First, we examine the effect of infrastructure in a given district in the year 2001 on FDI inflows over the period 2002-07. The economic magnitude of the effect of public infrastructure on FDI inflows is quite significant.
Is the relationship driven by omitted variables? A preliminary check
As Coughlin and Segev (2000) and Blonigen et al. 2004) show that FDI inflows to a certain district can be the result of agglomeration externalities, i.e. Specifically, the plots in the middle row repeat the plots described above, but only for manufacturing FDI inflows.
Preliminary Evidence
Effect of infrastructure
Therefore, in column 3, we use a linear spline specification to test for this nonlinear form. In column 3, we find that we are statistically indistinguishable from zero, while it is positive and statistically significant at the 5% level.
Control Variables
Third, wage rates may be negatively correlated with the level of economic development in a district. Fifth, since wage rates may be lower in districts that exhibit a high level of violent crime, we control for the number of violent crimes in the district.
Discussion
- Agglomeration externalities
- Demand-side effects
- Network effects stemming from Political factors
- Wage rates
Among these control variables, we find that GDP per capita, population, and metropolitan incidence are positively related to FDI inflows in a district. We also find that the level of violent crime in a district is, ceteris paribus, positively related to FDI inflows, which, as argued above, may be because violent crime can determine rates of FDI. wages in a non-human way. development index, population, GDP per capita or metropolitan city dummy. We find that the coefficient of human development is negative, which is consistent with higher wage rates in districts that have a higher level of human development and more FDI flowing to such districts.
For example, demand for consumer durables may be greater in districts bordering metropolitan cities. Although the inclusion of state fixed effects as well as other control variables such as the level of human development, population, economic development, GDP per capita allows us to control for the actual wage rates prevailing in a district, it is still possible that these variables do not fully capture the effect of actual wage rates prevailing in a district. Since FDI is more likely in districts where wage rates are lower, such omitted variables at the district level may also affect identification.
In general, omitted variables at the district level can be the source of endogeneity that corrupts the identification using the above tests.
Identification by netting out average FDI inflows into surrounding districts
Tests netting out maximum FDI inflows into surrounding districts
Since agglomeration externalities accounting for FDI in a particular district may manifest due to the most attractive destinations among the surrounding districts, we go one step further with our identification strategy by using these surrounding districts by equalizing the maximum FDI inflow among the surrounding districts. -runs our tests. This test enables us to control for network effects, unobserved demand-side factors, and the presence of a powerful legislator who uses the most attractive destination among surrounding districts. Having found similarly strong results using these neighboring district tests, we now examine the predicted relationship and estimate the economic size of infrastructure's effect on FDI.
Predicted relationship
In fact, as seen in section 3.2.1, the inflection point obtained with the quadratic functional form was also very close to the sample median.
Economic magnitudes
Within-district tests exploiting inter-sectoral differences in FDI propensity
Note that given the district fixed effects, the effect of infrastructure is included in the above specification. We find that while there is no excessive effect in the low infrastructure districts, in high infrastructure districts, the effect of infrastructure is more pronounced in sectors that have a greater propensity to attract FDI. Thus, our results in Table 9 districts indicate that the non-linear relationship between infrastructure and FDI inflows is more pronounced in sectors that have a greater propensity to attract FDI compared to sectors that are less likely to attract FDI.
Because the variation we exploit is entirely cross-sectional, these within-county tests control for all unobserved factors at the county level and provide the strongest evidence in support of the hypothesized relationship between infrastructure and FDI inflows.
Additional robustness tests
- Effect of Infrastructure on FDI in each year
- Tests controlling for effect of domestic demand
- Tests controlling for potential lobbying by multinational enterprises
- Relative effect in manufacturing and service industries
First, we test separately for the effect of infrastructure on FDI for the upper and lower quartiles of FC projects. Since lobbying is disproportionately more likely for the large projects but not for the small projects, our results would not be obtained for both subsamples if they were primarily driven by such lobbying. In particular, the fact that the relationship is quite evident for the lower quartile is reassuring, as lobbying is most likely to be a negligible consideration for such small projects.
We also note that the infrastructure coefficients in columns 5 and 6 are very similar to those in column 2 of Table 7 , implying that lobbying is unlikely to affect our results. We find that the results hold equally well for both, highlighting the fact that quality physical infrastructure is not only important for capital-intensive, large-scale manufacturing plants, but across the board. These tests also examine the possibility that our results are a manifestation of the competitive advantage that certain districts have in certain industries. The fact that our results are equally good in both of these sectors ensures that our results may not depend on it.
In summary, we conclude that our results remain robust even after being subjected to several robustness tests.
4 A theoretical explanation
Three conditions ensure equilibrium in the home country: clearing of the market for primary factors, clearing of the market for intermediate goods, and an iso-cost condition so that the multinational faces the same costs in the home market as if it had chosen to locate in to settle in another country. Furthermore, knowledge flows from one intermediate firm to another, so that the costs of setting up production decrease as the size of the intermediate goods industry increases. The models thus include complementarity between multinationals and local firms through input-output linkages, and positive externalities between local producers of intermediate goods.
However, this phenomenon is counteracted by the increased pressure in the labor market which leads to rising labor costs. The government that wants to encourage domestic production can provide a production subsidy for each unit produced by the MNE in the domestic economy. A non-discriminatory subsidy reduces the private marginal cost of production for all MNEs that choose to establish production facilities in the domestic economy.
The result is a multifaceted balance: any subsidy that exceeds the threshold level can lead to an inflow of foreign direct investment into a cluster of multinational companies that establish themselves in the local economy; without a marginal level of subsidy, no multinational company invests in the domestic economy.
5 Conclusion
The producers of intermediate products are assumed to operate with a technology with increasing returns to scale, which may be due to learning by doing, local agglomeration effects or division of labor. The larger the market (the more multinational companies there are), the greater the demand for intermediate products and thus lower production costs for all intermediate companies. If production costs fall due to the benefits of a growing intermediate sector, more firms may choose to enter this subsidy threshold level.
First, it explains why a small expansion of physical infrastructure in an impoverished country is unlikely to produce a commensurate increase in foreign direct investment. It also explains why Special Economic Zones, such as those in China, have achieved spectacular success; our results suggest that the policy has helped exceed the infrastructure threshold needed to attract foreign direct investment. Finally, our research sheds light on regional variation in FDI flows into India – the second-largest emerging market economy that received nearly USD 35 billion in FDI in 2009.
A better understanding of the nature and drivers of FDI inflows in India is an important topic in itself and the present paper is one of the first systematic studies of the reality of FDI in India.
In column 3, we show the FDI in the district after the net average of FDI from the surrounding districts. In column 3, we show the FDI in the district after the net average of FDI from the surrounding districts; this plot is also restricted to counties with non-zero FDI. The dependent variable is equal to the logarithm of the total value of FDI in the district in columns 1-3 and the logarithm of the number of FDI projects in the district in columns 4-6.
The dependent variable is equal to the logarithm of the total value of FDI in a district minus the average total value of FDI in its surrounding districts in columns 1-3 and the logarithm of the number of FDI projects in a district minus the number average of FDI projects in his district. the surrounding districts in columns 4-6 FDI are measured during the time period 2002-2007 while the independent variables are measured in 2001. The dependent variable is equal to the logarithm of the total value of FDI in a district, after fixing the maximum of FDI in the surrounding districts in columns 1-3 and the logarithm of the number of FDI projects in a district, after the maximum number of FDI projects in the surrounding districts has been settled in columns 4-6. The dependent variable is equal to the logarithm of the total value of FDI in a district in columns 1-2 and the logarithm of the Number of FDI projects in a district in columns 3-4.
FDI trend is measured as the ratio of FDI in a sector to total FDI in India for 2001. The dependent variable is equal to the logarithm of the total value of FDI in a district in columns 1-5 and the logarithm of the Number of FDI projects in a circle in columns 5-10. The dependent variable is equal to the logarithm of the total value of FDI in a district minus the average value of FDI in its surrounding districts in columns 1-2 and the logarithm of the number of FDI projects in a district minus the average number of FDI projects in his district. circles in columns 3-4.
The dependent variable is equal to the logarithm of the total value of FDI in a district minus the average value of FDI in its surrounding districts in columns 4-6. The dependent variable is equal to the logarithm of the total value of FDI in a district minus the average value of FDI in its surrounding districts in columns 1-2 and the logarithm of the number of FDI projects in a district minus the average number of FDI projects in his district. districts in columns 3-4 for service industries.
