My research interests cover topics in both industrial organization and international trade, with an emphasis on measurement of productivity, research and development (R&D), innovation, and the interaction between firm heterogeneity and firm activities. I emphasize firm heterogeneity in productivity, technology usage, and demand shocks when analyzing firm activities such as participation in importing/exporting, R&D, entry/exit decisions, and investment decisions. My thesis title is: "Essays on Firm Heterogeneity and Activities in Industrial Organization and International Trade".
1. "Static and Dynamic Gains from Importing Intermediate Inputs: Theory and Evidence", 2012. (Job Market Paper) Online Technical Appendix
EXTENDED ABSTRACT: Studies using micro data have found that firms that import some of their inputs from abroad have higher productivity than non-importing firms. There are several reasons for this correlation. On the one hand, more productive firms are more likely to import inputs, as they may have the largest potential gains from importing inputs. On the other hand, importing may have a positive impact on firm productivity through different channels. Existing studies have emphasized the role of “quality and variety effects”, arguing that the higher quality and increased variety of imported inputs can improve the productivity performance of importing firms. Alternatively, importing may have a dynamic impact on firms' future productivity, through exposure to foreign advanced technologies, productivity, and R&D.
This paper constructs a dynamic model to characterize a firm's decision to purchase imported inputs or rely exclusively on domestically-supplied inputs, which, in turn, has an impact on the firm's future productivity. This model provides a unified framework to analyze the determinants of a firm's import decision and to quantify its effect on firm value and productivity. A novel aspect of this model is that it allows an empirical decomposition of the profit gains from importing into a static effect and a dynamic effect. The static effect arises from the quality and variety effects as discussed in the literature, and the dynamic productivity effect arises from the dynamic impact of importing on firm's future productivity.
I structurally estimate the dynamic model of import demand using a plant-level data set from Colombia. The empirical results show that more productive plants and larger plants (measured by capital size) tend to import intermediate inputs and that the total gain from importing inputs from abroad is large. The counterfactual analysis shows that importing increases firm value--defined as the discounted future profit--by 0.87% to 22.28% in the six industries examined. These industries include Basic Industrial Chemicals, Pharmaceuticals, Plastics, Leather Shoes, Printing and Publishing, and Clothing. The decomposition of the import gain into its static and dynamic components reveals that the major effect of importing occurs through the dynamic effect on future firm productivity.
2. "Production Function Estimation with Unobserved Input Price Dispersion", with Paul Grieco and Shengyu Li, 2013, submitted. [Last updated January 2013].
ABSTRACT: We propose a method to consistently estimate production functions when intermediate inputs are not observed in the presence of input price dispersion. The traditional approach to dealing with unobserved input quantities--using deflated expenditure as a proxy--requires strong assumptions for consistency. In particular, we show that the traditional approach tends to underestimate the elasticity of substitution and bias estimates of the distribution parameters. Our approach applies to a general class of production functions with a mild identification restriction. As a demonstration, we apply our approach to the CES production function. A Monte Carlo experiment illustrates that the omitted price bias is significant in the traditional approach, while our method consistently recovers the production function parameters. We apply our method to a firm-level data set from Colombian manufacturing industries. The empirical results are consistent with the predictions that the use of expenditure as a proxy for quantities biases the elasticity of substitution downward. Moreover, using our preferred method, we provide evidence of significant input price dispersion and even wider productivity dispersion than is estimated using traditional methods.
3. "Unraveling Effects of Demand Shocks on Production Function Estimation and Firm Behavior", with Pradeep Kumar, 2012.
ABSTRACT: The productivity measure estimated using revenue based data has both demand side and production side shocks embedded in it. The existing literature uses price data to separate these two shocks, which is not commonly available to researchers. In this paper, we use inventory data to disentangle and separately identify demand shock and productivity shock without using any price data. Introducing demand shock in the model also addresses the multi-collinearity bias argued by Ackerberg, Caves and Fraser (2006). Finally, we use our estimates to explain entry/exit dynamics. We find that demand shock is a more important driver of firm-turnover than productivity shock.
4. "Biased Technology and the Contribution of Technological Change to Economic Growth: Firm-Level Evidence from China", 2012. (Draft available upon request)
ABSTRACT: Virtually all of the empirical literature on production function estimation that has followed the seminal paper by Olley and Pakes (Econometrica, 1996) begins with a framework in which technology differences, both across firms and over time, are Hick's neutral. In this framework, technology differences cannot explain variations in factor shares across production observations. In the Chinese manufacturing sectors we observe large variations in the capital-labor ratio across production units. These variations are unlikely to be explained by factor price variations combined with reasonable magnitudes of the elasticity of substitution and Hick's Neutral technology differences.
In this paper, I develop and estimate an empirical model with factor-biased technology differences and use it to analyze the sources of productivity differences across Chinese firms. The basic idea of the estimation is that the choice of inputs contains information about the unobserved factor-augmenting productivities; therefore, we can invert the input demand functions to recover the unobserved productivities. I estimate the model using a firm-level data set of four Chinese Manufacturing industries: Clothing, Paper and Paper Board Making, Production Equipments, and Motor Vehicles. The empirical results provide firm-level evidence of biased technological change over time and biased technological dispersion across firms. The results also show that large firms have a higher capital-labor efficiency ratio and that biased technological dispersion explains a large part of the dispersion of capital-labor ratios across firms.
I apply the recovered firm-level factor-biased productivities to analyze the contribution of technological change to the growth of the four Chinese industries. The results show that technological change contributes to the growth of gross output by 1.81%-3.10% annually and the value added by 12.67%-21.16%. This is higher than the combined contribution of the growth of capital and labor. I also find that capital efficiency grows much faster than labor efficiency in China, and the contribution of technological change to economic growth is mainly due to the growth of capital efficiency. These results shed light on the sustainability of growth in these industries.
Work in Progress:
- Endogenous Import and Export Decisions and Productivity
- Product Innovation, Process Innovation, Firm Investment in R&D and Productivity, with Mark Roberts, Bettina Peters, and Van Anh Vuong
- Export Types, Markups, and Exporter Performance in China, with Pei Li
Hongsong Zhang's Homepage, Economics, Penn State (PSU)