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Monetary Valuations of Life Conditions in a Consistent Framework: The Life Satisfaction Approach

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Abstract

The life satisfaction approach (LSA) enables researchers to attach monetary values to nonmarket circumstances that affect people’s life satisfaction. Previous studies employ LSA to examine how the environment, health, and social capital, etc. affect life satisfaction. This study focuses on the OECD Better Life Index and intends to evaluate more comprehensive socioeconomic characteristics. Considering comprehensive factors about life enables us to avoid biased estimation. This study also considers more consistent estimation methods. Previous studies tend to attach monetary valuations in different settings. Thus, it is difficult to compare the effects of different socioeconomic circumstances. This study employs LSA to estimate the happiness functions of Japanese survey respondents by incorporating extensive socioeconomic characteristics as explanatory variables. Controlling for multiple factors affecting subjective wellbeing, we more accurately attach monetary values to each factor within a consistent analytical framework. By doing so, we assess factors’ relative comparable influence on subjective wellbeing. We adopt three estimation procedures to check robustness against model specifications. Results indicate that respondents value small changes in their socioeconomic circumstances more highly than other factors. Especially, circumstances surrounding quality of life carry higher equivalent monetary valuations than material living standards.

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Notes

  1. The terms “happiness,” “subjective wellbeing,” and “life satisfaction” are used interchangeably, as is common in the literature.

  2. Menz and Welsch (2010) and Menz (2011) employ aggregate cross-country data to provide a monetary evaluation of an increment of PM10 concentration. Ambrey and Fleming (2013) deal with greenery in Australia’s main cities (public parks, community gardens, cemeteries, sports fields, national parks, and wilderness areas).

  3. Ferrer-i-Carbonell and van Praag (2002) use German household panel data to compute a monetary evaluation of various types of illness such as diabetes and epilepsy. Dolan et al. (2013) also apply LSA for various illness under different measures of wellbeing.

  4. Powdthavee (2008) computes the monetary values gained by an increase in the frequency of interaction with friends, relatives, and neighbors.

  5. Ambrey et al. (2013) show a one-person decrease in the number of annual property crime victims per 1000 members of the population in New South Wales in Australia has an annual household income equivalent of $3135. Cohen (2008) conducts a similar analysis using data for the US.

  6. OECD (2011) discusses how to select the underlying indicators.

  7. Subjective well-being is also one of the well-being topics considered essential. Thus, OECD specified 11 topics in total. However, as we are interested in socioeconomic conditions affecting people’s SWB, we focus on 10 topics listed in Table 1.

  8. A related study is by Plagnol (2011), who finds that assets relate positively to economic satisfaction only for the elderly, whereas income relates positively among all ages.

  9. Putnam (2000) finds that social capital has declined over the past several decades in the United States. Paxton (1999), Rothstein (2001), and Costa and Kahn (2003) find similar trends in Europe. Reduced social capital accompanied by economic growth is considered one explanation for the Easterlin Paradox (Helliwell 2003; Pugno 2009).

  10. People possessing high life satisfaction may volunteer more, so a reverse causality might exist between life satisfaction and volunteering. Meier and Stutzer (2008) show that volunteering enhances life satisfaction by taking advantage of a natural experiment: German reunification.

  11. They call this direct effect “self-confidence” obtained by knowledge accumulation.

  12. Other studies that consider air pollution include those by Rehdanz and Maddison (2008), MacKerron and Mourato (2009), Luechinger (2009, 2010), Ferreira and Moro (2010), Menz (2011), Ferreira et al. (2013), and Ambrey et al. (2014).

  13. BLI incorporates only household financial wealth. However, as non-financial assets account for much of total wealth, we adopt the sum of financial and non-financial assets as our measure of total household wealth.

  14. The regression approach based on an ordered probit model assumes that the happiness function is constant across countries. The two other approaches assume that it differs.

  15. Following a similar approach, we can derive monetary valuations for changes in individual attributes (\(n\), \(SP_{n}\)) such as \(SP_{n} = - dy/dz_{n} = \left( {\partial f/\partial z_{n} } \right) / \left( {\partial f/\partial y } \right)\).

  16. Mizobuchi (2014) is the first to apply DEA to estimate macroeconomic happiness function.

  17. We can also derive monetary valuations for a change in demographic characteristics \(\left( n \right)\) such as \(WTP_{n} = - \gamma_{n} /\beta\).

  18. See Das and Soest (1999) and Ferrer-i-Carbonell and Frijters (2004).

  19. This treatment of individual characteristics comes from the original SFA study measuring firms’ productive efficiency. In that setting, \(v^{k}\) indicates the inefficiency term, and smaller values mean less efficient firms. Just as efficient firms can produce more from given inputs, intrinsically happy people might report higher life satisfaction in any given socioeconomic situation.

  20. See Cooper et al. (2011).

  21. To apply DEA, each explanatory variable must exceed 0. As these variables increase, the happiness function should increase. Socioeconomic variables associated with employment and working hours do not satisfy this condition, as we sampled many persons who are unemployed and students or housewives who are not employed outside the home. They are treated in the same manner as demographic variables.

  22. By construction, \(v^{k}\) is positive, and lower values of \(v^{k}\) indicate that respondent \(k\) reports relatively higher SWB from given socioeconomic circumstances. Moreover, \(v^{k}\) is normalized in a range between 0 and 1. This procedure differs from the treatment in SFA.

  23. For example, we adopt the following approximation \(\frac{\partial f}{{\partial x_{m} }} \approx \frac{{\left( {g\left( {x_{m}^{k} ,\varvec{x}_{ - m}^{k} } \right) - g\left( {0,\varvec{x}_{ - m}^{k} } \right)} \right)h\left( {\varvec{z}^{k} } \right)}}{{x_{m}^{k} }}\), here \(\varvec{x}_{ - m} = \left( {x_{1}^{k} , \ldots ,x_{m - 1}^{k} ,x_{m + 1}^{k} , \ldots ,x_{M}^{k} } \right)\).

  24. As \(v^{k}\) increases, the corresponding SWB \(SWB^{k} = f^{k} \left( {\varvec{x}^{k} ,y^{k} } \right)v^{k}\) declines.

  25. All monetary values hereafter are converted into 2013 US$ using the CPI-U.

  26. Exchange rate was approximately 100 yen to the dollar in November 27 and 30, 2013.

  27. Their estimation is small compared with that of other studies: the results by Ferreira and Moro (2010) of approximately US$1500 for Ireland and the results by Levinson (2012) of US$497 to US$964 for the US.

  28. We focus on two approaches that derived comparable estimates. DEA computed shadow prices for more socioeconomic characteristics but is likely to go beyond the range of OPM and SFA. Furthermore, the fact that the happiness function estimated by DEA is not differentiable makes it difficult to do the same exercise for DEA.

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Acknowledgments

We appreciate Hideyuki Mizobuchi of Ryukoku University for computing results applying DEA. The authors thank Stephanie Rossouw (co-editor), and the anonymous referees for helpful comments.

Funding

This research was partially funded by a Grant-in-Aid for Specially Promoted Research (26000001) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan, and Ministry of Environment, Japan. The results and conclusions of this paper do not necessarily reflect the views of the funding agency.

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Authors and Affiliations

  1. Nanzan University, Seto, Aichi, Japan

    Tetsuya Tsurumi

  2. Urban Research Center, Departments of Civil Engineering, School of Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, Japan

    Shunsuke Managi

  3. QUT Business School, Queensland University of Technology, Brisbane, Australia

    Shunsuke Managi

Authors
  1. Tetsuya Tsurumi
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  2. Shunsuke Managi
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Correspondence to Tetsuya Tsurumi.

Appendix

Appendix

See the Table 8.

Table 8 Prefectural average monetary valuations based on DEA
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Tsurumi, T., Managi, S. Monetary Valuations of Life Conditions in a Consistent Framework: The Life Satisfaction Approach. J Happiness Stud 18, 1275–1303 (2017). https://doi.org/10.1007/s10902-016-9775-4

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