EFFECT OF FDI ON REAL PER CAPITA GDP GROWTH: A ROLLING

Applied Econometrics and International Development Vol. 17-1 (2017)

EFFECT OF FDI ON REAL PER CAPITA GDP GROWTH: A ROLLING WINDOW PANEL ANALYSIS OF 60 COUNTRIES, 1982-2011

Jeffrey A. EDWARDS1 Cephas B. NAANWAAB2

Alfredo A. ROMERO2

Abstract: The research regarding the effect that foreign direct investment (FDI) has on economic growth has morphed into a quagmire that the economics discipline can't seem to find a way out of, and in some ways, is sinking deeper into. In fact, the positive, negative, and dependent impact views many times blatantly contradict one another, leaving someone on the outside to wonder whether there may be 'something else' that is causing the differences in the empirical estimations of this effect. In this study, we employ a rolling window methodology across 60 countries that has been tailored for use with a perfectly balanced panel data set spanning 30 years, to investigate whether some of this variation in the effect FDI has on growth could be caused by shocks to the effect itself. What we find is that conservatively, 25% of our estimates are indeed dynamically sensitive, with this sensitivity continuing across economic sectors as well. In the end, at least some of this heretofore variation in the effect's estimates may simply be due to investigators ignoring significant levels of parameter heterogeneity, and nothing to do with the inherent attributes of the economies being studied. Keywords: FDI, Growth, Economic Sectors, Rolling Window JEL: F21, F43, F47, E50 1. Introduction For decades, the empirical evidence of a cross-sectional relationship between foreign direct investment (FDI) and growth in real per capita GDP has been tenuous at best, with little if any consistency in the estimates of this nexus, in spite of the relatively large amount of research on the subject. This is shocking since economic intuition gives FDI an important, positive, role in economic growth, especially in developing countries. However, when FDI is the variable of interest, researchers have been unable to reach a consensus regarding its marginal effect on growth, finding sometimes positive, negative, or insignificant coefficient estimates. Although many researchers have theoretically and empirically defended the salient results from each camp, a new strain of inquiry has implied that the inconsistencies observed in the data could be the result of parameter heterogeneity both across economic sectors and time (Edwards, et al., 2016). In this paper, we pursue these implications by tracking the relationship between FDI and growth over time within the industrial, services, and agricultural sectors of an economy. Specifically, using a perfectly balanced panel of 60 countries over the 30-year period from 1982 to 2011, we employ a rolling window regression methodology to plot

1 Corresponding author. Department of Economics, North Carolina A&T State University, Greensboro, NC, USA. Email: [email protected]. Postal address: Department of Economics, College of Business and Economics, North Carolina A&T State University, 1601 East Market Street, Greensboro, NC 27411. 2 Department of Economics, North Carolina A&T State University, Greensboro, NC, USA


20

FDI's coefficient estimates over this period; in addition, we allow these plots to vary by real growth in industrial, service and agricultural value added--the three sectors that account for 100% of a country's GDP. What we find is that the estimates of the marginal effect vary considerably from positive to negative (and vice versa) across sectors and over time. In fact, we find that after controlling for development level and a host of typical growth determinants, there exists enough parametric abnormality in the cross-sectional relationship between FDI, GDP growth, and sectoral growth that some of the estimates (not all, mind you) other researchers get may not be a good reflection of their true long-run value. To our knowledge, a few growth-related studies have tackled panel data parameter heterogeneity, but none investigating the growth-FDI relationship. For instance, Edwards and Kasibhatla (2009) investigate dynamic panel heterogeneity using a rolling window methodology on standard growth determinants, but did not include FDI. They found that roughly 25% of the estimates were indeed time heterogeneous. Balcilar and Ozdemir (2013) use a rolling window approach but on time series data; they also do not address FDI but rather the export-output nexus and only for one country, Japan. Lin, Kim and Wu (2013) and Deng and Lin (2013), on the other hand, investigated parameter heterogeneity in the relationship between FDI and income inequality using a threshold model and a semiparametric model, respectively. Similarly, Hossain (2014) addresses parameter heterogeneity in the Savings – Foreign Capital and Remittance Inflows (that included FDI) using a common correlated effects mean group estimator model. Lastly, Guisan (2015) proposes that the relationship may be heterogeneous depending upon how the dependent and independent variables are measured. Referencing Guisan (2009), she finds significant differences between growth on growth relationships, versus growth on ratio relationships. But again, these papers seem quite rare, and none address this particular nexus. 2. The Background for FDI's Effect on Growth The growth effect of FDI has been the subject of intense research for decades. Even more so since growth in FDI has been higher than that of trade over the last 20 years. From the standpoint of the neoclassical model (Solow, 1956), FDI can be thought of as an important source of capital, thus, augmenting/complementing domestic capital stocks. In accordance with these tenets, the economy will grow in the short run, but then as diminishing returns set in, the economy converges to a steady state with no long-run impact on growth (De Mello 1997). A similar model was put forth by Malley and Motous (1994), whereby the rate of technological transfer resulting from FDI affects only the level of national income and not the growth rate. Early empirical evidence was unable to corroborate the one-path convergence of this model, leading to the proposal of threshold growth models, where no long-run impact of FDI on growth can be a steady-state equilibrium unless some economic conditions (or thresholds) are reached (e.g., Azariadis and Drazen, 1990; Borensztein, De Gregorio, and Lee, 1998).

On the other hand, for endogenous growth theorists (e.g., Romer, 1990; Barro and Sala-i-Martin, 1995), growth in the long-run would indeed be influenced by FDI vis-a-vis technological spillovers and human capital dissemination. As foreign capital flows into a country, assuming it is more efficient than domestic capital, domestic technologies will improve and so will training of the indigenous workforce needed to operate them. This has been evidenced by several researchers that find enhanced labor augmentation, relaxing of foreign exchange markets, and increased financial penetration in the receiving countries

Edwards,J.A.,Naanwaab,C.B.,Romero,A.A. Effect of FDI on real per capita GDP of 60 countries

21

(e.g., Aitken et al. 1997; Aghion et al., 1999; Habib and Zurawicki 2002; IMF 2003; Ozturk 2007; Alfaro et al. 2010; and Adeniyi et al. 2012).

Both of these theories make perfect sense. The problem is when a researcher attempts to estimate this marginal effect, sometimes it is insignificant and sometimes it is negative; and it is these outcomes that cause the most consternation among economists. The need for explaining these results has led to the creation of three points of view: the positive impact view, the negative impact view, and the dependent impact view (El-Wassal, 2012). The positive impact view most closely correlates to the exposition outlined above, however, the explanations for a negative correlation are seemingly more laborious than the positive view's explanations. They rely mostly upon the idea that (1) foreign investment crowds-out domestic investment (Omran and BolBol, 2003; Esso 2010), (2) can entrench domestic production in less efficient techniques if domestic producers take a competitive stance against the new technologies and production processes (Dimelis, 2005), or (3) by the deterioration of an economy's current accounts (Mencinger 2003). Complicating this picture further are estimates of this relationship that are entirely insignificant. So, in addition to explaining the negative results, theories became even more disparate--this is the dependent impact view. The dependent impact view is perhaps the most encompassing of these viewpoints as researchers have pretty much included the proverbial kitchen sink. Starting with Bhagwati (1978) who suggested that the impact foreign investment has on growth may be dependent upon the receiving economy's trade policy, it runs the gamut to include social and cultural determinants, the role(s) of government, property rights, taxes, openness, and most obviously, infrastructure (e.g., Borensztein et al. 1998; De Mello 1999; Hsiao and Shen 2003). Perhaps the best exposition for this reasoning can be found in Borensztein et al., (1998) and De Gregorio (1992). For the most part, economies with highly skilled human capital can be expected to reap the full benefits of FDI, because a sufficient amount of human capital is needed to absorb the new technologies. Not surprisingly, those countries that have attracted record amounts of FDI have tended to be those with better developed human capital (Lucas, 1990; Benhabib and Spiegel, 1994; Zhang and MarKussen, 1999; Lin, Kim and Wu, 2013). But, what if many of the empirical results, and subsequent theoretical analyses, are mostly outdated or being driven by only a few years of estimated effects; and what if many of these effects are finding their origins in specific sectors of the economy? The time-sensitivity angle that we investigate is new, but an obvious one. If one harks back to the literature on observational outliers, the idea that a transitory shock to a marginal effect (not necessarily a shock to the data per se, but a shock to the effect itself) may produce an estimate that is well outside the long-run empirical regularity of the effect, is an easy argument to make. If that effect is large enough, it certainly could be driving the long-run inference drawn from the estimate, just like only a few observational outliers in a data set can influence a point estimate of a parameter. Furthermore, the Edwards and Kasibhatla paper mentioned earlier definitely shows that it is possible for an estimate to transition to a new long-run path after such a shock (albiet not necessarily a transitory one). In each of these cases, an empiricist estimating this effect would get different results depending upon what years are covered in the data--i.e., the estimate would be different if the model covers years that hold the shocks and/or transitions, versus a model that didn't.


22

But, the extent of the heterogeneity may also hinge on the value-added content of FDI-related production and the spillovers of FDI to domestic firms (Li et al., 2001). If FDI inflows for a country are all pouring into one sector, the value added numbers of that sector should reflect it. This accords well with the fact that the effects of FDI seem different across groups of countries (say Latin American countries to East-Asian countries) or from nation to nation (say Ecuador to Chile). In essence, a country’s growth-promoting initial conditions, e.g., what sector dominates an economy, are essential in determining whether FDI has an effect at all on growth, and what that effect might be (Edwards et al., 2016). Evidence for this is starting to appear. Focusing on India alone, Chakraborty and Nunnenkamp (2008) found that the effects of FDI on growth vary by sector. Using sector-specific FDI and output, their findings show that FDI stock promotes output growth in the manufacturing sector, produces short-term effects on services output, and does not affect output in the agriculture and mining sectors. In a similar vein, Wang (2009) investigated the growth effects of FDI in 12 Asian countries, essentially coming to the same conclusion as Chakraborty and Nunnenkamp (2008): manufacturing FDI promotes economic growth whereas FDI inflows into non-manufacturing sectors (agriculture and services) do not significantly impact economic growth. Aykut and Sayek (2007) also consider the growth effects of FDI on the aggregate economy as well as the sectoral composition of these effects. They found that FDI flowing into the manufacturing sector generally had a positive effect on economic growth, while FDI in services and agriculture had a negative effect on economic growth. Lastly, Alfaro and Charlton (2007) found evidence that the effect is indeed heterogeneous across sectors, and this heterogeneity should be accounted for when investigating this relationship. So, it does appear as though both dynamic and sectoral heterogeneity in the marginal effect that FDI has on growth is indeed possible. We now outline exactly how we will persue investigating this area of empirical research.

3. Methodology The general model we use takes the form

, and the dataset we use comes from the World Bank's World Development Indicators database, covering the 30-year period from 1982 to 2011 and covers 60 countries. The variable is a dummy for each country that covers the years 2008 to 2011 in order to capture the Great Recession and its subsequent recovery period. The inclusion of this dummy variable is critical for holding constant what would perhaps be the most dominant source of exogenous shock in the last 30 years or so. The 's include total trade (Trade), gross domestic investment (GDI), population growth (Pop), and the natural log of the initial level of real per capita GDP for each country in 1981 (i-GDP). FDI and the X's are estimated using a two-staged least squares method on the assumption that they are endogenous, and will be covered in greater detail later. We chose the World Bank dataset simply because it is probably the most popular data set used in empirical growth; but more importantly, it behooves us to maintain homogeneity in our data so we don't introduce bias by merging together data from different sources. Furthermore, because we ultimately conduct a rolling window analysis, it is imperative that the panel data is perfectly balanced and consistent (Edwards, 2014). This means that the data for each country not only covers exactly the same number of years, but exactly the same years. This consistency is necessary as panel regressions are


23

much like simple, stacked cross-sectional regressions. If data is covering different years across countries, then it would be impossible to draw inference for any particular year as one year would include more (or fewer) countries than other years. This exercise in consistency does limit our data somewhat, but is necessary in this paper. A separate regression for (1) is run for each income group— high, upper-middle, and lower per capita real income levels— as defined by the World Bank (see Appendix A); these groups serve as a proxy for a country's development level, roughly defined as developed, emerging, and developing economies respectively. Running these regressions separately not only allows the conditional mean to vary by group, but the conditional variance as well; in other words, we try to make as few empirical assumptions as possible by allowing the errors to more closely resemble the true standard errors of the coefficients, and relying less on asymptotics. The important thing to note about equation (1) is the coefficient for FDI. This is FDI's marginal effect. Currently, researchers assume that , in other words, is constant over time. This is the assumption we relax in our analysis. Taking a page from Edwards and Kasibhatla (2009), they were able to show that relaxing this assumption for a general growth regression is necessary in some cases in order to draw accurate long-run inference from the estimated marginal effects of standard growth determinants; they did not, however, investigate the case of foreign investment. The idea they have is simple enough to understand. Since a panel regression essentially averages a "stack" of yearly cross-sectional estimates, it might be the case that if enough weight were applied to a certain period of positive estimates, those estimates would dominate the long-run estimate, even though there could be a substantial period whereby the estimates were negative. Given this argument, one can quickly realize why it is possible that researchers have been getting different results for FDI's marginal effect—it could all depend upon the years the data set spans. Lastly, even though much of the literature distinguishes between how FDI affects economic growth differently for countries in different levels of development, sectoral heterogeneity seems to be an obvious specification issue that should also be investigated. Therefore, in addition to tracking our estimates over time, we also break it down by the sectors of the economy—industry, services and agriculture. If a researcher is modeling the growth effects of FDI for a country whereby firms are concentrated in just one or two particular sectors, then it makes sense that the effect FDI has on growth in that economy would more reflect the relationship in that specific sector(s). For instance, for the United States in 2014, the industry sector made up 20.5% of GDP, services 78.1% of GDP, and agriculture 1.4% of GDP. One would assume, then, that any estimated relationship between growth and FDI would largely reflect that of the services component in the U.S. economy, since that sector is disproportionately large relative to the other two sectors of the economy. This thought experiment gives us models (2) - (4)

where GrInVA, GrSeVA, and GrAgVA are real growth in value added of the industry, services, and agricultural sectors, respectively. Like equation (1), these regressions are


24

performed separately for each income group: high, upper-middle, and lower income countries. There are three variables in (2) - (4) that are not in growth regression (1). These are VInVA, VAgVA, and VSeVa, which are the absolute values of the residuals from regressions (2) - (4), but lagged one year. We felt that in the value added regressions, it was important to hold constant past volatility in the other two sectors as this could interfere with the FDI effect; it could be the case that domestic resources would flow from the more volatile to less volatile sector. If these flows coincide with inflows of foreign investment into the less volatile sector, then the effect FDI has on that sector would be overestimated. We lag the volatility variables one year to eliminate any contemporaneous feedback that may occur from growth in one sector to volatility in another. In addition to lagging sectoral volatility one year, we perform a two-stage least squares instrumentation of FDI, GDI, Trade, and Pop, using a two-step dynamic GMM first-stage regression of the contemporaneous values of these variables on their respective lags and the dummy variable that represents the Great Recession shock (Arellano and Bover 1995; Blundell and Bond 1998). Feedback from growth to FDI and the X's would likely occur at least to some degree, and this instrumentation would eliminate much of this phenomena. Specifically, in the first stage we run the regressions

(5) (6)

Then we estimate and using the parameter estimates from (5) and (6) and plug the forecasted values back into regressions (1) - (4). Since these are dynamic panel regressions, using the GMM process reduces/eliminates any correlation between the first lags of the variables and errors. Even though our conditioning set is rather limited, it is still too large to use an unrestricted GMM method as the number of instruments would quickly grow quite large and over-specification of the instruments would be an issue (Roodman 2006).3 To circumvent this problem, we collapse our instrument matrix to reduce the number of instruments. This procedure creates one instrument for each right-hand side variable and lag distance instead of for each variable, lag distance, and time period.4 We also use a finite-sample correction to the two step covariance matrix by Windmeijer (2005), and test for second-order serial correlation (first-order is expected given the design of the method). A Hansen test will check for independence between the residuals and the lagged variables within the first-stage regressions. Once the right-hand-side variables have been estimated, we plug them back into (1) - (4) to attain our baseline long-run estimates—i.e., proxies for the estimates that other researchers would get after performing the estimation procedures that are standard in the literature (obviously, we assume the marginal effect is constant over time in these baseline estimates). That said, since our data must take on a specific form in order to perform the rolling window procedure properly, our baseline estimates may not necessarily reflect those found in the literature for a specific group of countries. However, establishing someone else's estimates is not the purpose of this paper; the purpose of this paper is to

3 This program was constructed by David Roodman of the Center for Global Development in Washington, DC. 4 A recent instance of this method being employed is Edwards et al. 2016.


25

determine dynamic irregularities in the coefficient estimates themselves, and compare them to the long-run estimates—in other words, it is the relative estimates that count here, and not absolute comparisons. After establishing the baselines, we then conduct the rolling window regressions, the results from which are the crux of this paper. Since our goal is simply to look for evidence of abnormal coefficient estimates, the rolling window regressions and resulting time plots are sufficient to illustrate this; we feel strongly that it is simply not necessary to cloud one's judgement with quantitative testing methods that rely heavily on asymptotic performance. These methods depend upon a relatively large set of observations, but as the reader will see, the resultant plots will only be 28 observations long. Quantitative structural testing methods with this short of an observational period will have very low power, and would likely cloud our judgement when determining whether an irregularity has occurred or not. The window size we use is three years long. We decided to use a three-year window to sufficiently smooth and reduce the noise from yearly estimates while allowing us to closely examine the variability in the marginal effect without sacrificing critical turning-points in the estimates themselves. In our case, our first window starts in 1984 and ends in 1986 (we lose two observations for each country because of instrumentation). Regressions (1) - (4) are then run over the entire breadth of our data using this window, and the parameter estimates for FDI are documented; the window is then moved forward one year to cover the years 1985 to 1987, another regression is performed, and the new parameters are documented. This process continues until the last window spans the years 2009 to 2011. The algorithm ends at that point. The estimates that are collected along the way are then plotted from 1986 to 2011 and a 90% confidence interval is constructed around them. Since we are trying to compare rolling window estimates to static long-run estimates, the long-run standard errors will be used to calculate the confidence interval. We do not control for country-specific fixed effects as our window is short, and therefore, underestimation of FDI's true effect may result. However, including i-GDP, which is constant over time, and separating out individual sectors of growth, should alleviate at least some of the concerns in this area.

4. Setting Baseline Estimates We will start off by evaluating results from the instrument regressions (5) and (6); afterwards we will outline a set of results called our "long-run," or baseline, results. These long-run results assume the effect FDI has on growth is constant over time as in existing literature, and is perhaps the most common type of parametric assumption made. The first-stage estimates (Table 1) show that using lagged values of FDI, GDI, Pop, and Trade for estimating current values of these variables, is indeed justifiable. Each of the first-lag coefficients are highly significant, while all four second lag coefficient estimates are insignificant indicating that the autoregressive effect wanes after one period. The insignificance of the second lag is important because it tells us that we have sufficiently captured all of the statistically significant auto dependency in these variables. It should also be noted that the number of instruments used in the GMM process is well below the number of countries, hence, over-specification should not be an issue; furthermore, the AB and Hansen statistics fall in line with what is reasonable for this set of test statistics (i.e., there are no values close to zero, and none close to one).


26

Table 1: First-stage GMM estimates for FDI and the X's FDI GDI Pop Trade First Lag Second Lag Dum08

0.648 ** (0.000) 0.096

(0.114) 2.122 ** (0.034)

0.710 ** (0.000) -0.038 (0.296) 1.382 * (0.077)

1.250 ** (0.000) -0.313 (0.341) 0.001

(0.998)

0.824 ** (0.000) 0.059

(0.224) 4.611 ** (0.011)

No. Observations No. Countries No. Obs/Country No. Instruments AB Hansen

1740 60 29 10

0.672 0.194

1740 60 29 10

0.493 0.126

1740 60 29 10

0.317 0.116

1740 60 29 10

0.774 0.153

* indicates significance at 10%, ** at 5%. The AB statistic supports the null of second order serial independence, while the Hansen statistic supports the null of independence between the instruments and the errors. It is also interesting to note that three of the four recessionary dummy variable coefficients are significant and positive. This may be counterintuitive, but it actually makes sense given the time period involved. For the most part, beginning in 2007 until roughly 2014, FDI and Trade flows were structurally higher than they were before this period--the only exception being 2009 (http://www.oecd.org/ daf/inv/investment-policy/FDI-in-Figures-April-2016.pdf; http://data.worldbank. org/topic/trade).

Foreign investment inflows increased during this period as investors and firms sought higher returns vis-a-vis lower input prices around the globe, and firms/consumers in developed countries sought cheaper products. Domestic investment also has a similar pattern during this period, although far less statistically significant (http://data.worldbank.org/indicator/ NE.GDI.TOTL.ZS). Gross domestic investment was considerably higher from roughly 2006 through the sample period, but took such a great hit in 2009, that post 2009 investment has only moderately regained its pre-recessionary levels; that said, overall, it is still structurally higher than it has been since the mid-to-late 1990's. Now we move to the static long-run estimates (Table 2) that we will compare to our dynamic estimates.

The columns labeled (1) will reflect a conditioning set of only FDI and a constant; the columns labeled (2) will reflect estimates of FDI's marginal effect that are calculated using the complete conditioning set described earlier. Even though we only comment upon the fully conditioned model, we include the smaller model to show the reader how different the estimates can be depending upon the conditioning set used. And regardless of the fact that this difference does not matter within the context of this particular study, it does give the reader a sense of why some of the current literature tends to get such different results across studies--i.e., the choice of conditioning variables could be driving at least some of these differences. But, let us first make sense of the non-FDI coefficient estimates, and whether they perform as expected. If they do, it will indicate that our models are correctly calibrated (for the most part).


27

Table 2: Baseline Results for Growth in Real Per Capita GDP Pooled High Income Upper-Middle Income Lower Income

1 2 1 2 1 2 1 2 FDI i-GDP GDI Trade Pop Dum08 Constant

0.078 ** (0.006)

1.881 ** (0.000)

0.004 (0.905)

-0.260 * (0.078)

0.050 ** (0.047)

0.009 ** (0.000)

-0.576 * (0.100)

-0.855 ** (0.002)

3.345 * (0.100)

0.030 (0.630)

2.245 ** (0.000)

-0.238 ** (0.001)

-1.575 ** (0.000)

0.020

(0.665)

0.015 ** (0.000)

-1.572 ** (0.000)

-2.669 ** (0.000)

18.291 **

(0.000)

0.143 ** (0.025)

2.019 ** (0.000)

0.096 (0.208)

-1.387 ** (0.000)

-0.153 ** (0.015)

0.014 ** (0.002)

-0.878 ** (0.002)

-1.912 ** (0.000)

17.401 **

(0.000)

0.089 ** (0.017)

1.489 ** (0.000)

-0.001 (0.983)

-0.287 (0.397)

0.127 ** (0.000)

-0.006 (0.146)

-0.033 (0.955)

0.535

(0.245)

1.320 (0.730)

R2 No. Obs.

0.005 1680

0.043 1680

0.001 336

0.259 336

0.010 616

0.108 616

0.005 728

0.037 728

Note: * indicates significance at 10%, ** at 5%. P-values are calculated using heteroskedastic-corrected standard errors.

The models do in fact seem to be performing as expected. For instance, the estimated coefficient for population growth is negative, indicating a Malthusian effect on growth when holding domestic investment constant, and the Great Recession dummy is also negative (when significant), indicating an across-the-board reduction in growth during that period of anywhere from 0.80% to 2.5%. A negative coefficient for initial GDP tells us that there is at least some conditional convergence in incomes across economies. There really is no prior attached to the expected sign for Trade simply because it is not intended for calibration purposes, but to control for omitted variable bias. Lastly, for domestic investment, one would expect a positive relationship with growth; however, this is only the case for three of the four regressions. A negative domestic investment coefficient estimate for emerging economies may seem counterintuitive to some; but upon further investigation, the reason becomes quite clear. Not shown here, when growth is modeled on domestic investment by itself, the magnitude of the coefficient is very small (-0.013) in absolute value and highly insignificant (p-value = 0.749). The coefficient only increases in absolute value and significance when FDI and Trade are added to the regression. And this makes sense given the fact that these 'emerging' economies, to a large extent, are almost solely dependent upon foreign investment with domestic investment taking not only a backseat in advancing growth, but actually competing with foreign resources. If the separation between savers and investors in these economies is still quite large (Aghion, Banerjee, and Piketty 1999), then domestic resources may actually be competing against foreign resources, creating a drag on growth itself. Now moving on to the most important part of the analysis—i.e., the effect foreign investment has on growth. The first thing to notice is that the effect FDI has on economic growth is highly sensitive to both development level and conditioning set—i.e., there is nothing robust about this marginal effect. For instance, by development level we find that FDI positively affects growth for upper-middle and lower income groups, but negatively affects growth


28

for high income countries. By conditioning set, we see that in three of the four cases, the effect is significant and positive when FDI is modeled alone, which is not the case when additional variables are added; on the other hand, the polar opposite is true for high income economies. We now turn to the baseline estimates that cut across economic sectors (Table 3). Table 3: Baseline Regressions for Growth in Real Value Added for Industry, Services, and Agriculture Pooled High Income Upper-Middle Income Lower Income 1 2 1 2 1 2 1 2 FDI-Ind FDI-Serv FDI-Ag

0.064 (0.335)

0.092 ** (0.008)

-0.130 * (0.091)

0.004 (0.951)

0.037

(0.442)

-0.010 (0.909)

-0.042 (0.704)

0.216 ** (0.000)

-0.287 * (0.097)

-0.374 ** (0.014)

-0.135 * (0.058)

-0.015 (0.940)

0.144 (0.234)

-0.004 (0.947)

0.019

(0.918)

0.086 (0.559)

0.008

(0.915)

0.174 (0.406)

0.160 (0.221)

0.144 ** (0.040)

-0.030 (0.716)

0.044 (0.764)

0.129

(0.175)

-0.041 (0.713)

Note: The coefficient estimates listed here are only for FDI. Ind is the FDI coefficient estimate for real growth in industry value added, while Serv and Ag are for real growth in services value added and agricultural value added respectively. Sub-column 1 are estimates with the limited conditioning set as it is in Table 2, while sub-column 2 include all variables. * indicates significance at 10%, ** at 5%. These estimates for FDI, in some ways, become even more inconsistent. (To save space, we only tabled the estimates for FDI's coefficient, and didn't table the estimates for the other variables; but they are available upon request.) Using just the basic model setup in Column 1 for the pooled results, the estimate is insignificant for industry, positive for services, but negative to agriculture. When the full conditioning set is applied, however, all estimates become insignificant. For high income economies, when all of the determinants are included in the regression, FDI negatively affects industry and services, but has no effect on agriculture. Oddly, foreign investment has no effect on upper-middle income economies, regardless of the conditioning set. And lastly, for low income economies, FDI positively affects growth in services when modeled by itself, but has no effect otherwise. However, all of these baseline results assume that the effect is constant over time. Next we relax this assumption and proceed with estimating the marginal effect of FDI on growth using a 3-year rolling window.

5. Dynamic Heterogeneity in General In the previous section, we set our baseline estimates for both the general growth model and sectors, now we explore the time dimension by graphing the coefficient estimates over time and comparing those to the growth models' baseline estimates. This section will show four sets of plots of the coefficient estimates for FDI over the years 1986 to 2011. The figures will show estimates of FDI pooled across countries, and then separated by high income, upper-middle income, and lastly, lower income countries, respectively. Only the results with the full set of control variables will be analyzed to save space, for ease of exposition, and because it is the conditioned results upon which most researchers comment. The solid line in each plot traces the estimated marginal effect while the two dotted lines reflect a 90% confidence interval constructed using the long-run estimated standard errors. These are the same standard errors used to calculate the p-values in tables 2 and 3. We use these errors precisely because it compares the significance of the estimates over the long-run, which is the purpose of this research paper--i.e., to expose statistical reasons why the long-run estimates are inconsistent across studies. A


29

significantly positive estimate is one whereby the lower bound of the confidence interval is above the horizontal line of zero; a negative estimate is one where the upper bound is below the zero line. And insignificance is when the confidence interval spans the zero line. What becomes quite clear is that over time, the effect FDI has on growth is anything but constant (Figure 1). In fact, with the exception of upper-middle income countries, the coefficient estimate has at least one period where it is statistically significant and positive, and at least one where it is statistically significant and negative.

Figure 1: Rolling Window Estimates for FDI on Real Per Capita GDP Growth

-.20

.2.4

.6C

oeffi

cien

t Est

imat

e

1985 1990 1995 2000 2005 2010Years

GDP Growth: Pooled

-1-.5

0.5

1C

oeffi

cien

t Est

imat

e

1985 1990 1995 2000 2005 2010Years

GDP Growth: High

-.50

.51

1.5

2C

oeffi

cien

t Est

imat

e

1985 1990 1995 2000 2005 2010Years

GDP Growth: Upper-Middle

-10

12

Coe

ffici

ent E

stim

ate

1985 1990 1995 2000 2005 2010Years

GDP Growth: Lower

Specifically, harking back to the pooled baseline results in Column 2 of Table 2, FDI's coefficient estimate was 0.004 and highly insignificant with a p-value of 0.905. And while that estimate may be reasonable given the fairly consistent oscillation around zero seen in the upper left-hand plot of Figure 1, the results may be biased upward because of the large hump in the plot prior to 1993. Dropping these years does increase the absolute value of the estimate to -0.028, but the p-value continues to show insignificance at 0.445; in other words, the long-run estimate may indeed be a good reflection of the true empirical regularity.

Separating the countries by income level, the baseline estimate for high income economies was highly significant at -0.238 and a p-value of 0.001, which also appears consistent with the upper right-hand plot in Figure 1 regardless of the highly positive


30

'bump' in the coefficient estimate in the middle of the plot. As evidence, dropping the years 1998 - 2000 from this sample simply makes the long-run estimate more negative with a value of -0.300 and a p-value of 0.000. This means that the positive area between 1997 and 2001 wasn't enough to draw off the consistency in the estimate. For lower income countries, we see pretty much the same outcome—i.e., the bump around 1992 does not appear to be significant enough to artificially skew the long-run estimate of -0.001 with a p-value of 0.983. Even when the years 1990 - 1993 are dropped from this sample of countries, we get an estimate of -0.031, which is considerably higher in absolute value than the baseline estimate, but the p-value still indicates substantial insignificance at 0.541. However, none of this is the case for upper-middle income countries. For upper-middle income economies, the plot shows that there are very brief periods of insignificant, negative coefficient estimates, but nothing of the magnitude in the other three plots. The baseline estimate from Table 2 is 0.096 with a p-value of 0.208—quite insignificant. However, rerunning this regression with only the years 1995, 1996, and 2011 dropped from the sample, increases the estimate to 0.137 with a marginally significant p-value of 0.094. If we also drop 2010 from the sample, the estimate becomes highly significant and positive at a value of 0.183 and a p-value of 0.027. In other words, out of nearly 30 years of data, estimates from only 3 or 4 years were enough to considerably skew the results. In fact, this is the difference between a researcher applying the dependent impact view to this set of countries, when the positive impact view probably should have been applied instead.

6. Dynamic Heterogeneity Across Sectors Using the same sort of analysis as we did in the previous section, we now extend our inference across the industrial, services, and agricultural sectors of an economy. Since economic literature is increasingly investigating the impact foreign investment has on individual sectors, this analysis could prove to be quite informative for future work. In Figure 2, the pooled and high income plots for growth in industrial value added look very much like those for GDP growth. In other words, there are no obvious areas, when purged, would generate a coefficient value that would be substantially different than the value that we got in Table 3. The baseline pooled value was 0.004 and highly insignificant, and the high income value was -0.374 and highly significant. In the former case, the oscillation around this value is fairly consistent, and in the latter case, there are no highly positive areas that would generate either an insignificant or positive estimate. Figure 2: Rolling Window Estimates for FDI on Real Growth in Industry Value Added.

-2-1

01

2C

oeffi

cien

t Est

imat

e

1985 1990 1995 2000 2005 2010Years

Industrial Value Added: High

-10

12

Coe

ffici

ent E

stim

ate

1985 1990 1995 2000 2005 2010Years

Industrial Value Added: Upper-Middle


31

-20

24

6

Coe

fficien

t Est

imat

e

1985 1990 1995 2000 2005 2010Years

Industrial Value Added: Lower

For the upper-middle income countries, unlike the pooled and high income plots, it does seem as though a marginally positive value might be attained by dropping the years 2001 and 2002 where there is a negative spike in the estimate. However, when these years are dropped, the value remains about what it was in Table 3 of 0.086, and still highly insignificant. The reader also might conclude that since the dynamic values are marginally significant after 2003, and since more recent studies should perhaps rely on more recent data, that dropping everything before 2003 might produce a significantly positive estimate. That said, we want to stay cautious because the 2011 value does seem to be heading toward negative values, indicating that a reversal of the switch that we see from 2002 to 2003 could be occurring. We see the same relative trend for lower income economies as well. Even though the latter part of the plot seems to be positive, it is trending downward into insignificance. In other words, to us anyway, there doesn't appear to be any obvious areas that would be generating sensitivity in the parameter estimate. Now we move on to an inspection of real growth in services value added. Unlike the plots for GDP growth and growth in industry, the pooled results for growth in services seem to have a definite positive-to-negative structural change from 2003 to 2004. The baseline estimate from Table 3 is 0.037 with an insignificant p-value of 0.442. This finding is supported when a regression is run on just the years up to 2004 with a p-value of 0.310, although the estimate does double to 0.078. However, when a regression is run on the observations since 2004 inclusive, the estimate becomes negative at -0.102, and highly significant with a p-value of 0.019; and note that the last observation in 2011 appears to be trending even more into negative territory. This means that a researcher investigating the cross-country effect of foreign investment on growth in countries with a dominant services sector, will likely not be drawing the most recent, and therefore, the most important inference in this relationship. Policies driven by this researcher's outcome may be one of ambivalence toward FDI, when the most recent policy perspective should probably result from the latter half of this analysis whereby FDI actually hinders growth in this sector of the economy. That said, it certainly does not appear as though this pooled analysis is being driven by high income economies. For high income economies, the baseline value of -0.135 with a significant p-value of 0.058, seems to be a reasonable reflection of the true long-run parameter estimate; and for lower income economies, there are no obvious negative values that could throw into significance the baseline estimate of 0.129 with a p-value of 0.175. This is not the case for upper-middle economies whos general pattern appears similar to the pooled plot. For these economies, if we reflect upon the pooled results and drop observations before 2004, the coefficient estimate goes from 0.008 and insignificant with a p-value of 0.915, to


32

marginally significant at -0.169 and a p-value of 0.075. So more recent estimates for FDI's effect on real growth in services for upper-middle income countries do indicate that FDI hurts growth in this industry. Lastly, we look at growth in agricultural value added. Figure 3: Rolling Window Estimates for FDI on Real Growth in Services Value Added

-.50

.51

Coe

ffici

ent E

stim

ate

1985 1990 1995 2000 2005 2010Years

Services Value Added: Pooled

-1-.5

0.5

Coe

ffici

ent E

stim

ate

1985 1990 1995 2000 2005 2010Years

Services Value Added: High

-10

12

3

Coe

ffici

ent E

stim

ate

1985 1990 1995 2000 2005 2010Years

Services Value Added: Upper-Middle

-10

12

3

Coe

ffici

ent E

stim

ate

1985 1990 1995 2000 2005 2010Years

Services Value Added: Lower

Figure 4: Rolling Window Estimates for FDI on Real Growth in Agricultural Value Added

-.50

.5

Coe

ffici

ent E

stim

ate

1985 1990 1995 2000 2005 2010Years

Agriculture Value Added: Pooled

-2-1

01

23

Coe

ffici

ent E

stim

ate

1985 1990 1995 2000 2005 2010Years

Agriculture Value Added: High

-10

12

3C

oeffi

cien

t Est

imat

e

1985 1990 1995 2000 2005 2010Years

Agriculture Value Added: Upper-Middle

-3-2

-10

12

Coe

ffici

ent E

stim

ate

1985 1990 1995 2000 2005 2010Years

Agriculture Value Added: Lower


33

The most benign time plot for agriculture is lower income economies. This plot is almost perfectly balanced on either side of zero, leading us to conclude that the insignificant coefficient estimate of -0.041, likely reflects the true long-run value of this estimate. The other plots, however, are far more complicated.

For high income countries, there seems to be a definite structural shift in the coefficient estimate around 1999. The baseline estimate from Table 3 for this group of countries is -0.015 and highly insignificant with a p-value of 0.940. But, when the observations before 1999 are dropped from the regression, the estimate becomes far more negative at -0.459 and a p-value of 0.038. In other words, a researcher employing the full breadth of years available in this data set would conclude that FDI has no effect on growth in the agricultural sector for high income economies, when the true long-run coefficient value for nearly the last 20 years indicates that FDI actually hurts growth in this sector. But the more complicated picture is in the pooled and upper-middle income countries. In both plots, it may appear that by purging just a few select years, the estimates may become positive. But that appearance would be deceiving as we have tried many of the obvious selections, and the parameter estimate stays substantially insignificant. For instance, dropping the years 1991, 1994, and 2001 from the upper-middle income graph only lowers the p-value from 0.406 in Table 3, to 0.315; and going so far as dropping post 2007 observations only lowers it to 0.112. But, this is where the actual concern about drawing inference from the long-run value of these estimates really is—i.e., post 2007. In both the pooled and upper-middle income case, the coefficient estimate reflecting FDI's effect on real growth in agricultural value added is unambiguously trending downward. So, if we extend this analysis into the future, would this trend continue, becoming an empirical regularity? Only time will tell, but while it might be reasonable to conclude that foreign investment does not have a significant effect on growth in agriculture given the current data that is accessible, and this does appear to be an historical regularity, it would also be reasonable (in our opinion) to conclude that any current inference and policy prescription in this area should remain wary of the possibly negative effect moving into the future. Pulling all of our results together from the entire paper, we can conclude that, in some areas at least, researchers should be cautious when drawing inference from their static results. Specifically, there were 16 plots overall—4 each for GDP growth, growth in industry, services and agricultural value added. A full 25% of these plots exhibited either obvious structural changes in parameter estimates whereby the marginal effect transitioned to a new empirical regularity after some date (high income agricultural value added, upper-middle income and pooled for the services sector), or had only a few coefficient values driving the overall long-run result (upper-middle income countries with growth in GDP). And if we include the fact that the parameter estimate after 2007 for pooled and upper-middle income countries for agriculture may itself become a regularity, we can increase the number of suspect plots to 6 out of 16, or 37.5% of the long-run parameter estimates that we investigated.

7. Conclusion The growth effect of foreign direct investment has received a considerable amount of attention in the economics discipline as evidenced by the large body of literature surrounding it. However, the inconsistency of the marginal effect itself may, to some extent, be due to dynamic heterogeneity in the parameter estimate. Couple this


34

phenomenon with the fact that most of these studies only look at the aggregate effect FDI has on the national economy, leaves a gaping hole in the literature. Indeed, the sectoral effect of FDI as a line of research is only now gaining traction among economists. The rise in interest in sectoral heterogeneity is due in part to the realization that, as economies transition sequentially from agriculture to manufacturing to services, the contributions of these sectors (and FDI thereof) to economic growth will inevitably change. In this paper, we examine the growth effects of foreign investment, first on real per capita GDP growth and then across the major sectors of the economy—industrial, services, and agriculture—that together comprise 100% of a nation's economy. Most often, economists estimate the growth effects of foreign investment assuming that these effects are constant over time and across sectors, both are assumptions we relax in this paper. In addition, our analysis controls for sectoral volatility in order to avoid conflating any other volatility effect. Our findings show that the marginal effect FDI has on growth is indeed sensitive to many specification issues. Not only does it vary by income level and conditioning set, but also varies by sector and over time. On overall growth, the dynamic estimates are fairly consistent with the predictions of the baseline model in that the pooled estimates have no obvious areas that when removed could produce different results. Nonetheless, there is some degree of dynamic heterogeneity when the countries are separated by income group—and this is especially the case for upper middle income countries where the dynamic effects appear to be driven by a few years of estimates. With regard to dynamic heterogeneity across sectors, the estimates of the marginal effect of FDI for industrial growth are, for the most part, consistent with the long run estimates. This implies that merely dropping a few years from the dataset would not substantially change the estimates from their long run values. The same, however, cannot be said of services and agriculture sectors; there appears to be significant structural changes in each. Interesting as the findings presented in this paper are, they are by no means conclusive: as they are conditional on the time period and sectors covered. Going forward, it is suggested that future research should explore whether the dynamic and sectoral heterogeneity of FDI effects found in this study continue over extended periods beyond what is covered in this paper. In particular, sectoral heterogeneity deserves further research to determine whether the structural changes found in this study are an aberration or a trend that would continue into the future. References Adeniyi O, Omisakin O, Egwaikhide FO, Oyinlola A (2012) Foreign direct investment, economic growth and financial sector development in small open developing economies. Econ Anal Policy 42(1):105–127. Aghion P, Banerjee A, Thomas P (1999) Dualism and Macroeconomic Volatility. Quarterly Journal of Economics 114(4): 1359-1397. Aitken B, Hanson G, Harrison A (1997) Spillovers, foreign investment, and export behavior. J Int Econ 43(1):103–132. Alfaro L and Charlton A (2007) Growth and the Quality of foreign direct investment: Is all FDI equal? HBS Finance Working paper No. 07-072. Alfaro L, Chanda A, Kalemli-Ozcan S, Sayek S (2010) Does foreign direct investment promote growth? Exploring the role of financial markets on linkages. J Dev Econ 91(2):242–256. Arellano M, Bover O (1995) Another look at instrumental variable estimation of error-components models. Journal of Econometrics 68: 29-51.


35

Aykut D, Sayek S (2007) The role of the sectoral composition of foreign direct investment on growth. In: Piscitello L, Santangelo GD (eds) Do multinationals feed local development and growth? Elsevier, Oxford, pp 35–59. Azariadis C, Drazen A (1990) Threshold externalities in economic development. The Quarterly Journal of Economics 105(2): 501-526. Balcilar, Mehmet and Zeynel Abidin Ozdemir (2013) The export-output growth nexus in Japan: a bootstrap rolling windows approach. Empir Econ 44:639-660. Barro R, Sala-i-Martin X (1995) Economic Growth, McGraw-Hill, Cambridge, MA. Basu P, Chakraborty C, Reagle D (2003) Liberalization, FDI, and Growth in Developing Countries: A panel cointegration approach. Economic Inquiry 41(3): 510-516. Benhabib J, Spiegel MM (1994) The role of human capital in economic development: evidence from aggregate cross-country data. Journal of Monetary Economics 34 (2): 143 – 173. Bhagwati JN (1978) Anatomy and consequences of exchange control regimes. Vol. 1, Studies in International Economic Relations, 10 (New York: National Bureau of Economic Research). Blomstrom M, Lipsey R, Zejan M (1992) What Explains Developing Country Growth. NBER Working Paper No. 4132. Blundell R, Bond S (1998) Initial conditions and moment restrictions in dynamic panel data models. Journal of conometrics 87: 115-143. Borensztein E, De Gregorio J, Lee JW (1998) How does foreign direct investment affect economic growth? Journal of International Economics 45: 115-135. Chakraborty C, Nunnenkamp P (2008) Economic Reforms, FDI, and Economic Growth in India: A Sector Level Analysis, World Development 36(7): 1192-1212. Chan V-L (2000) Foreign Direct Investment and Economic Growth in Taiwan’s Manufacturing Industries. In: Ito T, Krueger AO (Eds.) Role of Foreign Direct Investment in East Asian Economic Development. Pp 349- 366. University of Chicago Press. Chen C, Chang L, Zhang Y (1995) The role of foreign direct investment in China’s post-1978 economic development. World Development 23: 691-703. Chowdhury,A.,Mavrotas,G(2006) FDI and Growth:What causes what? The World Economy,29(1):9-19. Dash RK, Parida PC (2012). FDI, services trade and economic growth in India: empirical evidence on causal links, Empir Econ (2013) 45:217–238 DOI 10.1007/s00181-012-0621-1. De Gregorio J(1992) Economic growth in Latin America. Journal of Development Economics 39:59–84. De Mello LR (1997) Foreign direct investment in developing countries and growth: A selective survey. The Journal of Development Studies 34(1): 1- 34. De Mello LR (1999) Foreign direct investment-led growth: Evidence from time series and panel data, Oxford Economic Papers 51 (1): 133-51. Deng, Wen-Shuenn and Yi-Chen Lin (2013) Parameter heterogeneity in the foreign direct investment-income inequality relationship: a semiparametric regression analysis. Empir Econ. 45:845-872. Dimelis S (2005) Spillovers from foreign direct investment and firm growth: technological, financial and market structure effects. International Journal of the Economics of Business 12(1): 85 – 104. Edwards (2014) Building Better Econometric Models Using Cross Section and Panel Data. Business Expert Press. New York, NY. ISBN 13: 978-1-60649-984-9 (paperback), and 13: 978-1-60649-985-6 (e-book). Edwards JA, Kasibhatla K (2009) Dynamic heterogeneity in cross-country growth relationships. Economic modeling 26(2): 445-455. Edwards JA, Romero AA, Madjd-Sadjadi Z (2016) Foreign direct investment, economic growth, and volatility: a useful model for policymakers. Empirical Econ 51(2): 681-705. El-Wassal KA (2012) Foreign direct investment and economic growth in Arab countries (1970-2008): an inquiry into determinants of growth benefits. Journal of Economic Development 37(4): 79-100.


36

Esso LJ (2010) Long-run relationship and causality between foreign direct investment and growth: evidence from ten African countries. Int J Econ Finance 2(2):168–177. Guisan MC (2009) Rates, Ratios And Per Capita Variables In Cross-Section Models Of Development, Investment And Foreign Trade: A Comparative Analysis Of OECD Countries. Applied Econometrics and International Development 6(2). Guisan MC (2015) Selected Readings on Econometrics Methodology, 2001-2010: Causality, Measure of Variables, Dynamic Models and Economic Approches to Growth and Development. Applied Econometrics and International Development 15(2). Habib M and Zurawicki L (2002) Corruption and Foreign Direct Investment. Journal of International Business Studies 33 (2): 291 – 307. Hossain D (2014) Differential impacts of foreign capital and remittance inflows on domestic savings in developing countries: a dynamic heterogeneous panel analysis. Economic Record 90: 102-126. Hsiao C and Shen Y (2003) Foreign direct investment and economic growth: The importance pf institutions and urbanization. Economic Development & Cultural Change 51(4):883-896. IMF (2003) Foreign direct investment in emerging market countries. Report of the working group of the capital markets consultative group. Li X, Liu X, Parker D (2001) Foreign direct investment and productivity spillovers in the Chinese manufacturing sector. Economic Systems 25: 305-321. Lin S-H, Kim D-H, and Wu Y-C (2013) Foreign direct investment and income inequality: Human capital matters. Journal of Regional Science 53(5): 874-896. Lucas RE (1990) Why doesn't capital flow from rich to poor countries? American Economic review 80: 92-96. Malley J. and Motous T (1994) A prototype macroeconomic model of foreign direct investment. Journal of Development Economics 43(2): 295-315.. Mencinger J (2003) Does foreign investment always enhance economic growth? Kyklos 56(4):491-508. Nair-Reichert U, Weinhold D (2001) Causality Tests for Cross Country Panels: A New Look at FDI and Economic Growth in Developing Countries. Oxford Bulletin of Economics and Statistics 63: 153-71. Omran M and BolBol A (2003) Foreign direct investment, financial development, and economic growth : Evidence from the Arab Countries. Review of Middle East Economics and Finance 1(3): 37-55. Ozturk I (2007) Foreign direct investment-growth nexus: a review of the recent literature. Int J Appl Econom Quant Stud 4(2):79–98. Romer PM (1990) Endogenous technical change. Journal of Political Economy 98(5): S71-102 Roodman D (2006) How to do xtabond2: An introduction to “Difference” and “System” GMM in Stata. Working paper 103, Center for Global Development. Solow RM (1956) A contribution to the theory of economic growth. The Quarterly Journal of Economics 70(1): 65-94. Wang M (2009) Manufacturing FDI and Economic Growth: Evidence from Asian Economies, Applied Economics. 41(8): 991–1002. Wei Y, Liu X (2001) Foreign Direct Investment in China: Determinants and Impact. Edward Elgar, Cheltenham. Wen M (2007) Foreign direct investment, regional market conditions, and regional development: A panel study on China. Economics of Transition 15 (1): 125 – 151. Windmeijer F (2005) A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics 126(1): 25-51. Zhang K, Markusen J (1999) Vertical multinationals and host-country characteristics. Journal of Development Economics 59: 233-252.

Annex on line at the journal Website: http://www.usc.es/economet/eaat.htm


37

Appendix A Countries by Income Groups (World Development Indicators) Low Income Burkina Faso Bangladesh Bolivia Central African Republic Congo, Rep. Egypt, Arab Rep. Guatemala Honduras Indonesia India Kenya Sri Lanka Lesotho Morocco Mali Mauritania Malawi Pakistan Philippines Rwanda Sudan Senegal Sierra Leone El Salvador Swaziland Togo

Upper-Middle Income Antigua and Barbuda Botswana Chile Colombia Costa Rica Dominica Dominican Republic Ecuador Gabon St. Lucia Mexico Mauritius Malaysia Panama Peru Seychelles Thailand Tunisia Turkey St. Vincent and the Grenadines Venezuela, RB South Africa

High Income Australia Austria Barbados Finland France St. Kitts and Nevis Korea, Rep. Netherlands Norway Saudi Arabia Singapore Sweden


Appendix B

Descriptive Statistics

Pooled High Upper-Middle Lower Mean Std.

Dev. Mean Std.

Dev. Mean Std.

Dev. Mean Std.

Dev. GDP Growth 2.113 4.286 2.288 3.309 2.477 4.473 1.652 4.540 Growth Ind

3.874 7.707 3.203 6.590 4.008 7.933 4.119 8.045

Growth Serv

4.090 4.973 3.408 3.043 4.452 4.743 4.115 5.948

Growth Ag

2.470 9.206 1.372 7.698 2.469 9.426 3.092 9.708

FDI 3.378 4.875 4.300 5.782 4.144 4.849 2.092 3.988

Documents

EFFECT OF FDI ON REAL PER CAPITA GDP GROWTH: A ROLLING