A Generalized Empirical Model of Corruption, FDI and Growth

8/4/2019 A Generalized Empirical Model of Corruption, FDI and Growth

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A Generalized Empirical Model of Corruption, Foreign Direct

Investment, and GrowthMichael S. Delgado

Department of Economics

Binghamton University

Subal C. Kumbhakar


Binghamton University

Nadine McCloud


University of the West Indies at Mona

March 27, 2011

Abstract

We propose a generalized empirical model for estimating the effect of foreign direct in-

vestment on GDP growth rates, as well as for determining the effect of corruption on the

growth rate, and on the relationship between foreign direct investment and growth. Our

model allows for parameter heterogeneity between all conditioning variables (including for-

eign direct investment) and growth, as well as in the effects of corruption on growth. We

estimate the regression using a recently developed nonparametric method of moments esti-

mator that allows us to concurrently use instrumental variables to mitigate any endogeneity

bias that may be present in the relationship between foreign direct investment and growth,

and model parameter heterogeneity. We find that there is substantial heterogeneity in the

relationship between foreign direct investment and growth, and that foreign direct invest-

ment has a positive and significant effect on growth for many of the countries in our sample.

Corruption is shown to significantly diminish the effectiveness of foreign direct investment

at improving growth rates, but overall has an insignificant net effect on growth.

Keywords: Foreign direct investment; corruption; parameter heterogeneity; economic growth;

nonparametric method of moments; instrumental variables.

Michael S. Delgado, Department of Economics, State University of New York at Binghamton, PO Box 6000,Binghamton, NY 13902-6000. Email: [email protected]

Corresponding author: Subal C. Kumbhakar, Department of Economics, State University of New York atBinghamton, PO Box 6000, Binghamton, NY 13902-6000. Phone: 607-777-4762. Fax: 607-777-2681. Email:

[email protected] McCloud, Department of Economics, University of the West Indies at Mona, Kingston 7, Jamaica.

Email: [email protected]

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1 Introduction

Foreign direct investment (FDI) is generally thought to be an important factor of growth and

development in developing countries. It is through the investments of large multinational corpo-

rations that developing countries have access to advanced technologies, management practices,

and research and development that are crucial for growth, but are otherwise unavailable in the

developing world (e.g., Borensztein et al. 1998 and Carkovic and Levine 2005). Unfortunately,

while there has been a broad consensus as to the theoretical importance of FDI for growth and

development in developing countries, there has yet to be a consensus among empirical researchers

as to the significance of FDI at increasing growth rates. Blomstrom (1986), Borensztein et al.

(1998) and Alfaro et al. (2004) all find evidence that FDI positively contributes to economic

growth, whereas Haddad and Harrison (1993), Aitken and Harrison (1999), and Carkovic and

Levine (2005) find no evidence in support of growth-enhancing effects of FDI.

These conflicting empirical results among studies on the FDI-growth relationship may be

due to the failure to appropriately incorporate parameter heterogeneities, which can lead to a

misspecified model and inaccurate estimation of the relationships of interest. Durlauf (2001), for

example, advocates modeling all the parameters in growth regressions as functions of develop-

mental variables, rather than as constants. Constant parameter growth models may suffer from

misspecification since they ignore crucial heterogeneities induced by the developmental variables

that are fundamental to the growth process. Moreover, constant-parameter models will most

likely be sensitive to different specifications of functional forms, or samples of observations.

Growth models that allow for constant parameters provide a description of the average relation,

at best. In the presence of substantial heterogeneity in the growth process, constant-parameter

models are unlikely to accurately estimate the relationship between FDI and growth.

One important developmental element that is likely correlated with the absorptive capabil-

ities of host countries and ultimately influences the effectiveness of FDI at improving growth

rates is institutional quality. While there are different measures of institutional quality that may

result in heterogeneity in the effect of FDI on growth across developing countries, corruption

may be of extra importance because of its effect on many avenues that all ultimately influence

absorptive capabilities and growth rates. Mauro (1998), Gupta et al. (2002) and Tanzi et al.

(2002) all document a negative relationship between corruption and human capital. Countries

that are more corrupt tend to invest less in human capital, which ultimately decreases the

ability of the country to absorb new technologies from developed nations (Borensztein et al.

1998). Bribery, for example, which is associated with higher levels of corruption, may lead to an

imbalance in the relative payoffs between productive and unproductive sectors in the economy

(Baumol 1990 and Murphy et al. 1991). Workers are less likely to move to domestic from foreign

firms (i.e., the multinational corporations) where their payoffs are relatively higher; the result

is less diffusion of technology from the domestic firms to foreign firms, and a weakening of the

effect of FDI on growth.

Building on previous studies that have identified heterogeneity within the relationship be-

tween FDI and GDP growth (e.g., Borensztein et al. 1998 and Alfaro et al. 2004), as well as

studies that have shown an important interaction between institutional factors (e.g., corrup-

tion) and the effectiveness of FDI at improving growth rates (e.g., McCloud and Kumbhakar

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2011), we present a generalized empirical growth model with which to re-analyze the relationship

between corruption, FDI, and GDP growth (as well as the relationship between growth rates

and other conditioning variables). Our generalization is based on a standard growth regres-

sion that assumes homogeneous parameters. We generalize the standard model to allow for a

heterogeneous relationship between GDP growth and all conditioning variables, by making thecoefficients unknown smooth-functions of an index of corruption, and country- and time-specific

indicators. Thus, our model allows us to obtain estimates that are specific to each country in

each year, and estimates that depend on the level of corruption in each country and in each year.

While we present an alternative approach that complements previous studies that have allowed

for heterogeneity within the corruption-FDI-growth relationship, our approach also allows us to

analyze the effect of corruption on GDP growth rates through its effect on all conditioning vari-

ables (e.g., trade openness, or inflation), and not solely through its influence on the FDI-growth

relationship.

To estimate our generalized regression model, we use a recently developed nonparametricversion of a standard method of moments estimator (Cai and Li 2008) that assumes the primary

conditioning variables (e.g., FDI) enter linearly into the regression model, but allows the inter-

cept and slope coefficients to vary nonparametrically (i.e., either linearly or nonlinearly) with

respect to certain environmental factors (e.g., corruption). Hence, this model is a version of the

varying coefficient models of Hastie and Tibshirani (1993), or more recently Li et al. (2002).

One advantage of this nonparametric generalized methods of moments (NPGMM) estimator

over other smooth coefficient models, e.g., Durlauf et al. (2001) and Li et al. (2002), is that it

allows all of the conditioning variables to be endogenous. This, in part, addresses one concern

raised by Durlauf (2001), who argues that in a growth specification, all conditioning variablescan be taken to be endogenous; that is, all variables typically included in a growth specification

are determined, in part, by omitted factors that also determine growth rates. In particular,

Borensztein et al. (1998) provide a discussion of the potential endogeneity of FDI in a growth

regression. Hence, it is important to consider an instrumental-variables approach to estimating

the relationship between FDI (or any other conditioning variables of interest) and growth in

order to obtain consistent estimates.1

Although the generalized model differs from standard homogeneous models by incorporat-

ing parameter heterogeneity in the coefficients, the generalized model maintains the traditional

functional form assumptions embedded in the standard models. The advantage of maintain-

ing such assumptions (e.g., additive separability and linearity of the regressors), is that the

standard model exists as a special case of the generalized model. We can econometrically test

whether the data support the assumption of parameter homogeneity inherent in the standard

model. Another advantage of maintaining such functional form assumptions is that we can avoid

dimensionality issues that often arise in fully-specified nonparametric models. In the present

context, dimensionality issues can only arise when estimating the coefficient functions in the

smooth coefficient model; because the number of continuous environmental factors is likely to

1In the empirical growth literature, Liu and Stengos (1999) and Durlauf et al. (2001) also use semiparametricmodels to examine parameter heterogeneity. Their works differ from that of the present paper in many ways

including the use of cross-sectional and not panel data, exclusion of FDI from the set of independent variables,sample selection (inclusion of OECD and non-OECD countries), assumed sources(s) of parameter heterogeneity,and analysis of endogeneity.

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be relatively small, or at least smaller than the entire conditioning set in a fully nonparametric

regression, the curse of dimensionality can often be avoided.

Our results confirm that there exists substantial heterogeneity in the relationship between

FDI and growth, and we find strong evidence that FDI has a positive and significant influence

on growth rates for about 80 percent of the developing countries in our sample. In addition toproviding observation-specific estimates of the coefficients (e.g., the FDI coefficient), our model

also provides estimates of the marginal effect of corruption on each of the coefficients. Our

estimates show that corruption significantly reduces the effectiveness of FDI on growth, which

supports previous studies that suggest that corruption influences the absorptive capabilities of

developing countries. However, when considering the total effect of corruption on growth rates

(i.e., the sum of the indirect effects of corruption on all of the coefficients in the model), we find

that corruption does not significantly influence growth rates.

Through our heterogeneous parameter estimates, we analyze separate groups of countries

that have substantially different coefficients and isolate characteristics common within suchgroups. This type of analysis is useful for international investment policies: knowledge of whether

there is a positive and significant relationship between FDI and growth rates for any particu-

lar country, or how this relationship varies with respect to corruption is crucial for designing

policies aimed at improving growth rates. While we do not find evidence of regional or geo-

graphical groups, we find that with respect to heterogeneity within the FDI-growth relation,

many countries with the highest returns to FDI also have the lowest returns to corruption.

Conversely, countries with an insignificant or relatively low correlation between FDI and growth

have the highest estimated returns to corruption. Hence, our results suggest that developing

countries with relatively low correlations between FDI and growth may benefit substantiallyfrom a reduction in corruption.

Our empirical results are robust to using different instruments for FDI, allowing for all

conditioning variables to be endogenous, using different measures of corruption, controlling

for other measures of institutional quality that may be correlated with the dependent and

independent variables. Moreover, a specification test suggests our semiparametric model that

allows for endogeneity is more consistent with the data than the standard-homogeneous model.

The structure of the rest of the paper is as follows. Section 2 presents and discusses our

generalized empirical growth model and its nonparametric method of moments estimator. Sec-

tion 3 discusses the data. Section 4 provides the main empirical results and discusses their

implied policy prescriptions. Section 5 investigates the robustness of our main results. Section

6 concludes. The excluded empirical results can be furnished on request.

2 Empirical Methodology

2.1 Growth Models

We consider a standard growth model with the growth rate of real GDP per capita as the

dependent variable and a set of control variables. Letting git denote the real GDP per capita

growth rate in country i at time t, we write the standard model as:

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git = 0 + Yit1 + Xit2 + it, i = 1, . . . , n t = 1, . . . , T , (1)

in which Yit is our measure of FDI, Xit is a vector of control variables, (0, 1, 2) is a

vector of parameters to be estimated, and it is a zero-mean random error. The advantage of

(1) is that, under certain regularity assumptions, it is easy to consistently estimate the effect of

FDI on growth using a least squares criterion. One primary drawback of this model, however,

is that it fails to incorporate parameter heterogeneity that more likely exists in a cross-country

panel of observations. In particular, model (1) does not allow the effect of FDI on growth, 1,

to vary with respect to the index of corruption.

Allowing the parameter estimates to vary with respect to corruption is a pragmatic way

to identify the indirect effect of corruption on growth. An alternative way to incorporate the

level of corruption into the growth regression would be to add the corruption index as another

conditioning (i.e., X) variable, but this approach does not identify indirect channels through

which corruption influences growth. Corrupt governments (or officials) are more likely to embez-

zle funds and redirect public expenditures towards personal and private ventures, rather than

direct them towards more publicly beneficial avenues. The effect is that FDI and other corre-

lates of economic growth may be directly influenced by the level of corruption. Through these

channels, corruption may indirectly influence GDP growth rates. However, it is important to

incorporate the direct effects of corruption in the regression model to obtain an accurate picture

of the effect of corruption on growth rates and on the relationship between the conditioning

variables (e.g., FDI) and growth, and results that are comparable to those of existing growth

studies.

Since our interest is on the estimation of the effect of FDI on growth, and how this effect

varies with respect to the level of corruption, we generalize the model to incorporate the effect of

corruption on the -parameters. Specifically, we generalize (1) by allowing the -parameters in

the model to vary with respect to a particular set of environmental variables, Zit, which contains

the index of corruption. Hence, we write our generalized model as:

git = 0(Zit) + Yit1(Zit) + Xit2(Zit) + it, i = 1, . . . , n t = 1, . . . , T . (2)

An advantage of using the generalized model in (2) is that it provides observation-specific

estimates of the coefficients of the model thereby allowing us to analyze the heterogeneity in the

effect of FDI (and other control variables) on growth rates. As previously argued, an accurate

modeling of parameter heterogeneity is crucial for designing cross-country policies to increase

growth rates in developing countries; if the effect of FDI on growth rates varies substantially

across different countries (or different groups or types of countries), investment policies governing

FDI should be tailored to each specific country (or group of countries). The policy prescriptions

for boosting growth through FDI, which are implied by homogeneous parameter estimated, may

be too passive or active for some developing countries.

If we assume that corruption is orthogonal to the error term, then it is straightforward to

extract the direct and indirect effects of corruption on growth. The direct effect of corruption

on growth comes through the effect of corruption on the intercept function, 0(); we can obtain

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an estimate of this direct effect through the partial derivative of the intercept function with

respect to corruption at a particular point, z: 0/z . The indirect effects come through the

effect of corruption on the other coefficient functions in the model, j=0(); we obtain these

effects through the partial derivatives of each of the slope coefficient functions with respect to

corruption, j=0/z. The total effect of corruption on GDP growth rates is the sum of thedirect and indirect effects of corruption on growth. Hence, taking a partial derivative of the

growth rate in (2) with respect to corruption at a particular point yields the total effect of

corruption on growth:

gitz

=0z

+ Yit1z

+ Xit2z

. (3)

2.2 Estimation

To exploit the generality of our model in (2), we assume that the coefficient functions are

unknown smooth functions of Z. For ease of exposition, we rewrite (2) more compactly as:

git = Xit(Zit) + it, i = 1, . . . , n t = 1, . . . , T , (4)

in which Xit is a vector of dimension k with the first column containing a one and the remainingcolumns containing the (k 1) regressors (including FDI); () is a vector of smooth coefficient

functions of unknown form; Zit is a vector of dimension p containing environmental factors that

are assumed to be the sources of parameter heterogeneity. If we assume also that all regressors

in X are exogenous then (4) is a standard semiparametric smooth coefficient model that can beconsistently estimated using the nonparametric kernel estimator proposed by Li et al. (2002).This exogeneity assumption seems strong in the present growth application, hence we allow the

variables in X to be endogenous. This key endogeneity assumption distinguishes our model in(4) from other semiparametric smooth coefficient models. In the special case where all regressors

in X are exogenous, our model is equivalent to a standard semiparametric smooth coefficientmodel.

If any element in X is endogenous, then E[git| Xit, Zit] = Xit(Zit) and estimation usingtypical semiparametric estimators (e.g., Li et al. 2002) will provide inconsistent estimates of the

unknown coefficient functions. Several nonparametric estimators have been proposed to deal

with the problem of endogeneity in smooth coefficient models, for example, Das (2005), Cai et

al. (2006), and Cai and Li (2008). Both the estimators in Das (2005) and Cai et al. (2006) are

two-step estimators that require nonparametric estimation of the endogenous variables on the

instruments and exogenous variables in the first step followed by semiparametric regression of the

dependent variable on the first stage estimates of the endogenous variables. Cai and Li (2008),

however, propose a one-step estimator of (4) when X is allowed to be endogenous. We apply thisone-step Cai and Li (2008) estimator to reap the gains in efficiency that the one-step estimator

likely has over the two-step estimators. We note that the Cai and Li (2008) framework allows

for all X variables to be endogenous, and explicitly assumes that the environmental variablesin Z are exogenous.

To circumvent the endogeneity problem and obtain consistent estimates of the coefficient

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functions, Cai and Li (2008) propose the following conditional moment restriction:

E[Q(it)it|it] = E[Q(it){git Xit(Zit)}|it] = 0, (5)

in which it = (Wit, Z

it)

, Wit is a vector of instrumental variables such that E[it|Wit] = 0,

and Q(it) is some vector function such that the conditional moment restriction in equation (5)

is satisfied. While in principle any vector for Q(it) that satisfies the conditional moment

restriction in (5) can be used, Cai and Li (2008) suggest using Q(it) =

WitWit(Zitz)/h

, where

h is a smoothing parameter, and is the Kronecker product operator, to make use of the

instrumental variables in Wit. Cai and Li (2008) suggest estimating the coefficients, (Zit),

with nonparametric kernel methods which, combined with the conditional moment restriction

in (5), yields a nonparametric equivalent of a GMM estimator (or NPGMM).

We assume the coefficients, (Zit), are twice continuously differentiable, so that we can apply

local-linear least-squares to estimate the unknown functions. A first order Taylor expansion

around a given point z yields an approximation to the function j(Zit) given by j(z)+j(Zitz),

in which j is a gradient vector of the partial effects j(z)/z . Thus the local-linear procedure

provides a vector of estimated coefficient functions, j(Zit), along with their first order gradient

vectors, j(z)/z . Letting Uit = XitXit(Zitz)

and = (j(z),

j) be the vector of coefficients

and their first order partial derivatives, the conditional moment restriction in equation (5) gives

rise to the following locally weighted orthogonality condition:

ni=1

Tt=1

Q(it)(git Uit)Kh(Zit z) = 0 (6)

in which Kh(Zit z) is a generalized product kernel of dimension p that admits a mix of

continuous and discrete environmental factors contained in Z (see Racine and Li 2004), and h

denotes a vector of smoothing parameters. Cai and Li (2008) show that a consistent estimate

of can be obtained from:

= (SnSn)1(SnTn), (7)

in which

Sn =1

n

ni=1

Tt=1

Q(it)UitKh(Zit z) (8)

and

Tn =1

n

ni=1

Tt=1

Q(it)Kh(Zit z)git. (9)

To avoid any pitfalls associated with an ad hoc choice of smoothing parameters, we use

least-squares cross-validation to select the parameters in h. The least-squares cross-validation

criterion function is given by

minh

nTi=1

(gi gi)2 (10)

in which gi is the leave-one-out estimate of the conditional mean, X(Z). All standard errors

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are estimated using a wild-bootstrap.

3 Data

3.1 Overview

The data set comes from McCloud and Kumbhakar (2011). It consists of a balanced panel of

60 non-OECD countries spanning the period 1985-2002 giving a total of 1080 observations. Our

primary interest is in estimation of the effect of FDI on GDP growth and how this effect varies

with respect to the level of corruption in each country in each year. Our secondary interest is in

identifying the overall effect of corruption on GDP growth, both directly through its influence

on the intercept term and indirectly through its role in the effects of other control variables on

growth. Our measure of GDP growth is the per capita GDP growth rate that comes from the

Penn World Table (version 6.2).

3.2 FDI

Our measure of FDI is the percentage of FDI inflow relative to GDP in constant 2002 dollars,

which comes from the United Nations Conference on Trade and Development online statistical

database. It is generally believed that FDI may be correlated with any factors that influence

growth rates but are omitted from the regression model; that is, FDI may be endogenous in

a growth specification such as (1). The empirical FDI literature has been unable to identify

an ideal instrumental variable to completely control for any endogeneity bias. Several studies

have proposed several different instrumental variables that have been shown to mitigate, at least

part of, the endogeneity of FDI. Borensztein et al. (1998), for example, suggest using lagged

values of FDI or measures of institutional quality. Carkovic and Levine (2005) suggest using

lagged FDI as well as lagged differences of FDI as instrumental variables. We find that in our

data set, lagged values of FDI work reasonably well and appear to mitigate, at least part of,

the endogeneity of FDI; see section 4.1. Measures of institutional quality (i.e., ethnolinguistic

fractionalization and latitude from La Porta et al. (2009), and the log of the life expectancy

and log of the fertility rate from the 2005 World Development Indicators) and lagged differences

of FDI appear irrelevant based on their low explanatory power in the first-stage parametric

regressions. Moreover, semiparametric regressions using these latter instrumental variables did

not yield meaningful estimates or appear to mitigate any endogeneity bias.

In addition to the above instrumental variables proposed in previous studies, we propose

using total world FDI flows and total FDI flows to developing countries as alternative instru-

mental variables. Currently, we are unaware of other studies that use these total FDI flows as

instrumental variables for individual country FDI flows. Our rationale for using these variables

is that measures of total FDI flows will cause fluctuations in individual country FDI flows, but

are uncorrelated with growth rates of individual countries. Parametric first-stage regressions

suggest that total world FDI flows and total FDI flows to developing nations may be reasonable

alternative instrumental variables for FDI. The sample correlations between FDI (the endoge-

nous variable of interest) and each of the instrumental variables are 0.74, 0.32, and 0.13, for

lagged FDI, world FDI flows, and developing world FDI flows, respectively. We use lagged FDI

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as our preferred instrumental variable since it provides the strongest first-stage correlations with

FDI (as well as strongest sample correlation), but we consider both measures of total FDI flows

as alternative instrumental variables in our sensitivity analysis.

3.3 CorruptionWe combine two indices of corruption that are widely used in the existing empirical literature.

One index of corruption is from Knack and Philip (1998), which is for the period 1984 to

1996. This index ranges from 0 to 6 and lower scores indicate lower levels of corruption in

that high government officials are likely to demand special payments and illegal payments

are generally expected throughout lower levels of government in the form of bribes connected

with import and export licenses, exchange controls, tax assessment, police protection, or loans.

The other corruption index is from Transparency International (TI) for the period 1997 to

2002.2 The TIs corruption index measures the overall extent of corruption and therefore does

not distinguish between administrative and political corruption, nor between petty and grandcorruption. It ranges from 0 to 10 with higher values indicating lower levels of corruption.

For ease of exposition, we rescale the TI index so that lower values represent lower levels of

corruption.

An important assumption in this analysis is that our proxies for corruption are time invari-

ant. Our rationale is based on the fact that the extent to which corruption is entrenched in many

non-OECD countries makes it difficult for these countries to lower their corruption levels in the

absence of proper legal recourse through institutional reform. Consequently, we transform the

Knack and Philip index to be within the range of 0 to 10 and then construct an aggregated cor-

ruption index by using the time average of the Knack and Philip and TI indices as the measureof corruption for the entire time span. We note that the Spearmans rank correlation coefficient

for the average PRS and TI indices is 0.6151 with a p-value of 0 for the null hypothesis of inde-

pendence. Hence, combining the different measures of corruption from these two sources should

not bias the qualitative implications about the effect of corruption on FDI-growth relation.

3.4 Additional Control Variables

We use the following list of covariates to control primarily for any omitted variables bias between

GDP growth and FDI, but also to serve as possible channels through which corruption may effect

growth. The variables include initial GDP per capita defined as GDP per capita in the previous

year; openness, defined as the ratio of exports plus imports as a percentage of GDP; government

consumption, defined as the ratio of general government consumption as a percentage of GDP;

domestic investment as a percentage of GDP; the US treasury bill rate; and the inflation rate.

All variables come from the Penn World Table (version 6.2) except for the US treasury bill

rate which comes from the IMF International Financial Statistics Database. We include the US

treasury bill rate to control for changes in the growth rate that are caused by macroeconomic

conditions that are exogenous to each individual country.

2The TI index was first launched in 1995 with only a small number of countries. Using earlier years of thisindex would have reduced our effective sample size.

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In addition to corruption, the vector of environmental variables, Z, also contains an un-

ordered categorical country indicator and an ordered categorical indicator for year to control

for country and year fixed effects, respectively. Alternative specifications include the fertility

rate (total births per woman) and an index of democracy as additional environmental variables.

Our index of democracy comes from the Polity IV database and ranges from -10 to +10 with+10 representing complete democracy and -10 complete autocracy. With the exception of the

variables already measured in percentage terms or growth rates, all continuous variables are

measured in logs.

4 Results

4.1 Ordinary Least Squares

We first estimate the standard homogeneous model in (1) using ordinary least squares. Since

the coefficients do not vary in this model, we include corruption and country and time dummy

variables as standard conditioning (i.e., X) variables. The purpose of estimating the homoge-

neous model is to provide estimates that are directly comparable to other studies that do not

use semiparametric estimators, and to anchor our semiparametric results to the standard case.

Table 1 contains the results from the different model specifications.

The first three columns in Table 1 show estimates from three standard models: the first

column reports the results from a parsimonious model in which the only regressors are FDI

and fixed effect dummy variables; the second column adds corruption; and the third column

adds the rest of the conditioning set. We find that FDI has a positive and significant effect

on growth rates in columns 1 and 2; that is, including corruption in the regression does noterode the effect of FDI on growth. In particular, an increase of 10 percent in the FDI inflows

to GDP is associated with an increase of 3 percent in economic growth rate. Interestingly, the

coefficient on corruption is positive and significant. A positive coefficient on corruption implies

that holding everything else constant, increasing the level of corruption in a country will increase

its rate of real GDP per capita growth. We find this result to be counter-intuitive; our prior

expectation is that corruption has a negative (or perhaps insignificant) effect on GDP growth

rates. Moreover, the R2 does not improve substantially after including corruption into the

regression, which suggests that corruption may not contain much predictive power. Including

the rest of the conditioning set (column 3) does not change the effect of corruption on growth,but it does erode the effect of FDI on growth by approximately 67 percent. This large reduction

in the estimated FDI coefficient suggests that the FDI-growth effects in the columns 1 and 2

may be driven by omitted variables bias. Indeed, there may be other factors that are subsumed

in the errors and are correlated with both FDI and economic growth. To explore this possibility,

we use instrumental variables methods.

Columns 4 through 6 report estimates from two-stage least squares regressions that use the

one-period lagged value of FDI to control for any possible endogeneity of FDI in the standard

models. Specifically, we include all exogenous variables including corruption in the first stage

regression, as well as country and time dummy variables to control for country and time fixedeffects. We find that for each first-stage specification, the FDI instrument is positive and sta-

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tistically significant, and the F-statistic for the null hypothesis of no regression exceeds 10,

suggesting strength of the instrumental variable (Staiger and Stock, 1997).

We find that in the second-stage regressions, including the fully specified model in column

6, the instrumented FDI variable has a positive and significant effect on growth rates. In

addition, the magnitude of the FDI coefficient is substantially larger than in the simple ordinaryleast squares models. This suggests that there is a downward bias in the ordinary least squares

estimates of FDI, most likely caused by endogeneity; the lagged FDI instrument is able to correct

for (at least part of) the downward bias on the FDI coefficient. Corruption remains positive and

significant, and many other conditioning variables maintain their sign and significance observed

in their ordinary least squares counterparts. The explanatory power in the two-stage least

squares models is comparable to that from the ordinary least squares models.

The aforementioned ordinary least squares and two-stage least squares homogeneous esti-

mates yield three important observations: One, on average, FDI has a positive and significant

effect on GDP growth rates in non-OECD countries, but the effect is subject to a downwardendogeneity bias that will potentially mask the significance of FDI. Two, the use of lagged FDI

as an instrument for FDI is able to mitigate this downward bias and provides more precise esti-

mates of the effect of FDI on growth. This positive mean effect of FDI on growth, as implied by

the homogeneous models, should not be taken to imply that in all countries, FDI has a positive

and significant effect on growth rates. Three, corruption appear to have strongly positive and

significant, albeit counter-intuitive, effect on growth rates. This latter counterintutive result

may be a manifestation of model misspecification, at least with regards to the way in which

corruption is included in the model. A maintained hypothesis is this paper is that corruption

itself is an environmental variable and thus it should be included as such in the regression, andnot included as a typical conditioning (i.e., X) regressor. In light of these observations, we moveon to the results from our generalized semiparametric specifications that concurrently allow for

(a) corruption to enter into the model indirectly through its influence on the relationship be-

tween the conditioning variables and GDP growth rates, (b) parameter heterogeneity and (c)

endogenous regressors.

4.2 Semiparametric Smooth Coefficient Models

Columns 7 through 10 in Table 1 report the mean coefficients and standard errors for four

semiparametric smooth coefficient specifications. The first two specifications (columns 7 and 8)do not control for endogeneity of FDI; hence, these models are standard semiparametric smooth

coefficient models (SPSCM). Columns 9 and 10 control for endogeneity of FDI using lagged

FDI and the Cai and Li (2008) NPGMM estimator. Columns 7 and 9 include only FDI as a

conditioning variable, whereas columns 8 and 10 include all other conditioning variables. Each

of the four models include corruption and indicators for country and year as environmental

variables.

4.2.1 Mean Parameter Estimates

We find that the mean coefficient on FDI is positive and statistically significant in all semipara-

metric specifications. In the semiparametric models that do not allow for instrumental variables

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(columns 7 and 8), we observe FDI coefficients that are similar in magnitude to their ordinary

least squares estimates in columns 1 and 2. After controlling for endogeneity, we observe that the

mean FDI coefficients in the semiparametric models (columns 9 and 10) are close in magnitude

to their counterparts from the two-stage least squares models. In essence, the FDI coefficients

are substantially larger in magnitude in the semiparametric models that control for endogeneity,suggesting that the endogeneity of FDI biased its coefficients downward bias. Thus, we again

find evidence that supports the validity of using lagged FDI values to correct for any endogeneity

bias associated with FDI.

Turning to the additional conditioning variables in the fully-specified models (columns 8 and

10), we find that regardless of whether we instrument for FDI, the mean estimates of initial

income, openness and the inflation rate are negative and highly statistically significant, whereas

those associated with domestic investment are positive and highly statistically significant. The

mean estimates of all other conditioning variables are insignificant.

Turning to the total effect of corruption on growth, g/Z, we find in each of the foursemiparametric models that corruption has an insignificant effect on GDP growth rates. This

result is more in line with our prior expectations, and is in contrast to the results from the

ordinary least squares and two-stage least squares estimates that found corruption to have a

positive and significant effect on growth rates. Table 2 shows the means and standard errors of

the direct effect of corruption on the coefficients in the semiparametric models. At the mean,

we find that corruption has a negative and significant effect on the coefficient on FDI (columns

2 to 4). Thus, coupling this result with the positive and significant FDI coefficients in Table 1

(columns 8 to 10) implies that an increase in corruption will decrease the effectiveness of FDI

on growth, and through this channel, holding everything else constant, decrease GDP growth.From Table 2, we also find that corruption has a positive and significant effect on the coefficient

on openness (column 2) and on inflation (column 4). Since both the openness and inflation

coefficients are negative and significant (see columns 8 and 10 of Table 1), these positive partial

derivatives imply that the effect of openness and inflation on growth is dampened by an increase

in corruption.

In addition, we find that in each of the four semiparametric models, the R2 is substantially

higher than in the corresponding homogeneous regressions. These higher R2 values suggest

that there are sizeable parameter heterogeneities across countries and modeling these parameter

heterogeneities substantially improves the fit of the model to the data. We now turn to the

distribution of the coefficients and their partial effects with respect to corruption to further

assess the degree of parameter heterogeneity that exists in the estimates, and to understand the

policy implications of heterogeneity in the relationship between FDI and growth.

4.2.2 Heterogeneous Parameter Estimates

To present the distribution of coefficients in a concise manner, we use 45-degree gradient plots

to simultaneously show the magnitude, sign, standard errors, statistical significance and density

of the coefficients. To construct these plots, we first plot the observation-specific coefficients on

the 45 degree line. The location of any particular coefficient to the horizontal axis determinesthe sign and magnitude of the coefficient, whereas the density can be seen by the proximity

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of surrounding observations to any particular observation. Areas with a high concentration of

coefficients are areas of higher density. We then calculate observation-specific confidence bounds

by adding (and subtracting) twice the observation-specific standard error from each coefficient.

We then overlay the confidence bounds above (and below) the scatterplot of coefficients. This

allows us to assess whether each observation is statistically significant; if the horizontal lineat zero runs between the coefficient and its upper or lower confidence bound, the observation

has a statistically insignificant coefficient. If the horizontal line at zero does not intersect the

confidence bound for a particular observation, that observation is statistically significant.

Figure 1 displays the 45-degree gradient plots for the distribution of observation-specific FDI

coefficients and standard errors for each of the four semiparametric models that are described

in Table 1. In each model, many of the observations are positive. Specifically, for the standard

semiparametric models that do not use instrumental variables (SPSCM models), 56 percent and

65 percent of the FDI coefficients are positive and significant, respectively SPSCM1 and SP-

SCM2. In the semiparametric models that use lagged FDI as an instrumental variable (NPGMMmodels), the FDI coefficients are positive and significant for 59 percent and 76 percent of the

observations, respectively NPGMM1 and NPGMM2. While the plots show a substantial amount

of heterogeneity in the parameter estimates, it is clear that, on average, FDI has a positive and

significant effect on GDP growth rates. Moreover, after instrumenting for any endogeneity bias

on the coefficients, we find that the fraction of coefficients that are positive and significant in-

creases. These results provide evidence that the lagged value of FDI is also able to mitigate (at

least part of) the downward bias on the FDI coefficients in the semiparametric models.

In reference to the additional conditioning variables in the fully-specified models that have

statistically significant mean estimates, we find that 59 percent of the initial income coefficientsare negative and significant in the model that does not instrument for FDI, whereas 72 percent

of the coefficients are negative and significant in the instrumental variables model. We find that

46 percent of the coefficients on openness are negative and significant in the non-instrumental

variables model, and 75 percent of the coefficients are negative and significant in the instru-

mental variables model. In the non-instrumental variables model we find that 94 percent of the

coefficients on domestic investment are positive and significant, and 94 percent of the coefficients

on inflation are negative and significant. In the instrumental variables model, we find that 95

percent of the coefficients on domestic investment are positive and significant, and 91 percent of

the coefficients on inflation are negative and significant. These results suggest that endogeneity

of FDI induces biases in the estimates associated with the other regression coefficients, and the

size and direction of these biases appear to differ across countries and regressors.

Figure 2 contains 45 degree plots for the partial effects of the FDI coefficients with respect

to corruption. For the standard semiparametric models, 42 percent and 74 percent of the FDI

coefficients show a significantly negative partial effect with respect to corruption, respectively

SPSCM1 and SPSCM2. In the semiparametric instrumental variables models, 45 percent and

84 percent of the FDI coefficients show a negative and significant partial effect with respect to

corruption, respectively NPGMM1 and NPGMM2. These results, especially in the fully specified

models, lend additional support to the view that corruption decreases the effectiveness of FDI

on GDP growth.

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The effect of corruption on the additional coefficients in the fully-specified models is insignif-

icant, except for the coefficients associated with openness and inflation. In the non-instrumental

variables model, we find that corruption significantly reduces the effectiveness of openness on

growth rates for 58 percent of the observations. In the instrumental variables model, the effect

of corruption on openness ceases to be statistically significant; however, we find that 56 percentof the observations have a positive and significant partial effect of the inflation coefficient with

respect to corruption. In general, these results suggest that corruption strongly affects the FDI

coefficient, and has a neutral effect on the coefficients of (many of) the other conditioning vari-

ables in the model. For the fully-specified models, 45-degree gradient plots for the additional

conditioning variables are available upon request from the authors.

Figure 3 shows the 45 degree plots for the total effect of corruption on growth rates. In the

first two semiparametric models (that do not instrument for endogeneity), we find 64 percent

and 70 percent of the partial effects to be insignificant, respectively SPSCM1 and SPSCM2.

In the semiparametric instrumental variables models, we find 66 percent and 72 percent of thepartial effects to be insignificant, respectively NPGMM1 and NPGMM2. Hence, we find strong

evidence that although corruption decreases the effectiveness of FDI on growth rates, corruption

has an overall insignificant effect on growth.

With regards to the estimated bandwidths, we find in each of our four semiparametric mod-

els that the bandwidth on corruption exceeds several standard deviations of the data. In the

local-linear least-squares context, a relatively large bandwidth on corruption implies that cor-

ruption enters linearly into the parameters, j(). Local-linear least-squares is nothing more

than weighted least-squares through the kernel function providing a local weight for each ob-

servation; a large bandwidth means that the local neighborhood includes all observations, andhence a globally linear estimate with respect to corruption.3 We leave further analysis of any

potential linearity between corruption, FDI, and GDP growth for future research.

Our empirical results so far are in favor of parameter heterogeneity. To formally test whether

the semiparametric models yielding heterogeneous parameter estimates are indeed preferred to

the homogeneous parameter models, we use the model specification test of Cai et al. (2000).

The Cai et al. (2000) test allows us to determine whether the data support the null hypothesis

of the simple two-stage least-squares model. We test the null hypothesis against two alterna-

tive hypotheses: the two primary semiparametric smooth coefficient models that include the

entire conditioning set of regressors (one using instrumental variables and the other without).

We are able to reject the null hypothesis of correct specification for the constant parameter

model in both tests with a p-value of 0.0000. Hence, the data support our generalized semipara-

metric specification that admits parameter heterogeneity as a function of corruption over the

homogeneous parameter specification.

3This does not imply any particular parametric functional form for the coefficients, j(), since a large band-width on corruption does not necessarily point towards any specifications regarding interactions between cor-ruption and other environmental variables, or towards correct parametric specification for other environmentalvariables.

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4.2.3 Policy Implications

One of the advantages of incorporating parameter heterogeneity into the regression model is

that it allows us to identify the country specific returns to both FDI and a marginal change in

corruption. We can analyze the placement of countries in the distribution of FDI coefficients to

ascertain which countries have the highest returns to FDI. In addition, we can identify which

group of countries may benefit the most from a reduction in corruption; countries that have the

highest partial effect of the FDI coefficient with respect to corruption may benefit substantially

from FDI if their level of corruption were to decrease. Moreover, the present analysis may help

directly with international FDI policies. One stipulation in an international FDI agreement

may be a mandatory reduction in the level of corruption in the developing host country. At the

very least, this analysis can assist policymakers in determining in which countries FDI will most

effectively improve growth rates, and in which countries FDI may have a neutral effect.

Focusing now on the fully-specified semiparametric instrumental variables model (NPGMM),

we analyze which countries appear to have the highest and lowest returns to FDI. Table 3 shows

the lists of countries divided based on their relative returns to FDI. For those observations

with positive and significant FDI coefficients, we divide the countries into separate lists for each

of the four quantiles based on the magnitude of the coefficient. That is, the countries with

FDI coefficients that are in the highest 75 percentile are grouped together in the 4th quantile

group; we do the same for each quantile.4 Since all of the countries in these quantile lists

realize positive returns to FDI, we also include a list of countries that have insignificant FDI

coefficients.5 Because we have a panel data set, some countries have FDI coefficients that

appear in each category for at least one year. To provide a bit more clarity as to which countries

receive high or low returns, we group the countries based on their modal classification: if acountry appears most frequently in the column for insignificant returns to FDI, we classify it as

insignificant. It is important to highlight which countries have consistently insignificant returns

to FDI; policymakers may want to reconsider investment policies or investment stipulations

aimed at these particular countries.6

We can see in Table 3 that there does not appear to be any geographical similarities between

the groupings of countries. Each group of countries contains countries from each continent or ge-

ographical region. This result implies that there exists heterogeneity in FDI returns even within

geographical regions. Hence, geographical-oriented investment policies may be inappropriate

for enhancing growth-effects of FDI, the best FDI policies may most likely be country-specific.With this in mind, the country lists in Table 3 provide preliminary estimates of the potential

returns each country may realize from further FDI.

Table 4 shows a similar breakdown of countries based on the partial derivative of the FDI

coefficient with respect to corruption.7 Here, we classify the modal observation into quantiles of

4The smallest positive and significant coefficient is 0.004, and the largest positive and significant coefficientis 3.15. The quartile values are 0.37, 0.61, and 0.78, respectively, for the 25th, 50th, and 75th percentiles. Allestimated values are in percentage terms.

5Only 2 percent of the coefficients were negative and significant; none of the negative and significant coefficientsrepresented the modal classification, so we do not include a category for negative returns.

6It is important to acknowledge that although these classifications are based on the modal observations, many

of the countries appear in the same category for many of the years in the panel. Hence the modal classificationprovides an accurate depiction of the distribution of the countries across the classification groups.

7Specifically, the largest negative and significant partial effect is -4.16, and the smallest negative and significant

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negative and significant coefficients and a separate category for insignificant returns. While we

again fail to find any apparent geographical groupings emerging from the lists, comparing groups

of countries between Tables 3 and 4 yields interesting results that complement our previous

findings. Of the countries with insignificant returns to FDI, we find approximately 67 percent

fall into the group of countries that record the highest marginal returns to corruption; and ofthis same group of FDI coefficients, approximately 83 percent fall into the groups that record the

highest and second highest returns to corruption. A similar analysis of countries with the lowest

positive returns to FDI shows that approximately 63 percent fall into the group with the highest

returns to a decrease in corruption, and approximately 88 percent fall into the groups with the

highest and second highest returns to a decrease in corruption. These comparisons strongly

suggest that many countries with insignificant or low returns to FDI may benefit substantially

from a reduction in their levels of corruption.

A similar comparison is done for countries with the highest returns to FDI. We find that

approximately 28 percent of the countries with the highest returns to FDI have insignificantreturns to a decrease in corruption, and approximately 61 percent have insignificant or low

returns to a decrease in corruption. This suggests that the countries with the highest returns to

FDI do not stand to gain as much from a marginal reduction in corruption. Overall, our estimates

suggest that corruption does indeed weaken the relationship between FDI and growth.

5 Sensitivity Analysis

We now turn to additional model specifications that can be used to examine the robustness of

our core results. Our primary concern is that we have inadequately controlled for any potentialendogeneity that may exist between GDP growth rates, FDI, corruption, or any other variables

in our conditioning set. Our secondary concern is that we have failed to incorporate related

environmental variables into our assessment of the relationship between corruption and the

FDI-growth relationship. All results from this section are available on request.

We first address our concerns regarding any additional biases arising from endogeneity. To

examine whether any possible endogeneity bias associated with the X variables (excluding FDI)

is driving our core results, we re-estimate our fully-specified NPGMM model using one-period

lags of all X variables, with the exception of the U.S. treasury bill rate.8 Indeed, one of the

strengths of the NPGMM model proposed by Cai and Li (2008) is its ability to allow for the

potential endogeneity of all X variables. Lagging all regressors reduces our sample size to 1020.

Although the estimated model has fewer number of statistically significant coefficients for the

other X variables, as well as a slightly lower R2 value, our core results remain unchanged. We

find a positive and significant relationship between FDI and GDP growth rates, and a negative

and significant relationship between corruption and the FDI-growth relationship for a large

subset of observations in our sample.

To address any endogeneity brought on by serial correlation within the regressors for any

partial effect is -0.50. The quartile values are 1.08, 1.28, and 1.45, respectively, for the 25th, 50th, and 75th

percentiles. All estimated values are in percentage terms.8The U.S. treasury bill rate is explicitly assumed to capture exogenous fluctuations in macroeconomic condi-

tions.

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of the X regressors (including FDI), we re-estimate our fully-specified NPGMM model using

two non-overlapping, aggregated panel data sets. The first aggregated panel uses observations

aggregated over 4-year intervals, and the second aggregates observations over 3-year intervals.9

In each case, we aggregate the data by taking the average of the annual observations over each

time-interval, and use the level of initial GDP at the beginning of each time interval to be ourmeasure of initial GDP. There are several advantages of using time-aggregated panels. One,

time-aggregation can substantially reduce serial correlation within regressors that may cause

correlation between the regressors and the error term. Two, time-aggregated panels can capture

more general movements in macroeconomic conditions by filtering out business cycle fluctuations

and minimizing attenuation bias from measurement error, which are usually prevalent in annual

data. The aggregation into a shorter panel reduces our number of observations to 300 and 360,

respectively, for the 4-year and 3-year models. We caution, however, that with such few obser-

vations we may introduce dimensionality issues in terms of estimating the unknown coefficient

functions. In both models we find a positive and significant relationship between FDI and GDPgrowth rates, as well as a negative effect of corruption on the FDI-growth relationship. We note,

however, that the number of statistically significant coefficients for other conditioning variables

has been reduced, and for some observations, the sign of the coefficient has been switched. This

suggests that dimensionality issues may be present when estimating the coefficients. Hence,

the sample size may be insufficient for accurate estimation of the model. Nevertheless, these

results are generally consistent with our core results, suggesting that any serial correlation that

may be present within the regressors is not causing us to obtain inconsistent estimates in our

semiparametric models.

Although our instrumental variable for FDI, lagged FDI, provides favorable results, thisinternal instrument may not be fully valid. It is well-known that factors that induce correlation

between an endogenous regressor and the error term can also induce correlation between an

internal instrument and the error term. For this reason, instruments that are external to the

data can be more credibly valid than their internal counterparts. To assess whether our core

results are influenced by invalid instruments, we estimate our fully-specified NPGMM model

using both total world FDI flows and total FDI flows to developing countries as instruments

for FDI. As previously mentioned, total world FDI flows and total FDI flows to developing

countries are observable variables that should cause fluctuations in FDI, which are unrelated to

conditions within an individual host country. Using these external instrumental variables, we

obtain similar results as those derived from our preferred specification. Hence, our core results

do not appear to be driven by invalid instruments.

So far, we have only considered one measure of corruption, and have ignored any potential

endogeneity and other biases associated with this corruption variable. In particular, although

the Spearmans correlation test suggests that the TI and Knack and Philip indices are strongly

correlated, merging different corruption indices may result in a conceptual ambiguity since each

index aims at capturing different aspects of corruption. In addition, the scale of measurement

differs across indices and also, aggregation causes measurement errors in these various indices

to be dependent, hence increasing the variance in the measurement error. The extent of this

9For the 4-year panel, the last interval contains only 2 years.

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attenuation bias will further exacerbate if our assumption of a time-invariant corruption index

is inappropriate for the data. To address these concerns, we consider (a) the time average of

the Knack and Philip index for the period 1984 to 1996 and (b) the Knack and Philip index for

1984 in lieu of our aggregated corruption index. Given that the initial year of our time span

is 1984, this latter measure of corruption seems more credibly exogenous, at least in theory,than the other measures. We find that our core results hold intact against these alternative

measures of corruption. Specifically, we find that these other measures support our earlier

results that corruption weakens the relationship between FDI and GDP growth. These results

suggest that any potential endogeneity or inappropriateness of the time-invariant assumption of

our aggregated corruption measure is not driving our core results.

Turning now to our secondary concern, we add two different variables to our set of environ-

mental factors, namely the fertility rate (total births per woman) and an index of democracy.

Democratic economies can better guarantee and protect property and contract rights than auto-

cratic economies, and North (1990) argues that secure property rights are crucial for economicgrowth. Furthermore, Barro (1996) finds a heterogeneous democracy-growth relation in which

more democracy is growth-enhancing at low levels of democracy but is growth-deterring at high

levels of democracy. High fertility rates can reduce investments in health and human capital,

which in the long-run can result in reduced physical work capacity in the labour force. Thus,

democracy and fertility rate may affect the efficacy of FDI to promote growth, and may also

be directly related to economic growth. We therefore include fertility rate and an index of

democracy to control for unobserved environmental factors that may cause heterogeneity in the

FDI-growth relationship but are omitted from our initial list of environmental variables. We find

that including either variable in Z does not alter the conclusions drawn from our core results.

6 Conclusion

In this paper we present a generalized empirical growth model that concurrently allows (a)

for heterogeneity within the FDI-growth relationship, as well as between the growth rate and

all other conditioning variables, (b) each of the coefficients in the model (e.g., the coefficient

governing the FDI-growth relationship) to be a function of corruption within each country, and

(c) each of the conditioning variables to be endogenous. The advantage of such a generalization

is that it allows us to analyze heterogeneity within the corruption-FDI-growth nexus and address

many of the concerns discussed by Durlauf (2001), in a unified framework.

We find that there exists substantial heterogeneity within the FDI-growth relationship, and

that FDI has a positive and significant effect on growth rates for many of the countries in our

sample. In addition, we find that corruption significantly reduces the effect of FDI on growth

rates for a large subsample of our data. We find, however, that corruption has an insignificant

net effect on growth rates. The finding that corruption weakens the relationship between FDI

and growth suggests that international investment policies aimed at improving growth rates

through FDI should carefully consider corruption levels in the developing host country prior

to implementing FDI policies. Our results suggest that many countries with relatively low

returns to FDI may substantially benefit, in terms of the returns to FDI on growth rates, from

a reduction in corruption.

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References

[1] Aitken, B. and A. Harrison, 1999. Do Domestic Firms Benefit from Foreign Direct Invest-

ment? Evidence from Venezuela American Economic Review, 89, 605-618.

[2] Alfaro, L., A. Chanda, S. Kalemli-Ozcan and S. Sayek, 2004. FDI and Economic Growth:The Role of Local Financial Markets, Journal of International Economics, 64, 89-112.

[3] Barro, R.J. 1996. Democracy and Growth, Journal of Economic Growth, 1, 1-27.

[4] Baumol, W. J., 1990. Entrepreneurship: Productive, Unproductive, and Destructive, Jour-

nal of Political Economy, 98, 893-921.

[5] Blomstrom, M., 1986. Foreign Investment and Productive Efficiency: The Case of Mexico,

Journal of Industrial Economics, 35, 97-110.

[6] Borensztein, E., J. De Gregorio, and J. W. Lee, 1998. How Does Foreign Direct InvestmentAffect Economic Growth? Journal of International Economics, 45, 115-135.

[7] Cai, Z., M. Das, H. Xiong, and X. Wu, 2006. Functional coefficient instrumental variables

models, Journal of Econometrics, 133, 207-241.

[8] Cai, Z., J. Fan, and Q. Yao, 2000. Functional-Coefficient Regression Models for Nonlinear

Time Series, Journal of the American Statistical Association, 95, 941-956.

[9] Cai, Z. and Q. Li, 2008. Nonparametric Estimation of Varying Coefficient Dynamic Panel

Data Models, Econometric Theory, 24, 1321-1342.

[10] Carkovic, M. and R. Levine, 2005. Does Foreign Direct Investment Accelerate Economics

Growth? Does Foreign Direct Investment Promote Development, eds. T. H. Moran, E. M.

Graham and M. Blomstrom, Washington: Center for Global Development and Institute for

International Economics, 195-220.

[11] Das, M., 2005. Instrumental variables estimators of nonparametric models with discrete

endogenous regressors, Journal of Econometrics 124, 335-361.

[12] Durlauf, S. N., 2001. Manifesto for a Growth Econometrics, Journal of Econometrics,

100, 65-69.

[13] Durlauf, S. N., A. Kourtellos and A. Minkin 2001. The Local Solow Growth Model,

European Economic Review, 45, 928-960.

[14] Gupta, S., H. R. Davoodi and E. R. Tiongson, 2002. Corruption and the Provision of

Health Care and Education Services, Governance, Corruption, Economic Performance,

eds. G. T. Abel and S. Gupta, Washington: International Monetary Fund, 245-279.

[15] Haddad, M. and A. E. Harrison, 1993. Are There Positive Spillovers from Direct Foreign

Investment?: Evidence from Panel Data for Morocco, Journal of Development Economics,42, 51-72.

19


20/27

[16] Hastie, T. and R. Tibshirani, 1993. Varying-Coefficient Models, Journal of the Royal

Statistical Society, Series B, 55, 757-796.

[17] Knack, S. and K. Philip, 1998. IRIS-3: File of International Country Risk Guide (ICRG)

Data, 3rd Edition. New York: The Political Risk Service Group, Inc.

[18] La Porta, R., F. Lopez-de-Silanes, A. Shliefer, and R. Vishny, 1999. The Quality of Gov-

ernment, Journal of Law, Economics, and Organization, 15, 222-279.

[19] Li, Q., C. J. Huang, D. Li, and T. Fu, 2002. Semiparametric Smooth Coefficient Models,

Journal of Business and Economic Statistics, 20 (3), 412-422.

[20] Liu, Z. and T. Stengos, 1999. Non-Linearities in Cross-Country Growth Regressions: A

Semiparametric Approach, Journal of Applied Econometrics, 14, 527-538.

[21] Mauro, P., 1998. Corruption and the Composition of Government Expenditure, Journal

of Public Economics, 69, 263-279.

[22] McCloud, N. and S. C. Kumbhakar, 2011. Institutions, Foreign Direct Investment, and

Growth: A Hierarchical Bayesian Approach, Journal of the Royal Statistical Society: Series

A, Forthcoming.

[23] Murphy, K.M., A. Shleifer, and R.W. Vishny, 1991. The Allocation of Talent: Implications

for Growth, The Quarterly Journal of Economics, 106, 503-530.

[24] North, D. (1990) Institutions, Institutional Change and Economic Performance. New York:

Cambridge University Press.

[25] Racine, J. S. and Q. Li, 2004. Nonparametric Estimation of Regression Functions With

Both Categorical and Continuous Data, Journal of Econometrics, 119, 99-130.

[26] Staiger, D. and J. Stock, 1997. Instrumental Variables Regression with Weak Instruments,

Econometrica, 65, 557-586.

[27] Tanzi, V. and H. R. Davoodi, 2002. Corruption, Public Investment, and Growth, Gov-

ernance, Corruption, Economic Performance, eds. G. T. Abel and S. Gupta, Washington:

International Monetary Fund, 280-299.

[28] Transparency International, Corruption Perception Index. URL: http :

//www.transparency.org/policyresearch/surveysindices/global/cpi.

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Table 1: Summary of results from the parametric and semiparametric regression

Variable OLS Models 2SLS Models SP

(1) (2) (3) (4) (5) (6) (7)

Intercept -0.0846 -0.1148 0.6014 -0.0855 -0.1161 0.5814 0.0066

0.0156 0.0186 0.1022 0.0156 0.0187 0.1024 0.0041 FDI 0.3013 0.3031 0.1024 0.6152 0.6215 0.4916 0.3829

0.0827 0.0824 0.0840 0.1811 0.1804 0.2006 0.1406 Corruption 0.0210 0.0200 0.0212 0.0216 0.0048

0.0071 0.0069 0.0071 0.0069 0.0062 Initial Income -0.1039 -0.0993

0.0126 0.0128 Openness -0.0153 -0.0230

0.0122 0.0127 Government Consumption 0.0809 0.0576

0.0504 0.0515

Domestic Investment 0.3401 0.2766 0.0525 0.0602

US T-Bill -0.5278 -0.3858 0.1925 0.2032

Inflation Rate -0.0004 -0.00020.0003 0.0003

R2 0.1241 0.1307 0.2128 0.1226 0.1293 0.2164 0.5929

1. Dependent variable in each regression is the growth rate of GDP per capita.2. All OLS and 2SLS models include dummy variables to control for country and time fixed effects;

coefficients to vary with respect to corruption and country and time fixed effects.3. Semiparametric models report mean coefficients and standard errors.4. OLS and SPSCM models do not control for endogeneity of FDI.5. First stage 2SLS models regress FDI on lagged FDI, exogenous variables, and country and year dumm6. The effects of corruption on GDP growth reported in both the SPSCM and NPGMM models is the toin section 2.1.

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Table 2: Summary of the effect of corruption on the coefficients.

Variable SPSCM NPGMM

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

Intercept 0.0098 0.0706 0.0128 0.0702

0.0077 0.0618 0.0069 0.0722FDI -0.3132 -0.3992 -0.4091 -1.1656

0.2270 0.1142 0.1332 0.4245Initial Income -0.0078 -0.0059

0.0058 0.0086Openness 0.0186 0.0163

0.0075 0.0160Government Consumption -0.0281 -0.0582

0.0402 0.0715Domestic Investment -0.0591 0.0129

0.0879 0.0965

US T-Bill 0.1892 0.05060.2243 0.3599

Inflation Rate -0.0002 0.00060.0003 0.0003

1. Table reports mean partial effects and standard errors of corruptionon each of the conditioning coefficients.

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Table 3: Countries grouped according to FDI returns.

Insignificant Positive and Significant

1st 2nd 3rd 4th

Bangladesh Egypt Botswana Angola Albania

Bolivia El Salvador Chile Cameroon ArgentinaEthiopia Ghana India Colombia BrazilHaiti Guatemala Korea Dominican Republic B ulgariaHungary Honduras Peru* Ecuador Burkina FasoIndonesia Pakistan South Africa Kenya ChinaJamaica Philippines Sri Lanka Mexico Costa RicaNigeria Romania* Tunisia Morocco JordanPanama Uruguay Peru* MadagascarParaguay Vietnam Senegal MalawiSingapore Thailand MalaysiaZambia Trinidad and Tobago Mongolia

Uganda MozambiqueVenezuela Nicaragua

PolandRomania*TanzaniaZimbabwe

1. Countries are grouped by modal appearance across categories.2. 1st, 2nd, 3rd, and 4th denote the relative quantiles for the positive and signifi-cant coefficients.3. Countries with * are listed more than once.

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Table 4: Countries grouped according to corruption returns.

Insignificant Negative and Significant

1st 2nd 3rd 4th

Angola Burkina Faso Bolivia* Argentina Albania

Botswana Chile* Brazil Bangladesh* Bangladesh*Chile* China Dominican Republic* Bolivia* BulgariaCosta Rica El Salvador Egypt* Colombia CameroonHungary Kenya* Kenya* Dominican Republic* Egypt*Jordan Korea Mexico Ecuador EthiopiaNicaragua Madagascar Morocco Ghana* Ghana*Poland* Malaysia Paraguay* Honduras GuatemalaSingapore Mongolia Peru Kenya* HaitiSouth Africa Mozambique Poland* Malawi IndiaTanzania Philippines Sri Lanka Uganda IndonesiaZambia Senegal Thailand Jamaica

Tunisia Trinidad and Tobago NigeriaUruguay Vietnam Pakistan

PanamaParaguay*RomaniaVenezuelaZimbabwe

1. Countries are grouped by modal appearance across categories.2. 1st, 2nd, 3rd, and 4th denote the relative quantiles for each of the negative and significantcoefficients.

3. Countries with * are listed more than once.

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1 0 1 2 3 4 5

2

0

2

4

6

8

SPSCM 1

^

1

1

0.0 0.5 1.0 1.5

0.5

0.0

0.5

1.0

1.5

2.0

2.5

SPSCM 2

^

1

1

2 1 0 1 2 3

4

2

0

2

4

NPGMM 1

^

1

1

0 1 2 3

4

2

0

2

4

NPGMM 2

^

1

1

Figure 1: 45 degree gradient plot of the effect of FDI on growth for each of the four semipara-metric models in Table 1.

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8 6 4 2 0 2

10

5

0

SPSCM 1

(^1) (Z1)

(1

)

(Z1

)

1.5 1.0 0.5 0.0 0.5

3

2

1

0

1

SPSCM 2

(^1) (Z1)

(1

)

(Z1

)

5 4 3 2 1 0 1 2

4

2

0

2

4

NPGMM 1

(^1) (Z1)

(1

)

(Z1

)

4 3 2 1 0

6

4

2

0

2

4

NPGMM 2

(^1) (Z1)

(1

)

(Z1

)

Figure 2: 45 degree gradient plot of the effect of corruption on the FDI coefficients for each ofthe four semiparametric models in Table 1.

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0.1 0.0 0.1 0.2

0.2

0.0

0.2

0.4

SPSCM 1

(g) (Z1)

(g)

(Z1

)

0.1 0.0 0.1 0.2

0.4

0.2

0.0

0.2

0.4

SPSCM 2

(g) (Z1)

(g)

(Z1

)

0.2 0.1 0.0 0.1 0.2

0.2

0.1

0.0

0.1

0.2

0.3

NPGMM 1

(g) (Z1)

(g)

(Z1

)

0.2 0.1 0.0 0.1 0.2 0.3

0.8

0.6

0.4

0.2

0.0

0.2

0.4

NPGMM 2

(g) (Z1)

(g)

(Z1

)

Figure 3: 45 degree gradient plot of the effect of corruption on growth for each of the foursemiparametric models in Table 1.

A Generalized Empirical Model of Corruption, FDI and Growth

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