We now turn attention to the intermediated economy at these parameter<sup>values.If weweighteach</sup> year and all the variables equally, the calibrated parameters<sup>are</sup><sup>_v_=0.004,</sup><sup>_w_=0.267and</sup><sup>**-ygr**=.006.</sup> The scaling factor chosen for the initial distribution is<sup>15</sup> percent<sup>of theone used toconvert wealthusing</sup> in the ML estimation. The corresponding graphs<sup>are</sup> presented<sup>inFigure6,page43.</sup> The model here also does well at explaining the<sup>levelsand changesin allvariables, evenbetterthan</sup> above with the SES data. Striking in particular is the growth<sup>rate of income,whichalthoughsomewhat</sup> low in levels, tracks the Thai growth experience well. The<sup>modelalsodoes remarkablywelin matching</sup> labor share and the Gini measure of inequality. It under-predicts,<sup>however,the fractionof entrepreneurs,</sup> although it is able to replicate a positive trend. As usual,<sup>themodel featuresaflattersavingsrate</sup> although it matches well the last subperiod, 1988-1996.<sup>Economywidegrowthisdrivenprimarilyby</sup> growth in the intermediated sector. That is<sup>wherethebulk oftheeconomy'sentrepreneurslieanda</sup> relatively high number of workers, from both the intermediated<sup>andnon-intermediatedsector.</sup>

# 8 Sensitivity Analysis **of** MILE parameters

We address the robustness of the model in two ways. First,<sup>wechange one parameterat a timeand check</sup> whether the new simulation differs significantly from the benchmark<sup>one.Alternatively,we couldseehow</sup> sensitive the model is to changes in **all** the estimated parameters<sup>at the sametime.We nowexplaineach</sup> approach in detail.

From the estimated parameters and their standard errors, confidence<sup>intervals canbeconstructed3 3.</sup> One can then set one parameter at a time to its<sup>confidenceintervallowerorupper boundwhilefixing the</sup> rest of the parameters at their original values. Keeping the calibrated<sup>parametersalso fixed,one can then</sup> simulate the economy. When we do this, it becomes clear that<sup>thesimulationsaremore sensitiveto some</sup> parameters than others. The reason is that some parameters<sup>areclose to the valuethat wouldmakethe</sup> constraints described in Footnote 9 bind. When we perturb these<sup>parametersbychanging themto their</sup> confidence interval bounds, we approach the constraints,<sup>sothemodeldeliversverydifferentdynamics.</sup> This is especially true for the parameters p and (. In fact, the<sup>lowerbound of theconfidenceintervalfor</sup> p obtained from the Townsend-Thai dataset violates<sup>someof the restrictionsthatthe modelmust satisfy</sup> to be well-behaved. Indeed, the unconstrained labor demand<sup>iszero,inwhichcasenoagentwiUlever</sup> want to become an entrepreneur regardless of his setup<sup>cost</sup><sup>_x._</sup> When we change the setup distribution parameter m beyond its<sup>confidenceintervaltoits extreme</sup> values of [-1, 1], and still fix the rest of the parameters,<sup>weobtainsomewhatmoredistortedpictures</sup> than if m were contained in the confidence interval. However,<sup>wedo notobtainthecyclesdiscussedby</sup> Lloyd-EUis and Bernhardt.

From the confidence intervals of the estimated parameters, we<sup>drawat random5,000differentsets of</sup> parameter values. It turns out, by chance, that none violated<sup>theconditionsinFootnote9.Noticethat</sup> since we also vary the scale parameter s, we are examining<sup>sensitivityto theinitialwealthdistribution</sup> when we use the SES dataset. Fixing the calibrated parameters<sup>attheiroriginallevel,werun5,000</sup> simulations for each the SES and the Townsend-Thai dataset.<sup>Wethencomputethemeanandstandard</sup> deviation at each date over these 5,000 simulations of<sup>eachofthefivevariables.Figures5and6also</sup> display (in dots) the 95 percent confidence intervals around<sup>themean.</sup> Figure 5 shows that income growth, the savings rate and the fraction<sup>of entrepreneursare quite</sup> 33We construct standard asymptotic 95% confidence intervals<sup>using thenorrnaldistribution.</sup> 18