Policy, Planning, and Reserch WORKING PAPERS Women In Development Population and Human Resources Department The World Bank August 1989 WPS 277 The Effect of Formal Credit on Output and Employment in Rural India Shahidur R. Khandker and Hans P. Binswanger Improving credit in rural India greatly improves rural nonfarm employment and output. It has only a modest effect on crop out- put - more because of increased use of fertilizer than because of capital investments, which merely substitute for farm labor. The Policy, Planning, and Research Conplex distributes PPR Woiking Papers to disseminate the findings of work in progress and to encourage the exchange of ideas among Bank staff and all others intercsF..d in development issues. These papers carry the names of the authors, reflect only theii views, and should be used and cited accordingly. he findings, interpretations. and conclusions are the authors' own.They should not be attributed to the World Bank, its Board of Directors, its management, or any of its member countries. Polay nning, and Reuach Women In Development Using a two-stage model to distinguish demand Credit decreases farm employment, yet for formal credit from supply, Khandker and increases the real agricultural wage because of Binswanger conclude that increased formal its overwhelmingly positive effect on rural credit has a positive effect on crop production, nonfarm employment. on the use of fertilizer, and on private invest- ment in machines and livestock. In short, improved financial intermediation in rural India greatly improves rural nonfarm The effect of expanded credit on crop output employment and output, has a modest effect on is small, however. Crop output improves more crop output, and tends to substitute capital because of increased use of fertilizer than investment for farm labor. because of capital investments, which merely substitute for labor. This paper is a product of the Women in Development Division, Population and Human Resources Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Belinda Smith, room S9-125, extension 35108 (28 pages with tables). The PPR Working Paper Series disseminates the rmdings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these ftndings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank. Produced at the PPR Dissemination Center The Effect of Formal Credit on Output and Employment in Rural India by Shahidur R Khandker and Hans P. Blnswanger Table of Contents I. Introduction 3 II. An Econometric Framework 6 III. Data and Variable Description 10 IV. The Results 12 V. Conclusions 17 Footnotes 19 References 21 Tables 23 Introduction Gover=cent financial intermediation in rural economies is geared to mobilize rural savings and foster agricultural output and invertment via lending. There is a growing body of literature that has focused on the linkages between credit market development and economic growth, the role of financial institutions in savings mobilization and the effect of credit on agricultural investment and output (Braverman and Guasch, 1986; David and Heyer, 1980; Feder et al., 1988; Giovannini, 1985; Gold*mith, 1969; Gupta, 1987; Tqbal, 1986; Shaw, 1973; Von Pischke et al., 1983). Credit can play an important role in agriculture. Because farmers often suffer from a cash-flow problem, their liquidity constraint produces sub-optimal inputs use and hence output. The role of credit, therefore, is to bring the sub-optimal outcome to optimal' level and enhance farmer investment and output. Government credit institutions often grant larger volumes of credit for longer terms and at lower interest rates than the informal market. For examle, according to Reserve Bank of India (RBI), a sum of Rupees 101.3 billion was advanced to the rural sector of India by formal lending agencies in 1980/81, about 78 percent increase compared to the amount advanced in 1969/70. In subs!dizing the growth of an institutional credit program for agriculture, the policy makers face the questi.on: to what extent formal credit contributes to agricultural investment and output and consequently rural employment and wages. - 49 - This paper estimates the output, investment, employment and wage effect of institutional credit using district-level panel data from India. The central problem of estimating the causal relationships is how to disentangle the aggregate demand for credit from its supply. What we observe here is only a total amount of credit advanced by different government lending agencies. This amount represents both the demand for and supply of institutional credit. However, identification of the effects of exogenous increases in credit supply is critical. There is an additional simultaneity problem: credit demand, output supply, and farmer investment are jointly determined in the farmer's utility maximization. In an earlier paper (Binswanger, Khandker and Rosenzweig, 1988) we circumvented the issue of simultaneity of credit demand and supply by estimating the effect of the number of rural commercial bank branches on agricu'tural output and investment rather than the effect of the volume of credit. Because the growth of rural commercial bank branches is controlled entirely by the banks and public policy, it is exogenous to the credit demand of farmers. Our findings suggested that commercial bank expansion had significant positive effects on farmer's fixed investment, fertilizer demand and output but the input effect is stronger than the output effects. Commercial banks also increase agricultural wage and helps reduce the incidence of rural poverty. However, because banks promote private capital investment in agriculture that replaces farm labor, the positive wage effect of improved financial intermediation is due to its strong (positive) effect on rural nonfarm employment and output (Khandker, 1989). -5- The commercial banks (CB) are, however, not the only agencies that advance credit to agriculture. Agencies such as the Primary Agricultural Co-operative Societies (PACS) and the Land Development Banks (LDBs) also provide credit to Indian farmers.1 The earlier paper could not estimate the direct effect of the PACS and the LDBs on output and investment as the growth of PACS and LDBs, unlike CBs, does not measure the level of their operations. This is because in recent years while their lending has increased their numbers have been declining as smaller societies or branches have been merged. The question thus emerges: do our findings differ if we estimate the direct effect of total formal credit advanced to the ural sector? This paper addresses this issue. To identify the credit supply from its demand, the number of rural branches of CBs, PACS, LDBs and Central Coperative branches (CCBs) are used as instruments to predict the volume of credit advanced. The predicted volume of credit is then used in the second stage of a two-stage procedure to estimate the effect of the volume of institutional credit on agricultural investment and output. Although the number of PACS may be negatively related to the volume of total lending, the purpose essentially here is to exploit the presence of correlation between the exogenous and the predicted variables. The paper is structured in the following manner. Section two discusses the model framework and its estimation technique. Section three discusses the data. Section four reports the results. Finally, the results are summarized in the concluding section of the paper. II. An Econometric Framework Additional credit supply can raise output, input use, and hence investment, employment and wages when the farmer faces a credit constraint. This is the liquidity effect of credit. Credit has another role to play. In moat developing countries where agriculture still remains a risky activity, better credit facilities, by enabling the smoothing of consumption, can increase the willingness of farmers to take risk and hence increase agricultural investment, output, employment and wages. This is the consumption smoothing effect of credit. Thus, better rural credit markets may lead to a volume of agricultural investment and output and consequently rural employment and wages which may not be attainable with a less developed or less efficient credit system. The informal credit sector consisting of a large professional money lenders, commission agents, traders, relatives and friends plays an important role in rural India (Timberg and Aiyer, 1984). However, with the growth of formal credit market development, the importance of the private lending has reduced. According to All India Debt and Investment Survey, the proportion of farmers' cash debt from formal sources rose from 18 in 1961 to 32 percent in 1971. In contrast, the proportion of loan fron rural money lenders has declined from 83 to 36 percent over this period. Although Indian official statistics provide district-level information on institutional credit advanced, the information on informal credit does not exist. Thus, without information on informal credit it is difficult to quantify to what extent this transition in farmer's source of credit from private to government haq helped increase agricultural investment, output, rural employment and wages in India. - 7 - Nevertheless with data on formal credit it is possible to quantify its effect on agricultural output and investment. Bur, because money is fungible and farmers also get loan rem the private lenders, the lack of information on informal credit may influence the effects of formal credit on output and investment. However, because the terms of credit in the formal system are better than from money lenders, farmers often try first to satisfy their credit demand by approaching the formal lending agencies. If they fail to satisfy their need for credit they then perhaps would approach the informal lenders at a higher rate of interest. This suggests that the absence of information regarding informal loans may not affect the estimates of the effects of institutional credit (Feder et. al., 1988). The credit advanced by formal lending agencies is an outcome of both the supply of and demand for formal credit. The amount of formal credit available to farmer, his credit rations. enters into the output supply, input demand (e.g., fertilizer, employment) and wage functions as an independent argument.2 We, therefore, need to disentangle the supply of formal credit from its demand. A two-stage procedure can solve this identification problem. Since financial institutions decide how many branches or offices a district should have, the number of offices is exogenous to farmer demand. In estimating the output or input effect of institutional credit, I first estimate a credit equation with credit advanced as the dependent variable with, among others, the uumber of branches of financial institutions as explanatory variables. This provides a predicted amount of credit supplied to each district by formal financial intermediaries which is then used in the second-stage estimation of output supply or input demand and wage equations. 8 - Formal agricultural lending is not exogenously given or randomkly distributed. As discussed in Binswanger, Khandker and Rosenzweig (1988), both the farmers and financial institutions are influenced by agricultural opportunities implied in the agroclimatic endowments of a district. That means, the lending agencies will lend more in areas where agricultural opportunities are better, risk is lower, and hence loan recovery is higher (Binswanger and Rosenzweig, 1986). An unobserved variable problem thus arises for the econometric estimation which can be overcome by the use of district-level panel data. The system o' equations to be estimated with the district-level time-series data are the followings (1) ICrit - ICr(Xjt. Zjt. pjt. 6j) (2) Qjt - Qjt(Xjt, ICrjt, pit, d;) (3) INpjt - INpjt(Xjt. ICrjt, Pjt. S) (4) INvjt - INvjt(Xjt. ICrjt. INvj(t-l), jSjt 6j) (5) WAGEjt - WA7Ejt(Xjt, ICrjt, sit, Sj) where equation (1) is the district's prediction equation for institutional credit advanced to rural sector by the formal lenders; (2) is tbe output supply equation; (3) is the input dcmand equation; (4) is the investment equation and (5) is the wage equation. Here ICr stands for institutional credit advanced, X is a vector of exogenous explanatory variables (e.g., the output and input prices, government infrastructure, interaction terms between year and agroclimatic endowments, the rate of interest); Z is a vector of the number of formal lending agencies; Q is aggregate crop - 9 - output; INp is the level of input (fertilizer and employment) utilized; INv stands for investment in pumps, draft animals, milk animals and small stocks; WAGE is daily wage of agricultural workers; A is vector of observable district-specific permanent characteristics; 6 is district- specific unobservable characteristics influencing investment and output; j stands for district and t stands for time. The interaction terms between year (t) and agroclimates (tpj) allow for a district-specific time trend which, among other factors, allows for district-specific rate of technical change. In order to estimate the causal relationships between, say, output growth and government infrastructure the simultaneity problem arising out of the response of both government and farmers to the heterogenous district endowments must be overcome. This is done by the use of panel data with either the fixed ur random effects technique. If the unobserved endowments are time-invariant and specific to each district, then a fixed effects procedure is appropriate. The randoi effects procedure accounts for the existence of both time-invariant and time-varying error components. The random effects procedure, however, ignores any correlation between the persistent errors (endowment effects) and time-varying observed variables. We use Hausman-Wu specification test to determine whether the fixed or random effects model is appropriate for the given data and present results accordingly. - 10 - 111. Data and Vatriable Description The data used in this paper are drawn from 85 districts of India for a period of 9 years beginning from 1972/73 to 1980/81. The number of observations vary depending on the data available for particular dependent variable. Thus, 765 observations (9 years x 85 districts) are used for the output supply and wage equations, 738 (9 year x 82 districts) observations for the fertilizer equation, 228 (3 years x 76 districts) observations f the investment equations, and only 170 (2 years x 85 districts) observations for the farm and nonfarm employment equations. The investment data are computed frc livestock censuses of 1966, 1972, 1976 5nd 1982, while fertilizer, crop output, and wage data are from yearly fertilizer, wage, and agricultural statistics published by the Ministry of Agriculture of India. Crop output is the aggregate index of 20 crops using 1975/76 as the base year, fertilizer is measured in nutrient tons of nitrogen, phosphate and potash, and the wage rate is the daily wage rate of agricultural field workers. The investment variables are the net additions over each census interval to the stock of draft animals (male bullocks and male buffalos), milk animals (female bullocks and female buffalos), small stocks (sheep and goats) and pumps (both diseal and electric).3 Employment data are dravn from the population censuses of 1970 and 1980 which are comparable with agricultural census years of 1971 and 1981. By employment we mean here the number of persons who were employed in farm or nonfarm activities for atleast 183 mandays in one year. The government infrastructure variables include road length, regulated markets, primary school density, rural electrification and canal irrigation. All the infrastructure and dependent variables are normalized - 11 - by the district's size. The price variables are the aggregate price index based on the internat4onal commodity prices, an all India price index of fertilizer, the district-level urban wage income, and the PACS rate of interest. The agroclimate variables include annual rainfall and permanent characteristics such as soil moisture capacity, length of rainy season, exceesive rainy months, irrigation potential, number of cold months and flood potentials. For a detailed discussion of these variables see Binswanger, Khandker and Rosenzweig, (1988). The data for the CBs and the CCBs are published by the Reserve Bank of India in Banking Statistics. The National Bank for Agriculture and Rural Development (NABARD) of India has kindly provided unpublished data on the PACS and the LDBs which were collected by sending questionnaires to the State headquarters of these institutions.4 Note that the CCBs primarily advance credit to agriculture via lending to the PACS and the LDBs. Thus, rural credit is defined in this paper as the amount of institutiorial credit advanced to the rural sector by the CBs and the credit advanced to agriculture by the PACS and the LDBs. Rural credit thus reflects both the subsidized agricultural credit advanced by the PACS, LDBs and CBs and non- subsidized nonagricultural credit advanced by the CBS. The variable such as subsidized agricultural credit cannot be constructed because district- level data on agricultural credit advanced by the CBs are not available. Since moiey is fungible, it is the effect of rural credit that is perhaps important to look at. However, I report the effects of credit advanced by the cooperative sector (i.e., PACS and LMBs) to compare with those of rural credit advanced by the banking system (i.e., PACS, LDBs and CBs). The mean and standard deviation of variables involved in this paper are presented in table 1. - 12 - IV. The Results The estimates of the credit supply equation are shown in table 2. As the Hausman-Wu test suggests, the fixed effects procedure is appropriate to explain variations in the amount of rural credit advanced. The real urban wage has a negative effect on the amount of institutional credit advanced. An increase in urban wage which is correlated with the urban upswing may tend to divert credit from rural to urban sector. The roads improvement and regulated marketfi development have positive effects on the credit amount advanced by the lending agencies because of the induced demand effect via their positive infrastructural effects on agricultural output and investment. Rural electrification has a negative effect on the rural loan advanced by government agencies. The number of branches generally have a positive effect on the volume of institutional credit advanced to rural households. The negative effect of PACS on credit supply is not surprising given the reduction in the PACS associated with consolidation of primary societies. Better agroclimates such as high irrigation potential and high soil moisture capacity lead to higher credit use. Lending is also higher in areas with low flood risk as measured by flood potential. In contrast, the credit volumes are lower in areas with longer rainy seasons. Based on the estimates of table 2, we predict the amount of credit supplied to each district by formal lending agencies each year. Using this predicted credit amount as an explanatory variable, among others, we then estimate the fertilizer demand and aggregate output supply equations. These estimates are presented in table 3. The Hausman-Wu test suggests - 13 - that the fixed effects procedure is appropriate for explaining variations in both the fertilizer demand and output supply over time. The institutional credit has a positive effect on both the fertilizer demand and aggregate output. A 10 percent increase in the formal credit leads almost 3 percent increase in fertilizer consumption and only 0.2 percent in aggregate crop output. The output effect of credit is thus fairly low. If the fertilizer elasticity of crop output is. says 0.01, it appears that fertilizer consumption increased by formal credit explains more than the increase in output due to credit. The fertilizer price has a negative effect on fertilizer demand indicating a negative own- price effect. However, the fertilizer price has a perverse positive effect on crop output. The urban wage has a positive effect on both the output and fertilizer, perhaps indicating a positive income effect induced by increased urban demand for farm goods. Regulated market and rural electrification have a positive effect on both the fertilizer demand and output supply, suggesting a positive induced infrastructural effect on agricultural production. Road length, however, has an unexpected negative effect on fertilizer demand. Canal irrigation increases fertilizer consumption. Better agroclimates such as higher rainfall, high irrigation potential and high soil moisture capacity have a positive effect on the growth in fertilizer demand and output supply. In contrast, poor agroclimatic conditions such as excess rain have a negative effect on the growth of fertilizer demand and hence crop output. The investment effect of institutional credit on draft and milk animals, small stocks and irrigation pumps is shown in table 4. The Hausman-Wu test indicates that the random effects model is more appropriate -14 - than the fixed effects in explaining variations in the private investment over time. Institutional credit has an overwhelming positive effect on all types of private agricultural investment. A 10 percent increase in the amount of institutional credit advanced raises private investment in irrigation pumps by 4 percent, 6 percent in milk animals, about 5 percent in draft animals and almost 7 percent in sheep and goats. The credit effect of investment is thus much higher than its effect on fertilizer use and aggregate crop output. The crop output price has an expected positive e^fect on investment in draft animals, small stocks and irrigation pumps, indicating a positive farm profit effect on the private investment. The fertilizer price has a negative effect on draft animals and small stocks, while a positive effect on milk animals. Real urban wage has a negative effect on Investment in milk animals and small stock. Real urban wage has two possible effects: one is the opportunity cost effect of labor and the other is an income effect. The results suggest that the opportunity cost of human labor (negative) is outweighed by the positive income effect of urban wage for the private investment in draft animals and pumps. The road investment has a negative effect on investment in draft and milk animals, indicating that private investment in animals reduces as roads communication improves. Primary school expansion, rural electrification and regulated markets have expected positive effects on private investment in some capital goods. The past stock has an expected negative effect on current investment, because of an adjustment process in an equilibrium regime. Private investment on pumps and small stocks increases over time in wheat - 15 - producing areas where the mean temperature falls below 18 degree Farenheit. In contrast, irrigation potential reduces investment in small stocks over time. Better agroclimates such as the length of rainy season encourage private investment in milk animals over time, while the poor agroclimates such as excess rain discourage it. The effect of formal credit on farm and nonfarm employment and agricultural real wage is shown in table 5. The Hausman-Wu test confirms that the random effect model is more appropriate than the fixed effect model in explaining variations in employment and wage over time. Institutional credit decreases agricultural employment by increasing private capital investment in agriculture that replaces farm labor, and yet increases agricultural real wage because of its strong positive effect on rural nonfarm employment. A 10 percent increase in institutional credit increases nonfarm employment by almost 18 percent, while reduces farm employment by only 0.4 percent and consequently increases agricultural real wage also by 0.4 percent. As can be seen from table 5, rural electrification like formal credit reallocates labor from agriculture to rural nonagricultural activities and thus helps increase agricultural real wage. In contrast, the aggregate crop output price increases both the farm and rural nonfarm employment and hence agricultural real wage. Real urban wage has a negative effect on farm employment but a positive effect on agricultural real wage because of its demand-pull effect on the rural sector. Regulated market and primary school expansion have a negative effect on both the rural nonfarm employment and agricultural real wage, although they have a positive effect on farm employment. A 10 percent - 16 - increase in the rural market regulation increases farm employment by only 6 percent, but decreases rural nonfarm employment by almost 10 percent and consequently agricultural real wage by about 6 percent. Better agroclimates such as higher annual rainfall and irrigation potential increases agricultural real wage and employment.5 In contrast, farm employment has declined over time in wheat producing areas where the number of cool months (i.e., when the mean temperature falls below 18 degree Farenheit) is higher. The summary results of the effect of rural credit, cooperative credit and number of commercial bank branches on agricultural output, investment, wage and rural employment are presented in table 6. As this table suggests, the results do not differ substantially whether number of commercial bank branches or volume of rural lending or volume of cooperative lending is used. More bank branches and more credit (either agricultural or nonagricultural) increase agricultural output with an elasticity of about 0.02, and fertilizer use with an elasticity in the range of 0.1-0.3. They lead to higher investment in tractors, pumps, draft animals and small stocks, with investment elasticities of between 0.14 to 0.71. Although cooperative credit seems to have no significant effect on rural employment and wages, commercial bank branches and rural credit have significant impact on these outcomes. For example, they increase rural nonfarm employment with an elasticity of 0.2 to 0.3, while higher bank branches decrease agricultural employment with an elasticity of 0.07. Nevertheless banking expansion or formal credit expansion increase agricultural real wage with an elasticity in the range of 0.04 to 0.06. - 17 - V. Conclusions This paper has estimated the effect of institutional credit on agricultural output, investment, fertilizer demand, farm-nonfarm employment and real wage using district-level panel da.a from India. In India special credit programs were launched after the nationalization of commercial banks in 1969 to support the country's green revolution in agriculture. An important policy quesu.Jon thus emerges: to what extent low-interest insitutioi l credit has helped increase private investment and output in Indian agriculture and consequently rural employment and wage. A panel data analysis is used to estimate the output and input effect as well as wage effect of formal credit. The number of branches of lending agencies are determined by the financial intermcdiaries and thus exogenous to farmer demand for credit. They can, therefore, be used as instruments to identify the aggregate supply of formal credit from its demand. These instruments also help solve the simultaneity between the credit supply, output supply, input demand and wage equations. By using panel data we circumvent the unobserved variable problem that could otherwise produce inconsistent estimates in cross-section data analysis. Econometric estimates suggest that formal credit plays an important role in fertilizer demand, private fixed investment, crop output, farm-nonfarm employment and agricultural real wage in India. A 10 percent increase in formal credit supply increases fertilizer use by almost 3 percent. A similar percentage increase in the supply of institutional credit spurs a 4 percent increase in private investment in irrigation pumps, 5 percent each in draft animals, 6 percent in milk animals, and - 18 _ about 7 percent in small stocks. In contrast, a 10 percent increase in formal credit supply increases aggregate crop output by only 0.2 percent. Compared to the credit effect of investment and fertilizer demand, the crop output effect appears fairly small. Since increased fertilizer consumption induced by formal credit can explain more than the credit effect of output, it appears, therefore, that additional capital investment has worked more for substituting agricultural labor than for increasing crop output. Thus, a 10 percent increase in the formal credit has reduced agricultural employment by 0.4 percent. However, institutional credit has a modest positive effect on agricultural real wage. This is because it has created more jobs in the rural nonfarm activities than it has substracted in agriculture. For example, a 10 percent increase in formal credit increases rural nonfarm employment by almost 18 percent and agricultural real wage by 0.4 percent. Formal credit expansion in rural India, therefore, has had a major effect on rural nonfarm sector and a modest effect in agriculture despite the considerable directed policy to increase formal credit supply for agriculture. Finally, the results do not vary substantially whether one uses the number of commercial bank branches or volume of lending (rural or agricultural) as a measure of growth of rural financial intermediation. - 19 - Footuotes It is worth noting that the CBs who advance more rural credit than the PACS and the LDBs. For example, RBI reports that in 1981 the CBs advanced 776.3 billion rupees to the rural sector, while the coopearive sector (i.e., PACS and LDBs) advanced only 236.7 billion rupees, a third of what the CBs advanced. 2 Credit can enter into the output supply and hence input demand and investment or wage functions if credit is a binding constraint in rural household's input-output decision-making. Assume that a farmer maximizes output function, Q - Ka (i) subject to a liquidity constraint, rx - 5 (ii), where Q is crop output, K is fixed capital such as livestock and irrigation pumps, r is the price of variable inputs (X) such as labor and fertilizer, 6 is the total credit available to purchase variable inputs; and equation (i) is the familiar Cobb-Douglas production function. By simple manipulation, one can derive the input demand-equation as Xc, r_16 (iil) and the output supply equation as , QC( - r-PP (iv) where Xc and Qc are, respectively, credit-constrained level of input use and crop output. If competitive labor market exists and equilibrium condition is satisfied, one can also show agricultural wage as a function of credit ration available to the farmers. - 20 - 3 A second-stage equation for tractors could not be estimated because none of the explanatory variables has a significant effect on the tractors investment. Thus, the tractor variable was dropped. 4 Thanks to Dr. Gad&Ll of NABARD who has kindly opened the data base and personally organized the assembly of the unpublished banking data. This paper would not have been feasible without his kind help in collecting the banking data. Since employment equations represent occupational status of rural households over the decade of 1970, the annual rainfall variable does not enter into these equations. This is, however, not the case with agricultural wage which comes from annual data. - 21 - References Binswanger, H.P., S.R. Khandker, and M.R. Rosenzweig, 1988, 'The Impact of Infrastructure and Financial Institutions on Agricultural Output and Investment in India,* mimeo, World Bank: Washington, D.C. Binswanger, H.P., and M.R. Rosenzweig, 1986, 'Behavioral and Material Determinants of Production Relations in Agriculture," Journal of Development Studies, Vol. 21, P. 503-539. Braverman, A. and L. Guasch, 1986, 'Rural Credit Markets and Institutions in Developing Countries: Lessons for Policy Analysis from Practice and Modern Theory," World Development, Vol. 14, No.10/11, P. David, C. and R. Meyer, 1980, "Measuring the Farm Level Impact of Agricultural Loans,' in J. Howell (Ed.), Borrowers and Lenders: Rural Financial Markets and Institutions in Developing Countries. London: Overseas Development Institute. Feder, G. et al., 1988, Land Policies and Farm Productivity in Thailand. Baltimore: Johns Hopkins University Press. Giovannini, A., 1985, 'Savings and the Real Interest Rate in LDCs.' Journal of Development Economics, Vol. 18, (Aug./Sept.): 197-217. Goldsmith, R.W., 1969, Financial Structure and Development. New Haven: Yale University Press. Gupta, K.L., 1987, 'Aggregate Savings, Financial Intermediation, and Interest Rate.' Review of Economics and Statistics, Vol. - 22 - Iqbal, F., 1986, "The Demand and Supply of Funds among Agricultural Households in India." in Singh, I.J., L. Squire and J. Strauss (Eds.), Agricultural Household Models: Extensions, Applications dnd Policy, Baltimore: The Johns Hopkins University Press. Khandker, S.R., 1989, "Effect of Agroclimatic Endowments and Infrastructure on Rural Wage and Employment in India," mimeo, World Bank, Washington, D.C. Reserve Bank of India, 1981, Banking Statistics, Bombey, June. Shaw, E.S., 1973, Financial Deepening in Economic Development. New York: Oxford University Press. Timberg, T., and C.V. Aiyer. 1984, ";aformal Credit Markets in India." Economic Development and Cultural Change, Vol. 33 (October): Von Pischke, D. Adams and G. Donald (Eds.), 1983, Rural Financial Markets in Developing Countries. Baltimore: The Johns Hopkins University Press. - 23 - TANLE 1. DESCRIPT?M STAT!ST!CS DeDendent Variable Number of Mean Standard ObFW rvatons doviation Aggregate crop output index 785 1.38S 1.168 Fertilizer zonsumption, nutrient tona/ 10 sq. km. 788 23.784 30.997 Not investment in draft animalo, number/ 10 eq. km. 223 6.756 17.102 Net Investment In milk ani"als, number/ 10 sq. km. 228 17.974 27.691 Not investment In snall stocks, number/ 10 sq. km. 228 5.948 16.426 Net Investment in pumps, nunber/ 10 sq. km/ 228 1.846 2.034 Credit advanced to rural aector, '000 Rs./ 10 sq. km. 765 283.991 421.446 Cooperative credit advanced to agriculture 765 9a.616 203.583 Agricultural real wage (Re. /an day) 765 5.294 2.165 Agricultural employment, persons/10 sq. km 170 236.492 196.889 Nonagricultural employmnt, persons/10 sq. km 170 153.989 206.158 Independent Variable Aggregate roal crop price Index 766 0.861 0.328 Real price ot fertilizer 766 8.459 0.493 Annual urban wage (real) 785 4373.277 1406.924 Canal irrigation, '000 ho/10 sq. km. 765 0.0 8 0.101 Number of regulated markete/ 10 sq. km. 765 0.019 0.026 - 24 - Independent Vegiable Number of Mean Standard Observattone d-vivtion Number of villagea with primary school/10 sq. km. 765 1.289 0.668 Number of village with *lectricity/ 10 sq. km. 765 0.976 0.865 Total road length/10 sq. km. 7?6 5.369 4.986 Number of rural and a*il-urban branches of commrcial banks/10 eq. km. 765 0.101 0.132 Number of cooperative bank branches/ 10 q. kM. 765 0.031 0.026 Number of agricultural co-operative societloe/10 sq. km. 765 0.436 0.277 Number of land development banks/ 10 sq. km. 766 0.010 0.006 Annual rainfall in _ 765 1120.059 9S4.609 Soil molture capacity Index a6 2.349 1.01 Length of rainy *"noon, onths 86 8.653 1.868 Excess rainy months, number a6 1.236 1.394 Number of cold months e6 0.935 1.313 Percentage of area liable to flooding e6 1.389 3.532 Percentage of aroa potential for Irrigation 85 S0.001 31.909 - 25 - TAKE t. EKTER ANTS OF INMUTONAL C1EDT ADVANE TO R3AL SECTOR Exolanetorv varlable Institutional Credit (FI xed Effet.) Aggregate real crop (real) price (Iaged) -0.064 (-0.841) Real price of fortilizoer -0.249 (-1.008) Real urban wago -0.a2a (-2.828)o Rainfall x 1O0 1.6894 (0.724) Roadns 2.489 (5.523). Rogulated mrketae 0.490 (8.254)* Primry *chooln' 0.984 (1.328) Rural *lectrificationa -0.a68 (-1.925). Canal irrlgationa -0.278 (-1.S8C) Commercial bankna 0.861 (9.185). Cooperative Bankaa 0.259 (2.1)36)o Primry cooperative socletiesa -0.81S (-4.499)* Land developmnt bankus -0.228 (-1.467) Year -0.003 (-0.027) Year x Irrigation potential 0.847 (2.873)* Year x excess rain months 0.883 (0.265) Year x length of rainy soason -6.942 (-2.563)e Year x *oil molsture capacity 7.029 (2.473)* Year x flood potential -1.9S4 (-1.990)* Ye-r x no. of cold months -.8.59 (-1.339) F-Statistic 44.62 Housman-Wu (chl-square, 20 df.) 42.6 Number of observatipns 765 Note: t-statistic are, In parenthesee. Asterisk rofers to significance level of 10 percent or better. a coefficienta are In elasticity form. - 26 - TABLE S. EFMC_ OF KSIMIUTUNAL OMEIT ON FERTIlUZER CONSUWTIN AND AGRICtINJAL OUTPUT Explanatory Variable Fertilizer Consumption Acareasto Crop Output Institutional credit (predicted)a 0.2865 0.021 (6.949)o (1. 344)§ Aggregate real output price 0.05U 0.012 index (lagged)' (1.128) (0.414) Real price of fertilizers -0.506 0.114 (-4.041). (1.660)* Real urban wage 0.185 0.131 (2.917)* (3.124)* Rogulated market 0.249 0.091 (3.111)e (2.223)- Canal irrigatlona 0.289 -0.078 (2.446). (-1.389) Rurol *l etrificationa 0.242 0.060 (2.710)o (1.031) Road length* -0.727 -0.140 (-2.862). (-1.022) Primary schoola 0.68S 0.219 (1.626) (1.066) Annual rainfall x 103 1.008 1.078 (1.081) (3.778)* Year -2.951 -0.000 (-4.496)* (-0.021) Yoar x irrigation potential 0.022 0.001 (3.858)* (5.618)* Year x *xcees rain -0.677 -0.003 (-4.733)* (-1.628) Year x soil moisture capacity 0.6e 0.008 (4.087). (2.262)* Year x length of rainy season 0.869 -0.008 (2.566)* (-2.810)* Year x flood potential -0.022 -0.001 (-0.616) (-0.988) Year x no. of cold months 0.560 0.001 (3.411)- (0.142) F-Statistic 60.446 19.99 Hausman-Wu (chl-square, 17 df.) 36.974 34.098 Number of observations 738 765 Note: .t-statistices no in parentheses. Asterisk refers to significance iev-l of 10 percent or better on a two-tall test. a coofficients are in elasticity form. § refers to a 10 percent level of significance on a one-tail test. - 27 - TAILe 4. EFFECT OF INSTrITTlONAL CREDIT ON AGRICULTURAL INVESTMENT (No. of Obeervatlonc a 22,) Investment In Exolanatory Draft Milk verinbl- animalcs 'nials Small stocks Pumps Institutional credit 0.488 0.140 o.67' 0.444 (predicted)& (2.229). (6.189). (2.822)* (3.908)* Aggrgat. real crop output 2.844 0.017 1.432 0.395 price, lagged (3.288). (0.042) (1.860)* (1.742)* Real fertllizer price a -14.291 -11.953 -19.819 0.135 (-5.004). (7.682)* (-4.569)* (0.093) Real urban wage a 0.076 -1.052 -3.939 0.037 (0.068) (-1.718). (-4.668). (0.066) Road a -1.621 -1.789 1.205 -0.265 (-1.839). (-3.363). (1.165) (-0.669) Canal Irrigation a -0.679 -0.190 0.008 -0.312 (-1.001) (-0.517) (0.011) (-0.977) Primary schools a 6.477 -0.670 0.489 0.121 (3.706)* (-0.639) (0.242) (0.132) Electriflcation 0.28s 0.406 -0.754 0.231 (0.727) (1.826)* (-1.774). (1.129) Regulated uarkotsa -0.094 0.396 0.279 -0.023 (-0.218) (1.643)* (0.589) (-0.104) Rainfall x 103 2.376 22.89s -S.986 0.732 (0.477) (2.8W)* (-1.138) (1.097) Past stock -0.239 -0.041 -0.20S -0.100 (-14.693)* (-0.901) (-14.260)* (-9.543)* Year -0.499 2.971 1.367 0.002 (-0.807) (2.894)* (2.061)* (0.020) Year x cool months 0.105 -0.137 0.486 0.026 (1.434) (-0.904) (4.913)* (2.319)* Year x rainy *seon 0.091 0.691 0.119 0.003 (0.965) (3.709). (0.960) (0.189) Year x flood potential -0.006 0.036 0.095 0.002 (-0.193) (0.669) (2.316)s (0.496) Year x lrrigation 0.006 0.002 -0.015 -0.001 potential (1.424) (0.216) (-3.087)* (-0.103) Year x Soil moisture (-0.041) -0.239 -0.116 -0.008 capacity (-0.460) (-1.332) (-0.967) (-0.634) Year x excess rain 0.101 -0.419 -0.168 -0.006 months (1.273) (-2.601)* (-1.464) (.4.390) Constant 120.182 -75.226 106.846 0.426 (3.173)* (-1.286) (2.896)* (0.089) F-Statistic 23.440 30.939 19.6s8 6.988 Hausmn-Wu (Chi-square, 18 df) 14.196 17.891 20.Sie 13.888 Notes: t-Statistics are in parenthesis. Asterisk refors to significance level of 10 percent or better on a two-tail test. a Coefficiente of thes variables are In elasticity fore. - 28 - TABLE S. EFFECT OF INSTITUTONAL CREDrT ON FARM ND NONFARM EWPLOYMIBT AND ACRICULilRAL WAGE Explanatory Varlablo Nonfarm Form Agricultural employment employment waG9 Institutional credit (prodicted)* 0.176 -0.044 0.040 (5.789). (-1.881)* (2.709)* Aggregate re-l crop output price 0.141 0.114 0.038 index (lagged)" (1.831)§ (2.642)* (1.624)* Real prico of fertilizora 0.420 0.08 0.01 (0.979) (0.207) (0.868) Real urban wagea -0.049 -0.208 0.394 (-0.289) (-1.589) (11.738)* Regulated markot -0.098 0.057 -0.059 (-2.091)* (1.628). (-1.829)o Canal irrigationa 0.064 -0.088 -0.046 (0.661) (-1.868) (-1.106) Rural *loctrificationa 0.1B8 -0.058 0.061 (3.428)* (-1.966). (1.512)§ Road lengtha 0.168 0.003 -0.174 (1.142) (0.656) (-1.889). Primry echoolO -0.607 0.134 -0.261 (-3.251). (0.933) (-1.766)* Annual rainfall x 10o 0.272 - - (2.817)a Year 0.061 0.725 0.025 (0.044) (-0.411) (0.679 Year x Irrigation potential 0.001 0.044 0.001 (0.069) (3.163). (1.642)* Year x excess rain months 0.888 -0.619 0.007 (3.265)e (-1.494) (1.039) Yer x soil moisture capacity -0.455 0.289 -0.008 (-1.729)* (0.841) (-1.200) Year x length of rainy samon 0.318 0.853 0.003 (1.020) (2.104)* (0.358) Year x flood potential 0.021 0.164 -0.001 (0.248) (1.439) (-0.286) Year x no. of cold months 0.286 -0.878 0.004 (1.211) (-2.209). (0.683) F-Statistic 29.686 10.945 28.822 HNusman-Wu (chi-square) 14.965 18.428 16.439 Number of observations 170 170 765 Note: t-statistics are In parentheses. Asterisk refers to significance levol of 10 percent or better on a two-tail teat. a coefficients are In elasticity form. § refers to a 10 percent level of significance on a one-tall test. PPR Wnrklng PEr Serbes Contact Iwa At Datfo Japr WPS258 The Role of Voluntary Organizations L David Brown in Development David C. Korten WPS259 Dealing with Debt: The 1930s and Barry Eichengreen August 1989 S. King-Watson the 1980s Richard Portes 33730 WPS260 Growth, Debt Accumulation, and Jagdeep S. Bhandari Sovereign Risk in a Small Open Nadeem Ul Haque Economy Stephen J. Turnovsky WPS261 Inflation, External Debt and Sweder van Wiinbergen Financial Sector Reform: A Roberto Rocha Quantitative Approach to Consistent Ritu Anand Fiscal Policy WPS262 Economic Policy and Extemal Dermot McAleese Shocks in a Small, Open Economy: F. Desmond McCarthy The Irish Experience WPS263 How Has Instability in World Markets Peter Hazell August 1989 C. Spooner Affected Agricultural Export Mauricio Jaramillo 30464 Producers in Developing Countries Amy Williamson WPS264 Case Studies of Two Irrigation Herve Plusquellec Systems in Colombia: Their Performance and Transfer of Management to Users' Associations WPS265 The Influence of Imperfect Alexander Yeats Competition in International Markets: Some Empirical Evidence WPS266 Policy Changes that Encourage Mansoor Dailami August 1989 M. Raggambi Private Business Investment In 61696 Colombia WPS267 Issues in Income Tax Reform in Cheryl W. Gray Developing Countries WPS268 The Market for Developing Country John Wakeman-Linn Debt: On the Nature and Importance of Market Shortcomings WPS269 Women in Development: Issues for Barbara Herz Economic Sector Analysis WPS270 Fuelwood Stumpage: Keith Openshaw Co,nsiderations for Developing Charles Feinstein Country Energy Planning PPR Working PAr Series Contact a AAor Datefor paper WPS271 The Industrial Labor Market and Katherine Terrell Economic Parformance in Senegal: Jan Sveinar A Study of Enterprise Ownership, Export Orientation, and Government Regulation WPS272 Women's Changing Participation in T. Paul Schultz the Labor Force: A World Perspective WPS273 FY88 Annual Sector Review: Population and Human Population, Health and Nutrition Resources Department WPS274 The Demography of Zaire: Review Miriam Schneidman of Trends In Mortality and Fertility WPS275 Worldwide Estimates and Fred Arnold Projections of Internatinal Migration, 1980-2000: An Assessment WPS276 Improving Rural Wages in India Shahidur R. Khandker August 1989 B. Smith 35108 WPS277 The Effect of Formal Credit on Shahidur R. Khandker August 1989 B. Smith Output and Employment in Rural Hans P. Binswanger 35108 India WPS278 Inflation and the Company Tax Anand Rajaram Base Methods to Minimize Inflation-Induced Distortions WPS279 Cross Country Determinants of Paul M. Romer Growth and Technological Change WPS280 Adjustment Policies in East Asia Bela Balassa WPS281 Tariff Policy and Taxation in Bela Balassa Developing Countries