This paper uses a structural model to understand, predict, and evaluate the impact of an exogenous microcredit intervention program, the Thai Million Baht Village Fund program. We model household decisions in the face of borrowing constraints, income uncertainty, and high-yield indivisible investment opportunities. After estimation of parameters using pre-program data, we evaluate the model’s ability to predict and interpret the impact of the village fund intervention. Simulations from the model mirror the data in yielding a greater increase in consumption than credit, which is interpreted as evidence of credit constraints. A cost-benefit analysis using the model indicates that some households value the program much more than its per household cost, but overall the program costs 20 percent more than the sum of these benefits.
This paper uses a structural model to understand, predict, and evaluate the impact of an exogenous microcredit intervention program, the Thai Million Baht Village Fund program. Understanding and evaluating microfinance interventions, especially such a large scale government program, is a matter of great importance. Proponents argue that microfinance allows the provision of credit that is both effective in fighting poverty and more financially viable than other means; detractors point to high default rates, reliance on (implicit and explicit) subsidies, and the lack of hard evidence of their impacts on households. The few efforts to evaluate the impacts of microfinance institutions using reduced form methods and plausibly exogenous data have produced mixed and even contradictory results. 1 To our knowledge, this is the first structural attempt to model and evaluate the impact of microfinance. Three key advantages of the structural approach are the potential for quantitative interpretation of the data, counterfactual policy/out of sample prediction, and well-defined normative program evaluation.
The Thai Million Baht Village fund program is one of the largest scale government microfinance initiatives of its kind. 2 Started in 2001, the program involved the transfer of one million baht to each of the nearly 80,000 villages in Thailand to start village banks. The transfers themselves sum to about 1.5 percent of Thai GDP and substantially increased available credit. We study a panel of 960 households from sixty-four rural Thai villages in the Townsend Thai Survey (Townsend et al, 1997 ). In these villages, funds were founded between the 2001 and 2002 survey years, and village fund loans amounted to eighty percent of new short-term loans and one third of total short-term credit in the 2002 data. If we count village funds as part of the formal sector, participation in the formal credit sector jumps from 60 to 80 percent.
Though not a randomized treatment, the program is viewed as a quasi-experiment that produced plausibly exogenous variation in credit over time and across villages. The program was unanticipated and rapidly introduced. More importantly, the total amount of funding given to each village was the same (one million baht) regardless of the number of households in the village. Although village size shows considerable variation within the rural regions we study, villages are administrative geopolitical units and are often subdivided or joined for administrative or political purposes. Indeed, using GIS maps, we have verified that village size patterns are not much related to underlying geographic features and vary from year to year in biannual data. Hence, there are a priori grounds for believing that this variation and the magnitude of the per capita intervention is exogenous with respect to the relevant variables. Finally, village size is not significantly related to pre-existing differences (in levels or trends) in credit market or relevant outcome variables.
Our companion paper, Kaboski and Townsend (2008). examines impacts of the program using a reduced form regression approach and many of the impacts are puzzling without an explicit theory of credit-constrained behavior. 3 In particular, households increased their borrowing and their consumption roughly one for one with each dollar put into the funds. A perfect credit model, such as a permanent income model, would have trouble explaining the large increase in borrowing, since reported interest rates on borrowing did not fall as a result of the program. Similarly, even if households treated loans as a shock to income rather than a loan, they would only consume the interest of the shock (roughly seven percent) perpetually. Moreover, households were not initially more likely in default after the program was introduced, despite the increase in borrowing. Finally, household investment is an important aspect of household behavior. We observe an increase in the frequency of investment, but, oddly, impacts of the program on the level of investment were difficult to discern. This is a priori puzzling in a model with divisible investment, if credit constraints are deemed to play an important role.
The structural model we develop in this paper here sheds light on many of these findings. Given the prevalence of income shocks that are not fully insured in these villages (see Chiappori et al. (2008 )), we start with a standard precautionary savings model (e.g. Aiyagari (1994 ), Carroll (1997). Deaton (1991)). We then add important features central to the evaluation of microfinance but also key characteristics of the pre-program data: borrowing, default, investment, and growth. Short-term borrowing exists but is limited, and so we naturally allow borrowing but only up to limits. Similarly, default exists in equilibrium, as does renegotiation of payment terms, and so our model incorporates default. Investment is relatively infrequent in the data but is sizable when it occurs. To capture this lumpiness, we allow households to make investments in indivisible, illiquid, high yield projects whose size follows an unobserved stochastic process. 4 Finally, income growth is high but variable, averaging 7 percent but varying greatly over households, even after controlling for life cycle trends. Allowing for growth requires writing a model that is homogeneous in the permanent component of income, so that a suitably normalized version attains a steady state solution, giving us time-invariant value functions and (normalized) policy functions.
In an attempt to quantitatively match central features of the environment, we estimate the model using a Method of Simulated Moments (MSM) on only the pre-program data. The parsimonious model broadly reproduces many important aspects of the data, closely matching consumption and investment levels, and investment and default probabilities. Nonetheless, two features of the model are less successful, and the overidentifying restrictions of the model are rejected. 5
For our purposes, however, a more relevant test of the estimated model’s usefulness is its ability to predict out-of-sample responses to an increase in available credit, namely the village fund intervention. Methodologically, we model the microfinance intervention as an introduction of a borrowing/lending technology that relaxes household borrowing limits. These limits are relaxed differentially across villages in order to induce an additional one million baht of short-term credit in each village; hence, small villages get larger reductions of their borrowing constraint.
Given the relaxed borrowing limits, we then simulate the model with the stochastic income process to create 500 artificial datasets of the same size as the actual Thai panel. These simulated data do remarkably well in reproducing the above impact estimates. In particular, they predict an average response in consumption that is close to the dollar-to-dollar response in the data. Similarly, the model reproduces the fact that effects on average investment levels and investment probabilities are difficult to measure in the data.
In the simulated data, however, these aggregate effects mask considerable heterogeneity across households, much of which we treat as unobservable to us as econometricians. Increases in consumption come from roughly two groups. First, hand-to-mouth consumers are constrained in their consumption either because they have low current liquidity (income plus savings) or are using current (pre-program) liquidity to finance lumpy investments. These constrained households use additional availability of credit to finance current consumption. Second, households who are not constrained may increase their consumption even without borrowing, since the increase in available credit in the future lowers their desired bufferstock savings. Third, for some households, increased credit induces them to invest in their high yield projects. Some of these households may actually reduce their consumption, however, as they supplement credit with reduced consumption in order to finance sizable indivisible projects. (Again, the evidence we present for such behavior in the pre-intervention data is an important motivation for modeling investment indivisibility.) Finally, for households who would have defaulted without the program, available credit may simply be used to repay existing loans and so have little effect on consumption or investment. Perhaps most surprising is that these different types of households may all appear ex ante identical in terms of their observables.
The estimated model not only highlights this underlying heterogeneity, but also shows the quantitative importance of these behaviors. Namely, the large increase in consumption indicates the relative importance of the first two types of households, both of whom increase their consumption. Also, the estimated structural parameters capture the relatively low investment rates and large skew in investment sizes. Hence, overall investment relationships are driven by a relatively few, large investments, and so very large samples are needed to accurately measure effects on average investment. The model generates these effects but for data that are larger than the actual Thai sample. Second and related, given the lumpiness of projects, small amounts of credit are relatively unlikely to change investment decisions on the large projects that drive aggregate investment.
Finally, our normative evaluation compares the costs of the Million Baht program to the costs of a direct transfer program that is equivalent in the sense of providing the same utility benefit. The heterogeneity of households plays an important role, and indeed the welfare benefits of the program vary substantially across households and villages. Essentially, there are two major differences between the microfinance program and a well-directed transfer program. First, the microfinance program is potentially less beneficial because households face the interest costs of credit. In order to access liquidity, households borrow more, and while they can always carry forward more debt into the future, they are left with larger interest payments. Interest costs are particularly high for otherwise defaulting households, whose debts is augmented to the more liberal borrowing limit, and so they bear higher interest charges. On the other hand, the microfinance program is potentially more beneficial than a direct transfer program because it can also provide more liquidity to those who potentially have the highest marginal valuation of liquidity by lowering the borrowing constraint. Hence, the program is relatively more costeffective for non-defaulting households with urgent liquidity needs for consumption and investment. Quantitatively, given the high frequency of default in the data 6 and the high interest rate, the benefits (i.e. the equivalent transfer) of the program are twenty percent less than the program costs, but this masks the interesting variation among losers and gainers.
Beyond the out-of-sample and normative analyses, we also perform several alternative exercises that build on the strengths of the structural model: long run out-of-sample predictions showing the time-varying impacts; a counterfactual “investment contingent credit” policy simulation that underperforms the actual policy; and re-estimation using the pooled sample, which confirmed the robustness of our exercise.
The paper contributes to several literatures. First, we add a structural modeling approach to a small literature that uses theory to test the importance of credit constraints in developing countries (e.g. Banerjee and Duflo (2002 )). Second, we contribute to an active literature on consumption and liquidity constraints, and the bufferstock model, in particular. Studies with U.S. data have
also found a high sensitivity of consumption to current available liquidity (e.g. Zeldes (1989 ), Souleles and Gross (2002), Aaronson, Agarwal, and French (2008) ), but like Burgess and Pande (2005). we study this response with quasi-experimental data in a developing country. 7 Their study used a relaxation of branching requirements in India that allowed for differential bank expansion across regions of India over twenty years in order to assess impacts on poverty headcount and wage data. Third, methodologically, our quasi-experimental analysis builds on an existing literature that has used out-of-sample prediction, and experiments in particular, to evaluate structural models (e.g. Lise et al. (2005a. 2005b ), Todd and Wolpin (2006) ). Finally, we contribute to the literature on measuring and interpreting treatment effects (e.g. Heckman, Urzua, and Vytlacil (2004 )), which has emphasized unobserved heterogeneity, non-linearity and time-varying impacts. We develop an explicit behavioral model where all three play a role.
The remainder of the paper is organized as follows. The next section discusses the underlying economic environment, the Million Baht village fund intervention, and reviews the facts from reduced form impact regressions that motivate the model. The model, and resulting value and policy functions, are presented in Section 3. Section 4 discusses the data and presents the MSM estimation procedure and resulting estimates. Section 5 simulates the Million Baht intervention, performs policy counterfactuals, and presents the welfare analysis. Section 6 concludes.
2 Thai Million Baht Credit Intervention
The intervention that we consider is the founding of village-level microcredit institutions by the Thai government, the Million Baht Fund program. Former Thai Prime Minister Thaksin Shinawatra implemented the program in Thailand in 2001, shortly after winning election. One million baht (about $24,000) was distributed to each of the 77,000 villages in Thailand to found self-sustaining village microfinance banks. Every village, whether poor or wealthy, urban 8 or rural was eligible to receive the funds. The size of the transfers alone, about $1.8 billion, amounts to about 1.5 percent of GDP in 2001. The program was overwhelmingly a credit intervention; no training or other social services were tied to the program, and although the program did increase the fraction of households with formal savings accounts, savings constituted a small fraction (averaging 14,000 baht or less than two percent) of available funds, and we measured no effect on the actual levels of formal savings during the years we study.
The design of the program was peculiar in that the money was a grant program to village funds (because no repayment was expected or made), yet the money reaches borrowers as microcredit loans with an obligation to repay to the fund. As noted earlier default rates to these funds themselves were low (less than 3 percent up through available 2005 data), and all village funds in the sample we use continue in operation, indicating that the borrowers obligation to repay was well understood in the rural villages we study. (In contrast, default rates to village funds in urban areas are substantially higher, roughly 15 percent.) Also, the quasi-experiment is quite different and less clean than typical randomizations, since the villagers themselves get to organize the funds, and in randomizations there is typically much greater control over what happens. Thus, one must be careful not to extrapolate our results across all environments and microfinance interventions. We are not evaluating a microfinance product via randomized trials.
The design and organization of the funds were intended to allow all existing villagers equal access to these loans through competitive application and loan evaluation handled at the village level. Villages elected committees who then drew up the rules for operation. These rules needed to satisfy government standards, however, and the village fund committees were relatively large (consisting of 9–15 members) and representative (e.g. half women, no more than one member per household) with short, two year terms. In order to obtain funds from the government, the committees wrote proposals to the government administrators outlining the proposed policies for the fund. 9 For these rural villages, funds were disbursed to and held at the Thai Bank of Agriculture and Agricultural Cooperatives, and funds could only be withdrawn with a withdrawal slip from the village fund committee. Residence in the village was the only official eligibility requirement for membership, and so although migrating villagers or newcomers would likely not receive loans, there was no official targeting of any sub-population within villages. Loans were uncollateralized, though most funds required guarantors. Repayment rates were quite high; less than three percent of funds lent to households in the first year of the program were 90 days behind by the end of the second year. Indeed, based on the household level data, ten percent more credit was given out in the second year than in the first, presumably partially reflecting repaid interest plus principal. There were no firm rules regarding the use of funds, but reasons for borrowing, ability to repay, and the need for funds were the three most common loan criteria used. Indeed many households were openly granted loans for consumption. The funds make short-term loans – the vast majority of lending is annual – with an average nominal interest rate of seven percent. This was about a five percent real interest rate in 2001, and about five percent above the average money market rate in Bangkok. 10
2.1 Quasi-Experimental Elements of the Program
As described in the introduction, the program design was beneficial for research in two ways. First, it arose from a quick election, after the Thai parliament was dissolved in November, 2000, and was rapidly implemented in 2001. None of the funds had been founded by our 2001 (May) survey date, but by our 2002 survey, each of our 64 villages had received and lent funds, lending 950,000 baht on average. 11 Households would not have anticipated the program in earlier years. We therefore model the program as a surprise. Second, the same amount was given to each village, regardless of the size, so villages with fewer households received more funding per household. Regressions below report a highly significant relationship between household’s credit from a village fund and inverse village size in 2002 after the program.
Our policy intervention is not a clean randomized experiment, and so we cannot have the same level of certainty about the exogeneity of the program. Several potential problems could contaminate the results. First, variables of interest for households in small villages could differ from those in large villages even before the program. Second, different trends in these variables across small and large villages would also be problematic, since the program occurs in the last years of the sample. If large villages had faster growth rates, we would see level differences at the end of the period and attribute these to the intervention during those years. Third, other policies or economic conditions during the same years could have affected households in small and large villages differentially. 12
Other issues and caveats arise from all of our variation coming at the village level. On the one hand, village-level variation has important benefits because, in many ways, each village is viewed as its own small economy. These village economies are open but not entirely integrated with one another and the rest of the broader economy (nearby provinces, regions, etc.) in terms of their labor, credit, and risk-sharing markets and institutions. This gives us confidence that program impacts are concentrated at the village level. 13 On the other hand, one could certainly envision potential risks involved with our use of village size. For example, even if credit itself were exogenous, its impact could differ in small and large villages. Small villages might be more closely connected, with better information or less corruption, and so might show larger impacts not only because they received more credit per household but because the credit was used more efficiently. Conversely, small villages might have smaller markets and so credit might have smaller impacts. Keeping this caveat in mind, our approach is to take a stand on a plausible structural model in Section 3. Within this structural model, village size will be fully excluded from all equations. So that when we introduce the policy in Section 4, the only role of village size will be in determining the expansion of credit. We are encouraged that the simple model does well in replicating the out-of-sample patterns in the data.
Despite the potential risks and caveats, there are both a priori and a posteriori reasons for pursuing our exclusion restriction and accepting inverse village size as exogenous with respect to important variables of interest.
First, villages are geopolitical units, and villages are divided and redistricted for administrative purposes. These decisions are fairly arbitrary and unpredictable, since the decision processes are driven by conflicting goals of multiple government agencies. (See, for example, Pugenier (2002) and Arghiros (2001) ), and splitting of villages is not uncommon. Data for the relevant period (1997–2003 or even the years directly preceding this, which might perhaps be more relevant) are unavailable, but growth data is available for 1960–2007 and for 2002–2007, so we know that the number of villages grew on average by almost one percent a year both between 1960 and 2007 and during the more recent period. Clearly, overall trends in new village creation are driven in part by population growth, but the above literature indicates that the patterns of this creation are somewhat arbitrary.
Second, because inverse village size is the variable of interest, the most important variation comes from a comparison among small villages (e.g. between 50 and 250 households). Indeed, the companion paper focuses its baseline estimates on these villages, but show that results are robust to including the whole sample. That is, the analysis is not based on comparing urban areas with rural areas, and we are not picking up the effects of other policies biased toward rural areas and against Bangkok.
Third, village size is neither spatially autocorrelated, nor correlated with underlying geographic features like roads or rivers, which might arise if village size were larger near population centers or fertile areas. Using data from Community Development Department (CDD), Figure 1 shows the random geographical distribution of villages by decile of village size in the year 2001 over the four provinces for which we have Townsend Thai data (Chachoengsao, Lopburi, Buriram and Sisaket). The Moran spatial autocorrelation statistics in these provinces are 0.019 (standard error of 0.013), 0.001 (0.014), 0.002 (0.003), and 0.016 (0.003), respectively. 14 Only the Sisaket autocorrelation is statistically significant, and the magnitudes of all of them are quite small. For comparison, the spatial autocorrelation of the daily wage in villages ranges from 0.12 to 0.21. We also checked whether village size was correlated to other underlying geographic features by running separate regressions of village size onto distance to nearest two-lane road or river (conditioning on changwat dummies). The estimated coefficients were 0.26 (standard error of 0.32) and −0.25 (0.24), so neither was statistically significant. Small villages did tend to be located closer to forest areas however, where the coefficient of 0.35 (0.03) was highly significant, indicating that forest area may limit the size of villages. 15 Nonetheless, these regressions explain at most five percent of the variation in village size, so the variation is not particularly well explained by geographic features. We have included roads, rivers, and forest in Figure 1 .
Number of Households per Villages, Four Provinces, ThailandSource: www.ncbi.nlm.nih.gov
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