This paper assesses the role of intra-sectoral spillovers in total factor productivity across Chinese producers in the chemical industry. We 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 our 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. We introduce spatial spillovers in total factor productivity through contextual effects of observable variables as well as spatial dependence of the disturbances. We 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, we provide spatial variants that allow for both homoskedasticity and heteroskedasticity. Monte Carlo results show, that our estimation procedure performs well in small samples. We find evidence of positive spillovers across chemical manufacturers and a large and significant detrimental effect of public ownership on total factor productivity.

Document Type

Working Paper


Fall 12-2014


Technology Spillovers, Spatial econometrics, Panel data econometrics, Firm-level productivity, Chinese firms




Working Papers Series


Chemistry | Econometrics | Industrial Organization | Labor Economics

Additional Information

Working paper no. 173

The authors gratefully acknowledge numerous helpful comments by the editor (Hashem Pesaran) and two anonymous reviewers. Moreover, they are indebted to the participants at the Annual World Conference of the Spatial Econometrics Association at Toulouse 2011, the European Annual Meeting of the Econometric Society at Oslo 2011, CESifo Venice Summer Institute on China and the Global Economy Post Crisis 2011, the 17th International Conference on Panel Data held at McGill University Montreal 2011, the Singapore Economic Review Conference at Singapore 2011, the Annual Meeting of the European Economic Association at Gothenburg Singapore 2011, the Annual Meeting of the European Economic Association at Gothenburg 2013, and the Conference on Cross-sectional Dependence in Panel Data Models at Cambridge 2013.

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