### **Remaining issues**

A remaining challenge for the analysis may pertain to reported FDI data. A first concern here may be that this does not capture FDI from all countries, but only OECD countries. However, due to the prominence of OECD-countries in outward FDI, we find that in most cases this data set captures a large share of a country’s FDI stock (see data section for details). In cases where the share of FDI from OECD countries is lower (e.g. Hong Kong SAR, China, or India), this would not introduce a bias (as the estimate would still be consistent), but changes the interpretation of the estimate to reflect an IPA’s ability to attract FDI _from OECD countries._ As such, this may potentially affect the external validity of the estimate, if it is expected that different strategies are more effective for non-OECD countries (but we are not aware of any such theories) _._ An additional limitation comes from the analysis of characteristics of effective IPAs. Using t-tests to distinguish between high-performing and other IPAs does not allow us to make causal inference about IPA characteristics to drive FDI dynamics. Instead, these results should be considered indicative only. This is partly because there may be other factors (e.g. strong political leadership) which may drive both IPA characteristics and FDI performance. These IPAs may then just be part of a government’s wider strategy of FDI sectoral targeting. It also does not allow us to gauge exactly how important specific characteristics are. That said, we do think that if there is a strong, consistent pattern emerging across all countries that see a strong FDI effect in sectors targeted by the IPA that it is not unreasonable to assume that at least part of this contribute this to the underlying IPA characteristics. In addition, to the extent that IPAs can align with the wider government’s sectoral growth strategy (or push sector-specific reforms through internal advocacy), this itself reflects an “effective” IPA.

# **5. Results**

## **5.1 Sectoral Gravity Model**

The results for the sectoral gravity model are presented in Table 5. The first equation uses traditional gravity model variables from the CEPII database (Head and Mayer, 2014) for the OLS and PPML model, respectively, and combines this with simple year fixed effects. Both models operate in line with expectations, as GDP source country and GDP host country are significant and positively correlated to FDI stock, while source-host distance is negatively correlated. Countries that are more culturally proximate (e.g. through a common language, colonizer or religion) also appear to be more likely to invest in one another in the OLS model, though the common religion variable is the only cultural approximation variable that is significant in the PPML model. In both cases, IPA sector targeting is highly significant and positive.

In the second set of regressions (equation 2 and 4), the traditional gravity variables are replaced with country-pair fixed effects, and time-varying dynamics are captured through source-year and host-year fixed effects. In both cases, the IPA sector targeting coefficient remains significant and positive, though reduces in size (thus capturing part of the unobservable country-pair FDI dynamics through fixed effects).

The third set of regressions further include sector-year and host-sector fixed effects. Again, the IPA sector targeting coefficient remains significant and positive, but further reduces in size (suggesting that IPAs are likely targeting those sectors which see larger increases in their FDI stock across the board). Our most conservative, and preferred, estimate is from the PPML model with the widest set of controls (equation 6). This suggests that an IPA’s targeting of a specific sector is associated with around 10 percent increase in the value of their country’s FDI stock in that sector over the timeframe of the analysis.

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