Does foreign direct investment affect domestic investment?

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Does foreign direct investment affect

domestic investment?

*

Rodolfo G. Campos

Banco de Espa˜

na

Julia Estefan´ıa Flores

Banco de Espa˜

na

Gonzalo G´

omez Bengoechea

Universidad Pontificia Comillas

29/08/2018

Abstract

We study the relationship between inbound and outbound foreign direct investment and domestic capital formation. We test the hypothesis that outward Foreign Direct Investment (FDI) crowds out domestic capital formation and that inward FDI crowds in domestic capital formation using a world-wide sample of countries over the period 1970 to 2017. We attempt to address potential endogeneity problems by using a gravity-based instrument that has been successfully employed in the literature studying the impact of trade and migration on growth. Except for a reduced sample of advanced economies, we do not find strong evidence of inward or outward FDI affecting domestic investment.

*_{We are grateful for comments by seminar participants at the FREE seminar. The views expressed in} this paper are those of the authors and do therefore not necessarily reflect the views of the Banco de Espa˜na or the Eurosystem.

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1. Introduction

Policy institutions such as the IMF (2018) are issuing warnings about the dangers of increasing protectionism and economic nationalism for economic growth and global prosperity. For instance, current economic policies being enacted in the USA as part of the renegotiation of NAFTA attempt to prevent outward FDI to increase investment at home. But does outward FDI lower domestic investment? And, does inward FDI foster domestic investment? This paper addresses these questions by revisiting an old line of research started by Feldstein (1995), but using new tools.

In an influential article, Feldstein (1995) documented a relationship between in-bound and outin-bound foreign direct investment and domestic capital formation—domestic investment—. Using a cross-section of a group of advanced countries in the 1970s and 1980s he found a correlation pattern that implies that one dollar of outbound FDI is associated with a displacement—“crowding-out”—of 0.20–0.38 dollars of domestic investment.

Our paper has two research goals. The first one is to update the study of cross-sectional patterns documented by Feldstein (1995) to a more recent period and using the widest cross section of countries possible. The second, and more important, is to test the existence of a causal link between FDI inflows and outflows and domestic capital formation by adapting a strategy used by Frankel and Romer (1999) to study the effect of trade on income levels.

Following Frankel and Romer (1999), we use a gravity equation to project bilate-ral FDI flows on geographical and cultubilate-ral variables and then obtain predicted FDI flows from this estimation. Because these predicted flows are constructed only from geographical and cultural explanatory variables, they can plausibly be argued to be exogenous and can be used as instruments for FDI flows to study their impact on domestic investment.

When we update the work of Feldstein (1995) to more recent decades and to a wider sample of countries, our results show that the relationship between FDI and domestic investment becomes less clear than in his initial sample of advanced countries in the 1970s and 1980s. Moreover, in the different regressions using our gravity-based instrument, we do not find evidence for the existence of a causal link between FDI flows and domestic investment, except for the subset of advanced countries originally considered by Feldstein (1995), and only at the beginning of the period that we consider. The paper is structured as follows. In Section 2 we summarize the literature on the relationship between FDI and domestic capital formation. In Section 3 we update Feldstein’s original results to a wider set of countries and to a longer time frame —until the 2010s. In Section 4 we explain the details of the methodology of our gravity-based

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instrument and in Section 5 we present the main results related to it. Finally, Section 6 contains the conclusions of the article.

2. Literature Review

It is commonplace to emphasize that FDI plays an important role on the productive fabric, employment, innovation, knowledge and productivity, as well as on the current and capital transactions, of a country.

The empirical results obtained in (Feldstein and Horioka, 1980), showed that the is a strong relation between domestic saving and investment rate, which is contrary to economic theory. From their point of view, the apparent contradiction —puzzle— was explained by structural factors, such as the lack of information, investors’ risk aversion and differences in legal systems.

However, OECD countries results suggest that there is an arbitrage in similar risk-free assets comes very close to perfection. According to this, if capital markets are integrated, domestic investment is financed by foreign savings and domestic saving could seek higher returns in other countries. These controversial results gave start to widespread debates in the economic literature, as in Obstfeld and Rogoff (2000).

A growing body of research investigated this puzzle using different methodologies, with inconsistent results. From the methodological point of view, the puzzle has been studied using using cross-sectional regressions (Obstfeld, 1994) and time series ap-proaches (Pelagidis and Mastroyiannis, 2003); (Caporale et al., 2005). However, the majority of the studies investigated it using panel data techniques (Bangake and Eg-goh, 2011); (Ketenci, 2013). More recently, researchers have focused on the effect of structural breaks on the puzzle dynamics (Akadiri et al., 2016); (Chen and Shen, 2015). Following the line of thoughy originally developed by himself, Feldstein (1995) stu-died the relationship between domestic investment (gross fixed capital formation) and foreign direct investment inflows and outflows at the light of the puzzle. In this paper, Feldstein analyzed the impact on domestic investment of an increase in inward and outward foreign direct investment flows.

In his seminal work, Feldstein considered a firm level approach. According to it, it would be possible to know if when companies invest in subsidiary firms abroad they reduce or increase investment on the parent company and, at the same time, if inward direct investment flows increased fixed capital on foreign subsidiary firms on domestic ground.

This firm perspective, followed by Desai et al. (2005), was only theoretically conside-red by Feldstein (1995). From the empirical point of view, an aggregated country-level approach was necessary to answer properly to the research question.

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Feldstein (1995) analyzed the impact of FDI inflows and outflows on domestic in-vestment in two different groups of 15 and 18 countries of the OECD for the 1970s and 1980s. In his results, foreign direct investment flows abroad are negatively related to domestic investment. Outward FDI flows are barely offset by inward portfolio in-vestments and other flows. As it was mentioned in the introduction, he documented a crowding-out effect of FDI outflows on domestic investment.

For the whole set of OECD countries, as well as for the individual case of the United States, Feldstein finds that outward FDI crowded out domestic investment in the 1970s and 1980s. At the same time, for each dollar of inward FDI, domestic investment in the host countries increased was crowded in in the same period. There is a substitutive relationship between outward FDI and domestic investment, whereas inward FDI and domestic investment are complements.

The articles that followed this line of research examined either the puzzle or this crowding out effect in other countries (Guzel and Ozdemir, 2011), at the firm or in-dustry level, (Desai et al., 2005); (Onaran et al., 2013), on larger samples (Yildirim and Orman, 2016), or using novel econometric strategies (Yıldırım and Koska, 2018).

One of the main issues with this literature is the lack of an econometric consensus methodology. This, partially, explains the absence of a global consensus view on the Feldstein-Horioka puzzle and on the crowding-out effect documented by the aforemen-tioned authors (Ma and Li, 2016).

3. Revisiting Feldstein: an update for 1970–2017

In this section we revisit Feldstein’s original specification to measure how domestic investment is related to FDI inflows, FDI ouflows and gross national savings, all mea-sured as percentages of GDP. We estimate the original cross-sectional specification of Feldstein (1995), where i indexes countries. Variables are measured as decennial ave-rages, as in Feldstein (1995). Data has been retrieved from the World Bank’s World Development Indicators

Investmenti=α+β1F DIiinward+β2F DIioutward+β3savingsi+εi, (1)

We estimate this regression on three different samples and present the results in Tables 1–3. In Table 1 we estimate the main specification for the original 16 OECD countries, as in Feldstein (1995). We update his results until the 2010s decade. In Table 2 we also include all the current OECD countries. Finally, in Table 3, we compute the model for the all countries with available information, and not only for OECD members.

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In all tables we show split the results by decade. We also estimate the main model pooling all decades and adding year dummies to the specification. Standard errors are clustered by country in all specifications that include repeated observations on the same country.

Table 1: OLS for Feldstein’s sample by decade

(1) (2) (3) (4) (5) (6) VARIABLES All Years 1970 1980 1990 2000 2010 Inflows ( % of GDP) 0.398*** -0.399 0.592 0.070 0.147 0.800**

(0.123) (1.006) (0.748) (0.548) (0.245) (0.303) Outflows ( % of GDP) -0.456*** -1.415 -1.799** -0.766 -0.249 -0.836**

(0.112) (0.832) (0.572) (0.436) (0.180) (0.294) Gross savings ( % of GDP) 0.461*** 0.569** 0.903*** 0.614*** 0.297 0.585***

(0.072) (0.210) (0.188) (0.121) (0.167) (0.148) decade = 1980 -0.117

(0.867) decade = 1990 -0.791

(0.879) decade = 2000 -0.695

(0.845) decade = 2010 -1.618* (0.864)

Constant 13.261*** 11.706* 4.258 10.428*** 16.387*** 8.997** (1.962) (5.385) (4.160) (2.837) (3.763) (3.211) Observations 65 10 11 12 16 16 R-squared 0.584 0.642 0.769 0.798 0.287 0.605

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

In Table 1, inflows are significant only for the pooled estimation and for the 2010s decade. FDI outflows are negatively significant for all periods with the exception of the 70s, 90s and 2000s. Gross savings are only not significant, with a positive sign, for the 2000s decade. The overall significance of all the estimations is above 0.5 with the exception of the 2000s decade, when it falls below 0.3.

Table 2 shows a similar pattern. Inflows are positively significant only for the pooled estimation and for the 2000s decade. FDI outflows show, for all decades, a negative relationship with domestic capital formation. It is significant for all periods with the exception of the 2010s decade. Gross savings, on the other hand, results in positive coefficients, all of them significant except for the 2000s decade. As in Feldstein (1995), R2 is always above 0.5, except for the two last periods.

Table 3 includes both advanced and developing countries. This fact explains the differences with the first two estimations. In this case, FDI inflows are significant for all periods except the 2000s decade. FDI outflows alternate positive and negative signs with no significance in any of the decades; only the pooled regression show a significant negative relation between outflows and domestic capital formation. Gross savings are significant in all periods, with the exception of the 2000s decade.

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Table 2: OLS for OECD’s sample by decade

(1) (2) (3) (4) (5) (6) VARIABLES All Years 1970 1980 1990 2000 2010 Inflows ( % of GDP) 0.389*** 0.682 0.439 0.188 0.411** 0.247 (0.103) (1.487) (0.617) (0.233) (0.199) (0.186) Outflows ( % of GDP) -0.432*** -2.416** -1.098** -0.902*** -0.378** -0.292

(0.092) (1.086) (0.419) (0.219) (0.145) (0.179) Gross savings ( % of GDP) 0.479*** 0.769*** 0.732*** 0.742*** 0.151 0.443***

(0.047) (0.156) (0.073) (0.074) (0.099) (0.087) decade = 1980 -0.651

(0.867) decade = 1990 -1.099

(0.822) decade = 2000 -0.541

(0.818) decade = 2010 -2.788***

(0.817)

Constant 13.278*** 6.960 7.203*** 7.250*** 19.969*** 11.381*** (1.325) (3.943) (1.773) (1.870) (2.408) (2.083) Observations 138 17 22 29 35 35 R-squared 0.512 0.659 0.859 0.816 0.198 0.483

In the last specification the overall significance of the model falls dramatically. R2 is below 0.3 in all periods, with the exception of the 1990s decade, when it rises slightly above 0.6.

Some conclusions are derived from this results. First, when we pool all decades, and allow for a decade-specific constant, the pattern uncovered by Feldstein (1995) emerges. In Tables 1–3 (Col. 1) the average relationship between outward FDI and investment is negative and strongly significant. The relationship between inward FDI and domestic capital formation is positive and strongly significant.

This result suggest that outward FDI would be crowding out domestic investment Inward FDI seems to be crowding in domestic investment. The coefficient on national gross savings is positive and strongly significant in all columns, as shown in Felds-tein and Horioka (1980). The coefficients stay roughly constant across the two OECD samples in Tables 1 and 2 and are only slightly lower for the pooled estimation.

Second, coefficients and significance are not stable for all decades. In Table 1, for the first three decades, FDI outflows seem to be strongly crowding out domestic investment. The magnitude of the point estimate drops in the 2000s and recovers in the 2010s. For inward FDI, the evidence in decade by decade regressions is weak. Only in the 2010s crowding in seems to be occurring.

In Table 2, where we extend the sample to all current OECD countries, the crowding-out effect seems to be stronger at the start of the sample, drops strongly in the 2000s and only partially recovers in the 2010s. In the pooled regression, the coefficient is

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Table 3: OLS for all countries by decade

(1) (2) (3) (4) (5) (6) VARIABLES All Years 1970 1980 1990 2000 2010 Inflows ( % of GDP) 0.332*** 1.502** 2.177*** 2.081*** 0.046 0.121*

(0.054) (0.675) (0.663) (0.151) (0.061) (0.072) Outflows ( % of GDP) -0.283*** 0.860 -0.068 0.037 -0.039 -0.102

(0.069) (1.702) (1.467) (0.305) (0.091) (0.069) Gross savings ( % of GDP) 0.120*** 0.168* -0.045 0.309*** 0.085*** 0.191***

(0.021) (0.098) (0.079) (0.076) (0.021) (0.027) decade = 1980 -1.467

(1.528) decade = 1990 -2.310

(1.442) decade = 2000 -3.889***

(1.412) decade = 2010 -2.645* (1.415)

Constant 21.835*** 18.883*** 21.552*** 10.725*** 19.764*** 18.471*** (1.338) (2.749) (1.917) (1.752) (0.682) (0.874) Observations 571 42 84 127 161 157 R-squared 0.112 0.199 0.130 0.607 0.093 0.258

similar to the one obtained by (Feldstein, 1995). Point estimates for FDI inflows are all positive.

Finally, in Table 3, FDI outflows show no significance except for the pooled esti-mation, whereas FDI inflows have positive and significant coefficients until the 1990s, suggesting strong crowding in effect. Significance disappear in the 2000s, weakly retur-ning in the 2010s.

There are also two important limitations that emerge from this OLS analysis. First, it is true that the pooled regression captures (after removing decade fixed effects) the relationship between between inward and outward FDI and investment in the same way tha Feldstein (1995). However, the coefficients of inward and outward FDI vary very much across decades, suggesting the probable omission of additional explanatory factors such as the global financial crisis in the 2000s.

Second, the overall significance of the model changes dramatically across decades. Whether the relationship in the previous estimations is a mere correlation or a causal link is something that we analyze in the following sections of this paper through the construction of an adequate instrument.

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4. Methodology: a gravity-based instrument

4.1. Specification

Because both inward and outward FDI is potentially endogenous, we construct an instrument for each of these two variables for countryiusing predicted values from the regression.

logfijt=z0ijφt+γjt+ηijt, (2)

whereiis called the domestic andjthe foreign country andfijtis bilateral FDI between

the domestic countryiand the foreign countryj at timet (either inward or outward) expressed as a percentage of GDP of countryi. The vectorzij contains geographical and

cultural bilateral variables related to the costs of performing investments betweenito

j, such as geographical distance, the presence of a common border, common language, etc. We allow the effect of the bilateral costs to vary over time by estimating a time-varying vector of coefficients φt. We include factors related to country j in the most

flexible way by includingj-time dummy:γjt. We do not include dummy variables for the

domestic countryibecause they are likely to be correlated with domestic investment. The predicted bilateral FDI share between countries iand j at timetis

ˆ

fijt= exp(zij0 φˆt+ ˆγjt). (3)

We obtain the share of FDI predicted by geography for any specific domestic country

iby summing predicted bilateral FDI shares over all foreign countries:

ˆ

fit=

X

j6=i

ˆ

fijt. (4)

We estimate the bilateral gravity equations in (2) by using the Poisson Pseudo Maximum Likelihood (PPML) estimator (Santos Silva and Tenreyro, 2006) for inward and outward FDI. We then use ˆfit obtained from this procedure as instruments.

Data for bilateral FDI flows are taken from two databases, from the OECD and UNCTAD. For each of these databases we construct two instruments: one for inflows and another one for outflows. We estimate the general form given in (2) in practice by estimating the coefficients in the following specification decade by decade:

logfijt=α(t) +β1(t) log(distij) +β2(t)Continenti+β3(t)Colonyi

+β4(t)Ethnoi+β5(t)Areai+β6(t)Landlockedi+β7(t)Cj+β8(t)Bi+εijt,

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where t stands for a decade, i for the reporting country in a dataset and j for the partner country. VariableCj is a dummy variable for the partner country and Bi is a

dummy variable used to single out four economies that we classify as outliers in the UNCTAD database and do not use in the final estimation.

Some of the bilateral data showed negative values. To solve this problem, we used the absolute value of this negative value to run the regression and converted the value back to negative after the prediction step. Thus, the conversion was only used to the development of the instrument and it did not interfered in any other calculations.

4.2. Dataset

To construct the instrument we relied on the methodology by Frankel and Romer (1999). In their article, they explore the relationship between trade openness and per-capita income levels by creating an instrumental variable based on distance and cultural patterns. When applying this for our research, to develop the instrument we require bilateral FDI data for the countries in our sample covering, at least, a decade.

There are twodatasets that meet these requirements: the one by the United Nations’ Conference on Trade and Development (UNCTAD) and the one by the Organization for Economic Cooperation and Development (OECD). The first one contains data from 2000 to 2012 – we use the period from 2000 to 2010 Regarding the OECD database, it covers from 1985 to 2016. In this case we created four different decades: 1980, 1990, 2000, 2010. The OECD also separates FDI by inflows and outflows.

For the gravity model based instrument, part of the data came from Centre d’ ´Etudes Prospectives et d’Informations Internationales (CEPII). The rest of the data - total FDI/GDP data - were taken from the World Bank’s World Development Indicators.

From CEPII, we selected indicators that measure the area of a country (Area), the distance between the countries (Dist), their continent (Continent), if they are landlocked or not (Landlocked), or if they have colonial (Colony) or ethnical (Ethno) linkages.

Because bilateral FDI flows from UNCTAD and OECD do not coincide with total FDI/GDP data from WDI we adjust data in the following way. We distribute the proportions of FDI flows per country from UNCTAD and OECD data using WDI data as a target.

F DIij/GDPi = (F DIij/

X

i

F DIi)d∗(F DIi/GDPi)W DI (6)

Where i stands for the reporting country; j for the partner country and dfor the bilateral database in each case - UNCTAD or OECD -.

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By analyzing our data, we realized there were some countries that showed large numbers on their FDI average flows across the decades. These countries required special treatment because they were significantly different from the rest. We established the threshold to select them at more than a 30 % GDP on average of FDI flows in a decade -measuring this FDI with WDI statistics. This provided us with a list of six countries for UNCTAD database (Malta, Cayman Islands, Marshall Islands, Liberia, Luxembourg and The Netherlands). We decided to exclude Luxembourg and The Netherlands from this list, given that they are big Eurozone economies and Feldstein included them both in his original sample. For OECD countries it was not necessary to extract any economy given that they are all big economies, and most of them are in Feldstein’s sample. To take into account the differential characteristics of these countries, we created a dummy variable for these countries (Bi).

5. Results

In Table 4 we present the main results for the instrumented regressions. The first three columns use the instrument constructed using bilateral UNCTAD data. They therefore use only the 2000s decade. The three columns cover the three samples we have used. The two remaining columns use the instrument constructed using bilateral data from the OECD. This instrument is available for the 1980s, 1990s, 2000s, ans 2010s but only for OECD destination countries. Therefore the estimates are only for OECD countries and for the Feldstein subsample.

The estimations using UNCTAD data do not show coefficients that are significantly different from zero. It can therefore not be concluded that inflows or outflows have a causal effect on investment, at least for the 2000s, the decade covered by these regressions. First-stage regressions and F-statistics point to strong instruments with the possible exception of inflows in the Feldstein sample. For the widest sample of countries, the point estimate for inflows is negative and the coefficient for outflows is positive, the opposite of what would be expected. For the two UNCTAD sub-samples of OECD countries the point estimates towards crowding in by FDI inflows and crowding out by FDI outflows but, although economically large, the imprecision of the estimates does not allow to conclude that these numbers are in effect statistically different from zero.

The remaining two columns use the instrument constructed from OECD data, allo-wing to extend the timeframe to decades other than the 2000s. Column 4 shows that, after controlling for decade dummies there is no noticeable effect, both economically or statistically, of FDI inflows or FDI outflows on domestic investment.1

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The results in Column 5 are for the subsample of OECD countries considered by Feldstein in his original work. This is the only sample in which there seems to be a robust effect of FDI on investment in the IV regressions.

Table 4: Instrumented regressions

(1) (2) (3) (4) (5) VARIABLES UNCTAD All UNCTAD OECD UNCTAD Feldstein OECD OECD Feldstein Inflows ( % of GDP) -0.178 1.132 0.783 -0.067 0.566***

(0.553) (1.444) (0.678) (0.356) (0.137) Outflows ( % of GDP) 0.235 -0.743 -0.616 -0.050 -0.608***

(0.320) (0.821) (0.403) (0.287) (0.114) Gross savings ( % of GDP) 0.078** 0.180 0.275* 0.382*** 0.509***

(0.034) (0.193) (0.143) (0.105) (0.079) decade = 1990 -0.697 -0.587

(0.569) (0.575) decade = 2000 0.104 -0.453

(0.841) (0.784) decade = 2010 -1.798** -1.494***

(0.838) (0.560) Constant 20.416*** 17.274** 15.893*** 15.067*** 12.060***

(2.364) (7.411) (3.507) (2.574) (2.011) Observations 154 35 16 112 55 R-squared 0.040 0.406 0.512 F test Inflows 19.38 17.40 7.662 47.09 489.6 F test Outflows 22.34 16.26 28.50 70.07 1067

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

For the regressions using the instrument constructed using OECD data we can also study each decade separately. This is what we do in Tables 5 and 6. From Table 5 we learn that after the 1990s there appears to be no causal relationship between FDI, either inward or outward, and domestic investment in the OECD sample.

In Table 6, on the other hand, presents evidence for crowding in and crowding out of domestic investment by, respectively, inward FDI and outward FDI in the reduced sample considered by Feldstein, with the exception of the decade covering the 2000s.

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Table 5: Instrumented Regressions by decade (OECD)

(1) (2) (3) (4)

VARIABLES 1980 1990 2000 2010

Inflows ( % of GDP) 0.991 -0.042 -0.118 -0.037 (1.364) (0.330) (0.866) (0.400) Outflows ( % of GDP) -0.523 -0.647** -0.046 -0.023

(1.027) (0.259) (0.528) (0.378) Gross savings ( % of GDP) 0.717*** 0.713*** 0.116 0.379***

(0.085) (0.068) (0.125) (0.122) Constant 6.564*** 7.668*** 21.745*** 13.043***

(1.608) (1.626) (3.983) (2.975)

Observations 20 24 34 34

R-squared 0.792 0.831 0.009 0.446

F test Inflows 2.861 4.907 10.76 32.54

F test Outflows 3.333 51.52 61.03 49.35

Table 6: Instrumented regressions by decade (OECD Feldstein sample)

(1) (2) (3) (4)

VARIABLES 1980 1990 2000 2010

Inflows ( % of GDP) 2.254*** 0.632 0.011 0.585*** (0.801) (1.176) (0.120) (0.150) Outflows ( % of GDP) -2.499*** -1.096** -0.200*** -0.639***

(0.636) (0.553) (0.058) (0.138) Gross savings ( % of GDP) 1.001*** 0.618*** 0.339** 0.521***

(0.231) (0.105) (0.131) (0.135) Constant 1.392 10.171*** 15.845*** 10.401***

(5.159) (2.267) (2.947) (3.031)

R-squared 0.605 0.771 0.251 0.585

F test Inflows 5.807 3.523 20.80 952.5

F test Outflows 8.036 14.69 116.7 1473

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6. Conclusions

The existing literature studying the effect of inward and outward FDI on domestic investment is mainly based on empirical approaches in which potential endogeneity concerns are not entirely ruled out. In this paper we have borrowed an approach from the trade literature that has been successfully used to study the causal effect of trade on economic growth by Frankel and Romer (1999). We develop a gravity-based ins-trument for inward and outward FDI by projecting bilateral FDI flows on distance and geographical determinants and then using predicted FDI from these projections as instruments in the benchmark regression proposed by Feldstein (1995).

Our main findings are that the strong empirical relationships found by other authors between inward and outward FDI with domestic investment are not necessarily causal. They do not survive our IV strategy. Moreover, we also document that the empirical association between FDI, both inward and outward, and domestic investment weakens in recent decades both in OLS and in IV regressions. Except when focusing on the small sample of developed countries used by Feldstein (1995) the evidence for crowding in or crowding out by FDI is extremely weak.

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Appendix

Table 7: First-Stage Regressions. Regressor: Inflows

(1) (2) (3) (4) (5) VARIABLES UNCTAD All UNCTAD OECD UNCTAD Feldstein OECD OECD Feldstein Inflows Instrument 0.142*** -0.031 -0.283 0.030** 0.017**

(0.054) (0.141) (0.258) (0.013) (0.007) GFCF ( % of GDP) 0.150*** 0.299** 0.157 0.238* 0.367

(0.042) (0.145) (0.197) (0.132) (0.224) Gross savings ( % of GDP) -0.034* -0.122 -0.178 -0.246** -0.379* (0.019) (0.077) (0.131) (0.090) (0.202) Outflows ( % of GDP) 0.554*** 0.685*** 0.852*** 0.864*** 0.916***

(0.104) (0.110) (0.174) (0.042) (0.027) Constant 0.299 -2.296 2.220 0.632 -0.096

(0.802) (2.948) (4.049) (1.548) (1.734) Observations 154 35 16 112 55 R-squared 0.538 0.812 0.894 0.905 0.957

Table 8: First-Stage Regressions. Regressor: Outflows

(1) (2) (3) (4) (5) VARIABLES UNCTAD All UNCTAD OECD UNCTAD Feldstein OECD OECD Feldstein Outflows Instrument 0.830*** 0.464** 0.545** 0.388** 0.352

(0.150) (0.189) (0.207) (0.166) (0.252) GFCF ( % of GDP) -0.061 -0.406** -0.128 -0.219 -0.378* (0.037) (0.165) (0.247) (0.147) (0.196) Gross savings ( % of GDP) 0.017 0.122 0.180 0.241** 0.402** (0.011) (0.098) (0.103) (0.092) (0.155) Inflows ( % of GDP) 0.508*** 1.003*** 0.962*** 0.822*** 0.822***

(0.126) (0.081) (0.120) (0.115) (0.192) Constant -0.945 4.535 -2.471 -1.194 -0.369

(0.572) (3.702) (4.690) (1.774) (1.971) Observations 154 35 16 112 55 R-squared 0.715 0.879 0.950 0.921 0.969

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Table 9: First-Stage OECD. Regressor: Inflows

(1) (2) (3) (4) VARIABLES 1980 1990 2000 2010

Inflows Instrument 0.517** 1.037 0.050 0.026*** (0.222) (0.621) (0.179) (0.008) GFCF ( % of GDP) -0.013 0.058 0.296* 0.222

(0.112) (0.120) (0.147) (0.163) Gross savings ( % of GDP) -0.005 -0.101 -0.130 -0.299***

(0.090) (0.112) (0.077) (0.064) Outflows ( % of GDP) 0.184 0.038 0.656*** 0.953***

(0.188) (0.313) (0.129) (0.035) Constant 0.642 0.844 -2.336 2.260

(0.858) (1.146) (3.182) (3.094)

R-squared 0.315 0.342 0.811 0.966 Robust standard errors in parentheses

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Table 10: First-Stage OECD Feldstein. Regressor: Inflows

(1) (2) (3) (4) VARIABLES 1980 1990 2000 2010

Inflows Instrument 1.250 -0.511 0.010 0.017*** (0.720) (0.685) (0.386) (0.005) GFCF ( % of GDP) -0.022 0.001 0.201 0.339

(0.241) (0.168) (0.254) (0.266) Gross savings ( % of GDP) 0.082 -0.017 -0.137 -0.378***

(0.230) (0.123) (0.138) (0.111) Outflows ( % of GDP) 0.208 0.819** 0.693** 0.978***

(0.467) (0.314) (0.278) (0.017) Constant -1.926 1.065 -0.641 0.518

(2.933) (1.913) (5.373) (3.761)

*** p<0.01, ** p<0.05, * p<0.1

Table 11: First-Stage OECD. Regressor: Outflows

(1) (2) (3) (4) VARIABLES 1980 1990 2000 2010

Outflows Instrument 0.586** 0.989*** 0.640*** 0.157 (0.251) (0.161) (0.172) (0.173) GFCF ( % of GDP) -0.237** -0.245** -0.309* -0.245

(0.092) (0.109) (0.177) (0.191) Gross savings ( % of GDP) 0.249*** 0.206** 0.106 0.323***

(0.076) (0.097) (0.097) (0.096) Inflows ( % of GDP) 0.457 0.183 0.876*** 0.918***

(0.297) (0.178) (0.083) (0.102) Constant -0.150 0.637 2.769 -2.763

(1.104) (1.004) (3.815) (3.332)

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Table 12: First-Stage OECD Feldstein. Regressor: Outflows

(1) (2) (3) (4) VARIABLES 1980 1990 2000 2010

Outflows Instrument 0.316 0.740** 0.685*** -0.186* (0.257) (0.249) (0.211) (0.095) GFCF ( % of GDP) -0.279*** -0.077 -0.069 -0.514**

(0.072) (0.204) (0.235) (0.207) Gross savings ( % of GDP) 0.366*** 0.056 0.153 0.497***

(0.081) (0.143) (0.116) (0.085) Inflows ( % of GDP) 0.418 0.679*** 0.830*** 1.105***

(0.265) (0.141) (0.123) (0.064) Constant -1.390 0.193 -2.997 0.696

(2.189) (2.364) (3.974) (3.392)