Challenges Faced By The Select Urban Public Sector Bank Customer’s While Using Atm/ Debit Card –

A Descriptive Analysis

* S.Bulomine Regi.,

ABSTRACT

“Banking is essential, banks are not”. It is noted that, traditional bank branches (bricks and mortar) are going to vanish through innovative banking services i.e. electronic banking and plastic cards which continue to attract new users. The main objective focused in this paper is to measure the challenges faced by the customers’ using ATM/Debit Card offered by selected public sector banks i.e. State Bank of India and Canara Bank. 360 respondents were selected using purposive stratified random sampling. This paper mainly focused on the challenges faced by the customers using ATM/Debit card.

KEYWORDS: ATM, Debit Card, ATM user, Challenges, Public Sector Banks

INTRODUCTION

ATMs are now a routine part of banking transactions but when they were introduced in 1960s, they were the high- tech technology. The Automated Teller Machine (ATM) is now such a normal part of daily life that it’s strange to think it was ever cutting-edge technology. But in 1960s, when the first cash-dispensing ATM was installed at a branch of Barclays Bank in London, it was innovative and revolutionary. What’s more, over the decades, ATMs have become much more than just cash dispensers. They also allow customers to carry out a range of banking activities, including deposits and mobile phone top-ups. Given that the ATM is such a prominent feature in people’s lives, it’s important to understand its background, technical development and its capabilities. Here’s a quick introduction to the ATM and its global significance.

While the first card-accepting ATM was introduced by Barclays in London in 1968, this was not in fact the very first incarnation of the automated teller. CitiBank, then known as First National City Bank, launched a version of the ATM called the Bankograph in American branches in 1960. This machine did not let customers withdraw money but instead allowed them to pay bills without the assistance of bank staff. Moreover, Barclays’ 1968 addition was not foolproof and cards were regularly swallowed by these early ATMs.

Following these early developments, growth in North America and Western Europe was rapid. In 1969, the first machine to use magnetically encoded plastic was installed at Chemical Bank in New York, although initial take-up was slow as the running costs for these machines, known as Docutellers, outstripped the cost of hiring a human teller. However, as the modified Total Teller was introduced in the early 1970s, ATMs began spreading in banks across the two continents.

Today, ATMs have been popularised across the globe. Experts estimate that developed countries like the USA, Canada, the UK and Japan have a high concentration of ATMs per capita, while steady economic growth in India and China has meant that the number of bank machines in these countries has been growing in the last decade. However, it’s not just the number of ATMs throughout the world that has increased but also its functions. As well as withdrawing and depositing cash, modern ATMs also allow you to put credit on a mobile phone just by entering your phone number of the keypad. What’s more, some machines will let pay money into a beneficiary’s account, while others will print mini bank statements of your last few transactions.

However, as software changes, it does concern over ATM security. Today’s biggest worry for ATM industry professionals is how to maintain the security of global systems beyond the traditional advice to consumers to keep their PIN secret. The development of chip cards and Chip and Pin technology has helped to combat ATM fraud but there are still advances to be made.

STATEMENT OF THE PROBLEM

            Nowadays majority of the customers are using ATMs to withdraw cash from their account. The debit cards are used in very occasion for payments made through online, payments for purchases in shopping mall and so on. The use of ATM is increasing day-by-day, it is important to study the challenges towards use of ATM services. The customers were facing different types of problems with which ATM is directly related. Machine complexity, machine breakdown, poor quality notes, network failure, unsuitable location, forgot ATM pin number, High frequency of use, safety and security are the major problems of ATM users. Customers do not like ATMs because of impersonality, vision problem, fear of technology and reluctance to change and adopt new mode of delivery of service.

 

 

OBJECTIVES OF THE STUDY

The following are the objectives of the study:

  1. To study the socio-economic conditions of the respondents using ATM/Debit Card from select public sector banks in Tirunelveli District.
  2. To identify the challenges faced by the customers while using ATM/Debit Card from select public sector banks in Tirunelveli District.
  3. To give suggestions for the improvement of using ATM/Debit Card.

METHODOLOGY

Research design

A  research  design  is  a  plan  of  the  research  project  to  investigate  and  obtain answers  to research questions. Three types of research designs identified from the literature are exploratory, descriptive and explanatory design.[1] In  the  beginning  of  the  study, an  exploratory research  was  undertaken by an  in-depth review of literature in order to identify the research  problem,  constructs  and  to formulate hypotheses. Descriptive  research design  was  used  in  the  next  stage  of  the research for the purpose of describing the profile of the respondents and to determine the frequencies,  percentages, mean and standard  deviation  of  the  measures  and constructs used  in  the  study. Descriptive research could not explain the relationship among the variables [2] and therefore, to establish relationship and association between variables used in the study, explanatory research was used.

Survey  method  using  a  pre-structured  interview schedule was  used  for  collecting  primary data from the respondents because it offers more accurate means of evaluating information about  the  sample  and  enables  the  researcher  to  draw  inferences about  generalising  the findings  from  a  sample  to  the  population.[3]  The study  also  made  use  of secondary  data  collected  from  published  sources  such  as  records  and  reports of RBI and IRDBT, books, bank official websites, bank magazines, reports, newspapers, journals and websites.

Two banks were selected for the study and 180 customers were selected from each bank purposively those who are using innovative banking services namely ATM/Debit Card, Credit Card, Internet Banking and Mobile Banking. Two banks were selected based on IBA Banking Technology Awards 2014-2015.[4] The select banks are State Bank of India and Canara Bank which are public sector.

Sample design

Details of customers using innovative banking services (IBS) could not be obtained from the banks due to banks’ privacy issues and topic sensitivity. Therefore, the researcher decided to contact the respondents from ATM outlets of the select banks and other urban ATM outlets in the district. Simple random sampling method was adopted to select the ATM outlets and purposive sampling method was adopted to select the respondents. Customers who are using innovative banking services (IBS) visiting ATM outlets on the days of survey were selected as sample respondents. The respondents were selected after having ensured that they have account with any of the two banks and they are using all the two selected IBS. It was also ensured that the respondents have been using IBS for a minimum period of two years.

 Determination of Sample Size

Where

         Z       =       Standardized value corresponding to a confidence level of 95% = 1.96

         S       =       Sample SD from Pilot study of 60 sample = 0.484

         E       =       Acceptable Error =5% = 0.05

         Hence, Sample size = n = (ZS/E) 2

                                                = (1.96*0.484/0.05)2

                                                = 359.96

Hence, Sample Size n= 360

360 respondents who were selected for the study out of those 180 respondents are from State Bank of India and 180 respondents are from Canara Bank. The collected data were analysed with the help of SPSS 21 and AMOS. In order to obtain the score of the attitude of customers Likert Five Point Scaling Technique was used.

Results of Reliability Test Using Cronbach’s Alpha

Variables No. of Items Cronbach’s Alpha
Measuring  level of attitude of  the customers’ towards IBS 28 0.892

LIMITATIONS OF THE STUDY

Each research work is subjected to certain limitations and this study is also not an exception. The present study has the following limitations:

  • The responses for the study have been solicited from the District of Tirunelveli in Tamilnadu alone. The expectation and attitude of the customers in Tirunelveli may vary from those of the rest of the Districts in Tamilnadu and other states in India.
  • The study may suffer from the element of biasness.
  • The customers of two banks were selected for the study to study the attitude towards IBS. As a result, the generalisation of the findings of the present research has to be done with utmost care.
  • Furthermore, the sample was restricted to commercial banks. The other major banks like private, co-operative banks and foreign banks are excluded from the study.
  • The analysis of innovative banking services offered to corporate banking customers are excluded from the study.
  • No published data were available on number of customers availing all the four select services and no banks provided much data.
  • As regard users of card, no categorisation has been done such as users of classic, platinum and alike.
  • The study was restricted to urban customers only.

ANALYSIS AND INTERPRETATION

MILIEU OF THE RESPONDENTS- A DESCRIPTIVE ANALYSIS

  • Majority (61.4%) of the customers using innovative banking services (IBS) are male.
  • Majority (31.7%) of the respondents using innovative banking services (IBS) belongs to the age group of 31-35.
  • Majority (75.3%) of the customers using innovative banking services (IBS) are married.
  • Majority (45.8) of the IBS users are graduates.
  • Majority (44.7) of the respondents are employed.
  • In public sector, customers using innovative banking services (IBS) are earning above
  • Overall 78.3 per cent of the respondents are having savings account.
  • Majority (47.2%) of the respondents are having account with the bank between 2-5 years.

 

Problems faced by the customers while using ATM/Debit Card

         Customers are using maximum ATM/Debit Card service at the maximum in their day to day transactions. It is evident that, majority of the customers are using Debit Card up to 5 times. While using ATM/Debit Card customers are facing problems in performing their task. The below table shows the major problems faced by the customers while accessing ATM/Debit Card.

Table No. 1

Mean of Problems Faced by the Respondents while using ATM/Debit Card

ATM/DEBIT CARD Public Sector of Bank
Poor network 2.05
Lack of infrastructure 1.99
Long waiting queue 3.40
Machine out of service 2.52
Out of cash 1.81
Limited ATM centres 2.52
Unable to print statement 2.41
Letters printed in the statement disappear after few days 3.62
Card blocked 2.58
Misuse of card and frauds 2.27
Lack of confidence 2.18
Swiping is difficult 1.86
ATM centre doors are always open 3.67
Without security guards 3.44
Non-availability of CCTV  (Inside and Outside ATM centre) 2.37
Damaged Currency 2.23
Reduction of balance without cash disposal 2.36
Over/Under value of withdrawal amount 2.29
Location of ATM centre  is safety 2.56
No proper Air Conditioner 1.74
No parking  facilities in front of ATM centres 3.60
2 or more people in a single machine 3.57
Not giving proper intimation about charges 3.54
Magnetic Strip easily damaged 1.89
If misplaced, blocking card is difficult 2.29
Prompt service to get new card and PIN 3.12
Time Consuming 2.29
ATM premises are full of Receipts on the floor. 3.17
Shoppers also charging for using card 2.76

    Source: Primary Data

            Based on the mean score, public sector customers using ATM/Debit Card services are facing problems like ATM outlet doors are always open (3.67), letters in printed statement disappear after few days (3.62), no parking facilities in front of ATM outlets (3.60), two or more people tend to use a machine at a time (3.57), banks are not giving proper intimation about charges (3.54), lack of security guards (3.44) in the ATM outlets and long waiting in queue (3.40).

Inference: It is inferred that, public sector customers are facing the similar problems i.e. lack of infrastructure facilities and not proper maintenance of ATM outlets. It is evidence that, urban ATM outlets in the study area are accurately having these types of problems cited by the sample respondents.

SUGGESTIONS

  • Nowadays, there is sufficient number of ATMs but no proper facilities to access the ATM outlets like parking, shed to stay in queue, paper free ATM center, Air Conditioner, Security guards and CCTV camera in and out of ATM outlets to avoid physical attack and theft occurred in the place of ATM outlet. So, proper care should be given to maintain ATM outlets.
  • The banks should instruct the outsourcing agents to put quality paper for printing receipt. Because, the letter in the printed receipt disappear after few days.
  • The customers should follow the security guidelines given by the banks while accessing ATM/Debit Card.
  • The customers should not disclose the PIN to anybody.
  • The customers should avail ATM/Debit Card with utmost care.

CONCLUSION

Banking sector plays a vital role in the growth of economic development in India. Banking is still under evolutionary stage as it is adopting new technologies to facilitate further the customer convenience in the secured environment. IBS is becoming popular amongst customers who are familiar with the technology up graduation but it is gradually spreading to mass especially at metropolitan and urban cities. Few banks have taken an early lead by introducing technology based banking services. The study on the customers’ attitude towards innovative banking services (IBS) in banking sector reveals that customers are satisfied in some aspects and they want to continue with their respective banks. The shift from cutomerised service to personalized services is highly essential to satisfy all groups of customers. The findings of the study stress upon the importance of the security and safety expected by the customers especially in the case of innovative banking services (IBS) like ATM/Debit Card. The future of internet banking lies in offering personalized internet based services that are not only valued by their customers but are also unique to them. This would help distinguish themselves in the crowd. This would also help them evolve continuously to meet customers’ needs, capitalizing on new technology to build stronger customer relationship.

REFERENCES

  1. Eugine, F. D. C. & Regi, S. B., “Advantages and Challenges of E-Commerce Customers and Businesses: In Indian Perspective” International Journal of Research–Granthaalayah,4(3), 7-13.
  2. Golden, S. A. R. (2015). Regional Imbalance affecting quality of e-banking services with special reference to Tuticorin District-An Analysis.International Journal of Research2(3), 788-798.
  3. Golden, S. A. R., & Regi, S. B. (2014). Attitude of Rural People Towards Technology Inclusion In Banking Services At Tirunelveli District, IGJAE – Indo Global Journal Of Applied Management Science, 2(2).
  4. Golden, S. A. R., & Regi, S. B. (2014). Customer Preference Towards E- Channels Provided By State Of Bank Of India, Kongunadu College Of Arts And Science, Special Edition 1(1).
  5. Golden, S. A. R., & Regi, S. B. (2015). Satisfaction of Customers towards User Friendly Technological Services offered by Public and Private Sector banks at Palayamkottai, Tirunelveli District.International Journal of Research2(3), 775-787.
  1. http://ezinearticles.com/?A-Brief-Introduction-to-the-Automated-Teller-Machine&id=5397483
  2. http://worldwidejournals.com/paripex/file.php?val=July_2013_1374047900_e453d_54.pdf
  1. Regi, S. B., & C. Eugine Franco, “MEASURING CUSTOMERS’ ATTITUDE TOWARDS INNOVATIVE BANKING SERVICES OF PUBLIC AND PRIVATE SECTOR IN TIRUNELVELI DISTRICT” International Journal of Research – Granthaalayah, Vol. 4, No. 5: SE (2016): 58-66.
  2. Regi, S. B., & Golden, S. A. R. (2014). Customer Preference Towards Innovative Banking Practices Available In State Bank Of India At Palayamkottai.Sankhya International Journal Of Management And Technology, 3 (11 (A)), 3133.
  3. Regi, S. B., & Golden, S. A. R. (2014). Customer Preference Towards E-Channels Provided By State Of Bank Of India.
  4. Regi, S. B., and Dr.C. Eugine Franco, “MEASURING CUSTOMERS’ ATTITUDE TOWARDS INNOVATIVE BANKING SERVICES OF PUBLIC AND PRIVATE SECTOR IN TIRUNELVELI DISTRICT” International Journal of Research – Granthaalayah, Vol. 4, No. 5: SE (2016): 58-66.
  5. Regi, S. B., Golden, S. A. R., & Franco, C. E. (2014). ROLE OF COMMERCIAL BANK IN THE GROWTH OF MICRO AND SMALL ENTERPRISES.Golden Research Thoughts, 3 (7), 15.

[1] Cooper, D.R. and Schindler, P.S. (2001).  Business Research Methods (7th edition). Singapore: McGraw-Hill- Irwin.

[2] Zikmund, W.G. (2000).  Business Research Methods (6th edition). Chicago: The Dryden Press.

[3] Creswell,J.W.  (1994). Research Design: Qualitative and Quantitative Approaches. Thousand Oaks: Sage Publication

[4] http://www.iba.org.in/Documents/FINAL_AWARDS.pdf dated 10/04/2015 time 23.59 p.m

Econometric Models  to Water Use Estimation in Power Plants: An Experiential Analysis

 PERINI PRAVEENA SRI

 

ABSTRACT

The purpose of this paper is to examine water use estimation in hydel and thermal electric power plants in selected regions i.e. Coastal, Rayalaseema and Telangana regions of Andhra Pradesh. The study primarily focuses on the realistic fundamental premise that thermal electric and hydro electric energy generation is responsible for the largest monthly volume of water withdrawals in four seasons (i.e. summer, rainy, winter and post monsoon season) of a year. These enormous water withdrawals by these hydel and thermal power plants can have significant influence on local surface water resources. However there are very few studies of determinants of water use in hydel and thermal electric generation. Analysis of hydel and thermal electric water use data in the existing power plants clearly indicates that there is wide variability in unitary hydel and thermal electric water use within the system. The multivariate regression procedures were used to identify the significant determinants of thermal and hydel water withdrawals in various power plants i.e. five hydel and four thermal power plants. The estimated regression coefficients indicate that the best explanatory variables for the total quantity of hydel water withdrawals are storage capacity, tail water level and actual generation and thermal water withdrawals are condenser cooling and ash disposal. The unit variability of unit water usage indicates that there is significant potential for water conservation in existing power plants.

Keywords:

Thermal water withdrawals, hydel water withdrawals, storage capacity, tail water level, actual generation, condenser cooling and ash disposal.

  • INTRODUCTION

Water has become a growing source of tension especially in power sector in many parts of the World. For India hydro and thermal power projects are vital to fill in the serious electric energy shortfalls that crimp its economy. About 40 percent of India’s population is off the power grid and due to this the welfare of the economy was badly affected. The main stumbling block for this kind of worse situation are a genuine water shortage problem in India and the country’s inability to properly manage large quantities of water during rainy season has made matters worse, exposing it to any small variation in rainfall or river flow. Though the country has invested heavily on nuclear power to generate 30,000 MW and $ 19 billion to produce factories of major thermal, hydro and nuclear power stations, the electric energy shortages were very much prevalent in most parts of the country. For this the first and foremost thing is to judiciously manage the vital resource “water”. The country also planned for setting up of 20,000 MW solar power by 2020. The Government of India has an ambitious mission of Power for All By 2012. This would require an installed generation capacity of atleast 20,000 MW by 2012 from the present level of 144,564.97 MU. However the power requirement will double by 2020 to 400,000 MW. How India is able to meet this target with the on-going water shortage plight in Electricity Generation Industry is a matter of great concern. However the Electricity Generation Industry strategy should primarily focus on this invisible culprit “Water” causing huge generation losses through better water efficiency techniques and lay emphasis on technology up gradation and massive utilization of renewable sources of energy.

The purpose of this paper was to examine water use estimation at hydel and thermal electric power plants in selected regions i.e. Coastal, Rayalaseema and Telangana regions of Andhra Pradesh. The study primarily focuses on the realistic fundamental premise that thermal electric and hydro electric energy generation is responsible for the largest monthly volume of water withdrawals in four seasons (i.e. summer, rainy, winter and post monsoon season) of a year. These enormous water withdrawals by these hydel and thermal power plants can have significant influence on local surface water resources. Water use at the power station level (by fuel type) can be estimated indirectly by using multiple regression analysis. In regression models, water use relationships are expressed in the form of mathematical equations, showing water use as a mathematical function of one or more independent (explanatory) variables. The mathematical form (eg. Linear, multiplicative and exponential) and the selection of the Right hand side (RHS) or independent variables depend on the category and on aggregation of water demand represented by Left Hand side (LHS) or dependent variable.

2.0  THEORETICAL AND CONCEPTUAL REVIEW OF LITERATURE: DIFFERENT APPROACHES OF WATER USE ESTIMATION

The various studies relating to water demand for thermal power plants and its significant determinants are reviewed for explicit understanding of thermal electric energy water use. Cootner, Paul and George O Golf (1965) have build upon a systematic model for estimating water demand in conventional steam electric utility industry. They have regarded   water as a common factor input along with fuel. Here

TWD= f (Qf, Cw, EHe, CWH )

Where in TWD = Thermal water withdrawal demand,    Qf = Quantity and cost of fuel,   Cw = Cost of water,  EHe = Economics of heat exchange and recycle and  CWH= other costs of thermal power plant associated with the disposal of waste heat.

In other words the quantity of the fresh water withdrawals depends upon the above mentioned factors. In another study Wollman and Bonem (1971) found that the quantity of fresh water withdrawals for steam electric power generation depends upon (1) Thermal efficiency (with higher thermal efficiency less heat will be dissipated. Due to this smaller amount of cooling water are needed) (2) The extent to which sea or brackish water can substitute for fresh water (3) The rate of recirculation. Recirculation is a function of price of water availability. Young and Thompson (1973) in their study identified three factors that affect water use   in thermal electric energy generation. They can be listed as water pricing, change in generation, technology, price of electricity, price of substitutes used in electricity i.e. oil and gas, population and level of general economic activity. The other factors include waste and heat discharge to water and the changes in cooling technologies.

Gleick (1993) in his study reviewed the water requirement of electric energy. Taking as base of earlier studies, he estimated the consumptive water use in Electricity Generation Industry using different technologies. The system efficiency for conventional coal combustion (Once through Cooling Towers), natural gas combustion (Once Through Cooling Towers) and nuclear generation (CTs) stood at 35 percent, 36 percent and 40 percent. The estimates specifies that with the help of Once Through Cooling Technologies, the average consumptive use ranges from  1.2 m3/MWH  in case of conventional coal, for oil and natural gas consumption the average consumption use is less by 1.1 m3/MWH  , where as with cooling towers it was 2.6 m3/MWH. For nuclear power generation the average consumptive use of water with the aid of CTs was more that stood at 3.2 m3/MWH. There is a need for use of high efficient technology in cooling towers for water conservation. Electric Power Research Institute 2002, estimated the evaporation water loss from recirculating towers i.e., roughly 480 gal/MWH for a coal fired power plant. Mortenson, 2006 in his study have provided a technological breakthrough i.e. small scale tests of one technology (that uses cross-currents of ambient air for condensation) as a counteracting measure for these evaporation losses. By this technology the evaporation losses can be reduced to about 60-140 gallons/MWH (that can be applied even to hotter climates). In value terms, EPRI 2004 notified that the savings from reduction of evaporation losses will be $870,000.

There are very few studies of determinants of water use in hydel and thermal electric generation. The literature available relating to water use estimations is very few. Water use experts have to opt for estimation methods for many of the water withdrawals classes i.e. domestic, agriculture and industry because of the true fact that many legal, financial and political constraints limit for getting the hard data. For instance water withdrawals in domestic and live stock water use are usually estimated by multiplying population figures by coefficient. In case of agricultural sector, the irrigation water withdrawals are often estimated by multiplying the acreage by assumed water requirements of the crop rather than by measuring actual water pumped and applied.

Snavely (1986), explicitly details the water use data collection programs and maintaining regional data base of the Great Lakes St. Lawrence River Basin States.  The results are very much appealing indicating as how broad the range of estimation coefficient for water use can be within a geographic area with similar water availability. Mostly the estimated coefficients used for agriculture and domestic use vary by a factor of 10. The econometric studies relating to water use estimation in public supply use and thermo electric power use have the potential to explain temporal and geographic variability across USA. The aggregated water use estimates were provided by the National water Use Information Programme. These estimates primarily focus on measuring total water withdrawals (that includes annual extraction of fresh  surface water and ground water) for the period 1980-1985 to 1990-1995 in each of 48 states of USA for public supply water withdrawals , domestic, commercial, irrigation and live stock. The saline water withdrawals were estimated for industrial, mining and thermal electric categories. The public supply water withdrawals are estimated within geographical area i during year t using a set of explanatory variables that includes air temperature, precipitation, price of water, median household income and others.

Cohn et.al (1989) and Christensen et.al (2000) have used examples of such kind by using statistical techniques. The shorter time period used has the advantage of highlighting the recent trend of declining water use since the 1980 compilation. The mean withdrawal for the period (1980-1995) clearly indicates that it was 183.7 gallons per capita per day. This average water withdrawals would decrease by 7.8 gpcd, if the state GDP per capita increased by $1000. The inclusion of this state GDP captures the effects of relative volume of non residential uses (along with their ability to pay for water). The model also indicates that US was able to withdraw 17.2 gpcd, because of its surface water rights in comparison with riparian law states. The inclusion of temperature and precipitation variables also clearly shows the effect of weather on water withdrawals and can be used in normalizing water use for weather. The model indicates that average per capita demand for water in the state decreases by 2.1gallons per day per one inch increase in precipitation and vice versa i.e. water demand increases during summer months. i.e. average temperature.

Billings and Jones, 1996 employed indirect estimation of water use in urban and municipal planning using coefficient based methods. It calculates water use for commercial, residential and industrial categories. They assume constant water use rates and ignores trends i.e. changes in water use due conservation, technological change or economic forces. Mullusky et.al (1995), Wood Well and Desjardin (1995) for Washington D.C. metropolitan area have employed this water use coefficients for three categories of water users i.e. simple family homes, multiple family homes and employment water use.   Another approach of estimating National Water Use in USA includes Stratified random sampling followed by Census of Agriculture. They employed various methods of collecting data such as telephone, mail survey instruments to develop detailed country level estimates of national agricultural activities. According to Hutson et.al 2004 the thermo electric power water use refers to water that is removed from the ground or diverted from surface water sources (that includes fresh water and saline water) for use in the process of generating electricity with steam driven turbine generators. In this paper the term water withdrawals is used more often precisely. The term designates the amount of water that is extracted from natural water sources. Again it is essential to demarcate between water withdrawals and discharge as consumptive use. Water consumption is the quantity of water with drawn that is evaporated, transpired, incorporated in to crops, consumed by human or live stock.

At the end it can be said that different authors have notified different methods for estimation of water use for various uses of the economy. This paper employs multivariate models of water use for estimation of significant determinants of thermal and hydel water withdrawals.

Objectives of the paper

The objective is to determine if multiple regression models of unit hydel and

thermo electric water use have the potential

To identify significant determinants of total hydel and thermo electric water withdrawals across selected region wise power plants in AP using aggregated category wise water use estimates.

To estimate the future water withdrawals for hydel and thermal electricity generation plants expressed as cubic meters per second. (Cumecs) and cubic meters using the growth rate phenomenon.

The types of data used for estimation are monthly water withdrawals data (For surface fresh water resources)

Region level models for hydro and thermo electric water withdrawals

The potential dependent and independent variables for water withdrawals are identified for estimation purpose. Regional level data for thermal and hydel water withdrawals are more accurate data. The underlying reason being water withdrawals are usually metered.

Dependent Variable: Total Hydel Water Withdrawals

     Total Thermal Water Withdrawals

Independent Variables of Hydel Power Plant:

(a) Reservoir levels, (b) Inflows, (c) Storage capacity, (d) Evaporation losses, (e) Tail water level and (f) Gross Head

Independent Variables of Thermal Power Plant:

(a) Demineralised water, (b) Boiler Feedback, (c) Condenser Cooling (d) Ash disposal, (e) Others include colony domestic, drinking, sanitation, fire fighting, back wash filter, (f) Installed generation capacity, (g) Actual electric energy production (h) Total no. of cooling towers, (i) Water temperatures in summer, rainy and winter.

Multiple Regression analyses were performed using the data related to category wise water use in power plant, generating facility and weather conditions from month wise 1995-96 to 2008-09 data in respective thermal and hydel power plants. The effect of variables such as quantities of water used exclusively for the production of electricity i.e. Boiler feed, Demineralised water, Condenser cooling, Ash Disposal, colony domestic (Drinking, Sanitation, Fire Fighting, Back wash filter ), installed capacity generation, number of cooling towers, cooling temperature and electric energy generation on total water withdrawals of thermal power plants are explicitly analyzed. In addition to this, the effect of variables such as reservoir elevation, storage capacity, tail water level, evaporation losses, inflows, gross head, actual generation on total hydel withdrawals have also been looked in to. This paper explores the structure of power plant level aggregated water use data based on corresponding and routinely collected economic and climatic data. The purpose of this enquiry is to determine if multiple regression models have the potential to explain the temporal and climatic variability across various thermal and hydel power plants in Andhra Pradesh using aggregated water use estimates and most importantly to identify significant determinants of total water withdrawals of thermal and hydel power plants. The statistical models examined here are derived using data estimates of total water withdrawals for hydel and thermo electric power use.

Specification of Mathematical Model

WHEim = a +∑ bj Xj

                    j

Where WHEim  = Fresh water withdrawals for Hydel Electric Energy within region wise i during particular months m in a year.

     Xj is a set of explanatory variables. (Mentioned above)

WTEim = a +∑ bj Xj

                    j

WTEim = Fresh water withdrawals for Thermal Electric Energy within region wise i during particular months m in a year.

      Xj is a set of explanatory variables. (Mentioned above Coefficients a and bj can be estimated using multiple regression model.

Specification of the Econometric Model:

In Linear forms, these equations can be estimated as follows

Yt = B1+B2X2+B3X3+B4X4+B5X5+B6X6+B7X7+ µ

Model: 1 WTEim = B1+B2 CT+B3DB+B4CD+B5AS+B6WT+B7AG+µ ……… (1)

Where, WTEim = Water withdrawals for thermal electric energy in region i for particular months m.

CT = Condenser cooling (with Cooling Towers), DB = Demineralized water and Boiler Feed

CD = Colony Domestic, AS = Ash Slurries, WT= Water Temperature, AG= Actual generation

µ= random error term

Condenser Cooling: Water required for cooling hot turbines and condensers

Demineralized Water:  Water that is, free of minerals and salts. Water runs through active resin beds to remove metallic ions and filtered through sub micron filter to remove suspended impurities.

Colony Domestic: Water that is used for the purpose of colony maintenance, drinking purpose and plantation.

Ash Slurries: As coal burns, it produces carbon –di-oxide, sulphur –di-oxide and nitrogen oxides. These gases together with lighter ash are called fly ash. The electro static precipitators remove all the fly ash and are mixed with water to make in to ash slurries.

Water temperature: Recording the temperature of water during summer, rainy and winter seasons.

Actual Generation: The generation of electricity that is actually generated apart from installed generation.

Model 2: WHEim = B1+B2 RE+B3SC+B4 TW+B5GH+B6WT+B7AG+µ ……. (2)

Where WHEim= Water withdrawals for hydel electric energy in region i for particular months m.

RE = Reservoir Elevation, SC= Storage Capacity ,TW= Tail water level, El= Evaporation losses, GH= Gross Head, WT= Water Temperature, AG= Actual Generation,µ= random error term

Reservoir Elevation: This is defined as the foot of the dam. i.e. the level from which the reservoir storage level and the height of the dam are measured.

Storage Capacity: This corresponds to the flood level usually designated as the upper limit of the normal operational range, above which the spill gates come in to operation

Tail water Level:  Water immediately below the power plant. Tail water elevation refers to the level that water which can rise as discharges increase. It is measured in the feet above sea level.  1 foot = 0.305 meters.

Inflows: The inflow may be monsoonal rains or lakes, rivers. The average volume of incoming water, in unit period of time.

Evaporation Losses: Conversion of liquid to vapor state by latent heat. Water gets saturated in the form of vapor due to rise in water temperature.

Discharge: Volume of water released from power dam at a given time measured as cubic feet per second.

Gross Head: A dam’s maximum allowed vertical distance between upper stream’s surface water fore bay elevation and the down stream’s surface water (tail water) elevation at the tail race for reaction wheel dams.

Actual Generation: The amount of electricity actually generated apart from installed generation.

The collection of data includes a monthly time series data analysis during the period (1995-96 to 2008-09). Analysis of hydel and thermal electric water use data in the existing power plants clearly indicates that there is wide variability in unitary thermal and hydel electric water use within the system. The multi- variate regression  procedures were used to identify the significant determinants  of thermal and hydel water withdrawals in various power plants i.e. five hydel and four thermal power plants. The unit variability of unit water usage indicates that there is significant potential for water conservation in existing hydel and thermal electric power plants.

3.0 Approach and Methodology

 The study includes three main components. (a) A series of site visits and interviews with power plant personnel. (b) Field surveys of selected hydel and thermal power plants of Andhra Pradesh (c) The multiple regression analysis of power generation data and other associated information.

Summary of site visits: Site visits for selected five hydel namely Nagarjuna Sagar Main Power House, Nagarjuna Sagar Left Canal Power House, Nagarjuna Sagar Right Canal Power House, Srisailam Left canal power house and Srisailam right Canal Power House and four thermal namely Rayalaseema Thermal Power Plant, Kothagudaem Thermal Power Station O & M, Kothagudaem Thermal Power Station Stage V and Narla Tata Rao Thermal Power Plant have been made to assess the overall performance scenario of power plants and also to examine the extent of water irregularities .Appraisal of Power Plant Survey:  The research estimates of hydel and thermal Electric Energy water withdrawals are based upon the authenticated sources of data provided by respective hydel and thermal power plants of Andhra Pradesh Generation Corporation of India Limited. In order to transparently clarify the way that power generation officials responded to this kind of field survey in practice and to solicit information from them on factors responsible for water use at power generation facilities, site visits have been taken up.  At various Power plants several personal interviews with power plant officers helped to identify the types of onsite water uses, the measurement of these uses and provision of information on various types of cooling systems and water use procedures employed by hydel and thermal electric energy generation facilities.

The purpose of conducting a series of personal interviews with power plant officials can be listed as follows:

(a)    Scrutinize and examine the power generation water use and water withdrawals from intake (surface water) to discharge in various types of facilities.

(b)   Observing the fact that all the water with drawals (hydel and thermal) are metered.

(c)    Detailed analysis about important onsite uses of water and its significant determinants

(d)      To obtain feedback on the cooling system level of water use in power stations.

Multiple Regression Models of Water Use

The principal sources of data used in the multi variate analyses of thermal and hydel power plants are most accurate and provides a fairly comprehensive review of plant characteristics, power generation and water withdrawal details. The data in electronic format and in official records was available for the years 1996-97 to 2008-09. The weather data i.e. especially related to water temperatures during summer, rainy and winter was collected in order to examine the influence of it on total thermal and hydel water withdrawals.

At the end it can be concluded that the site visits and field surveys helped to identify important concerns about water measurement and use at thermal and hydel electric power plants. Added to this, these factors have received attention in the development of models to describe hydro and thermal electric water use. All the above mentioned information proved very much useful in the design of data analysis that was used to develop water use bench marks.

4.0 RESULTS AND DISCUSSION: ESTIMATION AND INTERPRETATION OF MODEL SPECIFICATIONS

Hydel based Electric Energy Power Plants

Model Specification I Nagarjuna Sagar Main Power House

 (Appendix table: A1)

In model 1 the estimated regression equation for total hydel water withdrawals is in the linear form as follows:

*              * *                          *

WHE = -146.238-0.080RE-0.258SC+0.350TW+0.133GH+50.67AG

                                               (-3.96)         (3.144)                      (119.87)

N= 154, R2 =0.99, f= 5543.05

  • The estimated equation indicates that the total hydel water withdrawals are inelastic with respect to storage capacity. This kind of negative relationship indicates that the hydel water withdrawals are somewhat in responsive to changes in the storage capacity. The coefficients are statistically significant at 1 % level.
  • The total hydel water withdrawals are elastic with tail water level and actual generation that hold a positive relationship. The coefficients are statistically significant at 5 % and 1 % level.
  • The t-ratio of regression coefficients is highly significant for three independent variables namely SC, TW and AG. As the t ratio value is greater than 2.58 indicates that the relation between dependent variable and independent variables observed in the sample holds good.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as reservoir elevation and gross feet, as the t- value is very small.
  • The R2 (coefficient of determination) is 0.99. It means that the independent variables tail water level, actual generation and storage capacity can explain 99 percent of variation in the dependent variable (WD) and remaining 1 percent variation is unexplained by the model. As R2 is very high, the estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher and more significant at 1% level. This clearly indicates that the regressors are significantly associated with dependent variable.

Model SpecificationII Nagarjuna Sagar Left Canal Power House

         (Appendix Table: A2)

*                                 *            *                    *

WHE = 1660.770-3.516RE-21.705SC+9.653TW+491.286AG+0.130EL

            (3.314)                       (4.16)        (3.84)         (15.67)

 N= 166, R2= 0.78, f = 116.22

  • The estimated regression coefficients indicate that the best independent that have significant effect are storage capacity and actual generation with significant levels at 1 % for each of independent variables.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. As t ratio value is greater than 2.58, it indicates that the relation between Hydel Water withdrawal and independent (SC) and (AG) observed in the sample holds good.
  • The R2 is 0.78. It means that the independent variables SC and AG can explain 78 percent variation in the dependent variable and the remaining 22 % variation is unexplained by the model. The estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (116.22) and more significant at 1 % level. This indicates that the regressors SC and AG are significantly associated with dependent variable.

Model Specification III Nagarjuna Sagar Right Canal Power House 

         (Appendix Table: A3)

             *                                      *                                                     *

WHE = 6133.252+0.628 RL-58.029 SC+0.414EL+37.493TW+486.057 AG

          (7.314)                        (6.063)                                          (16.232)

N= 166, R2= 0.78, f value = 116.22

  • The estimated regression coefficients indicate that the best independent variables that have significant effect are storage capacity and actual generation with significant levels at 1 % for each of independent variables.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. The relation between water withdrawals and Storage capacity and actual generation in the sample holds good as the t-value is greater than 2.58.
  • The t-ratio of regression coefficients is not at all significant for other independent variables such as reservoir level, storage capacity and evaporation losses.
  • The R2 is 0.78. It means that the independent variables SC and AG can explain 78 % variation in the dependent variable and remaining 22 % variation is unexplained by the model. The estimated equation is considered as the equation of very good fit.
  • The overall model is statistically significant as f value is higher (116.22) and more significant at 1 % level. This indicates that the regressors are significantly associated with dependent variable (WD)

Model Specification IV Srisailam Left Bank Power House

                  (Appendix Table: A4)

                                                                *                          *

WHE = -2243.501-0.766RE+1.195SC+57.47AG+0.592EL+4.24TW+0.000IF

                              (-2.27)                         (18.81)                     (2.69)

N= 58   , R2= 0.96, f value = 221.872

  • The estimated regression coefficients indicate that the best independent variables that have significant effect are actual generation and tail water level with significant levels at 1 % and 10 % for independent variables.
  • The t-ratio of regression coefficients is highly significant with three independent variables namely reservoir elevation, actual generation and tail water level. The t-ratio value is greater than 1.96 value for reservoir level and greater than 2.58 value for actual generation and tail water level. This indicates that the relation between WD and independent variables AG and reservoir elevation observed in the sample holds good.
  • The t- ratio of regression coefficients is not at all significant for other independent variables such as evaporation losses and inflows.
  • The R2 is 0.96. It means that the independent variables reservoir level, actual generation and tail water level can explain 96 % of variation in the dependent variable and remaining 4% is unexplained by the model. Thus the estimated regression coefficient is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (221.872) and more significant at 1 % level. This indicates that the regressors AG and TW are significantly associated with dependent variable. (WD)

 

Model Specification V Srisailam Right Bank Power House

                   (Appendix Table: A5)

                 *                        *        *

Y = -7630.380-1.78RE+0SC+56AG+0.051EL+0.627TW+0.289GH

              (-4.199)             (-4.3)  (122.65)

  N= 138    , R2    = 0.99 and f value = 4.59

  • The estimated regression coefficients indicate that the best independent variables that have a significant effect are storage capacity and actual generation with significant levels at 1 % level each of independent variable.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. The t- ratio value is greater than 2.58 for SC and AG that indicates that the relation between WD and independent variables SC and AG holds good.
  • The t- ratios of regression coefficients is not at all significant for other independent variables such as evaporation losses, tail water level and gross head.
  • The R2 is 0.99. It means that the independent variables such as storage capacity and actual generation can explain 99 % variation in the dependent variable and remaining 1 % is unexplained by the model. Thus the estimated regression coefficient is considered as an equation of very good fit.
  • The overall relationship was statistically significant as f value is 4.59 and more significant at 1 % level. This indicates that the regressors SC and AG are significantly associated with WD.

Thermal based Electric Energy Power Plants

Model Specification VI Kothagudaem Thermal Power Plant O &M

      (Appendix Table: A6)

                                                     *                                                     *   

Y= -787978.047 + 1.021CC-2.130DB-12.190CD+146.699 OT +1.152 AD+4616.497 CT-817.112AG

                              (3.259)                                                        (3.841)

N= 84, R2 = 0.55, f value = 13.710

  • The estimated regression coefficients indicate that the best explanatory (independent) variables with significant effect on quantity of water with drawals per Kilowatt hour are condenser cooling with cooling towers (Natural Draft cooling system) and ash disposal with significant levels of 5 % and 1 % level.
  • The estimated equation indicates that the total thermal water withdrawals are elastic with respect to condenser cooling and ash disposal. This kind of positive relationship indicates that the thermal water withdrawals are responsive to changes in condenser cooling and ash disposal.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely condenser cooling with Natural Draft CTs and Ash Disposal. The t ratio value is greater than 2.58.
  • This indicates that the importance of technological alternatives (i.e. Condenser Cooling with natural draft CTs) is the significant determinant of water withdrawals. Next ash disposal takes second place as significant determinant of total thermal water withdrawals.
  • The t-ratio of regression coefficient is not at all significant for other independent variables such as DM and Boiler feedback, colony domestic, others (Drinking, Sanitation, Fire fighting, Back Wash Filter), cooling temperature and actual  electric energy generation.
  • The R2 is 0.55. It means that the independent variables such as condenser cooling and ash disposal can explain 55 % of variation in the dependent variable and remaining 45 % variation is unexplained by the model. The estimated equation is considered as good fit.
  • The overall model is statistically significant as f value is higher (13.710) and highly significant at 1 % level. This indicates that the regressor’s condenser cooling with Natural Draft CT’s and Ash Disposal are significantly associated with dependent variable WDs.

Model Specification VII Kothagudaem Thermal Power Station Stage V

          (Appendix Table: A7)

                                   *                *

Y= 98233.879+0.873 CC+1.186AD+0.111 DB-1688.373CT+32.019 AG

                               (20.91)       (15.247)

              N= 83, R2= 0.97, f value = 706.164

  • The estimated regression coefficients indicate that the best independent variables with significant effect on quantity of WD per million tonnes are Condenser cooling and ash disposal with significant levels at 1% level each.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely Condenser cooling with natural draft CT’s and Actual Generation. The t- ratio value is greater than 2.58. Here the significant determinant of WD’s are CC with natural draft CT’s. Next comes ash disposal as second good determinant.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as BF & DM, cooling temperature and Energy Generation.
  • The R2 is 0.97. It means that independent variables such as CC and AD can explain 97 % of variation in the dependent variable (Water withdrawal) and remaining 3 % variation are unexplained by the model. Thus the estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (706.164) and highly significant at 1 % level. This indicates that the regressors condenser cooling with NDCT’s and Ash Disposal are significantly associated with Water withdrawal’s (Dependent Variable)

Model Specification VIII Rayalaseema Thermal Power Plant

          (Appendix Table: A8)

                           *

Y = 10334.674+0.745 CC+8.725 BF+0.847 AS-4.143 AG-145.408 CT

     (2.677)                (3.007)

N= 35, R2 = 0.87 and f value = 33.145

  • The estimated regression coefficients indicate that the best independent variables with significant effect on quantity of Water Withdrawal Condenser cooling with significant levels at 5%.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for one independent variables namely Condenser cooling with natural draft CT’s .The t- ratio value is greater than 2.58. Here the significant determinant of WD’s are CC with natural draft CT’s.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as BF & DM, Ash Disposal cooling temperature and Energy Generation.
  • The R2 is 0.87. It means that independent variables such as CC can explain 87 % of variation in the dependent variable (WD) and remaining 13 % variation are unexplained by the model. Thus the estimated equation is considered as an equation of very good fit.
  • The over all model is statistically significant as f value is higher (33.145) and highly significant at 1 % level. This indicates that the regressors condenser cooling with NDCT’s are significantly associated with WD’s (Dependent Variable)

Model Specification IX Narla Tata Rao Thermal Power Plant

                     (Appendix Table: A9)

                          *                               *   

Y = 139993.709 + 1.002CC -0.863CD + 1.031 AS- 373.483 CT- 56.843 AG

                                    (1277.966)                 (19.88)

N=      R2 = 1.00, f value = 907849.564

  • The estimated regression coefficients indicate that the best explanatory (independent) variables with significant effect on quantity of water with drawals per Kilowatt hour are condenser cooling with cooling towers ( Induced l Draft cooling system) and ash disposal with significant levels of 1 % and 1 % level.
  • The estimated equation indicates that the total thermal water withdrawals are elastic with respect to condenser cooling and ash disposal. This kind of positive relationship indicates that the thermal water withdrawals are responsive to changes in condenser cooling and ash disposal.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely condenser cooling with Induced Draft CTs and Ash Disposal. The t ratio value is greater than 2.58.
  • This indicates that the importance of technological alternatives (i.e. Condenser Cooling with Induced draft CTs) is the significant determinant of water withdrawals. Next ash disposal takes second place as significant determinant of total thermal water withdrawals.
  • The t-ratio of regression coefficient is not at all significant for other independent variables such as, colony domestic, cooling temperature and actual electric energy generation.
  • The R2 is 1.00. It means that the independent variables such as condenser cooling and ash disposal can explain 100 % of variation in the dependent variable. This shows that we have accounted for almost all the variability with the variables specified in the model. The estimated equation is considered as very good fit.
  • The overall model is statistically significant as f value is higher (907849.564) and highly significant at 1 % level. This indicates that the regressor’s condenser cooling with Induced Draft CT’s and Ash Disposal are significantly associated with dependent variable WDs.

The pertinent conclusion of this study is there may be significant potential for water conservation after having identified the significant determinants of total thermal water withdrawals i.e. condenser cooling and ash disposal. The choice of explanatory variable for eg: Induced draft and natural draft technological innovation was able to address the significant changes of water withdrawals.

5.0  CONCLUSION AND RECOMMENDATION

The thermal and hydel power plants sustenance is very much under stake due to major reason of fresh water shortages in power generation. The most sophisticated technology followed in advanced countries namely Concentrated solar thermal power integrated with combined system of conventional steam plant, Fresnel Solar Collector and  Solar Flower Tower can be used as a replica even in developing countries like India though not cost effective in order to counteract the water shortage problem

REFERENCES

Benedy Kt Dziegielewski, Thomas Bik (August 2006), “ Water Use Bench Marks for Thermo Electric Power Generation” Project report, Southern Illinois University, United States

Geological Survey, 2004, USGS National Competitive Grants Program.

Gbadebo Oladosu, Stan Hadley, Vogt D.P. and Wilbanks J.J. (September, 2006), “Electricity

Generation and Water Related Constraints: An Empirical Analysis of Four South Eastern

States”, Oak Ridge National Laboratory, Oak Ridge Tennessee.

Sitanon Jesdapipat and Siriporon Kiratikarnkul, “ Surrogate pricing of water: The Case of micro Hydro –Electricity Co-operatives in Northern Thailand”.

 Xiaoying Yang & Benedy Kt Dziegielewski (February,2007), “ Water Use by Thermo Electric power plants in the United states” Journal of the American Water Resources Association, Vol 43, No.1.

“Estimating Water Use in United States: A new Paradigm for National Use Water Use Information Programme”(2002),

http://books.nap.edu/openbook.php?record_id=10484&page=95

 

Data Sources

Annual Report on the Working of SEBs and Electricity Departments, Planning Commission, Various Issues

Administrative Reports of Andhra Pradesh Generation Corporation of India Limited (APGENCO),Various Issues. Field Level data of selected thermal and hydel power stations authenticated  by APGENCO.

Econometric Models  to Water Use Estimation in Power Plants: An Experiential Analysis

 PERINI PRAVEENA SRI

Department of Social Science, Faculty of Economics

 Ethiopia, Aksum University

ABSTRACT

The purpose of this paper is to examine water use estimation in hydel and thermal electric power plants in selected regions i.e. Coastal, Rayalaseema and Telangana regions of Andhra Pradesh. The study primarily focuses on the realistic fundamental premise that thermal electric and hydro electric energy generation is responsible for the largest monthly volume of water withdrawals in four seasons (i.e. summer, rainy, winter and post monsoon season) of a year. These enormous water withdrawals by these hydel and thermal power plants can have significant influence on local surface water resources. However there are very few studies of determinants of water use in hydel and thermal electric generation. Analysis of hydel and thermal electric water use data in the existing power plants clearly indicates that there is wide variability in unitary hydel and thermal electric water use within the system. The multivariate regression procedures were used to identify the significant determinants of thermal and hydel water withdrawals in various power plants i.e. five hydel and four thermal power plants. The estimated regression coefficients indicate that the best explanatory variables for the total quantity of hydel water withdrawals are storage capacity, tail water level and actual generation and thermal water withdrawals are condenser cooling and ash disposal. The unit variability of unit water usage indicates that there is significant potential for water conservation in existing power plants.

Keywords:

Thermal water withdrawals, hydel water withdrawals, storage capacity, tail water level, actual generation, condenser cooling and ash disposal.

  • INTRODUCTION

Water has become a growing source of tension especially in power sector in many parts of the World. For India hydro and thermal power projects are vital to fill in the serious electric energy shortfalls that crimp its economy. About 40 percent of India’s population is off the power grid and due to this the welfare of the economy was badly affected. The main stumbling block for this kind of worse situation are a genuine water shortage problem in India and the country’s inability to properly manage large quantities of water during rainy season has made matters worse, exposing it to any small variation in rainfall or river flow. Though the country has invested heavily on nuclear power to generate 30,000 MW and $ 19 billion to produce factories of major thermal, hydro and nuclear power stations, the electric energy shortages were very much prevalent in most parts of the country. For this the first and foremost thing is to judiciously manage the vital resource “water”. The country also planned for setting up of 20,000 MW solar power by 2020. The Government of India has an ambitious mission of Power for All By 2012. This would require an installed generation capacity of atleast 20,000 MW by 2012 from the present level of 144,564.97 MU. However the power requirement will double by 2020 to 400,000 MW. How India is able to meet this target with the on-going water shortage plight in Electricity Generation Industry is a matter of great concern. However the Electricity Generation Industry strategy should primarily focus on this invisible culprit “Water” causing huge generation losses through better water efficiency techniques and lay emphasis on technology up gradation and massive utilization of renewable sources of energy.

The purpose of this paper was to examine water use estimation at hydel and thermal electric power plants in selected regions i.e. Coastal, Rayalaseema and Telangana regions of Andhra Pradesh. The study primarily focuses on the realistic fundamental premise that thermal electric and hydro electric energy generation is responsible for the largest monthly volume of water withdrawals in four seasons (i.e. summer, rainy, winter and post monsoon season) of a year. These enormous water withdrawals by these hydel and thermal power plants can have significant influence on local surface water resources. Water use at the power station level (by fuel type) can be estimated indirectly by using multiple regression analysis. In regression models, water use relationships are expressed in the form of mathematical equations, showing water use as a mathematical function of one or more independent (explanatory) variables. The mathematical form (eg. Linear, multiplicative and exponential) and the selection of the Right hand side (RHS) or independent variables depend on the category and on aggregation of water demand represented by Left Hand side (LHS) or dependent variable.

2.0  THEORETICAL AND CONCEPTUAL REVIEW OF LITERATURE: DIFFERENT APPROACHES OF WATER USE ESTIMATION

The various studies relating to water demand for thermal power plants and its significant determinants are reviewed for explicit understanding of thermal electric energy water use. Cootner, Paul and George O Golf (1965) have build upon a systematic model for estimating water demand in conventional steam electric utility industry. They have regarded   water as a common factor input along with fuel. Here

TWD= f (Qf, Cw, EHe, CWH )

Where in TWD = Thermal water withdrawal demand,    Qf = Quantity and cost of fuel,   Cw = Cost of water,  EHe = Economics of heat exchange and recycle and  CWH= other costs of thermal power plant associated with the disposal of waste heat.

In other words the quantity of the fresh water withdrawals depends upon the above mentioned factors. In another study Wollman and Bonem (1971) found that the quantity of fresh water withdrawals for steam electric power generation depends upon (1) Thermal efficiency (with higher thermal efficiency less heat will be dissipated. Due to this smaller amount of cooling water are needed) (2) The extent to which sea or brackish water can substitute for fresh water (3) The rate of recirculation. Recirculation is a function of price of water availability. Young and Thompson (1973) in their study identified three factors that affect water use   in thermal electric energy generation. They can be listed as water pricing, change in generation, technology, price of electricity, price of substitutes used in electricity i.e. oil and gas, population and level of general economic activity. The other factors include waste and heat discharge to water and the changes in cooling technologies.

Gleick (1993) in his study reviewed the water requirement of electric energy. Taking as base of earlier studies, he estimated the consumptive water use in Electricity Generation Industry using different technologies. The system efficiency for conventional coal combustion (Once through Cooling Towers), natural gas combustion (Once Through Cooling Towers) and nuclear generation (CTs) stood at 35 percent, 36 percent and 40 percent. The estimates specifies that with the help of Once Through Cooling Technologies, the average consumptive use ranges from  1.2 m3/MWH  in case of conventional coal, for oil and natural gas consumption the average consumption use is less by 1.1 m3/MWH  , where as with cooling towers it was 2.6 m3/MWH. For nuclear power generation the average consumptive use of water with the aid of CTs was more that stood at 3.2 m3/MWH. There is a need for use of high efficient technology in cooling towers for water conservation. Electric Power Research Institute 2002, estimated the evaporation water loss from recirculating towers i.e., roughly 480 gal/MWH for a coal fired power plant. Mortenson, 2006 in his study have provided a technological breakthrough i.e. small scale tests of one technology (that uses cross-currents of ambient air for condensation) as a counteracting measure for these evaporation losses. By this technology the evaporation losses can be reduced to about 60-140 gallons/MWH (that can be applied even to hotter climates). In value terms, EPRI 2004 notified that the savings from reduction of evaporation losses will be $870,000.

There are very few studies of determinants of water use in hydel and thermal electric generation. The literature available relating to water use estimations is very few. Water use experts have to opt for estimation methods for many of the water withdrawals classes i.e. domestic, agriculture and industry because of the true fact that many legal, financial and political constraints limit for getting the hard data. For instance water withdrawals in domestic and live stock water use are usually estimated by multiplying population figures by coefficient. In case of agricultural sector, the irrigation water withdrawals are often estimated by multiplying the acreage by assumed water requirements of the crop rather than by measuring actual water pumped and applied.

Snavely (1986), explicitly details the water use data collection programs and maintaining regional data base of the Great Lakes St. Lawrence River Basin States.  The results are very much appealing indicating as how broad the range of estimation coefficient for water use can be within a geographic area with similar water availability. Mostly the estimated coefficients used for agriculture and domestic use vary by a factor of 10. The econometric studies relating to water use estimation in public supply use and thermo electric power use have the potential to explain temporal and geographic variability across USA. The aggregated water use estimates were provided by the National water Use Information Programme. These estimates primarily focus on measuring total water withdrawals (that includes annual extraction of fresh  surface water and ground water) for the period 1980-1985 to 1990-1995 in each of 48 states of USA for public supply water withdrawals , domestic, commercial, irrigation and live stock. The saline water withdrawals were estimated for industrial, mining and thermal electric categories. The public supply water withdrawals are estimated within geographical area i during year t using a set of explanatory variables that includes air temperature, precipitation, price of water, median household income and others.

Cohn et.al (1989) and Christensen et.al (2000) have used examples of such kind by using statistical techniques. The shorter time period used has the advantage of highlighting the recent trend of declining water use since the 1980 compilation. The mean withdrawal for the period (1980-1995) clearly indicates that it was 183.7 gallons per capita per day. This average water withdrawals would decrease by 7.8 gpcd, if the state GDP per capita increased by $1000. The inclusion of this state GDP captures the effects of relative volume of non residential uses (along with their ability to pay for water). The model also indicates that US was able to withdraw 17.2 gpcd, because of its surface water rights in comparison with riparian law states. The inclusion of temperature and precipitation variables also clearly shows the effect of weather on water withdrawals and can be used in normalizing water use for weather. The model indicates that average per capita demand for water in the state decreases by 2.1gallons per day per one inch increase in precipitation and vice versa i.e. water demand increases during summer months. i.e. average temperature.

Billings and Jones, 1996 employed indirect estimation of water use in urban and municipal planning using coefficient based methods. It calculates water use for commercial, residential and industrial categories. They assume constant water use rates and ignores trends i.e. changes in water use due conservation, technological change or economic forces. Mullusky et.al (1995), Wood Well and Desjardin (1995) for Washington D.C. metropolitan area have employed this water use coefficients for three categories of water users i.e. simple family homes, multiple family homes and employment water use.   Another approach of estimating National Water Use in USA includes Stratified random sampling followed by Census of Agriculture. They employed various methods of collecting data such as telephone, mail survey instruments to develop detailed country level estimates of national agricultural activities. According to Hutson et.al 2004 the thermo electric power water use refers to water that is removed from the ground or diverted from surface water sources (that includes fresh water and saline water) for use in the process of generating electricity with steam driven turbine generators. In this paper the term water withdrawals is used more often precisely. The term designates the amount of water that is extracted from natural water sources. Again it is essential to demarcate between water withdrawals and discharge as consumptive use. Water consumption is the quantity of water with drawn that is evaporated, transpired, incorporated in to crops, consumed by human or live stock.

At the end it can be said that different authors have notified different methods for estimation of water use for various uses of the economy. This paper employs multivariate models of water use for estimation of significant determinants of thermal and hydel water withdrawals.

Objectives of the paper

The objective is to determine if multiple regression models of unit hydel and

thermo electric water use have the potential

To identify significant determinants of total hydel and thermo electric water withdrawals across selected region wise power plants in AP using aggregated category wise water use estimates.

To estimate the future water withdrawals for hydel and thermal electricity generation plants expressed as cubic meters per second. (Cumecs) and cubic meters using the growth rate phenomenon.

The types of data used for estimation are monthly water withdrawals data (For surface fresh water resources)

Region level models for hydro and thermo electric water withdrawals

The potential dependent and independent variables for water withdrawals are identified for estimation purpose. Regional level data for thermal and hydel water withdrawals are more accurate data. The underlying reason being water withdrawals are usually metered.

Dependent Variable: Total Hydel Water Withdrawals

     Total Thermal Water Withdrawals

Independent Variables of Hydel Power Plant:

(a) Reservoir levels, (b) Inflows, (c) Storage capacity, (d) Evaporation losses, (e) Tail water level and (f) Gross Head

Independent Variables of Thermal Power Plant:

(a) Demineralised water, (b) Boiler Feedback, (c) Condenser Cooling (d) Ash disposal, (e) Others include colony domestic, drinking, sanitation, fire fighting, back wash filter, (f) Installed generation capacity, (g) Actual electric energy production (h) Total no. of cooling towers, (i) Water temperatures in summer, rainy and winter.

Multiple Regression analyses were performed using the data related to category wise water use in power plant, generating facility and weather conditions from month wise 1995-96 to 2008-09 data in respective thermal and hydel power plants. The effect of variables such as quantities of water used exclusively for the production of electricity i.e. Boiler feed, Demineralised water, Condenser cooling, Ash Disposal, colony domestic (Drinking, Sanitation, Fire Fighting, Back wash filter ), installed capacity generation, number of cooling towers, cooling temperature and electric energy generation on total water withdrawals of thermal power plants are explicitly analyzed. In addition to this, the effect of variables such as reservoir elevation, storage capacity, tail water level, evaporation losses, inflows, gross head, actual generation on total hydel withdrawals have also been looked in to. This paper explores the structure of power plant level aggregated water use data based on corresponding and routinely collected economic and climatic data. The purpose of this enquiry is to determine if multiple regression models have the potential to explain the temporal and climatic variability across various thermal and hydel power plants in Andhra Pradesh using aggregated water use estimates and most importantly to identify significant determinants of total water withdrawals of thermal and hydel power plants. The statistical models examined here are derived using data estimates of total water withdrawals for hydel and thermo electric power use.

Specification of Mathematical Model

WHEim = a +∑ bj Xj

                    j

Where WHEim  = Fresh water withdrawals for Hydel Electric Energy within region wise i during particular months m in a year.

     Xj is a set of explanatory variables. (Mentioned above)

WTEim = a +∑ bj Xj

                    j

WTEim = Fresh water withdrawals for Thermal Electric Energy within region wise i during particular months m in a year.

      Xj is a set of explanatory variables. (Mentioned above Coefficients a and bj can be estimated using multiple regression model.

Specification of the Econometric Model:

In Linear forms, these equations can be estimated as follows

Yt = B1+B2X2+B3X3+B4X4+B5X5+B6X6+B7X7+ µ

Model: 1 WTEim = B1+B2 CT+B3DB+B4CD+B5AS+B6WT+B7AG+µ ……… (1)

Where, WTEim = Water withdrawals for thermal electric energy in region i for particular months m.

CT = Condenser cooling (with Cooling Towers), DB = Demineralized water and Boiler Feed

CD = Colony Domestic, AS = Ash Slurries, WT= Water Temperature, AG= Actual generation

µ= random error term

Condenser Cooling: Water required for cooling hot turbines and condensers

Demineralized Water:  Water that is, free of minerals and salts. Water runs through active resin beds to remove metallic ions and filtered through sub micron filter to remove suspended impurities.

Colony Domestic: Water that is used for the purpose of colony maintenance, drinking purpose and plantation.

Ash Slurries: As coal burns, it produces carbon –di-oxide, sulphur –di-oxide and nitrogen oxides. These gases together with lighter ash are called fly ash. The electro static precipitators remove all the fly ash and are mixed with water to make in to ash slurries.

Water temperature: Recording the temperature of water during summer, rainy and winter seasons.

Actual Generation: The generation of electricity that is actually generated apart from installed generation.

Model 2: WHEim = B1+B2 RE+B3SC+B4 TW+B5GH+B6WT+B7AG+µ ……. (2)

Where WHEim= Water withdrawals for hydel electric energy in region i for particular months m.

RE = Reservoir Elevation, SC= Storage Capacity ,TW= Tail water level, El= Evaporation losses, GH= Gross Head, WT= Water Temperature, AG= Actual Generation,µ= random error term

Reservoir Elevation: This is defined as the foot of the dam. i.e. the level from which the reservoir storage level and the height of the dam are measured.

Storage Capacity: This corresponds to the flood level usually designated as the upper limit of the normal operational range, above which the spill gates come in to operation

Tail water Level:  Water immediately below the power plant. Tail water elevation refers to the level that water which can rise as discharges increase. It is measured in the feet above sea level.  1 foot = 0.305 meters.

Inflows: The inflow may be monsoonal rains or lakes, rivers. The average volume of incoming water, in unit period of time.

Evaporation Losses: Conversion of liquid to vapor state by latent heat. Water gets saturated in the form of vapor due to rise in water temperature.

Discharge: Volume of water released from power dam at a given time measured as cubic feet per second.

Gross Head: A dam’s maximum allowed vertical distance between upper stream’s surface water fore bay elevation and the down stream’s surface water (tail water) elevation at the tail race for reaction wheel dams.

Actual Generation: The amount of electricity actually generated apart from installed generation.

Selected power plants in three regions of Andhra Pradesh

Power Plant by

Fuel Type

Rayalaseema Region Telangana Region Coastal Region
Thermal Rayalaseema Thermal Power Plant .Kothagudaem Thermal Power Station  O & M

 

.Kothagudaem Thermal Power Station Stage V

 Narla Tata Rao Thermal Power Plant
Hydel Nagarjuna Sagar Main Power House

 

Nagarjuna Sagar Left Canal Power House

 

Nagarjuna Sagar Right Canal Power House

Srisailam Left canal power house

 

Srisailam right Canal Power House

 

 

The collection of data includes a monthly time series data analysis during the period (1995-96 to 2008-09). Analysis of hydel and thermal electric water use data in the existing power plants clearly indicates that there is wide variability in unitary thermal and hydel electric water use within the system. The multi- variate regression  procedures were used to identify the significant determinants  of thermal and hydel water withdrawals in various power plants i.e. five hydel and four thermal power plants. The unit variability of unit water usage indicates that there is significant potential for water conservation in existing hydel and thermal electric power plants.

3.0 Approach and Methodology

 The study includes three main components. (a) A series of site visits and interviews with power plant personnel. (b) Field surveys of selected hydel and thermal power plants of Andhra Pradesh (c) The multiple regression analysis of power generation data and other associated information.

Summary of site visits: Site visits for selected five hydel namely Nagarjuna Sagar Main Power House, Nagarjuna Sagar Left Canal Power House, Nagarjuna Sagar Right Canal Power House, Srisailam Left canal power house and Srisailam right Canal Power House and four thermal namely Rayalaseema Thermal Power Plant, Kothagudaem Thermal Power Station O & M, Kothagudaem Thermal Power Station Stage V and Narla Tata Rao Thermal Power Plant have been made to assess the overall performance scenario of power plants and also to examine the extent of water irregularities .Appraisal of Power Plant Survey:  The research estimates of hydel and thermal Electric Energy water withdrawals are based upon the authenticated sources of data provided by respective hydel and thermal power plants of Andhra Pradesh Generation Corporation of India Limited. In order to transparently clarify the way that power generation officials responded to this kind of field survey in practice and to solicit information from them on factors responsible for water use at power generation facilities, site visits have been taken up.  At various Power plants several personal interviews with power plant officers helped to identify the types of onsite water uses, the measurement of these uses and provision of information on various types of cooling systems and water use procedures employed by hydel and thermal electric energy generation facilities.

The purpose of conducting a series of personal interviews with power plant officials can be listed as follows:

(a)    Scrutinize and examine the power generation water use and water withdrawals from intake (surface water) to discharge in various types of facilities.

(b)   Observing the fact that all the water with drawals (hydel and thermal) are metered.

(c)    Detailed analysis about important onsite uses of water and its significant determinants

(d)      To obtain feedback on the cooling system level of water use in power stations.

Multiple Regression Models of Water Use

The principal sources of data used in the multi variate analyses of thermal and hydel power plants are most accurate and provides a fairly comprehensive review of plant characteristics, power generation and water withdrawal details. The data in electronic format and in official records was available for the years 1996-97 to 2008-09. The weather data i.e. especially related to water temperatures during summer, rainy and winter was collected in order to examine the influence of it on total thermal and hydel water withdrawals.

At the end it can be concluded that the site visits and field surveys helped to identify important concerns about water measurement and use at thermal and hydel electric power plants. Added to this, these factors have received attention in the development of models to describe hydro and thermal electric water use. All the above mentioned information proved very much useful in the design of data analysis that was used to develop water use bench marks.

4.0 RESULTS AND DISCUSSION: ESTIMATION AND INTERPRETATION OF MODEL SPECIFICATIONS

Hydel based Electric Energy Power Plants

Model Specification I Nagarjuna Sagar Main Power House

 (Appendix table: A1)

In model 1 the estimated regression equation for total hydel water withdrawals is in the linear form as follows:

*              * *                          *

WHE = -146.238-0.080RE-0.258SC+0.350TW+0.133GH+50.67AG

                                               (-3.96)         (3.144)                      (119.87)

N= 154, R2 =0.99, f= 5543.05

  • The estimated equation indicates that the total hydel water withdrawals are inelastic with respect to storage capacity. This kind of negative relationship indicates that the hydel water withdrawals are somewhat in responsive to changes in the storage capacity. The coefficients are statistically significant at 1 % level.
  • The total hydel water withdrawals are elastic with tail water level and actual generation that hold a positive relationship. The coefficients are statistically significant at 5 % and 1 % level.
  • The t-ratio of regression coefficients is highly significant for three independent variables namely SC, TW and AG. As the t ratio value is greater than 2.58 indicates that the relation between dependent variable and independent variables observed in the sample holds good.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as reservoir elevation and gross feet, as the t- value is very small.
  • The R2 (coefficient of determination) is 0.99. It means that the independent variables tail water level, actual generation and storage capacity can explain 99 percent of variation in the dependent variable (WD) and remaining 1 percent variation is unexplained by the model. As R2 is very high, the estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher and more significant at 1% level. This clearly indicates that the regressors are significantly associated with dependent variable.

Model SpecificationII Nagarjuna Sagar Left Canal Power House

         (Appendix Table: A2)

*                                 *            *                    *

WHE = 1660.770-3.516RE-21.705SC+9.653TW+491.286AG+0.130EL

            (3.314)                       (4.16)        (3.84)         (15.67)

 N= 166, R2= 0.78, f = 116.22

  • The estimated regression coefficients indicate that the best independent that have significant effect are storage capacity and actual generation with significant levels at 1 % for each of independent variables.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. As t ratio value is greater than 2.58, it indicates that the relation between Hydel Water withdrawal and independent (SC) and (AG) observed in the sample holds good.
  • The R2 is 0.78. It means that the independent variables SC and AG can explain 78 percent variation in the dependent variable and the remaining 22 % variation is unexplained by the model. The estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (116.22) and more significant at 1 % level. This indicates that the regressors SC and AG are significantly associated with dependent variable.

Model Specification III Nagarjuna Sagar Right Canal Power House 

         (Appendix Table: A3)

             *                                      *                                                     *

WHE = 6133.252+0.628 RL-58.029 SC+0.414EL+37.493TW+486.057 AG

          (7.314)                        (6.063)                                          (16.232)

N= 166, R2= 0.78, f value = 116.22

  • The estimated regression coefficients indicate that the best independent variables that have significant effect are storage capacity and actual generation with significant levels at 1 % for each of independent variables.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. The relation between water withdrawals and Storage capacity and actual generation in the sample holds good as the t-value is greater than 2.58.
  • The t-ratio of regression coefficients is not at all significant for other independent variables such as reservoir level, storage capacity and evaporation losses.
  • The R2 is 0.78. It means that the independent variables SC and AG can explain 78 % variation in the dependent variable and remaining 22 % variation is unexplained by the model. The estimated equation is considered as the equation of very good fit.
  • The overall model is statistically significant as f value is higher (116.22) and more significant at 1 % level. This indicates that the regressors are significantly associated with dependent variable (WD)

Model Specification IV Srisailam Left Bank Power House

                  (Appendix Table: A4)

                                                                *                          *

WHE = -2243.501-0.766RE+1.195SC+57.47AG+0.592EL+4.24TW+0.000IF

                              (-2.27)                         (18.81)                     (2.69)

N= 58   , R2= 0.96, f value = 221.872

  • The estimated regression coefficients indicate that the best independent variables that have significant effect are actual generation and tail water level with significant levels at 1 % and 10 % for independent variables.
  • The t-ratio of regression coefficients is highly significant with three independent variables namely reservoir elevation, actual generation and tail water level. The t-ratio value is greater than 1.96 value for reservoir level and greater than 2.58 value for actual generation and tail water level. This indicates that the relation between WD and independent variables AG and reservoir elevation observed in the sample holds good.
  • The t- ratio of regression coefficients is not at all significant for other independent variables such as evaporation losses and inflows.
  • The R2 is 0.96. It means that the independent variables reservoir level, actual generation and tail water level can explain 96 % of variation in the dependent variable and remaining 4% is unexplained by the model. Thus the estimated regression coefficient is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (221.872) and more significant at 1 % level. This indicates that the regressors AG and TW are significantly associated with dependent variable. (WD)

 

Model Specification V Srisailam Right Bank Power House

                   (Appendix Table: A5)

                 *                        *        *

Y = -7630.380-1.78RE+0SC+56AG+0.051EL+0.627TW+0.289GH

              (-4.199)             (-4.3)  (122.65)

  N= 138    , R2    = 0.99 and f value = 4.59

  • The estimated regression coefficients indicate that the best independent variables that have a significant effect are storage capacity and actual generation with significant levels at 1 % level each of independent variable.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. The t- ratio value is greater than 2.58 for SC and AG that indicates that the relation between WD and independent variables SC and AG holds good.
  • The t- ratios of regression coefficients is not at all significant for other independent variables such as evaporation losses, tail water level and gross head.
  • The R2 is 0.99. It means that the independent variables such as storage capacity and actual generation can explain 99 % variation in the dependent variable and remaining 1 % is unexplained by the model. Thus the estimated regression coefficient is considered as an equation of very good fit.
  • The overall relationship was statistically significant as f value is 4.59 and more significant at 1 % level. This indicates that the regressors SC and AG are significantly associated with WD.

Thermal based Electric Energy Power Plants

Model Specification VI Kothagudaem Thermal Power Plant O &M

      (Appendix Table: A6)

                                                     *                                                     *   

Y= -787978.047 + 1.021CC-2.130DB-12.190CD+146.699 OT +1.152 AD+4616.497 CT-817.112AG

                              (3.259)                                                        (3.841)

N= 84, R2 = 0.55, f value = 13.710

  • The estimated regression coefficients indicate that the best explanatory (independent) variables with significant effect on quantity of water with drawals per Kilowatt hour are condenser cooling with cooling towers (Natural Draft cooling system) and ash disposal with significant levels of 5 % and 1 % level.
  • The estimated equation indicates that the total thermal water withdrawals are elastic with respect to condenser cooling and ash disposal. This kind of positive relationship indicates that the thermal water withdrawals are responsive to changes in condenser cooling and ash disposal.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely condenser cooling with Natural Draft CTs and Ash Disposal. The t ratio value is greater than 2.58.
  • This indicates that the importance of technological alternatives (i.e. Condenser Cooling with natural draft CTs) is the significant determinant of water withdrawals. Next ash disposal takes second place as significant determinant of total thermal water withdrawals.
  • The t-ratio of regression coefficient is not at all significant for other independent variables such as DM and Boiler feedback, colony domestic, others (Drinking, Sanitation, Fire fighting, Back Wash Filter), cooling temperature and actual  electric energy generation.
  • The R2 is 0.55. It means that the independent variables such as condenser cooling and ash disposal can explain 55 % of variation in the dependent variable and remaining 45 % variation is unexplained by the model. The estimated equation is considered as good fit.
  • The overall model is statistically significant as f value is higher (13.710) and highly significant at 1 % level. This indicates that the regressor’s condenser cooling with Natural Draft CT’s and Ash Disposal are significantly associated with dependent variable WDs.

Model Specification VII Kothagudaem Thermal Power Station Stage V

          (Appendix Table: A7)

                                   *                *

Y= 98233.879+0.873 CC+1.186AD+0.111 DB-1688.373CT+32.019 AG

                               (20.91)       (15.247)

              N= 83, R2= 0.97, f value = 706.164

  • The estimated regression coefficients indicate that the best independent variables with significant effect on quantity of WD per million tonnes are Condenser cooling and ash disposal with significant levels at 1% level each.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely Condenser cooling with natural draft CT’s and Actual Generation. The t- ratio value is greater than 2.58. Here the significant determinant of WD’s are CC with natural draft CT’s. Next comes ash disposal as second good determinant.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as BF & DM, cooling temperature and Energy Generation.
  • The R2 is 0.97. It means that independent variables such as CC and AD can explain 97 % of variation in the dependent variable (Water withdrawal) and remaining 3 % variation are unexplained by the model. Thus the estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (706.164) and highly significant at 1 % level. This indicates that the regressors condenser cooling with NDCT’s and Ash Disposal are significantly associated with Water withdrawal’s (Dependent Variable)

Model Specification VIII Rayalaseema Thermal Power Plant

          (Appendix Table: A8)

                           *

Y = 10334.674+0.745 CC+8.725 BF+0.847 AS-4.143 AG-145.408 CT

     (2.677)                (3.007)

N= 35, R2 = 0.87 and f value = 33.145

  • The estimated regression coefficients indicate that the best independent variables with significant effect on quantity of Water Withdrawal Condenser cooling with significant levels at 5%.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for one independent variables namely Condenser cooling with natural draft CT’s .The t- ratio value is greater than 2.58. Here the significant determinant of WD’s are CC with natural draft CT’s.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as BF & DM, Ash Disposal cooling temperature and Energy Generation.
  • The R2 is 0.87. It means that independent variables such as CC can explain 87 % of variation in the dependent variable (WD) and remaining 13 % variation are unexplained by the model. Thus the estimated equation is considered as an equation of very good fit.
  • The over all model is statistically significant as f value is higher (33.145) and highly significant at 1 % level. This indicates that the regressors condenser cooling with NDCT’s are significantly associated with WD’s (Dependent Variable)

Model Specification IX Narla Tata Rao Thermal Power Plant

                     (Appendix Table: A9)

                          *                               *   

Y = 139993.709 + 1.002CC -0.863CD + 1.031 AS- 373.483 CT- 56.843 AG

                                    (1277.966)                 (19.88)

N=      R2 = 1.00, f value = 907849.564

  • The estimated regression coefficients indicate that the best explanatory (independent) variables with significant effect on quantity of water with drawals per Kilowatt hour are condenser cooling with cooling towers ( Induced l Draft cooling system) and ash disposal with significant levels of 1 % and 1 % level.
  • The estimated equation indicates that the total thermal water withdrawals are elastic with respect to condenser cooling and ash disposal. This kind of positive relationship indicates that the thermal water withdrawals are responsive to changes in condenser cooling and ash disposal.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely condenser cooling with Induced Draft CTs and Ash Disposal. The t ratio value is greater than 2.58.
  • This indicates that the importance of technological alternatives (i.e. Condenser Cooling with Induced draft CTs) is the significant determinant of water withdrawals. Next ash disposal takes second place as significant determinant of total thermal water withdrawals.
  • The t-ratio of regression coefficient is not at all significant for other independent variables such as, colony domestic, cooling temperature and actual electric energy generation.
  • The R2 is 1.00. It means that the independent variables such as condenser cooling and ash disposal can explain 100 % of variation in the dependent variable. This shows that we have accounted for almost all the variability with the variables specified in the model. The estimated equation is considered as very good fit.
  • The overall model is statistically significant as f value is higher (907849.564) and highly significant at 1 % level. This indicates that the regressor’s condenser cooling with Induced Draft CT’s and Ash Disposal are significantly associated with dependent variable WDs.

The pertinent conclusion of this study is there may be significant potential for water conservation after having identified the significant determinants of total thermal water withdrawals i.e. condenser cooling and ash disposal. The choice of explanatory variable for eg: Induced draft and natural draft technological innovation was able to address the significant changes of water withdrawals.

5.0  CONCLUSION AND RECOMMENDATION

The thermal and hydel power plants sustenance is very much under stake due to major reason of fresh water shortages in power generation. The most sophisticated technology followed in advanced countries namely Concentrated solar thermal power integrated with combined system of conventional steam plant, Fresnel Solar Collector and  Solar Flower Tower can be used as a replica even in developing countries like India though not cost effective in order to counteract the water shortage problem

REFERENCES

Benedy Kt Dziegielewski, Thomas Bik (August 2006), “ Water Use Bench Marks for Thermo Electric Power Generation” Project report, Southern Illinois University, United States

Geological Survey, 2004, USGS National Competitive Grants Program.

Gbadebo Oladosu, Stan Hadley, Vogt D.P. and Wilbanks J.J. (September, 2006), “Electricity

Generation and Water Related Constraints: An Empirical Analysis of Four South Eastern

States”, Oak Ridge National Laboratory, Oak Ridge Tennessee.

Sitanon Jesdapipat and Siriporon Kiratikarnkul, “ Surrogate pricing of water: The Case of micro Hydro –Electricity Co-operatives in Northern Thailand”.

 Xiaoying Yang & Benedy Kt Dziegielewski (February,2007), “ Water Use by Thermo Electric power plants in the United states” Journal of the American Water Resources Association, Vol 43, No.1.

“Estimating Water Use in United States: A new Paradigm for National Use Water Use Information Programme”(2002),

http://books.nap.edu/openbook.php?record_id=10484&page=95

 

Data Sources

Annual Report on the Working of SEBs and Electricity Departments, Planning Commission, Various Issues

Administrative Reports of Andhra Pradesh Generation Corporation of India Limited (APGENCO),Various Issues. Field Level data of selected thermal and hydel power stations authenticated  by APGENCO.

APPENDIX TABLES

Table: A1: Nagarjuna Sagar Main Power House

Variables Entered/Removed  
Model Variables Entered Variables Removed Method  
1 acutal_generation, tail_water_level, Reser_elevation, Gross_feet, Storage_capacitya . Enter  
a. All requested variables entered.    
b. Dependent Variable: water_discharge_cums  
Model Summary  
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .997a .995 .995 512.92868  
a. Predictors: (Constant), acutal_generation, tail_water_level, Reser_elevation, Gross_feet, Storage capacity  
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 7291771208.745 5 1458354241.749 5543.053 .000a
Residual 38675087.446 147 263095.833    
Total 7330446296.191 152      
a. Predictors: (Constant), acutal_generation, tail_water_level, Reser_elevation, Gross_feet, Storage capacity
b. Dependent Variable: water_discharge_cums      
 

Coefficientsa

 
Model Unstandardized Coefficients Standardized Coefficients t Sig.  
B Std. Error Beta  
1 (Constant) -146.238 1555.816   -.094 .925  
Reser_elevation -.080 .093 -.012 -.865 .389  
Storage capacity -.258 .065 -.091 -3.966 .000  
tail_water_level .350 .111 .031 3.144 .002  
Gross_feet .133 .094 .026 1.419 .158  
acutal_generation 50.669 .423 1.041 119.869 .000  
a. Dependent Variable: water_discharge_cums        

Table: A 2 Nagarjuna Sagar Left Canal Power House

Variables Entered/Removedb  
Model Variables Entered Variables Removed Method  
1 evaporation, energe_bus, twl_ft, storage capacity, reservior_levela . Enter  
a. All requested variables entered.    
b. Dependent Variable: water_drawals  
Model Summary  
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .864a .747 .739 2350.84646  
a. Predictors: (Constant), evaporation, energe_bus, twl_ft, storage capacity, reservior_level  
 

ANOVAb

Model Sum of Squares df Mean Square F Sig.
1 Regression 2626964399.664 5 525392879.933 95.068 .000a
Residual 889763133.646 161 5526479.091    
Total 3516727533.310 166      
a. Predictors: (Constant), evaporation, energy bus, twl_ft, storage capacity, reservior_level  
b. Dependent Variable: water_drawals        
Coefficientsa  
Model Unstandardized Coefficients Standardized Coefficients t Sig.  
B Std. Error Beta  
1 (Constant) 1660.770 501.102   3.314 .001  
reservior_level -3.516 3.411 -.157 -1.031 .304  
storage capacity -21.705 5.219 -.538 -4.159 .000  
twl_ft 9.653 2.510 .394 3.846 .000  
energy bus 491.286 30.765 .987 15.969 .000  
evaporation .130 .508 .015 .255 .799  
a. Dependent Variable: water_drawals        

Table: A3 Nagarjuna Sagar Right Canal Power House

Model Variables Entered      
1 generation bus, reservior_level, evaporation, storage capacity, tailwaterlevela      
b. Dependent Variable: water_drawals

Model Summary

 
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .885a .784 .777 3767.05581  
a. Predictors: (Constant), generation bus, reservior_level, evaporation, storage capacity, tailwaterlevel  
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 8246365913.182 5 1649273182.636 116.222 .000a
Residual 2270513515.133 160 14190709.470    
Total 10516879428.315 165      
a. Predictors: (Constant), generation bus, reservior_level, evaporation, storage capacity, tailwaterlevel  
b. Dependent Variable: water_drawals        
Coefficientsa  
Model Unstandardized Coefficients Standardized Coefficients t Sig.  
B Std. Error Beta  
1 (Constant) 6133.252 838.604   7.314 .000  
reservior_level .628 7.571 .016 .083 .934  
storage capacity -58.029 9.570 -.832 -6.063 .000  
Evaporation .414 .810 .027 .511 .610  
Tailwaterlevel 37.493 21.598 .263 1.736 .084  
generation bus 486.057 29.945 1.045 16.232 .000  
a. Dependent Variable: water_drawals        

Table:  A4 Srisailam Left Canal Power House

Variables Entered/Removedb  
Model Variables Entered Variables Removed Method  
1 inflow, Reservoir, evaporat, Actual generation, Tail water, storage_capacitya . Enter  
a. All requested variables entered.    
b. Dependent Variable: water_withdra  
Model Summary  
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .981a .963 .959 1454.18057  
a. Predictors: (Constant), inflow, Reservoir, evaporat, Actual generation, Tail water, storage capacity  
                                                                                   ANOVAb  
Model Sum of Squares df Mean Square F Sig.  
1 Regression 2815082375.894 6 4.692E8 221.872 .000a  
Residual 107846697.597 51 2114641.129      
Total 2922929073.491 57        
a. Predictors: (Constant), inflow, Reservoir, evaporat, Actual generation, Tail water, storage capacity    
b. Dependent Variable: water_withdra          
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -2243.501 2527.275   -.888 .379
Reservoir -.766 .337 -.239 -2.272 .027
storage capacity 1.195E-6 .000 .000 .004 .997
Actual generation 57.476 3.055 .953 18.814 .000
evaporat .592 .939 .081 .631 .531
Tail water 4.237 1.572 .248 2.695 .010
inflow .000 .002 -.017 -.339 .736
a. Dependent Variable: water_withdra        

Table: A5 Srisailam Right Canal Power House

Model Variables Entered Variables Removed Method  
1 Gross head, Tailwaterlevel, actual generation, Evaporation, storage, Reservoir . Enter  
a. All requested variables entered.    
b. Dependent Variable: water withdrawals  
 

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate
1 .998a .995 .995 631.39218
a. Predictors: (Constant), Gross head, Tailwaterlevel, actual generation, Evaporation, storage, Reservoir

ANOVAb  
Model Sum of Squares df Mean Square F Sig.  
1 Regression 1.099E10 6 1.832E9 4.596E3 .000a  
Residual 5.222E7 131 398656.090      
Total 1.105E10 137        
a. Predictors: (Constant), Gross head, Tailwaterlevel, actual generation, Evaporation, storage, Reservoir  
b. Dependent Variable: water withdrawals        
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -7630.380 1817.341   -4.199 .000
Reservoir -.178 .322 -.027 -.553 .581
storage .000 .000 -.068 -4.288 .000
actual generation 56.314 .459 1.022 122.651 .000
Evaporation .051 .139 .005 .365 .716
Tailwaterlevel .627 .334 .059 1.874 .063
Gross head .289 .320 .036 .904 .368
a. Dependent Variable: water withdrawals      

Table: A6 Kothagudaem Thermal Power Plant O &M

Variables Entered/Removedb  
Model Variables Entered Variables Removed Method  
1 energy generation , cooling temp, DM Water & Boiler Feed back , Ash Disposal , Condenser Cooling , Colony domestic , (Drin, Sani, Firefigh, Backwarhfiler) a . Enter  
a. All requested variables entered.    
b. Dependent Variable: Total water consumption  
 

Model Summary

 
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .747a .558 .517 289298.132  
a. Predictors: (Constant), energy generation , cooling temp, DM Water & Boiler Feed back , Ash Disposal , Condenser Cooling , Colony domestic , (Drin, Sani, Firefigh, Backwarhfiler)  
ANOVAb  
Model Sum of Squares df Mean Square F Sig.  
1 Regression 8.032E12 7 1.147E12 13.710 .000a  
Residual 6.361E12 76 8.369E10      
Total 1.439E13 83        
a.     Predictors: (Constant), energy generation , cooling temp, DM Water & Boiler Feed back , Ash Disposal , Condenser Cooling , Colony domestic , (Drin, Sani, Firefigh, Backwarhfiler)  
       
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -787978.047 1.334E6   -.591 .557
Condenser Cooling 1.021 .313 .551 3.259 .002
DM Water & Boiler Feed back -2.130 5.717 -.038 -.373 .710
Colony domestic -12.190 15.642 -.250 -.779 .438
(Drin, Sani, Firefigh, Backwarhfiler) 146.699 201.477 .467 .728 .469
Ash Disposal 1.152 .300 .409 3.841 .000
cooling temp 4616.497 10000.955 .039 .462 .646
energy generation -817.112 1096.318 -.295 -.745 .458
a. Dependent Variable: Total water consumption      

Table:  A7 Kothagudaem Thermal Power Plant Stage V

  Variables Entered/Removedb  
  Model Variables Entered Variables Removed Method  
  1 Energy Generation, ASH DIS-POSAL (MT), Cooling Temperature , Boiled Feed and DM plant Regeneration, COOLING TOWER MAKEUP        (MT)a . Enter  
  a. All requested variables entered.    
  b. Dependent Variable: TOTAL CONS.  (MT)  
  Model Summary  
  Model R R Square Adjusted R Square Std. Error of the Estimate  
  1 .989a .979 .977 64726.513  
  a. Predictors: (Constant), Energy Generation, ASH DIS-POSAL (MT), Cooling Temperature , Boiled Feed and DM plant Regeneration, COOLING TOWER MAKEUP        (MT)  
 

ANOVAb

Model Sum of Squares df Mean Square F Sig.
1 Regression 14792454121098.932 5 2958490824219.786 706.164 .000a
Residual 322593153570.889 77 4189521474.947    
Total 15115047274669.820 82      
a. Predictors: (Constant), Energy Generation, ASH DIS-POSAL (MT), Cooling Temperature , Boiled Feed and DM plant Regeneration, COOLING TOWER MAKEUP        (MT)
b. Dependent Variable: TOTAL CONS.  (MT)      
  Coefficientsa  
  Model Unstandardized Coefficients Standardized Coefficients t Sig.  
  B Std. Error Beta  
  1 (Constant) 98233.879 76676.230   1.281 .204  
  COOLING TOWER MAKEUP        (MT) .873 .042 .577 20.912 .000  
  ASH DIS-POSAL (MT) 1.186 .078 .484 15.247 .000  
  Boiled Feed and DM plant Regeneration .111 .978 .003 .114 .910  
  Cooling Temperature -1688.373 2158.260 -.014 -.782 .436  
  Energy Generation 32.019 115.619 .005 .277 .783  
  a. Dependent Variable: TOTAL CONS.  (MT)          

Table: A 8 Rayalaseema Thermal Power Plant

  Variables Entered/Removedb  
  Model Variables Entered Variables Removed Method  
  1 Cooling Temp, Ash slurry, Actual Generation, Power Generation, Boiler feed, Condenser cooling, BCWa . Enter  
  a. All requested variables entered.    
  b. Dependent Variable: Water consumption  
  Model Summary  
  Model R R Square Adjusted R Square Std. Error of the Estimate  
  1 .934a .873 .846 1324.085  
  a. Predictors: (Constant), Cooling Temp, Ash slurry, Actual Generation, Power Generation, Boiler feed, Condenser cooling, BCW  
  ANOVAb  
  Model Sum of Squares df Mean Square F Sig.  
  1 Regression 3.487E8 6 5.811E7 33.145 .000a  
  Residual 5.084E7 29 1753200.788      
  Total 3.995E8 35        
  a. Predictors: (Constant), Cooling Temp, Ash slurry, Actual Generation, Power Generation, Boiler feed, Condenser cooling, BCW  
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 10334.674 3861.078   2.677 .012
Condenser cooling, BCW .745 .248 .432 3.007 .005
Boiler feed 8.725 4.628 .244 1.885 .069
Ash slurry .847 .501 .230 1.692 .101
Power Generation -.595 .388 -.138 -1.532 .136
Actual Generation -4.143 5.478 -.077 -.756 .456
Cooling Temp -145.408 94.141 -.109 -1.545 .133
a. Dependent Variable: Water consumption        

Table : A 9 Narla Tata Rao Thermal Power Plant

  Variables Entered/Removedb  
  Model Variables Entered Variables Removed Method  
  1 Energy Generation, Condenser cooling & BCW (KL), Cooling Temperature , Ash slurry water (KL), Colony Domestic (KL)a . Enter  
  a. All requested variables entered.    
  b. Dependent Variable: Totalwaterconsumption  
  Model Summary  
  Model R R Square Adjusted R Square Std. Error of the Estimate  
  1 1.000a 1.000 1.000 50290.302  
  a. Predictors: (Constant), Energy Generation, Condenser cooling & BCW (KL), Cooling Temperature , Ash slurry water (KL), Colony Domestic (KL)  
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 11480277367590772.000 5 2296055473518154.000 907849.564 .000a
Residual 42994946072.977 17 2529114474.881    
Total 11480320362536844.000 22      
a. Predictors: (Constant), Energy Generation, Condenser cooling & BCW (KL), Cooling Temperature , Ash slurry water (KL), Colony Domestic (KL)
b. Dependent Variable: Totalwaterconsumption      
  Coefficientsa  
  Model Unstandardized Coefficients Standardized Coefficients t Sig.  
  B Std. Error Beta  
  1 (Constant) 139993.709 137540.088   1.018 .323  
  Condenser cooling & BCW (KL) 1.002 .001 .987 1277.966 .000  
  Colony Domestic (KL) -.863 .584 -.001 -1.476 .158  
  Ash slurry water (KL) 1.031 .052 .018 19.879 .000  
  Cooling Temperature -373.483 3763.081 .000 -.099 .922  
  Energy Generation -56.843 138.469 .000 -.411 .687  
  a. Dependent Variable: Totalwaterconsumption        

Econometric Competencies and Entrepreneurship Development 

Adebayo G ADEBAYO

Department of Accountancy

Rufus Giwa (Formally Ondo State) Polytechnic.

Owo, Ondo State, Nigeria

Abstract

This study is designed specifically to demonstrate the application of econometrics or quantitative analysis especially in helping the numerous small and medium enterprises in Nigeria in critical decision making. An Agro-business labour saving fabricating firm in Nigeria was selected as a case and pilot study for all other small or medium enterprises fabricates labour saving machines such as the cassava frying machine (CFM) and palm-oil extracting machine (PEM). The agro-business firm also fabricate cassava grater (GRT) and appliances (APP). The entrepreneur is worried on some critical problems. First, whether these special offers would affect the sale of the major products CFM and PEM since the same customers buy the major products and the special offers. This implies that money spent on special offers will not be spent on CFM and/or PEM. An increase in the sales of special orders may correspond to a decrease in the sale of CFM and PEM. Second is what would be the total sales taking into consideration especially the seasonal fluctuations and third how to identify existing customers who are most likely to respond to proposed improved offer on CFM and PEM. This is important as the Firm fabricates these products on demand. Three models were designed to find solution to each of the problems. The first model was the two-stage least square regression. Findings revealed that the special offer sales GRATER and APPLIANCES) had no negative effect on the sales of the major products (CDM and PEM). All the products significantly contributed to the total sales. The second model was forecasting. The model satisfied forecasting requirements and was able to forecast total sales from April to December 2016 taking into consideration seasonal variations referred to as fluctuations. The third model is the recency, frequency and monetary (RFM) analysis. This marketing tool is used to classify customers according to how recent they patronize, how often and how much is involved in individual customer’s cumulative patronage. The RFM analysis carried out on the customers identified the firm’s customers that would likely respond to new offer. An RFM score of 300 and above qualified a customer to be selected. Part of the recommendations was that entrepreneurs should avail themselves of the decision making tools for better management of their enterprises.

 Keywords: 

 Introduction

The world is facing many economic challenges and issues. These do not isolate the developed economies. Both are debt ridden, with regional economic imbalances and geo-political challenges. There is general economic meltdown in the world market. The Nigeria economy has been acutely affected because of the fall in the price of crude oil in the world market as a result of these economic imbalances and trade policies that are not conducive to Nigerian oil market. Since revenue from crude oil takes about 90% of the Federal government total revenue, there is the critical need to raise non-oil revenue to ensure fiscal sustainability while maintaining infrastructure and social spending.

The Federal government has taken a bold step towards revamping agriculture and overhauling its solid mineral resources. From an entrepreneurial perspective, the present economic meltdown in Nigeria would eventually be a blessing in disguise. The Central Bank of Nigeria has been instructed by President Buhari to create a “synergy” and organize soft loans to agro-based industries and other export businesses.

The main objectives of the 2016 Buhari government’s budget are to make the “synergy” work among all the different players in the country’s economy. These include the banking system, financial institutions, government entities, regulators and other arms of the government.

  • Entrepreneurial Development.

The credibility of the Buhari government among the Nigerian populace as a result of his zero tolerance to corruption and his “Big Bang” [a rapid reform which is economically necessary as a result of severe macroeconomic imbalances (Gelb, Jefferson and Singh 1993) such as this period of economic crisis in Nigeria] approach to major economic reforms, has spurred many investors into agro-based industries. They believe that the government meant business.

One of these respondents is a medium scale firm in Nigeria that fabricates machines to boost agricultural produce. The Firm has recently reactivated two of its machines-the Cassava Frying Machine (CFM) and the Automated Palm oil Extracting Machine (PEM)

  • Objectives of the Firm

The major objectives of the Firm in fabricating these labour saving machines is the issue of health hazard on the one hand. Many local manual cassava frying and palm oil producers especially women, had been subjected to untimely death due to incessant heat from the local frying system and the palm oil production. These machines would reduce drastically the feminine life wasted on daily basis. On the other hand, these machines will boost agricultural produce of “GARI”(a local name for the fine grain output from the frying process of cassava) and palm oil to contribute to increase the recent low GDP rate in Nigeria. [Economic growth in the last quarter of 2015 was 2.1% while total growth in the year was 2.8%, the slowest since 1999 to date (NBS, 2016). This statistics seems to toe the line of the global GDP growth projection of 2.5% which is 0.3% point less than November 2015 outlook (GEO, 2016)]. Therefore any entrepreneurial effort to boost the Nigerian GDP at this trying period is a right decision in the right direction.

  • The Cassava Frying Machine

The CFM is powered by electricity and is capable of frying about 5-10-kg of already peeled, washed and had been cut into smaller sizes and loaded into the machine drum. The machine grinds the cassava, presses it cause fermentation and fry the cassava into very fine grains. Adjustments by the use of some special appliances sold by the firm to its customers may cause the machine to dry cassava from the normal “GARI” into smoother form or into powder. It is an automated machine with 100% local components. A single CFM costs N45, 000 ($225)

  • The Automatic Palm oil Extracting Machine..

The PEM is an automated machine that produces fine, clear, well heated palm oil. Palm fruits are removed from the bunch after some period for partial fermentation, washed and then loaded into the machine tank or drum to the brim. The machine twists and separate kernels from tissues, extract the paste and heat into fine glossy oil. The slag is released from an outlet. It is also powered by electricity. The machine is made with 90% local components, that is, 10% components have to be imported. A single machine costs N50, 000 ($250), about N5000 ($25) more than CFM probably as a result of the cost of the imported components.

  • Statement of the Problem.

The two machines fabricated by the firm are the Cassava Frying Machine (CFM) and Palm oil Extracting (PEM) and are sold to customers on demand. Apart from these machines, there are two special offers that are also sold. These are grater (GRT) and appliances (APP). The APP is a device to enhance either the CFM or PEM, especially as a power saving device, at the customers’ option and is sold at the rate of N12, 000 per unit. These appliances are capable of making adjustments possible to CFM and PEM and other agro-based machines.  The GRT powered by electricity, the pealed, and washed cassava are loaded into its receptor and it grinds cassava very well. This is sold at the rate of N15, 000 per unit. Every month the Firm makes these special offers to customers who need the APP on previously bought machines or on a proposed purchase of CFM and /or PEM. The GRT is mostly purchased by customers who could not afford CFM. The firm is now concerned about the following problems:

  1. Whether these special offers would affect the sale of the major products CFM and PEM since the same customers buy the major products and the special offers. This implies that money spent on special offers will not be spent on CFM and/or PEM. An increase in the sales of special orders may correspond to a decrease in the sale of CFM and PEM.
  2. How to make a good forecast of the total sales from major products and the special offers.
  3. How to identify existing customers who are most likely to respond to proposed improved offer on CFM and PEM. This is important as the Firm fabricates these products on demand.
  • Objectives of the Study.

The primary objective of this study is to underscore the importance of econometric or quantitative analysis in solving most of the problems of entrepreneurs and hence enhance entrepreneurship development in Nigeria. In realization of this objective, the study had focused on helping to collect all relevant data on the Firm’s customers. These include each customer’s date of transactions with the Firm, amount of purchases each time, total number of transaction and the most recent transaction. There will also be collection of data on total monthly sales on CFM, PEM, APP and GRT for the past 51 months. Other secondary objective, in addition are:

  1. To create compactible models to find solution to each of the Firm’s area of concern.
  2. To discuss the findings and give expert recommendations on the findings.

  1. Review of Related Literature

2.1 The Nigerian Cassava

Cassava is well known as manihot esculenta or manilot utilissima (Yakasi, 2010). In Nigeria, cassava is grown in all the ecological zones andit is planted all the year round on the availability of moisture (Odoemenem and Otanma, 2011). Production is vital to the economy of Nigeria as the country is the world’s largest producer of the commodity. The crop is produced in 24 of the country’s 36 states. In 1999, Nigeria produced 33 million tonnes, while a decade later, it produced approximately 45 million tonnes, which is almost 19% of production in the world. The average yield per hectare is 10.6 tonnes.(Wikipedia, n.d.)

In Nigeria, cassava production is well-developed as an organized agricultural crop. It has well- established multiplication and processing techniques for food products and cattle feed. Cassava is processed in many processing centres and fabricating enterprises set up in the country. Cassava is used in the preparation of several household foods and derivatives such as paste, biscuits, bread sagos and sauce. Its starch is for industrial use such as baby food, jelly, custard poeders and confectioneries (Echebiri and Edeba, 2008).  Roots or leaves are made into flours. Flours are of three types, yellow garri, white garri, or intermediate colour. These varieties are a matter of choice and traditional attachment. Therefore it may be erroneous to classify any type as the best product in Nigeria. Its other products are as dry extraction of starch, glue or adhesives, modified starch in pharmaceutical as dextrines, as processing inputs, as industrial starch for drilling, and processed foods.

 2.2 Palm Oil in Nigeria

 

Palm oil is as old as Nigeria itself and has been an important subsistence, but until recently a supportive factor in the diet of many Nigerians. Palm oil is the world’s largest source of edible oil, accounting for 38.5 million tonnes or 25% of the global edible oil and fat production (MPOC, 2007). Palm oil is a product extracted from the fleshy mesocarp of the palm fruit (Elaeis guineensis). The global demand for palm oil is growing thus, the crop cultivation serves as a means of livelihood for many rural families, and indeed it is in the farming culture of millions of people in the country. Akanbge et al (2011), referred to this product as capable of having multiple values, a feature that underscores its acclaimed economic importance. Eventually oil graduated from domestic use to industrial application which had appreciated its production geometrically (Omereji,2005). Ekine and Onu(2008) estimated palm oil consumption of about two litres per a family of five per week for cooking. Today, consumption must have tripled since Nigerian house hold now uses palm oil beyond normal consumption. Palm oil is also an essential multipurpose raw material for both food and non-food industries (Armstrong, 1998). Palm oil is used in the manufacturing of margarine, soap candle, base for lipstick, waxes and polish bases in a condense form, confectionary (Embrandiri et at., 2011; Aghalino, 2000), and other uses in pharmaceuticals.

2.3 Forecasting

Because economic and business conditions vary over time, managers must find ways to keep abreast of the effects that such changes will have on their organizations. One technique that can aid in plan­ning for future needs is forecasting. Although numerous forecasting methods have been devised, they all have one common goal—to make predictions of future events so that projections can then be incorporated into the planning and strategy process.

2.3.1 Time-series forecasting meth­ods involve the projection of future values of a variable based entirely on the past and present obser­vations of that variable. Examples of economic or business time series are the monthly publication of the Consumer Price Index, the quarterly statements of gross domestic prod­uct (GDP), and the annually recorded total sales revenues of a particular company.(Levine et al, 2005)

2.3.2 Least-Squares Trend-Fitting and Forecasting. The component factor of a time series most often studied is trend. Trend is studied as an aid in making intermediate and long-range forecasting projections. As depicted in Figure 1 to obtain a visual impression of the overall long­term movements in a time series, a chart is constructed in which the observed data (dependent variable) are plotted on the vertical axis, and the time periods (independent variable) are plotted on the horizontal axis.(See figure 1 below)

The Forecasting add-on module provides two procedures for accomplishing the tasks of creating models and producing forecasts.

The Time Series Modeler procedure estimates exponential smoothing, univariate Autoregressive Integrated Moving Average (ARIMA), and multivariate ARIMA (or transfer function models) models for time series, and produces forecasts. The procedure includes an Expert Modeler that automatically identifies and estimates the best-fitting ARIMA or exponential smoothing model for one or more dependent variable series, thus eliminating the need to identify an appropriate model through trial and error. Alternatively, one can specify a custom ARIMA or exponential smoothing model.

AR(1)        = a first-order autoregressive to correct for residual serial correlation. It is regressing the dependent variable(s) with linear combination of its past values or lagged values.

MA(1)       = a first-order moving  average model ,i.e., regressing the dependent error with linear combination of its past error or lagged error. It also corrects serial correlation.

2.4 Research Questions.

The following research questions are formulated by the researcher

  1. What are the total monthly sales of the Firm for 51 months?
  2. How many are the numbers of customers of the Firm are their variable such as name, address, city, state- province, post code, gsm numbers, country, gender, age category are available?
  3. How can customers that would respond to new offer be identified?
  4. What is the forecast total sales from April to December 2016?

2.5 Research Hypotheses.

The following research hypotheses are formulated by the researcher at 5% level of significance.

  1. There will be no significant relationship between total sales of special offers and CDM sales.
  2. There will be no significant relationship between total sales of special offers and POE sales.
  3. There will be no significant relationship between total sales of special offers and APP sales.
  4. There will be no significant relationship between total sales of special offers and GRTsales.

 

  1. Methodology

 3.1 Data Collection

The data collected for this study is a demonstrative data only on behalf of the firm. It is characteristic of most small and medium enterprises of nearly the same status. This case study Firm is having about 90 customers. Sales records on CFM, PEM, APP and GRT were collected for 51 months (January 2012 to March 2016). In the Table below, the author computed the total sales for CFM, PEM, APP and GRT and special offer total sales (SPSALES). Others computed are discount on CDM at 3.5% from records and discount on PEM at 4.8%. A log discount on CFM and PEM and their lagged variables are automatically computed in the SPSS 21 and E-View 7.1 work files. The data are presented in Table 1 below.

 Table 1    Company Data for 51 Months – from January 2012 to March 2016

tsales cfm pem spsales grt app discfm dispem
N N N N N N N N
109.50

123.50

130.00

147.00

154.10

174.00

166.50

178.00

166.50

153.20

132.50

132.70

148.80

167.80

180.50

192.50

192.00

202.50

193.50

183.20

196.70

198.80

179.10

169.80

193.10

194.10

213.00

241.40

231.80

226.40

226.50

245.70

231.20

207.50

219.90

214.00

241.30

265.00

267.50

246.90

237.00

234.20

251.20

266.00

270.80

264.80

258.00

243.80

244.00

246.50

263.30

57.50

60.00

62.50

67.50

75.00

78.00

75.00

73.00

58.00

55.00

56.00

57.00

58.00

65.00

84.00

78.00

77.50

74.50

62.50

68.00

83.00

87.50

78.50

73.00

71.00

73.00

83.00

95.00

92.50

82.00

87.00

95.00

100.00

97.00

100.00

95.00

103.00

112.00

113.00

110.00

105.00

97.00

94.00

95.00

94.00

93.00

100.00

103.00

102.00

102.50

103.00

40.00

50.00

52.50

65.00

64.00

60.00

75.00

86.50

87.50

77.50

60.00

52.00

65.00

73.00

67.00

85.00

85.00

97.50

100.50

85.00

83.00

80.00

68.00

60.00

82.50

80.00

90.00

110.00

108.00

105.00

100.00

110.00

90.00

80.00

78.00

72.00

90.00

105.00

107.50

90.00

85.00

90.00

108.00

120.00

125.00

120.00

105.00

85.00

86.00

85.00

100.00

12.00

13.50

15.00

14.50

15.10

16.00

16.50

18.50

21.00

20.70

22.50

13.70

25.50

29.80

29.50

29.50

29.50

30.20

30.20

30.70

30.70

31.30

32.60

36.80

39.60

41.10

40.00

39.40

39.30

39.40

39.50

40.70

41.20

40.50

41.90

47.00

48.30

48.00

47.00

46.90

47.00

47.20

49.20

51.00

51.80

51.80

53.00

55.50

57.00

59.00

60.30

4.00

5.00

6.00

6.00

7.00

8.00

8.50

10.00

12.00

11.50

13.00

14.00

16.00

20.00

19.50

19.00

18.50

18.50

18.50

17.80

18.00

18.80

19.60

20.00

20.80

21.40

22.00

22.40

22.80

23.00

22.50

22.00

21.20

21.50

23.50

26.00

26.30

26.00

26.00

26.10

26.50

27.00

27.20

28.00

27.00

26.80

27.00

29.00

29.00

29.20

29.30

8.00

8.50

9.00

8.50

8.10

8.00

8.00

8.50

9.00

9.20

9.50

9.70

9.80

9.80

10.00

10.50

11.00

12.00

12.00

12.40

12.70

12.50

13.00

16.80

18.80

19.70

18.00

17.00

16.50

16.40

17.00

18.70

20.00

19.00

18.40

21.00

22.00

22.00

21.00

20.80

20.50

20.20

22.00

23.00

24.80

25.00

26.00

28.80

28.00

29.80

31.00

2.01

2.10

2.19

2.36

2.63

2.73

2.63

2.56

2.03

1.93

1.96

1.99

2.03

2.28

2.94

2.73

2.71

2.61

2.19

2.38

2.91

3.06

2.77

2.56

2.49

2.56

2.91

3.33

3.24

2.87

3.04

3.33

3.50

3.40

3.50

3.33

3.61

3.92

3.96

3.85

3.68

3.40

3.29

3.33

3.29

3.26

3.50

3.61

3.57

3.59

3.07

1.92

2.40

2.52

3.12

3.07

2.88

3.60

4.15

4.20

3.72

2.88

2.50

3.12

3.50

3.22

4.08

4.08

4.68

4.82

4.08

3.98

3.84

3.26

2.88

3.41

3.50

4.32

5.28

5.84

5.04

4.80

5.28

4.32

3.84

3.14

3.46

4.32

5.04

5.16

4.32

4.08

4.32

5.18

5.76

6.00

5.76

5.04

4.08

3.57

4.08

4.80

       *The average Sales discount on CFM is 3.5% and on PEM is 4.8%. No discounts for APP and GRT.

            The LAG variable for CFM and LAG for PEM are automatically Created Series in the SPSS 21 and E-                           View 7.1 work files.

    Figure 1    Graph of the Relationship between Total Sales, and the sales from Cassava Frying Machine, Palm oil Extracting Machine, Appliances and Grater

3.2 Models Specification

Three models are specified to the three major problems faced the Firm as stated under the statement of the problem. These are (1) Two-Stage Least Squares Regression (2) Forecasting Models and (3) The RFM Customer Analysis

 

3.2.1 Model 1.Two Stage Least Square Regression

A careful observation of the relationship between CFM, PEM and the special offers shows that there is a feedback loop between the response and the two major products which are predictors. One of the basic assumptions of the ordinary least-squares (OLS) regression model is that the values of the error terms are independent of the values of the predictors. When this “recursivity assumption” is broken, the two-stage least-squares (2SLS) model can help solve these problematic predictors. The 2SLS model assumes that there exist instruments, or secondary predictors, which are correlated with the problematic predictors but not with the error term.

Given the existence of instrument variables, the 2SLS model:

  1. Computes OLS models using the instrument variables as predictors and the problematic predictors as responses.
  2. The model-estimated values from stage 1 are then used in place of the actual values of the problematic predictors to compute an OLS model for the response of interest.

Fifty two months of sales information is collected. The file also includes a variable, special offer, displaying each month’s special offer, which has also been recorded into two indicator variables, Appliances offer and Grater offer that can be used as predictors in the regression procedures. Lastly, the monthly discounts (and log-discounts) offered to customers are also listed. Since the monthly discounts are chosen independent of special offer sales but do not affect CDM and POE sales, they should make good instrument variables. Additionally, the lagged CDM and POE should also make good instruments. The independent, predictors and instrumental variables are in the model description as in Table 2 below.

Table 2: Model Description

   Variables Type of Variable
SpecialOfferSales Dependent
CDM Predictor
POE Predictor
Appliances predictor & instrumental
Grater predictor & instrumental
Logdiccfm Instrumental
Logdispoe Instrumental

Special Offer Sales = α0 + α1CFM + α2POE + α 3APP + α4GRT + ε                                                        Eq 1

where

α 0             =    the intercept or constant term

α      α 4   =    the coefficients of both the predictors and instrumental variables

 ε            =    the stochastic error term

All other variables are as described in the model description in Table 2 above.

3.2.2 Model 2. Forecasting Models

The Model of Forecasting from an Equation, can be dynamic or Static. The static model is chosen because the static forecasting model performs a series of one-step ahead forecasts of the dependent variable:

For each observation in the forecast sample:

 yg+k = c(l) + c(2)xs+k+c(3)zs+k+c(4)ys+k1                                                         Eq 2

Such equation is always using the actual value of the lagged endogenous variable. This is translated into sales forecast of the Firm:

Total Sales    = βo + β1Total Sales (-1) + β2Pm Ar(1) + µi                                                 Eq 3

Where:

Total Sales        =     the total sales from CFM, POE, APP and GRT for 51 months. This is the dependent variable.

Total Sales (-1) = a lagged variable of the dependent variable. This is a one step ahead static forecasts that makes the static forecast more accurate than the dynamic forecast since, each period, the actual value of Total Sales(-1) is used in forming the forecast of Total Sales.

Pmi                  =     the period expressed in months with a total of 51 months. This is the independent variable.

  β 0                            =     the constant term or the model intercept.

  β1                   =     the coefficient of the lagged variable.

  β2                    =     the coefficient of the independent variable.

 µi                    =     the stochastic or error term

The tolal sales above is transformed into:

tsalesFp+k = Ф0 + Ф1 tsalesFp+k-1 2PMp+k + AR(1) +  ε                                                    Eq 4

after being subjected to the static forecasting model

where

tsalesF = total sales forecast

 tsalesFp+k-1 =   the previous month’s (lagged) sales to be added to current sales forecast.

Ф I              = constant and coefficients.

p                 = the base period (month) of start of forecast.

k                 = any month from the forecasting period (April to December 2016)

AR(1)        = a first-order autoregressive to correct for residual serial correlation. It is regressing the dependent variable(s) with linear combination of its past values or lagged values.  .

ε i               = the error term.

For seasonal adjustment of the forecast (fitted) sales.

tsalesfSA  = ⨍i(tsalesFi)                                                                                                     Eq 5

where

⨍i = multiplicative scoring factor for a 12 month period.

tsalesFi = total sales forecast for April to December.

The tsalesfSA  = the seasonally adjusted tsalesF for months (p52-60) i.e. 52, 53, 54, 55, 56, 57, 58, 59 and 60 for April, May, June, July, August, September, October, November and December.

The actual trend base is P51. In Table 1 the total sales corresponding to P51 is N263300. A forecast for p52-60 is required; i.e. April – December 2016.

Figure 3.  Graphical Relationship between Total Sales (tsales) and Fitted Sales (tsalesF)

3.2.3 Model 3.  RFM Analysis         

RFM (Recency, Frequency and Monetary) analysis is a direct marketing option that provides a set of tools designed to improve the results of direct marketing campaigns by identifying demographic, purchasing, and other characteristics that define various groups of consumers and targeting specific groups to maximize positive response rates.

It is a technique used to identify existing customers who are most likely to respond to a new offer. This technique is commonly used in direct marketing. RFM analysis is based on the following simple theory:

The most important factor in identifying customers who are likely to respond to a new offer is RECENCY. Customers who purchased more recently are more likely to purchase again than are customers who purchased further in the past.’The second most important factor is FREQUENCY. Customers who have made more purchases in the past are more likely to respond than are those who have made fewer purchases.

The third most important factor is total amount spent, which is referred to as MONETARY. Customers who have spent more (in total for all purchases) in the past are more likely to respond than those who have spent less.

How RFM Analysis Works

Customers are assigned a recency score based on date of most recent purchase or time interval since most recent purchase. This score is based on a simple ranking of recency values into a small number of categories. For example, if you use five categories, the customers with the most recent purchase dates receive a recency ranking of 5, and those with purchase dates furthest in the past receive a recency ranking of 1. The recency ranking for this Firm is based on the past 20 months as below:

Month Interval Ranking
Jan to Apr 2016 5
Sept to Dec 2015 4
May to Aug 2015 3
Jan to Apr 2015 2
Sept to Dec 2014 1

In a similar fashion, customers are then assigned a frequency ranking, with higher values representing a higher frequency of purchases. For example, in a five category ranking scheme, customers who purchase most often receive a frequency ranking of 5.

 

The number of times a customer made purchases up to a maximum of 5,(or simply, the Transaction Counts) represent frequency ranking for the Firm.

 

Finally, customers are ranked by monetary value, with the highest monetary values receiving the highest ranking. Continuing the five- category example, customers who have spent the most would receive a monetary ranking of 5. The monetary ranking for the firm is:

Naira Value of Purchases Ranking
N120,000 and above 5
N100,000 to N120,000 4
N75,000 to N100,000 3
N40,000 to N75,000 2
Less than 40,000 1

The result is four scores for each customer: recency, frequency, monetary, and combined RFM score, which is simply the three individual scores concatenated into a single value. The “best” customers (those most likely to respond to an offer) are those with the highest combined RFM scores. For example, in a five-category ranking, there is a total of 125 possible combined RFM scores, and the highest combined RFM score is 555.

 Results and Discussion

The results of the models had produced numerous tables in their outputs. The option of  table by table explanation and discussion had been taken.. Where appropriate, results had been discussed. Only the summary points need be discussed further. .Summary of the findings revealed that the special offer sales did not affect the sales of CFM and PEM. The assumption that there exist instruments, or secondary predictors, which are correlated with the problematic predictors but not with the error term may not hold. When CFM, PEM GRT AND APP were regressed on the total sales, they were all significant at less than 5%.

A multiplicative scoring factor to produce the total sales forecast with seasonal adjustments from April to December 2016.

The RFM analysis produced a combined RFM score for each customer at the concatenation of the three individual scores, computed as (recencyx100) + (frequencyx10) _ monetary. The recency, frequency, monetary score is 5 5 5 respectively. A customer must score at least 3 points for recency to qualify for customers that are likely to respond to new offer on CFM and PEM.

 

Conclusion and Recommendations

This study had been able to highlight the importance of econometric applicaton in business decision making especially for small and medium scale enterprises. Decision making is a prerogative of the entrepreneur but using the tools would help the effective management of their enterprises. I is important to add a caveat that professionals should be used and that the outcome of decision making tools are to assist entrepreneur only and should not be forced on them.

Notwithstanding, entrepreneurs are advised to avail themselves of the decision making tools from professionals with econometric competences. The present day business environment needs the sixth sense which business decision making tools afford entrepreneurs.

 

APPENDIX: (Al, A2 and A3 for Models 1, 2 and 3 respectively.)

 MODEL 1: TWO-STAGE LEAST SQUARES REGRESSION*

 

Table Al-1 The Lagged Variable Infusion into the Model

Series Name Case Number of Non-Missing No of Valid Creating
Values Cases Function
First Last
1 cfm_1 2 51 50 LAGS(cfm,1)
2 pem_1 2 51 50 LAGS(pem,1)

 

 

Table A1-2 Model Description

Type of Variable
                          Spsales Dependent
                                Cfm Predictor
                                Pem Predictor
Equation 1              Grt predictor & instrumental
                               App predictor & instrumenta  l
                       Logdiscfm instrumental
                      Loadispem instrumental

The model description table gives a summary of the model being fit. Variables specified as predictor will be regressed on the instrumental variables, and the model- estimate4d values will then be used in place of the actual values of these problematic predictors when computing the model for the dependent

 

Table A1-3 Model Summary

                                          Multiple R .995
                                           R Square .990
Equation 1
                             Adjusted R Square .989
                    Std. Error of the Estimate 1.410

The model summary table reports the strength of the relationship between the model and the dependent variable.

Multiple R, the multiple correlation coefficient, is the linear correlation between the observed and model- predicted values of the dependent variable. Its relatively high value indicates a very strong relationship.

R Square, the coefficient of determination, is the squared value of the multiple correlation coefficients. It shows that about 99 percent of the variation in Special offer sales is explained by the model.

Adjusted R Square is an r-squared statistic that is “corrected” for the complexity of the model, and is useful for comparing competing models. The larger values of the statistic indicate better models. It showed that the predictors were able to explain 98.9% of the variations in special offer sales.

Std. Error of the Estimate is the standard error in estimates of Special offer sales based on the model. The value this standard error of estimate compare to the standard deviation of 13.649 of Special offer sales shows that the model has reduced the uncertainty in the “best guess” for next month’s sales.

TableA1-4 ANOVA

Sum of Squares Df Mean Square F Sig
Regression 9221.545 4 2305.386 1160.333 0.000
Equation 1 Residual 91.394 46 1.987
Total 9312.940 50

The ANOVA table tests the acceptability of the model from a statistical perspective

The Regression row displays information about the variation accounted for by the model

The Residual row displays information about the variation that is not accounted for by the model

The regression sum of squares is considerably higher than the residual sum of squares, which indicates that about most of the variation in Special offer sales is explained by the model

The significance value of the F statistic is less than 0.05, which means that the variation that is explained by the model is not simply due to chance.

While the ANOVA table is a useful test of the model’s ability to explain any variation in the dependent variable, it does not directly address the strength of that relationship

Table A1-5 Coefficients

Unstandardized Coefficients Beta T Sig.
B Std. Error
(Constant) -3.237 8.202 -.395 .695
cfm .032 .202 .039 .158 .875
Equation 1 pern .026 .030 .036 .857 .396
grt .916 .146 .484 6.276 .000
app .988 .254 .481 3.892 .000

Dependent Variable  Special Sales

This table shows the coefficients of the regression line. It states that the expected special offer sales is equal to : -3.237 + 0.032CFM + 0.026PEM = 0.916GRT + 0.988APP.

The significance value for GRT and APP are less than 0.05, indicating that the effect of GRT and APP distinguishable from sales of CFM and PEM. In other words, the sales of GRT and APP may not be affecting sales from CFM and PEM. When CFM, PEM GRT and APP are regressed against Total Sales, in Table A1-6, all the variables are significant at 1% level. This shows that they all contribute and no “special sales’’ is limiting the sales of CFM and PEM.

 

 Table A1-6 Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients T Sig.
B Std. Error Beta
1 (Constant) -.193 3.180 -.061 .952
cfm 1.054 .053 .404 19.745 .000
pem .991 .036 .437 27.209 .000
grt .812 .173 .133 4.702 .000
app 1.000 .176 .151 5.683 .000
a. Dependent Variable: tsales

Table A1-7 Coefficient Correlation

cfm Pem Grt App
Equation 1 Correlations cfm 1.000
pem -.866 1.000
grt -.885 .661 1.000
app -.966 .843 .757 1.000

*SPSS 21 OUTPUT

 

 

MODEL 2: FORECASTING MODELS**

TableA2-1 Sales Forecast and Seasonally Adjusted Trend

S/N p MONTHS Tsalesf (rounded to a whole number) N’000 Multiplicative scoring Factor(𝒇i) TsalesfSA (rounded to a vhole number) N’000
1 0.89
2 0.99
3 51 MARCH 1.02
4 52 APRIL 269 1.05 282
5 53 MAY 273 1.06 289
6 54 JUNE 275 1.02 281
7 55 JULY 278 1.02 284
8 56 AUGUST 280 1.01 283
9 57 SEPTEMBER 283 1.05 297
10 58 OCTOBER 286 1.02 292
11 59 NOVEMBER 288 0.96 276
12 60 DECEMBER 291 0.91 265

The model equation is: tsalesF = 93.79 + 0.341tsalesf (-1) + 1.64pm +[(AR(1)= 0.233]

  For example April Forecast: tsalesF 52 = 93.79 + 0.341tsalesF51 + 1.64(Mouth 52) + 0.233      (See Eq 4)

                                                     = 93.79 + 0.341(263.3 = March Total Sales) + 1.64(52) + 0.233

                                                     = 93.79 + 89.78 + 85.28 + 0.233

                                                     = 269.09

                                    tsalesF53  = 93.79 + 0.341(269.08) + 1.64(53) + 0.233

                                                    = 272.7  e.t.c

Table A2-2     Augumented Dickey-Fuller Unit Root Test
Test Statistics Coefficients
Variable Intercept Intercept &Trend None Intercept Intercept& Trend None
At Level Tsalesf(Total Sales Forecast) -1.53 -4.152 1.183 -0.065 -0.577 0.0095
1st Diff -6.59 -6.557 -6.385 -0.982 -0.989-1 -0.939
2ndDiff -7.34 -7.253 -7.426 -1.835 -1.835 -1.834
Critical

Value

1%    -3.59

 

5%    -2.93

 

10%    -2.6

1%     – 4.18

 

5%     -3.51

 

10%    -3.19

1%        -2.62

 

5%        -1.95

 

10%       -1.61

TsalesF is stationary at First Difference in Table A2-2 above. This satisfies its expected forecasting property.

   Table A2-3 Model is Stationary at the First and Second difference:

 

 

 

AR Root(s)

  0.233330  0.233330
 No root lies outside the unit circle.
 ARMA model is stationary.
MA Root(s) Modulus Cycle
 -0.236191  0.236191
 No root lies outside the unit circle.
 ARMA model is invertible.

 

 

      **E-View 7.1 OUTPUT

MODEL 3: THE RFM ANALISIS

 

The Firm is having a total of about 90 customers on records. A random sample of 14 customers were made using Random Numbers, but customer ID-01 was purposive. RFM Analysis was limited to customers who made purchases between September 2014 and March 2016.

Table A3-1 Transaction Data

ID DATE AMT N’000 ID DATA AMT N ‘000
01 08/24/2014 212 16 03/15/2015 15
16 09/09/2014 50 83 03/20/2015 105
75 09/30/2014 12 30 04/12/2015 15
75 10/15/2014 15 01 04/14/2015 50
39 10/20/2014 50 72 04/15/2015 30
72 11/15/2014 15 35 05/18/2015 15
28 11/20/2014 50 16 05/25/2015 12
24 11/22/2014 12 89 07/16/2015 45
28 11/25/2014 12 16 07/18/2015 45
24 12/03/2014 15 49 07/25/2015 195
28 12/14/2014 15 08 08/10/2015 12
77 12/18/2014 15 01 18/18/2015 15
16 12/20/2014 15 72 08/20/2015 30
75 12/22/2014 12 35 08/22/2015 30
77 01/15/2015 15 81 09/14/2015 27
02 01/19/2015 62 08 10/20/2015 12
02 03/04/2015 69 08 01/13/2016 12
30 03/10/2015 15 89 01/20/2016 12

When you compute RFM scores from transaction data, a new dataset is created that includes the new RFM scores.

By default, the dataset includes the following information for each customer:

  • Customer ID variable(s)
  • Date of most recent transaction
  • Total number of transactions
  • Summary transaction amount (the default is total)
  • Recency, Frequency, Monetary, and combined RFM scores

The new dataset contains only one row (record) for each customer. The original transaction data has been aggregated by values of the customer identifier variables. The identifier variables are always included in the new dataset; otherwise you would have no way of matching the RFM scores to the customers.

 

Table A3-2 Customers with Transaction Summaries

ID DATE AMT N’000 ID DATE AMT N’000
01 08/24/2014 12 30 10/202014 50
01 04/14/2015 50 30 03/10/2015 15
01 08/18/2015 15 30 04/12/2015 15
02 01/19/2015 62 35 05/18/2015 15
02 03/04/2015 69 35 08/22/2015 30
08 08/10/2015 12 49 07/25/2015 195
08 10/20/2015 12 72 11/15/2014 15
08 01/13/2016 12 72 04/15/2015 30
16 09/09/2014 50 72 08/20/2015 30
16 12/20/2014 15 75 09/30/2014 12
16 03/15/2015 15 75 10/15/2014 15
16 05/25/2015 12 75 22/12/2014 12
16 07/18/2015 45 77 12/18/2014 15
24 11/22/2014 12 77 01/15/2015 15
24 12/03/2014 15 81 09/14/2015 27
28 11/20/2014 50 83 03/20/2015 105
28 11/25/2014 12 89 07/16/2015 45
28 12/14/2014 15 89 01/20/2016 12

The dataset must contain variables that contain the following information:

  • A variable or combination of variables that identify each case (customer).
  • A variable with the date of each transaction.
  • A variable with the monetary value of each transaction.

Table A3-3 RFM Analysis from Transaction Data -The New Data Set.

ID Date Most Recent Transa

ction

Counts

Amount

N’000

Recency Frequency Monetary

Score

RFM

Score

Customer with High Response to an Offer
01 08/18/2015 3 77 3 3 3 333 ü
02 03/04/2015 2 131 2 2 5 225
08 01/13/2016 3 36 5 3 1 531 ü
16 07/18/2015 5 137 3 5 5 355 ü
24 12/03/2014 2 27 1 2 1 121
28 12/12/2014 3 77 1 3 3 133
30 04/12/2015 3 80 2 3 3 233
35 08/22/2015 2 45 3 2 2 322 ü
49 07/25/2015 1 105 3 1 5 315 ü
72 08/20/2015 3 75 3 3 3 333 ü
75 12/22/2014 3 39 1 3 1 131
77 01/14/2015 2 30 2 2 1 221
81 09/14/2015 1 27 4 1 1 411 ü
83 03/20/2015 1 105 2 1 4 214
89 01/20/2016 2 57 5 2 2 522 ü

The combined RFM score for each customer is simply the concatenation of the three individual scores, computed as: (recency x 100) + (frequency x 10) + monetary.

For example, the RFM score for ID-16 is (3×100 + 5×10 + 5) = 300 + 50 + 5 = 355

The marked customers are those that are likely to respond to new offer on CFM and PEM. To qualify, a customer must score at least 3 points for recency.

 

References

Aghalino, S.O. (2000). British Colonial Policies and the Oil Palm Industry in the Niger Delta

        Region of Nigeria, 1900- 1960. African Study Monographs 21(1): 19-33.

Akangbe, J. A., Adesiji, G. B., Fakayode, S. B. and Aderibigbe, Y. 0. (2011). Towards Palm Oil

      Self-sufficiency in Nigeria: Constraints and Training needs Nexus of Palm Oil Extractors. J. Hum. Ecol. 33(2): 139-145.

Echebiri R.N. and Edeba, M.E.I. (2008) Production and Utilisation of Cassava in Nigeria:

      prospects for Food   Security and Infant Nutrition; PAT:Production Agriculture and

     Technology; 4 (1); 38-52

Embrandiri, A., Singh, R.P., Ibrahim, H. M. and Ramli, A.A.(2011). Land application of

      biomass residue generated from palm oil processing: its potential benefits and threats.

     Springer Science. Environmentalist, DOI 10.1007/s 10669-011-9367-0.

Gelb, A.; Jefferson, G. and Singh, I. (1993) Can Communist Economies Transform                         Incrementally? China’s Experiwnce: World Bank Working Papers WP 1189, October.

Levine, D.M.; Stephan, D.; Krehbiel, T.C. AND Berenson, M.L.(2005) Statistics for Managers,

      Upper-Saddle River, NJ. Pearson Educational Inc.

NBS (National Bureau of Statistics)(2015) Growth of Gross Domestic Product in Nigeria .

       Annual Abstract of Statistics. Federal Republic of Nigeria.

Odoemenem, I.U.,and Otanma, L.B. (2011) Economic Analysis of Cassava Production in Benue

        State, Nigeria. Current Research Journal of Social Sciences; 3(5), 406-411

Omereji, G.O. (2005) The Oil Palm Industry in Nigeria: cultivation, Processing and Trade.

       Mindex  Publishers, Benin City. Pp. 131-156

Cassava Production in Nigeria (n.d.) in Wikipedia. Reviewed April 10, 2016 from

          http://en.wikipedia.org/wiki/cassava production

Yakasi, M.T. (2010) Economic Contribution of Cassava Production (A Case Study of Kuje Area

        Council, Federal Capital Territory , Abuja Nigeria; Bayero Journal of Pure and Applied

       Sciences; 3 (1) ; 215-219.

 

The Effect of Project Management Information System on  Project Managers and Project Success

 

Shadi Fallah

Department of  management,  Islamic Azad University, Qaemshahr, Iran

shfallaah@gmail.com

Yousef Gholipour-Kanani

Department of Industrial Engineering, Islamic Azad University, Qaemshahr, Iran

Abstract. Project management information system (PMIS) helps managers in decision making, planning, organizing and controlling the project. Based on the importance of decision making, the aim of this study is assessing the effects of PMIS on the project management success. This study designed a comprehensive model to evaluate the impact of PMIS on the project management functions. This model included five factors such as: PMIS quality, the quality of output information of PMIS, PMIS application, PMIS impact on project management and the impact of PMIS on the project success. This study conducted in Iran in three dependent firms in Tehran petroleum. Data of this research is collected by using questionnaires from project managers. Results demonstrated the crucial role of PMIS on the project management success due to better planning, proper scheduling, regulating and controlling the project. At the end limitation and suggestion for future study is discussed.

Keywords: Project management information system, decision making, planning, project control.

INTRODUCTION

Information is one of the most important capitals in the organizations, because all physical facilities and environmental decision making are affected by information. Information can change competitive aspects withing the organization and lead to competitive advantage. Morever, successful organizations in information technology can change competition characteristics in the industry and benefit by being pioneer. Due to the importance of project management information system (PMIS) to implement project faster, less expensive and with higher quality, the general expectation from project managers enhanced (Welsch, 2006). Organizations apply information systems in their organizations to improve effeciency and profitibility; through this way they can adopt themselves easier to environmental changes and attain updated information. Also managers can estimated the project costs,  profits and budget by using PMIS. In general, PMIS can be used in Implementing activities, goods and materials management, collecting and classifying financial and non-financial information, and storage of information.

Despite of the importance of PMIS, there are still limited studies in this filed. Previous studies mostly considered on implementing information technology instruments in the organizations. Morever, due to importance of oil and gas projects more research about PMIS is needed. Because, in this industry PMIS can play the key role in recognizing the suitable resources, planning and scheduling, this study has considered on the effect of project management information system on  project managers and project success.

Forasmuch as PMIS accompanies managers in all process of implementing project, this study aims to assess the effect of PMIS on  project managers. Also  this study targeted to evaluate the effect of  PMIS on project success. Furthermore, the effects of PMIS on its application, project management characteristics and quality of outputs.

LITERATURE RIVIEW

An information system defined as ‘’ a set of related components that assist to collect, recovery, processing, storage and distribution of information within the organization’’ (Olson, 2004). This information is very useful for decision making and controlling the operation in an organization. Managerial team can use this information for analysis and making decision about future product line. Also Management information system defined as ’’ series of tools which provide to managers the required information in their professional fields on time, accurate and in appropriate conditions’’. Study of Welsch (2006) indicated that MIS assists middle managers through the provision of information in appropriate formats according to what they need.

According to Olson (2004) projects due to their diverse and uniqueness are structurally different from the current organizational routines. Project management is always more difficult than regular administration and requires more attention to different aspects of organization. The project combines the demands of the custodians, contractors and other stakeholders; so to create unity between all factors involved in the project, appropriate techniques and utilities should be exploited (Baker et al., 2009). Belout (2003) in an empirical study considered on the effects of PMIS on multi-project environment. Results showed that using project management information system is beneficial for project managers. However, no adverse effects have been observed due to the project, and information overload.

Commonly projects implement in certain time, cost and quality; they mostly done only for one time. Therefore it is essential to apply an instrument which helps organization to do projects more effective (Dietrich and Lehtonen, 2005).  Also another study has investigated the Challenges in information systems projects (Finch, 2003). Adams and Barndt (2008) in their study showed that PMIS has a positive effect on the management and execution of construction projects and managers should give careful consideration to overcome the weaknesses of the project. According to study of (Cooper et al., 2001) during the projects some changes may happen in process. Despite the theoretical accuracy in the preparation of plans and administered programs, managers should carefully pay attention to using on time and accurate information. Also project control team requires different expertise to accomplish the responsibilities as good as possible (Dai and Wells, 2004). For the purpose of control, various units should send information to control project department under proper discipline and coordination. Particularly, the activities which locate on the critical path of the project should regularly monitor and inspect to ensure that occurred delays don’t lead to delay in entire project. Bozeman and Kingsley (2007) showed that data quality of PMIS is related positively to the correct decisions, managers’ satisfaction from PMIS and increasing usage of PMIS within the organization. Also this study discussed about implementing multiple projects simultaneously causes that the project managers expand the results of the quality information for a project to all existing projects. Based on abovementioned literatures the following hypotheses were proposed:

H1. PMIS quality is positively related to output information quality.

H2. PMIS quality is positively related to PMIS application and project management factors.

H3. Output information quality of PMIS is positively related to PMIS application and project management factors.

H4. PMIS application influences positively on project management factors.

H5. PMIS application influences positively on project success factors.

H6. Project management factors influences positively on project success.

METHODOLOGY

Data of this research attained by distributing questionnaire which included 20 questions based on 6 main categories. This study targeted project control department of three firms which dependent to Tehran Petroleum Company. Targeted employees were 80 and all of them participated in this research. According to table of Morgan number of participants is 63. This study applied SPSS to test normality of study population and hypothesized relationships. Testing normality of population evaluated by using Kolmogorov–Smirnov test; also binominal test applied to assess the hypothesized relationships.

To consider on validity of questionnaire, all questions reviewed by several scholars and their opinions applied in the study instrument. After discussion and making some changes, the validity of questionnaire approved by them.  To test reliability of questionnaire, this study applied Cronbach’ alpha and all values ranged between .76 and .85; Therefore, result demonstrated the reliability of questionnaire. Scale format of questionnaire was based on five-point Likert scale (1=very low to 5=very high).

Table 1. Respondents information

                              Number           Percentage

Gender:

Male                                                      51                      81%

Female                                      12                  19%

Total                                  63                100%

Organizational tenure:

<5 years                                                19                   30%

6-10 years                       30                       48%

>11 years                                              14                        22%

Total                                        63              100%

Educational level:

Bachelor degree                 42                  67%

Master degree                              21                  33%

Total                                       63                100%

 

RESULTS

Results of Kolmogorov–Smirnov test showed that data of this study is not normal; therefore assessing data is done by using Binominal test which is one of the nonparametric tests. As can be seen in Table 2, P-Value related to H1 is less than 0.05; this result indicated that there is significant difference between two groups. Furthermore, according to the reality that 95% of responses were ‘’high’’ ad ‘’very high’’; it conclude that PMIS has positive and significant effect on quality of output information. Thus, H1 is supported. Similarly, P-Value related to H2 is less than 0.05 and majority of responses (94%) were ‘’high’’ ad ‘’very high’’. This finding reveals that quality of PMIS influences positively and significantly on application of PMIS and project management factors. Therefore, H2 is supported. Also table 2 shows that the responses of participants about H3 were mostly (93%) above “fair’’ with P-Value<0.05. This result indicates that quality of PMIS output information is positively and significantly related to PMIS application and project management factors. Therefore, it reveals the empirical support for H3. To assess H4 which indicates to the positive relationship between PMIS application and project management factors; Table 2 shows that 90% of responses were ‘’high’’ ad ‘’very high’’ and P-Value<0.05. Thus, H4 is empirically supported. Also 86% responses related to H5 are above ‘’fair’’; also P-Value related to H5 is less than 0.05; this result indicated that there is significant difference between two groups. Abovementioned values indicated the positive relationship between PMIS application and project success factors. Therefore, H5 is supported. The last hypothesis is also empirically approved with majority of ‘’high’’ and ‘’very high’’ responses (94%) and P-Value<0.05. Therefore, there is positive significant relationship between project management factors on project success. Generally, all hypotheses were supported.

Table 2. Results

                        Maen          SD         %Responses ≤3       %Responses>3          α          P-Value

H1                     4.56           .33             5%                     95%                  .05            0.00

H2                     4.40           .39                 6%                           94%                .05            0.00

H3                     4.43           .48                 7% 93%                .05                           0.00

H4                     4.46           .50                10%                         90%                 .05            0.00

H5                     4.44           .40                14%   86%                 .05            0.00

H6                     4.55           .47                  6%                          94%                .05            0.00

Discussion

This study is done based on direct and indirect effects of PMIS on project success. Easy usage of system, flexibility, respond time, easy learning, integrity of system, all have critical roles on quality of provided information. When quality of PMIS is high, information is more Reachable, reliable, accurate, comprehensive and secure. Result demonstrated that quality of information directly and intensively is related to applying PMIS and its effects on project management. But quality of information is not the only factor that should be considered; because it will be useful when it applies in organization through qualified managers. Using affective PMIS makes performance more professional due to its application in planning, controlling, setting and reporting in different steps of projects.

Results indicated that PMIS quality influences positively on output information quality, PMIS application and project management factors, project management project factors and project success factors. Results of this study were consistent with the result of existing empirical researches (e.g., Belout, 2003; Bozeman and Kingsley, 2007; Olson,  2004).

Limitation and suggestion for future research

The first limitation of this study is related to generalizability because data of this study collected from three dependent firms to Tehran Petroleum Company. It is beneficial if future studies consider on larger number of participants.   Also this study conducted in Iran which is known as developing country; therefore to generalizability of results to developed countries some problems may occur.  Also data of this study collected on limited period of time and does not cover information and changes may happen over time.  Therefore, it is useful to collect data using longitudinal design. Finally, further consideration should be done about other internal and external factors may effect on the quality of PMIS.

References

– Baker, B., Murphy, D., Fisher, D. 2009. Factors affecting project success. In Cleland, D., King, W. (Eds),Project Management Handbook, Wiley, New York, NY, 902-19.

– Kostalova, J., Tetrevova, L., Syedik, J. 2015. Support of Project Management Methods by Project Management Information System. Procedia – Social and Behavioral Sciences, 210, 96-104.

– Bozeman, B., Kingsley, G. 2007. Risk culture in public and private organizations. Public Administration Review, 58 (2), 109-18.

– Iyer, K.C., Banerjee, P.S., 2016. Measuring and benchmarking managerial efficiency of project execution schedule performance. Measuring and benchmarking managerial efficiency of project execution schedule performance, 34 (2), 216-236.

– Dietrich, P., Lehtonen, P., 2005. Successful management of strategic intentions through multiple projects. International Journal of Project Management 23 (5), 386–391.

– Ghaffari, M., Sheikhahmadi, F., Safakish, G. 2014. Modeling and risk analysis of virtual project team through project life cycle with fuzzy approach. Computers & Industrial Engineering, 72 (June), 98-105.

– Jafarzadeh, M., Tareghian, H.R. Rahbarnia, F., Ghanbari, R. 2015. Optimal selection of project portfolios using reinvestment strategy within a flexible time horizon. European Journal of Operational Research, 243(2), 658-664.

– Liu, S., Wang, L. 2016. Influence of managerial control on performance in medical information system projects: The moderating role of organizational environment and team risks. International Journal of Project Management, 34(1), 102-116.

– Welsch, W. 2006. “Input: state and local IT spending surge begins”, available at: http://www.washingtontechnology.com/news/1_1/daily_news/28292-1.html (accessed July 29, 2006).

 

ACCOUNTABILITY IN EDUCATION IN KENYA: CHALLENGES AND STRATEGIES

Main Author: Dr. Reuben Nguyo Lecturer under mentorship Programme

Department of Educational, Administration and Planning University of   Nairobi

Co-Author: Jedidah  Nyawira  Kimathi, North-Eastern Hill University, India. Department of Education P.O. Box 793022, Meghalaya, Shillong

Abstract

School accountability—the process of evaluating school performance on the basis of student performance measures—is increasingly prevalent around the world. Kenya has not been left behind. Therefore, the paper will explore the challenges and strategies of accountability in education in Kenya. Accountability in education is a broad concept that could be addressed in many ways, such as using political processes to assure democratic accountability, introducing market-based reforms to increase accountability to parents and children, or developing peer-based accountability systems to increase the professional accountability of teachers. The most commonly considered definition of accountability involves using administrative data-based mechanisms aimed at increasing student achievement. The study found out that the Government of Kenya is struggling with many challenges related to accountability in education. The challenges include: enrolment policy, education for individuals with disabilities, staff and performance, quality assurance and standards, management and governance among others. As far strategies to enhance accountability include devising performance indicators through The National Education Sector Plan (NESP) 2013-2018.

Key Terms: Accountability, Indicators of accountability, challenges

 Introduction

As the economies of nations compete for strong positions within a competitive global market place, many governments have become increasingly interested in the performance of all aspects of their education systems. This trend, coupled with the enormous expenditures that are devoted to education, has also precipitated widespread public requests for higher levels of scrutiny concerning the quality of education. These demands for information about school system performance can only be addressed through the implementation of systematic accountability systems.

Historically, the education profession has conformed to the requirements of regulatory or compliance accountability systems (usually based on government statutes), and has also subscribed to professional norms established by associations of educators. However, at the beginning of the 21st Century, accountability systems have also been required to respond to demands that professional performance be judged by the results that have been achieved (UNESCO,2005).

Accountability has been an educational issue for as long as people have had to pay for and govern schools. The term covers a diverse array of means by which some broad entity requires some providers of education to give an account of their work and holds them responsible for their performance.

Anderson (2005) asserts that Educational accountability targets either the processes or results of education. A desired goal is identified (e.g., compliance with the legal mandates of providing special education, highly qualified teachers, improved student performance), and measures are identified for determining whether the goal is met (e.g., a checklist of indicators that the legal mandates have been met, a target of 90% correct for teachers taking a test of current knowledge and skills, a target of 60% of students performing at grade level by the end of each school year). Criteria for determining whether the goal has been met can involve specific determinations of ways that the goal may and may not be met (e.g., deciding how many indicators in the checklist must be marked to be considered meeting the legal mandates, determining the specific content that does or does not count for specific types of teachers, determining how to calculate the percentage of students performing at a proficient level, and how to define grade level performance).

Models of Accountability in Education

A number of models of accountability in education have been developed, chiefly by Kogan (1986), Ranson (1986), Elliot et al. (1981) and Day and Klein (1987). These models illustrate different codes which specify, for example, alternative methods of presenting and evaluating the account. Whilst there are some differences of classification and nomenclature, four main models of educational accountability emerge from the literature: professional, hierarchical, market and public. Although it is unlikely that any of the ideal models will exist in its pure form.

 

(a) Professional Accountability

The emphasis on accountability for process is characteristic of professional accountability. Sockett (1980, p. 11) illustrates this, arguing that ‘the question (professionals) debate is not whether certain results have been achieved, but whether professional standards of integrity and practice have been adhered to’. In this form of accountability, teachers, and hence schools, are accountable to professionals. Ranson (1986) states that the educational process is so complex that only professionals can hold other professionals to account.

(b) Hierarchical Accountability

In contrast to professional accountability where accountability is ‘sideways’, the hierarchical model involves accountability ‘upwards’. This is exercised through the managerial hierarchy and stresses the contractual relationship with the state. Becher et al. (1981) describe it as an obligation to render an account to an employer.

(c) Market Accountability

In the market model, accountability is to the consumer (normally the parent). The emphasis is on accountability for outputs, mainly measured by examination results. In this system, schools are accountable to the consumer who chooses their product or an alternative in the marketplace. In order for the market to operate effectively, information (for example, examination results) needs to be available so consumers know the full specifications of the product they are ‘buying.’

 

(d) Public Accountability

Both market and public accountability involve an active role for parents. In the public model, this active role is required of the community more widely. The method of accounting stresses parental and community participation in determining the purpose and process of education (Ranson 1986). This operates collectively through the democratic process as well as indi-vidually, and therefore involves all individuals within an electoral ward. It stresses mutual accountability and partnership between politicians, professionals, parents and the community.

Forms of Accountability in Education

Accountability occurs in many ways in educational systems:

  1. a) System Accountability

Educational accountability in which the system is held responsible for the results of its students gained popularity in the early 1990s. Schools, local education agencies, and states are held accountable for the performance of all students in the public education system.

  1. b) Accountability for the Process of Education

This is a common form of educational accountability. Schools are required to meet accreditation criteria. Special education programs must demonstrate that they have provided services and maintained Individualized Education Programs (IEPs) in a manner consistent with the law. The desired goal of educational accountability focused on process is to improve the process that is targeted. Special education IEPs are an example of a process targeted for accountability. Meeting the process requirements means demonstrating compliance with a number of requirements in the law and in regulations for IEPs. Measurement occurs through the completion of a checklist, for example, that identifies the requirements (such as providing notice within a certain period of time, having specific signatures on the IEP document, and so on). The criteria for determining when the measures show that the goal has or has not been met are defined in terms of numbers of elements that must be checked. The consequences for not meeting the goal generally include a letter identifying the problems in the process. In some cases, repeated failure to meet the criteria results in penalties, such as reduction of funding, to the educational system.

  1. c) Individual Accountability

Student accountability implemented via promotion or exit exams is a common type of individual accountability in schools. Students are required to pass a test to demonstrate that they are ready to move either from one grade to the next (promotion) or leave the educational system with a credential certifying successful completion (exit). The tests that are administered to students generally cover those topics that the school system or its public have deemed important for individual students to demonstrate at a certain point in time. The criteria for determining when the measures show that the goal has been met (for instance, that the student is ready to move from one grade to the next) are defined in terms of passing scores on the test. In some cases alternative criteria are available to certain students who either are not able to pass the tests or who need to demonstrate that they have met criteria through other means.

Individual accountability for the adults in the education system include such variations as teachers being held responsible for passing tests to obtain or keep jobs, or principals and educators receiving salary bonuses on the basis of student achievement. This type of accountability includes the same components as other educational accountability systems, with goals, measures, and other criteria for determining when the goal has been met, and rewards and sanctions for meeting or not meeting the criteria.

Challenges to Accountability in Education

Enrolment Policy

The Education Sector has been making improvements in terms of access to institutions of basic education and provision of services. However, the challenge of attributing learning outcomes to the investment in the sector still remains. The resource investment over the years, both for development and recurrent expenditures would have by know translated into exemplary results at the ECDE, primary and secondary school levels; however this is not the case.

At the ECDE level for instance, though the enrolment increased from 1.914 million children (967,544 boys and 946,678 girls) in 2009 to 2.37 million (1,175,530 boys and 1,194,518 girls) in 2011, the Gross Enrolment Rate (GER) was still at 65.5 per and at 41.8 percent in 2011. Clearly, this is an indication that many children of nursery school going age were still not enrolled in the

ECDE centers, a clear violation of the children’s right to basic education.

At the primary school level enrollments increased from 8.8 million (4.5 million boys and 4.3 million girls) in 2010 to 9.86 million (4.98 boys and 4.86 girls) in 2011; the gross Enrolment Rate was at to 115.0% while the Net Enrolment Rate was at 95.7% in 2011. While this demonstrate good progress in terms of access from a national point of view, the situation is not the same especially in ASAL areas where many children of school going age can still be seen staying at home during school days. The Completion rate stood at 74.6 per cent in 2011 clearly showing that children are dropping out of school. The question is, why do they drop where do those who drop go to?

In terms of transition from primary to secondary schools, the rate was at 73.3 per cent (68.9 per cent for male and 75.3 per cent for female) in 2011; again it is clear that some learners do not access secondary education; where do they go to? What alternatives do they have? Does the government know where they are?

At the Secondary School level the enrolment grew from 1.18 million students in 2007 (639,393 boys and 540,874 girls) to 1.5 million (804,119 boys and 695,896 girls) students in 2009 to 1.7 million (916,302 boys and 792,818 girls) students in 2010 and further 1.8 million (948,706 boys and 819,014 girls) in 2011. The GER for secondary level was at 48.8 per cent (51.0 per cent for boys and 46.8 per cent for girls) and the NER was at 32.7 per cent (32.6 per cent for boys and 33.1 per cent for girls) in 2011; a clear indication that many children of secondary school going age are not in schools.

 

Education for Individuals with Disabilities

While Kenya government recognizes the need to educate all children, including those with exceptional needs, there lacks a mechanism to ensure and oversee that all students have equal access to education. The crucial question regarding persons with disabilities, especially those with intellectual disabilities is: how will the rights of persons with exceptionalities be protected from economic, social, and political neglect? An estimated 80% of all individuals with disabilities reside in isolated areas in developing countries (Oriedo, 2003) with 150 million of them being children (Eleweke & Rodda, 2002). Disability-related issues affect approximately 50% of the population in these countries (Oriedo, 2003, Mukuria, Korir & Andea, 2007). In most cases, disability problems are compounded by the fact that most of the people with disabilities are extremely poor and live in areas where medical and educational services are not available (Eleweke & Rodda, 2002; Meja-Pearce, 1998; Oriedo, 2003; Mukuria & Korir 2006). According to the 2009 census, this group makes up approximately 20% of the Kenya’s population (Kenya Bureau of Statistics, 2009); unfortunately, only 2% of individuals with disabilities receive any form of special education (Eleweke & Rodda, 2003; Mukuria & Korir, 2007).

 

Staffing & Teacher Performance:

Though the outcry on teacher shortage continues to be heard, additional concerns also revolve

around teacher distribution with allegations of some schools having more teachers than they require while in other schools, at every given time, some classes remain untaught because of teacher shortage. Teachers’ absenteeism also remains an issue that cuts across many schools in the country with concerns that some teachers chose to be away based on a mutual agreement with the head teacher as opposed to an official documentation of leave of absence.

 

Quality Assurance & Standards support:

Teacher performance records are lacking in many schools: While at the Classroom level, it is not easy to determine the extent to which the teachers are delivering the right content; but instead the performance of the teacher is left to be reflected in the performance of the learners (often during external examinations)

Another critical gap is that the school terms often begin with the teachers not aware of the specific dates that the QASOs would be visiting their schools. The criterion that determines which schools to be visited during a particular term is also not readily available. Some schools also indicate that one calendar year ends without any QASO visiting their schools and as such no quality assurance support is received from MoE throughout the year. Most of the QA&SO are not clear on the kind of support teachers require and they also have capacity gaps.

Even though feedback is given to the schools after visits have been conducted by MoE officials, the feedback never trickles down to the learners and their parents / guardians. Most of the time the feedback is discussed at the teachers level while other actors in education service provision are left out. The feedback at times reaches the headquarters of MoE but there are no clear mechanisms of responding to such feedback until a crisis emerge.

Management and Governance:

While some schools have School Management Committees and Board of Management in place, that have undergone trainings conducted by Ministry of Education officials, the functioning of

these committees is not reflected in the manner in which school programmes and activities are implemented. In some instances, the head teachers continue to make decisions by themselves (as

individuals) and the SMC &  BoM members hardly question such decisions. There is also lack of School development Plans in most schools and this creates an opportunity for poor plans.

The information on the funds received that is displayed on the school notice boards has been limited to the FPE funds with many head teachers not displaying any other funds the school receive, especially those collected from parents towards other programmes, for example the school feeding programmes. In terms of purchases of school items, there have been outcry among parents that some head teachers collude with suppliers to increase the prices of goods (often way ahead of the market prices) with the aim of receiving “kickbacks.”

Very few schools do generate annual financial reports for discussion with parents; majority of schools choose to discuss the financial reports with the MoE officials and ignore the parents, guardians and children. In addition, auditing of the funds that the schools receive every year is irregular and such audit reports are never shared with the parents, guardians and children.

 

Access to Information:

Information flow between the school administration and the teachers is another gap that exists in many primary and secondary schools. For instance, some teachers are only aware of the data in regards to the learners in the school and their performance but have no idea on the resource requirements of their schools and the management of resources that the schools receive. The level of awareness of some teachers in regards to various policies and guidelines in education service provision is also minimal; this is however attributed to lack of access to such documents at the school level; there are cases where the school head teachers limit such information to themselves and do not share with the other teachers in the school.

While the children are aware that the government has been financing the Free Primary Education, many of them are not aware of how much they have been entitled to over the years. Worse is the fact that some of the parents and guardians too are not aware of what their children

are entitled to under the FPE programme. This is attributed to lack of clear communication modes between the school administration and the children, and their parents and guardians.

 

Holistic Focus on Learners:

On an annual basis, the schools over concentrate on discussing the performance of the children in regard to KCPE and KCSE results; and very minimal is discussed in reference to performance of the children at other levels (class 1 – 7 & Form 1-3). While many of the school teachers are aware that some of the learners do not transit to secondary schools, it appears that majority of the teachers have no role in following up where such learners go to. For instance, there are cases where some teachers interact with their former pupils in the neighborhood such as in the market centers; while they are very much aware that such children have not enrolled in secondary schools; they do not bother to find out whether such children require support.

Some parents have also  have left their children in the hands of teachers and do not care to follow up on what their children do in school; some do not even attend school meetings throughout the year and do not event care to find out what deliberations and decisions are made in such meetings.

 

Finances:

Though the government supports the Free Day Secondary Education (FDSE) programme, there is a general feeling among the public that secondary education in Kenya is largely expensive. The fact that various categories of schools charge different amounts of fees is something that continues to amaze the public. There are cases of schools that get to acquire the National level status then increases the fees payable by about 50%; goes ahead to demand the same from parents and the government does nothing.

Various secondary school heads continue to incur exorbitant expenditures with completely no oversight. For instance who pays for the cost of Kenya Secondary Schools Heads Association Annual meeting? Is it the Government of Kenya? is it from the Head teachers personal incomes? or from the poor parents and guardians (from the fees paid to schools)? This is something that the public is seeking for accountability on the part of the government. Secondary schools in Kenya continue to manage millions of shillings annually; but majority of the schools do not report to the students, parents and guardians on their incomes and expenditure on annual basis. Reports that are shared publicly are largely in regards to performance and very minimal information on finances. A part from the details of the fees to be paid in the subsequent year, the secondary schools heads often give very minimal information on the expenditure of the previous

years.

 

Public Participation:

 Even though various districts have a culture of annual education stakeholders meetings, the participants in such meetings are often limited to head teachers, teacher unions, FBOs and NGOs. Public participation in such meetings, for example through the Chairpersons of schools and other representatives of parents and children continue to remain very minimal.

 

Strategies towards Accountability in Education

The National Education Sector Plan (NESP) 2013-2018 is an all-inclusive, sector-wide

programme whose prime goal is: Quality Basic Education for Kenya’s Sustainable

Development. The sector plan builds on the successes and challenges of the Kenya Education Sector Support Programme (KESSP), 2005-2010. Sector governance, management and accountability in a decentralized setting with devolved responsibilities and diverse partnerships have been emphasized. Clear guidelines for coordination, transparency, and reporting at the national, county, sub-county and institutional levels are paramount. The focus on improvement of education quality specifically targets: improvement of schooling outcomes and impact of the sector investment; development of relevant skills; improved learning outcomes; and improved efficiency and effectiveness in use of available resources.

 

Four major sets of performance indicators for NESP are identified as;

  1. Social development and economic growth for the 21st century are dependent on a broad base of capable, literate, numerate, confident and motivated citizens. These citizens will actively contribute to a knowledge-based society. The National Education Sector Plan (NESP) sets out to shape the education system to complement and support the national aspirations of Kenya.

  1. The sector plan as set out in NESP emphasizes a holistic and balanced development of the entire education sector, and is embodied in recent legislation, including the Basic Education Act 2013. The NESP Implementation Plan focuses on the urgent need to enroll all students in basic education, raise literacy and numeracy levels, reduce existing disparities, and improve the quality of education with a focus on teacher quality, school level leadership, more effective applications of teacher training in the classroom, increasing resources to the education sector, and targeting improvements and monitoring key results.

  1. NESP sets out to expand on the national aspirations set out in Vision 2030 through a statement of comprehensive goals and objectives. It further aligns a commitment to the UN Convention on the Rights of the Child and the education related goals of the Millennium Development Goals with a vision for the wider educational aspirations of Kenya.

  1. The foundation priorities of the education system provide the strategic implementation processes set out in NESP. Through the extensive consultation processes undertaken for developing NESP, stakeholders elaborated that the vision and goals embody and cluster around four principles:

 

Inclusiveness

This principle is about the fullness of the range of learning opportunities provided for all children, young people and communities. The NESP describes the social circumstances and barriers to learning that present challenges to implementation planning.

 

Integrated and Unified System

This principle is about the characteristics of an efficient education system that effectively and coherently integrates all learning institutions, central authorities and administrative agencies, through their mandates, processes and procedures. NESP describes governance, management and administrative expectations (institutional arrangements) that will ensure all students are exposed to opportunities of shared knowledge and culture.

 

Equitable School Environment

This principle is about the provision of safe, stimulating and innovative learning places of modern pedagogy for all children and young people. NESP sets out goals and objectives for the fair provision of infrastructural, teaching and learning resources and support systems to benefit all learners.

 

Quality of Learning

This principle is about the setting of rigorous quality benchmarks in the curriculum and its delivery and assessment so as to ensure that the learning opportunities for all children and young people are maximized. NESP describes expectations for minimum standards of the physical learning environment, curriculum development, teacher performance, and prescribes the work of agencies to monitor and assure ongoing quality.

  1. NESP also implies four central and interdependent policy pillars to underpin the

development of each of the described implementation strategies:

 

Pedagogy Enhanced by Technology

NESP makes a very strong representation of the role of technology in a modern, vibrant and successful society. NESP envisages a solid technology base through information and communication technology (ICT) to be reflected within the curriculum at all levels, its delivery and the system support mechanisms. The principles described above clearly focus on the fundamental place of pedagogy in lifting and maintaining quality of learning. This policy pillar establishes the place of technology as a powerful support to pedagogy but not the determinant of pedagogy.

 

Systemic Solutions

The NESP principle of an integrated and unified system demands that meeting the challenges requires the design, development and implementation of agencies, approaches and processes that support the interdependencies of all elements within the system. The setting of priorities and sequencing of implementation strategies will take account of the expected growth and impact of the education system. The NESP elaborates the mechanism (the National Education Board, NEB) whereby growth and impact is considered across the social and wider sectors.

 

Collaboration

The achievement of the sector plan requires a high commitment by all key stakeholders in the education system to working together as a team. Collaboration as an approach, however, goes beyond individuals working together for the common goal with a focus on the learner. It includes the establishment of conditions and relationships between the central administration, agencies and learning institutions to facilitate collaborative processes and approaches. The design of new and strengthened strategies is expected to stimulate and maintain a focus on group, rather than individual, effort through to the very top of the system.

Capacity Development

Achieving the NESP goals in a decentralized system requires significant capacity building at all levels of the education system. The strengthening and establishment of new ways of working through clearly defined roles, expectations, responsibilities, accountabilities and mandates are best achieved by capacity building of both human and resources. This policy pillar will strive to incorporate capacity building as a prime driver for reform.

Conclusion

The Government of Kenya is encountering many challenges as it deals with accountability in education owing to the fact that the idea of accountability has not yet been embraced fully neither by the assessors nor those being assessed. Mechanisms have been formulated to enhance accountability but have not yet been implemented fully.

Recommendation

There are should be concerted efforts among all the education stakeholders to ensure there accountability in education. There also needs sensitization about accountability in education as many stakeholders are not aware of their role as far as accountability is concerned. Follow up measures on accountability should be to put in place.

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Sockett, H. (1980). ‘Accountability – the contemporary issues’ in H. Sockett (ed.), Accountability in the English educational system. London: Hodder and Stoughton.

World Vision Kenya (2012). Enhancing Responsiveness and Effectiveness of Basic Education Service Delivery in Kenya Project Reports

http://www.education.com/reference/article/accountability/

http://www.unesco.org/iiep

 

Language Awareness in the Workplace

Mustafa Wshyar Abdullah AL-Ahmedi

Lecturer at Koya University – Koya, Erbil/ Iraq

Abstract:

This article is exploring the use of language by the individuals of a group who are active members of a team. Real examples are given to further investigate the language awareness in real life communications.

Key Words: Language, Awareness, Workplace, Team, Individuals

  1. Introduction

      Working in a team can enhance a great experience for a participant in a group work. The enhancements can be new information, the way of dealing with subject matters, and communication with people who may have different ideas. Those features are mostly very important to be existed in a person who looks for a good employment opportunity. It is a reality that most employers try to employ someone with those and some other characteristics like a good command in using technology. Integrating to a work environment is significant for an employee to perform an acceptable performance. To be a successful person in a workplace, being aware of cross cultural and different types of community of practice has its significance.

As a part of Language Awareness in the Workplace, the students had to work in one of the projects which were offered to participate. One of them was “Wish upon a Star” and the other one was “Equality and Diversity at UCLan”. All the students decided to go with the first one because the majority desired to take part in it and the minority of students did not like the idea of creating a small group. “Wish upon a Star” is a project which leads to publishing a book about star and constellation stories for children. The aim of the book is to provide the readers, who are supposed generally to be children, with constellation and star beliefs in India and the UK. The master students in English Language and Literature were requested to contribute some cultural facts and beliefs about sky issues to the content of the book.

The preparation for the project started in early December 2011 and the book is expected to be published around July 2012. This report is written to show what were done to contribute to the project and the way which steps were taken. It explores the writer’s, who was a group member, weaknesses and strengths which are very important to be discovered for improvement. The benefits will also be mentioned as the most participants could obtain new skills or at least improving the existed ones.

  1. Community of Practice

  • An Introduction to Community of Practice

 

The term community of practice (CofP) is frequently mentioned in the modern world, especially when individuals exist in a group form to take a responsibility. Lave and Wenger (1991: 98) define the term: “A community of practice is a set of relations among persons, activity, and world, over time and in relation with other tangential and overlapping communities of practice.” That is to say, communities of practice are groups of codes which understood and used by the same members of a group. In other words, communities of practice are the ways of communication among the members of a group to achieve the common aims.

Hardcastle and Powers (2004) see community of practice as an application to practice skills to make communication in community groups, organizations or institutions. The relation among individuals is based on the community practice. In other words, it is a community of practice which arranges an acceptable relationship among members of a group. The members can communicate with each other through some common communication rules which are known by all the other members as they are familiar with the community they gathered in.

      Communities of practice are everywhere; human beings belong to several communities of practice in different time and places: at home, work, school, or in hobbies (Wenger, 1998). It can be said that a community of practice can be found in all aspects of life and it is the main tool of communicators. It is a tool which helps human beings to communicate and share ideas. They can understand each other through it and they can express themselves easily. As a result of this argument, community of practice involves in all forms of communication or it is the basic and it’s the way which makes communication.  Wenger (1998: 84) claims that there are three dimensions of a community of practice which are: mutual engagement, joint enterprise and shared repertoire. To understand these dimensions, looking at an example can be helpful before defining them. A large company which consists of many departments can be a good sample to give the reader a clear overview about the dimensions. The managers of departments can altogether engage in a group meeting which is arranged for all of them to discuss the goals of the company and they mutually engage. They may have a joint enterprise from discussing the achievements among themselves. The managers can also have shared repertoires by exchanging stories of achievements and other events.

  • Mutual Engagement

This is a regular interaction which people in pairs or groups discuss general issues (Holmes and Meyerhoff, 1999). This type is very simple which usually does not need arguments or debates. It is general discussions and it is the basis of relations. It can be said this one is used when the members have a shared goal and they all work to achieve it. As a result, the members communicate through this type of CofP because the target is the same. The communication can be while drinking a coffee or small meetings.

  • Joint Enterprise

It is more than a shared goal and needs process; complex relationships are involved in this dimension (Wenger, 1998). Members usually debate and have more serious meetings for longer hours.

  • Shared Repertoire

This dimension is the most complex one which linguistic resources involve in and The meetings are more serious in which gestures, pictures and regular meals become a part of the CofP (Holmes and Meyerhoff, 1999). The members of a group negotiate the meaning which shows disagreement sometimes.

  • Community of Practice of the MA English Language and Literature team

The group consisted of different people from different cultural and educational backgrounds. There was a main goal which was a contribution to publish a book and all the members worked for the same aim. It is worth mentioning that there was a main group in which sub groups involved in. My course mates and two of the tutors were members in a subgroup. This sub group mainly arranged meetings for its members and attended once the general meeting of the whole group. The whole group, which was the main one, included MA English Language and Literature team, MA Publishing Team and MA Creative Writing for Children Team.

            The common goal of the main group and subgroups was the same which was preparing the content for the book. Each group and individual had different duties; the duties were divided among the subgroups through their educational backgrounds. My group was supposed to carry out a research and collect data related to cultural beliefs as our educational background relates to language, culture and literature. Our part was exploring the beliefs in India and the UK about constellations and stars. Two members of the group had an Indian background and two members were British who were familiar with the beliefs in the UK. The other three members had no background about both countries and they tried to help those members to collect data and find information about both cultures.

            Regarding the power, all the members were equal in terms of controlling or managing the project. None of the members in the whole group including supervisors were more powerful. All the ideas were discussed and they were arranged to create a good content to the project. In brief, all the members decided on the content of the project and there was not anyone to have a final decision as the group generally decided on the final decision.

  1. Discursive Chosen Community of Practice

Discursive means everyday talk and it is a multi-utterance of talk; in order to analyze a conversation, meeting or talk, discursive should be analyzed (Tracy, 2002). Tracy (2002) argues that a talk is called discursive practice rather than a talk because it leads us to recognize the talk not like just a single thing, but to see it like an activity which has different parts. To interpret a talk, all the aspects like culture, speakers’ power status, politeness/ impoliteness and the others should be analyzed. After considering all these aspects, the exact meaning behind that speech might be explored and understood. As a result, it can be said that discursive practices are mainly used in conversation analysis to see how a conversation is conversed to show how successful it is.

            The language of communication for the MA English Language and Literature team was English as nobody could talk the first language of some other participants. Some of the members of the team had different accents but it did not affect the communication. All members’ speech tone was fine and it was always stayed at an average level which was considered polite. All the members of the team started studying in the same course since September 2011 and continued till late May 2012, but still some members of the group sometimes confused some other members’ names or even they did not know. This case shows that there was a lack of communication among some of the members and they did not have tight relations with each other. If the confusion or not knowing the names does really prove this, that means the relation among members of the team had to be tighter to enhance higher achievements.

In terms of the practices of the MA English Language and Literature team, it can be said that power was essentially equal as all the members worked all together. The decisions were taken through evaluating all the individual ideas and the ones, which were believed to be the best, were chosen. Generally speaking, the power did not have an important role as a community of practice in the group. The tutors who worked with us did not have a power of leading the project to any direction which they might desire. Their role was to guide us and answering our questions about the project to do our part very well as all student members were new for such a research and project.

            The practices like jocular and politeness were very common. There were many different ideas about the project and all the participants tried to show their thoughts to be admitted by other members. All the discussions were done in a very polite and friendly environment. To the best of my knowledge, cultural differences never made a problem. The obstacles or problems were solved through jokes and indirectly without harming any member. For example, a member of the group had always a problem with time managment; the member usually arrived to the meetings late. Other members did not react or sometimes I used jokes as a reaction, like telling that the member was supposed to arrive later and the moment was taken humorously. I personally tried to solve all the problems without making any trouble for the team. As it has been mentioned, the late arrivals or not attending was the biggest issue for the team. In such cases, other members volunteered to do the job which was considered to be done by someone else. In spite of this, those members who had such problems were encouraged to participate the meetings and to share towards the project.

            Collectivism and individualism had their influence on the members but they did not change the direction of the activities. The members of the team were divided equally on both these cultural backgrounds. The members who came from collectivistic cultures desired to arrange more meetings, while people from individualistic cultures did not mind and they thought the discussions could be done by using Facebook and emails. A member who was from a collectivist culture was not very happy as we did not have as many meetings as that member preferred to have. In spite of this concern, all other members including others from collectivist cultures were quite happy about the progress of the project. High context and low context was another difference like collectivism and individualism, but I never felt about its existence. All the members were very friendly and as it had been mentioned earlier, the power was equal which did not make any difference among us to recognize this matter.

  1. Employability Outcomes

The project was an opportunity for me to improve the skills which directly relate to my future employment. Communication skills were among the most important ones that I needed. In my undergraduate study, I did not have any group works; though, this project was the first one in my life which I became a group member to carry out a research. Communicating with people from different academic level and nationalities made me more communicable. This is a very important feature for a person to have it while seeking an employment opportunity. After the period of the project and communicating to the members of the group, now I am more confident in terms of contacting other people. The key point here is confidence because it can be increased through practicing the skills in real workplaces.  To be honest, I was not very confident in my communication in the past, but now a serious improvement can be seen. This improved skill is a significant outcome of working on the project. It can definitely be said that I will communicate easier in the future and it is a very important feature to have to be more employable.

Time management is another issue which I see as an employability outcome of this project. I usually had been good at managing my time, but when started this project, the skill needed improvements. During the work of the project, many weaknesses could be found about the time management skills. Through consulting other members and external sources, some of the negative points were changed to positives. This point is very important for my future employment and career because success can be the result of good time management. I was provided with very useful leaflets and sources by the tutors about this issue. After reading them, I found that I had to manage my time better as it was not great. Now, my time is managed better and I can have more activities as the least amount of time is spent carelessly or without a plan. I have many goals that I work hard to achieve them. My main targets are obtaining higher degrees and learning more to have a better employment opportunity. Trying to reach all the achievements need a good plan which requires managing time very well. This skill can have a main role in my future employability.

The dream employment for me is working in a group which consists of people from different cultures and nations. Such an employment essentials cross cultural skills, that is to say, someone with a good knowledge of different cultural aspects. The group of people which involved in the project was quite diverse. I worked with people who had different ideas and attitudes. It was a good introduction for me to learn some basics of dealing with people who may think differently especially with different customs. It does not look like having a conversation with someone who the same culture is shared with. In this sense, communicators should be more careful to avoid misunderstanding. After communication with all the members of the team, my cross cultural information increased significantly.

We had many meetings to discuss the project; a great number of people from different courses and positions attended to the meetings. In the meetings, I usually could improve my leadership skills as I always tried to participate and contribute something to the meetings. It can be said that being active in a group can enhance leadership skills because usually active people become leaders. Problem solving was another issue which I could recognize it from myself; I could solve some of the problems in a few meetings. While my leadership and problem solving skills were improving, my self-confidence increased significantly. As people relied on me sometimes and they wanted me to speak at the meetings, I became more confident in communicating with other people. This helped me to recommend solutions and new ideas with less hesitation.

Using technology to enhance a group aim was another new thing for me. Facebook was generally used to update group members about the recent changes of the project and sometimes, the ideas were discussed there. We had two Facebook groups: one of them was shared between students of my course and MA publishing students, the other one was dedicated to the students of my course. The second one was not very active as we usually preferred face to face communication, but I usually became an active member of the first group. I used the group to update MA publishing students about our works. It was proven to me that a space on a social network can be very useful for a group to work on a project. People from a work place can have such a group to discuss issues and problems about their job or they can easily ask questions and search for the answers there.

Team work was very new for me and I found that it is more beneficial than doing a type of work individually. It could be seen that a team work was more successful than a work of an individual. In the project, I realized that to get great achievements for a group, a shared aim of the team is very important. That is to say, all the members of a group should work to achieve a target and the group achievements should be more important than individual attainments.

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Tanzania Unique Status in the Opulence of the East African Community

Article Type: Journal Article

 Author: Dr. Philemon Sengati

 Affiliation:

Dr. Philemon Sengati

The University of Dodoma

P.O. Box 395, Dodoma- Tanzania

1.0 Abstract

This paper argues the fact that, whatever circumstances come on, Tanzania stands as a strategic actor with unique status in the development and prosperity of the East African Community. This position is built on a variety of premises, one of which are, the records of Tanzania in the struggle to build unity, equality, true democracy and peace among nations in Africa, thus the Great Lakes Region and the East Africa community. There are numerous instances to justify this assertive position, such as Tanzania has been the designated honest broker in crisis prone regions of Kenya, Burundi, Democratic Republic of Congo, Comoro and Uganda at different phases in its existence as a state.

Again Tanzania strategic position is vibrant in the growth potential of the economy of Rwanda, DRC, and Uganda; this is reflected by the strategic geographical location in serving other countries in the region. Tanzania is the heart of EAC transport network, in sense that four out of the five transport corridors start from Tanzania to EAC countries. The existence of sea ports (Dar es Salaam, Mtwara and Tanga and one is in plan to be constructed in Bagamoyo) dependable by the EAC for economic activities like importations and transportation of goods in their destinations consolidate the fact. More importantly Tanzania is a country that has sustainable peace situation as such for years it never entered into war rather peaceful coexistence amongst Tanzanians and its neighbors has been its tradition. In this paper we argue that, despite of the challenges that the EAC faces, Tanzania has a vital status in its development and prosperity of which citizens and leaders at local, national, regional and international levels have to uphold and nurture.

2.0 Key words

Regional Integration, Strategic Position, the East African Community.

3.0 Introduction

African frontrunners have long recognized the need for closer regional connections as a way to overcome the fragmentation of the continent which is one of the major constrictions toward its economic development. The economic integration of Africa was the central theme of the 1980 Lagos Plan of Action, the special United Nations Session on Africa in 1986 and numerous other high level statements and reports on African policy and development strategy (Ojo et al., 1985).  It is no doubt that, more recently the dreams have found expression in the creation of the African Union and regional and sub-regional integration.

In view of that, Sub-regional and regional groupings is a dominating agenda to the attainment of socioeconomic, political development; the approaches complements as necessary for improving Africa’s competitiveness, mindful of the fact that as most African countries are small by standing independently in terms of their domestic markets (EAC Annual Trade Report, 2008). In line with the vision and objectives of the region, East African Community was formed to create a well-connected, economically prosperous and peaceful region by supporting both public and private sector engaged in the regional integration process (Munster, 2009).

Five countries in the region (Burundi, Kenya, Rwanda, Tanzania and Uganda) constitute the EAC. The Permanent Tripartite Commission for East African Co-operation was first formed in 1967, but collapsed in 1977 due to political differences among the participating countries; again it was re-established in January 2001 by a Treaty, which entered into force on 7 July 2000. Burundi and Rwanda joined the Community on 18 June 2007.

“One people, one destiny” – so runs the slogan of the East African Community (EAC), which was re-established through signing of the EAC treaty on 30th November, 1999 and came into force in 2001. The future conceived EAC will comprise 13 countries including: Burundi, Comoros, Djibouti, Eritrea, Ethiopia, Kenya, Rwanda, Seychelles, Somalia, Sudan, Uganda and Tanzania as of 9 July 2011, the newly-independent Republic of South-Sudan. It draws on the analyses and conclusions of various sector studies and benefits from discussions with the country and regional stakeholders (Heinz, 2005). At the same time, it pays close attention to specific concerns in the region such as fragility, insecurity, cross-border conflicts, governance challenges, as well as cross-cutting issues related to gender, the environment and climate change.

The EAC is a key driver of the regional integration process and has achieved positive results, including a common market status in July 2010. The target date for establishing a monetary union is 2012. The vision of EAC is to create a prosperous, competitive, secure and politically united Eastern Africa. The objective, according to Article 5 (1) of the Treaty, is to develop policies and programs aimed at widening and deepening cooperation among the partner states in political, economic, social and cultural fields, research and technology, defence, security and legal and judicial affairs for mutual benefit (EAC Annual Trade Report, 2008).

The East African Community is organized into different organs provided in the Treaty which formed the integration and are found in Chapter III Article 9(1) of the union Charter. These organs include, the Summit of the EAC that consists of the Heads of State of the Partner States and at present these are:

“ President Pierre Nkurunzinza of Burundi, President Uhuru Kenyatta of Kenya, President Paul Kagame of Rwanda, President Dr. John Pombe Magufuli of  Tanzania, and President Yoweri Kaguta Museveni of Uganda”. The five presidents take the chair of the summit in turns of one year and the present chairperson of the Summit is Dr. John Pombe Magufuli of Tanzania. It is equally important to unravel that, the Summit meets at least once in a year (Chapter Four Article 10 of the EAC Charter of, 1999).

The other organ of the East African Community is the Council of Ministers consisting of the ministers for regional co-operation of each Partner State and other ministers to be determined by the Partner States. The Council of Ministers meets twice a year; one of the meetings is held immediately preceding a Summit Meeting (Chapter Five Article 13 Charter of the East African Community). In connection to this organ, there is a Co-ordination Committee consists of the Permanent Secretaries responsible for regional co-operation in each Partner State. It reports to the Council of Ministers and co-ordinates the activities of the Sectorial Committees (The EAC Charter of 1999, Chapter 6 (Article 17).
There is another organ called Sectorial Committees of the EAC which reports to the Co-ordination Committee and are established by the Council of Ministers. Their task is to prepare programmes and to implement the objectives of the Treaty (Chapter 7 (Article 18) of the 1999 Charter of the EAC). Another organ is the East African Court of Justice has the major responsibility to ensure the adherence to law in the interpretation and application of and compliance with the Treaty. This includes for example disputes between Partner States regarding the Treaty, disputes between the Community and its employees or the compliance of national laws with the Treaty (Chapter 8 (Article 23) of the 1999 EAC Charter).

The East African Legislative Assembly is the Parliament of the East African Community. It has 52 members – nine members from each Partner State – plus 7 ex-officio members, namely the five Ministers responsible for regional co-operation, the Secretary General and the Counsel to the Community (Chapter 9 (Article 48) of the 1999 EAC Charter). The Secretariat is the executive organ of the EAC and runs the day-to-day business. It is headed by the Secretary General. He is supported by four Deputy Secretary Generals who deputies for him and have the following special responsibilities. The Counsel to the Community is appointed by the Council of Ministers and acts as the principal legal adviser to the Community. The Counsel is also entitled to appear in the Courts of the Partner States in matters regarding the Community and its Treaty (Chapter 10 (Article 66) of the 1999 EAC Charter).

There are other autonomous institutions with special responsibilities to perform in the EAC, one of which is the Lake Victoria Basin Commission, this oversees the management and development of Lake Victoria Basin and serves as a centre for promotion of investments and information sharing among the various stakeholders. Its headquarters are situated in Kisumu, Kenya. The other institution is Lake Victoria Fisheries Organization (LVFO), this coordinates fishery issues in Lake Victoria to ensure that fish and fish products are available in East Africa and has access to international markets (Chapter Four Article 10 of the EAC Charter of 1999).

The other institution is called Inter-University Council of East Africa (IUCEA),
IUCEA encourages and develops mutual beneficial collaboration between member universities and Governments and other public and private organizations.
East African Development Bank (EADB). EADB was established in 1967 to redress the development disparities between the member states of the former East African Community. EADB has a critical role to play in setting up the East African Common Market in terms of mobilising external lendable resources for the East African Market. Civil Aviation Safety and Security Oversight Agency (CASSOA).CASSOA is a specialized agency of the East Community responsible for ensuring the development of safe and secure civil aviation system in the region. The main objectives of the Agency are to ensure coordinated development of an effective and sustainable civil aviation safety and security oversight infrastructure in the Community (Chapter Four Article 10 of the EAC Charter of 1999).

 

4.0 The Predicament on board

There are initiatives to promote a strong and well founded East African Community among member states like creation of customs union, common market and an EAC legislative. In 2013, the diplomatic rifts between President Kagame of Rwanda and former Tanzanian president Jakaya Kikwete constrained and retarded these efforts.
President Kikwete had suggested – during an auspicious AU Summit in Addis – which Kigali should negotiate with the rebels based in the DR Congo. The Summit then had serious security issues facing the continent on the table, including the running instability in eastern DR Congo. Conversely, in an interview with Radio France International (RFI), on 4th  June, 2013 Rwanda’s Minister of Foreign Affairs, Louise Mushi Kiwabo said Rwanda will not consider negotiating with people who were responsible for the 1994 Genocide against the Tutsi.

Those who think that Rwanda today should sit down at the negotiating table with FDLR simply don’t know what they are talking about,” she said, adding that it was unfortunate that the rebel group had sympathizers in the region, including President Kikwete himself, should he not retract his comments.” (The Guardian 8th June, 2013).

There are many spokespeople for the FDLR; some are ideologically aligned to the FDLR. We stopped the genocide but we didn’t stop the ideology,” she added. Tanzania categorically said it won’t apologize over remarks by President Jakaya Kikwete, and reiterated its call upon Rwandan authorities to initiate peace talks with rebels of the Democratic Liberation Forces of Rwanda (FDLR) (The Guardian 8th June, 2013).

Of recent years, the presidents of Kenya, Uganda and Rwanda met in Kigali for the third time, in what came to be known as “the coalition of the willing.” this brought a negative view on which media went on commenting that perhaps the new EAC could go back to its old history of 1977. Still Tanzania used its diplomatic strategy to make the situation cool by upholding the vision and principles of regional integration. When addressing the nation, on his monthly speech former President Kikwete affirmed to Tanzanians that Tanzania will take the last position to go out the EAC community, by the time it will use any diplomatic means to make sure all things are calm and bring positive results in the community.

“Tanzania will never quit the East African Community and will do everything in its power to make sure the community survives and becomes prosperous despite efforts by Kenya, Rwanda and Uganda to side-line it……. We are in the EAC to stay. We have come from so far. We have sacrificed so much to give up now. We will do everything in our power to make sure the EAC survives and achieve its ultimate goal of political federation,” President Kikwete told the Parliament. (Daily News 7, Nov. 2013).

Tanzania is currently endeavouring to improve its cooperation with Governments and with regional organizations, this is concomitant with Tanzania strategy towards integration where it believes that the key player in the EAC  must be driven by clear vision and political will and be guided by four “Cs” namely Communication, Commitment, Cooperation and Completion.

While situation is not calm between Tanzania and Rwanda the United Nation passed special resolution No 2098 of 2013 to send a special peace keeping force in the Eastern DRC, a special zone for Rwandese interest as conceived by different scholars (Sengati, 2014; Mpangala, 2004; Rummel, 1995 and Rupiya, 2005). That the region is full of mineral potential but a conflict Zone with M23 and FDLR rebel groups causing chaos and alleged supported by the Rwanda government. This UN special force is largely composed of Tanzanian soldiers, this has increased hostility between Tanzania and Rwanda, because the initiative to forcing out all rebel groups in Eastern DRC impliedly mean the call to peace and freedom for the DRC of which jeopardizes the Rwandese dominance and interest especially in mineral resource in the eastern DRC. In this paper we argue that, the development and prosperity of the East African Community is linked to its member commitment. It is of no imagination that Tanzania can be isolated in the move to building a strong East African Federation, because of many reasons but mostly its strategic position is the potential for the development of the EAC.

5.0 Materials and Methods

The materials used in this paper are secondary materials and qualitative methods are employed to describe the strategic status Tanzania has in the opulence of the East African Community. The materials reviewed include the journal articles, books, News Papers, the EAC Charter and paper presented in different forums to enhance validity and reliability of key arguments in the article. The analysis of the information is done by using a thematic method that is used in qualitative research data analysis.

 

6.0 Theoretical and Empirical debates

The theory of regional integration has been associated with Haas (1950) a prominent neo-functionalist known for his concept of “The uniting of Europe”. This is because Europe remained the focal point for most of the works on regional integration theory although in the recent past the application of integration theory to Latin America, Africa and Asia has increased. Haas and Schmitter developed a conceptual framework that has spread the process of regional integration beyond Europe in industrial and non-industrial settings with a concept approach that is applicable to both. The basic postulation of neo functionalists is the decline of nationalism and nation-states and their replacing by large units more suited for the roles they play in society. The neo functionalist thus does not see nation-states as units of analysis but the whole region as a unit. Modern neo-functionalist who were inspired by European integration still exist and put emphasis on supranational institutions, among them are Sandholz and Sweet (1997) and multilevel governance, Marks, Hooghe and Blank (1996) among the opponents of regional integration was Haas himself, Lindberg and Scheingold. This was after the European integration process started to experience a crisis in the mid 1960s. Haas and these scholars concluded that his theory was too deterministic and Haas admitted that he had not foreseen a rebirth of nationalism and resilience of sovereign nation-states within functionalist organization of supra-national institutions referred to as regionalism.

Lindberg and Scheingold singled out some of the major mechanisms and dynamics. It was concluded that neo-functionalists had not studied domestic politics sufficiently and that they could have exaggerated the role of supranational institutions The other opponent of neo-functionalism is Pieson, Pollock (1996), Scheneider and Aspinwall (2011) who used the new institutionalism approach to integration studies. According to Pierson there are gaps that emerge among the member states which are difficult to close. These gaps are created by autonomous action of integration institutions, the restricted time horizons of political decisions makers, unanticipated consequences and shifts in policy preferences of governments. This makes the gaps very difficult to close because of the reluctance of supranational actors, institutional barriers to reform and various costs to change. Due to this gaps and the difficulty in closing them, Pieson, Pollock and Scheneider and Aspinwall argue that this forms the foundation of disintegration rather than integration. Therefore these authors see nothing than disintegration as states pursue their own agenda defined as state interest among community of states. This disintegration and the consequent pursued by individual interest is therefore a source of disharmony since it is equivalent to a chaotic state of nature. With this state of nature, states are likely to disagree and by extension war erupts. The war is a war in a whole community of states. As states push and shove over their interests, there is likely war in the whole community while in the individual states, there will be peace. This in Nye phrase is the “peace in parts”. The parts are individual states which internally are at peace but externally in relation to other states are not, as each state attempts to promote and protect its own self interests, there is no peace i.e. the states are in a state of war always in their protection and promotion of self interest. Nye’s thesis rests on rather simple question of how there can be integration as proposed by neo-functionalists when there is no peace in the whole but only in the parts. Rather how can the peace existent in parts be utilized to guarantee peace in the whole. Simply how can states be at peace while they all pursue their own self interest in the same environment? This according to Nye’s thesis is impossibility. This theory is relevant because it talks about collective decision making. Policies in EAC are determined by consensus which covers a varying number of functional areas. Ernst Haas came up with the concept of spillover which “refers to a situation in which a given action, related to specific goals, creates a situation in which the original goals can be assured only after taking further actions, which in turn create a further condition and a need for more action and so forth”60. This refers to policies that are agreed upon and the partner states need to implement them for the prosperity and continuous existence of the integration.

 

Liberalism is the theory related with the formation of the East African Community. Liberals argue that the universal condition of world politics is globalization. States are, and always have been, embedded in a domestic and transnational society, which creates incentives for economic, social and cultural interaction across borders. State policy may facilitate or block such interactions. Some domestic groups may benefit from or be harmed by such policies, and they pressure government accordingly for policies that facilitate realization of their goals. These social pressures, transmitted through domestic political institutions, define “state preferences” –that is, the set of substantive social purposes that motivate foreign policy (Hurrel, 1995).

 State preferences give governments an underlying stake in the international issues they face. Since the domestic and transnational social context in which states are embedded varies greatly across space and time, so do state preferences. Without such social concerns that transcend state borders, states would have no rational incentive to engage in world politics at all, but would simply devote their resources to isolated existence. To motivate conflict, cooperation, or any other costly foreign policy action, states must possess sufficiently intense state preferences. The resulting globalization-induced variation in social demands, and thus state preferences, is a fundamental cause of state behavior in world politics (Durgesh, 1984). This is the central insight of liberal international relations theory. It can be expressed colloquially in various ways: “What matters most is what states want, not how they get it- “Ends are more important than means.”

Three specific variants of liberal theory are defined by particular types of preferences, their variation, and their impact on state behavior. Ideational liberal theories link state behavior to varied conceptions of desirable forms of cultural, political, socioeconomic order. Commercial liberal theories stress economic interdependence, including many variants of “endogenous policy theory.” Republican liberal theories stress the role of domestic representative institutions, elites and leadership dynamics, and executive-legislative relations. Such theories were first conceived by prescient liberals such as Immanuel Kant, Adam Smith, John Stuart Mill, John Hobson, Woodrow Wilson, and John Maynard Keynes-writing well before the deep causes (independent variables) they stress (e.g. democratization, industrialization, nationalism, and welfare provision) were widespread (Duncan, 2008)

What basic assumptions underlie the liberal approach? Two assumptions liberal theory makes are the assumptions of anarchy and rationality. Specifically, states (or other political actors) exist in an anarchic environment and they generally act in a broadly rational way in making decisions. The anarchy assumption means that political actors exist in the distinctive environment of international politics, without a world government or any other authority with a monopoly on the legitimate use of force. They must engage in self-help.  The rationality assumption means that state leaders and their domestic supporters engage in foreign policy for the instrumental purpose of securing benefits provided by (or avoiding costs imposed by) actors outside of their borders, and in making such calculations, states seek to deploy the most cost-effective means to achieve whatever their ends (preferences) may be (Daniel,1984).

 Liberal theory shares the first (anarchy) assumption with almost all international relations theories, and it shares the second (rationality) assumption with realism and institutionalism, but not non-rationalist process theories. The second core assumption shared by liberal theories is that the interdependence among of state preferences influences state behavior. Rather than treating preferences as a fixed constant, as do realists or institutionalists, liberals seek to explain variation in preferences and its significance for world politics. The precise distribution and nature of the “stakes” explains differences in state policy and behavior (Willis, K. 2005).

States, liberals argue, orient their behavior to the precise nature of these underlying preferences: compatible or conflictual, intense or weak, and their precise scope. States require a “social purpose” a perceived underlying stake in the matter at hand in order to pay any attention to international affairs, let alone to provoke conflict, inaugurate cooperation, or take any other significant foreign policy action. If there is no such interdependence among state objectives, a rational state will conduct no international relations, satisfying itself with an isolated and autarkic existence. Conflictual goals increase the incentive for political disputes. Convergence of underlying preferences creates the preconditions for peaceful coexistence or cooperation (Duncan, 2008).

 Rational choice Theory is also one of the theories related with the formation of East African Community. An economic principle that assumes that individuals always make prudent and logical decisions that provide them with the greatest benefit or satisfaction and that are in their highest self-interest. Most mainstream economic assumptions and theories are based on rational choice theory (Ojo et al. 1985)

Indeed, the East African Community might have put in perspectives rational choice theory in devising coercive apparatuses among member states such like the Interpol-to crack down criminality beyond borders. The road map into the formation of East African Monetary fund is related with the concept of rational choice theory which looks into maximizing members’ states advantage or gain, and to minimize their disadvantage or loss.

Realism is the last theory in the analysis of the formation of East African Community. Descriptive political realism commonly holds that the international community is characterized by anarchy, since there is no overriding world government that enforces a common code of rules. Whilst this anarchy need not be chaotic, for various member states of the international community may engage in treaties or in trading patterns that generate an order of sorts, most theorists conclude that law or morality does not apply beyond the nation’s boundaries (Holst, 1990).

Arguably political realism supports Hobbes’s view of the state of nature, namely that the relations between self-seeking political entities are necessarily a-moral. Hobbes asserts that without a presiding government to legislate codes of conduct, no morality or justice can exist: “Where there is no common Power, there is no Law, no justice if there be no power erected, or not great enough for our security; every man will and may lawfully rely on his own strength and art, for caution against all other men. In this case integration of countries is the best strategies to enforce moral behaviors or acts amongst actors within regional agreements (Pentland, 1973).

Either descriptive political realism is true or it is false. If it is true, it does not follow, however, that morality ought not to be applied to international affairs; what ought to be does not always follow from what is. A strong form of descriptive political realism maintains that nations are necessarily self-seeking, that they can only form foreign policy in terms of what the nation can gain, and cannot, by their very nature, cast aside their own interests.

However, if descriptive realism is held, it is as a closed theory, which means that it can refute all counter-factual evidence on its own terms (for example, evidence of a nation offering support to a neighbor as an ostensible act of altruism, is refuted by pointing to some self-serving motive the giving nation presumably has it would increase trade, it would gain an important ally, it would feel guilty if it didn’t, and so on), then any attempt to introduce morality into international affairs would prove futile (Breen,  and  Rittman, 1995).

 Examining the soundness of descriptive political realism depends on the possibility of knowing political motives, which in turn means knowing the motives of the various officers of the state and diplomats. The complexity of the relationship between officers’ actions, their motives, subterfuge, and actual foreign policy makes this a difficult if not impossible task, one for historians rather than philosophers. Logically, the closed nature of descriptive realism implies that a contrary proposition that nations serve no interests at all, or can only serve the interests of others, could be just as valid.

Realism under the East African Community hinges on the assumption that some leaders, because of their ethnic background, would always think of remaining in power and controlling others. It has been assumed that President Museveni and Kagame belong to Tutsi ethnic background. The motive behind Tutsi generation is hegemonic power. They (Tutsi) have a propensity of ruling others forever and evermore. Example of tyrannical utterance once put forward by Museveni justifies this contention.

 President Museveni has been in power for almost 28 consecutive years 40 per cent of his lifetime. Given the country’s very young population, 75 per cent of Ugandans have only had one president all their life. When asked if he would run again in 2016, Museveni’s response was, “one of the real points for me politically is the East African Federation. I cannot leave this issue if I think there is a possibility of advancing it. This is something I have been working for all my time in politics and is one of the reasons why I continue to be in power (The guardian 17 August 2015)

This is the classic case of a leader thinking that he is indispensable, a very dangerous mind-set for democracy. In 2011, when President Museveni was asked how he would react if Ugandans contested election results with demonstrations, Museveni responded that “we just lock them up … bundle them into jail and bring them to the courts.” There you have it – a theoretical model for democracy.

The maturation of region integration elsewhere in Africa is engulfed by both optimists and pessimists leaders, and scholars.  Empirically there are vast literature by both African and Africanist scholars which point out a dark picture about the prospects of getting it right in terms of bringing together different countries in a specific region in Africa. Dieter (1997) for example, writes: “in Africa, attempt to create regional integration prospect have a long, albeit discouraging history”. Odhiambo (1981) writing specifically about East Africa, shares the same view by arguing that: “when it comes to the question of African attempts at territorial politics, the experience is one of failure, or alternatively of inability”.  There are a few other scholars who concur with this trajectory (Hentz, 2005) writes: “Thus schemes in Africa such as the Economic Community of West African states and the East African Community adopted a blueprint from a very different place and time, and like others such schemes in sub Saharan Africa, they failed”.

These views are credible and can be substantiated by facts. For example west and central African states tried regional integration soon after gaining political independence from European colonizers but all these attempts failed. The French colonies of Mali and Senegal formed a federation but a few months later Senegal seceded from the federation and declared itself as the Republic of Senegal. In other areas Ivory Coast, Dahomey and Niger formed the council of the intent but this too collapsed (Melady, 1961).

Patrice Lumumba of the present day the Democratic Republic of Congo and Kwame Nkrumah once contemplated combing DRC and Ghana, an idea that never materialized. Some of the post independence regional organization includes the West African Economic Community (WAEC) and the central African customs and Economic Union (CACEU), which were established in the 1960s later disintegrated too. Even the Pan African Freedom Movement of East, Central and Southern Africa (PAFMECSA) did not survive due to ideological differences among African leaders and their excitement about their newly found freedom from colonial rule. Thus the argument by the pessimists is tenable and can be substantiated

Increasingly, in spite of these features the spirit of regional integration did not die out amongst Africans. Consequently, when the East African Community territories their political independence in early 1960s they also tried to follow the same route by establishing the East African Community. Unfortunately, like its predecessor organization the EAC’s life was also ephemeral as it collapsed after a single decade. It is in this context therefore that the view expressed by those scholars who state that the African experience with regional integration or territorial politics is one of failure can not be summarily refuted (Veit, 2010).

However it would be wrong to overgeneralized and argue that all attempts to establish regional institutions in post colonial Africa failed because some of these regional organizations are still operational even today.  Among these that have survived to date is the ECOWAS, established in 1976. In this paper we stand out to argue that the survival, development and prosperity of the current East African Community is totally dependent on the commitment of its member states to forming the political federation. Uniquely is the strategic status of Tanzania in promoting such development and prosperity within the East African Community.

Musonda (2006) is of the view that, Western European countries started experiencing regionalism in the 1950’s. From these countries, the project of regionalism spread to other parts of the world including Asia, Latin America, and Africa among others. The formation of the European Economic Community EEC   and later the signing of the Treaty of Maastricht in 19936 ushered in a big leap as far as European integration was concerned. These were to later emerge as case studies of successful regional integration. The change of name from EEC to EU signified the expansion growth the union was undergoing. These developments were not only political, but also economic, social, cultural and linguistic changes. The institutions created under this community played a significant role in strengthening and buttressing the community to what it is today. That the EU integration is developed to the extend of having a full legal system and jurisprudence is pointer to how successful integration can be. A reference to EU law8 which has become part of comparative legal studies across the world is one such proof. Among the EU laws are legislations on and provisions of the EU treaty on immigration, visa regulation, and free movement of persons within the union9 and outside the union who are citizens of member states or non members. A study of this EU law will reveal the impact of the aforesaid law on integration in the EU.

7.0 Figure 1: CONCETUAL FRAMEWORK:

Source: Authors’ Creativity

The figure 1 shows a conceptual framework with varies variable pointing to the prospect of a strong integrated EAC.  The dependent variable in this model are the other members of the EAC currently they include Kenya, Uganda, Rwanda, Southern Sudan and Burundi, with a future prospective members of Democratic Republic of Congo, Somalia and Ethiopia. The dependent variable as argued is a strong and composed EAC with achieved vision of having a common market, custom union, monetary union, and political federation. The key driving force toward success of the respective is having Tanzania as catalysts member to enforce a strong integrated EAC in terms of its potentials in resources, population, political, infrastructure and energy.

8.0 Results and Discussions

8.1 Rationale of the EAC Integration for Tanzania

Reith S. et all (2011) has contended that “country’s neighborhood matters for regional integration and growth spillovers from across borders are among the main benefits of regional integration. In a more integrated economic space, the long-run growth prospects of countries become interlinked as markets of neighboring countries become more accessible”. Kiraso (2010) argues that, when two or more states come together to trade as a block thereby creating a bigger consumer base for their products and services. Thus, growth in neighboring countries enhances domestic growth, which benefits neighbors. This spatial multiplier enhances the rewards to good policy and contributes to convergence in living standards.

The idea that economic integration can promote regional (or global) co-operation among states finds its sources in several theories of International Relations. Neo-functionalism, for instance, which was particularly influential in its time as a theory of European integration, predicted that two kinds of “spillover” would occur to sustain and deepen integration. The first kind of spillover was functional, “whereby partial small initial steps down the integration road would create new problems that could only be solved by further cooperation.” The second kind of spillover was political: “the existence of supranational institutions would set in motion a self-reinforcing process of institution building” (Folayan O.  1975). Tanzania stands for this theory by aspiring to deepening and broadening integration as one important way to contribute to sustained rapid growth and greater poverty reduction for the people of EAC. The end result of integration should be greater inclusiveness among Tanzania and East African people.

According to Binto (2012) in the paper of Ngowi (2009), Tanzania joined different regional integration as an essential plank of their development strategy, and an important ingredient in stimulating increased social, political and economic progress. This is in line with the Treaty of the East African Community (EAC), signed on 30 November 1999, seeks to promote and strengthen the balanced and sustainable integration of economic, social, cultural and political aspects of the three member states: Tanzania, Kenya, and Uganda. To this end Meredith (2005) argues that the EAC will promote regional projects, facilitating the movement of people and vehicles across borders, harmonizing policies and regulations for trade and investments and promoting regional infrastructure. EAC is implementing its Development Strategy launched in April, 2001 with programmes such as Lake Victoria Basin development, agriculture and food security, energy, tourism, civil aviation safety, postal services, meteorology, and inter-university cooperation. To enhance good governance in the region, two organs of the Community, namely the EAC Court of Justice and the EAC Legislative Assembly were launched in November, 2001. Efforts for concluding the Customs Union Protocol are at an advanced stage (Kimario, 2011).

The EAC will promote regional and continental inter-linkages, the involvement of the private sector, exports to the region and beyond, and facilitate cross border movements.

Tanzania believes that the EAC will assist the region to create a promising future and stability, peace, security, democracy, prosperity and equity. It is, therefore, to Member States, collectively and individually, that place the hope for the effective and timely realization of the goals that with regard to politics, defence and security cooperation, priorities include preventing, managing and resolving conflicts so as to strengthen and sustain national and regional stability, peace and security. This is in concurrence with Article 5(3) (f) on the objectives of the EAC, which reads:

For purposes set out in paragraph 1 of this Article and as subsequently provided in particular provisions of this Treaty, the Community shall ensure: and sub paragraph (f) continues “the promotion of peace, security, and stability within, and good neighborliness’ among, the Partner States”.

8.2 Tanzania Unique status in the EAC

Kamala (2012), identified factors which qualify Tanzania as the gateway in the East African Community, which includes macroeconomic stability, strategic geographical location, the heart of East African Community transport network, hub of EAC Master Power Plan, nucleus of EAC single Customs Union Territory, focal point of the planed COMESA -EAC – SADC Free Trade Area, regional hub of EAC intra-regional trade, regional hub of investments opportunities, EAC food basket and EAC regional tourism hub. Salim,A and Eyakuze, A. (2012) narrowed the scope in four distinct areas that Tanzania stands as the strategic member in the development and prosperity of the East African Community, These include: Political capital, Demography ,Geography and Resources. All of these areas can and in many ways should be the bedrock on which Tanzania can anchor its self-assured engagement with regional integration.

Political Capital: Despite having been ruled by the same political apparatus since its independence in 1961 the country has enjoyed peace, harmony, democracy and governance to a great extent than any other country in East Africa. Good politics and good governance has made Tanzania an icon for peace and tranquility in Africa. Tanzania has been the designated honest broker in a crisis prone region. Historically, Tanzania has mediated many regional conflicts and has been viewed as a neutral stakeholder whose orientation is peace building (Baregu, 2004). In the late 1990s Mwalimu Nyerere served as a mediator trying to bring the different factions in Burundi towards a peace agreement. In 2006, former Tanzanian President Benjamin Mkapa was sent to mediate Zimbabwe’s political crisis in attempts to resolve the diplomatic stand-off between Britain and Harare. Most recently, Tanzania was heavily involved in solving the post-election crisis that gripped Kenya for months during the 2007 general election. With its historical reputation of being a leader and consensus builder, Tanzania has enough political capital to mediate many of the challenges East Africa faces (Salim, A and Eyakuze, A.2012).

Demography: Tanzania is by far the largest and most populous member of the East African Community. As of 2012, there were an estimated 45 million Tanzanians accounting for 32% of the 139 million East Africans. This share is projected to increase to 34% of the expected 237 million East Africans by 2030 translating to an 82 million Tanzanians as according to Tanzania National Bureau of Statistics (Khadiagala, 2009). Therefore, Tanzanian population gives recommendable domestic market in the EAC goods and services, which other countries can not dare to miss.

Geography: Geographically, Tanzania accounts for 52% of East Africa’s Total combined area of 2.01 million sq km and even though it has the largest population in the region there is significant land available. This is demonstrated by its small population density, which is the lowest in the region at 47 people per square kilometer. In comparison, Rwanda and Burundi have the highest with 403 and 301 people per square kilometer respectively. By 2030, Tanzania will be the only country in the region with less than 100 people per square kilometer. As a result Tanzania has a commanding advantage when it comes to land availability and usage (Tanzania government portal, 2014)

Tanzania is the only member of East African Community which shares the border with all EAC partner states. Currently, EAC is constructing “One-Border-Posts” with the purpose of facilitating EAC intra regional trade. The Border Posts under construction are: Rusumo/Rusumo(Tanzania and Rwanda); Namanga/Namanga (Tanzania and Kenya); Sirari/Isebania (Tanzania and Kenya); Holili/Taveta (Tanzania and Kenya) Horohoro/Lungalunga (Tanzania and Kenya); Mutukula/Mutukula (Tanzania and Uganda); and Kabanga/Kobero (Tanzania and Burundi). Therefore, the geographical Location of Tanzania makes Tanzania A hub of the East Africa Integration of which can never be excluded (Sezibera, 2016).

Infrastructure: Given Tanzania strategic geographical location, Tanzania is the heart of EAC transport network. EAC partner states agreed on five transport corridors which constitute EAC Road Network. Four out of the five transport corridors starts from Tanzania to EAC countries.  Five East African Community major transport corridors are: Mombasa – Malaba- Kigali – Bujumbura, Dar es Salaam- Rusumo, with branches to Kigali, Bujumbura, Masaka and Kampala, Biharamuro- Sirari-Lodwar – Lokichogio, Nyakanazi- Kasulu- Tunduma with a branch to Bujumbura and Tunduma – Dodoma- Namanga-Moyale. Thus, 80% of East Africa Road network transit corridors start from Tanzania to Kenya, Uganda, Rwanda and Burundi.

 

Tanzania also boasts the largest coastline in the region and has three ports Dar es Salaam, Mtwara and Tanga and one is in plan to be constructed in Bagamoyo. Countries like Uganda, Rwanda and Burundi are reliant on Tanzania for direct access to the Indian Ocean. The opportunities in this are endless and Tanzania should be used as a gateway to middle Africa.

Resources: Tanzania also has a wealth of natural resources that it can use to boost its economic development and invest in regional development. Its reach natural resources including iron ores soon will be in effective use and hence giving rise to Tanzania as a source of Iron and iron materials in the region. It also has more arable land than any other country in the community. Tanzania arable land accounts to 44 million hectors, which potentially not only make it the EAC’s central breadbasket, but Africa’s in general. Southern Tanzania is rich with natural re-sources and has the capability of feeding those in need in southern Kenya and Ethiopia. From the natural Resources endowment of tourism attractions Tanzania is the EAC Regional tourism hub. Tanzania has over 46,000 square kilometers of land reserved for National Parks. There is no any other East African country with such a huge piece of land dedicated to National Parks. Tanzania, has many more tourism attractions such as Mount Kilimanjaro, Ngorongoro Crater, Zanzibar, Serengeti National Park, Katavi National Park and Ruaha National Park to mention a few. A number of investments opportunities are available in tourism sector (Uwe, 1999).

Energy Hub: Tanzania has become an energy potential than the other four countries combined and will become soon the region’s energy powerhouse, after the discovery of large amount of gases and the amount of coal deposit. Energy is potential for industrialization and manufacturing, therefore after the discovery of this potential supply of energy Tanzania opens for vast investment and industrialization. Tanzania is also the Hub of East African Community Master Power Plan. In response to the recurring shortage of electrical energy, in EAC partner states, EAC established EAC Master Power Plan. Most of the projects identified in EAC Master Power Plan are based in Tanzania. The identified project are: Singida-Arusha-Nairobi 400kV Interconnector; Masaka-Mwanza 220kV Interconnector; Rusumo-Nyakanazi 220kV Interconnector; Stieglers Gorge Hydro-Power Project 2100MW; Kiwira Coal 200MW; and Rusumo Hydro Power Plant 90MW (Kamala,2012).

 

8.3 Achievements of the East African Community

The cross border movement of persons and goods has been eased through a number of measures, example, the introduction of the East African passport, special immigration desks for East African citizens at international airports, re-introduction of interstate passes, and withdrawal of visa charges for students and harmonization of vehicle transit procedures. The free convertibility of the currencies of Kenya, Tanzania and Uganda already introduced in 1977 (Durgesh, 2010). There has also been progress in a number of measures to improve East African infrastructure, for example in road improvement, telecommunication, civil aviation, postal services, energy and related areas and meteorology.

The customs union launched in 2005 eliminates all internal tariffs and other similar charges on trade between the partner states. It was agreed that the customs Union would be gradually implemented over a period of five years. Partner states immediately agreed that goods to and from Uganda and Tanzania shall be duty free.  From the start, imports of goods from Uganda and Tanzania into Kenya were free of duty, while goods from Kenya into Uganda and Tanzania were subject to two categories of import duty. C category A goods were duty free, and Category B goods from Kenya into Uganda and Tanzania have the present tariffs phased out over a five-year period. (Ojo et al. 1985)

The customs Union Protocol also established three-brand common external tariffs (CET) with a minimum rate of 0%, a middle rate of 10% and a maximum rate of 25%. The highest CET rate of 25% is to be reviewed by the partner states after a period of five years and possibly be reduced to 20%. The partner states also agreed that all non-tariff barriers should be removed and that no new non-tariff barriers should be imposed (Durgesh, 2010).

9.0 Conclusions

In conclusion, it can be stated that aspirations and prospects for an East African Federation are neither new nor unrealistic. However, unity, peace, true democracy and equality within and amongst member states, are a pre-requisite for a viable federation. These variables provide a vitally important environment for an honest and better, and a meaningful referendum on a federation. Members of the EAC need to work first on developing their environment including governance systems and on these elements Tanzania is outstanding in the region. Despite the weakness that Tanzania is experiencing, the country has enjoyed peace, harmony, democracy and governance to a great extent. It stands as a strategic player with unique status in the EAC because of the Political capital, Demography, Geography and Resources variables. All of these areas can and in many ways should be the bedrock on which Tanzania can anchor its self-assured engagement and unquestionable status in the East Africa Community. Thus the argument asserted by the paper “Tanzania Unique Status in the Opulence of the East African Communityis valid and relevant.

This paper is concluding by making Tanzania to stand out of other EAC which are marred with more weaknesses compared to Tanzania. To highlight few issues like the post-election violence in Kenya in 2008 that killed more than 1,000 people was a painful reminder of the severe deficiencies in the political system and also a bold demonstration of the quest for free and fair elections. To its credit, Kenya has been able to pass a new constitution which puts more effective checks and balances into place for the governance of the country. If the spirit and the letter of the new constitution are fully implemented, one can argue that Kenya is in a positive trajectory in its governance. But Kenya needs to gain experience with its new constitution before it propels itself into a federation.

In Rwanda almost all economic indicators suggest that it is doing quite well. President Kagame deserves credit. However, he seems to be following in President Museveni’s footsteps in thinking that he is indispensable. Critical elements of governance are missing in the country including participation of citizen in issues that affect their lives, democracy, and freedom of speech and the rule of law. Many analysts consider Burundi as a failed state. What is perplexing about African politics is that in the last 20 years it has been the autocratic leaders who have been major lobbyists for the political unification of Africa. In the lead was President Gaddafi of Libya. In fact, the precursor to the establishment of the AU was a special OAU summit of African heads of state initiated and hosted by Gaddafi in Sirte, Libya, in 1999, at which point it was declared (in the Sirte Declaration) that steps towards integration must be accelerated. The 2015 elections brought a lot miseries and traumas to Burundians and thus absence of true peace and democracy is a common practice in the country.

Indeed, in often times, dictators and autocratic leaders seek to divert attention from discontent at home by engaging in grandiose international initiatives. There might be some elements of that phenomenon going on in East Africa. Nonetheless, an East African Federation cannot be an “arranged marriage” brought about by overzealous politicians who think they are indispensable or entitled to power because of what they believe they have accomplished. Such a union will, sooner or later, break. What is needed at this point is for the East African countries to continue to solidify their economic integration, implement policies that increase the standard of living for all people, improve domestic governance with checks and balances, and develop genuine democracies at home. This calls for an exemplary country in the respective region of which Tanzania could take that leading role as it has demonstrated to intervene the conflict in Kenya, Burundi and Rwanda during the 2007 election, during the 2015 election and in the DRC visas Rwanda conflict.

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            Willis, K., (2005) “Theories and Practices of Development”. London and New York: Routledge.

News papers:

The Guardian 8th June, 2013.

The Guardian 5th May, 2015.

Daily News 7th November, 2013

The East African, Saturday, November 9   2013.

Internet Sources:

Horst Köhler was speaking in June 2011 at a conference in Arusha organised by the Tanzania Government Portal: www.tanzaniagovernment.go.tz

http://www.Tanzania National Bureau of Statistics (2014)

The EAC “http://eac.int/politicalfederation/index.php?option=com_ docman&Itemid=28 (accessed January 13, 2016). Fast Tracking Report

Heinz-Michael Stahl, (2005) “Tariff Liberalization Impacts of the EAC Customs Union in Perspective,” tralac Working Paper.

“EAC Annual Trade Report 2008,” http://eac.int/statistics/index.php?option= com_docman& task=cat_view&gid=48&Itemid=153 (accessed January 3, 2016).

Chikwanha A, (2010) “The Anatomy of Conflcts in the East African Community (EAC): Linking Security With Development,” http://www.ascleiden.nl/Pdf/LectureAnnie Chikwanha.pdf (accessed January 9, 2016).

 East African Community, “EAC Facts and Figures 2010”, http://eac.int/statistics/index.php?option= com_docman& Itemid=153 (accessed January 10, 2016)..

Richard Sezibera, “EAC Secretary General Outlines Priorities. Press Statement,” http://eac.int/about-eac/eacnews/632. html?task=view (accessed January 10, 2016).

  1. Macmillan (1962), Britain, the Commonwealth and Europe, available at

http://www.toryeuropenetwork.org.uk, accessed last on 11 November 2008

Wikipedia, On Federalism, Available http://n.wikipedia.org/wiki/Federalism.Accessed last on 27 December 29, 2013.

 

Presented Articles:

Brief Overview Of The East African Community (2010). A Presentation by Hon. Beatrice

Kiraso, Deputy Secretary General Tanzania Society and Tanzania Development Trust, held at the Royal Commonwealth Club, London – United Kingdom

Rwekaza, M., 2000, “Political Cooperation”, Paper presented at the proceedings of the

1st Ministerial Seminar on East African Cooperation on Perspectives on Regional

Integration and cooporation in East Africa, at Arusha, Tanzania, 25-26 March 1999.

Uwe, L (1999), “Germany Federation Toward 2000: To be Reformed or Deformed?, in Charlie Jeffery, ed., Recasting Germany Federalism: The Legacy of Unification, London: Pinter

International and Regional Documents:

Treaty for the establishment of the East African Community, 30th November, 1999 (entered into force, 7th July 2000).

Charter of the United Nations, 1945

Steven Buigut, “A Fast-Track East African Community Monetary Union? Convergence Evidence from A Cointegration Analysis,” International Journal of Economics and Finance, 3 (2011) 1, 255-261.

 Reith S. et all, 2011 The EAC regional integration between aspiration and reality

Legislations:

Constitution of the Republic of Rwanda

Constitution of the Republic of Uganda, 1995

Constitution of the United Republic of Tanzania, 1997