Table 3 2SLS estimates - Jinshi density effects on contemporaneous per capita wage.

	(1)	(2)	(3)	(4)	(5)	(6)
Panel A: First stage					Labor -intensive industry	High-technology industry
Dependent variable:	Jinshi density in Ming-Qing(logged)
River distance to bamboo/pine (no log)	−0.135*** (0.007)	−0.132*** (0.007)	−0.104*** (0.014)	−0.104*** (0.014)	−0.094*** (0.005)	−0.102*** (0.006)
KP-F statistics	332.443	328.083	260.957	52.876	337.130	257.790
Panel B: Second stage
Dependent variable:	Wage (logged)
Jinshi density in Ming-Qing(logged)	0.122*** (0.014)	0.131*** (0.016)	0.079*** (0.021)	0.079*** (0.018)	0.114*** (0.024)	0.076*** (0.021)
Jinshi density × Labor intensive					0.079*** (0.024)
Jinshi density × High technology						0.031** (0.013)
Cluster	County	County	County	City	County	County
Observations	250,176	250,176	250,176	250,176	250,176	250,176
Firm controls	No	Yes	Yes	Yes	Yes	Yes
Other controls	No	No	Yes	Yes	Yes	Yes
Provincial fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Industrial fixed effects	Yes	Yes	Yes	Yes	Yes	Yes

Each Column in each panel represents a separate cross-sectional 2SLS regression. Panel A displays first-stage results of the 2SLS estimate, showing river distance to the bamboo/pine forest effects on the regional Jinshi density. In Panel B, Column (1) reports the effects of Jinshi density effects to wage per worker without any covariates. We add firm controls and other controls stepwise in Columns (2) and (3). In Column (4), we adjust the cluster to the city level. In Columns (5) and (6), to investigate whether capital-intensive and high technology firms benefit more, we add the interaction term respectively. Covariates include rainfall and air pollution, nightlight in 2004, population density in Ming-Qing, the urbanization rate in Ming-Qing, distance to coast, agricultural sustainability, and terrain ruggedness. All regressions are clustered at the county level, unless noted. These estimated Jinshi density effects can be interpreted as the percentage changes in wages per worker with a percent change in Jinshi density. The KP F-statistic is the Kleibergen-Paap Wald rk F-statistic for weak identification in the first stage (Kleibergen and Paap, 2006). *** denotes significant at 1% level, ** denotes significant at 5% level, * denotes significant at 10% level.

Search