The Swedes are supposed to be in a left-wing utopia. Welfare state, ample childcare and long maternity leave but their gender wage gap is almost as bad as in 1980. They must be a misogynist throwback.
Maybe Megan McArdle can explain:
There are countries where more women work than they do here, because of all the mandated leave policies and subsidized childcare — but the U.S. puts more women into management than a place like Sweden, where women work mostly for the government, while the private sector is majority-male.
A Scandinavian acquaintance describes the Nordic policy as paying women to leave the home so they can take care of other peoples’ aged parents and children. This description is not entirely fair, but it’s not entirely unfair, either; a lot of the government jobs involve coordinating social services that women used to provide as homemakers.
The Swedes pay women not to pursue careers. The subsidies from government from mixing motherhood and work are high. Albrecht et al., (2003) hypothesized that the generous parental leave a major in the glass ceiling in Sweden based on statistical discrimination:
Employers understand that the Swedish parental leave system gives women a strong incentive to participate in the labour force but also encourages them to take long periods of parental leave and to be less flexible with respect to hours once they return to work. Extended absence and lack of flexibility are particularly costly for employers when employees hold top jobs. Employers therefore place relatively few women in fast-track career positions.
Women, even those who would otherwise be strongly career-oriented, understand that their promotion possibilities are limited by employer beliefs and respond rationally by opting for more family-friendly career paths and by fully utilizing their parental leave benefits. The equilibrium is thus one of self-confirming beliefs.
Women may “choose” family-friendly jobs, but choice reflects both preferences and constraints. Our argument is that what is different about Sweden (and the other Scandinavian countries) is the constraints that women face and that these constraints – in the form of employer expectations – are driven in part by the generosity of the parental leave system
Most countries have less generous family subsidies so Claudia Goldin’s usual explanation applies to their falling gender wage gaps
Quite simply the gap exists because hours of work in many occupations are worth more when given at particular moments and when the hours are more continuous. That is, in many occupations earnings have a nonlinear relationship with respect to hours. A flexible schedule comes at a high price, particularly in the corporate, finance and legal worlds.
The Minister for Women Paula Bennett and the Ministry of Women published excellent research in February showing there cannot be a gender wage gap driven by unconscious bias. The Minister has blamed a large part of the remaining gender wage gap on unconscious bias.
… up to 84 per cent of the reason for the Pay Gap, that’s right, 84 per cent, is described as ‘unexplained factors.’ That means its bias against women, both conscious and unconscious.
It’s about the attitudes and assumptions of women in the workplace, it’s about employing people who we think will fit in – and when you have a workforce of men, particularly in senior roles then it seems likely you’re going to stick with the status quo – whether they do that intentionally or just because “like attracts like”.
It’s because there is still a belief that women will accept less pay than men – they don’t know their worth and aren’t as good at negotiating.
The reason why this February 2017 research on the motherhood penalty contradicts earlier Ministry of Women research on unconscious bias and the gender wage gap is simple.
There is a large difference in the gender wage gap from mothers and for other women. As the adjacent graphic from Ministry of Women research shows, the gender wage gap for mothers is 17% but it is only 5% from other women.
Source: Effect of motherhood on pay – summary of results Statistics New Zealand and Ministry of Women February 2017.
We men, us dirty dogs all, have no way of knowing whether a female applicant is a mother. Remember we are dealing with unconscious bias, the raised eyebrow, the prolonged pause, the lingering glance, not a conspiracy or a prejudice of which we are self-aware and take overt steps to implement. Unconscious bias is unconscious by definition.
Because the bias against women is implicit and unconscious, we men, dirty dogs all, do not know we are biased, so we do not know we have to make further enquiries to check if the female applicant is a mother so we can discriminate against her more than we do for other women.That is before we consider other drivers of the gender wage gap such as whether there are relatively large spaces between the births of her children.
Large spaces between the birthdays of children greatly increases the gender wage gap because women spend much more time out of the workforce and part-time if they spread births. This reduces their accumulation of on-the-job human capital and encourages women who plan large families to choose occupations and educational majors that do not depreciate rapidly during career interruptions.
I have no idea how an unconsciously biased employer can discover if a woman has children with spaced out ages and therefore discriminate against an even more, unconsciously, of course. We men, dirty dogs all, do not know that in order to discriminate against them, especially in shortlisting for initial hiring when we have no information beyond the application about them.
Do women have more unconscious bias against women than men? If not, there should be differences in the gender pay gap in firms with more women managers or owners.
Perhaps there is more unconscious biased in promotions because managers may have accidentally learnt are the ages of the children of female applicants and unconsciously taken a note to remember that when unconsciously discriminating against them in promotion. This unconscious bias involves a lot of very conscious data collection and retention.
All in all, the unconscious bias hypothesis simply cannot explain such a large difference between the gender wage gaps of parents and non-parents. There is too much evidence whose existence that is strictly forbidden by the hypothesis of unconscious bias against women in the workplace.
A major factor driving the gendered division of labour and household effort is technology. Tiny differences in comparative advantage such as in child rearing immediately after birth can lead to large differences in specialisation in the market work and in market-related human capital and home production related work and household human capital (Becker 1985, 1993).
These specialisations are reinforced by learning by doing where large differences in market and household human capital emerge despite tiny differences at the outset (Becker 1985, 1993). This gendered division of labour and household effort is hard to change because large payments must be made to influence choices about care giving by highly specialised people with large but different accumulations of market and household human capital.
From a luck egalitarian perspective, many of the differences in earnings and occupations flow accidents of birth in deciding gender and who parents might be. Social inequalities that flow from brute bad luck call for interventions to put them right, if they work.
Many laws already make up for brute bad luck such as job protections while on maternity leave, and government funded parental leave pay and child care subsidies. Employers can do little to redress these accidents of birth nor do they have sufficient resources to put them right. For this reason, for example, parental leave pay is usually taxpayer funded rather than employer funded.
Rosen (2004) suggests that the engineering market responds strongly to economic forces. The demand for engineers responds to the price of engineering services and demand shocks such as recessions and defence cuts. Supply and enrolment decisions are remarkably sensitive to career prospects in engineering. Students also appear to use some forward-looking elements to forecast demand for engineers. Many students also change their majors in light on more information on whether the like their current choices and other news (Bettinger 2010).
This evidence of students use forward-looking elements to forecasting the occupational demand for human capital suggests that better information may improve these choices. The government has made a distributional judgment to expand the choices open to women. The growing evidence of relatively accurate forward looking decisions making by students suggests that they will respond to additional information on prospects in different careers.
In addition, earnings from some occupations are also more uncertain than others. The STEM occupations are an example where shortages and, in particular, surpluses are more common because durable goods industries bear the bulk of business cycle risk. There is also the political unpredictability of defence and R&D spending. Women seem to prefer jobs that are more secure. Some occupations have higher risks of injury than others. Fewer parents, and both single fathers and single mothers, in particular, enter these more injury prone occupations.
These gender-based preferences about hazards and uncertainties will lead to fewer women entering occupations that are more injury prone or more at risk to recessions and industry-specific downturns. Occupational segregation will still persist in the labour market in the relative absence of either discrimination or a gendered division of labour and household effort.
The growing number of women in the workforce and the domination of women of the graduate labour supply will increase the incentive of employers to make the workplace more family-friendly. Those that do not will lose access to the majority of graduate and other talent.
Various work place amenities can be traded-off in salary packages. In industries and occupations where this is cheap to do, the wage offset will be least. These industries and occupations will attract a large number of women because the net returns to them in cash wages plus amenities is higher than for men who value the greater work life balance less.
Occupational segregation around the clock illustrates the delicate trade-off between cash wages and the costs of flexible hours. Men and women work in much the same occupations between 8 and 6. There are big gaps if you are an early starter or work over dinner time.
Changing the production processes of these industries to induce more women to work unsocial hours would require large reduction in production and pay. Fewer women will not enter occupations with more unsocial hours unless they are paid more than in other jobs where it is cheaper to provide work-life balance and still pay higher cash wages.
Occupations and industries where family friendliness is more costly will be male dominated because women qualified enough to enter these occupations will go elsewhere where the cash wages sacrifice is less for work-life balance. Influxes of women will occur in industries where technological trends lower the cost of work-life amenities and the growing number of female skilled workers forces employers’ hands. They must adapt or lose out in competition for talent. The large influx of women into male dominated higher skilled occupations and professions suggests that some occupations can provide work-life balance at a lower cost than others.
The main drivers of female occupational choice are supply-side (Chiswick 2006, 2007). This self-selection of females into occupations with more durable human capital, and into more general educations and more mobile training that allows women to change jobs more often and move in and out of the workforce at less cost to earning power and skills sets.
Chiswick (2006) and Becker (1985, 1993) then suggest that these supply side choices about education and careers are made against a background of a gendered division of labour and effort in the home, and in particular, in housework and the raising of children. These choices in turn reflect how individual preferences and social roles are formed and evolve in society.
These adaptations of women to the operation of the labour market, in turn, reflect a gendered division of labour and household effort in raising families and the accidents of birth as to who has these roles (Chiswick 2006, 2007; Becker 1981, 1985, 1993).
The market is operating fairly well in terms of rewarding what skills and talents people bring to it in light of a gendered division of labour and household effort and the accidents of birth. The issue is one of distributive justice about how these skills and family commitments are allocated and should be allocated outside the market between men and women when raising children. As in related areas such as racial and ethnic wage and employment gaps, these gaps are driven by differences in the skills and talents that people acquired prior to entering the labour market. …
Developments in recent decades greatly increased the options for women to combine careers and family. The unadjusted gender wage gap is narrow while the gender education gap has reversed. The progress with closing the gender gaps in employment and education in recent decades makes the crafting of further gender-based policy interventions more challenging.
The remaining gender gaps reflect much more thorny issues such as work-life balance rather than mid and late 20th century concerns such as large gender differences in education participation and attainment, sex discrimination and full-time motherhood raising much larger families.
Parental leave, early childhood education and child care subsidies have increased in New Zealand in recent years. Early childhood education spending is high in New Zealand by international standards but spending on child care subsidies is less generous (OECD 2012).
The main drivers of greater female labour force participation and greater investment in long-duration professional educations were access to reliable contraception, the rise of service sector and other jobs that depend on brains instead of brawn, the automation of housework with white goods, and rising incomes increasing the opportunity cost of having a large number of children.
This is a first in a series of blogs on occupational segregation and gender.
Gender gaps in injuries and fatalities go beyond those industries demanding physical.strength.
There are noticeable differences in the occupational choices of single people, parents, and single parents. Women choose safer jobs than men; single moms or dads are most averse to fatal risk because they have the most to lose. About one quarter of occupational differences between men and women can be attributed to the risks of injury and death.
All but 3 of the fatal workplace accidents in New Zealand in 2015 were men.
Source: Accident Compensation Corporation, Statistics New Zealand.
This gender gap in the risk of injury and death can lead to a significant gender wage gap because of the wage premium associated with these risks and in particular the risk of death as Viscusi explained.
The bottom line is that market forces have a powerful influence on job safety. The $120 billion in annual wage premiums referred to earlier is in addition to the value of workers’ compensation. Workers on moderately risky blue-collar jobs, whose annual risk of getting killed is 1 in 10,000, earn a premium of $300 to $500 per year.
The imputed compensation per “statistical death” (10,000 times $300 to $500) is therefore $3 million to $5 million. Even workers who are not strongly averse to risk and who have voluntarily chosen extremely risky jobs, such as coal miners and firemen, receive compensation on the order of $600,000 per statistical death…
Other evidence that the safety market works comes from the decrease in the riskiness of jobs throughout the century. One would predict that as workers become wealthier they will be less desperate to earn money and will therefore demand more safety.
A German study was able to reduce a raw gender wage gap of 20% to 1% after accounting for differences between gender in the risk of injury and death in addition to the usual factors. This 2007 study found that they were the 2nd study ever to make this adjustment.