By Bernard van Praag, Ada Ferrer-i-Carbonell
How will we degree happiness? this crucial and long-awaited ebook offers a brand new and unified method of the research of subjective pride and source of revenue review. Drawing on empirical analyses of German, British, Dutch, and Russian facts, it develops new technique to set up a version of future health such as delight with lifestyles as a complete and with a number of domain names of lifestyles. this system is utilized to review person and collective norms, to build family-equivalence scales, to estimate healthiness damages, repayment for externalities, and the development of tax price lists, and to outline subjective inequalities with recognize to healthiness, source of revenue, and different domain names of lifestyles. Written for a large readership of social scientists, the ebook provides a theoretical and empirical leap forward right into a new and fruitful method within the social sciences.
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Extra info for Happiness Quantified: A Satisfaction Calculus Approach
2. 021 Note: *^i = —oo because the first response category is empty in this sample. 2 we present three versions of the financial-satisfaction equation to be estimated. 2). We have two explanatory variables; namely, household income and family size. In the second version we replace family size by two variables. 5 The latter class will be called 'adults'. In the third specification we add a third variable 'family structure', which equals 0 if the respondent lives alone, 1 if the household has two working adults, and 2 if there are two adults in the household of which only one works.
Readers who are acquainted with this stuff are advised to skip this section. Before we turn to the economic interpretation of our estimates, the reader who is not acquainted with reading statistical tables may be helped by a few words on how to read these tables, because we will encounter many of them in the pages that follow. 1, the first of which presents the values of the estimates and the second of which gives the t-ratio. The t-ratio is a statistic that gives an idea of the statistical reliability of the estimate.
The combined chance of finding this sample is the expression L(^i, a, ft), which depends on the unknown parameters n, a, ft. The maximum-likelihood principle says that the most probable values of the unknown parameters are those that maximize the chance of the sample we have found in reality. Hence, we maximize the sample likelihood L(^i, a, ft) with respect to n, a, ft. In practice, we do not maximize this expression but its logarithm with respect to n, a, and ft. Maximization of the logarithm ln(I) obviously amounts to the same thing as maximizing L itself.