The effects of credit subsidies on development

Abstract

Under credit market imperfections, the marginal product of capital may not be equalized, resulting in misallocation and lower output. Preferential interest rate policies are often used to remedy the problem. This paper constructs a general equilibrium model with heterogeneous agents, imperfect enforcement and costly intermediation. Occupational choice and firm size are determined endogenously by an agent’s type (ability and net wealth) and credit market frictions. The credit program subsidizes the interest rate on loans and requires a fixed application cost, which might be null. We find that the credit subsidy policy has no significant effect on output, but it may have negative effects on wages. The program is largely a transfer from households to a small group of entrepreneurs with minor aggregate effects. We also provide estimates of the effects of reducing the frictions directly. When comparing differences in US output per capita in a baseline case to simulations with counterfactually high frictions, intermediation costs and enforcement explain about 20–25 % of the output gap. We include a transition analysis.

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Notes

  1. 1.

    Underinvestment can also occur due to political economy reasons since some agents might have a vested interest on credit market imperfections. See, for instance, Alexopoulos and Cavalcanti (2010) and Rajan and Zingales (2003b).

  2. 2.

    In a related article, Armendariz de Aghion (1999) develops a model of a decentralized banking system in which banks are shown to both underinvest in, and under transmit, expertise in long-term industrial finance. Stiglitz (1994) and Armendariz de Aghion and Morduch (2005) discuss the foundation of government interventions in financial markets, including credit subsidies.

  3. 3.

    Our models also differ regarding how we model financial frictions. Besides the intermediation costs, we have an enforcement constraint that the subsidized loan program affects by decreasing loan interest rates. We also have a corporate sector, as in Quadrini (2000) and Wynne (2005), where the credit market frictions may not bind. This is important since large corporations account for a significant fraction of output and do not face the same credit frictions as small entrepreneurs.

  4. 4.

    See Caselli and Gennaioli (2008), Rajan and Zingales (2003), Rajan and Zingales (2003b), among others to understand why some of these reforms are not implemented.

  5. 5.

    We also consider the case where the government finances subsidies by lump-sum taxes on households. Output and wages increase, but the wage minus the lump-sum tax decreases.

  6. 6.

    Since the marginal products of capital are not equal in equilibrium, the only way to improve allocations is to transfer income from entrepreneurs with a relatively low marginal product of capital to those with a high marginal product. However, there is no market mechanism to implement such a policy, which must be incentive compatible.

  7. 7.

    The exercises show the size of inefficiencies generated by the counterfactual financial policy.

  8. 8.

    See Antunes et al. (2013) for a model in which \(\eta \) arises endogenously due to an explicit financial intermediation technology that depends on capital and labor.

  9. 9.

    In an equivalent environment, we could also assume an oligopolistic banking sector in which banks compete à la Bertrand, where \(\eta \) is the marginal cost of financial intermediation.

  10. 10.

    See Koeppl et al. (2014) for a discussion of efficient contract enforcement and Peiris and Vardoulakis (2013) on default by intermediaries.

  11. 11.

    We set \(\tau ^k=0\) because the goal of the program is to expand access to capital, and this is consistent with a credit program we will analyze. As a consequence, our results provide a lower bound on the distortionary effects of this credit policy. We also analyze the case where the program is financed through a lump-sum tax.

  12. 12.

    The only role for \(g\) is to balance the budget constraint in the baseline economy. Given the value for \(\tau ^w\), consistent with some data statistics, \(g\) is chosen such that the government budget constraint in the baseline economy is in equilibrium. We vary the credit interest policy, and then adjust \(\tau ^w\) to balance the government budget constraint, keeping the value of \(g\) at its baseline level.

  13. 13.

    Using indicators of political connections constructed from campaign contribution data, Claessens et al. (2008) show that firms that provide contributions to (elected) officials experience higher stock returns. They also find that firms that contribute more have lower economic performance and interpret the contributions as a firm survival strategy. Since we abstract from political connection, our results may understate the effects of credit subsidies on development.

  14. 14.

    The results are similar when \(\zeta \) is a pure deadweight loss.

  15. 15.

    Results are not very different when \(J=1\) in the model, as in Galor and Zeira (1993). This is because we re-calibrate the parameters of the model to match the same statistics of the baseline economy. In general, financial frictions have a strong effect on output when \(J=1\), and if we were to change \(J\) but fix other parameters at the baseline, the results would differ.

  16. 16.

    This is consistent with Gollin (2002).

  17. 17.

    The estimated value of the capital-to-output ratio ranges from 2.5 (see Maddison 1995) to 3 (see Cagetti and Nardi 2009). Using the Heston et al. (2012), Penn World Tables 7.1 and the inventory method, we construct the capital-to-output ratio for the USA and estimate it to be 2.55. The value for \(\beta \) is 0.9225. Since the model period is 5 years, this implies that agents discount the future at a rate of about 1.63 % per year.

  18. 18.

    As is well known, labor income shocks can be added to increase the income and wealth Gini indexes, but they increase the complexity of the model without adding any new insights.

  19. 19.

    When \(\zeta =0\) the largest effect is at \(\tau ^c=3.927\,\%\) per year: output per capita increases 2.29 %.

  20. 20.

    In US data, entrepreneurs are 7.5 % of the labor force. In the experiment the share of entrepreneurs increases only slightly with credit subsidies: in the baseline with no fixed costs, it goes from 7.5 to 7.93 % when credit subsidy \(\tau ^c\) changes from 0 to 3.927 % per year. See panel (a) in Table 4 in the appendix.

  21. 21.

    Transitions for other experiments reported in Fig. 2 follow a similar qualitative path. For the sake of space we do not report them, but they are available upon request.

  22. 22.

    The intuition is that with the introduction of the program there is a demand for subsidized loans and the tax rate must increase to balance the budget, which decreases labor demand. However, there is more investment and capital accumulation, and the marginal product of labor increases.

  23. 23.

    Observe that the long-run interest rate decreases with credit subsidies. Although the demand effect pushes interest rates up, more production and capital accumulation decreases the marginal productivity of capital and therefore decreases the interest rate. In addition, the payroll tax rate increases significantly, and this decreases the demand for capital and production.

  24. 24.

    The share of entrepreneurs in the labor force increases from 7.5 to 7.79 %.

  25. 25.

    We are not arguing that other parameters in Brazil are the same as those observed in the USA. The goal is to isolate the effects of intermediation costs, enforcement and credit subsidies in a pure counterfactual exercise. In a technical note, we calibrate all parameters to match the Brazilian economy, see http://sites.google.com/site/tiagovcavalcanti/research-1. We consider several cases, including when there is a cap on the interest rate that financial intermediaries can charge on subsidized loans. The effects of credit subsidies on development are robust to different calibrations of the model and are consistent with the findings reported here.

  26. 26.

    The interest margin in Brazil reported by Beck et al. (2009) is about 14 %. However, the net interest margin also contains loan loss provisions and after tax bank profits, which are not explicitly modeled here.

  27. 27.

    We implicitly assume that the relationship between the index and the parameter is linear, at least locally. This is an approximation, and we know that the polar cases coincide.

  28. 28.

    We use the 2010 rule of law index, which varies from \(-\)2.5 to 2.5, normalized to a 0–10 interval. Higher scores indicate that agents have higher confidence in the rules of society.

  29. 29.

    In some credit programs borrowers can apply directly to BNDES, but the majority of loans are through commercial and regional development banks. See Ribeiro and DeNegri (2010) and Ottaviano and de Sousa (2008), for more details about how BNDES operates and its credit lines.

  30. 30.

    BNDES loans have a longer term than other types of credit, but require large collateral. The loan maturity for firms in general is within 60 months, the time period of our model economy.

  31. 31.

    BNDES also finances the corporate sector (see Torres-Filho 2009) where the marginal product of capital is lower than that for some credit constrained entrepreneurs. In our model, all credit subsidies go to entrepreneurs, and therefore, our results are an upper bound of the effects of credit subsidies in Brazil on output.

  32. 32.

    Available at http://www.bmfbovespa.com.br/.

  33. 33.

    Using the Heston et al. (2012) Penn World Tables 7.1 and the inventory method, we find a value of 2.2 for the Brazilian economy.

  34. 34.

    The equilibrium real interest rate is smaller than the one observed in the US since the financial market is more repressed in Brazil, which decreases the demand for loans. This is a risk-free rate.

  35. 35.

    Output per capita is taken from Heston et al. (2012) and corresponds to the average value from 2008 to 2010 of the series “PPP Converted GDP Per Capita (Chain Series), at 2005 constant prices”. For the wage rate, we use the 2010 hourly compensation costs in manufacturing provided by the US Bureau of Labor Statistics (BLS). See: http://www.bls.gov/news.release/pdf/ichcc.pdf.

  36. 36.

    The wage rate in the model with the de facto \(\phi \) is equal to 77 % of the US wage rate. In the data, wages in manufacturing in Brazil are about 30 % of the US level.

  37. 37.

    Output increases by 14 and 16 % points, while the wage rate increases by 14 and 15 % points, depending on which measure of enforcement is used.

  38. 38.

    Using manufacturing industry data, Lee (1996) shows that cheap credit programs had no significant effect on capital accumulation or TFP in Korea. Using firm-level data, Ribeiro and DeNegri’s (2009) estimates suggest that BNDES cheap credit had limited effects on TFP growth in Brazil. Using value added per worker, Ottaviano and de Sousa (2008) find that BNDES loans increase productivity only for large projects but not for small loans and the aggregate effect is not statistically different from zero. Lazzarini and Musacchio (2011) find a significant effect of BNDES minority equity stakes on firm performance (ROA), which they attribute to weaker capital constraints for publicly traded companies when the development bank is a shareholder.

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Correspondence to Tiago Cavalcanti.

Additional information

We thank Breno Albuquerque, Francesco Caselli, Mário Centeno, Fernando de Holanda Barbosa Filho, Giammario Impullitti, Marcelo Mello, Marcelo dos Santos, André Silva, Arilton Teixeira, and Pedro Teles for helpful comments and suggestions. We have also benefited from comments by audiences at the EPGE/CAEN Meeting, Lisbon Meeting on Institutions and Political Economy, LuBraMacro meeting, REAP Meeting, Rice University, SAET Meeting, INSPER-SP, PIMES/UFPE, PUC-RJ, Thema-Cergy, University of Cambridge, University of Illinois, and the Workshop on Advances in Economic Growth at University of St. Andrews. We thank for financial support, INOVA and Fundação para a Ciência e Tecnologia, grant PTDC/EGE-ECO/108858/2008, and Keynes Fund from the University of Cambridge.

Appendix

Appendix

See Tables 4,5,6.

Table 4 Policy experiments:long run credit subsidy effect; endogenous interest rate and payroll tax rate
Table 5 Policy experiments: long run credit subsidy effect; exogenous interest rate and payroll tax rate
Table 6 Policy experiments: long run credit subsidy effect; endogenous interest rate and lump-sum tax

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Antunes, A., Cavalcanti, T. & Villamil, A. The effects of credit subsidies on development. Econ Theory 58, 1–30 (2015). https://doi.org/10.1007/s00199-014-0808-0

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Keywords

  • Financial frictions
  • Credit subsidy
  • Entrepreneurship

JEL Classification

  • E60
  • G38
  • O11