The importance of the problem of corruption is explained by the fact that it affects the very roots of the economy. Corruption violates equity rights which has far-reaching consequences for both economic efficiency and asset ownership. It makes the work of social and political institutions less efficient and thus endangers democracy. Moreover, corruption activities are often undertaken tacitly which makes the fight against it a complicated and resource-consuming process.
Numerous economists including Tirole (1996) claim that inefficient government institutions form an environment that inhibits investment, entrepreneurship, and innovation. North (1991) underlined that an efficient judicial system is necessary to implement contracts as an essential driver of economic performance. Poor protection of property rights over different types of assets including fixed capital, know-hows and patents weakens stimuli and opportunities to innovate, invest and attract foreign technological companies into a country. Burdensome and indecent bureaucracies may postpone the allocation of permissions, licenses and access, thus slowing down the process of adoption and integration of technological advances into new productive processes and equipment (O’Driscoll and Hoskins, 2003).
While corruption is often considered a factor that hampers growth, academic literature has not formed a commonly accepted viewpoint on this phenomenon. Previous researchers formulated the two contradicting hypotheses on the effect of corruption on growth and development, namely “grease in the wheels” and “sand in the wheels”.
According to the “grease in the wheels” hypothesis, corruption may act as a positive factor that might enhance efficiency and facilitate growth. Leff (1964) and Huntington (1968) considered corruption as the essential “grease” to smear the inelastic mechanism of rigid government regulation. In the same vein, Montinolla and Jackman (2002) deemed corruption as piece-rate pay for officials to ensure a more efficient supplement of public services. Lui (1985) showed that bribes can reduce the costs of government participation and queuing, thereby increasing the productivity of public administration. Beck and Maher (1986) reported a similar result showing that a decrease in costs using corruption schemes allows a firm to win the bidding competition. After that, Acemoglu and Verdier (1998) argued that the costs of fighting corruption may be too high compared to the benefits attained, and moderate protection of property rights and executing contractual arrangements is often an optimal scenario for many countries.
In turn, the ‘sand in the wheels’ hypothesis asserts that corruption imposes additional costs on economic actors (Mauro 1995). Corruption entails disproportional distribution of resources for the benefit of particular individuals or groups but to the detriment of the rest of society. Distorted redistribution, in turn, adds risks to investors who will not be able to put their capital to work with full efficiency. Thus, by admitting inappropriate allocation of resources, corruption destroys both investing and savings (Rose-Ackerman 1999).
Since there is no agreement among academic researchers on the contribution of corruption to economic growth, it is interesting to explore how corruption affects developing countries. Such economies demonstrate high rates of growth compared to developed economies (World Bank, 2019a), but there is also a high degree of corruption found in developing economies (TI, 2019).
The aim of the study is to explore how corruption and the level of institutional development affect economic growth in emerging economies. To attain this aim, the following objectives are addressed:
- to determine the extent to which corruption affects economic growth;
- to find out how institutional quality contributes to economic growth.
The rest of the chapters are organised as follows. Chapter 2 is the literature review which outlines the main theories of growth and provides empirical evidence on the relationship between corruption and economic growth. Chapter 3 discloses the methodology of the study including the employed data and sources, methods of analysis, research hypotheses and the empirical model. Chapter 4 provides the outcomes of analysing three samples, including descriptive statistics, correlation matrix, regression analysis and the Granger causality test. Chapter 5 assesses whether the research objectives have been attained, lists limitations of the study and makes recommendations for further research.
The Domar Model
The first mathematical model reflecting determinants of economic growth was proposed by Domar (1946). The researcher suggested that capital accumulation was directly connected with full employment. He also opined that an economy would be balanced when national income and productive capacity of all resources are in equilibrium. In this case, demand would be balanced with supply. Thus, Domar (1946) concluded that the rate of economic growth depends on both increase in labour force and its productivity.
Domar’s model described a closed economy that could not be influenced by external factors. However, Domar’s (1946) approach looks to be quite ambiguous since it stipulates that for an economy to maintain full employment, the real growth rate of invested capital has to be the same as the rate of productivity growth. On the other hand, this implies that if demand decreases, firms have to reduce both productivity and investment (Chirwa and Odhiambo, 2018) which contradicts the logic of economic growth. Therefore, the use of Domar’s model of growth looks to be difficult since it is hard to maintain equilibrium of capital in this setting.
The Solow Model
To account for the limitations of the Domar model, Solow (1956) proposed another growth model. According to Solow (1956), Domar’s (1946) model had three main drawbacks. First, the equilibrium proposed by Domar was very unstable, and even a small change in the established proportions could distort the balance and entail inflation or unemployment. Second, this equilibrium could exist only in the short-term perspective and did not describe long-term state of the economy correctly. Third, Domar (1946) deemed productivity as the only factor that determined growth and stock of capital. To address these restrictions, Solow (1956) considered labour as the key variable in the production function. He opined that different combinations of capital and labour should be examined in order to determine an optimal proportion that would provide the highest growth rate.
In addition to capital and labour, Solow (1956) also suggested that other factors including a change in the savings rate, technological progress and increase in labour force would also influence economic growth (Brauninger and Pannenberg, 2002). Moreover, Solow (1956) argued that savings and labour force growth had definite limits whereas technological progress was, in fact, not restrained. Therefore, he considered technological progress as a factor that could stimulate economic growth even in the long-term perspective. Along with that, both income and consumption would also increase. On the other hand, this setting is relevant for a closed economy. Hence, in this model, growth can be influenced only by external factors such as the government policy and technological innovations.
The Frankel Model
Unlike exogenous growth models, endogenous growth theories explain the rate of technological development and savings rate in the economy as a function of other internal factors. In exogenous growth models, it was not explained how savings or technological innovations were determined. They were taken as exogenous. In particular, endogenous growth models pay large attention to the collection of physical capital and assert that the investment rate is a factor that affects the long‑run growth rate. Frankel (1962) opined that total production functions show that returns increase in case a share of the invested capital is directed at innovative development that entails the technological progress in an economy. In this sense, innovative development is considered as enhancement in the firm, its labour productivity, attained economies of scale or technical progress that leads to these improvements (Frankel, 1962). This model also suggests that the rate of economic growth is proportional to the rate of physical capital. Therefore, if the investment rate increases, this will entail an increase in the rate of economic growth in the long run.
The Cass Model
The model suggested by Cass (1965) attempted to determine the best growth path which would provide the highest level of social welfare. In order to determine the most appropriate growth path, it is essential to estimate the maximum level of utility provided by the current consumption per capita by means of assessing the savings rate. To do this, Cass (1965) equated national income to consumption and investment assuming that all national income should be directed either at consumption or investment.
The Romer Model
According to Romer (1986), most significant technological innovations have the endogenous character. In particular, they are determined by the volume of investment in technological progress and the quality of human capital. Romer (1986) asserted that it is intangible knowledge that stimulates economic growth in any technology‑based endogenous model (Romer, 1986). Unlike models stipulating diminishing returns, such as Cass (1965) and Coopmans (1965), Romer (1986) deemed knowledge as an input in production that is able to provide increasing marginal productivity. The author argued that increasing knowledge may entail growth with increasing rates over time. Thus, this model is opposite to the neo-classical exogenous growth models since it considers knowledge, a purely endogenous factor, to be the main driver of growth in the long-term perspective (Romer, 1990).
The Lucas Theory
Following Romer (1986), Lucas (1988) developed another endogenous growth model which considered productivity and human capital as significant drivers of economic growth. Unlike the Solow (1956) model, Lucas (1988) insisted that human capital should be represented not by the volume of labour force but rather by its quality. He claimed that the efficiency of labour force and thus economic growth was determined by its skill level. The higher the skills of workers, the higher productivity and efficiency they can provide.
According to North (1991, p.98), "Institutions are the rules of the game in a society or, more formally, are the humanly devised constraints that shape human interaction". Acemoglu et al. (2005) specified this definition by considering institutions as a combination of three elements, namely economic institutions, political power and political institutions. In this setting, economic institutions aggregate factors determining the structure of stimuli for economic actors to undertake economic activities such as making investments, execute transactions, exchange goods and distribute resources. Political power is the ability of a legitimate social structure to affect the behaviour of individuals or other groups. On the one hand, economic institutions are the outcome of collective choices of individuals. On the other hand, these choices should be legitimised in the form of official agreements. In this case, political power acts as a means for ensuring the implementation of these social agreements about the distribution of available resources. Thus, the distribution and structure of political power directly influences the design and the efficiency of economic institutions. The third element is political institutions. They represent the means of allocating political power in the society. Acemoglu et al. (2005) underlined that political institutions and the allocation of political power depend on the distribution of resources. Political institutions specify the design of economic institutions that in turn influence the level and ways of economic development as well as the dynamics of the allocation of resources. Knack and Keefer (1995) and Rodrik et al. (2004) asserted that functioning of institutions in general and the protection of property rights in particular are a cornerstone for providing the long-run economic growth.
In a seminal paper, Mauro (1995) disclosed a negative impact of corruption on both developed and emerging economies but, in addition to this, he also attempted to indicate the mechanisms of this detrimental effect. Following his previous work (Mauro, 1993), the author suggested that corruption is connected with political instability since members of the ruling elite cannot coordinate their actions and inhibit long-term growth which affects the economy in a negative way (Putnam, 1993; Murphy et al., 1993). Next, the author indicated that the mechanism through which corruption may harm growth is a low volume of investment. In particular, Schleifer and Vishny (1993) showed that higher corruption was connected with low investment in education since it does not provide immediate effect but takes a long time to obtain the desired outcomes. The final and most significant conclusion made by Mauro (1995) was that slow growth may be a consequence of badly developed institutions in the past. That is, since corruption persists over time, it could negatively affect economic growth in the past which entailed poverty and a gap in development in the present.
A large strand of research dedicated to studying the effect of corruption on economic growth captured various contexts of emerging and transitional economies. In particular, Mobolaji and Omoteso (2009) explored whether corruption affected economic growth in a sample of transitional economies during the period 1990 - 2004. The value of their paper is that it not only attempted to estimate the impact of corruption on economic growth but also assessed the contribution of institutions through evaluation of the level of accountability and law. The study's outcomes were in line with Mauro's (1995) conclusion about the negative effect of corruption on growth. This implies that the researchers failed to find statistical support for the “grease-the-wheels” hypothesis suggested by Leff (1964) and Huntington (1968).
Ensor (2004) estimated the corruption-growth nexus in the healthcare sector of developing economies. The researcher revealed that informal payments were peculiar for the explored sample of countries and affected the development of healthcare services negatively since they distorted the effect of legal and official financing in this sphere. The findings of the following researchers mostly confirm these conclusions. One of the scholars who also focused on the mechanisms through which corruption hinders economic growth was Dridi (2013). Instead of employing the popular method of decomposition which was used by Mo (2001) and Pellegrini (2011), this author applied the channel methodology (Tavares and Wacziarg, 2001) which has previously been used by Lorentzen et al. (2008). The outcomes attained by Dridi (2013) evidenced that the negative externalities entailed by corruption could be implemented via the channels of human capital and political instability that, in turn, inhibited economic growth.
According to Kolstad and Soreide (2009), corruption is the major reason for the appalling economic performance of numerous resource-rich countries. They considered two particular forms of corruption, namely patronage and rent seeking. Resource rents stimulate efforts to receive returns from selling commodities instead of using human capital including people’s time, skills and creativity, more productively. Along with that, resource revenues were claimed to entail patronage in the form of government pay-offs to supporters to stay in power. This, in turn, leads to lower accountability and poor allocation of public funds.
Meon and Sekkat (2005) explored the linkage between the influence of corruption on economic growth and investment and the quality of institutions using a sample of 71 countries during 1970 -1998. The outcomes of the analysis demonstrated a negative effect of corruption on growth regardless its influence on investments. However, the degree of the influence was different depending on the quality of governing institutions. Namely, the marginal effect of corruption on growth appeared to be positive in less politically stable regimes. That is, the corruption was positively linked with efficiency in economies with “ineffective" institutions. This supports the “grease the wheel” hypothesis. In addition, the authors attempted to disclose the channels through which corruption may affect growth. They argued that corruption entails lower accumulation of capital which inhibits growth. However, they suggested the existence of other channels that have yet to be indicated. This result was later supported by Meon and Weill (2010).
Another support for the “grease the wheel” hypothesis was provided by Egger and Winner (2005). These authors employed data for 73 developed and emerging economies to examine the association between corruption incentives for foreign direct investments (FDI). They found that corruption was useful for businesses in avoiding encumbered regulations and administrative restrictions and implementing beneficial transactions which would otherwise not occur. Thus, the researchers concluded that corruption raises the efficiency of the economy acting as a correction mechanism that allows the private sector to reimburse government failures.
Svensson (2005) was aware of the detrimental role of corruption and rent seeking in minimising the real potential of economic growth. However, the author argued that much of research was still to be made to understand the phenomenon of corruption and find the opportunities for fighting it. Svensson (2005) showed that corruption negatively affected the allocation of entrepreneurial efforts and talents (Murphy et al., 1993), disrupted firms’ choice of technology and innovation (Svensson, 2003; Mian and Khwaja, 2005) and devastated the potential of public spending on growth and social welfare (Reinikka and Svensson, 2005).
Aidt and Dutta (2008) made several contributions to the explored topic of corruption and growth. First, they formulated a theoretical model reflecting the effect of corruption on economic growth taking into account various institutional structures. Second, they showed that the mechanism of the influence of corruption is determined by peculiarities of the specific regimes and countries depending on their institutional quality. Specifically, these researchers demonstrated that countries where institutional quality was good observed a negative effect of corruption on growth. Meanwhile, countries in which institutional quality was poor, the association between corruption and growth was positive or at least had weaker negative implications.
Venard (2013) examined the associations between the quality of institutions, corruption and economic development using a sample including 120 countries worldwide. A unique feature of the sample was that the data had been aggregated for four separate years, namely 1998, 2001, 2004 and 2007 while the analysis was conducted using a partial least squares (PLS) regression. The author revealed that both institutional quality and corruption influenced economic development in a negative way. Along with that, the author estimated a mediating effect of institutions on the corruption-growth nexus. Specifically, the effect of corruption on economic growth was more significant in countries where the quality of institutions was low compared to countries with high institutional quality. Thus, regarding the effect of corruption on growth, this paper supported the ‘sand in the wheel’ hypothesis.
The data for the analysis is retrieved from two sources. In particular, economic variables have been gathered from the World Development Indicators whereas institutional and corruption variables have been downloaded from Worldwide Governance Indicators. GDP growth (%) is taken as the dependent variable. The economic drivers of growth include gross fixed capital formation (GFCF) as % of GDP, government expenditure (GovEx) measuring physical capital, the rate of labour force growth (Labour, %) measuring human capital and research and development (R&D) expenditure as % of GDP (RD) to represent innovation. All these variables are taken from the World Development Indicators database (World Bank, 2019a).
Along with that, the variables representing corruption and institutional quality are taken from the World Governance Indicators database (World Bank, 2019b). These include Control of Corruption (Corrupt) which reflects perceptions whether individuals benefit from the execution of public power; Government effectiveness (GovEff) which reflects perceptions of the quality of government services and the independence of the government from political pressure; Political stability and Absence of Violence (Stab) which represents the probability of political turmoil and execution of violence by the governing forces; Regulatory Quality (RegQual) which represents perceptions of the ability of the government to undertake measures for developing the private sector; Rule of Law (RoL) which reflects the confidence in the legal system adopted in the country; Voice and Accountability (VA) reflecting the confidence of citizens that they can participate in government elections and have civil liberties such as freedom of self-expression, freedom of receiving information and freedom of association. All these variables are represented by the indices that take values from -2.5 to 2.5.
The period of analysis is 1995-2018. Thus, the most recent trends in the relationship between corruption, institutional quality and economic growth are estimated in the study. The sample captures 20 emerging economies, namely 10 countries from Latin America and 10 countries from Asia. Thus, separate regressions are estimated for the full sample and the two subsamples. This allows for capturing region-specific distinctions.
The main method of analysis is the panel regression technique. This method allows for estimating the significance of linear relationships between the explanatory variables and the dependent variable across countries and time periods. A panel of countries is used since it is necessary to account for country specific differences not captured by the explanatory variables. Accounting for these differences is done by estimating the regression model with fixed effects (FE) and random effects (RE). To determine whether the RE-specification generates consistent coefficients, the Hausman specification test is conducted for each of the explored samples. If the null hypothesis of the Hausman test is rejected, the FE specification is used.
The null hypotheses tested in the study are the following:
H01: Control of corruption does not have a significant impact on economic growth in developing countries;
H02: The effect of institutional quality on economic growth is not significant in developing countries.
The model used for the regression analysis is specified as follows:
where for country i and year t, GDP is the GDP growth; GFCF is the gross fixed capital formation, Labour is the rate of labour force growth; RD is the research and development expenditure; Corrupt is the corruption level; GovEff is the government efficiency; Stab is the political stability and absence of violence; RegQual is the regulatory quality; RoL is the rule of law; VA is the voice and accountability, is the constant; are the slope coefficients; is the error term.
Prior to conducting the regression analysis, descriptive statistics are explored to assess the scope of the employed variables. Descriptive statistics for the full sample are presented in Table 1 and include the maximum, minimum, mean and standard deviations.
The mean value of the corruption index is -0.23. This implies that, on average, the level of corruption in the explored sample was higher than the average world level. Meanwhile, the extremum values, 1.58 and -1.40, respectively, show the sample comprises countries with very high and low levels of corruption. The mean value of the government efficiency index is -0.02 which indicates that, on average, the examined countries have a similar level to the global average. The variable of regulatory quality has the average value -0.07 which implies that the index reflecting this parameter for the countries in the sample was also close to the global average. Meanwhile, the minimum value equal to -1.82 shows a very low quality of regulation whereas the maximum value equal to 0.65 implies that none of the countries had a high level of regulation. The average value for rule of law was lower than the global average by 0.26. Similar to the previous variable, the minimum value was very low, namely -1.92 whereas the maximum level was not high, namely 0.65. This indicates that there is asymmetry in the distribution of the institutional variables. As for the stability variable, its mean value showed that the explored countries were mostly politically unstable. In the meanwhile, the minimum value -2.37 demonstrates the presence of extreme cases of instability. The average level of voice and accountability variable was almost equal to the global average, namely -0.08. In general, the descriptive statistics of worldwide governance indicators show that the explored countries had comparatively low level of democracy and poor quality of public institutions while the level of corruption was high.
The average rate of GDP growth was 4.5% per annum while the most significant growth was at the level of 14.23%. The mean level of investment in fixed capital was 23.53% of GDP whereas the highest level was 45.69%. On average, the share of government expenditure in the explored countries was at the level of 12.92% of GDP whereas the highest level was 20.39%. The average rate of labour force growth was 0.02% while the highest level was 0.08%. As for the R&D expenditure, the countries, on average, spent 0.49% of their GDP whereas the highest level was 2.11%.
The regression analysis is preceded with the analysis of pairwise correlations between explanatory variables. This is done in order to detect multicollinearity that may be present in the sample. Multicollinearity is the phenomenon of strong correlation between independent variables. If at least two explanatory variables are strongly correlated this makes the estimation of their individual effects on the dependent variables impossible. This especially refers to the worldwide governance variables since they represent perceptions of respondents and experts. The respondents may have a holistic attitude to the situation in the country. Therefore, these indices are likely to be correlated. In this case, some of the correlated independent variables should be omitted from the model to eliminate multicollinearity. Table 2 reflects pairwise correlations for the full sample.
As expected, some of the institutional variables appeared to be strongly correlated. In particular, corruption is strongly correlated with regulatory quality and rule of law. Also, government effectiveness has strong correlation with the same variables. All strong correlations are positive which implies that a high level of institutional quality in one aspect will also point to a high level in the corresponding aspect. Thus, if one institutional variable appears to be a significant determinant of growth in the model, an omitted variable will also be a significant driver of economic growth. Since corruption is the main explanatory variable in the study, it cannot be omitted. Therefore, government effectiveness should be removed from the model.
Next, the regression analysis for the full sample is conducted. Its output is presented in Table 3.
The R-squared of the FE-specification is 0.384. Since the R-squared is the ratio of explained variance to the overall variance of the dependent variable, it implies that the model is able to explain 38.4% of variability in the dependent variable, namely growth. The remaining share of variance is explained by other factors that were not captured by the model. Meanwhile, the RE-specification explains 29.4% of the dependent variable variance.
Table 3 shows that GFCF, government expenditure and corruption were significantly associated with GDP growth in the RE-specification. The level of control of corruption was positively associated with growth. That is, a higher degree of economy transparency and lower corruption contributed to growth. The coefficient was significant at the 1% level. As for GFCF, it was significant only at the 10% level. Its positive effect implies that higher level of investment in fixed assets entailed economic growth. The variables reflecting institutional quality beside corruption appeared to have no effect on growth in this specification.
For the RE-specification, GFCF and government expenditure were shown to have a significant impact on the dependent variable at the 1% level. Similar to the FE-specification, the influence of government expenditure was negative whereas GFCF affected growth positively.
The Hausman test has been conducted to choose between the RE and FE specification. The p-value of the test chi2 is equal to 0.005. This shows that the null hypothesis of the test that RE model is optimal has to be rejected at the 1% level. Therefore, the FE-model is chosen as the final model.
A similar analysis of the correlation and regression is conducted for the Asian sample which is presented in Table 4.
Similar to the full sample, some institutional variables are strongly correlated. In particular, corruption is correlated with regulatory quality, government efficiency and rule of law. In addition, government efficiency is strongly correlated with regulatory quality. In addition to this, economic variables such as RD and GFCF are strongly correlated as well. Thus, to remove multicollinearity, government efficiency and RD are removed from the model for the Asian sample.
The outcomes of the regression analysis for the Asian sample are presented in Table 5.
The R-squared of the FE-specification is 0.318 showing that 31.8% of the GDP growth variance can be explained by this model. Along with that, the p-value of the F-statistic is equal to 0.000. Therefore, the null hypothesis of the F-test on the overall insignificance of the model cannot be accepted. This means that at least one coefficient is significant in this specification.
The analysis shows that an increase in labour force affected GDP growth negatively at the 10% level. This might be explained by increase in productivity and replacement of labour with technologies. Along with that, voice and accountability had a positive impact on economic growth at the 10% level. This shows that social freedoms and an opportunity for citizens to elect government provides confidence in the future and contributes to economic wellbeing of a country. As for the effect of corruption, it was statistically insignificant.
For the RE-specification, the R-squared is 0.060. That is, only 6% of the dependent variable variance is explained by this specification. Along with that, the p-value of the F-statistic is 0.175. This implies that the null hypothesis of the F-test is accepted at the 5% level, and the model does not contain any significant determinants of economic growth.
The outcomes of the RE-specification confirmed the results of the F-test. A negative impact of labour force on economic growth was significant only at the 10% level, which is not enough considering the 95% confidence level used as a criterion in this study. In addition, rule of law appeared to have a negative effect at the 10% level as well. The p-value of chi2 in the Hausman test is equal to 0.676. Thus, the null hypothesis of the test cannot be rejected. This implies that the RE-specification is considered the final model for the analysis of this subsample.
The next step of the analysis is to explore the sample of South American economies. The correlation matrix for this sample is presented in Table 6.
Table 6 indicates that voice and accountability is strongly correlated with all institutional variables. Along with that, corruption is correlated with government efficiency, rule of law and regulatory quality. Besides, rule of law is strongly correlated with government efficiency. Finally, R&D spending is strongly correlated with the level of government expenditure. To remove multicollinearity, voice and accountability, government efficiency and R&D expenditures are omitted from the model. The outcomes of the regression analysis for the South American countries are demonstrated in Table 7.
Table 7 shows that the FE-specification is able to explain 26.2% of GDP growth variance. The output of the F-test points at the presence of significant determinants of growth at least at the 5% level. The same results of the F-test are obtained for the RE-specification. Along with that, the RE-model explains only 12.2% of the growth rate variance.
For the FE-specification, GFCF, labour force and corruption appeared to influence economic growth significantly. The effect of GFCF and control of corruption was positive. That is, greater investment in fixed assets and transparency of the economy contributed to growth in South American countries. On the other hand, the effect of government expenditure was negative. This can be explained by a higher role of other components of aggregate demand such as consumption and investments in total output.
As for the FE-specification, government expenditure and corruption had the same effect on GDP growth as in the FE-model. However, the effect of GFCF was significant only at the 10% level. Along with that, rule of law appeared to have a negative impact on growth at the 1% level.
The Hausman test shows that the null hypothesis has to be rejected at the 1% level since the p-value of the chi2 is lower than 0.01. Therefore, the FE-model is more appropriate for the analysis. This means that country specifics are taken into account by means of dummy variables.
The regression analysis assessed the significance of linear associations between explanatory variables and the dependent variable. However, regressions are unable to identify causality between the variables as potential issues with endogeneity may be present. In simple words, it is unable to explain whether changes in the corruption level lead to changes in GDP, or vice versa. In order to estimate the causal associations between the variables, the Granger causality test is conducted for the significant determinants indicated by the regression analysis. In particular, government expenditure and corruption appeared to have a significant impact on GDP growth in the full sample, whereas GFCF, government expenditure and corruption influenced growth significantly in the sample of South American countries. As for the sample of Asian countries, no significant determinants of growth at the 5% level were identified.
The Granger Causality test for a pair of variables examines the null hypothesis that changes in one variable do not cause changes in the other variable, and vice versa. This hypothesis is accepted if the p-value of the test is greater than 0.05. In the opposite case, the hypothesis is rejected, which implies that one variable Granger causes another variable.
The outcomes of the Granger test are presented in Table 8.
According to Table 8, both government expenditure and corruption caused GDP in the full sample of emerging economies and not vice versa. In particular, an increase in the control of corruption entailed GDP growth in the explored developing countries.
As for the South American countries, GDP caused government expenditure, which is consistent with the Keynesian explanation that governments should run budget deficit when economic growth slows down. Along with that, a bidirectional relationship was revealed for GFCF and GDP growth. That is, higher investment in fixed assets leads to an increase in GDP while GDP growth stimulates further fixed capital investments. As for the corruption-GDP nexus, no causal relationships were indicated in either direction in the South American sample. This may imply that while control of corruption is connected with GDP, this relationship is indirect and may be mediated by other factors.
The results of the regression analysis showed that the relationship between corruption and economic growth was significant at the 1% level in the full sample of emerging economies and in South American countries. The results support the “sand in the wheels” hypothesis, that is corruption is a detrimental factor for growth. This finding supports the main streamline in the corruption-growth research. In particular, this outcome is in line with the findings of Mauro (1995) who revealed a negative impact of corruption on growth. This result also accords with the findings by Dridi (2013). This researcher argued that corruption is connected with political instability, and both these factors inhibit growth. This dissertation showed that, on average, political stability in explored countries was lower compared to the global average. Moreover, the current analysis confirmed a positive effect of control of corruption on economic growth, which implies that corruption itself has a negative effect on growth.
Along with that, the study confirmed the findings of Aidt and Dutta (2008). These researchers not only underlined a negative impact of corruption on GDP but also showed that institutional quality had a mediating effect on this relationship. Namely, this effect was more pronounced in countries with higher institutional quality while less evident in countries with lower institutional quality. Institutional quality in South American countries appeared to be higher compared to Asian countries. Moreover, the effect of corruption in South American countries was significant whereas it was not significant in Asia. This supports the findings of the abovementioned authors.
The findings of this dissertation were to some extent contrary to those reported by Venard (2013). This researcher showed that the effect of corruption on growth was more significant in countries with lower institutional quality. On the other hand, his sample consisted of 120 countries, including both advanced and emerging economies. Meanwhile, the sample in the current study comprised only developing countries in which the quality of institutions was low compared to the global average.
Previous studies including Meon and Sekkat (2005) and Meon and Weill (2010) emphasised that the quality of institutions was significantly connected with the level of corruption and affected the corruption-growth nexus. On the one hand, the analysis of correlation matrices for all three samples showed that corruption was, indeed, strongly correlated with institutional quality. On the other hand, the analysis showed that institutions appeared to have no effect on economic growth.
The aim of the study was to explore the relationship between corruption, institutional quality and economic growth in emerging economies. The sample included twenty emerging economies, ten from South America and ten from Asia. The period of analysis was 1995-2018. Three regressions have been run on the total sample and two subsamples.
Two main objectives of the study have been met in the course of the analysis. First, the effect of corruption on economic growth had to be determined. The analysis showed that higher level of transparency had a positive impact on economic growth in the full sample of countries and in South American countries in particular. As for Asian countries, the effect of corruption on growth was insignificant. However, the Granger test showed that a lower level of corruption entailed economic growth in the full sample while it did not cause growth in South America.
The second objective was to determine the effect of institutional quality on economic growth in the explored countries. The analysis showed that the variables of institutional quality did not have a significant effect on growth in all the three explored samples.
The obtained results have contributed to the understanding of the role of corruption and institutional quality in economic growth. However, several limitations of the study should be mentioned. First, the chosen variables reflected only the growth theories and the institutional quality of the explored countries. Yet, they have not revealed the mechanisms through which corruption affects growth. Shleifer and Vishny (1993) supposed that corruption entailed lower investment in education and thus inhibited growth while Dridi (2013) asserted that the effect of corruption could be observed through the channels of human capital and instability. It is also necessary to underline that the research was conducted only at the national level. Meanwhile, some authors including Beck and Maher (1986) and Acemoglu and Verdier (1998) explored the corruption-performance nexus at the firm level and obtained different results. Thus, the study could be expanded in future research distinguishing between the micro and macro scale.
In light of the aforementioned limitations, several recommendations can be made for the further research. First, the effect of corruption on performance at the firm level should be estimated and compared to the relationship between these variables on the macro scale. It could turn out that the results are driven by a few outliers and are not representative. Second, particular mechanisms of corruption should be examined to find out what factors affect corruption and through which channels the effects of corruption are transmitted to economic growth. Third, the outcomes of the analysis demonstrate that the institutional quality did not play a significant role in economic growth in the explored countries. However, some other factors including culture and mentality may appear to be more influential. Thus, the effect of cultural variables, such as the Hofstede cultural dimensions, on growth may be also investigated in future to expand this research. Finally, the effect of corruption on growth may not be direct. Thus, this relationship can be explored using different methods such as with instrumental variables that might mediate the influence of corruption on growth and address possible endogeneity problems.
Along with that, recommendations for policy makers are provided. The analysis showed that a higher level of transparency is positively associated with economic growth. Therefore, it is recommended that policy makers should increase the level of accountability and quality of bureaucratic decisions if they want to achieve higher economic growth in their country. Moreover, a higher degree of political stability and certainty would also contribute to growth.
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