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Improving Well-Being in Bhutan: A Pursuit of Happiness or Poverty Reduction?

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Abstract

Increasing happiness is a key priority for the Bhutanese government. This priority displaces more traditional (economic) objectives such as the pursuit of income growth and the reduction of income poverty. This paper examines the implications of this approach by examining whether there are common correlates of the four following measures of human well-being in Bhutan: income poverty; multidimensional poverty; perceived poverty; and happiness. Our findings suggest that whilst there is a degree of commonality, determinants of the different measures of well-being are distinct. Common factors include having a savings account, levels of literacy and household size. Further we show that higher levels of income poverty, multidimensional poverty and perceived poverty are found to be negatively associated with happiness. Importantly, our findings suggest that a focus on increasing happiness might come at the expense of improving other measures of wellbeing.

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Notes

  1. Michaelson (2012, p. 1).

  2. It is noted that poverty can also be reduced via income redistribution in addition to growth in incomes.

  3. Commonly, happiness takes on a subjective form measured using a single question such as “In general, how satisfied or happy are you with your life?” Likert scale responses are elicited.

  4. We note this is lower than other comparable countries such as Nepal, where in 2011 poverty rates were measured at 25.2 per cent (Asian Development Bank [ADB], n.d) and multidimensional poverty at 44.2 per cent (Oxford Poverty and Human Development Initiative [OPHDI] 2013).

  5. Levels of education in the Bhutanese context include primary school, high school, degree colleges and vocational and post-graduate education.

  6. Primary Sampling Units (PSUs) are taken from Population and Housing Census of Bhutan and comprised chiwogs (villages) in rural areas and urban block counts in urban areas.

  7. Ngultrum (Nu) is the Bhutanese currency unit. The equivalent US$ PPP would be 71.09 per person per month.

  8. Bivariate probit and bivariate ordered probit regressions are also estimated as a means for testing the robustness of the univariate regression approach. Results are not presented in the paper but are available from the authors on request. Findings are consistent across the different forms fitted.

  9. Similar social setup here means having similar living standards, neighbourhood relationships, similar religious faiths and living in the same locality.

  10. Since perceived poverty and happiness are based on the response from the household head, explanatory variables such as, literacy, education, employment and marital status are also based on the household head for the univariate regression on perceived poverty and happiness.

  11. Under certain conditions, Lewbel (2016) demonstrates that this technique can be used where both the outcome and regressor are binary.

  12. The results are not provided in this article but can be provided on request from the corresponding author.

  13. Ideally, an adjusted pseudo R-squared would be compared to account for the inclusion of additional explanatory variables. Unfortunately, it was not possible to obtain these statistics from the ordered probit models.

  14. When interpreting the coefficients we are mindful that the dependent variable in each case is either ordinal or bivariate and therefore technically must be interpreted differently from traditional regression estimates. We have been careful to interpret the results with this in mind deliberately using the term association rather than causation as typically done in regression analysis. In addition we have been careful to contextualise our results in the broader literature. This interpretative approach empowers the reader to discern for themselves the validity of our discussion.

  15. The variables Age, Gender and Sickness are not included since the coefficients on these variables were never statistically significant in preliminary analyses.

  16. Since there are only 20 districts, degrees of freedom are of no concern. For example, the specifications with district dummies have 8,808 degrees of freedom (df) when all other well-being measures are included in the model.

  17. It is to be noted that marginal effects for happiness and perceived poverty are generated for their highest orders, i.e. when happiness is taking the rank of 5 and perceived poverty taking the rank of 4.

  18. Results from the Lewbel estimations should be interpreted with caution since they failed to pass the Sargan (overidentification) test.

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Authors and Affiliations

  1. Central Australian College, 8 Cross Street, Footscray West, VIC, 3012, Australia

    Jigme Nidup

  2. School of Economics, Finance and Marketing, RMIT University, 445 Swanston St., Melbourne, VIC, 3000, Australia

    Simon Feeny & Ashton de Silva

Authors
  1. Jigme Nidup
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  2. Simon Feeny
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  3. Ashton de Silva
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Correspondence to Jigme Nidup.

Appendix

Appendix

See Tables 5, 6, 7, 8, 9 and 10.

Table 5 Variable definition
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Table 6 Summary statistics
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Table 7 Cross tabulation of various well-being measures
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Table 8 Lewbel’s instrument variable regression (ivreg2 h) results
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Table 9 Marginal effects (mfx) from the base model
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Table 10 Variance inflation factor and tolerance level
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Nidup, J., Feeny, S. & de Silva, A. Improving Well-Being in Bhutan: A Pursuit of Happiness or Poverty Reduction?. Soc Indic Res 140, 79–100 (2018). https://doi.org/10.1007/s11205-017-1775-1

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