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Income distribution and total factor productivity: a cross-country panel cointegration analysis

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Abstract

This study investigates the relationship between income inequality and total factor productivity (TFP) across countries for a period covering the years 1990 to 2014. The research objective is to empirically assess the skill-biased technological change argument which supports the increasing income/wage inequality that has boosted productivity in recent decades. To achieve this objective, we utilized panel cointegration tests and a fully modified OLS and rolling window OLS regression. The findings show that income inequality significantly deters TFP in the long-term in developing countries. We found no evidence that income inequality affects TFP in the long-term in developed countries. These findings suggest that developing countries that are experiencing prolonged periods of rising income inequality are more exposed to: (i) low productivity and growth, (ii) a high risk of increase in the extreme poverty rate.

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Notes

  1. The 2018 World Inequality Report contains useful information on the evolution of income distribution across countries from 1980 to 2016. The report, data series and notes can be found online at the following link: wir2018.wid.world.

  2. There exist other determinants of productivity growth that are not emphasized on in this study. For instance, Kaldor (1966) point out on the positive effects of output growth on productivity growth due to existing increasing returns to scale in the sector of manufacturing. This is referred in the literature as the Kaldor-Verdoorn’s Law. Several studies have reported evidence in support of this law (McCombie 1983; Angeriz et al. 2009). Particularly, Romero and Britto (2017) investigate the productivity growth effects of output growth and research intensity in 15 OECD countries. The estimates provide strong evidence of the importance of demand growth for productivity growth. Second, the results indicate the existence of increasing returns to scale in the sector of manufacturing. Third, the study recognizes the positive effect of research intensity for productivity growth.

  3. Several studies have been conducted over the past decades on panel unit root and cointegration testing. The development of those studies was facilitated by the availability of newly released datasets that cover a long-time span and a large set of countries. Over the years, two generations of tests have been developed for panel unit root. The most notable tests for the first generation are Levin et al. (2002), Im et al. (2003) and the Fisher-type of tests suggested by Maddala and Wu (1999). The key limitation is that all the first-generation tests are all developed under the strict assumption of cross-sectional independence across panel units. In contrast, the second generation of tests rejects the assumption of cross-sectional independence and assumes that cross-units tend to cluster over time. Among the tests of the second generation we have Chang (2004), Bai and Ng (2004), Phillips and Sul (2003), Moon and Perron (2004) and Pesaran (2004).

  4. The lag order is automatically obtained via Schwarz information criteria (SBC) or Akaike information criteria (AIC) by starting qi =8.

  5. The confidence interval allows one to decide whether the estimated marginal effect is significant (positive/negative) or not. The rule of thumb is that an estimate is considered positive and significant if the lower bound of the confidence interval is beyond the line of zero. It can also be negative and significant if the upper bound of the confidence interval is below the line of zero. Finally, an estimate is considered insignificant if the confidence interval does not distance the line of zero.

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  1. School of Economics and Econometrics, University of Johannesburg, Auckland Park Kingsway Campus, PO Box 524, Auckland Park, Johannesburg, South Africa

    Delphin Kamanda Espoir & Nicholas Ngepah

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  1. Delphin Kamanda Espoir
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Correspondence to Delphin Kamanda Espoir.

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The authors declare no conflict of interest. The School of Economics and Econometrics of the University of Johannesburg had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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Appendices

Appendix 1 List of developed countries

Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Netherland, New Zealand, Norway, Spain, Sweden, Switzerland, the United Kingdome, the United States.

1.1 List of developing countries.

Armenia, Bolivia, Botswana, Brazil, Bulgaria, Central African Republic, Chile, China, Colombia, Costa Rica, Croatia, Czech Republic, Côte d’Ivoire, Dominican Republic, Ecuador, Estonia, Fiji, Guatemala, Honduras, Hungary, Indonesia, Iran, Jamaica, Jordan, Kazakhstan, Kenya, Kyrgyzstan, Lao People’s DR, Latvia, Lesotho, Lithuania, Malaysia, Mauritania, Mauritius, Mexico, Moldova, Mongolia Morocco, Mozambique, Namibia, Nicaragua, Niger, Nigeria, Panama, Paraguay, Peru, Philippines, Poland, Romania, Russian, Senegal, Serbia, Sierra Leone, Slovakia, Slovenia, South Africa, Sri Lanka, Swaziland, Tajikistan, Tanzania, Thailand, Trinidad and Tobago, Tunisia, Turkey, Ukraine, Uruguay, Bolivarian Republic of Venezuela.

Appendix 2

Table 8 Panel (2015) results for weak cross-sectional dependence
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Table 9 Results of ADF unit root test (model with intercept)a
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Espoir, D.K., Ngepah, N. Income distribution and total factor productivity: a cross-country panel cointegration analysis. Int Econ Econ Policy 18, 661–698 (2021). https://doi.org/10.1007/s10368-021-00494-6

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