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


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.

This is a preview of subscription content, log in to check access.


  1. 1.

    Michaelson (2012, p. 1).

  2. 2.

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

  3. 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. 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. 5.

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

  6. 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. 7.

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

  8. 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. 9.

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

  10. 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. 11.

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

  12. 12.

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

  13. 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. 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. 15.

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

  16. 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. 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. 18.

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


  1. Adeoti, A. I. (2014). Trend and determinants of multidimensional poverty in rural Nigeria. Journal of Development and Agricultural Economics.

    Article  Google Scholar 

  2. Alkire, S., & Foster, J. (2011). Understanding and misunderstanding of multidimensional poverty measurement. OPHI Working Paper, No. 43. Accessed July 23, 2014.

  3. Asian Development Bank. (n.d). Country Poverty Analysis. Accessed August 17, 2016.

  4. Ataguba, J. E., Ichoku, H. E., & Fonta, W. M. (2013). Multidimensional poverty assessment: applying the capability approach. International Journal of Social Economics, 40(4), 331–354.

    Article  Google Scholar 

  5. Banerjee, A. N., Banik, N., & Mudhopadhyay, J. P. (2015). The dynamics of income growth and poverty: Evidence from districts in India. Development Policy Review, 33(3), 293–312.

    Article  Google Scholar 

  6. Benfield, W. A. (2008). Determinants of poverty and subjective well-being. Social and Economic Studies, 57(3/4), 1–51. Accessed June 10, 2014.

  7. Berger, P. L., & Kellner, H. (1964). Marriage and the construction of reality: An exercise in the microsociology of knowledge. Diogenes, 46, 1–24.

    Article  Google Scholar 

  8. Bridge, S. (2013). Live in the country? Then you’re happier than your city-dwelling friends…Rural folk more optimistic than those in urban areas. Accessed September 19, 2014.

  9. Bruck, T., & Kebede, S. W. (2013). Dynamics and drivers of consumption and multidimensional poverty: Evidence from Rural Ethiopia. IZA Discussion Paper, No. 7364. Accessed September 22, 2014.

  10. Çaglayan, E., Kosan, N. I., & Astar, M. (2012). An empirical analysis of the determinants of household poverty in Turkey. Asian Economic and Financial Review. 2(1), 181–191.,2%281%29,%20pp.181-192.pdf. Accessed August 12, 2014.

  11. Caporale, G. M., Georgellis, Y., Tsitsianis, N., & Yin, Y. P. (2009). Income and happiness across Europe: Do reference values matter? Journal of Economic Psychology, 30(1), 42–51.

    Article  Google Scholar 

  12. Chyi, H., & Mao, S. (2012). The determinants of happiness of China’s elderly population. Journal of Happiness Studies, 13(1), 167–185.

    Article  Google Scholar 

  13. Coromaldi, M., & Zoli, M. (2012). Deriving multidimensional poverty indicators: Methodological issues and an empirical analysis for Italy. Social Indicators Research, 107, 37–54.

    Article  Google Scholar 

  14. Dartanto, T., & Nurkholis, N. (2013). The determinants of poverty dynamics in Indonesia: Evidence from panel data. Bulletin of Indonesian Economic Studies, 49(1), 61–84.

    Article  Google Scholar 

  15. Dema, K. (2013). Lowering the boom (p. 3). Thimphu: Kuensel.

    Google Scholar 

  16. Dhamija, N., & Bhide, S. (2013). Poverty in Rural India: Variations in factors influencing dynamics of chronic poverty. Journal of International Development, 25(5), 674–695.

    Article  Google Scholar 

  17. Di Tella, R., McCulloch, R., & Oswald, A. (2001). Preferences over inflation and unemployment: Evidence from surveys of happiness. American Economic Review, 19(1), 335–341. Accessed June 23, 2014.

  18. Easton, M. (2006, May 2). Britain’s happiness in decline. BBC news. Accessed April 10, 2014.

  19. Elmslie, B. T., & Tebaldi, E. (2014). The determinants of marital happiness. Applied Economics, 46(28), 3452–3462.

    Article  Google Scholar 

  20. Etim, N. A., & Edet, G. E. (2014). Factors determining urban poverty and farming households in a tropical region. American Journal of Experimental Agriculture, 4(3), 322–335.

    Article  Google Scholar 

  21. Feeny, S., McDonald, L., & Posso, A. (2014). Are poor people less happy? Findings from Malanesia. World Development, 64, 448–459.

    Article  Google Scholar 

  22. Fredrickson, B. L. (2004). The broaden-and-build theory of positive emotion. Philosophical Transaction of the Royal Society B: Biological Sciences, 359(1449), 1367–1377.

    Article  Google Scholar 

  23. Gerdtham, U. G., & Johannesson, M. (2001). The relationship between happiness, health, and socio-economic factors: results based on Swedish microdata. Journal of Socio-Economics, 30(6), 553–557.

    Article  Google Scholar 

  24. Giri, S. (2004). The vital link: Monpas and their forests. Thimphu: Galing Printing & Publishing.

    Google Scholar 

  25. Goh, C., Luo, X., & Zhu, N. (2009). Income growth, inequality and poverty reduction: A case study of eight provinces in China. China Economic Review, 20(3), 485–496.

    Article  Google Scholar 

  26. Graham, C., & Pettinato, S. (2002). Frustrated achievers: Winners, losers and subjective well-being in new market economies. The Journal of Development Studies, 38(4), 100–140.

    Article  Google Scholar 

  27. Helliwell, J. F. (2002). How’s life? Combining individual and national variables to explain subjective well-being. Economic Modelling, 20(2), 331–360.

    Article  Google Scholar 

  28. Helliwell, J. F., & Wang, S. (2011). Trust and wellbeing. International Journal of Wellbeing, 1(1), 42–78.

    Article  Google Scholar 

  29. Kingdon, G. G., & Knight, J. (2006). Subjective well-being poverty vs. income poverty and capabilities poverty? Journal of Development Studies, 42(7), 1199–1224.

    Article  Google Scholar 

  30. Lever, J. P., Pinol, N. L., & Uralde, J. H. (2005). Poverty, Psychological resources and subjective well-being. Social Indicators Research, 73(3), 375–408.

    Article  Google Scholar 

  31. Lewbel, A. (2012). Using heteroscedasticity to identify and estimate mismeasured and endogenous regressor models. Journal of Business and Economic Statistics, 30(1), 67–80.

    Article  Google Scholar 

  32. Lewbel, A. (2016). Identification and estimation using heteroscedasticity without instruments: The binary endogenous regressor case. Boston College Economics Working Paper 927.

  33. Michaelson, J. (2012). The Importance of measuring well-being. Accessed July 16, 2014.

  34. Michalos, A. C. (2008). Education, happiness and well-being. Social Indicators Research, 87(3), 347–366.

    Article  Google Scholar 

  35. Mukherjee, S., & Benson, T. (2003). The determinants of poverty in Malawi, 1998. World Development, 31(2), 339–358.

    Article  Google Scholar 

  36. National Statistics Bureau. (2012). Poverty analysis report. Thimphu: Author.

    Google Scholar 

  37. National Statistics Bureau. (2014). Bhutan: Multidimensional poverty index 2012. Thimphu: Author.

    Google Scholar 

  38. Neff, D. F. (2007). Subjective well-being, poverty and ethnicity in South Africa: Insight from an exploratory analysis. Social Indicators Research, 80(2), 313–341.

    Article  Google Scholar 

  39. Odingo, A. O. (2009). Determinants of poverty: Lessons from Kenya. GeoJournal, 74(4), 311–331.

    Article  Google Scholar 

  40. Oshio, T., & Kobayashi, M. (2010). Income inequality, perceived happiness, and self-rated health: Evidence from nationwide surveys in Japan. Social Science and Medicine, 70, 1358–1366.

    Article  Google Scholar 

  41. Oxford Poverty and Human Development Initiative. (2013). Nepal country briefing. Accessed July 5, 2014.

  42. Pankaj, P., & Dorji, T (2004). Measuring individual happiness in relation to Gross National Happiness in Bhutan: Some preliminary results from survey data. Accessed May 24, 2014.

  43. Preidt, R. (2009). As literacy improves, so might happiness. Accessed June 2, 2014.

  44. Putnam, R. (1995). Bowling alone: America’s declining social capital. Journal of Democracy, 6(1), 65–78.

    Article  Google Scholar 

  45. Robey, B. (1990). Family size and well-being: Evidence from Thailand. Asia-Pacific Population Policy, 12, 1–4. Accessed July 10, 2014.

  46. Rojas, M. (2011). Poverty and psychological distress in Latin America. Journal of Economic Psychology, 32(2), 206–217.

    Article  Google Scholar 

  47. Royo, M. G., Velazco, J., & Camfield, L. (2013). Basic needs and wealth as independent determinants of happiness: An illustration from Thailand. Social Indicator Research, 110, 517–536.

    Article  Google Scholar 

  48. Rupasingha, A., & Goetz, S. J. (2007). Social and political forces as determinants of poverty: A spatial analysis. The Journal of Socio-Economics, 36(4), 650–671.

    Article  Google Scholar 

  49. Ruprah, I. J., & Luengas, P. (2011). Monetary policy and happiness: Preference over inflation and unemployment in Latin America. The Journal of Socio-Economics, 40(1), 59–66.

    Article  Google Scholar 

  50. Ryan, J. (1981). Marital status, happiness, and anomia. Journal of Marriage and Family, 43(3), 643–649.

    Article  Google Scholar 

  51. Sarracino, F. (2013). Determinants of subjective well-being in high and low income countries: Do happiness equations differ across countries? The Journal of Socio-Economics, 42, 51–66.

    Article  Google Scholar 

  52. Senik, C. (2014). Wealth and happiness. Oxford Review of Economic Policy, 30(1), 92–108.

    Article  Google Scholar 

  53. Shaw, D. M. (2009). Euthanasia and eudaimonia. Journal of Medical Ethics, 35(9), 530–533.

    Article  Google Scholar 

  54. Silva, I. D. (2008). Micro-level determinants of poverty reduction in Sri Lanka: a multivariate approach. International Journal of Social Economics, 35(3), 140–158.

    Article  Google Scholar 

  55. Treffgarne, C. (2002). Improving livelihoods for the poor: the role of literacy. Accessed June 3, 2014.

  56. United Nations Development Program. (2013). The rise of south: Human progress in a diverse world. Accessed June 8, 2014.

  57. Ura, K. (2005). Beneficial labour contribution (woola). Thimphu: Centre for Bhutan Studies.

    Google Scholar 

  58. Ura, K., Alkire, S., Zangmo, T., & Wangdi, K. (2012). Short guide to GNH Index. Thimphu: Centre for Bhutan Studies.

    Google Scholar 

  59. Waite, L. J., & Gallagher, M. (2000). The case for marriage. New York: Doubleday.

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Jigme Nidup.



See Tables 5, 6, 7, 8, 9 and 10.

Table 5 Variable definition
Table 6 Summary statistics
Table 7 Cross tabulation of various well-being measures
Table 8 Lewbel’s instrument variable regression (ivreg2 h) results
Table 9 Marginal effects (mfx) from the base model
Table 10 Variance inflation factor and tolerance level

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation


  • Bhutan
  • Happiness
  • Income poverty
  • Multidimensional poverty
  • Perceived poverty
  • Well-being