Impact of Agricultural Finance on Production of Fruits: Evidence from Pakistan

Transcription

1 Pakistan Journal of Social Sciences (PJSS) Vol. 36, No. 2 (2016), pp Impact of Agricultural Finance on Production of Fruits: Evidence from Pakistan Adeel Akhtar (Corresponding Author) Assistant Professor Department of Commerce Bahauddin Zakariya University, Multan, Pakistan seeadeel@yahoo.com Muhammad Shaukat Malik, PhD Professor Institute of Banking & Finance Bahauddin Zakariya University, Multan, Pakistan shoukatmailk@bzu.edu.pk Abdul Ghafoor Awan, PhD Dean, Faculty of Management and Social Sciences, Institute of Southern Punjab-Multan-Pakistan ghafoor70@yahoo.com Abstract The current research aims to investigate the relationship of agricultural finance with the production of fruits in the context of Pakistan. Data were obtained by using the Pakistan Economic Survey Data were analysed using EViews 7.0. Ordinary Least Square method (OLS) was used to check the impact of agricultural finance on the production of fruits in Pakistan. The results shown that there is a strong and positive impact of the total amount of agricultural finance on the total production of fruits in Pakistan. Hence, the current study contributes a good addition to the available literature within the context of Pakistan. Keywords: Agricultural Finance, Production of Fruits, Pakistan I. Introduction Finance is concerned about raising and accumulation of funds from different sources and transferring them primarily from moneylenders to borrowers to commence any sort of spending and following repayment of loans, whereas agricultural finance focuses on the provision of funds to support the activities of agriculturalists and other people involved in production, storage, processing, handling and distribution of agricultural products. Pakistan is mainly an agricultural country and agriculture sector is considered as the backbone of the economy of Pakistan. According to Pakistan Economic Survey ( ), agricultural sector employs 45% of the population directly engaged in agriculture. Its contribution towards gross domestic product (GDP) is 25%. Its share in export earnings is round about 65%.

2 694 Pakistan Journal of Social Sciences Vol. 36, No. 2 As per State Bank of Pakistan (SBP) Agricultural Finance is Credit facility or equity financing allowed for agricultural production / development / grading / polishing of agricultural products etc. prior to any processing, financing of receivables against sale of agricultural products, lending to any institution / person. It includes such financing for Forestry, Horticulture, Fish farming, Dairy farming, Poultry farming, financing to agricultural value chain, export of agricultural goods or for any other purpose declared eligible by State Bank of Pakistan in its Methodology Report. The sources of agricultural finance are both formal and informal. The informal sources include private moneylenders, friends and relatives, well-to-do rural people, shopkeepers, and marketing intermediaries. The formal sources are the commercial banks, rural banks, agricultural development banks, agri-product credit corporations, various government agencies for agricultural development, land mortgage banks and cooperative banks and societies. Successful agricultural activities depend upon the proper availability of financial resources and their utilization. If agricultural finance is not adequately and timely available then it becomes a hurdle in the way of successful agricultural activities. Another point of consideration is the tendency of farmers regarding the utilization of agricultural finance in a proper way. If agricultural finance is availed and that is utilized for agricultural activities then that can improve the agricultural output but if that is not utilized for the agricultural purposes then that cannot improve the agricultural output. Agricultural finance must be seen as an integral part of equitable development within a framework of macroeconomic stability. The State Bank of Pakistan is actively pursuing new initiatives to expand quality and availability of agricultural finance. The objectives for agricultural growth for the current decade are to achieve self-reliance in agricultural commodities, ensure food security, and provide export orientation and improved productivity. The availability of adequate and affordable agricultural finance is central to the implementation of this strategy. There is also very limited access to shortterm and long-term financing needed for agricultural purposes. This is really unacceptable for Pakistan, where two-third of the population lives in rural areas. Therefore, the current study is really useful as it relates the amount of agricultural finance with the production of fruits within the context of Pakistan. II. Review of Literature Many researchers have identified the impact of agricultural finance on production of fruits and crops in Pakistan. Iqbal et al. (2004) investigated the impact of institutional credit on agricultural production in Pakistan. The study was aimed to estimate the production function relating to agricultural output with institutional credit and other independent variables like: land and water. The secondary data for the period to was collected through various publications of government of Pakistan and official records of the ZTBL. The purpose was to compute various credit indicators, calculate shares of various financial institutions in total agricultural loans granted, evaluate purpose wise composition of agricultural credit, and estimate the agricultural production function with the help of agricultural credit as one of the important explanatory variables. Since agriculture involves multiple products, Agricultural Gross Domestic Product (AGDP) was taken as the dependent variable whereas agricultural productivity was taken as an independent variable which is affected by water availability,

3 Adeel Akhtar, Muhammad Shaukat Malik, Abdul Ghafoor Awan 695 agricultural labour force, cropped area, and agricultural credit. The statistical analysis of the data helped to conclude that the coefficient for agricultural credit was positive and significant at 5 percent level that proved a very strong positive relationship between institutional credit and agricultural production. Therefore, it was recommended that financial institutions including commercial banks should be encouraged to expand their agricultural credit. Similarly, Khan et al. (2007b), for example, analysed the impact of short term credit scheme of Zarai Taraqiati Bank Ltd. (ZTBL) on Farm production in four villages of district Karak in the year Findings of the study revealed that short term agricultural credit had a positive impact on production of wheat, gram, and livestock. The response of the farmers was found encouraging as they managed to repay their loans on time. Therefore, study recommended that If ZTBL expanded the short term credit programme with increased credit limits, agricultural production can be increased significantly as more farmers could take advantage of the programme. Shah et al. (2008) studied the impact of credit on farm productivity and income of the farmers in District Chitral. Primary data were collected from borrowers of Zari Taraqiati Bank Ltd. (ZTBL) and non-borrowers from selected villages in District Chitral in the year The results of the study showed a positive relationship between agricultural credit and farm productivity and income of the sample farmers. Authors attributed the relationship to the on time access to the necessary inputs because of availability of credit by a financial institution like: ZTBL. Das et al. (2009) observed that share of agricultural GDP in total GDP of India was falling. Authors attempted to examine the relationship between agricultural credit and agricultural production in India. The study found that the agricultural credit had a positive and statistically significant relationship with agricultural productivity. Authors also noticed that there were several deficiencies in the existing institutional credit delivery system such as insufficient provision of loans to small farmers and limited availability of medium and long term loans etc. However, in spite of the problems with credit delivery system agricultural credit plays a crucial and important role in development of agricultural production of India. Akmal et al. (2012) analysed the relationship of agricultural credit and growth. The study is based on secondary data of institutional credit and agricultural GDP for the period The data showed a significant increase in the ratio of institutional credit as a percentage to agricultural GDP from merely 0.71 percent in to percent in Afterwards a gradual and continuous decline was visible as the ratio declined to 6.42 percent in and 3.29 percent in For the period once again institutional credit contributed in agricultural GDP. For the year this ratio is reported as percent. The regression analysis of the data showed that the coefficient for agricultural credit is positive and significant at 1 percent level that demonstrates the positive effect of institutional credit on agricultural production. It was also suggested in the study that water availability, labour availability and crop intensity are significant factors of Agriculture Gross Domestic Production (AGDP).

4 696 Pakistan Journal of Social Sciences Vol. 36, No. 2 Obilor (2013) evaluated the impact of commercial banks' credit to agricultural productivity under the Agricultural Credit Guarantee Scheme Fund in Nigeria for the period According to author the mineral sector has replaced the agriculture as a primary source of foreign exchange earner for Nigeria as by mid-seventies it was agricultural sector in that position. Inadequate capital may be considered as one of the most significant factor affecting the productivity of agriculture. For that reason author examined the impact of Agricultural Credit Guarantee Scheme Fund, agriculture product prices, government fund allocation and commercial banks' credit to agricultural productivity. Statistical analysis helped to conclude that Agricultural Credit Guarantee Scheme Fund and Government Fund allocation had a positive and strong relationship with agricultural productivity in Nigeria. However, agricultural product prices and commercial banks' credit had no significant positive relationship with the agricultural productivity. Findings of the study helped to suggest that governments should spend more on agricultural financing and give more attention on this sector as it helps to increase the agricultural productivity of the country. After the review of relevant literature it is evident that agriculture finance is helpful in improving the agricultural production (Iqbal et al., 2004; Khan et al., 2007b; Shah et al., 2008; Das et al., 2009; Akmal et al., 2012). Whereas, contrary to the other researchers, Obilor (2013) pointed out that commercial banks' credit had no significant positive relationship with the agricultural productivity. So the current study is important as well as interesting as it investigated the relationship of agricultural finance with the agricultural production in-terms of production of fruits within the context of Pakistan and tried to resolve the dichotomy between the earlier studies and study conducted by Obilor (2013). The objectives of this research were: To check the relationship of agricultural finance with the production of fruits within the context of Pakistan. To check the moderating effect of changes in rural population on the relationship between agricultural finance and production of fruits within the context of Pakistan. The research focused on the following research questions: Is production of fruits dependent on agriculture financing within the context of Pakistan? Does changes in rural population moderates the relationship between agricultural finance and production of fruits within the context of Pakistan? III. Research Methodology In order to test the hypotheses of the study, research methodology, discussed below was adopted, so that the appropriate data could be obtained and then analysed to meet the objectives of the study. Research methodology is discussed under four sub-parts. In first part, target population, sample and subjects of the study are described. In second section, data collection method and procedures are given. In third section data coding and data analysis techniques are discussed.

5 Adeel Akhtar, Muhammad Shaukat Malik, Abdul Ghafoor Awan 697 A. Population and Sample Agricultural Finance data from Financial Year (FY) to Financial Year (FY) were used. B. Data Collection Method and Procedure Agricultural finance data from FY to FY were obtained from Pakistan Economic Survey ( ), which were used to analyze the hypotheses. C. Data Analysis Techniques Linear regression model was applied for testing the hypotheses. Ordinary Least Square method (OLS) was used to estimate the parameters of the model. We assume that the Population Regression Function (PRF) denoted as E(Y Xi) is a linear function of Xi, say, of the type: E(Y Xi) = β1 + β2xi (1) Where β1 and β2 are unknown but fixed parameters known as the regression coefficients; β1 and β2 are also known as intercept and slope coefficients, respectively. Equation (1) itself is known as the linear population regression function. Some alternative expressions used in the literature are linear population regression model or simply linear population regression (Gujrati 2004; Kleiber and Zeileis 2008). In regression analysis our interest is in estimating the PRF that is, estimating the values of the unknownsβ1 and β2 on the basis of observations on Y and X. Population Regression Function is linear in variables and parameters. The PRF under the stochastic specification is shown in Equation (2). Yi = E(Y Xi) + ui (2) Where the deviation ui is an unobservable random variable taking positive or negative values; ui is known as the stochastic disturbance or stochastic error term. Sample Regression Function The task is to estimate PRF on the basis of sample information. The sample counterpart of Equation (2) is shown in Equation (3) Yi = ˆ β1 + ˆ β2xi + ˆui (3) The method of least square is used to estimate the unknown parameters of Equation (2). Model Goodness of Fit The quantity R² thus defined is known as the (sample) coefficient of determination and is the most commonly used measure of the goodness of fit of a regression line. R² measures the proportion or percentage of the total variation in Y explained by the regression model shown in equation (3). Two properties of R² are: It is a non-negative quantity. Its limits are 0 R² 1. An R² of 1 means a perfect fit, that is, ˆYi = Yi for each i. On the other hand, an R² of zero means that there is no relationship between the regressand and the regressor whatsoever (i.e., ˆ β2 = 0).

6 698 Pakistan Journal of Social Sciences Vol. 36, No. 2 D. Conceptual Framework of the Study A research model was developed in order to empirically investigate the effect of agricultural financing on production of fruits in Pakistan. Production of fruits was measured in 000 of Tons. Agricultural finance was taken as the independent variable, production of fruits was taken as dependent variables, whereas, rural population was taken as a moderating variable. The research focused to investigate the relationship of independent variable with the dependent variable and to measure the strength of the relationship between independent variables and dependent variable in presence of moderating variable. Figure for the Conceptual Framework of the Study is given below: Figure 1: Conceptual Framework E. Hypotheses of the Study This section elucidates the causal relationships among variables of the study based on the objectives and Conceptual Frameworks of the study, which are given below: H 1 : Agriculture financing is positively related with production of fruits within the context of Pakistan. H 2 : Changes in rural population moderates the relationship between Agriculture financing and production of fruits within the context of Pakistan. IV. Data Analysis and Discussions First objective of the research was to evaluate the impact of agricultural finance on the production of fruits in Pakistan. Data for agricultural finance, production of fruits, and changes in rural population from Financial Year (FY) to FY are shown in Table 1. Whereas, data for agricultural finance, production of fruits, and changes in rural population from Financial Year (FY) to FY are shown in Table 2. It can be observed from table 1 that agricultural financing in FY was Rs in millions (M) and production of fruits was in tons (T) in same year. It increased from Rs M to Rs. 293,850 M in and production of fruits increased from T in FY to T in FY Same trend is shown by graphs given below (Figure 2, and Figure 3).

7 Adeel Akhtar, Muhammad Shaukat Malik, Abdul Ghafoor Awan 699 Table 1: Agricultural finance in million rupees and production of fruits in 000 ton Total Agricultural Rural Population Fiscal Production of Fruits Finance (Rs. in in in Million Year in '000' of Tons Millions) Numbers , , , , , , , , , , , , , , , , , , , , , , Source: Pakistan Economic Survey Figure 2: Total Agricultural Finance (Rs. in Millions) 350, , , , , ,000 50,000 0 Figure 4 shows approximately a linear relationship between agriculture finance amount (X: independent variable) and production of fruits (Y: dependent variable). It provides a good feel to apply a linear regression model of Y on X.

8 Production of fruits (thousand tons) Pakistan Journal of Social Sciences Vol. 36, No. 2 Figure 3: Production of Fruits in '000' of Tons Figure 4 Scatter plot between agriculure finance & fruit production , , , , , , ,000 Amount (PKRs in millions) of agricuture finance

9 Adeel Akhtar, Muhammad Shaukat Malik, Abdul Ghafoor Awan 701 Table 2: Statistical Results using EViews 7.0 Research Model 1 Variable Coefficient Std. Error t-statistic Prob. Constant Agricultural Finance R-squared F-statistic Adjusted R-squared Prob(F-statistic) Akaike info criterion Durbin-Watson stat Note: Dependent Variable: Production of Fruits The estimated regression line for Model 1 is (see Table 2): Yi = (X) Where, Production of fruits (thousand tons) is Yi, Constant/ Intercept is , Coefficient is and Agricultural Finance is X. The value of ˆ β2 = , which measures the slope of the line, shows that, within the sample range of X (Agriculture Finance in Million Rs.) between Rs. 14,915 M and Rs. 293,850 M, as agricultural finance increases, say, by Rs. 1 million, the estimated increase in the mean or average production of fruits would be about thousand tons. The value of ˆβ1 = , which is the intercept of the line, indicates the average level of production of fruits when there is zero level of agricultural finance. The value of adjusted R² of (with P-Value < 0.05) means that about 88.8 percent of the variation in the production of fruits is explained by agricultural finance. Since R² can at most be 1, the observed R² suggests that the sample regression line fits the data very well. Moreover, it shows that the two variables, production of fruits and amount of agricultural finance are highly positively correlated so our H 1 is supported. Table 3: Statistical Results using EViews 7.0 Research Model 2 Variable Coefficient Std. Error t-statistic Prob. Constant Agricultural Finance*Rural Population E R-squared F-statistic Adjusted R-squared Prob(F-statistic) Akaike info criterion Durbin-Watson stat Dependent Variable: Production of Fruits The estimated regression line for Model 2 is (see Table 3): Yi = (X) Where, Production of fruits (thousand tons) is Yi, Constant/ Intercept is , Coefficient is and Agricultural Finance is X. The value of ˆ β2 = , which measures the slope of the line, shows that, within the sample range of X (Agriculture Finance in Million Rs.) between Rs. 14,915 M and Rs. 293,850 M, as agricultural finance increases, say, by Rs. 1 million, the estimated increase in the mean or average production of fruits would be about thousand tons in the presence of the moderator (changes in rural population). The value of ˆβ1 = , which is the

10 702 Pakistan Journal of Social Sciences Vol. 36, No. 2 intercept of the line, indicates the average level of production of fruits in the presence of the moderator (changes in rural population) when there is zero level of agricultural finance. The value of adjusted R² of (with P-Value < 0.05) means that about 89.2 percent of the variation in the production of fruits is explained by agricultural finance in the presence of the moderator (changes in rural population). Since R² can at most be 1, the observed R² suggests that the sample regression line fits the data very well. Moreover, it shows that the two variables, production of fruits and amount of agricultural finance are highly positively correlated in the presence of the moderator (changes in rural population) as well. So, our H 2 is also supported. The research has tested and proved the relationship of agricultural financing with the production of fruits in Pakistan, which would be helpful and informative for the researchers, students, teachers and agricultural finance policy makers of Pakistan. V. Conclusion The current research aimed to investigate the relationship of agricultural finance with the production of fruits in the context of Pakistan. Data were obtained by using the Pakistan Economic Survey Data were analysed using EViews 7.0. Ordinary Least Square method (OLS) was used to check the impact of agricultural finance on the production of fruits in Pakistan. The results shown that there is a strong and positive impact of the total amount of agricultural finance on the production of fruits in Pakistan. Findings are consistent with the findings of previous studies conducted by (Iqbal et al., 2004; Khan et al., 2007b; Shah et al., 2008; Das et al., 2009; Akmal et al., 2012). The notion given by Obilor (2013) is rejected with the help of our findings. Hence, the current study contributes a good addition to the available literature. Future research may be conducted by using data for larger span of time (i.e. from year 1947 onwards) for more precision and accuracy. Moreover, the use of pesticide and fertilizers in production of fruits in Pakistan may also be taken as moderators. References Akmal, N., Rehman, B., Ali, A. and Shah, H. (2012). The Impact of Agriculture Credit on Growth in Pakistan. Asian Journal of Agriculture and Rural Development, 2(4), Ayaz, Saima and Hussain, Zakir. (2011). Impact of Institutional Credit on Production Efficiency of Farming Sector: A Case Study of District Faisalabad. Pakistan Economic and Social Review, 49(2), Ayaz, Saima, Anwar, Sofia, Sial, Maqbool Hussain, and Hussain, Zakir. (2011). Role of Agricultural Credit on Production Efficiency of Farming Sector in Pakistan: A Data Envelopment Analysis. Pakistan Journal of Life and Social Sciences, 9(1), Das, Abhiman, Senapati, Manjusha, and John, Joice. (2009). Impact of Agricultural Credit on Agriculture Production: An Empirical Analysis in India. Reserve Bank of India Occasional Papers, 30(2). Gujarati, D. N. (2004). Basic Econometrics (Fourth ed), McGraw-Hill/Irwin, New York.

11 Adeel Akhtar, Muhammad Shaukat Malik, Abdul Ghafoor Awan 703 Iqbal, Muhammad, Ahmad, Munir, Abbas, Kalbe, and Mustafa, Khalid. (2004). The Impact of Institutional Credit on Agricultural Production in Pakistan. The Pakistan Development Review, 42(4), Khan, Naushad, Jan, Inayatullah, Rehman, Mujib Ur, Latif, MahboobUl, and Ali, Akhtar. (2007). The Impact of Micro Credit on Livestock Enterprise Development in District Abbottabad: A Case of SRSP Micro Credit Programme. Sarhad Journal of Agriculture,23(4). Khan, Naushad, Jan, Inayatullah, Rehman, Mujib Ur, Mahboob, Anwar, and Ali, Akhtar. (2007b). The Effects of Short Term Agricultural Loans Scheme of Zarai Taraqiati Bank on Increase in Farm Production in District Karak. Sarhad Journal of Agriculture, 23(4). Kleiber C, Zeileis A (2008). Applied Econometrics with R. Springer-Verlag, New York. Government of Pakistan. ( ). Economic Survey of Pakistan. Finance Division. Economic Advisors Wing, Islamabad. Obilor, Sunny I. (2013). The Impact of Commercial Banks' Credit to Agriculture on Agricultural Development in Nigeria: An Econometric Analysis. International Journal of Business, Humanities and Technology, 3(1). Shah, Mir K., Khan, Humayun, Khan, Jehanzeb, and Khan, Zalakat. (2008). Impact of Agricultural Credit on Farm Productivity and Income of Farmers in Mountainous Agriculture in Northern Pakistan: A case of Selected Villages in District Chitral. Sarhad J. Agric., 24(4).