metadata: authors: 0: author: Feder, Gershon
1: author: Lau, Lawrence J.
2: author: Lin, Justin Y.
3: author: X
count: China
docna: 0: docna: Credit's effect on productivity in
            Chinese agriculture : a microeconomic model of disequilibrium
docty: Policy Research Working Paper
subtopic: Agricultural Economics,Legal Institutions of the Market Economy,Nutrition,Educational Sciences,Food Security
teratopic: Finance and Financial Sector Development
abstracts: cdata!: Many government programs want to provide
            more credit to the farm sector to increase agricultural
            productivity.  If the marginal effect on productivity is
            small, those resources might be put to better use elsewhere.
            The authors conducted an econometric analysis of the effect
            of credit on output supply which recognizies that credit
            markets are not necessarily at equilibrium - so that credit
            rationing and nonborrowing are both possible.  Only about 37
            percent of the farmers in the study area were constrained by
            inadequate formal credit.  Informal credit sources provided
            funds for specific non-agricultural activities that were not
            fungible.  The results indicate that one additional yuan of
            liquidity yielded 0.235 yuan of additional gross value of
            output. These results suggest that for the area of China
            covered in the study, a good part of the short-term credit
            may actually be used for consumption and investment.  Two
            conclusions are suggested for evaluating the probable effect
            of expanding agricultural credit.  First, not all farmers,
            and sometimes only a minority, are constrained in their
            farming operations by inadequate credit. And second, greater
            supplies of formal credit will be diverted in part to
            consumption, so the likely effect on output will be smaller
            than what one might expect if all funds are assumed to be
            used productively.
geo_reg_key: 517198,517191,119225
repnme: repnme: Credit's effect on productivity in
            Chinese agriculture : a microeconomic model of disequilibrium
display_title: Credit's effect on productivity
            in Chinese agriculture : a microeconomic model of disequilibrium
listing_relative_url: /research/1991/01/700093/credits-effect-productivity-chinese-agriculture-microeconomic-model-disequilibrium
topicv3: Agriculture and Food Security m1326205 435,Farm Household Consumption m1326643 1969,Education m1327048 470,Agricultural Credit m1327605 1962,Financial Institutions m1505015 3797
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Finance and Financial Sector Development
origu: Policy, Research & Ect Affairs
no_of_pages: 32
model_extractions: **Policy, Rearch, and Exlornal AffaIrs**

## **Agricultural Podlces**

# WPS **571**

This paper - a product of the Agricultural Policies Division, Agriculture and Rural Development Departmint - is part of a larger effort in PRE to evaluate agricultural credit policies and review institutional designs so as to formulate better guidelines for Bank activities in rural credit. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Cicely Spooner, room N8-035, extension 30464 (27 pages).

Many government programs want to provide more credit to the farm sector to increase agricultural productivity. If the marginal effect on productivity is small, those resources might be put to better use elsewhere.

Feder, Lau, Lin, and Luo conducted an econometric analysis of the effect of credit on output supply which recognizes that credit markcts are not necessarily at equilibrium - so that credit rationing (with unsatisfied demand) and nonborrowing (when credit could be available) are both possible. Only about 37 percent of the farners in the study area were constrained by inadcquate formal credit. Informal credit sources provided funds for specific non-agricultural activities that were not fungible.

The results indicate that one additional yuan of liquidity (credit) yielded 0.235 yuan of additional gross value of output. These results suggest that for the area of China covered in the study, a good part of the short-term credit may actually be used for consumption and investment. Indeed, mediumand long-termn formal credit is practically nil among the agricultural households in the study area. Rolled-over shortterm credit is sometimes used for small-scale investments. The diversion of short-term credit for farmn investment is about 40 percent for an average household in the study area. This implies that almost a third of the forrnal credit is used for consumption (of current goods or durables).

What conclusions does this suggest in evaluating the probable effect of expandin, agricultural credit? First, not all farmers, and sometimes only a minority, are constrained in their farming operations by inadequate credit. And second, greater supplies of formal credit will be diverted in part to consumption, so the likely effect on output will be smaller than what one might expect if all funds are assumed to be used productively.

The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Research, and Extemal Affairs Complex. Anobjectiveof theseries is togetthesefindingsoutquickly, evenif presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the PRE Dissemination Center
Credit's Effect on Productivity in Chinese Agriculture: A Microeconomic Model of Disequilibrium by Gershon Feder, Lawrence j. Lau, Justin Y. Lin, and Xiaopeng Luo ,able of Contents

|I.<br>Intro Juction|I|
|---|---|
|II.<br>China's Farm Sector and Rural Credit Market|3|
|III.<br>A Model of Farm Household Consumption, Production, and Investment|_5_|
|IV.<br>Econometric Specification and Empirical Results|8|
|V.<br>Implications|13|
|References|1 4|
|Footnotes|1 5|
|Annex|1 6|
|Appendix|24|
# 1. **INTR(DUCTION**

Credit is an important element in agricultural productlon systems. It allows producers to satisfy the cash needs Induced by the production cycle which characterizes agriculture: preparation, planting, cultivation and harvesting of the crops are typically done over a period of several months in which very little cash revenue is earned, while expenditures on materials, purchased inputs and consumption need to be made in cash. Cash income is received a short time after the harvest. In the absence of credit markets, farmers would have to maintain cash reserves so as to facilitate production and consumption in the next cycle. The availability of credit allows both greater consumptlon and greater purchased Input use, and thus increases welfare of the farmers.

If a producer faces an infinite supply of liquidity at a given price, the production decisions will be independent from consumption decisions, as has been shown in the household models of Singh et al. However, asymmetric information and adverse selection typically prevail in credit markets, giving rise to credit rationing as an optimal behavior (Stiglitz and Weiss). Furthermore, government intervention in the form of interest rate ceilings or subsidized interest rates is common in many countries' agricultural sertors, necessitating rationing. When credit is rationed, some borrowers cannot obtain the amount of credit they desire at the prevailing interest rate, nor can they secure more credit by offering to pay a higher interest rate. In such circumstances, liquidity can become a binding constraint on many farmers' operations.

When liquidity is a binding constraint, the amounts and combinations of inputs used by a farmer deviate from their notiornal optimal levels (the levels that would have been utilized if liquidity were not a binding constraint). - **ie** marginal contribution of credit is therefore to bring input levels closer to the optimal levels, thereby increasing output and, since the quantity of land
**-2-**

is fixed, yie!d. This potential gain in productivity is one motivation underlying many government programs seeking to provide more credit to the farm sector. An Important issue in the context of agricuiltural credit policy is the magnitude of the expected productivity gain. If the marginal productivity effect of credit is small, then the resources may be more beneficially deployed elsewhere. Assessment of the expected productivity gain Is not trivial because the effect of credit is likely to differ between liquidity-constrained and unconstrained farm households.

Some studies attempt to identify the effect of credit by estimating separate production functions or supply functions for borrowers and non-borrowers, and then proceeding to compare the estimates (see review in David and Meyer, 1980, pp. 206-215). One major weakness of this approach is the implicit assumption that all borrowers and all non-borrowers are respectively homogenous with respect to their credit demand/supply situations. This assumption is often not valid, as many non-borrowers do not borrow because they actually have sufficient liquldity from their own resources and not because they cannot obtain credit, while some cannot borrow because they are not credit-worthy. Similarly, the marginal effect of credit may actually be zero for borrowers for whom liquidity is not a binding constraint.

The same criticism applies to other studies in which all sampled observations are pooled to estimate production functions (or output supply functions) with credit as a production input or as a supply determinant. As will be argued in a subsequent section, the supply function is alfferent (both in parameters and in variables) depending on whether- liquidity is a binding constraint. Estimates which do not take account of these restrictions on the specification are therefore flawed.

The present study reports an econometric analysis of the effect of credit on output supply which avoids some of the aforementioned pitfalls. The central feature is the recognition that credit transactions are not necessarily in equilibrium at the household level. That is, the amount of credit desired and tne amrourt offered are not necessar,,y **equ4l** so th:t creiit SL.ppy
- **3 -**

rationing **(with unsatisfied** demand) **and** non-borrowing (while **supply** Is potentially avalIabl) arre **both possible. The analysis** utilizes cross-sectional household-level data from a study area in northeast China, obtained in a recent farm survey designed **by** the authors. The pian for tloe paper **is as follows:** Section **II** provides background on the farm sector and the ru al cre sit market In China, and describes the specific study area and data utilized in the analys.s. Section **liI** discusses the formal model underlying the empirical analysis (the mathematical model ts pr e(ente 8 in an annex). It is followed by a discussion of the econometric procedure and tr e e j>cal results In Section IV. The last section discusses the implications of the results.

# **II. CHINA'S FARM SECTOR AND RURAL CREDIT MARKET**

China introduced a smaliolder agricultural production system in a series Of reFcrms between the years 1979-1984. The "household responsibility system" made individua! householes, rather than the communes to which they belonged, the decision-makers and mariagers of the C'N farms. Individual families were allocated land by the communes on leases that run tycicairl fc' 15 years. The improved incentives brought about a significant increase in agricuwtura! cut_tc ac in rural income (Lin). While prior to the reforrns there was only Vimited nter3act on retbe households and financial institutions, the emergence of smalilholder ag:-!cu'tur e im;: -s t',t households now need liquidity for seasonal production and consumption, or longer-rer . e finance investment, construction and ceremoniai social events.

Most of agricultural households' transact,ons w;th the fcrma f ' sector ?are the rural crecit cooperatives (RCCs).1 The interest rates for agricuitura oans tas _ve 2-:_ - loans) made by forma! credit institutions are fixed :by the government, vith some _a- a, according to loan categories. In 1987, the rates of interest for agrcu!tural Scans between 7 and 1 **4** percent. The degree of intc, est subsidy is believed to Wave been **s'L** There is evidence that following the introduction of r-efcr-ms the voiu;Tce of . obtained from informal sources is substantial in China. Jiang asserts that ron-;rsl tutic'a,
sources contribute roughly half **of** the credit volume In rural areas. Feder et al. (1989) report non-institutlonaTcredit shares of between one third and two thirds In several study areas. The most common sources of informal credit In China are relatives and friends. Most **of** such loans carry no Interest charges. Possible reasons for the **absence** of a substantial<sup>profit-motivated</sup> informal credit market In China are discussed In Feder et al. (1990a). They include, unclear legal status, residual ideological resisLance and absence of collateral assets.<sup>2</sup> The present study relies on data collected in December 1987 in Gongzhuling.<sup>Gongzhuling</sup> is located in Jilin province, within the corn belt of northeastern China, where agro-climatic conditions dictate essentially one corn season a year. The original sample consists of 2U0 households selected at random from eight rdndomly selected townships. The information<sup>gathered</sup> covers inputs, outputs, financial assets, credit transactions, and household characteristics. Thirteen households are deleted after determining that tneir main activity was not<sup>agriculture or</sup> that thei: situation was unusual (e.g., a widow maintaining a home garden<sup>plot).</sup> The data show that nearly three quarters of the sample borrowed from formal<sup>sources</sup> (essentially the RCCs) during the study season. The frequency of informal credit transactions is much lower than that of formal transactions (about one fifth of the sample), and three quarters of Lhese loans were provided free of interest. Given the significant differential between the rates of interest on the two types of loans, this may be taken as<sup>evidence that</sup> .nformal credit is not a good substitute for formal credit due to limited fungibility (otherwise every borrower would exhaust his or her informal credit first before going to the RCC). The share of formal credit in the total volume of new credit is 66.5 percent.

Table 1 presents the distribution of loans by purpose and by type of lender. It is readily apparent that the predominant stated purpose of formal loans (all of whicn<sup>are short-term)</sup> is for the financing of current production. Most of the informal credit is reported to have been obtained for purposes other than production, with construction and social<sup>expenditures appearing</sup>
**-5-**

dominant. Informal loans contracted for these purposes, however, cannot be easily diverted to finance day-to-7ay consumptlon or production, because the lenders, mostly relatives and friends, can easily monitor compliance. The bulk of the fungible credit, defined as credit which is not granted for easily monitored purposes, In the study area thus comes from the formal sector (87 percent).

Given the dominance of formal credit, a key issue for tha present study is the extent to which its supply is a constraini on households' desired activities. The survey data collected permit an answer to this question. Borrowing households were asked if at the go ng rates of interest they would have liked more institutional credit than the amount they were actually granted. Households which did not borrow were asked the reason for not borrowing. The most common reason for not borrowing was availability of sufficient own resources. The borrowers who indicated a desire for more credit, and the non-borrowers who responded that they could not obtain credit, are classified as credit-constrained. As reported in Table 2, about 37 percent of the farm households in Gongzhuling were constrained by credit accordina to this classification. The liquidity position of credit-constrained households as compared to non-constra ned households is compatible with intuitive expectations: They have significantly lower deposils in financial institutions, and overall, their liquid resources per unit of land are 12 percent teIcA those of unconstrained households.

# lll. A **MODEL OF FARM** HOUSEHOLD CONSUMPTION, PRODUCTION AND INVESTMENT

Suppose the household considers the allocation of resources at its disoosai at :"e beginning of the production period between the following uses: (i) current consumpt on, investment; (iii) the purchase of variable inputs for current production (inclueing lator a'ncw fertilizers). Variable inputs, in combination with land and existing capital, will produce this perici s output. Investment will not mature by the time this period's output is produced, but Its contribution to the household's welfare may be accounted for through a valuation function wlich
**TABLE 1:** Distribution of Loan Purposes **by** Type **of** Lender (Percent)

||||Pur p|o s e||||
|---|---|---|---|---|---|---|---|
||Sample<br>Size<br>|Prod-<br>uction|Farm<br>Equip-<br>ment|Constr-<br>uction|Consump-<br>tion|Social<br>(Wedding<br>Funeral,<br>etc.)|Other|
|S.U.lr<br>_ce_<br>|(Number|||||||
|of L oans|of Loans)|||||||
|_rO'rp.Sl._|209|92.3|4.3|1.9|0|1.0|0.5|
|l C mal|44|9.1|4.6|20.5|15.9|27.3|2 2.7|

TABLE 2: Extent of Formal Credit Constraint

|tite Crs<br>(Nulber|SamDle<br>s.ze<br>of Households)|% Constrained|
|---|---|---|
|.'.s<br>;frS|145|41.3|
|or' - tCr,D~n'es|42|28.3|
||187|37.4|
summarizes tne contribution of capital to the future consumption stream. The household's **Initial** endowments of-liquid resources, family labcr, capital, and lana (the latter two assumed not convertible to liquldity during the **perlod)** can be augmented by borrowing at the beginning of the period. Whether the household can borrow the entire desired **amou. t** or is constrained by a binding upper limit on the availability of credit Is of considerable consequence, as it determines whether production decisions are separable from the consumption decisions. The household is assumed to maximize a utility function defined over consumption per family member in the current and next period, plus the utility of future streams of consumption summarized by the valuation function of next period's capital, per family mcember. The optimization can be carried out under two scenarios: (I) The supply of credit is greater than or equ_l to the demand (i.e., credit constraint not binding); and (ii) The supply of credit is less than the notional demand for credit (credit constraint binding). The essence of the results of such a model 3 is that under case (i) above, the supply of output Is not affected by the level of licuidity (including credit), the size of the household'b own family labor force or the total size of the household. The parameters of the output supply function in this case are determined by the production function alone. Under case (ii), however, output supply is positively affected by increases in liquidity (e.g. increased credit supply) and in the household's labor endowment, while the effec. if total household size is indeterminate. 1,,creases in the initial endowments of land and capital would have a positive effect on output supply in both cases (i) and (i), while they would have an indeterminate impact on input demands, depending on substitutability. The parameters of the output supply function under case (ii) are determined by both the production function and the utility function.
- **8** -

# **IV. ECONOMETRIC SPECFICATION AND EPRICAL RESULTS**

The econometric model most suitable for estimating<sup>the output supply tunction with the</sup> data avallable to **us Is** the switching regression<sup>model</sup><sup>**with**an endooenous criterion</sup> function described in Maddala (pp. 223-228). The model postulates fo'r any observation I where X1;, X2; and Zi are vectors of exogenous or predetermined<sup>varlables,</sup><sup>_Pl, p2,_and7are</sup> the corresponding vectors of parameters, and U11<sup>, U</sup> 2<sup>1 and U</sup> 1<sup>are random disturbances.</sup> 'ii and Y2i are two possible values of the dependent variable,<sup>only one of which is actually observed</sup> for any given household, depending on the value<sup>of the (unknown) criterion function</sup><sup>**7**Z</sup> 1<sup>+ Ui.</sup> The random disturbances are assumed to have<sup>a trivariate norma; distribution, Identically and</sup> independently distributed across households. Applied<sup>to the particu!ar issue at hand, equations</sup> (1) and (2) may be viewed as the output supply<sup>equations under a non-binding and binding</sup> iquidity constraint respectively. The criterion for<sup>whether liquidity is binding or not is whether</sup> the demand for credit exceeds credit sapply, and the<sup>criterion function7Z + U, in our case, is</sup> the excess credit demand function (i.e., demand minus<sup>supply). Excess credit demand is not</sup> directly observable. However, from the survey responses,<sup>we know whether a given household</sup> is constrained or unconstrained by !iquidity. Using data on<sup>the dichiotomous responses, the</sup> vector of parameters 7 can be estimated up to a proportionality<sup>constant by a probit procedure.</sup> The estimated parameters are then used to generate Mills<sup>ra.-.s which are incorporated in tne</sup> second stage estimates, where the equations<sup>(1) and (2), with their Mills ratio corrections, are</sup> estimated by a linear regression. Under model<sup>assumptions, the estimated coefficients are</sup> consistent and asymptotically normal, and with appropriate<sup>corrections to their estimated</sup> variance-covariance matrix (dua to the heteroscedasticity of the<sup>stochastic disturbance terms</sup> in the second stage estimates) can be subjected to<sup>statistical tests based on normality.</sup>
- 9 -

The einpirical specificatlon of the variables which corstitute the vector Z involves both determinants orcredit demand and credit supply. Thus, In the case of variables which affect both demand and supply In the same directlon, one cannot predict a priori the expected sign. These variables are (with the expected effect on the probabillty of being credit constrained indicated In parentheses for those with an unambiguous effect): (1) Land; (2) Capital; (3) Number of adults (-); (4) Number of dependents;4 (5) Education; (6) Farm experience; (7) Savings in financial Institutlons (-); (8) Total initial liquid assets (-); (9) Outstanding debt to financial institutions (+)! (10) Total outstanding debt (+); (11) Last sedson's income (-); (12) Previous loan default dumrry (+). In ddditbon, eight dummy variables for townships were introduced. The r asults of the probit estimates are presented in Table :x.5 Two estirated coefficients are statistically significantly different from zero at the 5 percent level of significance and have the theoretically predic<sup>t</sup> ed sign: Savings in financial institutions and last season's income. Eighty-two percent of the -.-servations are properly classified as being credit constrained or unconstrained, implying a fc.irly good fit.

The reduced form output sunply equation for liquidity-constrained households, estimated with the double-log specification, involves the folkviing variables (with the direction of trie expected effect noted in parentheses): t1) Total liquidity 6 W; (2) Number of adults (+); (3) Number of dependents (?); (4) Land (+); (5) Capital (+); (6) Education (+); (7) Farm experience (+). The specification for the households not constrained by liquidiLy is similar exceot for the first three variables, which do not theoretically belong in the reduced form for output sup,z Y. The estimated coefficients are reported in Table 4.

In the output supply equation for constrained households, (column 1) the estimated coefficient of the total liquidity variable is positive and statistically significantly different from zero at the 5 percent level of significance, but the number of adults and the number of
- 10 -

TABLE 3: Estimated Coefficlents of Probit Model (probability of being credit-constralned)

|Variable a|Estimated<br>Coe,**ficlent**<br>(t-value)|
|---|---|
|Land|-.212<br>|
||(.505)|
|Capital|-.029|
||(.265)|
|Number of adults|.282<br>(1.950)|
|Number of dependents|.093<br>(.509)|
|Education|-. 101|
||(1.502)|
|Farm experience|-.025<br>(1.666)|
|Savings in financial Institutions|-.121<br>(2.223)|
|Total initial liquid assets|.376<br>(1.552)|
|Outstanding debt to financial institutions|-.053<br>(.977)|
|Total outstanding debt|.057<br>(1.182)|
|Last season's income|-.974|
||(2.973)|
|Previous loan default|.587|
||(1.260)|
|Percent correctly predicted|.820|
|<br>No. of observaticns|156|

a/ The equation also Included also eight township dummy variables. These are not reported.
> - **11** -

# TABLE 4: Estimated Coefficlents of Second Stage Switching Regression Model for Output Supply (Reduced Form)

||(1)|(2)|(3)|
|---|---|---|---|
|Variable a/<br>Regression|Credit<br>Constrained<br>(N-48)|Credit<br>Unconstralned<br>(N-108)|Credit<br>Unconstrained<br>Counter factual<br>(N-108)|
|Total liquidity|.183<br>(2.951) b/|-|.042<br>(1.261)|
|Number of adults|.015<br>(.641)||.001<br>(.004)|
|Number of dependents|-.020<br>(.538)|-|-.005<br>(.247)|
|Land|.863<br>(8.166)|.875<br>(18.120)|.846<br>(15.202)|
|Capital|.027<br>(1.193)|.051<br>(3.287)|.052<br>(3.306)|
|Education|-. 004<br>(.261)|.018<br>(2.216)|.018<br>(2.206)|
|Farm experience|-. 028<br>(.533)|.063<br>(2.324)|.062<br>(2.250)|
|R<sup>2</sup>|.863|.867|.869|

a/ Regressions Included also eight dummy variables for townships and the Mills ratios computed from the first stage probit. These are not reported.

b/ Numbers in parentheses denote t-values
- 12 -

dependents do not have statistically significant estimated coefficlents. The hypothesis that all three variablesdo not affect the supply of output for constrained households has a F-statistic of 2.96 and Is rejected at the 5 percent level of significance, confirming the theoretical predlctions of the model. The quantity of land Is an Important and statistically significant determinant of output supply for constrained and unconstrained households (the estimated coefficients of the output supply function for the latter group are reported In column 2). It is also worth noting that capital, education and farm experlence have statistically significant positive effects on output for the credit-unconstrained households but have statistically Insignificant effects for the credit-constrained households. This finding suggests that capital, education and experience are less likely to contribute to output If the farmer's choices are constrained by iquidity.

While under the assumptions of our model it is not approprlate to estimate the output supply equation for the unconstrained households with the inclusion of liquidity and household composition variables (liquidity is theoretically endogenous for such households and the estimated coefficients would be subject to simultaneity bias), we experimented with the estimation of such a hypothetical counter-factual case on the assumption that the classification was wrong and therefore these households were liquidity-constrained. The results (column 3 in Table 4) indicate that none of the estimated coefficients of the first three variables are statistically significantly different from zero (the hypothesis that all three are not significant has a F-statistic of 0.55 and cannot be rejected at any level of significance) implying that the counter-factual case is not borne out empirically. Another experiment was the estimation of the model using the whole sample without separation, that Is, as If all households were liquidity-constrained. The results show that the estimated coefficient of total liquidity in the output supply equatlon would have been about two-thirds of that in column 1. Predictions based on the wrong estimated
- **13 -**

**coefficients** would thus lead to significantly Inaccurate assessments of the effect of credit on output supply.-

# **V. NPLICATIONS**

Based on the estimated coefficients, if every credit-constrained household In the sample is given an additional credit of 17.82 yuan (equal to 1 percent of the average level of liquidity of the credit-constrained households), the total output of these households may be projected to increase by 201.08 yuan, or approximately 0.04 percent of the total output. Thus, on - average, one additional yuan of liquidity (credit) would yield 201.08/(17.82 x 48) 0.235 yuan of additlonal gross value of output. These results suggest that for the area of China covered in the present study, a significant proportion of the short term credit provided by the rural credit cooperatives as production ci-edit" may actually be utilized for consumDtion and investment. Indeed, medium and long term formal credit is practically nil amongst the agricultural households In our study areas, and a similar picture is given by aggregate statistics. Rolledover short term credit is sometimes utilized to finance small scale investments. A recent study by Feder et al. (1990b) finds that the diversion of short-term credit for farm investment is about 40 percent for an average household in the study area. This, in turn, Implies that almost a third of the formal credit is utilized for consumption (whether of current goods or durables).

The results of the study highlight two important factors which should be considered when evaluating the likely impact of agricultural credit expansion: (i) Not all farmers, and sometimes only a minority, are constrained in their farming operations by inadequate credit; (ii) Expanded supplies of formal credit will be diverted in part to consumption, thus the likely output effect will be smaller than that which is expected when all funds are assumed to be used productively. These ideas have been propounded by the Ohio State school critics of credit supply-led development schemes. The present paper thus provides empirical verification of these views.