The document analyzes how factors like corruption, government size, monetary policy, and business regulation impact GDP growth across countries. Using regression analysis of data from 87 countries in 2012, it finds: 1) Countries with less government involvement in business through state-owned enterprises tend to have higher GDP per capita, after controlling for other factors. 2) Countries with stronger legal systems protecting property rights and less burdensome business regulations also tend to have higher GDP per capita. 3) Money growth has a negative but insignificant impact on GDP per capita, suggesting price stability is important for economic performance. 4) Overall, the study finds government interventions around corruption, limited state involvement, strong legal protections and free markets

1. An Econometric Analysis of the Effects of Corruption, and Institutional Factors on GDP Growth
By: Nne Ekeogbede
Abstract:
This paper draws from a universal economic objective, that any given country aims to achieve economic
growth, to examine and show the factors that contribute to economic growth across various countries.
Using multiple regression analysis and joint hypothesis testing, our paper focuses on corruption (integrity
of legal system), business regulation, size of government, and sound money (monetary growth) as
relevant parameters to measure their economic effects on GDP per capita ($). RealGDP per capita,the
dependent variable, is regressed against multiple indexes of freedom from the Fraser Institute to uncover
empirical evidence that minimal government action leads to higher GDP per capita. Results indicate that
minimal corruption, size of government, and business regulation have a significant positive effects on
economic growth across countries, while money growth has an insignificant negative effect on Real GDP
per capita. From this, we make policy recommendations on the need for government interventions—
though limited—that will help promote economic growth.

2. I. INTRODUCTION
Economic growth, represented by RealGDP, is central to sustainable long-term development and
improved living standards. Over the years,considerable studies have argued and examined the impact of
government intervention—or lack thereof—on economic performance, citing impacts of government size,
government operations, regulatory processes,and monetary policies as powerful engines of economic
productivity. In the same breath, this research study follows free market principles of proper government
role, economic freedom, and neoclassical microeconomic theoretical framework to test the nature of
government contributions on national growth in an attempt to support or deny such claims. The idea that
government involvement is needed in harmonizing economic incentives and providing socially optimal
directions for growth and development is an important subject matter in today’s highly competitive,
globalized marketplace. Relative policies and institutions of individual countries pose varying degrees of
effects on economic freedom, which concurrently supports national productivity. At the foundation of
economic freedom are personal choice, voluntary exchange,freedom to enter markets and compete, and
security of the person and privately owned property. These indicators mediate mechanisms through which
country economies are fueled and impacted. With these organized policies and institutions, we posit that
multiple regression and joint hypotheses tests will provide evidence that set variables are individually and
jointly significant factors of economic growth. More specifically, a positive effect is expected on an
aggregated basis, for the sub-component parameters of government enterprises and investment, integrity
of the legal system, and business regulation on GDP per capita. For the sub-component parameter of
money growth, we expect results to yield a negative effect on GDP per capita. Both tools of analysis are
expected to validate our hypothesis that government policies and institutions interact to fuel economic
productivity.
Social science literature underpins the market economy theoretical framework that institutions
have the potential to stimulate efficient actions. Given specific incentives, economic agents have the
capacity to contribute to the production of more valuable output. The favorable impact of economic

3. freedom is evident in its ability to promote positive growth effects,and high return on productive efforts
(Berggren, 2003). Conversely, ineffective incentives such as corruption has been found to be negatively
associated with growth (Johnson et al., 2012). Evidence indicates that corruption adversely affects many
proxy causes of economic growth, including investments in human capital, due to the lack of political
accountability and property rights (Aidt, 2009). The most important function of government is
undoubtedly protection of persons and their rightfully acquired property—as such the legal system exists
to constrain the state and empower private economic agents. It was found that as policies shifted to more
market-oriented solutions, law became more important because enforced agreements and security are
substantive norms which fuel social and economic change (Ginsburg, 2000). Although our model differs
from Ram’s two-sector production function framework,we outline our estimates based on the theoretical
point of view that a larger government size is expected to create unfavorable economic performance
because government operations are often inefficient, regulatory processes inflict excessive burdens and
costs on the economic system, and government monetary policies tend to distort economic incentives
(Ram, 1986).
Relative to our study, Johnson et al. focus solely on the interaction between corruption and
economic freedom using single country data and their results show a negative link between corruption
and growth and a positive relationship between freedom and growth. We borrow from their regression
setup, though this study’s regression will focus on cross-country data with different parameters to capture
economic cost, freedom, and growth. The reasoning developed in these studies, and the economic model
previously used by Agwu (2014) are utilized in our estimation of the factors that contribute to economic
growth. However,this study tries to modify previous studies by employing (4) independent variables
which have not been modeled together. Secondly, to keep the causal relationship simple, the regression
will use data on 87 world countries exclusively from the year 2012.
While we borrow theoretically from indicated studies and accept the leading position that limited
government is generally good for economic growth, this paper contributes to empirical research by
showing the mediating effect of the regulatory environment on economic growth across world countries.

4. By focusing on a basic regression with cross-country data,we hope to set a framework that can be later
used to estimate more complicated models.
II. MODEL SPECIFICATION
The above arguments provide the theoretical and empirical guide on the relationship between
protective government roles and economic growth. The study’s independent variable, Real GDP per
capita,will be drawn from The World Bank National Accounts data and OECD National Accounts data
and is provided in current U.S. dollars. To determine country level GDP per capita—defined as the gross
domestic product divided by midyear population—the World Bank uses data from these accounts to
calculate the sum (GDP) of gross value added by all resident producers in the economy plus any product
taxes and minus any subsidies not included in the value of the products. The study’s independent
variables are obtained from the Fraser Institute’s Economic Freedom of the World (EFW) summary
index, constructed using forty-two data points that measure the degree of economic freedom in five broad
areas—(1) Size of Government, (2) Legal Structure and Security of Property Rights, (3) Access to Sound
Money, (4) Freedom to Trade Internationally, and (5) Regulation of Credit, Labor, and Business
(Gwartney et al. 2014). The EFW index borrows data from external sources such as the IMF, and
identifies the level to which institutions and policies of a country are supportive of a limited government
ideal, where the government protects property rights and organizes the provision of a limited set of
“public goods” such as access to money of sound value, but nothing beyond these core functions.
Several features characterize this study. The specifications used in this study follow both Johnson
et al. (2011) and Agwu (2014) and include controls from the Fraser Institute for government enterprises
and investment, a sub-component of size of government that measures the extent to which countries use
private investment and enterprises rather than government investment and firms to direct resources. The
logic follows that economic freedom is reduced as government enterprises produce a larger share of total
output because government enterprise does not operate under private enterprise rules, nor are they
dependent on consumers. Additional controls include the integrity of the legal system, a sub-component

5. of the legal system and property rights that measures how effectively the protective functions of
government are realized, and money growth—a sub-component of sound money that measures the
consistency of monetary policy (or institutions) with long-term price stability based on the idea that
inflation (high money growth) erodes the value of property held in monetary instruments. The last
control, business regulation, measures the degree to which entry into markets and freedom to engage in
voluntary exchange is inhibited. Data analyses were conducted using Stata statistical software and Excel.
Alpha was set at .05 for all tests. Given these specifications, the generated summary output is revealed in
Table 1.
Table 1
Furthermore, our OLS regression analysis is generated using the models shown below.
GDP per capita = f (GEI, ILS, MG, REG) (1)
GDP per Capita = Gross Domestic Product, such that 266.59 is the current dollar amount ($)
GEI = Government Enterprises and Investments index, such that 0 means more government enterprises and
investments while 10 means government investment share was generally less than 15% of total investment
ILS = Integrity of the Legal System index, such that 0 means less law and order and 10 means more law and order
MG = Money Growth index, such that 0 means the money growth was greater than the long-term growth of real
output and 10 means that money growth was equal to long-term growth of real output
REG = Regulation index, such that 0 means more centralization and 10 means less regulations
Re-writing equation (1) in linear form yields
R E G 8 7 7 . 0 2 9 3 5 6 . 9 2 0 1 1 5 4 3 . 8 9 5 2 7 9 9 . 0 1 9 0 4 7
M G 8 7 8 . 4 8 5 1 7 1 1 . 5 8 2 3 5 8 0 9 . 9 2 5 0 1
I L S 8 7 5 . 7 6 5 5 1 7 2 . 0 4 6 6 7 4 1 . 6 6 6 6 6 7 1 0
G E I 8 7 6 . 1 2 6 4 3 7 3 . 1 7 9 8 9 5 0 1 0
G D P p e r C a p i t a 8 7 1 3 3 9 2 . 9 6 1 6 5 1 9 . 6 8 2 6 6 . 5 8 9 6 7 5 1 1 . 6 9
V a r i a b l e O b s M e a n S t d . D e v . M i n M a x

6. Y = β0 + β1X (GEI) + β2X (integrity of the legal system) + β3X (money growth) + β4X (regulation) + u; Y= GDP per capita and u = error term
(2)
Model (2) is constructed with the purpose of obtaining correlation coefficients that estimate a linear
relationship between the response variable (GDP per capita) and the controls. Additionally, u represents
residual values and serves to include heteroscedastic and autocorrelation effects. The regression line will
be further tested with the coefficient of determination, to ensure variability within set variables has been
accounted for. Overall, the results between the dependent variable and predictor variables are expected to
fit a casualcausation model.
Our joint hypothesis tests consist of testing for the microeconomic theoretical framework on
economic freedom and functional institutions. Thus, our main null hypothesis and overall significance
test, provided in model (3), reads that government enterprises and investments, integrity of the legal
system, money growth, and regulation do not jointly affect RealGDP per capita,while the alternative in
model (4) says that these coefficients have some joint effect on RealGDP per capita.
H0 = β1=0, β2 =0, β3 =0, β4 =0 (3)
H1 = Not H0 (4)
Model (3) corresponds with our overall regression model which includes all specified parameters.
Accounting for interdependence within government involvement, we test the joint hypothesis that
government enterprises and investments, money growth, and business regulation are both zero, against the
alternative that at least one of these coefficients is nonzero. This is illustrated in model (5). We will use
the restricted regression Y = β0 + β2X (ILS) + u; Y= GDP per capita and u = error term to conduct the hypothesis in
model (5). Drawing on the effects of freedom and structure,we test the joint hypothesis—captured in
model (7)—that integrity of the legal system and business regulation have no effect on economic growth,
against the alternative that at least one of the coefficients has some effect on RealGDP per capita. We
use the restricted regression Y = β0 + β1X (GEI) + β3X (money growth) + u; Y= GDP per capita and u = error term to test
this hypothesis.

7. H0 = β1=0, β3 =0, β4 =0 (5)
H1 = Not H0 (6)
H0 = β2=0, β4 =0 (7)
H1 = Not H0 (8)
For this cross-country data analysis, the F-ratio test statistic is used to measure the efficiency and
goodness of fit for data in the overall regression model. The study’s data falls within a time period
marked by global economic recovery. Although economies tend to have higher rates of money growth
during financial downturns, we expect (β3) to be negative because promoting economic performance
requires low, stable inflation. By including money growth, we focus on short-run policies and mitigate the
bias created by preceding economic conditions on 2012. Also, comprehensive data collection and
increased political decision-making across world countries, lead us to believe that government enterprises
and investments (β1) and integrity of the legal system (β2) will be positive. By including these controls,
we eliminate the bias created by having rampant political processes in modern economies. Lastly,
economic freedom and concurrent liberalization of trade policies have increased since the dawn of the 21st
century. In lieu of this, we expect to see a positive (β4)and by including regulation as a control variable,
we eliminate the bias created by the nature of government regulation on business activities.
III. EMPIRICAL RESULTS
Given underlying OLS assumptions, conditional interpretations of the data results are allowed for
this multiple linear regression analysis. Stata generated output revealed practical differences across world
countries with regards to most of this study’s variables. The coefficient of determination (R2
) indicates

8. that 53.28% of the variability in Real GDP per capita is explained by the independent variables
(government enterprises and investments, integrity of the legal system, money growth, and business
regulation). After controlling for the effects of integrity of the legal system, money growth and regulation,
on average countries with less government enterprises and investments earn more, per capita, to the tune
of $608.99. Similarly, controlling for the effects of all other model parameters,on average more law and
order contributes to per capita earnings by $4,574.45—while less business regulation contributes to
increases in per capita earnings by $3,988.93. Equally, after controlling for the effects of GEI, ILS and
REG, on average countries with greater money growth than realoutput experienced diminished per capita
earnings by $1,359.20. Examining these outcomes, we see that the effects of law and order (integrity of
the legal system) and business regulations were individually significant at all levels while government
enterprises and investment and money growth were not significant in our main regression.
Next we apply model restrictions in our evaluation for joint significance testing. First, we identify
a null hypothesis similar to our main regression line for the F-test for overall significance (ANOVA). The
resultant F-test statistic (4, 82) for all variables 23.28 is greater than df4/df82 (critical value) which is 2.45
and the output shows us a p-value of 0.000. Therefore,after controlling for constant factors,government
involvement and institutions which are measured by GEI, ILS, MG, and REG jointly and significantly
affect GDP per capita of world countries. We reject the null hypothesis that all the slopes equal 0 and
conclude that at least one of the slopes differs. In model 5, given an organized legal system, we test
government involvement; the F-statistic (3, 82) for our parameters 2.73 is greater than the critical value of
2.68 and output shows a p-value of 0.04. Therefore, we reject the null hypothesis of no joint effect at the
5% level. Results indicate that after controlling for the integrity of legal system, government contributions
in world countries which are measured by GEI, MG, and REG jointly affect GDP per capita. Lastly,in
model (7) we test the effects of freedom and structure,measured by ILS and REG. The F-statistic (2, 82)
for our parameters 33.90 is greater than the critical value of 3.07 while output reveals a p-value of 0.000,
and so we reject the null hypothesis of no joint effect at the 5% level (all) levels. We find that after

9. controlling for the effects of government enterprises and investments and money growth, freedom and
structure which are measured by integrity of legal system and business regulation jointly affect country
GDP per capita. This indicates that our sample data provides enough evidence to support the importance
of a legal system and minimal business regulation for economic productivity on all world countries.
Regression 1:
Y = β0 + β1X (GEI) + u; Y= GDP per capita and u = error term
Regression 2:
Y = β0 + β1X (GEI) + β3X (money growth) + β4X (regulation) + u; Y= GDP per capita and u = error term
Regression 3:
Y = β0 + β2X (integrity of the legal system) + β4X (regulation) + u; Y= GDP per capita and u = error term
Regression 4:
Y = β0 + β1X (GEI) + β2X (integrity of the legal system) + β3X (money growth) + β4X (regulation) + u; Y= GDP per capita and u =
error term
Regression 5:
Y = β0 + β2X (integrity of the legal system) + u; Y= GDP per capita and u = error term
Regression 6:
Y = β0 + β1X (GEI) + β3X (money growth) + u; Y= GDP per capita and u = error term

10. Table 2 Baseline OLS regression results (dependent variable = RGDP per capita ($2012)
* Significance at 10% level
** Significance at 5% level
*** Significance at 1% level
Under regression 1, we find a positive relationship between free market interests and growth, which
remains fairly stable across the other specifications. Without controls, this regression produced the
highest correlation coefficient for the measure of government enterprises and investments, implying that
growth is fueled by private interests and limited government. We next estimate the free market core,
against political variables, all of which show their expected signs in specification (2). The results appear
to indicate that the marginal effects of government policies and roles are impactful. We next test the
Dependent
Variable
(1) (2) (3) (4) (5) (6)
Government
Enterprises and
Investments
1908.316***
(524.0875)
1289.259**
(487.507)
608.999
(422.225)
1894.842***
(523.8191)
Integrity of the Legal
System
4907.64***
(736.7629)
4574.45***
(752.046)
5627.467***
(627.6035)
Money Growth -1403.812
(1089.90)
-1359.2
(910.2652)
1122.292
(1052.663)
Regulation 8958.927***
(1932.854)
2957.329*
(1638.831)
3988.928**
(1809.244)
Intercept 1701.776
(3613.116)
-45569.5***
(11837.56)
-
35690.3***
(9967.612)
-
33218.7***
(10092.57)
-19052.3***
(3837.215)
-7738.516
(9562.301)
Observations 87 87 87 87 87 87
R-squared 0.1349 0.3220 0.5053 0.5328 0.4861 0.1465

11. impact of judicial sovereignty using regression 3, which includes the terms corruption (law and order) and
economic freedom (regulation). By including this specification, we account for legal procedures and
mechanisms that, when applied in a consistent manner, affect beneficial transactions such as trade and
investment. Lack of an efficient judicial system hampers private sector development and the stability of
individual property rights.
Lastly, we estimate our main hypothesis, and find a positive relationship between limited government
and protective economic institutions. Within regression 4, we see that OLS coefficient results show that
economic growth is positive and significant for the presence of a legal system and limited government
regulation. In conjunction with joint hypothesis testing, we run regressions (5) and (6) and find that
private enterprise and enforced legal systems are significant markers of economic growth. Yet, low R2
results suggest that these variables alone do not capture much of the variability within the response
variable. It’s also interesting to find that, across all regression models, money growth did not yield
significant effects on GDP per capita.
In regards to each regression test, coefficients vary with each additional control, perhaps due to the
complex interconnectedness of economic and political structures. Monetary growth did not prove to be
significant for any specified regressions, and could be attributed to the single year comparison since
monetary policies make gradual adjustments to interest rates over time. Changes in the betas with and
without controls could be suggestive of correlations between set variables and other unmeasured
observations, as well as the complexity of GDP per capita. Our results are indicative of individual
differences among countries in political, climatic, locational, cultural, and historical factors that are
determinants of economic growth and development. Also, the absence of panel data analysis, and other
political institutions and variables could explain the variation in regression results. Controls for
government type, such as democracy or communism, is needed to account for discrepancies in varying
government systems and functions on different societies.

12. IV. CONCLUSION
This paper has presented suggestive evidence for the free market hypothesis on global economic
objectives. It commonly thought that an over-reaching government distorts growth, and evidence
evaluated here supports the view that evasive government intervention curtails economic growth and
hinders freedom. Although additional variables distort the significance of private enterprise on economic
growth, we can still conclude with confidence from joint testing that minimal protective roles are best for
economic growth. Using multiple regression analysis, we estimate that the dynamic relationship between
market liberalization and economic growth fosters a causal empirical link. However,our finding is not
without limitations, particularly in time observations, data, sample size and structure. Possible bias could
be a result of differences in scales of measurement, given that the regressors are indexes while economic
growth is measured using Real GDP per capita—asopposed to RealGDP growth rate. In addition, the
focus on growth in GDP per capita is perhaps misguided because GDP per capita is not necessarily a
good measure of economic welfare because it fails to account for changes in price level, differences in
cost of living between countries, and underground transactions. Also, making international comparisons
with RealGDP per capita is somewhat convoluted when the measure has not been adjusted for
differences in purchasing power parity, or when it’s expressed in terms of a commonly used international
currency—in this case U.S. dollars. Similarly, the use of cross-country data potentially generates bias due
to the heterogeneity of sample data and unobserved factors. Omitted variables, such as government
consumption, also create limitations because various factors are intertwined in complex ways with size of
government and other political processes. Interpretation of results as a strong causalrelationship between
regressors and response variable may not be fully established for this empirical study because panel data
was not used, nor were other statistical tools of analysis. More research is needed on economic freedom,
and further development of the EFW index and statistical tests are important for future insights into the
nature of institutional roles on welfare and growth. Overall, in countries aiming to increase national
income and human well-being, we recommend policy proposals which rely on the processes of the market
economy within the context of a stable legal system. Political decision makers in these countries ought to

13. be wary of restricting economic freedom, particularly freedom to establish new businesses, regulation of
domestic and international trade, and flexibility of legal contracts. Although free markets are not perfect,
they are inherently more conducive for advancing development and economic productivity.
V. REFERENCES
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829-856.
Gwartney, J., Lawson, R et al. (2014). “Economic Freedom of the World 2014 Annual Report,” Retrieved
April 11, 2015, from http://www.freetheworld.com/2014/EFW2014-POST.pdf
Johnson, N. et al (2012). “Corruption, Regulation, and Growth: An Empirical Study of the United States,”
15 Economics of Governance pp. 51-69
Berggren, N (2003) “The Benefits of Economic Freedom,” 3 The Independent Review pp. 193-211
Ram, R (1986) “Government Size and Economic Growth: A New Framework and Some Evidence from
Cross-Section and Time-Series Data,” 76 The American Economic Review pp. 191-203
Agwu, C (2014) “Factors that Contribute to Economic Growth in Nigeria,” 2 International Journal of
Management and Commerce Innovations pp. 487-495
Aidt, T (2009) “Corruption, Institutions, and Economic Development” 25 Oxford Review of Economic
Policy pp. 271-291
Buscaglia, Edgardo (1999) “Law and Economics of Development,” Hoover Institute, Stanford University
pp. 562-592