Gender Matters: Female Policymakers Influence in Industrialized Nations

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The chart below plots cross-country estimates of the share of women who are not involved in decisions about their own income. The line shows national averages, while the dots show averages for rich and poor households i. As we can see, in many countries, particularly in Sub-Saharan Africa and Asia, a large fraction of women are not involved in household decisions about spending their personal earned income.

Why do countries with more gender equality have less women in STEM?

And this pattern is stronger among low-income households within low-income countries. Above we focus on whether women get to choose how their own personal income is spent. In the next chart we plot the share of currently married women who report having a say in major household purchase decisions, against national GDP per capita. We see that in many countries, notably in Sub-Saharan Africa and Asia, an important number of women have limited influence over major spending decisions.

Economic inequalites between men and women manifest themselves, not only in terms of wages earned, but also in terms of assets owned. For example, as the chart below shows, in nearly all low and middle-income countries with data, men are more likely to own land than women.

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Closely related to the issue of land ownership is the fact that in several countries women do not have the same rights to property as men. These countries are highlighted in the map below. This map from the World Development Report provides a more fine-grained overview of different property regimes operating in different countries.

Inheritance is one of the main mechanisms for the accumulation of assets.

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In the map below we provide an overview of the countries that do, and do not have gender-equal inheritance systems. If you move the slider to , you will see that while gender equal inheritance systems were very rare in the early 20th century, today they are much more common. And still, despite the progress achieved, in many countries, notably in North Africa and the Middle East, women and girls still have fewer inheritance rights than men and boys. Above we show that there are large gender gaps in land ownership across low-income countries.

Here we show that there are also large gaps in terms of access to borrowed capital. The chart below shows the percentage of men and women who report borrowing any money in the past 12 months to start, operate, or expand a farm or business. As we can see, almost everywhere, including in many rich countries, women are less likely to get borrowed capital for productive purposes.

This can have large knock-on effects: In agriculture and entrepreneurship, gender differences in access to productive inputs, including land and credit, can lead to gaps in earnings via lower productivity. Indeed, studies have found that, when statistical gender differences in agricultural productivity exist, they often disappear when access to and use of productive inputs are taken into account.

The previous discussion focused on particularly aspects one by one. What is the the picture on economic inequality in the aggregate? Tracking progress across multiple dimensions of gender inequalities can be difficult, since changes across dimensions often go in different directions and have different magnitudes. Because of this, researchers and policymakers often construct synthetic indicators that aggregate various dimensions.

Here is a map showing scores on this index higher scores denote more economic opportunities for women. The Human Development Report produced by the UN includes a composite index that captures gender inequalities across several dimensions, including economic status. This index, called the Gender Inequality Index, measures inequalities in three dimensions: reproductive health based on maternal mortality ratio and adolescent birth rates ; empowerment based on proportion of parliamentary seats occupied by females and proportion of adult females aged 25 years and older with at least some secondary education ; and economic status based on labour market participation rates of female and male populations aged 15 years and older.

Considering this, Sarah Carmichael, Selin Dilli and Auke Rijpma, from Utrecht University, produced a similar composite index of gender inequality, using available data for the period , in order to make aggregate comparisons over the long run. As we can see, the second half of the 20th century saw global improvements, and the regions with the steepest increase in gender equality were Latin America and Western Europe.

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Interestingly, this chart also shows that in Eastern Europe there was important progress in the period , but there was a reversal after the fall of the Soviet Union. When the gender pay gap is calculated by comparing all male and female workers, irrespective of differences in worker characteristics, the result is the raw or unadjusted pay gap. In contrast to this, when the gap is calculated after accounting for underlying differences in education, experience, and other factors that matter for the pay gap, then the result is the adjusted pay gap.

The idea of the adjusted pay gap is to make comparisons within groups of workers with roughly similar jobs, tenure and education. This allows us to tease out the extent to which different factors contribute to observed inequalities. The following chart, from Blau and Kahn 8 shows the evolution of the adjusted and unadjusted gender pay gap in the US. More precisely, the chart shows the evolution of female to male wage ratios in three different scenarios: i Unadjusted; ii Adjusted, controlling for gender differences in human capital, i. Blau and Kahn further break down the wage gap into contributing factors.

The following chart shows the relative importance of specific labor market characteristics in and Now we see that in the US, education and experience have become much less important in explaining gender differences in wages over time, while occupation and industry have become more important. This means the observable characteristics of workers and their jobs explain wage differences better today than a couple of decades ago. But is this really the case? The unexplained residual may include aspects of unmeasured productivity i.

For example, suppose that women are indeed discriminated against, and they find it hard to get hired for certain jobs simply because of their sex. This would mean that in the adjusted specification, we would see that occupation and industry are important contributing factors — but that is precisely because discrimination is embedded in occupational differences!

Hence, while the unexplained residual gives us a first-order approximation of what is going on, we need much more detailed data and analysis in order to say something definitive about the role of discrimination in observed pay differences. I will discuss the evidence on discrimination further below. The set of three maps below, taken from the World Development Report , shows that today gender pay differences are much better explained by occupation than by education.

This is consistent with the point already made above using data for the US: as education expanded radically over the last few decades, human capital has become much less important in explaining gender differences in wages. This blog post from Justin Sandefur at the Center for Global Development shows that education also fails to explain wage gaps if we include workers with zero income i.

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All over the world women tend to do more unpaid care work at home than men — and women tend to be overrepresented in low paying jobs where they have the flexibility required to attend to these additional responsibilities. Goldin shows that when one looks at the data on occupational choice in some detail, it becomes clear that women disproportionately seek jobs, including full-time jobs, that tend to be compatible with childrearing and other family responsibilities.

In other words, women, more than men, are expected to have temporal flexibility in their jobs. Things like shifting hours of work and rearranging shifts to accommodate emergencies at home. And these are jobs with lower earnings per hour, even when the total number of hours worked is the same. The importance of job flexibility in this context is very clearly illustrated by the fact that, over the last couple of decades, women in the US increased their participation and remuneration in only some fields.

In a recent paper, Goldin and Katz 13 show that pharmacy became a highly remunerated female-majority profession with a small gender earnings gap in the US, at the same time as pharmacies went through substantial technological changes that made flexible jobs in the field more productive e.

The chart below shows how quickly female wages increased in pharmacy, relative to other professions, over the last few decades in the US. Closely related to job flexibility and occupational choice, is the issue of work interruptions due to motherhood. Lundborg, Plug and Rasmussen 15 provide evidence from Denmark — more specifically, Danish women who sought medical help in achieving pregnancy. We explain the decline in annual earnings by women working less when children are young and getting paid less when children are older. We explain the decline in hourly earnings, which is often referred to as the motherhood penalty, by women moving to lower-paid jobs that are closer to home.

But this was not the case for men with children, nor the case for women without children. These patterns are shown in the chart below. The first panel shows the trend in earnings for Danish women with and without children. The second panel shows the same comparison for Danish men.

This is why women must play a greater role in the global economy

Note that these two examples are from Denmark — a country that ranks high on gender equality measures and where there are legal guarantees requiring that a woman can return to the same job after taking time to give birth. The next chart shows similar estimates, but for a larger selection of rich countries. These estimates rely on the same empirical approach, specification and sample selection, so results are comparable. As we can see there is a striking similarity: The arrival of children creates a gender gap in earnings. In all these countries women experience a large, immediate and persistent drop in earnings after the birth of their first child, while men are essentially unaffected.

Yet there are sharp differences in the magnitude of this effect. But there are also interesting differences in short-run dynamics e.

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Even when there is generous paid leave and subsidized childcare, as long as mothers disproportionately take additional work at home after having children, inequities in pay are likely to remain. The discussion so far has emphasised the importance of job characteristics and occupational choice in explaining the gender pay gap. This leads to obvious questions: What determines the systematic gender differences in occupational choice? What makes women seek job flexibility and take a disproportionate amount of unpaid care work?

One argument usually put forward is that, to the extent that biological differences in preferences and abilities underpin gender roles, they are the main factors explaining the gender pay gap. In their review of the evidence, Francine Blau and Lawrence Kahn 17 show that there is limited empirical support for this argument.

To be clear, yes, there is evidence supporting the fact that men and women differ in some key attributes that may affect labor market outcomes. You can influence tastes, and you can certainly teach people to tolerate risk, to do maths, or to negotiate salaries. In contrast, the evidence does suggest that social norms and culture, which in turn affect preferences, behaviour and incentives to foster specific skills, are key factors in understanding gender differences in labor force participation and wages.

Independently of the exact origin of the unequal distribution of gender roles, it is clear that our recent and even current practices show that these roles persist with the help of institutional enforcement.

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Goldin , 18 for instance, examines past prohibitions against the training and employment of married women in the US. These work prohibitions are important because they applied to teaching and clerical jobs — occupations that would become the most commonly held among married women after The map shown below highlights that to this day, legal barriers to fight discrimination are far from being the norm.

However, even after explicit barriers are lifted and legal protections put in their place, discrimination and bias can persist in less overt ways. The thesis is still discussed by feminist and labour economists today. She was dissuaded from pursuing a Ph. Her work on the formal and informal political institutions the influence an economy had enormous influence on a discipline that had become more scientific and mathematical, bringing back some of the more politically focused work more common years ago.

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