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Expanding Microfinance in Brazil: Credit Utilisation and Performance of Small Firms

2013; Taylor & Francis; Volume: 49; Issue: 9 Linguagem: Inglês

10.1080/00220388.2013.790961

1743-9140

Emmanuel Skoufias, Phillippe Leite, Renata Narita,

ICT Impact and Policies

Abstract We take advantage of the natural experiment generated by the exogenous change in government policy towards microcredit to evaluate the impact of the increased supply of microcredit on the utilisation of credit by micro-entrepreneurs. Based on micro-entrepreneurs' survey and administrative data from a microcredit programme in Brazil, we show that: the increased supply of microcredit raised formal credit utilisation and this does not crowd out the use of informal credit sources; formal credit taking improves business performance; and returns are larger for women- than for men-owned firms, but males employ significantly more workers after taking formal credit than females. JEL Classification: O16G21L25 Acknowledgements The authors are grateful to the CrediAmigo administration (Stelio, Iracema and Marcelo) that made possible this analysis, and to Susana Sanchez, Pedro Olinto, Edinaldo Tebaldi, Alinne Veiga, Francisco Haimovich, and two anonymous referees for valuable comments and inputs throughout the long gestation of this project. The findings, interpretations, and conclusions in this article are entirely those of the authors and they do not necessarily reflect the view of the World Bank. Notes An Online Appendix is available for this article which can be accessed via the online version of this journal available at http://dx.doi.org/10.1080/00220388.2013.7907961 1. In 1998, the per capita GDP in 2000 prices for the north-eastern states was from 1.51 to 3.65 thousand dollars, whereas for the relatively richer south-eastern states the figure was about three times as much or more: 5.89 to 10.35 thousand dollars. Source: IPEADATA, IPEA, Brazil. 2. In fact, the PME contains information on the labour incomes of the self employed and employers. Neri (Citation2008) uses these data as a measure of profit. 3. The reference month is October. It is also important to note that the ECINF surveys took place during the same calendar months in 1997 and 2003, thus minimising problems due to seasonal variation in the need for credit. 4. Enumeration areas (EA) were selected using a probabilistic sample proportional to size on the basis of the urban census enumeration areas. Each EA contains on average 300 households. 5. Actually each state was segmented into three strata: the municipalities of the capital city; urban areas of the municipalities of the metropolitan area; and urban areas of the remaining municipalities of the state. 6. These key design features included solidarity group lending, targeting the informal sector, charging interest rates high enough to provide a return on assets, and to permit financial sustainability with gradually increased loans, amortising loans regularly, offering incentives for regular repayment through discounts on the last instalment, and penalising borrowers if repayment falls behind schedule. 7. This rate only applies to loans under 1,000 Reais. Clients are also required to pay a few other fees when obtaining a loan. 8. Loan overdue rates were initially 4.2 per cent in the first year of the programme, but by writing off bad loans and modifying the performance-based incentive scheme for staff, by 2003 only 1.8 per cent of programme loans were overdue more than one day (Banco do Nordeste, Citation2003). 9. The fraction of credit that is not from CrediAmigo can be interpreted as measurement error in the dependent variable. Therefore, coefficients remain unbiased. This only decreases efficiency. Yet, our coefficients are significant and most at 1 or 5 per cent level, as we will show later. 10. We use variable V4331, which equals three for occasional or five for frequent access to credit. 11. The emphasis on the primary source of credit implies that we cannot distinguish whether micro-entrepreneurs used more than one source of credit. 12. We use variable V4332, which equals one if credit is from friends or relatives, two if from private or public banks, three if from suppliers, and four or five if from other firms, people or other sources. 13. For example, Neri (Citation2008) reports difference-in-differences estimates of the increased access to credit through CrediAmigo using the same surveys by simply comparing the utilisation of credit in the nine states comprising the north-east region of Brazil with all other states outside the north-east region. 14. The ECINF surveys can be obtained directly from IBGE (http://http://loja.ibge.gov.br). Please note that the municipality codes cannot be made available due to a confidentiality agreement. 15. Table A1 shows means by treatment status. As the simple t-test suggests, there are significant differences between treatment and control groups for most control variables in 1997. Thus, these factors need to be accounted for. In any case, later we also present estimates of the treatment effects with and without the control variables. 16. Treatment in this case is 'exposure to credit' from CrediAmigo. Because exposure is involuntary, βTR provides the intent-to-treat rather than the treatment-on-treated effect. 17. As a means of testing the sensitivity of the results, we have also estimated Equation (1) using the treatment variable T=1 if the micro-enterprise is located in any of the nine states (instead of the specific municipality) in the north-east region and in the Minas Gerais and Espirito Santo states in the south-east region, and equal to zero otherwise. In this case the comparison group consists of all other states not covered by CrediAmigo. The DD estimates in this case were slightly lower overall (for example, the utilisation of formal credit as a primary source increased by 1.3% instead of 1.5%). 18. Given the sampling design of the ECINF surveys, not every municipality sampled in the 1997 ECINF necessarily appears in the 2003 survey. However, when we use the DD estimator we do not have to limit our analysis to the set of municipalities that appears in both survey rounds. We run the DD regressions using the sampling weights in each year to account for the stratification procedure explained in Note 5. 19. Given that very similar estimates were obtained using a probit and logit model we chose to report the LPM estimates, since the coefficients of the LPM provide direct estimates of the marginal effect on the probability of utilising credit. 20. The full set of estimates of Equation (1) based on Samples A and B are presented in Tables A2 and A3. In particular, these show the bias correction by including the covariates. In the full sample, it accounts for the treatment group being worse than the control group in terms of human capital and business capacity in 1997, thus with more need of capital. In the north-east sample, it accounts for the treatment group being relatively better in 1997. 21. Specifically, we cluster our standard errors by enumeration area in each municipality and each year of the survey. As pointed out by Bertrand, Duflo,& Mullainathan (Citation2004), the DD estimator has potentially serious limitations, especially if there is serial correlation in the error term of the regression. We have also investigated the sensitivity of our results, taking into account the potential for serial correlation within municipalities (and states). We found that the correction for serial correlation did not have any substantial change in the results reported here. 22. The CrediAmigo programme focused on established entrepreneurs who are found among likely vulnerable groups, such as the self employed and small businesses, in which women tend to be overrepresented. 23. Revenues are defined as the sum of the value of sales of own product, resale of merchandise, provision of services and other receipts. Expenditures are the sum of expenditures on inventories (primary material and items for resale), labour costs, contributions to social security (INSS), severance pay (FGTS), electricity, water, telephone, rental, machines, equipment, vehicles, gasoline, repair and maintenance, taxes, financial and other expenditures. 24. The inclusion of a set of binary variables for municipality implies that micro-entrepreneurs with credit in any given municipality are matched with micro-entrepreneurs without credit in the same municipality.