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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: [email protected]
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
4
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.
5
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
6
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
7
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
8
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
10
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
14
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.
17
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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