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Journal of Happiness Studies

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01.08.2012 | Research Paper

Happiness No Matter the Cost? An Examination on How Efficiently Individuals Reach Their Happiness Levels

verfasst von: Martin Binder, Tom Broekel

Erschienen in: Journal of Happiness Studies | Ausgabe 4/2012

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Abstract

Happiness measures, reflecting individuals’ well-being, have received increasing attention by policy makers. Policies could target absolute happiness levels when aiming at increasing a society’s well-being. But given upper bounds of happiness measures, as well as the possibilities of decreasing returns to happiness resources, we argue that an important measure of interest is the efficiency with which individuals convert their resources into happiness. In order to examine the effects of policies on this efficiency and to better understand the trajectories of human well-being over time, we suggest an efficiency measure that is calculated via a nonparametric order-m approach borrowed from the production efficiency literature. Our approach is exemplified using micro level data from the British Household Panel Survey (BHPS). Between 20 and 27% of the British populace are efficient in attaining happiness during our sample period. A negative influence on “happiness efficiency” is being unemployed while a positive influence is cohabitation with a partner. Our results are robust with respect to using a more comprehensive subjective well-being measure, but there are gender differences, for example in the (positive) influence that retirement has on males’ efficiency, or the (positive) influence of maternity leave on females.

Vorheriger Artikel Global and School-Related Happiness in Finnish Children

Nächster Artikel De-Biasing the Age-Happiness Bias: Memory Search and Cultural Expectations in Happiness Judgments Across the Lifespan

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Ticking the highest value in a fixed scale measure of happiness does not need to entail that the “true” underlying level of happiness cannot be further increased. It might rather point at an inappropriateness in using fixed scales for assessing subjective well-being (see Ng 2008b, pp. 256–8). There might, however, exist limits due to our biological makeup regarding how much pleasure we can experience during a certain time period. If happiness measures were based on this affective evaluation, there might actually be “true” absolute happiness maxima per time unit. We remain agnostic on this issue but thank an anonymous referee for pointing this out.

This certainly is also of value on lower levels of well-being: unhappy individuals being able to more efficiently reach their (admittedly lower) level of happiness similarly profit in effectively having more resources for other purposes if happiness efficiency is increased. However, this case of the efficient unhappy makes clear that this relative view is a complement to the absolute measurement of well-being.

The Happy Planet Index has been criticized for a number of deficits (see Ng 2008a) and it bears a particularly misleading name: while it measures life satisfaction (times longevity) per ecological footprint—the happiness of a country in relation to its environmental pollution if one wishes—the media has by and large interpreted the measure as an index of a country’s happiness. In this regard, the index is misleading as for example the difference in life satisfaction between, say, Costa Rica and Denmark is only (0.4 on an 11 point scale) whereas their rank in the index is 1 and 105 (out of 143), a result that is mainly driven by the difference in ecological footprints. The example of this index highlights why the Stiglitz commission rightly states that such measures should not be lumped together: “To take an analogy, when driving a car, a meter that weighed up in one single value the current speed of the vehicle and the remaining level of gasoline would not be of any help to the driver. Both pieces of information are critical and need to be displayed in distinct, clearly visible areas of the dashboard” (Stiglitz et al. 2009, pp. 77).

Very similar to this tradition is research using the “satisfaction with life scale” (WLS, Diener et al. 1985). Proponents of this scale argue that both cognitive and affective components build the psychological construct “well-being”, but the cognitive components seem to dominate their scale. A third distinction is made by Veenhoven (1991), who differentiates between affective (“happiness”) and cognitive (“contentment”) concepts of well-being.

The reason for this divergence of findings is that the original findings did not account for breaks in the data, where the wording of the life satisfaction and happiness questions had been changed and thus led to different results.

With the method presented here, we put the non-parametric framework discussed in Binder and Broekel (2010, 2011) into the context of subjective well-being research. The idea that individuals differ in certain respects that influence the conversion of resources into well-being plays a prominent role in Sen’s capability approach.

Holding resources constant, individual happiness could be increased through “technological shifts”, examples of which might lie in effective happiness counseling, applying tenets of positive psychology, etc.

In light of the difficulties with defining a theoretical maximum happiness one “should” reach with a certain level of resources, this seems to be a sound approach.

In production theory this relates to the discussion about partial and total factor productivity/efficiency.

For an extensive discussion on the advantages and drawbacks of parametric and nonparametric approaches see Coelli and Perleman (1999) and Daraio and Simar (2007).

The value of m has to be specified by the researcher. It can be seen as a “trimming parameter” defining the sensibility of the estimation with respect to outliers in the data. We follow Bonaccorsi et al. (2005) in setting the level of robustness to below ten percent. This means that ten percent of the observations have efficiency values smaller than one. A sensible value in our case would be m = 500.

If one wants to impose the assumption of convexity, it is possible to create measures that allow for substitutability among input and output vectors (see Daraio and Simar 2005). In this case, individuals are also compared to linear combinations of observed resource/well-being relations.

The survey is undertaken by the ESRC UK Longitudinal Studies Centre with the Institute for Social and Economic Research at the University of Essex, UK (BHPS 2009). Its aim is to track social and economic change in a representative sample of the British population (or more information on the data set, see Taylor 2009). The sample comprises about 15,000 individual interviews. Starting in 1991, up to now, there have been 18 waves of data collected with the aim of tracking the individuals of the first wave over time (there is a percentage of rotation as some individuals drop out of the sample over time and others are included, but attrition is quite low, see Taylor 2009).

We have recoded this to values of one (lowest well-being) to 37 (highest scores in mental well-being).

Usually, the logarithm of income measures is used as a regressor in the literature, thus assuming that a given change in the proportion of income leads to the same proportional change in well-being (Stevenson and Wolfers 2008; Easterlin 2001, p. 468). Within the efficiency framework, it is not necessary to correct for the skewed income distribution by taking the logarithm of our income measure, of which we therefore refrain.

As in the case of mental well-being, we have reversed the numerical order of the Likert scale to consistently use higher values for higher “achievement” in these domains. The original coding in the BHPS codes a value of one to be excellent health and five to be very poor health.

Whether objective health is sufficiently well captured by subjective health assessments is still debated (Johnston et al. 2009).

While the aggregation of different indicators into one comprehensive measure is by no means trivial, the efficiency methodology used in the present paper implicitly solves this issue via the dominance principle for all indicators. Because of this property, efficiency analysis is often used to collapse multidimensional information organized in varying scales into one dimension (see e.g., Ramos and Silber 2005).

For more information see Taylor (2009), App. 2, pp. 18-9.

A Hausman test suggests that the fixed effects model is more efficient (χ²(23) = 217.97; p = 0.0000). Accordingly, we control for individual-specific time-invariant effects.

We transformed the efficiency scores here so that positive coefficients refer to an increase in efficiency values (i.e., the individual becomes more efficient).

Means by gender are: male mean is 1.3726 (mean of inefficient males: 1.4717); female mean is 1.3834 (mean of inefficient females: 1.5129) pooled over all years.

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Titel: Happiness No Matter the Cost? An Examination on How Efficiently Individuals Reach Their Happiness Levels
verfasst von: Martin Binder
Tom Broekel
Publikationsdatum: 01.08.2012
Verlag: Springer Netherlands
Erschienen in: Journal of Happiness Studies / Ausgabe 4/2012
Print ISSN: 1389-4978
Elektronische ISSN: 1573-7780
DOI: https://doi.org/10.1007/s10902-011-9283-5

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