Abstract
In this paper we study the effects of destination institutions and firm productivity on exporter dynamics in a heterogeneous firm setting. The empirical results, using a panel of Chinese firms, show that the quality of destination institutions has a significant and positive effect on the probability of entry and survival and that these effects are increasing in firm productivity. In contrast, firms have higher initial sales and faster growth in destinations with weaker institutions and this effect is decreasing in firm productivity. We also find that exporter performances are increasing in firm experience and in the level of foreign ownership whereas the importance of destination institutions is decreasing in firm experience and in the level of foreign ownership. We show that while firms from regions with better institutions enjoy higher probability of entry, initial sales, survival and growth in markets with better institutions; the importance of productivity for exporter performance diminishes as the quality of local institutions improves. Lastly, firms that are more dependent on contract enforcement perform better in entry probability, initial sales, survival and growth in destinations with better institutions.
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Notes
For a comprehensive survey on the effect of institution on trade, see Nunn and Trefler (2014).
Experience (Braymen et al. 2011; Albornoz et al. 2012), networks (Chaney 2014), learning dynamics (Eaton et al. 2014; Fernandes and Tang 2014), sunk costs (Melitz 2003; Bernard et al. 2007; Fernandes and Tang 2014; Castro et al. 2016), prevalence of other exporters (Alvarez et al. 2013; Bernard and Jensen 2004), demand uncertainty, capital requirements, adjustment costs and financial frictions (Blum et al. 2013; Kohn et al. 2016) and matching failures (Eaton et al. 2014) are shown to be important determinants of exporter dynamics.
For entry and survival status, we face the problem of censored data from both ends of the distribution. To correct this problem we assign the survival status for year 2006 as missing and define a firm-year-destination as a new entry only if it appears after 2001.
Total sales are deflated by 4-digit industry specific output deflators, wages by four-digit input deflators, and capital stock is by capital stock deflator, all from Brandt et al. (2012).
Supporting this argument, the survival rate (after one period of entry) among sample firms in the first versus fourth quartiles of countries in institutional development is significantly different: 46% versus 53%, respectively.
As in Bernard et al. (2014), we adjust initial sales using the number of months after entry. For example, if a firm enters a market in March and remains active in the rest of the year, its annual sales are compounded to include the missing 2 months, each month being weighted equally.
In Araujo et al. (2016, p. 9), better institutions slow down the effect of reputation building and reduce the “information content of past histories” as firms cannot easily know whether a partner complied because of being a good partner or the threat of a legal challenge.
Using the mean growth rate we avoid the problem of outliers caused by spikes in a single year and restrict the growth rate to − 2 and + 2.
In cleaning the raw data, we follow Brandt et al. (2012). We exclude the tobacco industry as it is highly regulated. More details on sample coverage are provided in the online Appendix.
The industrial surveys are reported in domestic currency and we used the average annual exchange rate to convert them to the USD.
Firms in the matched sample are relatively larger than the ones in the unmatched datasets. More details on the matched and unmatched samples are provided in the online Appendix.
The processing trade consists of “purely assembly” and “import-and-assembly” type trade flows and as such they are expected to have a different set of determinants than ordinary trade.
Including the top and bottom 1% of firms do not affect our results as reported in the Appendix.
We repeated this exercise by creating another Experience variable, defined as the total number of destinations previously served and are in the same quartile in institutional quality. The results, which are available in an online Appendix, are consistent with those reported here.
Feenstra et al. (2013) report that the effect of local institutions on export performance is more important for foreign than domestic firms in China as they are more dependent on formal local institutions in resolving business disputes.
Rauch (1999) provides two types of goods classification, “conservative” and “liberal. In the Appendix, we report results using three additional classifications: “differentiated” and “conservative”; “differentiated” and “liberal”, and “differentiated and reference priced” and “liberal”. Results from these exercises are similar to those reported in Table 14.
The correlation coefficients between WGI, and ICRG and Polity IV are 0.95 and 0.56, respectively. Details on the factor analysis are available in the online Appendix.
We have also experimented with a threshold level of 100% and found no change in the results.
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Acknowledgements
We thank Mustafa Caglayan, James Hartigan, Katheryn Russ, Deborah L. Swenson, and seminar participants at UC-Davis in 2016 and SEA conference in Tampa in 2017 for comments and suggestions on earlier versions of this paper. We also thank Jiandong Ju for sharing the data. Firat Demir thanks the Fulbright Commission and the Faculty of Economics at the University of Montenegro for his Fulbright visit during 2015–2016. All remaining errors and omissions are ours.
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Demir, F., Hu, C. Destination institutions, firm heterogeneity and exporter dynamics: empirical evidence from China. Rev World Econ 156, 183–217 (2020). https://doi.org/10.1007/s10290-019-00358-x
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DOI: https://doi.org/10.1007/s10290-019-00358-x
Keywords
- Exporter dynamics
- Firm heterogeneity
- Institutional development
- Total factor productivity
- Developing countries
- Chinese firms