Taking Stock of the Evidence on Microfinancial Interventions

Abstract

We review the empirical evidence on microfinance and asset grants to the ultra poor or microentrepreneurs and use quantitative economic theory to account for this evidence. Properly executed, these interventions can help segments of the population increase their income and consumption, but neither literature gives much reason to believe that such interventions can lead to wide-scale, transformative impacts akin to escaping aggregate poverty traps.

This article is based on the authors' book chapter of the same name (Buera, Kaboski, and Shin, 2019) in The Economics of Poverty Traps.

Introduction

Microfinancial interventions are often designed as responses to poverty traps, where the poor cannot invest because they lack wealth and without investment poverty persists. The past decade of empirical development research has produced a host of highly insightful, well-­­identified evaluations of the impacts of microfinancial interventions. These interventions include microcredit programs; asset grants to microentrepreneurs; and small asset transfers to the very poor, regardless of their entrepreneurial status. The aim of this article is to take stock of the state of our knowledge.

We approach the topic in two steps. First, we review the findings to crystallize the salient patterns. Second, and of equal importance, we assess our understanding of these empirical patterns through the lens of economic theory. Reflecting on the policy lessons of the East Asian miracles, Robert E. Lucas, Jr. once observed, 

If we understand the process of economic growth—or of anything else—we ought to be capable of demonstrating this knowledge by creating it in these pen and paper (and computer-equipped) laboratories of ours. If we know what an economic miracle is, we ought to be able to make one. (Lucas, 1993, p. 271) 

The same is true for poverty traps and financial interventions. If we truly understand why an intervention works, we ought to be able to recreate the empirical patterns in our theories. Such an understanding is necessary to design our policy interventions, apply them with confidence in new contexts, and make projections for larger-scale programs that will have macroeconomic consequences.

Toward the first step, this article reviews the lessons from the empirical literature on microfinancial interventions. At least three general lessons arise consistently. First, no policies produce large-scale miracle escapes from poverty traps. That is, although some of the policies have led to sustained gains, none has been shown to lead to permanent increases in income or consumption well beyond poverty levels or to extended and sizable increases in the rate of growth of income, consumption, and capital that predict such escapes. Second, take-up rates for microcredit are typically low, while those of asset transfer programs are understandably much higher. Third, heterogeneous responses to policies are evident in almost all studies, where impacts vary by initial wealth, size of the intervention, gender, ability, entrepreneurial status, financial access, and time frame. Variation in measurement and context (e.g., rural vs. urban settings and the degree of preexisting financial development) may also play a role.

The most interesting patterns emerge from a comparison across interventions. Although individual-level microcredit interventions can lead to increases in credit, entrepreneurial activity, and investments, they have been much less successful in leading to higher incomes or consumption. Among these interventions, only larger-scale village fund programs are shown to raise income and possibly consumption. Microcredit interventions often show relatively larger impacts on existing and marginal entrepreneurs. Small asset grants of less than $200—at purchasing power parity (PPP)—to entrepreneurs often lead to stronger increases in capital and profits, with typically high returns on assets. Grants to "ultra poor" households often have led to changes in income-generating activities, higher asset levels and capital, and increases in consumption of up to 30 percent.

Natural questions are, what leads to such very different outcomes, and what do these outcomes say about the relevant economic mechanisms at play? Even to replicate the outcomes of these different policies in varying contexts, we need an understanding of these mechanisms. Lucas (1993, p. 252) is again much more eloquent: 

[S]imply advising a society to "follow the Korean model" is a little like advising an aspiring basketball player to "follow the Michael Jordan model." To make use of someone else's successful performance at any task, one needs to be able to break this performance down into its component parts so that one can see what each part contributes to the whole, which aspects of this performance are imitable and, of these, which are worth imitating. One needs, in short, a theory. 

A purely qualitative theory is useful in terms of organizing ideas and checking the internal consistency of one's reasoning, but we also want to know how well such a theory can quantitatively explain our observations, which is undoubtedly a higher hurdle.

Toward the second step, we review existing quantitative theory on financially constrained entrepreneurial decisions. A representative model in this literature incorporates much of what seems a priori essential in the economics involved: ex ante heterogeneity in wealth and ability, entrepreneurial decisions on both the extensive (entry) and intensive margins (scale), stochastic shocks, "necessity" entrepreneurs, and financial constraints that interact with wealth and ability. The combination of heterogeneity, intensive margins, and stochastic shocks provide enough smoothness and mixing so that poverty traps at the individual level (where investment decisions and asset and income paths depend critically on initial wealth levels) become irrelevant at the economy level (where a unique stationary equilibrium exists). Using this model, we simulate analogs of microcredit programs and asset grants targeted toward the poor and small entrepreneurs. Within our microcredit programs, we further vary the interest rates faced by borrowers. Some of these simulations reproduce results from our earlier work (Buera, Kaboski, and Shin, 2012, 2014), while others are unique to this article.

We show that the model captures many of the qualitative and quantitative patterns observed empirically in the interventions, but we also learn lessons from where it fails. For asset grants, the model shows that marginal entrepreneurs enter and that capital, income, and consumption increase, while assets tend to decline over time. However, the model does not generate the large increases in income, and we conjecture that the model fails to account for increases in labor supply in certain economic situations (e.g., where the market labor is limited for women). Moreover, the training components of such interventions might increase the effective ability of livestock entrepreneurs, or the real-world projects may somehow target people with higher ability (i.e., marginal entrepreneurs). Indeed, we show that marginal products of capital for poor existing entrepreneurs are quite high in the model. For microcredit, the simulations capture low take-up rates, borrowing, and impacts concentrated in the higher end of the ability distribution, and small increases in entrepreneurship mostly due to the entry of marginal entrepreneurs. The baseline model somewhat overpredicts the increases in investment. However, with realistically higher interest rates on microloans, the model limits microcredit along the extensive and intensive margins and dampens the impacts of microcredit.

However, the simulations illuminate some long-run and general-equilibrium implications: First, microfinancial interventions can have substantial steady-state and transitional impacts on development measures (income, consumption, productivity, etc.). But the simulations show no escape from aggregate poverty traps that operate through wealth distributions and general-equilibrium effects, since these traps do not exist. In this sense, we are unable to "make a miracle." Second, the simulations show that one-time redistributions in the form of asset grants alone tend to have only short-run aggregate and distributional impacts, as infused assets are eventually depleted over time. In contrast, microcredit—at least subsidized low-interest microcredit—has potentially longer-run impacts because of its permanent availability and general-equilibrium impact through wages. The cost effectiveness of smaller but sustained subsidies to microcredit versus one-time asset grants is therefore of interest. This result also suggests the importance of proper targeting and technical training for asset grant programs to have persistent effects.