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The Limits of Mathematics and NP Estimation in ... - Chichilnisky

The Limits of Mathematics and NP Estimation in ... - Chichilnisky

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<strong>The</strong> Impact <strong>of</strong> Government-SponsoredTra<strong>in</strong><strong>in</strong>g Programs on the Labor Market Transitions <strong>of</strong> Disadvantaged Men<strong>The</strong> Impact <strong>of</strong> Government-Sponsored Tra<strong>in</strong><strong>in</strong>g Programs on the Labor Market Transitions <strong>of</strong> Disadvantaged Men 2773Welfare Welf. Tr. JRP UI UI Tr. Employ. OLFTWO-FACTOR LOADING MODELWEIBULL DISTRIBUTION -NO DUMMY INDICATORSWelfare 1.000 -0.343 0.993 0.987 0.237 0.988 -0.947(0.266) (0.004) (0.004) (2.413) (0.003) (0.012)Wel. Tr. 1.000 -0.228 -0.189 0.832 -0.195 0.627(0.278) (0.281) (1.390) (0.280) (0.218)JRP 1.000 0.999 0.351 0.999 -0.901(0.001) (2.325) (0.001) (0.026)U.I. 1.000 0.388 1.000 -0.883(2.288) (0.027)UI Tr. 1.000 0.383 0.089(2.294) (2.475)Emplo. 1.000 -0.886(0.026)TWO-FACTOR LOADING MODELWEIBULL DISTRIBUTION -WITH DUMMY INDICATORSWelfare 1.000 -0.181 0.993 0.987 -0.737 0.987 -0.945(0.337) (0.004) (0.004) (0.943) (0.003) (0.013)Wel. Tr. 1.000 -0.061 -0.018 0.798 -0.021 0.494(0.341) (0.344) (0.872) (0.344) (0.296)JRP 1.000 0.999 -0.650 0.999 -0.898(0.002) (1.062) (0.001) (0.027)U.I. 1.000 -0.617 1.000 -0.878(1.098) (0.000) (0.028)UI Tr. 1.000 -0.619 0.918(1.096) (0.552)Emplo. 1.000 -0.880(0.027)Table 6. (Cont<strong>in</strong>ued)Correlations Between Heterogeneity Variables(St<strong>and</strong>ard Errors <strong>in</strong> Parentheses)(7)–(9), respectively. Hence, the first section has the same <strong>in</strong>terpretation as the correlations <strong>of</strong>the previous panels. <strong>The</strong> correlation coefficients reported <strong>in</strong> this section differ considerablyfrom the previous ones. Accord<strong>in</strong>g to the estimates, it now appears that there is considerableselectivity <strong>in</strong>to welfare tra<strong>in</strong><strong>in</strong>g as well as <strong>in</strong> JRP. Indeed, those who are more likely toparticipate <strong>in</strong> the former are also more likely to tra<strong>in</strong> under JRP <strong>and</strong> to f<strong>in</strong>d employment.On the other h<strong>and</strong>, higher transition rates <strong>in</strong>to JRP or welfare tra<strong>in</strong><strong>in</strong>g is now associated withlower transition rates <strong>in</strong>to UI <strong>and</strong> UI tra<strong>in</strong><strong>in</strong>g. This is <strong>in</strong> stark contrast with the previousresults. Other correlation coefficients are relatively similar to the previous ones.<strong>The</strong> second section <strong>of</strong> the panel reports the correlation coefficients with respect to the orig<strong>in</strong>states. Large heterogeneity values <strong>in</strong> the orig<strong>in</strong> state translate <strong>in</strong>to short spell durations.Consequently, the correlations reflect the frequency with which <strong>in</strong>dividuals transit across thevarious states. <strong>The</strong> estimates show that <strong>in</strong>dividuals who are more likely to have long welfarespells are also likely to have short employment spells. <strong>The</strong> same holds with respect to welfaretra<strong>in</strong><strong>in</strong>g <strong>and</strong> employment, as well as JRP <strong>and</strong> employment. Those who are more likely to haveshort unemployment spells are more likely to have long JRP, welfare tra<strong>in</strong><strong>in</strong>g or UI tra<strong>in</strong><strong>in</strong>gspells.

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