Microfinance: Issue 3

Indirect effects of microcredit on other firms

While the impacts of microcredit on borrowers are well understood, few studies have tackled the indirect effects on other actors, such as peer firms through business stealing, information diffusion, and other channels, as well as on consumers through improvements in quality and price. Such indirect effects can be important for welfare and redistribution. Earlier evidence on the distributional effects of microcredit has not found evidence of business stealing or other such spillovers on small family businesses (Banerjee et al. 2015c, Angelucci et al. 2015, Banerjee et al. 2019).[1]1.A different form of spillover effect is, however, shown in Banerjee et al. (2024) who show that access to microfinance appears to weaken social networks in two settings in India, resulting in reduced access to informal insurance for those unlikely to borrow. See also Cai (2021) for the impacts of microcredit on risk sharing.

In recent work, Cai and Szeidl (2024) find much larger indirect effects of bigger sized loans on small and medium enterprises in China. In a field experiment with 3,173 retail and service firms organised in 78 local markets pre-defined by the local government, the authors provide randomised access to a new loan product on both market and firm level. This design enables them to measure direct effects of borrowing by exploiting within-market variation in a firm’s treatment status, and indirect effects by exploiting across-market variation in the share of the firm’s peers that were treated. The findings show that improved access to finance had a large positive direct effect on the performance of treated firms, but a similar-sized negative indirect effect on that of firms with treated competitors, leading to non-detectable gains in producer surplus. However, access to finance had a positive direct effect on business quality and consumer satisfaction, and a negative effect on price, which were not offset by indirect effects, implying net gains in consumer surplus. A key implication of their results is that accounting for potentially numerous indirect effects can be essential for evaluating firm policies. For example, in their setting, only accounting for the direct and indirect effects on firms while ignoring the effect on consumers would imply smaller and insignificant gains. A second implication is that interventions such as loan programmes can have large distributional effects, creating both winners and losers. This raises the policy question of whether the losers need to be compensated in some way.  

The general equilibrium effects of microfinance

In addition to the indirect effects on peer firms and consumers, microcredit programmes can also generate general equilibrium (GE) effects on society. There are at least three channels through which microcredit programmes might have multiplier effects (Breza and Kinnan 2021). First, if the impacts of microcredit on business outcomes grow over time (Banerjee et al. 2019), microcredit may stimulate firm investment and demand for labour. This may further lead to reductions in savings and higher interest rates, affecting the entry of new firms and the aggregate capital stock, and placing upward pressure on wages. Second, microcredit may increase aggregate demand because many borrowers use microcredit as a consumption loan (Kaboski and Townsend 2012, Tarozzi et al. 2015). Third, microcredit access may cause households to reduce precautionary savings and increase consumption (Kaboski and Townsend 2011). Estimating the GE effects of microcredit can generate important implications for policymakers regarding microcredit provision and targeting. 

Several papers use quasi-experiments and RCTs to measure the GE effects of microcredit. For example, Kaboski and Townsend (2012) find that Thailand’s ‘Million Baht Village Fund’ programme, which injected more than US$ 25,000 into villages for lending, has large impacts on consumption and wages. Fink et al. (2020) carry out a study in Zambia and show that access to lean-season credit increased consumption and village-level wages. Similarly, as noted earlier, Burke et al. (2019) show that providing access to credit to farmers in Kenya during harvest time affects local prices through helping farmers delay grain sales. In a recent working paper, Breza and Kinnan (2021) study a major lending shock in India: the Andhra Pradesh crisis, during which more than US$ 1 billion in credit was wiped out. To measure the causal impacts of credit reduction, they take advantage of the variation in the balance sheet exposure of each lender to loans in the affected state before the crisis. They find that the crisis did impact other districts of India through its effect on the balance sheets of lenders. In areas exposed, a majority of microcredit disappeared. The large negative credit shock significantly decreased daily wages, household wage earnings, and consumption. 

Using RCTs to identify GE effects is challenging because it requires large-scale credit shocks at the level of entire markets. Moreover, it is hard to evaluate the macroeconomic effects of economy-wide microcredit using existing data. Buera et al. (2020) study the short-run and long-run aggregate impacts of microcredit using a model of entrepreneurship and financial frictions. The model is disciplined and validated using two micro evaluations of microcredit programmes (Kaboski and Townsend 2012, Banerjee et al. 2015c). The authors then use the model to simulate and quantify microcredit impacts on several key macroeconomic measures of development, including output, capital, TFP, wages, and interest rates. They find that the general equilibrium effects differ substantially from the partial equilibrium impacts. In partial equilibrium, microcredit increases income and capital because it allows more people to invest, but it lowers TFP because of the entry of low productivity entrepreneurs. In general equilibrium, both wages and interest rates increase in the short run because of the rising demand for capital driven by microcredit. In the long run, the provision of microcredit lowers saving and the interest rate rises. This together with higher wages lead to only a small increase in the number of entrepreneurs. However, the average quality of entrepreneurs and the efficiency of capital allocation both improve. Consequently, the higher capital and lower TFP offset each other in the longer term, leading to a negligible impact of microcredit on output. Although the long-run GE effect is small, the vast majority of the population does benefit from microcredit, and the welfare gain is larger for the poor and marginal entrepreneurs.[2]2.Bruhn and Love's (2014) study on a large-scale expansion of financial services in Mexico provides further insights using a quasi-experimental approach of the labour market effects of increased access to finance. Bruhn and Love examine the impact of rapidly increasing financial access across previously underserved areas. Results indicate that this expansion supported the survival and growth of informal businesses and contributed to modest employment gains, particularly for informal business owners who could continue operating instead of transitioning to wage work or unemployment. As the authors point out, they are not able to establish whether the observed positive effects are driven by the direct impact of the new bank's services or by indirect effects from increased competition in the local financial sector, as the expansion may have influenced clients of other nearby financial institutions as well.

References

Angelucci, M, D Karlan and J Zinman (2015), “Microcredit Impacts: Evidence from a Randomized Microcredit Program Placement Experiment by Compartamos Banco”, American Economic Journal: Applied Economics 7(1): 151–182.

Banerjee, A, E Breza, E Duflo and C Kinnan (2019), “Can Microfinance Unlock a Poverty Trap for Some Entrepreneurs?”, National Bureau of Economic Research Working Paper No. 26346.

Banerjee, A, E Duflo, R Glennerster and C Kinnan (2015c), “The Miracle of Microfinance? Evidence from a Randomized Evaluation”, American Economic Journal: Applied Economics 7(1): 22–53.

Banerjee, A, E Breza, A G Chandrasekhar, E Duflo, M O Jackson and C Kinnan (2024), “Changes in Social Network Structure in Response to Exposure to Formal Credit Markets” The Review of Economic Studies 91(3): 1331–1372.

Breza, E and C Kinnan (2021), “Measuring the Equilibrium Impacts of Credit: Evidence from the Indian Microfinance Crisis”, The Quarterly Journal of Economics 136(3): 1447–1497.

Bruhn, M and I Love (2014), “The real impact of improved access to finance: Evidence from Mexico” The Journal of Finance 69(3): 1347-1376.

Buera, J F, J P Kaboski and Y Shin (2020), “The Macroeconomics of Microfinance”, The Review of Economic Studies.

Burke, M, L F Bergquist and E Miguel (2019), “Sell Low and Buy High: Arbitrage and Local Price Effects in Kenyan Markets”, The Quarterly Journal of Economics 134(2): 785–842.

Cai, S (2021), “The Impacts of Microcredit on Informal Risk Sharing: Experimental Evidence from China.” Available at SSRN 3859868.

Cai, J and Adam Szeidl (2024), “Indirect Effects of Access to Finance”, American Economic Review, 114(8): 2308–51.

Fink, G, B K Jack and F Masiye (2020), “Seasonal Liquidity, Rural Labor Markets, and Agricultural Production”, American Economic Review 110(11): 3351–92.

Kaboski, J P and R M Townsend (2011), “A structural evaluation of a large‐scale quasi‐experimental microfinance initiative”, Econometrica 79(5): 1357–1406.

Kaboski, J P and R M Townsend (2012), “The Impact of Credit on Village Economies”, American Economic Journal: Applied Economics 4(2): 98–133.

Tarozzi, A, J Desai and K Johnson (2015), “The Impacts of Microcredit: Evidence from Ethiopia”, American Economic Journal: Applied Economics 7(1): 54–89.