Our empirical analysis uses data from the Swedish Survey of Living Conditions (the ULF survey). The ULF survey is an annual systematic survey of living conditions conducted by Statistics Sweden since 1975. The data are collected during 1-h personal interviews with randomly selected individuals aged 16–84 years and complemented with information from various registers. On average 7,500 individuals are interviewed yearly. The database is primarily cross-sectional, but it also contains a longitudinal panel. The panel is complemented with immigrants and young individuals who have become old enough to be included in the population [41]. The questions are divided into four main themes: Health, Social relations, Physical environment and Work. The survey always contains some central questions from all themes. However, every 8 years each theme receives particular attention. This study uses unbalanced panel data from four 2-year waves, 1980–1981, 1988–1989, 1996–1997 and 2004–2005, covering a 25-year period and focusing on health-related issues. The last two survey waves had 75 % response rates [41].

At the outset, the sample consists of n = 22,855 observations. The sample is restricted to working-age individuals, i.e., those aged 20–64 years (n = 16,816) who have not retired (n = 15,779). We are only interested in individuals who appear at least twice (n = 10,048). The lag length in the final sample varies between 8 years and 16 years (<5 % of the sample observations). In addition, we require information on BMI and that BMI is lower than 45, thereby including individuals who are morbidly obese (12 observations) but excluding individuals who are super obese (3 observations).Footnote 2 Those who are or have been underweight are also excluded (n = 9,591), making normal weight the reference group for the two excess-weight categories, overweight and obese.Footnote 3 Furthermore, we eliminate missing observations regarding education (n = 9,570) and health measures (n = 9,567). The final requirement states that individuals must be employed and have a relatively strong connection to the labor market (thereby avoiding the analysis of individuals who work very little during a year, e.g., those who only have a summer job). We code this requirement as annual income from employment exceeding at least 100,000 SEK (approximately $15,750). Our final sample consists of n = 8,214 observations belonging to 2,415 men and 2,184 women (N = 4,599).

Dependent variables

This article examines the association between excess weight and income, measured as the logarithm of annual income from employment, above a threshold of 100,000 SEK. Income from employment is based on tax records and includes salaries and benefits such as sickness, unemployment and parental leave benefits. Benefit payments are conditioned on labor market activity, and the amount is related to the individual income level. If benefit payments mask differences in behavior related to weight, our analysis will generate biased results. This concern is particularly related to women, who tend to allocate more time to the care of children and of the home, and also tend to suffer from worse health than men, factors that may all affect labor supply negatively (see, e.g., [44–46]). Unfortunately, there is no measure of income from employment that excludes benefit payments available to us.Footnote 4 The consequence for our analysis is most likely an overestimation of the obesity penalty for women. However, in lieu of an income measure excluding benefits, we specify several health variables that should pick up conditions and circumstances that could influence both weight and income. We also include a control for having small children when analyzing excess-weight penalties for women, thereby taking account of any differences in the family situation that could influence the income level of excess-weight women.Footnote 5 Another factor that may influence the results for women is the income threshold itself. If women have a weaker connection to the labor market, it is possible that the excess weight penalty for women is not observable above the income threshold. We investigate this possibility in the “Sensitivity analyses” section where we perform various sensitivity analyses.

Our income measure is the product of the wage rate and the number of hours worked during a year. In consequence, any indication of income penalties due to excess weight may be associated with either fewer work hours or a lower wage rate or both. However, Antelius and Björklund [47], studying the returns on education in Sweden, observe that the analysis when excluding annual income below 100,000 SEK generates results that are similar to those obtained in an analysis of hourly income. To the degree that this relationship holds in other contexts, our analysis will contribute to elucidating the association between excess weight and wage rates for Swedish employees (see also Lundborg et al. [40], who apply the same income threshold when analyzing obesity and income for Swedish men). In addition, we have run regressions controlling for hours of work per week without observing any marked differences in our main results.

Independent variables

Excess-weight measures

We measure normal weight, overweight, and obesity using BMI, based on self-reported weight and height. This article relies on the WHO classification of weight categories: normal weight is a BMI of 18.5–25, overweight 25–30 and obesity ≥30.

Additional background variables

We control for individual age, age squared, and whether or not the individual is married or cohabiting, respectively. We also control for first generation immigrant status or second-generation immigrant status [born in Sweden by parents, one of which is or both are non-Swedish citizen(s)]. Pregnancy tends to increase weight and decrease income (due to work reduction during pregnancy and after birth). These pregnancy-related effects could bias the estimates for women, implying an amplification of the excess-weight penalties. Unfortunately, we cannot exclude pregnant women from the analysis because the ULF survey does not collect information about pregnancy at the time of the interview. However, the survey collects information about how many children the respondent has in different age ranges (0–6 years, 7–18 years, 0–12 years, etc.). Thus, in lieu of information about pregnant respondents, we use a dummy variable describing whether or not the individual has small children, aged 6 years or younger. (We also try using lagged values of the children dummy in the analysis without observing any material changes in the weight estimates.) The analysis also considers four levels of educational attainment, in the form of dummy variables: primary school, 2 years of secondary school, more than 2 years of secondary school and higher (post-secondary) education. In addition, we control for panel waves and region of residence; living in northern or southern Sweden, or in a large city (Stockholm, Gothenburg or Malmoe).

We use a set of variables to control for health: (1) self-assessed health, (2) pain or discomfort due to disease(s), (3) anxiety, nervousness and uneasiness, and (4) mobility. Self-reported health functions as the general measure of health, while the other measures reflect different dimensions of health; the impact of suffering from a disease, of mental health status and of physical ability. In the first two waves, the measure of self-assessed health uses a three-point scale (“good,” “between good and bad” and “bad”). In the last two waves, the measure uses a five-point scale (“very good,” “good,” “between good and bad,” “bad” and “very bad”). We construct a measure of self-assessed health using the three-point scale, merging assessments of “very good” and “very bad” health into the categories of “good” and “bad” health, respectively. Bad health receives the lowest score (1) and good health the highest score (3).Footnote 6 In the ULF survey, respondents are asked to specify up to six diagnoses from which they suffer and to assess the pain or discomfort experienced because of each diagnosis. Based on the reported frequency and intensity of the pain or discomfort, we construct a measure that ranks the pain along a three-point scale, where high levels of pain receive the highest score (3) and low levels of pain receive the lowest score (1). The variable measuring anxiety, nervousness and uneasiness is also constructed in the same way: a three-point scale indicating severe problems by the highest score (3) and no problems by the lowest score (1). The mobility variable indicates whether the respondent can run a short distance when necessary (e.g., when trying to catch a bus). Table 1 provides the descriptive statistics of our sample.

Table 1 Descriptive statistics Full size table

Attrition bias is a potential problem because individuals with certain characteristics may drop out of the panel between the survey waves. We investigate the extent of the attrition bias by comparing the variable means in the panel sample, separated into three groups. Group 1 contains observations belonging to individuals appearing once in the sample and group 2 contains observations belonging to individuals appearing twice in the sample. Because of our use of lagged weight variables, single and double appearances imply that the individuals have responded twice and three times respectively in the survey. In these two groups there are individuals who have responded on all possible occasions, individuals who have not responded on one or two occasions as well as individuals whose responses are excluded from our sample because of the age restrictions we set up. Group 3 contains the observations of individuals appearing three times in the sample, i.e., responding in all four survey waves. Table 4 in Appendix 1 shows the variable means per group (the first three columns) and presents the p values of the t tests when we compare the groups pairwise: group 1 to group 2, group 2 to group 3 and group 1 to group 3 (the last three columns). Generally, when we compare the p values of the pairwise t tests, we observe that the characteristics of group 1 differ significantly at 5 % from the other two groups (column 4 and 6) more often than the characteristics of group 2 compared to group 3 (column 5). Focusing on the comparison of group 1 and group 3 (last column), we note that among other things group 1 tends to earn less, have invested less in higher education (significant at 10 %), have worse health, be less overweight, be younger and have more immigrant representation. Notably, there is no significant difference in average obesity. In fact, across all three groups we observe increasing average income, as well as age, but no significant differences in obesity. However, group 3 is significantly more overweight on average. Partly these observations may indicate a positive relationship among age, income and weight. Indeed, when studying the means for income and weight variables of group 3, while decreasing the maximum age limit, we find that the means become more like the ones of group 1. Overall, we find little indication that attrition bias is a major problem for our analysis.Footnote 7