where zJ denotes the variable s, t denotes time, and **_w,t_** is the weight given to the variable s in year t. In order to focus on a particular period, more weight may be given to those years. Analogously, all the weight may be set to one variable to assess how well the model is able to replicate it alone. All weights are re-normalized so that they add up to unity. Finally, sim and ec denote respectively "simulated" and "Thai economy", and M., denotes the variable zJm ean from the Thai data.

We search over the cost of living **v,** subsistence level growth rate **-g,** and the bequest motive parameter _w_ using a grid of 203 points or combinations of parameters<sup>3 0</sup> .

All the statistics but the savings rate have natural counterparts in the model. We consider "savings" the fraction of end-of-period wealth bequested to the next generation. The savings rate then is computed by dividing this measure of savings by net income<sup>3</sup> l.

# 7 Resullts

In this section we present the simulation results using the calibrated and estimated parameters from both data sets.

## 7.1 Simulations using SES Data Parameters

We begin with the original LEB model without liberalization. If all periods and all variables are weighted equally, the parameters which minimize the squared error metric are _v_ = 0.079, _w_ = 0.479 and **7g.** = 0.042. Figure 3 in page 40 displays the model simulations against the actual Thai data displayed earlier.

The figures show how the model fails to explain the levels and changes in roughly all variables. In the simulation, the growth rate of income is flat at roughly 2 percent. Growth is driven mainly by the exogenous growth of the subsistence level which is set at 4.2 percent per year. Overall, the economy shrinks in the early periods, and then by 1983 it grows at the wage level<sup>32</sup> . The simulated labor share is able to roughly match the trend displayed in the data, although it is always higher in levels. The simulated savings rate is decreasing at first and then slightly increasing, always at a higher level than the actual data. Entrepreneurship in the model is somewhat low. The Gini coefficient is always decreasing and terribly low, in contrast to the first increasing and then decreasing pattern that the Thai data exhibits.

If we had tried to match the growth rate alone, we do somewhat better on that dimension. In fact, we are able to replicate the low growth - high growth phases seen in the data. Figure 4 in page 41 displays the model simulations when we restrict attention to the growth rate. The calibrated parameters here are _v_ = 0.074, _w_ = 0.7 and -g, = .1. The improvement in the growth rate comes at the expense of increasing the model's savings rate above one from 1985 onwards, far above the actual one. Labor share increases sharply in the model, but not in the data. The income Gini coefficient and the fraction of entrepreneurs are very poorly matched as both drop to zero. The reason for such drastic macroeconomic aggregates is _3 0 As_ mention earlier, when we use the Townsend-Thai data, we also search over a grid of 20 scaling factors for the mitial distribution of wealth.

> 31More formally we can express the savings rate (in an economy without credit) as

where income _Y(b, x,_ w) is given by _W(b, x, W) = Y(b, x,_ w) _+b_ as expressed in equation (4). Note that for some parameters, the savings rate may be larger than one. 32 Smce the cost of living v is roughly 3 times the subsistence income -y, wage growth rate wvll always be lower than **7yrg** 16