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This paper is part of a research project on macroeconomic policy in low-income countries supported by U.K.’s Department for International Development (DFID). This paper should not be reported as representing the views of DFID. We thank A. Banerjee, A. Auclert, A. Berg, F. Buera, S. Claessens, W. W. Dou, D. Marston, C. Pattillo, R. Portillo, A. Simsek, I. Werning, H. Zhang, and participants in the IMF Workshop on Macroeconomic Policy and Inequality and the MIT Macro and Development Seminar for helpful comments.
This problem is more acute for smaller firms and for those in the informal sector. For instance, in developing economies, 35 percent of small firms report that access to finance is a major obstacle to their operations, compared with 25 percent of large firms in developing economies and 8 percent of large firms in advanced economies (World Bank, 2014). In this paper, we focus primarily on formal sector firms.
The successor of an agent can be interpreted as the reincarnation of the original agent with potentially new talent.
The value of ω affects the amount of wealth transferred from the current period to the next period. Therefore, ceteris paribus a higher ω implies that the economy would have a higher level of wealth.
The shock to talent is interpreted as changes in market conditions that affect the profitability of individual skills as in Buera et al. (2011).
To simplify the computation, we do not explicitly track which firms hire which workers in our numerical simulation. All agents receive the full wage income ω with probability p, and receive nothing with probability 1 − p. Since agents are risk-neutral in our model, they only care about the expected wage income, which is (1 − p)w, when making occupational choice decisions.
Note that the diminishing returns to scale property implies that there exists an unconstrained level of capital
Implicitly assumed here is that entrepreneurs would not decline the repayment of the loan if they have sufficient funds because the bank monitors and seizes the face value of the loan when default happens.
This argument is trivial, since entrepreneurs would borrow to produce only if they can make profits. Therefore, when production succeeds the gross output should be at least higher than the capital input. On the other hand, if the entrepreneur defaults, the bank will monitor output and seize the face value of the loan. Thus, the entrepreneur has no incentive to default.
The threshold between low and high leverage ratio is derived by considering whether the value of interest-bearing collateral plus the recovered working capital is sufficient to repay the face value of the loan. In particular, as we discuss later, the loan contract is highly leveraged if η(1 − δ)Φ + (1 + rd)Δ < Ω.
Note that there might exist multiple optimal contracts for wealthy entrepreneurs, since they do not demand much credit. But all these contracts would result in an identical net outcome for both entrepreneurs and banks. The optimal contract we focus here is the one with the lowest leverage ratio, i.e., with all wealth b being posted as collateral.
Note according to (3.7), the inverse of leverage ratio is defined as
Note that we also use the same wage and interest rate while plotting the occupation choice map for the credit regime. This is to highlight the partial equilibrium result of moving an agent from the savings regime to the credit regime. When financial inclusion allows more agents to get credit, the wage and interest rate would also change in general equilibrium.
The selection of the countries is mainly driven by data availability. First and foremost, we need sufficient cross-section units to run our framework. The numbers of cross section of firms in our sample are 563 for Uganda, 781 for Kenya, 599 for Mozambique, 1115 for Malaysia, 1326 for Philippines, and 996 for Egypt. Second, we consider relatively recent cases but exclude countries with financial turbulence around the year of the survey.
In a more technical version of this paper, we prove that if η is set larger than
In our model, parameter λ refers to the maximum leverage ratio, or the minimum value of collateral held as a percentage of the loan. We calibrate this parameter from the average instead of the minimum value of collateral as a percentage of the loan, because some firms in the data post no collateral while borrowing. Calibration based on the minimum value would imply λ = +∞, which is not reasonable.
Many developing countries have conducted such kind of policies. For example, after a bank nationalization in 1969, the Indian government launched an ambitious social banking program which sought to improve the access of the rural poor to formal credit and saving opportunities (Burgess and Pande, 2005).
Note that it takes time for the economy to transition from one steady state to another when these parameters change. The transitional dynamics are also computable from the model. However, we only report the outcome of simulations in steady states because focusing on the transitional dynamics could be misleading for two reasons: (1) the transition is rapid at the beginning but becomes slower when the economy is approaching the steady state. This is inconsistent with reality, where the impact of financial reforms happens gradually, or at least the immediate impact is not significant; (2) the numerical error is large relative to that in the steady state, possibly leading to overshooting of some variables if parameters are adjusted a lot. These two problems associated with transitional dynamics exist for all quantitative macroeconomic models, although the first problem could be mitigated to some extent if agents were modeled as forward-looking.
As reflected in Figure 3 – Figure 4, at ψ = 0.15, the percent of firms with credit is about 50% in Malaysia while it is close to zero in Uganda. The position of Uganda (identified by the blue solid line in Figure 3) indicates that in 2006, Uganda was about to move from the initial stage of development (in Kuznets’ sense).
There is only slight increase (almost invisible from the figure) in the Gini coefficient of Uganda, Mozambique, and Philippines, because intermediation cost is not a binding constraint in these countries.
Using other countries’ calibrated parameters does not change the qualitative results.
Using the credit to investment ratio might bias the results on the effectiveness of different sources of financial deepening, since the credit to investment ratio itself is more responsive to some factors (e.g. λ), and significantly less responsive to some other factor (e.g. χ). Therefore, the impact of λ is likely to be underestimated, while the impact of χ is likely to be overestimated.
We expect this result to change if agents are infinitely lived and forward looking in the model, as talented agents would have time to accumulate wealth and eventually mitigate their credit constraint.
According to the labor demand function, the capital/labor ratio is increasing in wealth. Therefore, poor agents benefit less from a reduction in cost of intermediation.