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 ($). Real GDP 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.

I. INTRODUCTION

Economic growth, represented by Real GDP, 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

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.

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

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

REG 87 7.029356 .9201154 3.895279 9.019047 MG 87 8.485171 1.582358 0 9.92501 ILS 87 5.765517 2.046674 1.666667 10 GEI 87 6.126437 3.179895 0 10GDPperCapita 87 13392.96 16519.68 266.589 67511.69 Variable Obs Mean Std. Dev. Min Max

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

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 casual causation 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 Real GDP per capita, while the alternative in

model (4) says that these coefficients have some joint effect on Real GDP 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 Real GDP 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.

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

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 real output 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 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

Table 2 Baseline OLS regression results (dependent variable = RGDP per capita ($2012)

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

* 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

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.

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—as opposed to Real GDP 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 Real GDP 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 causal relationship 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

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

Ginsburg, Tom (2000) “Does Law Matter for Economic Development,” 34 Law & Society Review pp. 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