Influence of foreign direct investment on mass entrepreneurship
(2.School of Economics, Peking University)
【Abstract】China’s mass entrepreneurship is conducive to building new momentum for economic development. Whether FDI, one of the most important forces to promote China’s economy, has a direct impact on China’s mass entrepreneurship warrants further investigation. Based on data from China Household Finance Survey and China City Statistical Yearbook, this paper examined the impact of FDI on China’s mass entrepreneurship. FDI has two counterbalancing effects on China’s mass entrepreneurship: positive spillover effect and negative crowding-out effect. When the proportion of tangible assets of enterprises with FDI is relatively high, the positive spillover effect is strong. When the product markets of enterprises with FDI are mainly local markets, the negative crowding-out effect on local entrepreneurs is also strong. As for the impact on types of entrepreneurship, since FDI and markets of transformational entrepreneurship overlap significantly, FDI has an inhibitory effect on transformational entrepreneurship, but has no significant impact on subsistence entrepreneurship. As for the impact on particular industries, as the industry spillover index goes up, the promoting effect of FDI on entrepreneurship also increases. This paper founds that there is a direct relationship between FDI and mass entrepreneurship, which sheds light on how local governments can promote development of FDI and mass entrepreneurship with policies.
【Keywords】 foreign direct investment; mass entrepreneurship; spillover effect; crowding-out effect;
. ① In the robustness test, this paper discusses empirical results from measuring the intensity of foreign direct investment with other measurement methods. [^Back]
. ② This paper mainly uses the cross-sectional data of CHFS in 2011 (with information of the cities provided) as the key data, because data of CHFS in 2013 only cover the information of provincial areas and the matching of provincial data generates variables of foreign direct investment with little difference and low accuracy. The authors notice that in the empirical analysis with this regression model and basic data, the differences on which the regression depends on result from cross-city differences in individual entrepreneurship behaviors in the cross section. In order to make up the possible deficiency, different databases and the balance sections of the two years are used in the robustness test, and conclusions obtained are similar to the results of benchmark regression. [^Back]
. ① The data on the comprehensive utilization rate of urban industrial solid wastes are cited from China City Statistical Yearbook. The data of PM2.5 are cited from the China Environment Database of CNRDS. The ArcCIS is used to match the satellite monitoring data of global PM2.5 that SEDA of Columbia University announced with the geographic information, to obtain the average provincial and urban PM2.5 concentration in the period 1998–2016. [^Back]
. ① Meanwhile, this paper uses the data of the 1% national population sample survey of China in 2005 to test the robustness of different databases. The results can be seen in the open attachment on the website of China Industrial Economics (http://www.ciejournal.org/). [^Back]
. ① One problem of using panel logit regression is the decline in the samples observed caused by the matching. [^Back]
. ① Since China Industry Business Performance Data mainly covers enterprises with FDI above designated size, the use of data from this database may lead to the neglect of enterprises with FDI below designated size. Still, more comprehensive data about enterprises with FDI are unavailable at present. If there is no systematic difference between enterprises with FDI below designated size and enterprises with FDI above designated size, it will cause no significant error in the empirical results to use this database. In order to guarantee the robustness of the conclusion, this paper also uses the data of industrial enterprises in the economic census of year 2004, the custom database, and other databases that include both types of enterprises, to construct the index, and similar conclusions are acquired. [^Back]
. ② To be specific, this paper calculates the market focus (the proportion of the total export in the total industrial output) and the proportion of tangible assets (1 − the proportion of the total intangible assets in the total assets) of enterprises with FDI set up in the last two years (the business starting time was in and after year 2009, and the proportion of foreign capital is above 25%) at the city level. [^Back]
. ① According to research by Hoetker (2007) and Zelner (2009), there may be false significance in the coefficients of interaction terms in the logit model. This paper uses the “intgph” command by Zelner (2009) to test the result with the drawing method, and the result of the influencing mechanism is still significant. According to Wooldridge (2002) and Chen (2014), it is advantageous to calculate the coefficients of interaction terms with the linear model for robust standard errors. Thus, this paper uses this model to test the coefficients of interaction terms, and the results are still significant. According to those two methods, the test on the influencing mechanism in this paper is robust. The result details can be seen in the open attachment on the website of China Industrial Economics (http://www.ciejournal.org/). [^Back]
. ① In the CHFS survey, there are 19 industries in total and the classification standard is consistent with that of National Bureau of Statistics of China, but the industry of agriculture, forestry, animal husbandry and fishery is not included. This paper identifies the agriculture, forestry, animal husbandry and fishery according to what the correspondents fill in the item of “Others,” such as breeding industry, bee-keeping and honey selling, and animal husbandry. There are also supplements to other industries according to the content filled in. [^Back]
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