# **4. EMPIRICAL RESULTS**

# **4.1. Main Results**

**First, looking at all regions together, we see that growth is correlated with an increased share of industry, construction, and market services, plus innovation and FDI.** Table 9 reports the results of the two-way FE estimation of various versions of the baseline Equation (1), and its modified version accounting for business activities of inward FDI, for the whole sample of regions. The results show a general convergence process, as denoted by the negative coefficient on the variable capturing GDP per capita at the beginning of the period. Agglomeration forces, as captured by the employment density variable, are positively associated with GDP per capita growth. The results indicate that a 10 percent change in employment density is associated with a 0.4 to 0.6 percent change in GDP per capita. The correlation with population change is negligible.  Looking at specifications (4), (5), and (6), we see that GDP per capita growth is positively associated with increases in the industry, construction, and market services shares of GVA, while negatively associated with increases in the non-market services share of GVA. The agriculture sector, on the contrary, does not show a significant correlation with economic growth. Furthermore, both innovation and inward FDI emerge as growth-enhancing factors, and the coefficients on the industry variables are relatively consistent with the addition of these variables.

**Inward FDI is most beneficial to incomes growth when it is in production activities, logistics, distribution and transportation.** These results emerge looking at specification (7), which replaces the continuous variable for inward FDI with the categorical one ( r −1), showing the business activity most commonly observed in the host region among positive FDI. The evidence implies that regions have recorded a ‘growth premium’ when they attract inward FDI in MMNNMM IINNIINNaaII FFGGII production activities and logistics, distribution and transportation.

**Disaggregating the regions by income category, we see that correlates of growth differ for low-income regions – in which none of the industries is significantly correlated with growth, but the role of FDI is strongest.** Table 10 reports the results of two-way FE estimation of Equation (1) and its modified version obtained by accounting for regional heterogeneity in terms of income level. Drawing on the income-based taxonomy previously presented, regions have been split into ‘highincome’, ‘transition’, ‘less developed’, and ‘low-income’. Comparison of the results reported in specifications (1) to (4) indicates the existence of different growth pathways for different types of regions. First, looking at the industrial structure of regions, it emerges that increases in the industry, construction, and market services shares of GVA are positively associated with economic growth in all regions, except for ‘low-income’ regions where there is no pattern in the industries contributing to growth. For agriculture, there is a positive association with growth; and for non-market services there is a negative association with growth – but both of those relationships seem to affect only ‘transition’ and ‘less developed’ (‘high-income’) regions. Second, innovation (proxied by the number of patents) seems to be a growth-enhancing factor in all regions, except for ‘transition’ regions. This may be because transition regions rely more on technology adoption, rather than pioneering innovation, but this requires further investigation. Third, inward FDI seems to be associated with growth only in ‘low-income’ regions. In those regions, the relationship is very robust (significant at the 0.1 percent level), but it is relatively weak in impact: a doubling of the number of foreign investments per 100,000 inhabitants is associated with a 1.1 percent increase in GDP per capita growth. For example, a region with an average per capita income of €4,000 and 2 foreign investments per 100,000 inhabitants, would find on average that doubling the number of investments to 4 per 100,000 inhabitants is associated with an increase in per capita income of €44. This should not discourage low-income regions, since the relationship with FDI is still robust and positive, but indicates that increases in GDP per capita for the whole region are associated only with a dramatic increase in the number of foreign investments.<sup>32</sup>

> 32 Some readers will question why the number of investments is used, rather than the value of investments or the jobs created in the host region through inward FDI. Both values are available from the same _fDi Markets_ database. However, they are generally regarded

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