the impact of trade with china and india on argentina
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Table 9.4 Regression Results from Base Model
Dependent variable: Employment Employment (t-1) Wage Capital or output Total import penetration Total export penetration
(1) Capital constrained 0.456***
0.187***
[0.052]
[0.037]
–0.279***
–0.280***
[0.040]
[0.024]
0.222***
0.624***
[0.039]
[0.059]
–0.084**
–0.050**
[0.033]
[0.022]
0.007 [0.019]
Constant
(2) Output constrained
3.397**
0.029** [0.013] 1.424
[1.390]
[1.051]
Hansen J-Statistic/Sargan
0.00
0.07
2nd-order auto-correlation (AC) test (p-value)
0.63
0.80
Source: Authors. Note: Time and sector dummies are included in all regressions but are not reported. Robust standard error is in brackets. System GMM corresponds to one-step estimation. All continuous variables are expressed in logs. **Significant at 5 percent level. ***Significant at 1 percent level.
Table 9.5 reports the system GMM estimations only for the capitalconstrained specification, but including trade shares by partner, to assess the marginal impact of imports and exports with different trading partners. The coefficients on lagged employment, wage, and capital stock show the expected signs and are highly significant and stable across specifications. The total import penetration coefficient is always negative and significant around the 1 to 5 percent threshold. As shown, an increase of 1 percentage point in total import penetration generates a job loss of around 0.07 percent. Given that import penetration increased by 79 percent over the sample period (1991–2003), the decline in labor demand that can be attributed to the increase in import penetration is around 6 percent in the short run and 10 percent in the long run. Given that manufacturing employment declined by 31 percent over the sample period (1991–2003), the increase in import penetration can at most explain 32 percent of the observed loss in