Estimating Returns to Scale with Large, Imperfect Panels: An Application to Chilean Manufacturing Industries
利用智利工厂级面板数据，采用广义矩方法估计制造业规模报酬，发现多数行业规模报酬接近常数，且估计对测量误差和异质性具有稳健性。
This study exploits plant-level panel data from Chile to provide new evidence on the empirical significance of scale economies in manufacturing sectors. Particular emphasis is given to econometric problems induced by the presence of unobservable plant heterogeneity, measurement error, and selectivity. An analysis of the results suggests that estimates based on generalized method of moments (GMM) estimators that pool long differences (which eliminate heterogeneity effects) are robust to measurement error in the capital stock, heteroscedasticity, and selectivity. Returns to scale for three-digit industries are fairly evenly distributed over the plausible range of 0.8 to 1.2, and none is statistically significantly different from constant returns. Similar results hold for the four-digit industries for which sufficient data are available. Although general expansion of the manufacturing sector cannot be expected to yield strong plant-level scale economies, our results do not rule out scale economies from other sources, such as the spreading of start-up costs and external returns to scale. Finally, the analysis has generated several findings of methodological interest, including the notion that Stigler's survival test may indeed be useful as a quick first pass on the empirical importance of returns to scale.