(e.g. Harding and Javorcik 2011), or rely on newspaper-reported greenfield FDI announcements (e.g. Crescenzi, Di Cataldo and Giua 2021). In both cases, this only accounts for a subset of FDI and can deviate significantly from actual FDI dynamics. In addition, their models commonly do not account for the relative effects that IPA targeting may have on relative competitiveness vis-à-vis alternative host countries. Such omissions can bring about endogeneity concerns and potentially over-estimate the effect of investment promotion on FDI inflows. This challenge was first raised by Anderson and van Wincoop (2003), showing the importance of ‘multilateral resistance’ general equilibrium effects in correctly estimating the response of trade flows to trade costs.

In contrast, this paper makes use of a wide set of FDI source countries and makes use of actual FDI stock data (rather than newspaper reported FDI announcements). It also explicitly considers how an IPA’s targeting of a priority sector in one country may affect the relative effectiveness of IPAs in _other countries_ targeting the same sector, by including sectoral IPA targeting within a structural gravity model framework.

The paper also contributes to the more qualitative literature around identifying high-performing IPAs and considering which characteristics contribute to their performance. Other such studies have historically relied on IPAs’ self-reported statistics (e.g. Lim, 2018) which can be biased, or have defined IPA performance based on a set of “good practices” exhibited by their favored IPAs, which then runs the risk of circular reasoning (e.g. Whyte, Ortega and Griffin, 2011). Other, more empirical estimates tended to correlate FDI inflows with a narrow indicator of IPAs (e.g. Harding and Javorcik, 2012 show a relationship between an IPA’s information handling and aggregate FDI inflows). Yet, it can be difficult and misleading to reduce the effectiveness of an IPA to a single outcome indicator.

This paper avoids some of these limitations by taking a data-driven approach to identifying ‘effective’ IPAs. Namely, it uses a model to identify those countries which see a statistically significant relationship between their IPA’s sectoral targeting and increased FDI stock in that sector. For this, the paper uses an empirical specification with country-pair, host-sector, source-, host-, and sector-year fixed effects. It then compares these to other IPAs across a highly detailed set of characteristics using the 2019 WB-WAIPA survey, to assess the potential dynamics that may shape the effectiveness of IPAs in attracting FDI.

While this paper provides a wide range of controls to address endogeneity concerns, the non-randomness of priority sectors may still happen for reasons not accounted for by the comprehensive set of fixed effects. 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. As such, these results should be considered as indicative only.

# **2. Literature Review**

## **2.1 The effect of IPAs on attracting foreign investment**

The rationale for investment promotion agencies stems from the existence of information asymmetries and transaction costs in capital markets (Williamson, 1985; Wells and Wint, 2000, Loewendahl, 2001). International investors, who intend to invest in a foreign market, often lack specific information on the potential host country’s available business partners, government regulation, and wider investment climate (OECD, 2015). IPAs are expected to influence the investment decisions of multinational corporations (MNCs) by solving these information/perception gaps about the host economy (OECD, 2018).

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