To develop the structural gravity model of FDI, we follow the approach suggested by Kox and RojasRomagosa (2020). First, even though trade gravity models usually depend on flows, we use the stock of (sectoral) bilateral FDI as our dependent variable, as it is much less volatile than FDI flows and tends to be non-negative (which would be problematic for some of the estimation methods). We are careful to distinguish between real zero FDI stocks and missing (or non-reported, suppressed) data. Second, we employ a Pseudo-Poisson maximum likelihood (PPML) estimator, because it effectively deals with zero bilateral FDI stocks and accounts for the presence of heteroskedasticity in FDI data (Santos Silva and Tenreyro, 2006). Third, alongside our standard set of gravity variables we also follow the best practice of using country-pair fixed effects to account for any unobservable time-invariant FDI barriers, which has been proven to be a better measure of the bilateral costs (Agnosteva, Anderson, and Yotov, 2014; Egger and Nigai, 2015). Finally, we use source-year and host-year fixed effects to properly account for multilateral resistance terms in panel data gravity estimations (Olivero & Yotov, 2012). The country of origin generates the outward FDI stock and the destination country the inward FDI stock. The origin-time and destination-time fixed effects also absorb the country size variables from the structural gravity model, in addition to all other observable and unobservable time-varying country-specific characteristics, including different national policies, institutions and exchange rates (Yotov et al. 2016).

Based on this, our main specification is: FDIijst = β1 IPAstrategyjst + αij + αit + αjt + αst + αjs + εijst (2) This specification is similar to (1), but is now set up in a bilateral framework where FDIijst is the inward FDI stock from source country _i_ to the host country _j_ , for sector _s_ in period _t_ . β1 is still the coefficient of interest, capturing the average effect of the IPA strategy of the host country on sectoral FDI stocks. αij captures the country-pair fixed effects, while αit, αjt, αst and αjs reflect the source-year, host-year, sector-year and host-sector fixed effects, respectively. εijst are the idiosyncratic error terms. Standard errors are clustered at the country-pair-sector level and are heteroscedasticity-robust, correcting for possible bias induced by estimation of log-linearized models (Santos Silva and Tenreyro, 2006).

# **Characteristics of effective IPAs**

In the second part of the analysis, we also consider the _relative_ effectiveness of different IPAs. This is done by incorporating an interaction effect with the IPA strategy in our main specification: FDIijst = β1 IPAstrategyjst + β2 IPAstrategyjst ∗Countryj + αij + αit + αjt + αst + αjs + εijst (3) Here, the main coefficients of interest are β1 (the average effect of IPA targeting) and β2 (the countryspecific effect of IPA targeting). From this, we define three different groups: ‘high-performing IPAs’ are those in which country-interaction effects show a positive, significant effects of IPA targeting on FDI stocks. ‘Other IPAs’ are those in which country-interaction effects either show an insignificant or negative, significant effect of IPA targeting on FDI stocks. Finally, a select group of countries have no sectoral targeting or no variation in the timing of the sector-strategy, thus precluding any country-level analysis.

Next, we consider how high-performing IPAs differ from other IPAs across a range of characteristics. This is done by comparing the mean values of both groups, making use of t-test to consider any statistically significant effect. These variables are grouped according to the four pillars identified by Heilbron and Kronfol (2020): institutional arrangement, strategic alignment and focus, organizational framework and resourcing and investor service delivery.

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