Regression trees for growth models
Model | Regression tree | Final groups |
---|---|---|
Benchmark with Control of Corruption | ICRG Corruption level ≤ 0.916 ICRG Corruption level >0.916 | 2 |
Benchmark with Corruption level | ICRG Corruption level ≤ 0.916 ICRG Corruption level > 0.916 | 2 |
Model 1 | ICRG Corruption level ≤ 0.916 ICRG Corruption level > 0.916 | 2 |
Model 2 | ICRG Corruption level ≤ 0.916 ICRG Corruption level > 0.916 | 2 |
Model 3 | ICRG Rule of Law ≤ 1.150 ICRG Rule of Law > 1.150 World Bank Control of Corruption ≤ 0.378 ICRG Rule of Law > 1.150 World Bank Control of Corruption > 0.378 | 3 |
Model 4 | World Bank Control of Corruption ≤ 0.269 World Bank Rule of Law ≤ 0.484 World Bank Control of Corruption ≤ 0.269 World Bank Rule of Law > 0.484 World Bank Control of Corruption > 0.269 | 3 |
Regression tree estimations for the benchmark with control of corruption
Determinants | Estimation without subgroups | High corruption group (Indicator ≤ 0.916) | Low corruption group (Indicator > 0.916) |
---|---|---|---|
−0.344 (0.360) | 0.351 (0.509) | −0.662 (0.269) | |
( ) | 0.751*** (0.000) | 0.743*** (0.000) | 0.649*** (0.000) |
( ) | −0.154*** (0.000) | −0.209*** (0.000) | −0.092** (0.021) |
( + + ) | −0.583*** (0.000) | −0.873*** (0.000) | −0.422*** (0.002) |
0.044 (0.136) | 0.162 (0.127) | 0.021 (0.588) | |
0.603 | 0.727 | 0.486 | |
N | 103 | 45 | 58 |
Determinants | Estimation without subgroups | High corruption level group (Indicator ≤ 0.916) | Low corruption level group (Indicator > 0.916) |
---|---|---|---|
−0.539 (0.116) | 0.683 (0.884) | −0.837 (0.119) | |
( ) | 0.748*** (0.000) | 0.736*** (0.000) | 0.651*** (0.000) |
( ) | −0.139*** (0.000) | −0.200*** (0.000) | −0.064** (0.021) |
( + + ) | −0.587*** (0.000) | −0.900*** (0.000) | −0.432*** (0.000) |
( ) | −0.075 (0.324) | 0.037** (0.035) | −0.067 (0.560) |
0.598 | 0.741 | 0.487 | |
N | 103 | 45 | 58 |
Determinants | Estimation without subgroups | High corruption level group (Indicator ≤ 0.916) | Low corruption level group (Indicator > 0.916) |
---|---|---|---|
−0.539 (0.116) | 0.683 (0.884) | −0.837 (0.119) | |
( ) | 0.748*** (0.000) | 0.736*** (0.000) | 0.651*** (0.000) |
( ) | −0.139*** (0.000) | −0.200*** (0.000) | −0.064** (0.021) |
( + + ) | −0.587*** (0.000) | −0.900*** (0.000) | −0.432*** (0.000) |
( ) | −0.075 (0.324) | 0.037** (0.035) | −0.067 (0.560) |
0.598 | 0.741 | 0.487 | |
103 | 45 | 58 |
Determinants | Estimation without subgroups | High corruption level group (Indicator ≤ 0.916) | Low corruption level group (Indicator > 0.916) |
---|---|---|---|
−0.539 (0.116) | 0.683 (0.884) | −0.837 (0.119) | |
( ) | 0.748*** (0.000) | 0.736*** (0.000) | 0.651*** (0.000) |
( ) | −0.139*** (0.000) | −0.200*** (0.000) | −0.064** (0.021) |
( + + ) | −0.587*** (0.000) | −0.900*** (0.000) | −0.432*** (0.000) |
( ) | −0.075 (0.324) | 0.037** (0.035) | −0.067 (0.560) |
0.598 | 0.741 | 0.487 | |
N | 103 | 45 | 58 |
Determinants | Estimation without subgroups | Low institutional quality group | Medium institutional quality group | High institutional quality group |
---|---|---|---|---|
Constant | −0.344 (0.360) | 0.378 (0.504) | −1.292* (0.069) | 1.447 (0.209) |
Ln(Inv ) | 0.751*** (0.000) | 0.718*** (0.000) | 0.911*** (0.000) | 0.399* (0.077) |
Ln(Yi ) | −0.155*** (0.000) | −0.210*** (0.000) | −0.108** (0.020) | −0.228** (0.013) |
Ln(ni + δ + ζ) | −0.583*** (0.000) | −1.081*** (0.000) | −0.470*** (0.000) | −0.113 (0.611) |
Ln(Cor ) | 0.044 (0.137) | 0.066 (0.137) | −0.114 (0.369) | 0.006 (0.928) |
0.603 | 0.655 | 0.760 | 0.414 | |
N | 103 | 40 | 30 | 33 |
Determinants | Estimation without subgroups | Low institutional quality group | Medium institutional quality group | High institutional quality group |
---|---|---|---|---|
−0.344 (0.360) | 0.366 (0.493) | −0.891 (0.299) | −1.785* (0.069) | |
) | 0.751*** (0.000) | 0.619*** (0.000) | 0.968*** (0.000) | 0.471** (0.017) |
) | −0.155*** (0.000) | −0.177*** (0.000) | −0.166** (0.016) | −0.295*** (0.000) |
+δ + ζ) | −0.583*** (0.000) | −0.896*** (0.000) | −0.783*** (0.000) | −0.016 (0.906) |
0.044 (0.137) | 0.084 (0.493) | −0.168 (0.407) | 0.009** (0.049) | |
0.603 | 0.680 | 0.722 | 0.551 | |
N | 103 | 35 | 30 | 38 |
Groups with high corruption | Groups with middle corruption | Groups with low corruption | |
---|---|---|---|
Benchmark Control of Corruption | Non-significant | – | Non-significant |
Benchmark Corruption Level | Significant | – | Non-significant |
Model 1 | Significant | – | Non-significant |
Model 2 | Significant | – | Non-significant |
Model 3 | Non-significant | Non-significant | Non-significant |
Model 4 | Significant | Non-significant | Non-significant |
Determinants | High corruption level group (Indicator ≤ 0.916) | Low corruption level group (indicator > 0.916) |
---|---|---|
0.061 (0.947) | −0.938 (0.198) | |
( ) | 0.811*** (0.000) | 0.653*** (0.000) |
( ) | −0.216*** (0.000) | −0.061 (0.293) |
( + + ) | −0.968*** (0.000) | −0.429*** (0.000) |
) | −0.066 (0.870) | −0.019 (0.759) |
0.682 | 0.474 | |
N | 45 | 58 |
Determinants | High corruption level group (Indicator ≤ 0.916) | Low corruption level group (indicator > 0.916) |
---|---|---|
Constant | 0.122 (0.829) | −0.815 (0.141) |
( ) | 0.750*** (0.000) | 0.651*** (0.000) |
−0.217*** (0.000) | −0.068* (0.071) | |
( + + ) | −0.950*** (0.000) | −0.427*** (0.000) |
( ) | 0.246 (0.804) | −0.057 (0.729) |
0.719 | 0.485 | |
N | 45 | 58 |
High corruption group | Low corruption group |
---|---|
Algeria, Angola, Argentina, Albania, Armenia, Belarus, Bangladesh, Bolivia, Burkina Faso, Cameroon, China, Rep. Congo, Cote d’Ivoire, Dominican Republic, Egypt, Gabon, Ghana, Guatemala, Guinea-Bissau, Honduras, Indonesia, India, Jamaica, Kenya, Latvia, Mali, Malawi, Mexico, Mozambique, Niger, Nigeria, Pakistan, Papua New Guinea, Panama, Paraguay, Philippines, Russia, Sierra Leone, Togo, Thailand, Tunisia, Turkey, Uganda, Ukrania, Zimbabwe | Australia, Austria, Bahrain, Belgium, Botswana, Brazil, Bulgaria, Canada, Chile, Colombia, Costa Rica, Croatia, Czech Republic, Denmark, Estonia, Ecuador, El Salvador, Finland, France, Gambia, Germany, Greece, Hong Kong, Hungary, Iceland, Ireland, Israel, Italy, Japan, Jordan, Korea, Rep., Kuwait, Lithuania, Luxembourg, Malaysia, Madagascar, Morocco, Namibia, Netherlands, Nicaragua, New Zealand, Norway, Peru, Poland, Portugal, Romania, Singapore, Senegal, Slovak Republic, Slovenia, Spain, Sri Lanka, Sweden, Switzerland, UK, USA, Uruguay, Zambia |
Following Berkowitz et al. (2003 ) and Neyapti (2013 ), we do not consider the rule of law indicator as a potential explanatory variable. They, indeed, highlight that its effect is only indirect.
Previous papers also addressed nonlinearity through regime-dependent models, with different determinants of the regime changes. Meon and Sekkat (2005) find that the negative impact of corruption decreases with the quality of governments. Aidt et al. (2008 ) and Méon and Weill (2010 ) conclude that corruption has regime-specific effects on growth with weaker impact on countries with poorer institutional quality.
This indirect effect is also documented in Gwartley et al. (2006), Minier (2007 ) and Dort et al. (2014) , who obtain that the quality of institutions has an impact on the marginal effect of investment on growth.
In this aspect, we follow the strategy recommended by Tan (2010 ).
The classifications of regions come from Gründler and Krieger (2016) .
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Theory is divided over the effects of corruption on economic growth. However, the growing consensus based on the empirical literature is that corruption is associated with negative growth outcomes. This relationship is not necessarily linear, and causality between corruption and economic growth can run in both directions.
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Bose, N. (2018). Corruption and Economic Growth. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2920
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A key factor could be the indirect effects of corruption on economic growth through the transmission channels. This overall result is mainly due to the impact of corruption on economic growth through human capital, total investment, and other channels (Gründler & Potrafke, Citation 2019; Mo, Citation 2001). In models 4, 5, and 6, we replace ...
The literature identified that depending on the circumstances, corruption can have both good and bad consequences on economic growth. Besides, the impact of corruption on economic growth varies ...
Empirical model. Empirical studies generally opt for the nonlinear approach to study the impact of corruption on economic growth (Méon and Sekkat 2005; Mendez and Sepúlveda, 2006; Aidt et al. 2008; Mushfiq 2011; Allan and Roland 2013; Eatzaz et al. 2012; Saha and Gounder 2013 and Kolstad and Wiig 2013).This is a quadratic function assuming the hypothesis that the impact of corruption on ...
New panel data studies corroborate that corruption has direct negative effects on economic growth (Swaleheen, 2011, D'Agostino et al., 2016a, D'Agostino et al., 2016b, Cieślik and Goczek, 2018a).What is more, scholars investigate the functional form of the relationship between corruption and economic growth, transmission channels and the extent to which the relationship between corruption and ...
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effects (that is, coefficients) of corruption on economic growth from 41 different empirical studies (the studies are listed in appendix section A.l). The selection cri-teria we used are as follows. In order to be included, a paper has to investigate econometrically the relationship between corruption and economic growth across
Corruption has fierce impacts on economic and societal development and is subject to a vast range of institutional, jurisdictional, societal, and economic conditions. It is this paper's aim to provide a reassessment and a comprehensive state-of-the-art survey of existing literature on corruption and its causes and effects. A particularly strong ...
After separating the indirect effects of corruption on economic growth, Swaleheen (2011) concludes that corruption has a direct negative impact on economic growth. Kunieda et al.
In this article, a panel data analysis has been used to examine 65 countries over the 1987 to 2021 period. Our findings are that corruption can have a positive effect on growth. The results ...
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Corruption is often a source of contentious debate, covering different areas of knowledge, such as philosophy and sociology. In this paper we assess the effects of corruption on economic activity and highlight the relevance of the size of the government. We use dynamic models and the generalised method of moments approach for a panel of 48 countries, and as a measure of corruption the ...
Two Opposing Theoretical Views. consensus based on the empirical literature is that corruption is associated with negative One view argues that corruption can enhance ef-growth outcomes. This relationship is not nec- fi ciency and raise growth in the presence of cum-essarily linear, and causality between corrup-bersome bureaucratic regulations.
The empirical evidence tends to. suggest that corruption decreases economic growth, especially in countries with low investment. rates and low-quality governance (e.g., Mauro 1995, Mo 2001, Aidt et al. 2008, Méon and. Sekkat 2005, Hodge et al. 2011, Swaleheen 2011, d'Agostino et al. 2016a and 2016b, Huang.
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that corruption has a negative, direct impact on economic growth and development. Corruption also has an indirect effect on a country's economic performance by affecting many factors fuelling economic growth such as investment, taxation, level, composition and effectiveness of public expenditure.
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The main objective of this empirical study to examine the impact of corruption, unemployment and inflation on economic growth for seventy nine (79) developing countries of the world for the period from 2002 to 2018. This study uses Panel unit root tests (PUT), Pooled Mean Group (PMG), Fully modified ordinary least square (FMOLS), and Dynamic least square (DOLS), for the data estimation. The ...
Purpose. This paper aims to apply regression-tree analysis to capture the nonlinear effects of corruption on economic growth. Using data of 103 countries for the period 1996-2017, the authors endogenously detect two distinct areas in corruption quality in which the members share the same model of economic growth.
Corruption is considered a strong constraint on growth and development. The academic literature, however, finds different effects of corruption on economic performance. Some research considers corruption a 'grease the wheels' instrument. In this view, corruption helps to overcome cumbersome bureaucratic constraints, inefficient provision of ...
accountability, suggesting that some forms of corruption had been curtailed. Both essays suggest that despite substantial corruption in government and fraud in private dealings economic growth was curtailed far less in America than in today's developing economies. The volume then turns to the causes and consequences of reform. Reform and ...
Abstract. Theory is divided over the effects of corruption on economic growth. However, the growing consensus based on the empirical literature is that corruption is associated with negative growth outcomes. This relationship is not necessarily linear, and causality between corruption and economic growth can run in both directions.
may undermine long-term sustainable development, economic growth and equality (Transparency International, 2014). According to (Transparency International, 2014), corruption has a direct impact on economic growth and development and indirect effects on a country's economic performance by affecting many factors fueling economic growth,