The fraction of population with<sup>access tocreditin1976was estimatedat6 percentand increasedby</sup> 1996 to 26 percent. The data<sup>alsorevealthatasmeasure offinancial deepening,itgrew slowlyinthe</sup> beginning and from 1986 grew<sup>more sharply.We recognizethatat bestthismeasure ofintermediationis</sup> a limited measure of what<sup>wewouldliketohaveideally,anditseemslikelyweareoff inlevels.</sup>

# 6.2 Issues in the calibration method

## **6.2.1** Financial Liberalization

We begin with the standard, benchmark<sup>LEB model,shuttingdowncredit altogether.Wethen consider</sup> an alternative intermediated<sup>economy,withtwo sectors,oneopentocreditandsaving.Onlylaboris</sup> mobile, hence a unique wage rate,<sup>whereascapitalcannotmovetotheothersector.Inotherwords,a</sup> worker residing in the non intermediated<sup>sector mayfinda job inthe creditsector,even thoughshewill</sup> not be able to deposit her wealth<sup>in the financialintermediary.The relativesize27of eachsectoris taken</sup> to be exogenous and changing<sup>overtimegivenby thefractionof peoplewithaccesstocredit reportedin</sup> Table 2 in the Appendix. As<sup>mentioned,this isourkeymeasureof liberalization.</sup>

## **6.2.2** Initial **wealth** distribution

Relevant for dynamic simulations<sup>is theinitial1976economy-widedistributionof wealth28.Asmentioned</sup> before, Jeong (1999) constructs<sup>a measureof wealthfromtheSES data using observations on household</sup> assets and the value of owner<sup>occupiedhousingunits.</sup>

## **6.2.3** The metric

Any calibration exercise requires<sup>a metric toassess howwell the modelmatchesthe data.Asan example,</sup> the business cycles literature has<sup>focusedon modelsthatareabletogenerateplausibleco-movements</sup> of certain aggregate variables with<sup>output.Almost by definition,themetricrequiresthattheeconomy</sup> displayed by these models be<sup>ina steadystate.Eventhoughtheeconomyweconsiderhere eventually</sup> reaches a steady state, we are interested<sup>inthe(deterministic)transitiontoit, thusthe metricputforth</sup> as our objective function suffers<sup>frombeing somewhatadhoc.Inparticular,weconsiderthenormalized</sup> sum of the period by period squared<sup>deviations of the predictions of the modelfrom the actualThai data</sup> for the five time series<sup>29</sup> displayed<sup>inTable2intheAppendix.Wenormalizethe deviations in thefive</sup> variables by dividing them<sup>bytheircorrespondingmeansfromthe Thaidata.More formally,</sup>

> 27We assume that the intermediated sector, with its distribution of wealth, is scaled up period by period according to the exogenous credit expansion.<sup>Alternatively,wecouldhave sampledfromthe no-credit sector distributionof wealthand</sup> selected the correspondung fraction<sup>totheexogenousexpansion,buttheincreaseissmallandhiswouldhavemadelittle</sup> difference in the numerical computations.

r8Smce this estimated measure<sup>of wealthis likelyto differ in scaleand units to the wealthreportedin the Townsend-Thai</sup> data, we allow for a different scaling<sup>factorto convertSESwealthinto themodelunits. In otherwords,weuse</sup><sup>_two_scaling</sup> factors when we calibrate the model<sup>usingthe parameters estimatedwiththeTownsend-Thaidata.Oneisestimatedwith</sup> ML techniques and converts wealth<sup>andincomes reportedinthe data,whereas the otheris calibratedand convertstheSES</sup> wealth measure used to generate<sup>theeconomy-wideinitial distribution.</sup>

> 2 9Note that in computing the growth rate we lose one observation, so the time index in the formula given in (25) runs from 1977 to 1996 for the growth<sup>ratestatistic.</sup>

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