Every year, the Department of Labor issues a report on the pay gap between women and men.

Women earn a median of $30,0001 per year, while men earn $40,000 per year. In other words, working women earn 75% of what men earn.

But this gap doesn’t take into account the fact that on average, men work more hours than women. According to U.S. census data, men spend an average of 41.0 hours per week at their jobs, while women work an average of 36.3 hours per week.

Many argue that gender discrimination explains a large part of the difference in earnings. Others argue that parenthood and gender roles usually affect women's earnings more than men.

To better understand the pay gap, we classified the respondents according to their marital and parenthood status2. The gap is dramatically higher between married couples versus singles without children. For married parents, the gap is even greater.

But we also found that married fathers work even more than other men, while married mothers work less than married women without kids.

We analyzed the pay gap across hundreds of U.S. occupations. According to our research, in most occupations, the main source of the pay gap lies in the difference between the number of hours spent at work by women and men, and marital status and parenthood explain almost all this difference in working times.

The different behavior of women and men3 has an impact on the gender wage gap. As we will see below, the decision of who does most of the work outside versus who stays at home influences the pay gap in two ways: it modifies the nominal income, but it also influences how much women and men earn per each hour worked4.

A few specific examples

Let's take a look at the most common occupation in the US: Managers. This occupation is representative of the overall trend we see in the United States.

Single male managers without kids earn a median of $60,000 per year, while single female managers without kids earn $58,000 per year. On average, single male managers work 43.7 hours per week, while single female managers work 42.3 hours per week.

This means that men earn 3.4% more but work 3.5% more hours per week.

But when we look at the pay gap between married couples, we see a different picture. Both female and male married managers do have a higher salary. But men earn much more than women.

Male married managers without kids earn a median of $90,000, while female married managers without kids earn a median of $62,000. A pay gap of 31%. In other words, women earn $0.69 for each dollar earned by their male counterparts.

A large part of this gap is explained by the number of hours spent at work. Men tend to work more after they marry. The average weekly working hours of males increase 4.3%, while women keep working the same quantity of hours per week. This explains a part of the gap increase.

But the time spent at work does not explain all of the gender pay gap. Married men managers without kids also earn more for each hour at work: they earn $38.40 per hour while married women without kids earn only $28.70. That means that for each hour spent at their jobs, male married managers without kids earn about 34% more than women. As we will see in detail below, the different hourly rate is related to job market trends.

We can see the same pattern across occupations like school teachers, secretaries, nurses, customer service representatives, and a lot of other professions: a small pay gap for singles without kids and a larger pay gap for married people.

Exceptions to the overall trend

We have seen that, for the most common occupations, there is almost no absolute pay gap for singles without kids, and this gap could be explained by the difference in time spent at work. But there are some occupations that do show a gap for this group of people.

Notable examples are drivers, retail salespersons, supervisors and janitors. Interestingly, we can see the same general pattern in these occupations: the uncontrolled gap increases dramatically for married couples, even if they do not have kids.

The same general pattern repeats itself in occupations where single women without kids earn more than their male counterparts. Some of them are secretaries, customer service representatives, cooks, stock clerks, office clerks and receptionists.

In all of these occupations, the pay gap in favor of women reverts if they marry: married men still earn more than married women.

More time at work also means higher wages

Now, let’s look closely at the different hourly wages paid to women and men. The data shows that there is a persistent difference in the hourly rate earned by women and men, specially for married women and men. But the data also shows that men work more than women.

After taking a closer look at the data, we found a relationship between the hourly wage and the time spent at work. The average hourly pay increases as the number of hours worked per week increases. This is true for both sexes.

In the following chart, we plotted the hourly pay for women and men. To isolate the effect of marriage and parenthood, we took into account only singles without kids.

In the next chart, we can see the average number of hours worked for each group:

For the relevant range of hours worked per week, the average hourly pay increases as the time spent at work increases.

Because men tend to work more hours than women, especially if they are married, and even more if they are married parents, this could explain a large portion of the pay gap.

Also, the previous chart shows that on average, single women without kids are getting paid more than men for every hour spent at work. This could mean that if women worked the same amount of hours as men do, and other conditions remained the same, there would be no pay gap for this group5.

What about age and experience?

It is important to note that age and job experience are also relevant factors in the gender gap debate. To isolate the possible effects that age and job experience may have in the pay gap for each of the different groups, we plotted the weighted average of working hours per age for single women and men without kids.

For singles without kids, there is a very small gap at every age. But for married couples, there is a significant gap in working hours at every age.

If we take into account how the hourly wage varies as men and women get older, the hourly wage of men increases more than the hourly wage of women. The same pattern can be seen in all three groups.

The charts above demonstrate that job experience is correlated with the time spent at work through the years. As years pass, men accumulate more practice and training than women. The job market pays more if the worker has more experience. In other words, the gap widens as men acquire more experience than women6.

So what's the real cause of the gender wage gap?

In this article, we found that one of the main sources of the gender pay gap is the fact that, on average, women and men devote a different number of hours to their jobs, specially after marriage and parenthood.

The literature on gender pay gap is very extensive. Different papers focus on diverse causes to explain it. Two of the most mentioned reasons are gender discrimination and motherhood and gender roles.

Gender discrimination against women occurs if a woman is paid less than a man for doing the same job.

If we consider that the quantity of hours devoted to a job determines whether we consider a job to be the same as another, the data doesn’t support the idea of gender discrimination at the aggregate level.

The hourly pay rate for married women is lower than for married men on average, but a probable explanation is because the job market pays less per hour if the number of hours worked decreases, and married women tend to work less. The same pattern can be seen in almost every occupation.

Also, men tend to devote more time to work, thus acquire more experience as years pass by, and the job market pays more if the worker has more experience.

This doesn’t mean that gender discrimination doesn’t exist. Our analysis just shows that, at the aggregate level, most of the gap is not explained by gender discrimination.

Regarding the second aspect of the pay gap, societal ideas of gender roles influence the behavior of women and men. Also, biological factors related to parenthood do play a role in the creation of differences in preferences. Namely, women get pregnant and women breastfeed. These differences between sexes could be a plausible explanation of why women tend to spend more time at home versus their couples, especially after marriage and parenthood7.

To conclude and to recap, we can say that, according to our analysis, job market forces and gender preferences in relation to marital status and parenthood could explain almost all of the pay gap. Most of the gap is not the result of gender discrimination.

Methodology

Data Source

We used IPUMS USA to extract the data of the American Community Survey 2017.

Some characteristics of this sample are:

- 1-in-100 national random sample of the population.

- The data include persons in group quarters.

- This is a weighted sample.

- The smallest identifiable geographic unit is the PUMA, containing at least 100,000 persons. PUMAs do not cross state boundaries.

The ACS is the largest household survey that the Census Bureau administers.

The number of cases in this data set is 3,190,040.

We selected the following variables:

- NCHILD Number of own children in the household

- YNGCH Age of youngest own child in household

- SEX Sex

- AGE Age

- MARST Marital status

- OCC Occupation

- UHRSWORK Usual hours worked per week

- INCWAGE Wage and salary income

- PERWT Person weight

Description of the Variables

PERWT

PERWT indicates how many persons in the U.S. population are represented by a given person in an IPUMS sample.

PERWT should be used when conducting a person-level analysis of any IPUMS sample.

NCHILD

NCHILD counts the number of own children (of any age or marital status) residing with each individual. NCHILD includes step-children and adopted children as well as biological children. Persons with no children present are coded "0."

YNGCH

YNGCH reports the age of the youngest own child (if any) residing with each individual, regardless of the child's age or marital status. The highest legitimate age for YNGCH is 98. YNGCH includes step-children and adopted children as well as biological children. Persons with no own children present are coded 99.

SEX

SEX reports whether the person was male or female.

AGE

AGE reports the person's age in years as of the last birthday.

MARST

MARST gives each person's current marital status.

OCC

OCC reports the person's primary occupation, coded into a contemporary census classification scheme. Generally, the primary occupation is the one from which the person earns the most money; if respondents were not sure about this, they were to report the one at which they spent the most time. Unemployed persons were to give their most recent occupation. For persons listing more than one occupation, the samples use the first one listed.

Codenames can be obtained from this url:

https://usa.ipums.org/usa/volii/occ_acs.shtml

UHRSWORK

UHRSWORK reports the number of hours per week that the respondent usually worked, if the person worked during the previous year. The census inquiry relates to the previous calendar year, while the ACS and the PRCS uses the previous 12 months as the reference period.

UHRSWORK is a 2-digit numeric variable that reports the number of hours per week that the respondent usually worked, if the person worked during the previous year. The census inquiry relates to the previous calendar year, while the ACS and the PRCS uses the previous 12 months as the reference period. UHRSWORK specific variable codes for missing, edited, or unidentified observations, observations not applicable (N/A), observations not in universe (NIU), top and bottom value coding, etc. are provided below if applicable by Census year (and data sample if specified).

UHRSWORK Specific Variable Codes

00 = N/A

99 = 99 hours (Top Code)

INCWAGE

INCWAGE reports each respondent's total pre-tax wage and salary income - that is, money received as an employee - for the previous year. The censuses collected information on income received from these sources during the previous calendar year; for the ACS and the PRCS, the reference period was the past 12 months. Sources of income in INCWAGE include wages, salaries, commissions, cash bonuses, tips, and other money income received from an employer. Payments-in-kind or reimbursements for business expenses are not included. See the comparability discussion below for further information.

Amounts are expressed in contemporary dollars, and users studying change over time must adjust for inflation (See INCTOT for Consumer Price Index adjustment factors). The exception is the ACS/PRCS multi-year files, where all dollar amounts have been standardized to dollars as valued in the final year of data included in the file (e.g., 2007 dollars for the 2005-2007 3-year file). Additionally, more detail may be available than exists in the original ACS samples.

User Note: ACS respondents are surveyed throughout the year, and amounts do not reflect calendar year dollars. While the Census Bureau provides an adjustment factor (available in ADJUST), this is an imperfect solution. See the ACS income variables note for further details.

INCWAGE is a 7-digit numeric code reporting each respondent's total pre-tax wage and salary income - that is, money received as an employee - for the previous year. INCWAGE specific variable codes for missing, edited, or unidentified observations, observations not applicable (N/A), observations not in universe (NIU), top and bottom value coding, etc. are provided below by Census year (and data sample if specified).

User Note: Amounts are expressed in contemporary dollars, and users studying change over time must adjust for inflation.

INCWAGE Specific Variable Codes

999999 = N/A

999998 = Missing

Data Preparation

We used Python to import and filter the data. Python is a general-purpose programming language. The libraries Pandas and Numpy libraries were used to import and filter the data.

We removed observations less than 16 years old, unemployed who never worked, not in labor force who last worked more than 5 years ago.

We removed observations that reported N/A or missing wage or salary income.

We removed observations with a code of 0 hours worked

To isolate any possible effect caused by marked differences between women and men when they reach older ages, we took into account only people younger than 70 years old.

To calculate the hourly wage, we used the value 52.143 to calculate how many weeks are in a year. We calculated the number of hours worked per year for each observation, multiplying UHRSWORK ( the number of hours per week that the respondent usually worked, if the person worked during the previous year) by this value.

To arrive at the hourly wage, we divided the wage and salary income for the previous year (INCWAGE) by the number of hours worked per year.

To consider if a person is a parent, we took into account if there is an own child residing with each individual and the age of the youngest child. For this study, we chose the age of 18 as the limit to consider if a person is a parent.

We classified observation according to their marital and parenthood status (according to our definition of parent).

If an observation is single and no parent, we assigned it to the group “singles no children”

If an observation is married and no parent, we assigned it to the group “married no children”

If an observation is married and parent, we assigned it to the group “married with children”

After all this cases had been removed, our sample consisted in 1,509,403 observations.

Descriptive Statistics

To calculate the statistics, we used R and Python. R is a programming language for statistical computing. We used the R library dplyr to create subsets of the sample.

We calculated the weighted statistics for:

wage and salary income

hours worked

hourly wage

For the wage and salary income, we chose the median as the descriptive statistic. Because the mean can be influenced by extreme scores. That means that a small number of high wages could significantly affect the mean, but not the median.

The hourly wage also represent the median.

In the case of the hours worked we chose the mean because there is a natural limit on the worked hours per week, so extreme values cannot be present (the top code for UHRSWORK is 99).

For the occupations, we also merged the dataset with the occupations names. Occupation names can be found at:

https://usa.ipums.org/usa/volii/occ_acs.shtml

Notes