Table 2: Mechanism at play

|VARIABLES|(1)<br>CR5|(2)<br>CR5|(3)<br>CR5|(4)<br>CR5|(5)<br>HHI|(6)<br>log(# MNEs)|(7)<br>log(K)|
|---|---|---|---|---|---|---|---|
|Uncertainty (standardized)|0.927**|-0.298|1.460***|1.560***|0.059**|-0.067**|0.019|
||(0.440)|(0.600)|(0.520)|(0.501)|(0.025)|(0.027)|(0.031)|
|Uncertainty _×_ Developing||1.862***<br>(0.687)||||||
|Observations|2,349|2,349|1,489|1,472|1,472|1,472|1,472|
|R-squared|0.830|0.831|0.819|0.824|0.738|0.894|0.385|
|Controls|No|No|No|Yes|Yes|Yes|Yes|
|Data Coverage|All co|untries|||Developin|g||

_Note:_ Each observation corresponds to a destination-year, represented as _it_ . “CR5” denotes the concentration ratio of the top 5 investing firms. Countries averaging fewer than 5 foreign investors are excluded from these calculations. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

However, eliminating source countries one by one from the descriptive regression associating uncertainty with investment concentration at the destination does not yield major variation in the coefficient (see Tables B8 and B9). This result points to destinationspecific variation as the main driver of the pattern. Controlling for home-country concentration (see Tables B10 and B11) and disaggregating industry-level concentration (see Table B12) confirms the robustness of our findings.

# **5 Conclusions**

Using comprehensive data on cross-border investment projects from a wide range of countries, this study provides evidence that FDI has become more concentrated in the past decade. This trend is especially marked in developing countries and East Asian economies in particular (including China and India); it is also sharper when it comes to greenfield investment relative to M&A and has accelerated during the COVID-19 pandemic. Our analysis provides suggestive evidence that concentration is associated with both an increase in idiosyncratic macroeconomic uncertainty and, relatedly, a dramatic reduction in the number of foreign investors. Although the structural shift of FDI into services as well as rising market concentration in some major source economies 12