Gender and inequality of opportunity in Sweden

Economic inequality is of substantial academic and public-policy interest. There are many conceptual approaches to its empirical measurement. Although the economics literature has traditionally focused on inequality of outcomes, in recent years there has been an upsurge of interest in inequality of opportunity (see, for example, Almås et al. 2011; Björklund et al. 2012; Checchi and Peragine 2010; Ferreira and Gignoux 2011). The equality of opportunity literature leans on the idea that individual outcomes depend in part on individual effort and in part on circumstances that are beyond an individual’s control, such as parental income and childhood standard of living. The core argument is that inequalities due to effort differences are ethically defensible, while those due to differences in circumstances are not. Thus, a society is said to have achieved equality of opportunity if circumstances have no influence on outcomes.

A fast-growing empirical literature measures inequality of opportunity by estimating the share of outcome inequality that can be attributed to circumstances.¹ One circumstance that could be seen as a natural starting point – gender – has largely been ignored, although admittedly there is some debate in the conceptual literature as to whether it should be viewed as such (see Roemer and Trannoy 2015). The purpose of this paper is to address this gap in the empirical research by analyzing equality of opportunity in long-run income for Swedish men and women. Analyzing men and women separately, we examine if there are gender differences in inequality of opportunity and in the role of particular circumstances. Next we pool men and women and treat gender as a circumstance, allowing us both to assess inequality of opportunity in the overall population and to compare the importance of gender to that of other circumstances, such as parental income and parental education.

More insight into the extent to which income inequality can be attributed to unequal opportunities, and identifying the relative contribution of different circumstances, is informative for policymakers concerned with equality of opportunity. Sweden provides an interesting case for the analysis of equality of opportunity and gender, as the Swedish labour market is characterized by low levels of income inequality (Gottschalk and Smeeding 1997; Björklund and Freeman 1997) as well as by a low gender pay gap relative to other countries (Blau and Kahn 2003).²

Our paper extends the work of Björklund et al. (2012), who estimate inequality of opportunity in long-run income for Swedish men using the most comprehensive set of circumstances so far in the literature. As circumstances, they include parental long-run income, parental education, family structure, IQ and body mass index. They find that about one third of the inequality in long-run income among Swedish men can be attributed to these circumstances, and that the most important circumstances are parental income and own IQ. While using the same empirical approach and to some extent the same data, our paper differs from theirs primarily in that we study both men and women, allowing us to analyze gender differences in inequality of opportunity and to include gender among the circumstances. In addition, we include non-cognitive ability in the vector of circumstances (but exclude body mass index from our analysis as it turned out to be of minor importance among men).

Our data on IQ and non-cognitive ability stem from the military enlistment tests. Because these tests were mandatory and therefore widely available only for men, we approximate women’s IQ and non-cognitive ability by those of their brothers. Our dataset contains about 370,000 Swedish men and women born between 1952 and 1964. We examine the inequality of total market pretax income averaged over seven years (ages 37–43 in the main analysis).

Like Björklund et al. (2012), we measure inequality using four inequality indices (the Gini and the GE family using parameter values 0, 1, and 2), measure the contribution of each circumstance using a Shapley-value decomposition, and allow for circumstances to contribute to inequality both directly (by shifting expected income) and indirectly (by shifting income dispersion), as the distribution of income for given circumstances may vary.

We find that opportunities are slightly more equal among women than among men, as circumstances account for up to 25% of income inequality among women and up to 31% of income inequality among men, but that the same circumstances—parental income, IQ, non-cognitive ability and differences in the distribution of effort—are roughly equally important determinants of inequality. Pooling men and women and treating gender as a circumstance, circumstances account for at most 38% of income inequality, so roughly two fifths of inequality in long-run income in Sweden can be attributed to inequality of opportunity. Gender accounts for the largest share, up to 13% of overall inequality, or almost a third of overall inequality of opportunity.

The rest of the paper is structured as follows. In Sect. 2, we provide a literature review. In Sect. 3 we present the method and in Sect. 4 we describe the data. Section 5 reports the results. Finally, in Sect. 6, we summarize our findings and provide a concluding discussion.

In this section, we selectively review equality of opportunity research with a focus on gender. We then turn to discussing how our approach differs from that typically taken in studies of gender differences in labour market outcomes.

Research on empirical equality of opportunity is rapidly expanding and also varies along several dimensions, including the definition of equality of opportunity, the set of circumstances examined and the estimation method employed.³ One feature that many of the papers share, however, is that they rarely consider gender. The importance of gender in comparison to other circumstances, such as parental income and education, is largely unknown. There is also limited evidence on gender differences in inequality of opportunity as well as on the relative importance of different circumstances.

Most papers only study men, but we provide here an overview of empirical research on equality of opportunity that includes both men and women. We limit the review to papers that study inequality of opportunity in income. We start by summarizing four studies that include gender as a circumstance.⁴

Niehues and Peichl (2014) examine inequality of opportunity in Germany and the US, using as outcomes gross as well as net earnings, measured both annually and in the long-run. Measuring inequality using the mean log deviation, they estimate lower bounds on inequality of opportunity including as circumstances gender, height at birth, year of birth, indicators for whether the individual was born in a foreign country, born in East Germany (Germany), or born in the South (US), race (US), degree of urbanization of the place where the individual was born, father’s occupation and father’s education. They rely on kinship information to estimate upper bounds which obviates the need to specify the circumstances. However, they do not report the importance of gender in comparison to the other circumstances included in the analysis.

In addition to including gender as a circumstance, Niehues and Peichl (2014) also conduct separate analyses for men and women and, while they do not comment on gender differences, their results (in Table 4) allow us to assess this. For both countries, most lower bound estimates of inequality of opportunity are higher for men than for women, while there seems to be a gender difference in the opposite direction for the upper bound estimates. They do point out that when men and women are analyzed separately, inequality of opportunity is lower than when the samples are pooled and gender is treated as a circumstance. They argue that gender is an important driving force of inequality of opportunity and that the main reason for this is that women work fewer hours than men.

Peichl and Ungerer (2016) explore how inequality of opportunity varies when circumstances are expanded to include characteristics of their partners. The standard in the literature is that partners’ characteristics are not regarded as circumstances, but, examining the same inequality measure, outcomes, and individual circumstances as Niehues and Peichl (2014), they also include some of the individuals’ spouse’s characteristics to again explore lower bounds of inequality of opprtunity. They do not report how important gender is relative to the other circumstances but they do provide separate analyses of inequality of opportunity by gender. In line with the results in Niehues and Peichl (2014), they find that inequality of opportunity is generally higher for men than for women. When a partner’s characteristics are treated as circumstances, inequality of opportunity increases more for women than for men, decreasing the gender difference in inequality of opportunity.

Ferreira and Gignoux (2011), in turn, examine inequality of opportunity in Brazil, Colombia, Ecuador, Guatemala, Panama and Peru. Using household income and consumption expenditures per capita as well as individual labour earnings, they develop a scalar measure based on the mean log deviation. Their circumstance vector includes ethnicity, father’s occupation, father’s education, mother’s education and birth region. When examining individual labour earnings, gender is also included in the set of circumstances. They then find that gender accounts for between 0.2% (Colombia) and 5.8% (Guatemala) of inequality, suggesting that gender is much less important than other circumstances, such as parental education and father’s occupation. To put the importance of gender into perspective, it can be compared to that of mother’s education, which accounts for between 9.4 (Panama) and 11.9% (Brazil) of total inequality in individual labour earnings. It is not possible to draw any conclusions about whether there are any gender differences in the relative importance of circumstances from the results they present. A related study, de Barros et al. (2009) also includes Mexican data and find that gender accounts for 3–4% of inequality in individual labour earnings. In comparison to the importance of the other circumstances, gender plays a small role. The Mexican results on gender are thus in line with the results from the other Latin American countries.⁵

Having summarized the papers that include gender among the circumstances, we now proceed to the papers that investigate equality of opportunity separately for men and women. Studying Italy using the mean log deviation of gross earnings and parental education as the sole circumstance, Checchi and Peragine (2010) find that between 15 and 20% of income inequality in the overall population can be attributed to inequality of opportunity, depending on its definition.⁶ They do not comment on gender differences in inequality of opportunity but results reported in their Tables 4 and 5 suggest that parental education accounts for a larger share of income inequality for men than for women although it is unclear whether this gender difference is statistically significant (standard errors are not provided).

Bourguignon et al. (2003) analyze inequality of opportunity in Brazil in both individual earnings and household per capita income. Measuring circumstances by parental education, father’s occupation, race and region of origin, they find inequality of opportunity to be high in Brazil (compared to other countries), measured by both the Gini coefficient and the Theil index, and that parental education is the most important circumstance. They also find that, when circumstances are equalized, the Gini coefficient for individual earnings decreases by 8–10% points for both men and women. They do not comment on whether there is a gender difference in the shares of total earnings inequality that can be attributed to inequality of opportunity (and it is difficult to draw any conclusions about this from the graphs presenting the results).

Finally, Nilsson (2005) analyzes inequality of opportunity in Sweden. Unlike the above papers, Nilsson (2005) does not explicitly examine the share of inequality of opportunity in conventional income inequality measures. Instead, he regresses labour income and disposable income on circumstances, which include a wide range of parental, family and parish characteristics. Since the circumstances are associated with income, he concludes that Sweden has not achieved equality of opportunity. He also estimates conditional indirect opportunity sets showing that men whose parents belong to the 25th percentile in the income distribution must exert more effort to reach the average income than men whose parents belong to the 75th percentile. For women, the difference depending on parents’ income percentile is not statistically significant. These findings may thus suggest that women have more equal opportunities than men.

To summarize, we contribute to the existing literature on equality of opportunity in income by (1) including both men and women, (2) analyzing men and women separately, and (3) exploring the importance of gender, relative to other circumstances, in generating income inequality.

Above, we discuss papers on (in)equality of opportunity that address gender, either including it as a circumstance or by analyses separately for men and women. Here, we discuss how our paper relates to research on gender differences in labour market outcomes (see e.g. Altonji and Blank 1999). Our approach to measuring the importance of gender in the labour market differs from the approaches typically taken in this literature in two main ways.

First, an individual’s counterfactual income is defined differently. In typical studies of gender income gaps, female mean income is compared to that of men, so the female counterfactual mean is the male mean (“income” can mean hourly or annual wages or earnings as well as other concepts). In research that explores the extent to which gender income gaps can be attributed to discrimination, the female counterfactual income is typically taken to be that of a man with the same income-generating characteristics. By contrast, we define the counterfactual income of a woman as the income she would have, conditional on her other circumstances, if the gender difference in income was zero.

Second, our approach to aggregating individual incomes is different. Most papers on gender differences focus on the average difference between an counterfactual and actual incomes. Jenkins (1994) points out that one drawback of this is that it is consistent with very different income distributions. Differences across the male and female distributions can and have been studied e.g. using conditional quantile regressions. This has for instance been done to examine if there is a “glass ceiling” in the labour market (e.g. Albrecht et al. 2003; Arulampalam et al. 2007). We go one step further and investigate to what extent inequality can be attributed to inequality of opportunity and thus account for differences across the whole distribution.⁷

We now turn to our methods. We start by describing the conceptual framework, including the regression-based approach we take, followed by a description of our decomposition method. Finally, we outline the consequences of using proxy measures for IQ and non-cognitive ability.

Our approach is based on Keane and Roemer (2009), Betts and Roemer (2007), Lee (2008) and Björklund et al. (2012). We are interested in what fraction of inequality of long-run income, Y with distribution \(F_Y\), can be attributable to circumstances and effort, respectively. Inequality of long-run income is measured using four relative inequality indices namely the Gini coefficient (Gini), the mean log deviation (GE[0]), the Theil index (GE[1]), and one half of the squared coefficient of variation (\(2\times \) GE[2], CV\(^{2}\)). As is well known, the indices vary by their sensivity to income differences in the middle (Gini), lower tail (GE[0] and GE[1]), and upper tail (CV\(^{2}\)) (Jenkins and Van Kerm 2009).

Circumstances are captured by partitioning the population (and sample) into discrete types, indexed by t, one for each unique set of circumstances. The key idea is that an individual should not be held accountable for income differences due to type.

Denote each of the J circumstances by \(X_j\), which can take \(K_j\) specific values. The types t are elements of the set \(\mathcal T\), each element of which consists of a particular combination of circumstances \({\mathbf {X}}^t=(X_1=x^t_1, X_2=x^t_2, X_3=x^t_3,\ldots , X_J=x^t_J)\), so a sample individual i of type t has \({\mathbf {X}}^t_i\). The effort of an individual of a certain type is defined here as the deviation of the individual’s actual income \(Y^t_i\) from her expected income, given type, \(\text {E}[Y|{\mathbf {X}}^t]\), defined through the regressionCircumstances are included in the regression as a set of indicators, so with J circumstances, each with \(K_j\) categories, \(\sum _j K_j - J \) is the total number of indicators in the regression equation. We measure an individual’s effort by the residual from estimating this regression.⁸

One issue with this approach is that the distribution of the regression error may vary across types, i.e., the distribution of effort may be heterogeneous across types. Each type is characterized by an expected income \(\text {E}[\ln Y|{\mathbf {X}}^t]\), but may in addition be characterized by a distribution of effort \({\upvarepsilon }^t\), \(F_{\upvarepsilon }^t\). Recall that the key idea is that an individual should not be held responsible for income differences attributable to type. As the type-specific distribution of effort is a consequence of circumstances, individuals should not be held accountable for differences due to circumstance-related differences in the distribution of effort. Thus, we need to adjust also for differences in type-specific heteogeneity.

We follow Björklund et al. (2012) and treat the error in Eq. 1 as being heteroscedastic, so each type t has its own variance \({\upsigma }_t^2=\text {Var}[{\upvarepsilon }^t|{\mathbf {X}}^t]\). In order to capture the importance of that heterogeneity, we add and subtract a term that has a homogeneous variance. The most natural candidate for standardizing the distribution of effort across types is to choose the overall variance, which, since the expectation of the error is zero in all groups, is given by the weighted average of the variance within types as \({\upsigma }^2=\sum _t f_t {\upsigma }_t^2\), where \(f_t\) is the population share of type t. This allows us to distinguish between one error term, \(\widetilde{\upvarepsilon }^t_i={\upvarepsilon }^t_i - u_i\), whose variance \(\widetilde{\upsigma }_t^2\) varies across types and is treated as a circumstance, and one, \(u_i\), whose variance \({\upsigma }^2\) is constant and is treated as effort, leading to the regression equationTo implement this, we first estimate all \({\upbeta }\) coefficients and then, based on the OLS residuals, the type-specific variances \({\upsigma }_t^2\). In practice, however, some types have very few observations or very small estimated variances, leading to very large standardized residuals \(\hat{u}_i\). For this reason, we regress the estimated variances on the background characteristics, and use the fitted values from that regression as the basis for \({\upvarepsilon }^t_i {\upsigma }/{\upsigma }_t\). This procedure smooths out the more extreme values.

We now turn to the decomposition of income inequality. With J circumstances, type-specific effort and individual effort, we have \(J+2\) factors whose impact on inequality we want to measure. The importance of a factor is measured by comparing the inequality of long-run income when the factor is “on”—i.e., the factor is allowed to vary as it does in the population—to when it is “off”—i.e., variation due to the factor is eliminated by replacing for every observation its actual value with its mean value. For the circumstances, the mean value equals the average proportion in each category of the circumstance weighted by the corresponding coefficients.

Formally, from Eq. 1, a circumstance j contributes \({\mathbf {X}}_{ji}'{\varvec{\upbeta }}_j\) to income. We compare inequality when we allow the circumstance to contribute \({\mathbf {X}}_{ji}'\widehat{\varvec{\upbeta }}_j\) to the income of individual i (providing the actual variation in the circumstance j to income) with one in which we have replaced that by \( \overline{{\mathbf {X}}}_j'\widehat{\varvec{\upbeta }}_j\), thus eliminating variation across individuals from that circumstance.

The contribution of a factor to inequality may depend on what other factors vary (are “on”). That is, the contribution depends on the order in which we eliminate factors. We take this into account by estimating the contribution of a factor by its average contribution across all possible elimination sequences, a procedure that is called Shapley-value decomposition (Shorrocks 2013; Chantreuil and Trannoy 2013).

To do this, we first generate the powerset of the \(J+2\) factors. For each element in the powerset, we construct the income for each observation counterfactually by allowing the factors included in the element to vary and eliminating the rest. That is, for each element in the powerset, we have a set of factors that we turn “on”, while we let the rest of the factors be “off”. For instance, the element {parental income, gender} dictates that we should let parental income and gender be “on”, and the rest of the factors be “off”. We then take the antilog of the counterfactual incomes calculated in this way and compute the inequality indices.

Then, for each of the factors, we take every element of the powerset that does not include it, and compare inequality in that set with the set that is otherwise identical but does include the factor. The importance of a factor is measured as the average of all such comparisons.⁹ The Shapley-value decomposition approach has several benefits (see Shorrocks 2013; Chantreuil and Trannoy 2013). Among others, it results in an additive decomposition of inequality, that is, the sum of all contributions is the value of overall inequality.¹⁰

Our method is based on discrete types. In order to fit continuous circumstances into this framework, we divide them into groups. This approach ignores some within-type variation in circumstances and thus underestimates the importance of circumstances and overestimates the role of effort. However, one advantage of using types is that our underlying regression model has a quite flexible functional form. It is a challenging task for future research to develop the analysis of equality of opportunity to the case of continuous circumstances.¹¹

The inclusion of women and treatment of gender as a cirumstance is a key difference between this paper and Björklund et al. (2012). As enlistment data is unavailable for women, we use their brothers’ IQ and non-cognitive ability as proxies for own IQ and non-cognitive ability. For symmetry, in our main specification, we measure IQ and non-cognitive ability of men also using the information of their brothers. The use of proxy measures has several consequences for our analysis.

First, we need to restrict our main analysis to only those individuals who have a brother who has taken the military enlistment test.¹² Second, we need to make a strong assumption, which is that a brother’s IQ and non-cognitive ability are equally good measures of his sister’s abilities as they are of his brother’s abilities. That is, we assume that the measurement error is similar for mixed and same-sex siblings. Bouchard and McGue (1981) suggest that brother–brother and brother–sister correlations in IQ are very similar. In addition, using a large Swedish register dataset, Grönqvist et al. (2016) find that the brother–sister correlations in IQ and non-cognitive ability amount to 92 and 93% of the brother–brother correlations. These findings lend plausibility to the assumption that the measurement error is similar for mixed and same-sex siblings.

Third, our estimated regression coefficients will suffer from attenuation bias. We correct for this bias by comparing the coefficient estimates for men when using their own versus their brothers’ characteristics and applying the difference between the two sets of estimates to each coefficient for women. We also correct for this bias when using brother proxies for men.

Finally, the Shapley-value decomposition can also be biased, for two distinct reasons. First, in the Shapley-value decomposition, the estimated regression coefficients suffer from attenuation bias. This source of bias we do correct for as described above. However, the Shapley-value decomposition will also biased because of classification errors. Recall that we divide our circumstances into groups. When using proxy measures, we classify some individuals into other IQ and non-cognitive ability groups than we would have based on their own characteristics. We do not adjust for this bias.

We now proceed to describe the data we use. The data have been constructed by combining information from several Swedish administrative registers. A first and basic source is Statistics Sweden’s so-called Multi-generational register. This is a register of all individuals who were born 1932 and onward, and who have ever received a unique national registration number from 1961 and onward.¹³ For the Swedish population defined in this way, the register contains information about biological and adoptive parents along with their national registration number. Our analysis sample is based on a 35% random sample of the Swedish population born 1952–1964 as defined in this register. The multi-generational register is used to identify parents and siblings.

The second source is the set of quinquennial censuses conducted from 1960 to 1980. We identify our main sample children in the households of these censuses as well as other individuals in the household. Thus, we can determine whether the individuals in the child generation lived with their biological parents in the census years.

The third source is Statistics Sweden’s income register, which in turn comes from the Swedish tax assessment procedure. Such data are available starting in 1968. The income register provides data on total income from all sources of income—from work, self employment, capital, real estate—and from 1974 onward, some transfers. These incomes are used for both parents and children. The parents’ data stem from their self-reported compulsory tax assessments. The childrens’ data stem in part from compulsory reports by employers to the tax authorities.

The fourth source is the Swedish Military Enlistment Battery, which provides data on cognitive (IQ) and non-cognitive ability. For the cohorts we examine, military service was mandatory only for men. Thus, enlistment data are unavailable for women.¹⁴ The purpose of these tests is to classify Swedish men to different military positions with different demands on general intellectual and non-cognitive capacity. Generally, the tests were done during the year the men turned 18. In our main specification, we measure IQ and non-cognitive ability of both men and women using the information of their brothers.

To construct our analysis sample, we make use of the fact that all four data sources contain the unique Swedish national registration number, by means of which we can merge the information from the four sources.

Our outcome variable is total market income before taxes as provided by Statistics Sweden. It includes income from all sources, that is, labour, business, capital, realized capital gains as well as some taxable social transfers such as unemployment insurance, sickness pay, parental leave payment, and pensions. We use the average of real total income over the years when sons and daughters were 37–43 years old. This age interval is based on the findings in Böhlmark and Lindquist (2006), who study life-cycle variations in the associations between current and lifetime income for Swedish men and women. Böhlmark and Lindquist (2006) show that life-cycle bias is a more serious problem for women than for men, because there is greater heterogeneity in women’s labour supply and income profiles over the life-cycle. For the cohorts born closest to ours—born 1948–1950—they find that measuring women’s incomes around the age of 40 provides the best available estimate of their long-run income. Therefore, we center our age interval at 40. Further, the results in Böhlmark and Lindquist (2006) suggest that averaging over an appropriately chosen age interval eliminates much of transitory income variation while also eliminating much of the life-cycle bias. While the result is not necessarily a good measure of permanent income, it is as far as we an tell, the best available. Following earlier research in Sweden using a similar approach, we call this long-run income. In Sect. 5.3, we test the robustness of our results to measuring income at ages other than 37–43.

Next, we turn to our background characteristics, the circumstances that define our types (when characteristics are continuous, we divide them into groups). Apart from gender, we have six circumstances, namelythe combination of which gives us 1152 distinct types or, when combined with gender, 2304 distinct types.

IQ is measured by the summary measure of intellectual ability provided in the enlistment data, which is based on four different cognitive tests: instructions, synonyms, metal folding and technical comprehension. The subtests are designed to measure the primary IQ factors induction, verbal comprehension, spatial ability and technical comprehension.¹⁵ We split the summary measure of intellectual ability into four quartile groups.

Non-cognitive ability is measured based on a structured interview during military enlistment, which was mandatory for Swedish men aged 18–20. The approximately 25-min interviews are conducted by a psychologist charged with rating an individual’s suitability for military service. The conscript’s overall suitability for military service is given a score from 1 to 9. The psychologist determines the score based on a number of specific characteristics such as level of responsibility, independence, outgoing character, persistence, emotional stability, power of initiation and social skills (Lindqvist and Vestman 2011). We split the measure of non-cognitive ability into four quartile groups.

It is important to note that we treat non-cognitive ability and IQ as circumstances since we do not hold individuals responsible for their actions (including their effort) before the age of 18. As Björklund et al. (2012) point out, to classify IQ as a circumstance is potentially controversial, because these IQ test scores can depend on earlier educational choices and performance, which in turn may depend on individual effort. A similar reasoning may apply to non-cognitive ability. Ultimately, what counts as circumstances is a matter for the social planner. In Sect. 5.3, we check the robustness of our results by excluding IQ and non-cognitive ability from the vector of circumstances.

The variable definitions imply that we need to make some sample restrictions. We focus on individuals born in Sweden as there is limited information on the parents of foreign-born inhabitants and only include individuals for whom both the biological mother and biological father are non-missing in the Multigenerational register. Furthermore, we limit our main analysis samples to individuals who have at least one brother with non-missing information on both IQ and non-cognitive ability. Thus, we eliminate singletons from our main analysis samples. In case an individual has more than one brother, we take the average across all brothers as the measures of IQ and non-cognitive ability.

As mentioned in Sect. 3, find that the brother-brother correlation in IQ and non-cognitive ability is slightly higher than the brother–sister correlation. While, by contrast, Bouchard and McGue (1981) find no such difference in brother-brother and brother-sister correlations, this would suggest greater measurement errors for women than for men. On the other hand, conditional on having a brother, women have, on average, more brothers than men. This difference will make the proxy measures more precise for women than for men as we average across more brothers for women than for men. It is not clear which of these effects dominates.

In Table 1 we show some descriptive statistics for our main analysis sample. This sample contains more than 180,000 women and more than 180,000 men with a total sample of about 370,000 individuals roughly but not quite uniformly distributed across birth years 1952–1964; the share tends to increase toward later years indicating increased cohorts sizes.

In the offspring generation, the average income of men is about SEK 100,000 higher than that of women but with a substantially higher dispersion—a Gini of 0.30 as opposed to 0.24 for women. In the parental generation, the difference between men’s and women’s income is larger; the average income of fathers is more than SEK 150,000 higher than that of mothers. Moreover, in the parental generation, the income dispersion is smaller for fathers than for mothers. The Gini is 0.29 for fathers and 0.44 for mothers. In the first rows of Table 1, we show the share of fathers and mothers in each education group. On average, fathers have slightly higher education than mothers. Looking across the columns, we see that parental income and education are roughly equal for men and women, as we would expect them to be.

We begin this section by showing results first for men only, comparing results using own IQ and non-cognitive ability to those obtained using their brothers’ characteristics as proxies for their own. This is followed by our main analysis. In order to investigate whether there are gender differences in the level and structure of inequality of opportunity, we start by analyzing men and women separately, measuring in both cases IQ and non-cognitive ability by those of their brothers. Next we pool men and women and add gender to the circumstance vector. This analysis provides us with an estimate of inequality of opportunity in the overall population and allows us to compare the importance of gender to that of the other circumstances in accounting for income inequality. We close the section with two sets of robustness checks to check if the results are sensitive to the age at which income is measured and if the conclusions regarding the role of gender are robust to excluding IQ and non-cognitive ability from the circumstance vector.

In this paper, we explore equality of opportunity in long-run income for Swedish men and women by analyzing to what extent income inequality can be attributed to circumstances that individuals cannot be held accountable for. The key idea is that a society has achieved equality of opportunity if circumstances have no influence on income.

Our main contribution is to provide insights into the role of gender in inequality of opportunity. Relative to previous studies on equality of opportunity in income, we have the advantage of using a large register dataset with incomes of both men and women along with detailed background information. This dataset allows us to examine the role of gender in two different ways. First, by conducting separate analyses for men and women, we investigate gender differences in the share of income inequality that can be attributed to circumstances. This analysis also shows if the relative importance of each of the circumstances is similar for men and women. Second, and more importantly, we pool men and women and treat gender as a circumstance. Doing this, we analyze inequality of opportunity in the overall population and compare the importance of gender to that of the other circumstances.

Apart from using a large and detailed dataset and analyzing the role of gender in generating income inequality, one advantage of our study is that we examine both the direct and the indirect contributions of circumstances to income. The indirect contribution arises because the distribution of effort may vary with circumstances. As the distribution of effort is a consequence of an individual’s circumstances, we regard income differences due to differences in the distribution of effort as due to circumstances rather than to effort.

The individuals in our sample were born between 1952 and 1964 and we use total market pretax income averaged over seven years as the outcome variable. Our circumstances consist of parental income, parental education, number of siblings, family structure, IQ and non-cognitive ability. Analyzing men and women separately, we find that circumstances account for up to 31% of income inequality among men and up to 25% among women. This implies that there are slightly more equal opportunities among women than among men. Parental income, IQ, non-cognitive ability and differences in the distribution of effort are important contributors to income inequality for both men and women. Pooling men and women and including gender as an additional circumstance, we find that circumstances account for up to 38% of income inequality in the overall population. In other words, up to 38% of income inequality in long-run income in Sweden can be attributed to inequality of opportunity. Of the circumstances considered in this paper, gender turned out to be the most important determinant of inequality of opportunity, accounting for 13% of income inequality. Given the significant role played by gender, it seems important to include both men and women in the analysis sample and to treat gender as a circumstance in order to get as complete a picture as possible of inequality of opportunity in a society. In future research, it would be interesting to look further into potential mechanisms behind the importance of gender as a circumstance. It seems particularly useful to investigate the roles of gender differences in part time work and parental leave take-up.

Our results for the male sample are in line with those of Björklund et al. (2012), but differ from theirs slightly since they did not include non-cognitive ability. To the best of our knowledge, the only two previous studies that report how important gender is relative to other circumstances are Ferreira and Gignoux (2011) and de Barros et al. (2009). Our findings on the importance of gender stand in stark contrast to theirs. While we find that gender is the most important contributor to income inequality in Sweden, they conclude that in Latin America, gender is less important than family background variables such as parental education. However, we cannot draw any firm conclusions from the comparison of these studies since they differ along several dimensions, such as the empirical approach, outcome variable and the circumstances considered. These differences point to the need for more studies on this topic. Moreover, for international comparisons of the role of gender in generating inequality of opportunity, it seems particularly important to take differences in female labour force participation into account.

While we do account for the indirect contribution of circumstances to income inequality arising from differences in the distribution of effort, we do not analyze to what extent each of the circumstances contributes to the differences in the distribution of effort. That is, when we report the importance of each of the circumstances, we only report their direct contributions. In addition to the direct contributions, we also report the joint indirect contribution of all circumstances. Part of the differences in the distribution of effort are likely driven by gender, because of, for example, differences in the variation of hours worked between men and women. Thus, when we conclude that gender accounts for up to 13% of income inequality, we underestimate the total contribution of gender to income inequality since this number does not include the indirect contribution.

Our choice of outcome variable also merits further discussion. First, we use individual income despite the fact that family income may be a better measure of standard of living, especially for women. However, Swedish register data do not for the period we study provide a good measure of family income as we can only observe if individuals live together if they are married or if they have children together. Therefore, we base our analysis on individual income. Second, we use total income rather than wages since total income better reflects standard of living. The use of disposable income, which includes all public transfers and deducts direct taxes, might be better still, but we do not have access to that.

To estimate the share of income inequality in Sweden that is due to inequality of opportunity, we would ideally want to observe all the relevant background factors of individuals. Since we can only observe a subset of these factors, we are likely to overestimate the role of effort in generating income inequality. For instance, if individuals inherit their labour-leisure preferences from their parents, it is an open question to what extent they should be held accountable for income differences stemming from differences in labour supply. But we do not observe parental preferences, so we cannot include them as circumstances in the analysis. Moreover, one may question the fact that we interpret all of the residual as effort, as the residual also contains income differences due to differences in luck (Lefranc et al. 2009). While both the role of inherited labour supply preferences and of luck merit further investigation, doing so with the data at hand is very difficult and must be left for future study.