UNIFYING LIVABILITY AND COMPARISON THEORY: CROSS-NATIONAL TIME-SERIES ANALYSIS OF LIFE-SATISFACTION
Michael R. Hagerty Graduate School of Management University of California, Davis Davis, CA 95616 (510)522-7505
[email protected]
October 1997 Submitted to: Social Indicators Research Keywords: cross-national, time-series, life-satisfaction, comparison theory, livability theory. Abstract The two major theories of QOL judgment – livability and comparison theories – are tested. The first states that only absolute level of objective variables will affect QOL, whereas the second states that only differences in objective variables will. A 25-year, 8nation database was developed that allows more powerful tests than previous research. Consistent with previous studies and with livability theory, absolute level of GDP/person had the largest effect on life-satisfaction. Contrary to previous research, a reliable effect was also found for differences in GDP/person and the consumer price index. The length of these effects is 9 or 10 quarters. That is, consumers take into account changes as far back as 2 or 2 ½ years, in addition to their absolute level of GDP/person.
*Thanks are due to Ruut Veenhoven, for compiling the life-satisfaction data, and to Prasad Naik and Scott Davis for their many comments and suggestions.
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Two major theories of life-satisfaction have very different implications for improving peoples’ subjective Quality of Life (QOL). “Livability theory” (Veenhoven and Ehrha 1995) holds that people make judgments on their life-satisfaction based on absolute standards – the degree to which universal human needs are being met. In contrast, “comparison theory” (Lance, Mallard, and Michalos 1995) holds that people make the judgment based on relative standards, compared to their past experience or to other peoples’. If comparison theory is correct, then governments are running an endless treadmill when they improve objective QOL, because citizens will just adapt to the new level and be no happier. In contrast, if livability theory is correct, then governments that improves the objective QOL of their citizens will be rewarded by happier voters. Most researchers have treated these two theories as mutually incompatible. Some have completely rejected comparison theory, while others have completely rejected livability theory. In this paper, a more detailed database is developed that detects effects from both theories. We show conditions under which each theory is dominant in predicting life-satisfaction. The model of judgment that this paper develops is a combination of livability and comparison theory, in which people take into account both absolute levels of QOL, and recent changes in QOL. The absolute portion of the judgment might be typified by the question “How am I doing at filling all of my human needs?” The comparison portion can be typified by the question “Am I better off than I was 4 years ago?” Note that our model assumes that a person compares herself to her own prior QOL, not to other peoples’ circumstances. Diener et al. (1995) label this type of comparison as “adaptation theory.” Our general model may be described as:
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Life-Satisfactiont = a0 + b1X1t + b2X2t +… + c1∆ 1t + c2∆ 2t +…
(1)
where Xit is the absolute level of objective variable i that affects life-satisfaction at time t, and ∆ it is the difference in that variable since some earlier time. X is the set of objective variables that affect life-satisfaction, such as GDP per person, education, equality, etc. (See Veenhoven and Ehrhe 1995 or Diener et al. 1993 for a summary of these.) If the coefficients c in Equation 1 are all zero, then people respond only to absolute levels, and the model reduces to the livability theory. On the other hand, if the coefficients b are all zero, then the model reduces to pure comparison theory. However, if both coefficients are significantly different from zero, people are using both absolute and comparative standards to judge life-satisfaction. The relative size of the coefficients reflects how heavily each strategy is used. Evidence in previous research is strongly divided into two camps. When objective conditions in cross-national samples are used, livability theory is strongly confirmed. But when perceived conditions and individual-level analysis is used, comparison theory is strongly confirmed. For example, Veenhoven and Ehrha (1995) provide recent evidence in favor of livability theory. They analyze means and dispersions from surveys of 38 nations, and find consistent evidence for livability and against comparison theory. However, their data was cross-sectional only, containing observations for each country at only one point in time. While they were able to reject comparison theories where people compare themselves with others in the same time period, they could not test adaptation over time. Diener, Diener, and Diener (1995) went one step further by collecting data at two points in time for the 55 nations that have conducted at least one life-satisfaction survey.
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The nations compose over three-fourths of the earth’s population. They found that absolute level of GDP per person was highly related to life-satisfaction, but that change in income from 5 years before was not. This is contrary to the prediction of comparison theory. However, their data contained only two points in time, separated by 5 years. If the correct time interval is much more or less than 5 years, their test would have little power to detect adaptation. In contrast, Lance, Mallard and Michalos (1995) analyze surveys of 1354 individual U.S. college students, and find strong evidence in favor of comparison theory. They find that the direction of causation is “top-down” – that is, a person’s overall satisfaction causes their satisfaction with individual facets of life. They conclude that a person will not respond much to objective changes, but will be affected most by longlasting personality characteristics. However, this data, too, was cross-sectional and could not track changes across time. Further, comparison theory has not to this point been supported by data on objective QOL conditions. The present study performs such a test on objective QOL data for the presence of both comparison and livability judgments. The dataset used ensures far more power to detect both of these effects within-country, collecting up to 25 years of quarterly observations in each of 8 countries. Defining ∆: To perform such a test, the measurement of ∆ in Equation 1 becomes crucial. We consider two issues of definition – non-linear transformation of objective variables, and appropriate time lag. With respect to non-linear transformation, Fechner’s Law of psychophysics posits that a non-linear log-type transformation is needed to convert
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objective differences into perceived differences. Diener et al. (1995) confirmed this for GDP/person by noting a strong curvilinear relation between it and perceived lifesatisfaction. Therefore we shall use Fechner’s proposed measure of relative difference (defined as the percentage change rather than the absolute change) in measuring ∆ for all objective variables. (Lance, Mallard and Michalos (1995) do not apply a non-linear transformation, but they avoid the problem by measuring perceived differences directly.) The other major issue in measuring ∆ is, what is the appropriate time length that people use in judging change? Previous research has been quite limited in answering this because of the dearth of observations across time. Diener et al (1993) defined change as being across 9 years, because that is the only 2 time periods the data contained. Similarly, Diener et al (1995) defined change as being across 5 years, again because the database was limited to just 2 time-series. In contrast, Fair has found an effect on voting patterns in the US presidential elections for change in GDP/person. But his best-fitting time length was very short – only 2 or 3 quarters. This is far shorter than the 5 or 9 years previously analyzed in life-satisfaction research. To adequately test whether adaptation in life-satisfaction is similarly short, we therefore must have data that is not only a time-series, but is at a quarterly or monthly level. The analysis can then test different definitions of ∆ that last from as short as 1 quarter to as long as 10 years. The next sections describe how this data set was constructed, and how it was analyzed. Data The data consisted of quarterly observations of life-satisfaction ratings, GDP, and inflation from 8 countries. The life-satisfaction ratings were collected from Veenhoven’s
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(1993) World Database of Happiness. A country was included only if it had a history of at least 10 years of surveys using the same rating scale, employing national probability samples. Table 1 lists the countries. The countries performed life-satisfaction surveys an average of twice per year. Veenhoven gives the month that each survey was fielded, which was transformed to the appropriate quarter and year. The question wording for all European countries was (suitably interpreted) “On the whole, are you very satisfied, fairly satisfied, not very satisfied, or not at all satisfied with the life you lead?” Coding was originally on a 4-point scale, and was transformed by Veenhoven to a 10-point scale to allow easy comparison with other scales. Question wording for Japan was similar though not identical. Quarterly GDP and consumer price indexes were collected from OECD (1996). GDP/person was measured as real 1987 US dollars, to give a consistent scale across countries. Quarterly population was interpolated from the UN Statistical Service midyear population estimates. Quarterly GDP per person was then computed as the ratio of quarterly GDP to quarterly population. Table 1 lists the countries, with the number of quarters and the number of life-satisfaction surveys for each. The 25 years of observations result in almost 100 quarterly data points for the economic data, and from 20 to 40 life-satisfaction surveys per country. The data span the years 1970-1995. In 1972 the European countries in the sample began regular (usually twice per year) surveys of life-satisfaction in each country. Fortuitously, OECD began publishing quarterly GDP and inflation data in 1970. These countries are highly skewed toward the richest in the world, because only the richest countries have been able to afford the regular national probability sampling of
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life-satisfaction. Every nation in the sample showed a significantly increasing GDP/person over these 25 years. Hence the data offer less variation than the larger sample of countries examined by Diener et al. (1995) or by Veenhoven and Ehrha (1995). Nevertheless, they offer far more data within countries than have been available before. Table 1: Countries included in the data, with number of life-satisfaction surveys and quarters of economic data for each. Country Number of lifeNumber of Quarters of satisfaction Surveys Economic Data United Kingdom 40 99 Netherlands 34 79 France 39 99 Germany (West) 39 99 Spain 20 63 Italy 39 99 Denmark 28 63 Japan 26 106 Total 265 707
Results Simple Correlations: Simple correlations between the objective variables and life-satisfaction were first computed, replicating the analysis of Diener et al. (1995). Table 2 summarizes them, with a separate row for each country. The second column of Table 2 shows the simple correlation of GDP/person with life-satisfaction. Six of the eight countries are positive, with 3 significantly so. The third column shows the simple correlation of change in GDP/person with life-satisfaction. Four of the eight countries are positive, with 2 significantly so. The last row summarizes these simple correlations, recomputed across all 8 countries. The overall correlation between GDP/person is significantly positive, but the correlation with change (over 5 years) is not. This last row replicates the results from Diener et al. (1995) for their full sample, even though they analyzed different countries, and fewer time periods. Hence their basic
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results seem robust, that absolute level of GDP is significantly related to life-satisfaction, but that change in GDP/person is not, at least when measured over 5 years. However, the simple correlations do not exploit the power of multiple predictors and multiple time periods per country. In the next analysis, the countries were pooled together to allow testing for smaller effects and controlling for multiple variables. Table 2. Simple regressions predicting life satisfaction in 8 countries. Country Correlation with Correlation with GDP/person ∆GDP/person United Kingdom -.02 -.14 Netherlands .28 .25 France .15 -.16 Germany (West) .37* -.01 Spain -.12 .89** Italy .89** .10 Denmark .22 -.42 Japan .64** .60** All 8 countries .34** .03 *p
