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Inclusive wealth, total factor productivity, and sustainability: an empirical analysis


Sustainability can be assessed by non-declining inclusive wealth, which refers to man-made capital, human capital, natural capital, and all other types of capital that are sources of human well-being. As the previous studies—including Arrow et al. (J Econ Perspect 18(3):147–172, 2004) and the Inclusive Wealth Report (2012 and 2014)—suggest, total factor productivity (TFP) is one determinant of inclusive wealth, because it is related to the resource allocation mechanism. TFP is one important component of sustainability. When considering the contribution of TFP toward inclusive wealth, attention needs to be paid to the improvement in the usage of human and natural capital as well as the traditional man-made capital. However, in the previous studies, only man-made capital and labor force have been considered. This study extends current measures of sustainability by capturing the efficient utilization of natural resources, giving us inclusive wealth-based TFP. Therefore, in contrast to conventional TFP measures, we consider both human and natural capital in addition to man-made capital. We examine 43 countries and find that a new indicator which asserts countries previously considered sustainable by earlier studies such as Arrow et al. (J Econ Perspect 18(3):147–172, 2004) as no longer sustainable.

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  1. 1.

    GS is also called genuine investment (Arrow et al. 2003), inclusive investment (Dasgupta 2007), and adjusted net savings (World Bank 2006). However, these terms indicate a change in wealth as a source of well-being. These terms are sometimes used interchangeably, but some studies, e.g., Engelbrecht (2016), pay attention to the difference in each concept. The recent research project by the United Nations Development Corporation (UNDC) developed an inclusive wealth index and stressed the difference between this concept and GS in theoretical assumptions and empirical techniques (see UNU-IHDP and UNEP 2012, 2014). Since we need complete panel data to estimate TFP in our study, we use the data provided by the World Bank. The database is available at

  2. 2.

    Xepapadeas and Vouvaki (2009) apply GDP as an output and, therefore, their externality-adjusted TFP is different from that proposed in this study. Barndt et al. (2014) also estimated TFP considering natural capital and externality using the data of CO2, NOx, and SOx, but the output is also production of goods.

  3. 3.

    Dasgupta (2004) provides mathematical proof why non-declining IW coincides with non-declining V.

  4. 4.

    As explained in detail in Sect. 3, the World Development Indicators (WDI) database includes man-made capital, human capital, and natural capital. Each is measured by net national investment, education expenditure as a proxy for human capital, and a few types of natural capital depletions, such as energy depletion, mineral depletion, forest depletion, and carbon dioxide damage. Other capitals related to human well-being, such as knowledge capital and institution, are omitted from our formulation.

  5. 5.

    Arrow et al. (2012) also consider the effect of TFP. However, because they adopted another data handling approach, which is not generally available, we use Arrow et al. (2004) to focus on the different contributions of GDP- and IW-based TFP.

  6. 6.

    It may be misleading to use the term IW-based “TFP”, because this concept includes both production and output use. However, because we follow the same procedure for estimation, we use the term TFP.

  7. 7.

    Using the same strategy, Jumbri and Managi (2016) estimate TFP using stock data. On the other hand, this paper focuses on the effect of TFP toward temporal change in inclusive wealth. It measures the transformation technology from input to output and consumption (investment) propensity.

  8. 8.

    In general, variables including negative values cannot apply to DEA estimation. However, variables related to IW (GS, human capital, and natural capital) include negative value. In this study, we use the variables that are weighted by the minimum value of each variable. Previous studies use the same way to treat the negative value in DEA estimation (for example, Ali and Seiford 1990). Recent studies developed and applied a more suitable way to consider the negative value in DEA estimation (for example, Kerstens and Van de Woestyne 2011; Fujii and Managi, 2016). Thus, our estimation has the possibility to incur bias by the difference of weighting in each variable. Such biases become more severe in the both-oriented model that previous studies focus on. However, our model employs an output-oriented model based on only one output. Therefore, biases that were considered in previous studies are not strong in this estimation.

  9. 9.

  10. 10.

    Hence, our data set evaluates all variables in 2000 US dollars.

  11. 11.

    Based on the primary estimation by Sato (2015), we obtained the TFP results for each year from 1971 to 2004. Due to the space limitation, however, we only provided averages in Table 1. The overall result is available upon request.

  12. 12.

    We use data of the International Country Risk Guide ( for the institution variables.


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The authors also would like to express their gratitude to two anonymous reviewers and the editor for constructive and helpful comments and suggestions to improve the paper. This research was financially supported by Grant-in-Aid for Specially Promoted Research (26000001) by Japan Society for the Promotion of Science (JSPS).

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Correspondence to Masayuki Sato.

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Sato, M., Tanaka, K. & Managi, S. Inclusive wealth, total factor productivity, and sustainability: an empirical analysis. Environ Econ Policy Stud 20, 741–757 (2018).

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  • Sustainable development
  • Inclusive wealth
  • Malmquist index
  • Efficiency
  • Total factor productivity

JEL Classification

  • Q56
  • O33
  • O47