Abstract
This paper investigates whether and how regional industrial structure/concentration influences firm productivity. Based on the firm-level data from China, the paper estimates firm productivity with regional structure conditioning on agglomeration effects and concludes that regional industrial structure plays little role in firm’s output, but affects localization agglomeration, which in turn affects firm productivity. In other words, localization agglomeration is stronger in cities in which sectors are less dominated by a few large firms in their own sector. Our conclusions are robust to the classification of industries, intertemporal, and spatial dimensions of agglomeration externalities, alternative measures of regional industrial structure and agglomeration, and different spatial scales in which standard errors are clustered. An important policy implication of our findings is that China’s industrial policies favoring large firms may be harmful to local economic development in the long term.
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Notes
One may wonder whether the presence of dominant firms discourages other local firms’ intention in the improvement of productivity. While this may be possible, this paper does not find the evidence that the dominance directly lowers firm productivity, but suggests that the dominance reduces agglomeration economies.
In December 2018, we conducted a field survey for this research in Gansu Province, an underdeveloped province in China, and found that most local economies are dominated by one or few large firms and have been suffering economic stagnation in recent years. For example, according to the information provided by the bureau of finance of Honggu District in Lanzhou City, the capital city of Gansu Province, the largest firm, Fangda Carbon New Material Technology, contributes about 62% of the total local tax revenue in 2017. However, this firm is the only one in its industrial group and has little connection with other local firms or industries. Both the supplier and consumer industries are located in other regions.
A large part of these quantitative researches used aggregate data instead of micro-level data. It might be one reason for the mixed results, because using micro-level data helps to improve the measure of industrial structure and reduce aggregation bias in the estimation.
A comparison to the First Economic Census indicates that, in 2004, above-scale firms represent 22% of all registered firms, accounting for 78% of employment, and produce 90% of output in industrial sectors. As 53% GDP was produced by the secondary sector in 2004, firms in our data represent a crucial component in China’s economy.
See Section 3.3 of Brandt et al. (2014) for the details in the measurement of the real capital stock as well as the inspection on the measurement.
In the Herfindahl index, we use sales instead of other variables like employment for following reasons. First, sales are probably a direct measure on market dominance than employment. Second, using sales is consistent with the literature, so that our results are comparable to others (Drucker and Feser 2012; Drucker 2013). Third, firms in our data represent nearly 80% of total employment but more than 90% of total sales, so using sales improves the measurement in this paper.
The quality of employment data is better than population data (from China City Statistics Yearbooks or China City Construction Statistics Yearbooks). The city population data is notoriously inaccurate as they count only the registered population (residents with city hukou or temporary residents living at city over 6 months in a year) (Ding and Li 2019). Massive floating population is excluded from city statistics data. Therefore, using population data is likely to underestimate the agglomeration of large cities that attract many migrant workers and overestimate agglomeration of small cities that are losing population and labor force.
Certainly, in the decision of using the fixed effects estimator, there is a tradeoff between preserving the variation versus controlling unobserved factors. As the unobserved factors are likely to significantly influence firm productivity and the local environment (industrial concentration, agglomeration, etc.) at the same time, we chose to control fixed effects. This choice is also consistent with many influential works, for example, Henderson (2003), as discussed by Rosenthal and Strange (2004). In the following paragraph, we show the within variation in our data should be large enough to identify the key parameters.
After excluding three provinces of Qinghai, **njiang and Tibet that have extremely low population density, we end up with 29 provinces or equivalents, as compared to 650–670 cities.
Using longer lags may better satisfy the exclusion restriction, but there are at least two considerations in this research. The first is the whether the instrument is strongly correlated with the endogenous variable. Using longer lags generally makes the correlation weaker. The second consideration is data. This research uses a 10-year panel data, so the longest lag is 8 years in the first difference estimation. Further, when using longer lags, we have to drop more years of samples.
The correlation between other-industry concentration and the interaction term is 0.92, and the collinearity might more or less lower the statistical significance of the coefficient of the interaction term. On the other hand, the industrial concentration in certain industries that are strongly linked with the target industry might have some impact on firm productivity. The investigation of this mechanism, however, is beyond this paper.
These interpretations are based on the assumptions that the log-linear function form is a good approximation for the production function, and that the coefficients of employment and capital inputs do not vary across observations. While these assumptions are often made in the empirical literature, investigating the degree of the approximation is beyond this paper.
Whether it is lagged industrial concentration, contemporary industrial concentration, or both, that influence firm productivity is an important question, and we leave it for future research.
We believe the industrial dominance is largely determined by the largest 5% firms. Among firms bigger than 900 employees, the average employment is 2762 and the median is 1509. For comparison, among firms no larger than 900 employees, the average employment is 164 and the median is 102.
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Li, Z., Ding, C. & Niu, Y. Industrial structure and urban agglomeration: evidence from Chinese cities. Ann Reg Sci 63, 191–218 (2019). https://doi.org/10.1007/s00168-019-00932-z
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DOI: https://doi.org/10.1007/s00168-019-00932-z