Greater Happiness for a Greater Number: Did the Promise of Enlightenment Come True?

In the eighteenth century ‘Enlightened’ thinkers challenged the belief that happiness exists only in Heaven. They claimed that happiness is possible in earthly life and foresaw that greater happiness would be achieved using reason. Did this promise of greater happiness come true? Several scholars doubt that we have become any happier and some claim that happiness has declined. These critical claims are tested using the time trend data available in the World Database of Happiness, which cover the period 1950–2010 and involve 1531 data points in 67 nations yielding 199 time-series ranging for 10 to more than 40 years. The analysis reveals that happiness has risen in most nations. The average yearly rise in the 67 nations was +0.012 on scale 0–10, which equals a rise of one full point every 83 years. At this rate happiness must have improved by more than two points over the past two centuries and, together with increasing longevity, this denotes an unprecedented rise in happy life years.     

Why Social Policy Needs Subjective Indicators

There are many qualms about subjective indicators, and some believe that social policy would be better for not using them. This paper consists of a review of these objections. It is argued that policy makers need subjective indicators, the main reasons being: 1. Social policy is never limited to merely material matters; it is also aimed at matters of mentality. These substantially subjective goals require subjective indicators. 2. Progress in material goals can not always be measured objectively. Subjective measurement is often better. 3. Inclusive measurement is problematic with objective substance. Current sum-scores make little sense. Using subjective satisfaction better indicates comprehensive quality. 4. Objective indicators do little to inform policy makers about public preferences. Since the political process also does not reflect public preferences too well, policy makers need additional information from opinion polls. 5. Policy makers must distinguish between `wants' and `needs’. Needs are not observable as such, but their gratification materialises in the length and happiness of peoples' lives. This final output criterion requires assessment of subjective appreciation of life-as-a-whole     

Happiness Scale Interval Study. Methodological Considerations

The Happiness Scale Interval Study deals with survey questions on happiness, using verbal response options, such as ‘very happy’ and ‘pretty happy’. The aim is to estimate what degrees of happiness are denoted by such terms in different questions and languages. These degrees are expressed in numerical values on a continuous [0,10] scale, which are then used to compute ‘transformed’ means and standard deviations. Transforming scores on different questions to the same scale allows to broadening the World Database of Happiness considerably. The central purpose of the Happiness Scale Interval Study is to identify the happiness values at which respondents change their judgment from e.g. ‘very happy’ to ‘pretty happy’ or the reverse. This paper deals with the methodological/statistical aspects of this approach. The central question is always how to convert the frequencies at which the different possible responses to the same question given by a sample into information on the happiness distribution in the relevant population. The primary (cl)aim of this approach is to achieve this in a (more) valid way. To this end, a model is introduced that allows for dealing with happiness as a latent continuous random variable, in spite of the fact that it is measured as a discrete one. The [0,10] scale is partitioned in as many contiguous parts as the number of possible ratings in the primary scale sums up to. Any subject with a (self-perceived) happiness in the same subinterval is assumed to select the same response. For the probability density function of this happiness random variable, two options are discussed. The first one postulates a uniform distribution within each of the different subintervals of the [0,10] scale. On the basis of these results, the mean value and variance of the complete distribution can be estimated. The method is described, including the precision of the estimates obtained in this way. The second option assumes the happiness distribution to be described as a beta distribution on the interval [0,10] with two shape parameters (α and β). From their estimates on the basis of the primary information, the mean value and the variance of the happiness distribution in the population can be estimated. An illustration is given in which the method is applied to existing measurement results of 20 surveys in The Netherlands in the period 1990–2008. The results clarify our recommendation to apply the model with a uniform distribution within each of the category intervals, in spite of a better validity of the alternative on the basis of a beta distribution. The reason is that the recommended model allows to construct a confidence interval for the true but unknown population happiness distribution. The paper ends with a listing of actual and potential merits of this approach, which has been described here for verbal happiness questions, but which is also applicable to phenomena which are measured along similar lines.     

Conversion of Verbal Response Scales: Robustness Across Demographic Categories

Happiness and life satisfaction have traditionally been measured using verbal response scales, however, these verbal scales have not kept up with the present trend to use numerical response scales. A switch from a verbal scale to a numerical scale, however, causes a severe problem for trend analyses, due to the incomparability of the old and new measurements. The Reference Distribution Method is a method that has been developed recently to deal with this comparison problem. In this method use is made of a reference distribution based on responses to a numerical scale which is used to decide at which point verbally labelled response options transit from one state to another, for example from ‘happy’ to ‘very happy’. Next, for each wave of the time series in which the verbal scale is used, a population mean is estimated for the beta distribution that fits best to these transition points and the responses in this wave. These estimates are on a level that is comparable to that of the mean of the reference distribution and are appropriate for use in an extended time series based on the responses measured using a verbal and a numerical scale. In this paper we address the question of whether the transition points derived for the general population can be used for demographic categories to produce reliable, extended time series to monitor differences in trends among these categories. We conclude that this is possible and that it is not necessary to derive transition points for each demographic category separately.     

The cross-national pattern of happiness: Test of predictions implied in three theories of happiness

Predictions about level and dispersion of happiness in nations are derived from three theories of happiness: comparison-theory, folklore-theory and livability-theory. The predictions are tested on two cross national data-sets: a comparative survey among university students in 38 nations in 1985 and a collection of comparable general population surveys in 28 nations around 1980. Most predictions of comparison-theory and folklore-theory are defied by the data. The predictions of livability-theory are all confirmed.     

The ‘10 Excess’ Phenomenon in Responses to Survey Questions on Happiness

Happiness in nations is typically measured in surveys using a single question. A common question is: ‘all things considered, how satisfied or dissatisfied are you with your life as-a-whole these days on a scale from 0 to 10?’. The responses typically follow a uni-modal distribution with highest frequencies between 5 and 8. Yet in some nations, the percentage of 10 responses stands out and is higher than the percentage of 9 responses. This is particularly present in Latin America and in the Middle East. In this paper we explore the prevalence of the ‘10-excess’ pattern and check some possible explanations. We conclude that the 10-excess phenomenon is partly due to cultural influence.     

Happy life-expectancy

One of the aims of social indicator research is to develop a comprehensive measure of quality-of-life in nations that is analogous to GNP in economic indicator research. For that purpose, several multi dimensional indexes have been proposed. In addition to economic performance, these also acknowledge the nation's success in matters like schooling and social equality. The most current indicator of this type is the ‘Human Development Index’. In this approach QOL is measured by input; the degree to which society provides conditions deemed beneficial (‘presumed’ QOL). The basic problem is that one never knows to what extent the cherished provisions are really good for people. An alternative is measuring QOL in nations by output, and consider how well people actually flourish in the country. This ‘apparent’ QOL can be measured by the degree to which citizens live long and happily. This conception is operationalized by combining registration based estimates of length-of-life, with survey data on subjective appreciation-of-life. Life-expectancy in years is multiplied by average happiness on a 0–1 scale. The product is named ‘Happy Life-Expectancy’ (HLE), and can be interpreted as the number of years the average citizen in a country lives happily at a certain time. HLE was assessed in 48 nations in the early 1990's. It appears to be highest in North-West European nations (about 60) and lowest in Africa (below 35). HLE scores are systematically higher in nations that are most affluent, free, educated, and tolerant. Together, these country-characteristics explain 70% of the statistical variance in HLE. Yet HLE is not significantly related to unemployment, state welfare and income equality, nor to religiousness and trust in institutions. HLE does not differ either with military dominance and population pressure. The conclusion is that HLE qualifies as the envisioned comprehensive social indicator. It has both clear substantive meaning (happy life-years) and theoretical significance (ultimate output measure). HLE differentiates well. Its correlations fit most assumptions about required input, but also challenge some. The indicator is likely to have political appeal.     

Cultural bias in ratings of perceived life quality

The results of comparative surveys have demonstrated great differences of perceived life quality. Yet there is doubt whether these differences mean that people are really more satisfied in one country than in another. It is generally believed that the responses are distorted by factors such as language, familiarity with concepts like satisfaction, and social desirability pressures. Though often alleged, the truth of these charges has not yet been demonstrated empirically. Ostroot & Snijder (1985) now claim to have demonstrated that about 40% of the difference in satisfaction between the French and Americans is due to ‘cultural bias’: in particular to a rosier outlook of the latter. Yet their arguments labor under two defects: Firstly, their use of the word ‘bias’ is misleading. They do not demonstrate any discrepancy between avowed satisfaction and ‘true’ satisfaction. Speaking of a ‘cultural effect’ would be more appropriate. Secondly, Ostroot & Snyder do not demonstrate that the greater satisfaction of Americans is due to a rosier outlook on their part. The data do not allow the conclusion that Americans hold a rosier view than the French, nor that such a view is responsible for their greater satisfaction.