Working Paper

Abstract

This paper assesses the role of intra-sectoral spillovers in total factor productivity across Chinese producers in the chemical industry. The authors use a rich panel data-set of 12,552 firms observed over the period 2004-2006 and model output by the firm as a function of skilled and unskilled labor, capital, materials, and total factor productivity, which is broadly defined. The latter is a composite of observable factors such as export market participation, foreign as well as public ownership, the extent of accumulated intangible assets, and unobservable total factor productivity. Despite the richness of the authors' data-set, it suffers from the lack of time variation in the number of skilled workers as well as in the variable indicating public ownership. The authors introduce spatial spillovers in total factor productivity through contextual effects of observable variables as well as spatial dependence of the disturbances. They extend the Hausman and Taylor (1981) estimator to account for spatial correlation in the error term. This approach permits estimating the effect of time-invariant variables which are wiped out by the fixed effects estimator. While the original Hausman and Taylor (1981) estimator assumes homoskedastic error components, this study provides spatial variants that allow for both homoskedasticity and heteroskedasticity. Monte Carlo results show, that the authors' estimation procedure performs well in small samples. They find evidence of positive spillovers across chemical manufacturers and a large and significant detrimental effect of public ownership on total factor productivity.