informal sources. Indeed, in the model so far, we have not allowed any form of lending (formal or informal) for those households residing in the no-credit sector. We now relax that assumption and explore whether the welfare gains from liberalization would differ significantly from those reported in the previous section.

We follow Lloyd-Ellis and Bernhardt (2000) and introduce intermediation which is limited by a moral hazard problem<sup>4 0</sup> . In particular, given an interest rate of _r,_ possibly different from that in the formal sector, entrepreneurs borrow _L_ and put up their wealth _b_ as collateral. After production they can abscond losing _rb_ but escaping the repayment obligation _rL._ Absconders are apprehended with probability p and if so they can hide their income but receive a punishment corresponding to an additive disutility of _d._ Borrowers will renege on the loan contract if _rb + pd_ < _rL,_ so lenders only make loans that satisfy _L < b +_ A, where A = _pd/r._ Thinking again about help from friends and relatives, we set _r_ = _1_ so that the objective function for households that now can "borrow" from the informal sector is the same as before. However, the constraint in (3) is now modified to

# _k+z <b+A._

In effect, parameter A is treated as a lump sum addition to wealth but only for those who borrow from the informal sector and choose to start firms This would allow more talented households to alleviate credit constraints<sup>41</sup> .

As shown in the Appendix, parameter A would be identified in cross sectional maximum likelihood estimation. However, to give parameter A a greater opportunity to influence the dynamic paths relative to the earlier simulation, we calibrate the parameter A against the dynamic aggregate data for each dataset using the previously estimated and calibrated parameter values for the other parameters. The calibrated values of parameter A are A = 0.0163 for the Townsend-Thai dataset and A = 0.0082 for the SES dataset. These correspond to 1.1 times and 30% of the subsistence income in 1976 for each dataset respectively.

Similarly to the open economy version, the increased informal finance speeds up the creation of firms resulting in an earlier depletion of the fraction of subsisters. Again, labor share begins to increase along with a decline in the Gini coefficient, both at an earlier date.

Although the parameter A could in principle have a large welfare impact, at the calibrated parameter A that best fits the Thai economy, these welfare gains and losses remain much as before. In particular, the counterparts of Figures 7a - lOa in pages 44-47 still feature the tent-shaped gains for relatively talented but poor people. Recall that in the intermediated sector, only the most talented individuals will start a firm, irrespective of their wealth. However, even when we relax the constraint in the no-credit sector by alowing informal finance, wealth still matters in determining who becomes an entrepreneur, and thus the tent-shaped welfare gains still appear. In addition, the increased wage in the latter periods does benefit workers in the liberalized economy. In the no-credit economy which now has informal credit, some households become richer but the additional capital from informal sources is insufficient to trigger an increase in the equilibrium wage rate. Thus, in the welfare experiment comparing the intermediated to the no-formal-credit economies, wage earners benefit more from the liberalization, but not much. In contrast, firms lose from the relative increase in the wage, but not much.

4<sup>0</sup> We also tried modifying the constraint in (3) to k E _i4O, b -_ xz, where the parameter N. measures the severity of the financing constraints. Thus, if some of the assets that make up wealth are illiquid, **we** would expect rN < 1. Unfortunately, this specrfication **is** not useful because the parameter N cannot be identified.

4t We are **implicitly** assuming a partial equilibrium, unlimited supply of **funds** by setting r = 1. However, the magnitude of the calibrated parameter A as reported below is comparable to the average amount of informal borrowing found **in** the Townsend-Thai dataset by those households who borrow informally.

24