Skip to main content

We Do Not Have the Same Tastes! Evaluating Individual Heterogeneity in the Preferences for Amenities

Journal of Happiness Studies volume 21, pages 53–74 (2020)Cite this article

Abstract

It is widely recognized by scholars that amenities affect the individual well-being. Ample empirical evidence has been provided for developed countries, although this analysis for developing economies is scant. The aim of this paper is to study the association between locational specific characteristics and self-reported measures of subjective well-being. We focus our analysis in Chile, a developing country located in South America endowed with a rather heterogeneous set of amenities across cities. Using data from several sources to account for both natural and urban city amenities along with individual traits, our first results suggest that natural and urban amenities do affect the level of subjective well-being across Chilean cities. Afterwards, we allow for the estimated parameters associated to amenities vary to characterize the whole distribution rather than a single average parameter. This analysis uncovers the existence of unobserved individual heterogeneity, that is, individuals display different tastes for amenities not captured by observed traits, and consequently compensating variations associated to amenities differ across the sample. These results provide valuable elements to policy makers and city planners to the design of policies that enhance the population well-being and to the understanding of the development of cities in developing economies.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2

Notes

  1. The concept of SWB, defined as “a person’s cognitive and affective evaluations of his or her life” (Diener et al. 2005), has been used in different fields such as economics, sociology and psychology to evaluate the key factors affecting individuals’ well-being. In general, SWB is measured by survey questions about individuals’ self-perception about different domains such as life satisfaction, happiness and quality of life. Although, some debate of the correct interpretation of each of these domains persists (see for example, Veenhoven 2000, 2017), in economics—the field from which the theoretical framework used in this article is derived—the measurement of individual welfare using data on reported subjective well-being has become popular and has served as an empirically adequate and valid approximation for individually experienced welfare (Frey et al. 2009). Furthermore, the empirical evidence seems to support the theoretical link between SWB and the unobserved utility. For example, Benjamin et al. (2014) and Perez-Truglia (2016) using behavioral experiments and observational data, respectively, find that SWB is a reliable proxy for individuals’ utility and preferences. Thus, in the economics literature, these concepts are often used interchangeably (see Di Tella and MacCulloch 2006; Ferreira and Moro 2010) and we adopt the same approach in this study.

  2. In general, studies using a random coefficient approaches use stated preferences surveys.

  3. Although the CASEN has been conducted every 2 or 3 years since 1987, we only use the 2013 version because it is the only wave containing questions about individuals’ life satisfaction.

  4. For some applications, see Falco et al. (2015) and Greene et al. (2014).

  5. Note that for each random parameter we need an integral.

  6. For dummy variables, the marginal effects correspond to a discrete change from 0 to 1. The marginal effects for the rest of the categories are presented in Table 5 in “Appendix”.

  7. Standard errors for the marginal effects are computing using Delta Method.

  8. This ratio was multiplied by 10,000 to get a meaningful measure.

  9. This is computed as \(100 \times \varPhi \left( {\frac{{\hat{\alpha }_{k} }}{{\hat{\sigma }_{k} }}} \right),\) where \(\varPhi\) is the cumulative standard normal distribution, \(\hat{\alpha }_{k}\) and \(\hat{\sigma }_{k}\) are the mean and the standard deviation of the coefficients estimated in column 3 of Table 3, respectively.

  10. The unconditional distributions were computed using 10,000 draws from the estimated distribution.

  11. For more details about the CV distribution, please see Daly et al. (2012).

References

  • Albouy, D., Graf, W., Kellogg, R., & Wolff, H. (2016). Climate amenities, climate change, and American quality of life. Journal of the Association of Environmental and Resource Economists,3(1), 205–246.

    Google Scholar 

  • Ambrey, C., & Fleming, C. (2014). Public greenspace and life satisfaction in urban Australia. Urban Studies,51(6), 1290–1321.

    Google Scholar 

  • Ardeshiri, A., Willis, K., & Ardeshiri, M. (2018). Exploring preference homogeneity and heterogeneity for proximity to urban public services. Cities,81, 190–2002. https://doi.org/10.1016/j.cities.2018.04.008.

    Article  Google Scholar 

  • Benjamin, D. J., Heffetz, O., Kimball, M., & Szembrot, N. (2014). Beyond happiness and satisfaction: Toward well-being indices based on stated preference. American Economic Review,104(9), 2698–2735.

    Google Scholar 

  • Benjamin, D. J., Kimball, M. S., Heffetz, O., & Rees-Jones, A. (2012). What do you think would make you happier? What do you think you would choose? American Economic Review,102(5), 2083–2110.

    Google Scholar 

  • Berger, M. C., Blomquist, G. C., & Sabirianova Peter, K. (2008). Compensating differentials in emerging labor and housing markets: Estimates of quality of life in Russian cities. Journal of Urban Economics,63(1), 25–55.

    Google Scholar 

  • Bishop, K., & Timmins, C. (2011). Hedonic prices and implicit markets: Estimating marginal willingness to pay for large by. NBER working papers, 17611, pp. 1–38.

  • Blomquist, G. C., Berger, M. C., & Hoehn, J. P. (1988). New estimates of quality of life in urban areas. American Economic Review,78(1), 89–107.

    Google Scholar 

  • Boxall, P. C., & Adamowicz, W. (2002). Understanding heterogeneous preferences in random utility models: A latent class approach. Environmental & Resource Economics,23(4), 421–446.

    Google Scholar 

  • Brereton, F., Clinch, J. P., & Ferreira, S. (2008). Happiness, geography and the environment. Ecological Economics,65(2), 386–396.

    Google Scholar 

  • Brueckner, J., Thisse, J.-F., & Zenou, Y. (1999). Why is central Paris rich and downtown Detroit poor? An amenity-based theory. European Economic Review,43(1), 91–107.

    Google Scholar 

  • Cazzuffi, C., & López-Moreno, D. (2018). Psychosocial wellbeing and place characteristics in Mexico. Health and Place,50, 52–64. https://doi.org/10.1016/j.healthplace.2018.01.002.

    Article  Google Scholar 

  • Colombo, E., Michelangeli, A., & Stanca, L. (2014). La Dolce Vita: Hedonic estimates of quality of life in Italian cities. Regional Studies,48(8), 1404–1418.

    Google Scholar 

  • Cruces, G., Ham, A., & Tetaz, M. (2010). Well-being at the subcity level: The buenos aires neighborhood quality of life survey. In E. Lora, A. Powell, B. van Praag, & P. Sanguinetti (Eds.), The quality of life in Latin America cities: Markets and perception (pp. 91–115). Washington, DC: Inter-American Development and World Bank.

    Google Scholar 

  • Cuñado, J., & de Gracia, F. P. (2013). Environment and happiness: New evidence for Spain. Social Indicators Research,112(3), 549–567.

    Google Scholar 

  • Daly, A., Hess, S., & Train, K. (2012). Assuring finite moments for willingness to pay in random coefficient models. Transportation,39(1), 19–31.

    Google Scholar 

  • Di Tella, R., & MacCulloch, R. (2006). Some uses of happiness data in economics. Journal of Economic Perspectives,20(1), 25–46.

    Google Scholar 

  • Diener, E., Oishi, S., & Lucas, R. E. (2005). Subjective well-being: The science of happiness and life satisfaction. In S. Lopez & C. Snyder (Eds.), Handbook of positive psychology (2nd ed., pp. 63–73). New York: Oxford University Press.

    Google Scholar 

  • Falco, P., Maloney, W. F., Rijkers, B., & Sarrias, M. (2015). Heterogeneity in subjective wellbeing: An application to occupational allocation in Africa. Journal of Economic Behavior & Organization,111(1), 137–153.

    Google Scholar 

  • Ferreira, S., Akay, A., Brereton, F., Cuñado, J., Martinsson, P., Moro, M., et al. (2013). Life satisfaction and air quality in Europe. Ecological Economics,88(1), 1–10.

    Google Scholar 

  • Ferreira, S., & Moro, M. (2010). On the use of subjective well-being data for environmental valuation. Environmental & Resource Economics,46(3), 249–273.

    Google Scholar 

  • Ferrer-i Carbonell, A., & Frijters, P. (2004). How important is methodology for the estimate of the determinants of happiness? The Economic Journal,114(1997), 641–659.

    Google Scholar 

  • Frey, B. S., Luechinger, S., & Stutzer, A. (2009). The life satisfaction approach to valuing public goods: The case of terrorism. Public Choice,138(3), 317–345.

    Google Scholar 

  • Frey, B. S., Luechinger, S., & Stutzer, A. (2010). The life satisfaction approach to environmental valuation. Annual Review of Resource Economics,2(1), 139–160.

    Google Scholar 

  • Gabriel, S. A., & Rosenthal, S. S. (1999). Location and the effect of demographic traits on earnings. Regional Science and Urban Economics,29(4), 445–461.

    Google Scholar 

  • Gandelman, N., Piani, G., & Ferre, Z. (2012). Neighborhood determinants of quality of life. Journal of Happiness Studies,13(3), 547–563.

    Google Scholar 

  • Glaeser, E. L., & Gottlieb, J. D. (2009). The wealth of cities: Agglomeration economies and spatial equilibrium in the United States. Journal of Economic Inequality,47(4), 983–1028.

    Google Scholar 

  • Glaeser, E. L., Kolko, J., & Saiz, A. (2001). Consumer city. Journal of Economic Geography,1(1), 27–50.

    Google Scholar 

  • Goetzke, F., & Islam, S. (2017). Testing for spatial equilibrium using happiness data. Journal of Regional Science,57(2), 199–217.

    Google Scholar 

  • Graves, P. E. (1983). Migration with a composite amenity: The role of rents. Journal of Regional Science,23(4), 541–546.

    Google Scholar 

  • Greene, W., Harris, M. N., Hollingsworth, B., & Weterings, T. A. (2014). Heterogeneity in ordered choice models: A review with applications to self-assessed health. Journal of Economic Surveys,28(1), 109–133.

    Google Scholar 

  • Hajivassiliou, V. A., & Ruud, P. A. (1994). Classical estimation methods for LDV models using simulation. In R. F. Engle & D. L. McFadden (Eds.), Handbook of econometrics (Vol. 4, pp. 2383–2441). Amsterdam: Elsevier.

    Google Scholar 

  • Hensher, D. A., & Greene, W. H. (2003). The mixed logit model: The state of practice. Transportation,30(2), 133–176.

    Google Scholar 

  • Hess, S. (2014). Impact of unimportant attributes in stated choice surveys. European Journal of Transport and Infrastructure Research,14(4), 349–361.

    Google Scholar 

  • Hess, S., Bierlaire, M., & Polak, J. (2005). Estimating value-of-time using mixed logit models. In 84th Annual meeting of the transportation research board, Washington DC.

  • Iturra, V. (2018). Amenity decomposition: The role played by firms and workers in explaining spatial wage differences in Chile. Tijdschrift voor Economische en Sociale Geografie,109(4), 542–560. https://doi.org/10.1111/tesg.12311.

    Article  Google Scholar 

  • Krupka, D. J. (2009). Location-specific human capital, location choice and amenity demand. Journal of Regional Science,49(5), 833–854.

    Google Scholar 

  • Lee, L. (1992). On efficiency of methods of simulated moments and maximum simulated likelihood estimation of discrete response models. Econometric Theory,8(4), 518–552.

    Google Scholar 

  • Levinson, A. (2012). Valuing public goods using happiness data: The case of air quality. Journal of Public Economics,96(9–10), 869–880.

    Google Scholar 

  • Luechinger, S. (2009). Valuing air quality using the life satisfaction approach. The Economic Journal,119(536), 482–515.

    Google Scholar 

  • Maddison, D. (2001). The amenity value of the global climate. New York: Taylor & Francis.

    Google Scholar 

  • Maddison, D., & Bigano, A. (2003). The amenity value of the Italian climate. Journal of Environmental Economics and Management,45(2), 319–332.

    Google Scholar 

  • Medina, C., Morales, L., & Núñez, J. (2010). Quality of Life in Urban Neighborhoods of Bogotá and Medellín, Colombia. In E. Lora, A. Powell, B. van Praag, & P. Sanguinetti (Eds.), The quality of life in Latin America cities: Markets and perception (pp. 117–159). Washington, DC: Inter-American Development Bank and World Bank.

    Google Scholar 

  • Mentzakis, E. (2011). Allowing for heterogeneity in monetary subjective well-being valuations. Health Economics,20(3), 331–347.

    Google Scholar 

  • Partridge, M. D. (2010). The duelling models: NEG vs amenity migration in explaining US engines of growth. Papers in Regional Science,89(3), 513–536.

    Google Scholar 

  • Perez-Truglia, R. (2015). A Samuelsonian validation test for happiness data. Journal of Economic Psychology,49, 74–83. https://doi.org/10.1016/j.joep.2015.05.002.

    Article  Google Scholar 

  • Perez-Truglia, R. (2016). The effects of income transparency on well-being-evidence from a natural experiment. Available at SSRN: https://ssrn.com/abstract=2657808.

  • Rappaport, J. (2007). Moving to nice weather. Regional Science and Urban Economics,37(3), 375–398.

    Google Scholar 

  • Rehdanz, K. (2006). Hedonic pricing of climate change impacts to households in Great Britain. Climatic Change,74(4), 413–434.

    Google Scholar 

  • Rehdanz, K., & Maddison, D. (2005). Climate and happiness. Ecological Economics,52(1), 111–125.

    Google Scholar 

  • Roback, J. (1982). Wages, rents, and the quality of life. The Journal of Political Economy,90(6), 1257–1278.

    Google Scholar 

  • Rosen, S. (1979). Wage-based indexes of urban quality of life. In P. Mieskowski & M. Stratzhein (Eds.), Current issues in urban economics (pp. 74–104). Baltimore: Johns Hopkins Press.

    Google Scholar 

  • Sarrias, M. (2016). Discrete choice models with random parameters in R: The Rchoice package. Journal of Statistical Software,74(10), 1–31.

    Google Scholar 

  • Sarrias, M. (2018). Do monetary subjective well-being evaluations vary across space? Comparing continuous and discrete spatial heterogeneity. Spatial Economic Analysis. https://doi.org/10.1080/17421772.2018.1485968.

    Article  Google Scholar 

  • Schurer, S., & Yong, J. (2016). Happiness, income and heterogeneity. Singapore Economic Review,61(3), 1–23.

    Google Scholar 

  • Scott, J. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks: SAGE Publications Inc.

    Google Scholar 

  • Train, K. E. (2009). Discrete choice methods with simulation. New York: Cambridge University Press.

    Google Scholar 

  • van Praag, B. M., & Baarsma, B. E. (2005). Using happiness surveys to value intangibles: The case of airport noise. Economic Journal,115(500), 224–246.

    Google Scholar 

  • Veenhoven, R. (2000). The four qualities of life. Journal of Happiness Studies,1(1), 1–39.

    Google Scholar 

  • Veenhoven, R. (2017). Greater happiness for a greater number: Did the promise of enlightenment come true? Social Indicators Research,130(1), 9–25.

    Google Scholar 

  • Welsch, H., & Ferreira, S. (2014). Environment, well-being, and experienced preference. International Review of Environmental and Resource Economics,7(4), 205–239.

    Google Scholar 

  • Welsch, H., & Kühling, J. (2009). Using happiness data for environmental valuation: Issues and applications. Journal of Economic Surveys,23(2), 385–406.

    Google Scholar 

  • Winters, J. V., & Li, Y. (2016). Urbanisation, natural amenities and subjective well-being: Evidence from US counties. Urban Studies,54(8), 1956–1973.

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge and appreciate the wise comments of two anonymous referees and the financial support of CONICYT/Chilean Fondecyt 11170018 “City differences in the return to schooling”.

Author information

Authors and Affiliations

  1. IDEAR, Department of Economics, Universidad Católica del Norte, Avenida Angamos, 0610, Antofagasta, Chile

    Gustavo Ahumada, Victor Iturra & Mauricio Sarrias

Authors
  1. Gustavo Ahumada
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Victor Iturra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mauricio Sarrias
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gustavo Ahumada.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Table 5.

Table 5 Marginal effects on the probability of reporting a specific level of life satisfaction
Full size table

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ahumada, G., Iturra, V. & Sarrias, M. We Do Not Have the Same Tastes! Evaluating Individual Heterogeneity in the Preferences for Amenities. J Happiness Stud 21, 53–74 (2020). https://doi.org/10.1007/s10902-019-00081-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10902-019-00081-2

Keywords

  • Subjective well-being
  • Amenities
  • Heterogeneous preferences
  • Random parameters models
  • Compensating variation

JEL Classification

  • C35
  • I31
  • Q51