Article Title: The impact of foreign direct investment on institutional quality in Latin

America

Authors Contact Information:

Victor Owusu-Nantwi, Pennsylvania State University, Hazleton Campus, 76 University Drive,

Hazleton, PA 18202. Email: vze3@psu.edu

Abstract

There are several studies that have examine the relationship between foreign direct investment

and institutional quality, and they report a positive effect of institutional changes on foreign

direct investment. However, there is a sparse but growing literature that now examines the

reverse relationship, and this is because there is a reason to believe that FDI may influence

institutional quality. In this paper, we contribute to this literature by providing empirical

evidence from Latin America using a panel data of 18 countries over the period 1984-2016. The

results show that foreign direct investment has a positive effect on institutional quality. In other

words, higher levels of FDI are associated with improved institutional quality. The study finds a

bidirectional causality between FDI and institutional quality and a negative and significant error-

term which indicates the presence of long-run causality.

Keywords: Foreign direct investment; institutional quality; Latin America; Vector error

correction model

JEL Classification: C1; F21; 01

1

1. Introduction

Inward foreign direct investment (FDI) to developing economies has increased significantly

over the last two decades, and it is estimated to be about $671 billion in 2016, representing about

47% of global flows (UNCTAD, 2018). This is a significant source of investment and capital

formation for countries, and therefore, developing economies have embraced it as part of their

economic development and productivity-enhancement strategies (UNCTAD, 2014). Further, FDI

brings a bundle of capital, technological and managerial knowledge to host countries, and this

benefits their economies by enhancing productivity and competitiveness (Dunning, 1958;

UNCTAD, 1995). Recognizing this, countries have pursued varying degrees of strategies to

pursue and attract FDI. The impact of foreign direct investment on host countries has long been

of interest to academics and policy-makers and this has led to several empirical evidence with

varying degrees of perspectives.

A number of studies have examined the technological and managerial knowledge spillover

effects of FDI on host countries. A study by Liu (2008) investigates how foreign direct

investment generates externalities in the form of technological spillovers within the endogenous

growth framework using a sample dataset of 20,000 industrial firms in China for the period 1995

to 1999. The results suggest that FDI facilitates technological spillovers to domestic firms in host

economies and the benefits accruing to domestic firms are positive and substantial. This implies

that technological transfer is a critical ingredient in industrialization and human capital

development. Newman et al., (2015) test the relationship between FDI and technological

spillover using dataset from a designed survey of over 4,000 manufacturing firms in Vietnam.

The estimate identifies an indirect vertical spillover from FDI, and that there is a productivity

gain associated with FDI. These results support the hypothesis that FDI enables countries to gain

2

access to technology that may not be readily available to them. In the contrast, Germidis (1977)

assess the relationship between FDI and technology transfer in a sample of 65 multinational

subsidiaries from 12 developing countries. The study finds no evidence of technology transfer

from foreign to local firms. Haddad and Harrison (1993) find negative spillovers associated with

FDI in Morocco.

Other studies examined the importance of FDI to economic development. Pegkas (2015) test

the relative importance of FDI to economic growth in the Eurozone countries using time-series

data from 2002 to 2012. The results suggest a positive long-run relationship between FDI stock

and economic growth. Blomstrom et al, (1992) examine the FDI-economic growth nexus in a

panel dataset of 78 developing countries. The results show that foreign direct investment has

positive effect on economic growth. Further, the study suggests that technological and

knowledge spillover are the medium through which FDI stimulates growth. In a sample of 69

developing countries, Borensztein et al, (1998) analyze the impact of FDI on economic growth

for the period from 1970 to 1989. The study finds a positive effect of FDI on economic growth.

The medium through which this growth effect is achieved is through increasing technological

progress instead of capital accumulation in the recipient countries. The seminal work of De

Mello (1999) investigates the economic growth effect of FDI in a sample of OECD and non-

OECD countries for the period 1970-1990. The estimates suggest a long-run positive effect of

FDI on economic growth in the OECD countries. This growth is achieved through technological

progress and knowledge spillover. Further, the study finds no causality between FDI and

economic growth in OECD countries in the short-run, and a negative short-run growth impact of

FDI on non-OECD countries. Alfaro et.al (2004) examine the FDI-growth nexus for 71

developing countries, by focusing on whether countries with not well-developed financial

3

markets are able to attract and benefit from FDI. The study finds a negative impact of FDI on

economic growth for countries with less developed financial market. This implies that countries

with less developed financial markets are not likely to benefit from FDI inflows.

Other studies focused on the impact of FDI on exports. Wilamoski and Tinkler (1999)

investigates the relative importance of US foreign direct investment in Mexico to US exports to

and imports from Mexico from 1997 to 1994 using vector error correction model. The estimates

suggest that the US foreign direct investment in Mexico leads to increased exports and imports.

Within the framework of simultaneous equation system, Marchant et al (2002) examines the

relationship between US foreign direct investment and exports of processed foods into East

Asian countries – China, Japan, Singapore, South Korea and Taiwan for the period 1989-1998.

The study finds a complementary relationship between FDI and exports. Majeed and Ahmad

(2007) investigates the impact of inward FDI on exports in Pakistan from 1970 to 2004. The

results indicate that FDI has a positive effect on export in Pakistan.

Recently, some studies have examined how institutional quality affects inward FDI. The

importance of institutions to foreign direct investment has pushed many countries to improve and

harmonize their institutional environment. For the period 2000-2012, about 55 countries adopted

a total of 1,082 institutional policy changes to promote and facilitate a more conducive

environment for foreign investment (UNCTAD 2014). By the end of 2013, a total of 9,175

bilateral investment treaties and agreements have been signed among 201 countries (UNCTAD,

2014). Using country-level data of 144 developed and developing countries, Globerman and

Shapiro (2002) find a positive impact of governance on FDI. That is countries with good

governance and a strong legal system tend to receive more inward FDI. Daude and Stein (2007)

explore the importance of institutional quality on FDI in a panel of 58 countries using bilateral

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outward FDI stock data. The results suggest a positive relationship between institutional quality

and FDI. More specifically, five out of six governance indicators matter to inward FDI. Bénassy-

Quéré et al. (2007) re-evaluate the role of quality of institutions on FDI using panel data analysis

for a sample of 52 countries. The estimates indicate that institutional quality is an important

determinant of FDI. Focusing on Latin America and the Caribbean, Fukumi and Nishijima

(2009) examine the interaction between institutional quality and FDI within the framework of

simultaneous equation. The results find that better institutions attract more inward FDI.

In the empirical evidence, several studies have delved into understanding how foreign direct

investment relates to institutional quality. Majority of them focus on the impact of institutional

environment on FDI by treating institutional quality as a cause and FDI as an effect (e.g.,

Buhanan et al., 2012; Fukumi & Nishijima, 2009; Busse & Hefeker, 2007; Daude & Stein,

2007). However, a sparse but growing body of studies have sought to examine whether, and to

what extend FDI influences institutions. This study contributes to the latter block of literature by

focusing on Latin America which is among the top destinations for inward foreign direct

investment. Inward FDI to Latin America is about $151 billion which is 11% of global FDI

(UNCTAD 2018). This study utilizes a panel dataset for 18 countries over the period 1984 to

2016. The choice of the sample is due to the availability of data. The results show that increases

in the levels of inward FDI are associated with improved institutional quality. Further, the study

finds a bidirectional causality between FDI and institutional quality.

2. Literature

As a topic of importance, the study of institutional quality has generated numerous published

scholarly studies. In the academic literature, there are three identified streams of studies on this

subject matter.

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2.1 Institutional quality and economic growth

The first stream examines the effect of institutional changes on economic growth, and an

example is the study by Alexiou et al., (2014) which investigate the relationship between

institutional quality and economic growth in Sudan using the ARDL bound-testing approach for

the period 1972-2008. The results suggest that institutional quality is an important determinant of

economic growth in Sudan. Ahmad et al., (2012) explore the linear quadratic relationship

between corruption and economic growth in a panel of 71 countries for the period 1984-2009.

The findings indicate that a decrease in corruption raises the economic growth rate in an inverted

U-shaped way. Other studies such as Krueger (1974), Shleifer & Vishny (1993), Tanzi &

Davoodi (1997), and Mauro (1995, 2004) have argued that institutional quality is detrimental to

economic growth.

2.2 Institutional quality and foreign direct investment

The second research stream investigates the effect of institutional quality on foreign direct

investment, and the evidence is mixed. A study by Buhanan et al., (2012) establish a positive

effect of institutional quality on foreign direct investment in a sample of 164 countries for the

period 1996-2006. Another study by Busse & Hefeker (2007) find a positive effect of political

risk and institutions on inward foreign direct investment in a sample of 83 countries for the

period 1984-2003. Using corruption as a proxy for institutional quality, Wei (2000) reports a

negative impact of corruption on FDI in the Middle East and North African (MENA) region. A

study by Choi & Yiagadeesen (2008) re-examine the effect of democratic institutions on FDI

inflows in developing countries. They find a weak association between democratic institutions

and increases in FDI inflows. Ali et al (2010) investigates the importance of institutions to

foreign direct investment using a large panel of 107 countries for the period 1981-2005. The

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findings indicate that institutions are a robust predictor of FDI and that the most significant

institutional aspects are linked to propriety rights, the rule of law and expropriation risk.

The third stream which has not received considerable attention in the existing literature

examines the effect of foreign direct investment on institutional quality. One such study is Long

et al., (2015) which examine the effect of FDI on institutional quality in China using data from

the firm-level survey conducted jointly by the World Bank and the Enterprise Survey

Organization of the National Bureau of Statistics (NBS) of China. Employing ordinary least

squares, and two-stage least squares approach, the study finds a positive effect of FDI on

institutional quality in China. Demir (2016) investigates the effect of bilateral FDI flows on

institutional development gaps between countries, and whether such effects are conditional on

the direction of flows including South–South, South–North, North–South, and North–North

directions. Using dataset of 134 countries and a variety of institutional development measure for

the period 1990-2009, the study finds no significant effect of bilateral FDI on institutional

development in any direction. Using cross-country dataset of 140 countries, Kwok & Tadesse

(2006) examine how the presence of Multinational corporations (MNC) may shape the

institutional environment of corruption over time. The study suggests that foreign direct

investment generates positive spillover effects on the institutional environment of host countries.

However, it is important to point out that it would be naive to think that the influence of

foreign direct investment on institutions is always good. There are some anecdotal evidences that

foreign direct investment sometimes brings undesirable influences. Robertson & Watson (2004)

argue that foreign direct investment will lead to increases in the level of corruption in the host

country in three ways. First, the increase in FDI represents a larger amount of foreign money

flowing into country and, therefore, an expansion of opportunities for bribery. Second, the

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eagerness of foreign investors to enter the market may tempt host country nationals to resort to

corruption as a means of sharing in the opportunities for profit presented by their own country

(Robertson & Watson, 2004). Third, equipped with advanced knowledge in international

business and a vast international network, multinational companies may have developed

sophisticated skills of bribery. Such practices will “contaminate” firms in the host country

(Kwok & Tadesse, 2006).

3. Trends of FDI and Institutional Quality in Latin America

3.1 Foreign direct investment

Inward FDI to Latin America has increased significantly over the last two decades. In the

1990s, inward FDI surged in Latin America, and this was mainly attributed to the mergers and

acquisitions, privatization of state enterprises, and less government control in the private sector

(World Bank et al., 2013). Majority of these investments went to the service sector of the

economy as foreign investors took advantage of opportunities generated by the privatization of

government enterprises, and greater openness to foreign partnerships in the financial,

telecommunication and public utilities sectors (World Bank et al., 2013). FDI to the region

continuously increased in the 2000s. These inflows went to the manufacturing sector of the

region’s economy in response to the general economic and political liberalization in some

countries in the region such as Argentina, Brazil and Venezuela (World Bank et al., 2013). This

liberalization positioned the region to be strategically recognized by developed countries as a key

component of their economic development strategies, and hence a surge in inward FDI to Latin

America (World Bank et al., 2013).

Further, Latin America is one of the favorable destinations for FDI globally and this is shown

in figure 1. In figure 1, inward FDI to the region increased from 0.60 percent of GDP in 1990 to

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about 5.40 percent of GDP in 1999 which represent about 6.47 percent of world FDI. FDI

decreased to about 2.1 percent in 2003 and increased subsequently to about 3.6 percent of GDP

in 2011. By 2016, inward FDI to Latin America declined to about 2.3 percent of GDP which

represents 5.76 percent of world FDI. The fall in inward FDI to the region was due to three

fundamental factors. The decline in raw material prices impacted investments directed toward

the natural resources sector; several economies in the region experienced economic slowdown;

and technological sophistication and expansion of the digital economy that tends towards a

concentration of transnational investments in developed economies (Economic Commission of

Latin America and Caribbean 2018).

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

0

1

2

3

4

5

6Figure 1: FDI Inflows Across Latin America as a percentage of GDP

FDI (% of GDP)

Year

Perc

enta

ge o

f GD

P

Source: Word Development Indicators

Latin America is a major recipient of inward FDI from many developed countries and in

2016 as shown in Figure 2, twenty percent of inward FDI to Latin America originated from the

United States, twelve percent came from the Netherlands and eight percent from Luxembourg.

Spain accounted for eight percent, Canada and the United Kingdom, contributed five percent

each, Germany, Italy and France provided four percent each, Japan contributed three percent and

China accounted for 1.1% respectively.

9

Source: Economic Commission for Latin America and the Caribbean

3.2 Institutional Quality

Institutional quality is a concept that captures the overall country risk profile. This index is based

on a set of 22 components grouped into three major categories of risk: political, financial, and

economic, with political risk comprising 12 components (and 15 subcomponents), and financial

and economic risk each comprising five components (Political Risk Services Group, 2018). A

country’s composite risk rating is assessed as low, moderate, or high, with the direction of

change assessed as decreasing, stable or increasing for a specified time frame, depending on the

country’s circumstances, and the business and economic environment (Political Risk Services

Group, 2018). It is measured by a wide range of different political, economic, financial risk

rating for a country. The institutional quality index which is a composite score ranges from zero

to 100 with the highest number of points indicating the lowest potential risk and the lowest

number (0) indicating the highest potential risk. Latin American countries are among countries

that have experienced political, financial and economic instability, deep-rooted corruption and

worse laws and order (Adkisson, 1998). To improve the quality of institutions in the region,

countries have pursued reforms in their political, economic, financial, governance, and legal and

regulatory frameworks, and these have translated to improvements in their composite risk ratings

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as measured by the Political Risk Services. The institutional quality measure employed for the

study covers a sample of 18 countries in Latin America for the period 1984-2016.

4. Data and Methodology

4.1 Data

The study employs a panel dataset of 18 countries in Latin America (Argentina, Bolivia,

Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala,

Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay and Venezuela) for the period

1984-2016. Data availability was the main constraint in the country and period selection. The

final dataset is a panel of 574 country-year observation from 18 countries in Latin America.

Table 1: Descriptive StatisticsInstitutional

Quality FDI Inflation

Government

Consumption GDP Trade Population

Mean 6.409 2.914 114.612 12.055 1.936 61.214 1.546

Median 6.648 2.178 8.465 11.801 2.145 55.528 1.546

Maximum 8.238 16.229 111750 43.479 16.226 165.344 3.018

Minimum 2.913 -10.082 -1.167 2.976 -15.219 13.753 -0.064

Std. Dev. 0.976 2.755 75.478 3.778 3.602 29.209 0.601

Observation

s 574 574 574 574 574 574 574

Based on the academic literature, seven variables are selected for the study. Institutional

quality index and foreign direct investment (as % of GDP) are the key variables of interest. The

control variables include government expenditure (as % of GDP) which is a proxy for

government size (Lizardo and Mollick 2009), inflation (%) as a measure of macroeconomic

instability (Fischer, 1993), GDP per capita growth rate (%) as a measure of economic

11

performance (Vianna and Mollick, 2018), population growth rate (%) as a proxy for human

capital (Becker 1994), and trade (as % of GDP) as a measure of openness (Rodrik et al., 2004).

Source: Author’s calculations

The dataset for the variables is obtained from the world development indicators of the World

Bank except for the institutional quality index which is obtained from the International Country

Risk Guide (ICRG) published by the Political Risk Services (PRS Group). Table 1 reports the

descriptive statistics of the variables. Institutional quality ranges from 2.913 (in Bolivia in 1985)

to 8.238 (in Chile in 2007) with a mean of 6.409. FDI ranges from -10.082 (in Panama in 1988)

to 16.229 (in Panama in 2006). The variables mean-to medium is approximately 1 while the

range of variation between minimum and maximum is quite logical, indicating the normality of

the distribution. The variable with the highest maximum value is inflation and that of lowest

minimum value is GDP. The descriptive statistics show low coefficient of variation as the

standard deviation is quite low relative to the mean except for inflation. Inflation exhibited the

largest mean and standard deviation among the variables.

4.2 Methodology

The study examines the effect of FDI flows on institutional quality in Latin America by

following specification similar to Demir (2016), Long et al., (2015), Olney (2013), ElBahnasawy

& Review (2012), Ali et al., (2008), Chong & Gradstein (2007) and La Porta et al., (1999). The

model is specified below;

Instit = α1+ β1FDIit-1 + γiVit-1 + ɛit (1)

Where Instit is the institutional quality index for country i at time t. FDIit is the flows of FDI as a

percentage of GDP for country i at time t. Vis a vector of control variables for country i at time t.

To estimate Eqn.1, we followed the standard procedure of time series analysis using the

vector error correction model. First, we investigate the time series property of each variable.

12

Panel unit root test is examined to determine the stationarity of the data series. This test is

important because often time series data tends to be non-stationary, and if not checked, running

regression with such data series may yield spurious regression. The study employs Levin et al.,

(2002); Im et al., (2003); Fisher-type panel unit root tests to check the stationarity of the data

series. Except for Im et al., (2003)1, all the tests assume a common (identical) unit root test

process across the relevant cross-section (that is, pooling the residuals along the within-

dimension) (Ramirez,2007). They employ a null hypothesis of a unit root following the basic

Augmented Dickey Fuller specification:

Δyit = μ + Xitδ +αyit-1 + Σβij Δyit-j + εit (2)

where yit represents the variable in question, Xit represents the exogenous variables in the model

such as country fixed effects and individual time trends, and εit is the error term. If the panel unit

root tests confirm that the data series is stationary at levels, then the estimation is carried out

using ordinary least squares method or other estimation technique. However, if the data series is

stationary at first difference I(1), then the cointegration relationship among the variables is

examined. Second, the cointegration among the variables is determined by the Pedroni

cointegration test. This test is comprehensive and residual based but assumes cross-sectional

dependence. It employs four panel, and three group panel statistics to test the null hypothesis of

no cointegration against the alternative hypothesis of cointegration. Pedroni cointegration test is

based on the model:

yit = αi + δit + βi Xit + εit (3)

1 Im, Pesaran and Shin (2003) test has the form: Δ y i , t=c i+β i y i ,t−1+∑i=1

n

ψ i , n Δy i ,t−n+μ i ,t where Δ is the first difference

operator, y i ,t is the white noise disturbance term.

13

where αi and δi allow for country specific fixed effects and deterministic trends, Xit is a n-

dimensional column vector of explanatory variables for each country i, and βi is a n-dimensional

row vector for each country i. The variables yit and Xit are assumed to be integrated of order one

I(1). Prior to the cointegration test, the lag length is determined using five lag-length selection

criteria which includes Akaike information criterion (AIC), Schwarz information criterion,

Hannan-Quinn information criterion, sequential modified LR test statistic, and final prediction

error.

Third, the presence of cointegration implies that there exists a long-run relationship among

the variables, but it does not provide the long-run estimates and the direction of causality. Engle

& Granger (1987), indicates that if non-stationary variables are cointegrated, then a vector in the

first difference will be mis-specified, because the long-run information in the first difference

have been removed, and this shortcoming is avoided using vector error correction model

(VECM). Further, unlike the usual Granger causality test, the VECM can identify sources of

causation and can distinguish between long-run and short-run relationship in the series. The

short-run dynamics is diagnosed using the Wald test. The VECM is estimated as follows:

ΔINS=c i+∑i=1

n

α¿ INS¿−n+∑i=1

n

β¿ ΔFDI ¿−n+∑i=1

n

ψ¿ ΔGDP¿−n+∑i=1

n

γ ¿ ΔGC¿−n+∑i=1

n

δ ¿ ΔINF¿−n+∑i=1

n

μ¿TR ¿−n+∑i=1

n

ϕ¿ ΔPOP¿−n+φi ECT t−1+ε ¿(4 )

where Δ is difference operator, φ i is the coefficient of the error correction term (ECTt-1), n

indicates the lags of variables, β, γ, δ, μ,ψ , and ϕ represent the respective long-run estimates. In

this specification, the long-run causality is determined by the significance of the coefficient of

the error correction term. If the error correction term is negative and significant, then it implies

convergence to the long-run equilibrium.

5. Results and discussion

5.1 Panel Unit Root Test

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Table 3 presents the results of Levin et al., (2002), Im et al., (2003) and Fisher-type panel

unit root tests. As the results suggest, most of these tests do not reject the null hypothesis of non-

stationarity in levels, but the results of the panel unit root tests in first difference indicate that

data series for the variables are stationary which indicates that the data series are integrated of

order one I (1).

Table 2: Panel Unit Root Test EstimatesLevin,

Lee

&Chu

t*

Im,

Pesara

n &

Shin

W-stat

ADF-

Fisher

Chi-

square

PP-

Fisher

Chi-

square

Levin,Lee

&Chu t*

Im,

Pesaran

& Shin

W-stat

ADF-

Fisher

Chi-

square

PP-

Fisher

Chi-

square

Variables Levels First Difference

Institutional

Quality

-0.936

(0.175)

1.040

(0.851)

26.116

(0.887)

10.474

(1.000)

-11.253

(0.000)***

-12.410

(0.000)***

215.030

(0.000)***

239.532

(0.000)***

FDI 0.436

(0.667)

-0.804

(0.211)

34.514

(0.539)

108.14

(0.000)

-3.988

(0.000)***

-11.245

(0.000)***

191.105

(0.000)***

502.321

(0.000)***

Inflation 110.26

(1.000)

-1.225

(0.111)

46.657

(0.110)

94.587

(0.000)

-20.849

(0.000)***

-21.821

(0.000)***

360.136

(0.000)***

504.933

(0.000)***

Government

Consumptio

n

-2.096

(0.200)

-1.705

(0.044)

44.264

(0.162)

32.920

(0.616)

-3.032

(0.001)***

-6.483

(0.000)***

104.651

(0.000)***

297.757

(0.000)***

GDP 6.978

(1.000)

-3.641

(0.000)

65.736

(0.180)

163.62

7

(0.350)

-19.146

(0.000)***

-21.572

(0.000)***

397.966

(0.000)***

551.575

(0.000)***

Trade 1.365 1.101 29.474 24.958 -15.667 -15.297 250.663 317.241

15

(0.914) (0.865) (0.771) (0.917) (0.000)*** (0.000)***

(0.000)***

(0.000)***

Population 3.914

(1.000)

-1.367

(0.858)

50.724

(0.527)

14.145

(0.999)

-1.402

(0.080)**

-5.131

(0.000)***

97.414

(0.000)***

41.946

(0.023)**

Notes: p-values shown in parenthesis

***indicates significance at an alpha of 1%, ** indicates significance at an alpha of 5%, * indicates significance at an alpha of

10%

Source: Authors’ calculations

5.2 Panel Cointegration Test

Based on the panel unit root test results, which suggest the data series are stationary at first

difference, the study proceeds to test for the presence of cointegration among the variables using

Pedroni cointegration test. Seven test statistics under the Pedroni cointegration are used to test

the null hypothesis of no cointegation verses the alternative hypothesis of cointegation in the

panel data. Note that prior to this test, the lag length is determined using five lag-length selection

criteria and the results are presented in Table 3. All the lag-length selection criteria suggest two

lags. Overfitting of lag length reduces efficiency while not including an appropriate lag length

can introduce variable bias (Owusu-Nantwi and Erickson, 2016).

Table 3: Lag Length Selection Criteria Test ResultsLag LR FPE AIC SC HQ

0 NA 2.32e+12 48.339 48.395 48.361

1 7369.757 2547629.0 34.616 35.062 34.790

2 1075.845* 390760.5* 32.741* 33.578* 33.068*

Note: * indicates lag order selected by the criterion

LR: sequential modified LR test statistic (each test at 5% level); FPE: Final prediction error; AIC: Akaike information criterion;

SIC: Schwarz information criterion; HQ: Hannan-Quinn information criterion

16

Source: Authors’ calculations

Table 4 presents the Pedroni cointegration test results. The results show that five out the seven

test statistics reject the null hypothesis of no cointegration and indicate the presence of

cointegration among the variables. Panel rho-statistic and Group rho-statistic do not reject the

null hypothesis of no cointegration. Overall, the results of Pedroni test support the existence of

cointegration among the variables.

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Table 4: Pedroni Cointegration Test ResultsPanel Group Statistics Statistic Probability

Panel v-statistic -0.812 0.079**

Panel rho-statistic 1.958 0.995

Panel PP-statistic -1.896 0.003***

Panel ADF-statistic -2.033 0.021**

Group rho-statistic 3.961 1.000

Group PP-statistic -1.156 0.013***

Group ADF-statistic -1.177 0.012***

Notes: p-values shown in parenthesis

***indicates significance at an alpha of 1%, ** indicates significance at an alpha of 5%, * indicates significance at an alpha of

10%

Source: Authors’ calculations

5.3 Panel Long-run Estimates

The results of Pedroni test suggest the presence of long-run cointegration among the

variables but does not provide the long-run estimates. VECM provides the long-run estimates of

the variables in addition to the estimates of the short run and long-run causality. Table 5 presents

the panel long-run estimates of the variables. The results show a positive and significant

relationship between institutional quality and foreign direct investment. In other words, the flows

of FDI positively impact the institutional environment of Latin America. This is consistent with

studies by Long et al., (2015), Demir (2016) and Kwok & Tadesse (2006). The coefficient of

GDP is positive and significant. This indicates that increases in per capita GDP growth rate lead

to improvements in institutional quality in Latin America. The coefficient estimate of trade is

significantly positive. This implies that trade appears to have a significantly positive effect on

institutional quality. For inflation and population, the study finds that these variables have

significantly negative effects on institutional quality. The coefficient of government consumption

is positive but not significant. This indicates that government consumption has no significant

effect on institutional quality in Latin America.

Table 5: Long-Run Estimates of the Variables

FDI GDP Government

Consumption

Inflation Trade Population

Coefficient 0.174 0.907 0.008 -0.002 0.012 -1.025

t-statistics [2.100] [8.492] [0.162] [-4.067] [2.320] [-3.260]

Notes: Dependent variable is Institutional Quality and [ ] represents t-statistics

Source: Authors’ calculation

5.4 Panel Causality Test

18

Table 6 presents the panel vector error correction model estimates. The results show a

bidirectional causality between institutional quality and foreign direct investment in the short-

run. This indicates a two-way causation between the variables. The results confirm a

bidirectional causality between institutional quality and government consumption, and a

unidirectional causality from GDP and trade to institutional quality.

The study finds a bidirectional causality between GDP and government consumption, and a

unidirectional causality running from inflation, FDI, trade and population to GDP respectively.

The findings show a bidirectional causality between FDI and trade, and GDP and inflation

respectively. The causal relationship between government consumption and inflation is

bidirectional, and that of inflation and institutional quality; inflation and trade; trade and

government consumption; and population and government consumption is unidirectional running

from institutional quality, trade, and government consumption respectively in the short run. The

significance of the error correction term (ECT) is important for interpreting long-run causality.

The error correction term, which is also called the speed of adjustment, should be negative and

statistically significant to indicate convergence to the long-run equilibrium. The error correction

term explains the speed at which the dependent variable adjusts to correct any deviations from

the long-run equilibrium

Table 6: Panel Vector Error Correction Model EstimatesDependent

Variable

Independent Variables – Chi-square value (Wald Test)

Institutional

Quality

FDI GDP Government

Consumption

Inflation Trade Population ECT (-1)

T-stat

Institutional

Quality

2.228

(0.069)**

16.190

(0.003)***

11.214

(0.024)**

3.776

(0.437)

9.690

(0.0460)**

3.492

(0.479)

-0.410

[-3.190]

19

FDI 2.093

(0.072)*

0.896

(0.925)

3.738

(0.445)

2.196

(0.700)

10.2994

(0.036)**

4.373

(0.358)

-0.116

[-0.228]

GDP 1.573

(0.814)

9.333

(0.053)**

20.055

(0.001)***

28.253

(0.000)***

11.788

(0.019)***

6.339

(0.175)*

0.071

[7.913]

Government

Consumption

13.404

(0.010)***

4.641

(0.326)

18.298

(0.001)***

11.641

(0.020)**

4.640

(0.326)

1.994

(0.737)

-0.377

[-1.254]

Inflation 24.444

(0.000)***

4.049

(0.399)

97.296

(0.000)***

22.250

(0.000)***

9.085

(0.059)**

1.746

(0.782)

-6.313

[-6.356]

Trade 3.056

(0.549)

7.329

(0.120)*

5.685

(0.224)

10.204

(0.037)**

0.475

(0.976)

3.632

(0.458)

0.022

[1.285]

Population 0.268

(0.992)

3.078

(0.899)

4.034

(0.544)

7.281

(0.122)*

1.999

(0.736)

5.820

(0.213)

0.381

[-2.276]

Notes: p-values shown in parenthesis

***indicates significance at an alpha of 1%, ** indicates significance at an alpha of 5%, * indicates significance at an alpha of 10%

Source: Authors’ calculations

The results show that the estimated speed of adjustment for institutional quality index is -0.410,

and it is statistically significant. This implies that institutional quality adjusts by 41% annually to

correct any deviation from the long-run equilibrium. Overall, this suggests that there is evidence

of long-run causality from FDI, government consumption, GDP, inflation, trade and population

to institutional quality. Table 7 summarizes the main findings of the short-run causality.

Table 7: Summary of Main Findings of Short-run CausalityVariables Direction of causality

Institutional Quality ↔ FDI Bidirectional

Institutional Quality← GDP

Institutional Quality↔ Government Consumption

Unidirectional

Bidirectional

20

FDI ↔ Trade Bidirectional

Institutional Quality ← Trade

GDP↔ Government Consumption

Government Consumption ↔Inflation

GDP↔Inflation

Unidirectional

Bidirectional

Bidirectional

Bidirectional

GDP← FDI

GDP ←Trade

GDP←Population

Inflation ←Institutional Quality

Inflation ←Trade

Unidirectional

Unidirectional

Unidirectional

Unidirectional

Unidirectional

Trade ← Government Consumption Unidirectional

Population← Government Consumption Unidirectional

Notes: ↔ indicates causality running in both direction ←indicates causality from right to left →

indicates causality from left to right

6. Summary and conclusion

The debate on the relationship between foreign direct investment and institutional quality

remains inconclusive. This study contributes to this debate by providing empirical evidence from

Latin America by addressing the effect of FDI on institutional quality. The study uses a panel

data of 18 countries over the period 1984-2016 by implementing vector error correction model.

The results find a positive effect of FDI on institutional quality. In other words, increases in FDI

is associated with improvements in institutional quality. The study finds a bidirectional causality

between FDI and institutional quality, and a negative and significant error term which indicates

the presence of long run causality. The key implication of the findings is that Latin America

21

should continuously pursue policies that would attract and promote foreign direct investment as

this contributes positively to improving their institutional environment.

The institutional quality measure is based on data from the International Country Risk Guide

(composite based index), and the study is aware of other alternative measures of institutional

quality from World Governance Indicators database and Transparency International database.

Therefore, the study is hesitant to generalize these results, and it is recommending future

research in Latin America that could use these alternative measures to future throw more light on

the relationship between FDI and institutional quality.

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