Essays on estimating firm-level production function with spatial dependence - NG, Bo Lin (Institutional Knowledge at Singapore Management University, 2024)
This dissertation consists of two chapters on the estimation of firm-level production functions when spatial effects are present. In joint work with Pao-Li Chang, chapter 1 focuses on the impacts of global value chains (GVC) on the firm-level outcomes of Singapore. First, we quantify Singapore’s participation in global value chains (GVC) using the export decomposition framework of Borin and Mancini (2019), before using these indicators to analyse how GVC participation affects sectoral-level valueadded and employment. We find that gross exports and foreign final demand have become more important for Singapore’s value-added, largely driven by the Services sectors. We then use the GVC indicators to evaluate the impact of GVC participation on firm-level outcomes, including total factor productivity, labor productivity and employment. We find that firms tend to be more productive in sectors with stronger backward linkages (measured by the proportion of foreign content embedded in the production of a sector’s GVC-related exports). On the other hand, firms tend to be less productive in sectors with stronger forward linkages (measured by the proportion of domestic content embedded in a sector’s GVC-related exports). Our analyses provide policymakers with a better understanding of the impact of shifts in GVC on firm-level and sector-level performance measures. In joint work with Pao-Li Chang, Ryo Makioka, and Zhenlin Yang, Chapter 2 proposes a threestage efficient GMM estimation algorithm for estimating firm-level production functions given spatial dependence across firms due to supplier-customer relationships, sharing of input markets, or knowledge spillover. The procedure builds on Ackerberg, Caves and Frazer (2015) andWooldridge (2009), but in addition, allows the productivity process to depend on the lagged output levels and lagged input usages of related firms, and spatially correlated productivity shocks across firms, where the set of related firms can differ across the three dimensions of spatial dependence. We establish the asymptotic properties of the proposed estimator, and conduct Monte Carlo simulations to evaluate the finite sample performance of the estimator. The proposed estimator is consistent under DGPs with or without spatial dependence, and with strong/weak or positive/negative spatial dependence. In contrast, the conventional estimators lead to biased estimates of the production function parameters if the underlying DGPs have spatial dependence structure, and the magnitudes of the bias increase with the strength of spatial dependence in the underlying DGPs. We apply the proposed estimation algorithm to a Japanese firm-to-firm dataset during the period 2009-2018. We find significant and positive spatial coefficients in the Japanese firm-level productivity process via all three channels proposed above.