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A Note on Empirical Studies of Life-Satisfaction: Unhappy with Semiparametrics?

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Abstract

This article deals with the modeling of life-satisfaction, and estimating the impact of age on it. We investigate how findings and the interpretation of empirical studies hinge on the respectively assumed model. Assuming a specific model comprises various hypothesis made on the data generating process, like indicator selection, measurement, or functional form specifications. In this study we focus on the latter two issues. In particular, we show how different response behaviors (optimistic, pessimistic, extreme averse, etc.) lead to seemingly contradictory conclusions if the researcher does not address them adequately. In fact, we show that one can reproduce any shape found in the literature simply by modifying the way respondents rank life satisfaction on a bounded scale.

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Notes

  1. One may even think of the problem when turning from optimal data fitting to causal inference: In that case, the impact of a specific covariate is of interest, but not the model fit of the data as a whole.

  2. For a review of nonparametric identification in models with discrete (bounded) dependent variables see Matzkin (2007) .

  3. We are taking ‘relative wealth’, because people are inclined to compare their economic situation with their present distribution quantile and not their own past situation, see McBride (2001).

  4. The chosen parameters are the outcomes of different scenarios replicating figures that are similar to those we found in real data examples.

  5. For women the menopause is a clear factor for causing such LS-valley at around 50.

  6. These are the binary variables listed in Table 1, together with year fixed effects.

  7. For the sake of presentation we have plotted the estimate of \((- m_a)\) so that it can be directly compared to the estimates obtained for the other models.

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Acknowledgements

The authors gratefully acknowledge the participants of CompStat 2014, Swiss Statistics Meeting 2015, CMS 2016, the CUSO summer school, seminars at the Universities of Bern and St Gallen, two anonymous referees and Vanesa Jordá Gil for comments and discussion. Financial support from the Swiss National Science Foundation 100018–140295 is acknowledged.

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Authors and Affiliations

  1. HEC Lausanne, Batiment Anthropole, 1015, Lausanne, Switzerland

    Setareh Ranjbar

  2. Geneva School of Economics and Management, University of Geneva, 40 Bd du Pont d’Arve, 1211, Geneva, Switzerland

    Stefan Sperlich

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  1. Setareh Ranjbar
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  2. Stefan Sperlich
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Correspondence to Stefan Sperlich.

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Appendix: Diagnostics on SOEP Data Analysis

Appendix: Diagnostics on SOEP Data Analysis

See Tables 1, 2, 3 and Figs. 10, 11, 12.

Table 1 Descriptive statistics
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Table 2 Correlation coefficient between continuous covariates
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Table 3 PLM with Identity link: Fitted results for parametric components
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Fig. 10
figure 10

Distribution of LS by different age groups

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Fig. 11
figure 11

Diagnostic graphs of residuals for PLM (left) and GAM (right) with Poisson link for dissatisfaction

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Fig. 12
figure 12

AM with interaction: Marginal effect of age, where all interactions with the continuous variables are included in the model

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Ranjbar, S., Sperlich, S. A Note on Empirical Studies of Life-Satisfaction: Unhappy with Semiparametrics?. J Happiness Stud 21, 2193–2212 (2020). https://doi.org/10.1007/s10902-019-00165-z

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