_Table 2.2. 2SLS Regression Results: Explaining Wage Growth_

|||Model A|||Model B|||ModelC||
|---|---|---|---|---|---|---|---|---|---|
||Total|Unskilled|Skilled|Total|Unskilled|Skilled|Total|Unskilled|Skilled|
|∆Export Index|.024<br>|.047*<br>|.0072<br>|.0076<br>|.028*<br>|-.027<br>||||
||(.021)|(.027)|(.017)|(.011)|(.016)|(.023)||||
|∆Export US Index|||||||.010|.042*|-.029*|
||||||||(.017)|(.023)|(.017)|
|∆Skilled/unskilled status|-.056|.11|.028|-.083|.055|-.060|-.083|.055|-.064|
||(.062)|(.093)|(.062)|(.064)|(.090)|(.077)|(.064)|(.090)|(.077)|
|∆Marital status|-.13*|-.16**|-.017|-.10|-.14**|.0069|-.099|-.14**|-.00035|
||(.070)|(.078)|(.092)|(.061)|(.068)|(.091)|(.062)|(.069)|(.091)|
|Initial wage in log|<br>-.25***|<br>-.38***|<br>-.27***|<br>-.40***|<br>-.49***|<br>-.33***|<br>-.40***|<br>-.49***|<br>-.33***|
||(.023)|(.031)|(.036)|(.030)|(.034)|(.041)|(.030)|(.033)|(.041)|
|Female||||<br>-.096***|<br>-.24***|<br>.018|<br>-.096***|<br>-.24***|<br>.020|
|||||(.024)|(.034)|(.036)|(.024)|(.034)|(.036)|
|Minority||||<br>.0024|<br>-.12|<br>.093|<br>.0012|<br>-.13|<br>.10|
|||||(.087)|(.12)|(.073)|(.087)|(.12)|(.071)|
|Education||||<br>.041***|<br>.023***|<br>.025***|<br>.041***|<br>.023***|<br>.026***|
|||||(.0035)|(.0044)|(.0066)|(.0035)|(.0045)|(.0066)|
|Potential Experience||||<br>.0015|<br>-.00010|<br>.00057|<br>.0016|<br>-.000063|<br>.00052|
|||||(.0014)|(.0016)|(.0018)|(.0014)|(.0016)|(.0017)|
|Urban||||<br>.044|<br>.060|<br>.0029|<br>.043|<br>.060|<br>.0052|
|||||(.030)|(.041)|(.042)|(.030)|(.041)|(.042)|
|Agriculture share 2002<sup>a</sup>||||<br>-.38|<br>-.27|<br>-.62**|<br>-.35|<br>-.19|<br>-.72***|
|||||(.24)|(.30)|(.25)|(.23)|(.30)|(.25)|
|Industry share 2002<sup>a</sup>||||-.074|.084|-.16|-.0059|.27|-.48|
|||||(.54)|(.66)|(.57)|(.48)|(.65)|(.40)|
|Construction share 2002<sup>a</sup>||||<br>-.039|<br>1.50|<br>-1.46|<br>-.090|<br>1.34|<br>-1.26|
|||||(.74)|(1.00)|(1.06)|(.71)|(.99)|(1.07)|
|Government share 2002<sup>a</sup>||||<br>-1.34|<br>-.20|<br>-2.89**|<br>-1.34|<br>-.24|<br>-2.90**|
|||||(1.22)|(1.36)|(1.21)|(1.23)|(1.39)|(1.22)|
|Constant|3.57***|5.04***|3.89***|<br>5.40***|<br>6.52***|<br>4.99***|<br>5.37***|<br>6.44***|<br>5.08***|
||(.30)|(.39)|(.47)|(.46)|(.50)|(.62)|(.44)|(.48)|(.64)|
|Observations<sup>b</sup>|1718|1051|647|1718|1051|647|1718|1051|647|
|R<sup>2</sup>|.114|.178|.145|.210|.274|.179|.209|.272|.184|
|Wu-Hausman F test|2.52|7.26|.59|.76|0.00|4.43|1.36|.000|3.59|
|[p-value]|[.118]|[.0092]|[.447]|[.386]|[.966]|[.040]|[.248]|[.966]|[.063]|

_Notes_ : The dependent variable is the log difference of monthly wage ( _∆Ln W_ in Equation (2)). The regressions are estimated using STATA’s ivreg2 command with the robust and cluster options (Baum _et al_ ., 2007). The classification between skilled and unskilled workers is based on the status in 2002.

*, **, *** indicate that the coefficients are significant at the 10, 5, and 1 percent level respectively.

The standard errors in parentheses are based on heteroskedasticity-consistent estimates of the variance-covariance matrix and corrected for within province correlation (clustering).

a The share of workers employed in the service sector is the omitted category.

b The total sample includes those who worked for “Army force”. As the skill category is not assigned for the latter workers, the number of skilled and unskilled workers do not add up to total number.

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