On the nonlinear relationships between shadow economy and the three pillars of sustainable development: new evidence from panel threshold analysis
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Sami Saafi
,
Ridha Nouira
Abstract
Most previous studies that examined the relationship between the size of shadow economy and the pillars of sustainable development maintained that this relationship is linear. This paper provides an empirical contribution to the literature by arguing that this relationship is likely to be nonlinear, and it might be subject to threshold effects. For this purpose, in addition to the static threshold panel model of Hansen (1999. “Threshold Effects in Non-dynamic Panels: Estimation, Testing, and Inference.” Journal of Econometrics 93 (2): 345–68), the dynamic panel threshold model suggested by Seo and Shin (2016. “Dynamic Panels with Threshold Effect and Endogeneity.” Journal of Econometrics 195 (2): 169–86) has been applied to a larger panel-data set covering 83 developed and developing countries over the 1996–2017 period. Empirical results from both models yield evidence advocating the existence of threshold effects of the shadow economy on the economic, social, and environmental dimensions of sustainable development for the global sample as well as the sub-samples of developed and developing countries. Moreover, for the global sample and developing countries, our findings show that shadow economy would spoil the three sustainable development pillars only when its size exceeds a certain threshold critical size. While, the impact for developed countries was found negative even for low levels of underground activities. These finding are shown to be robust to alternative proxies for the size of the shadow economy and have important policy implications, especially for developing countries. In these countries, a moderate size of the shadow economy might have positive spillovers on long-term growth and sustainable development. Our research also suggests that, for developing and developed countries to achieve sustainable goal 8.3, the extent of the shadow activities should be taken into account.
Acknowledgement
The authors wish to thank warmly an anonymous referee for valuable comments and suggestions.
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
List of variables, description, and sources.
| Variable | Description | Sources |
|---|---|---|
| Growth | The growth rate of real per capita GDP | World bank development indicators |
|
|
The initial level of per capita GDP | World bank development indicators |
| School | Share of population enrolled in secondary schooling | World bank development indicators |
| Inv | Ratio of investment | World bank development indicators |
| Open | Openness rate (sum of imports and exports over GDP) | World bank development indicators |
| Gov | Government spending(%GDP) | World bank development indicators |
| SE | Shadow economy size(%GDP) | Medina and Schneider (2019) estimates |
| HDI | The human development index | United nations development program |
| EF | The economic freedom index | Heritage foundation |
| Dem | The polity index | Polity IV database |
| Urban | Urban population in percentage of total population | World bank development indicators |
| EPI | The environmental performance index | The yale university center for environmental law and policy |
Summary statistics.
| Growth |
|
School | Inv | Open | Gov | SE | HDI | EF | Dem | Urb | EPI | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean | 2.1 | 17,849.85 | 80.7 | 22.04 | 67.09 | 15.5 | 29.32 | 0.96 | 61.52 | 6.54 | 59.67 | 51.1 |
| Median | 2.14 | 5917.84 | 86.7 | 21.7 | 58.15 | 15.21 | 29.87 | 0.71 | 61.5 | 77.56 | 62.29 | 47.1 |
| Max | 23.93 | 91,565.73 | 163.7 | 43.5 | 226.7 | 27.93 | 70.57 | 0.95 | 83.7 | 10 | 97.9 | 90.4 |
| Min | −15.04 | 200.63 | 5.28 | 4.45 | 16.98 | 4.13 | 5.4 | 0.84 | 21.4 | 0 | 13.22 | 17.1 |
| Std.Dev | 3.17 | 21.49 | 34.21 | 5.24 | 34.10 | 4.89 | 13.21 | 0.17 | 9.75 | 5.15 | 21.01 | 17.7 |
| Skewness | −0.49 | −0.86 | −0.34 | 0.82 | 1.74 | 0.2 | 0.21 | −0.5 | −0.2 | −10.3 | −0.37 | 0.51 |
| Kurtosis | 7.68 | 2.67 | 2.58 | 4.79 | 6.6 | 2.4 | 2.51 | 2.35 | 3.33 | 87.6 | 2.16 | 2.06 |
| Jarque-Bera | 12.36 | 22.98 | 234.42 | 32.2 | 13.58 | 24.21 | 40.48 | 28.7 | 21.39 | 2.6 | 94.9 | 48.1 |
| Observations | 1296 | 1296 | 1296 | 1296 | 1296 | 1296 | 1826 | 1826 | 1826 | 1826 | 1826 | 1826 |
Countries classification by shadow economy regime, based on estimations from column 1 of Table 1.
| Shadow below threshold | Shadow above threshold |
|---|---|
| Austria, Canada, Denmark, Finland | Algeria, Bangladech, Belgium, Bolivia, Bulgaria |
| France, Ireland, Netherlands, New Zealand | Cameroon, Colombia, Ecuador, Egypt, El Salvador |
| Norway, Sweden, Switzerland, United States | Greece, Guatemala, India, Indonesia, Iran, Italy |
| Madagascar, Malaysia, Mali, Mexico, Morocco | |
| Mozambique, Pakistan, Peru, Philippines, Poland | |
| Portugal, Romania, Senegal, Spain, Sri Lanka | |
| Trinidad and Tobago, Tunisia, Turkey, Uganda,Uruguay |
Countries classification by shadow economy regime, based on estimations from column 2 of Table 1.
| Shadow below threshold | Shadow above threshold |
|---|---|
| Albania, Algeria, Armenia, Australia, Austria | Bolivia, Gabon, Guatemala, Haiti |
| Bangladesh, Belgium, Botswana, Brazil, Bulgaria | Honduras, Nigeria, Peru, Thailand |
| Burkina Faso, Cameroon, Canada, Chile | Ukraine, Uruguay, Zimbabwe |
| China, Colombia, Costa Rica, Croatia, Denmark | |
| Dominican Republic, Ecuador, Egypt | |
| El Salvador, Finland, France, Germany | |
| Ghana, Greece, Guinea-Bissau, Hungary | |
| India, Indonesia, Iran, Ireland, Italy, Jamaica | |
| Japan, Jordan, Kenya, Madagascar, Malawi | |
| Malaysia, Mali, Mexico, Morocco, Mozambique | |
| Netherlands, New Zealand, Niger, Norway, Oman | |
| Pakistan, Paraguay, Philippines, Portugal | |
| Romania, Senegal, Sierra Leone, Slovenia | |
| South Africa, Spain, Sri Lanka, Sweden | |
| Switzerland, Togo, Tunisia, Turkey, Uganda | |
| United ArabEmirates, United Kingdom | |
| United StatesVenezuela |
Countries classification by shadow economy, based on estimations from column 3 of Table 1.
| Shadow below threshold | Shadow above threshold |
|---|---|
| Albania, Algeria, Armenia, Australia | Bolivia, El Salvador, Gabon |
| Austria, Bangladesh, Belgium, Botswana | Guatemala, Haiti, Honduras |
| Brazil, Bulgaria, Burkina Faso, Cameroon | Nigeria, Peru, Thailand, Ukraine |
| Canada, Chile, China, Colombia, Costa Rica | Uruguay, Zimbabwe |
| Croatia, Denmark, Dominican Republic | |
| Ecuador, Egypt, Finland, France | |
| Germany, Ghana, Greece, Guinea-Bissau | |
| Hungary, India, Indonesia, Iran, Ireland | |
| Italy, Jamaica, Japan, Jordan, Kenya | |
| Madagascar, Malawi, Malaysia, Mali | |
| Mexico, Morocco, Mozambique | |
| Netherlands, New Zealand, Niger | |
| Norway, Oman, Pakistan, Paraguay | |
| Philippines, Portugal, Romania, Senega | |
| l Sierra Leone, Slovenia, South Africa, Spain | |
| Sri Lanka, Sweden, Switzerland, Togo | |
| Tunisia, Turkey, Uganda, United ArabEmirates | |
| United Kingdom, United States, Venezuela |
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Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/snde-2021-0099).
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Articles in the same Issue
- Frontmatter
- Research Articles
- On determination of the number of factors in an approximate factor model
- Clean energy consumption and economic growth in China: a time-varying analysis
- Panel data models with two threshold variables
- What will drive global economic growth in the digital age?
- On the nonlinear relationships between shadow economy and the three pillars of sustainable development: new evidence from panel threshold analysis
- Tail behaviours of multiple-regime threshold AR models with heavy-tailed innovations
- Stock price prediction using multi-scale nonlinear ensemble of deep learning and evolutionary weighted support vector regression
Articles in the same Issue
- Frontmatter
- Research Articles
- On determination of the number of factors in an approximate factor model
- Clean energy consumption and economic growth in China: a time-varying analysis
- Panel data models with two threshold variables
- What will drive global economic growth in the digital age?
- On the nonlinear relationships between shadow economy and the three pillars of sustainable development: new evidence from panel threshold analysis
- Tail behaviours of multiple-regime threshold AR models with heavy-tailed innovations
- Stock price prediction using multi-scale nonlinear ensemble of deep learning and evolutionary weighted support vector regression
