There has been considerable debate concerning the economic impacts of divorce on the livelihoods of women. Duncan and Hoffman (1988) state that, “the economic status of women fell an average of about 30 percent in the first year after divorce”(Duncan and Hoffman 1988, 641). However, others note that a negatively causal relationship between divorce and the economic well being of women is difficult to prove, as women who experience divorce often differ in ways that are difficult to measure (Holden and Smock 1991; Smock, Manning, and Gupta 1999). Important to note is the well-documented increase in the labor force participation of mothers throughout the 20^th century (Shapiro and Mott, 1978; Shapiro and Mott, 1994). Considering this trend, this post will address the effects of divorce and childbirth on the economic well being of a subset of the population: those in the labor force. By examining the relationship between marital status, number of children, and wage income, I hope to achieve a greater understanding of the impact that divorce has on the wage incomes of this smaller subset.

Data:
I collected my data for this analysis from the Integrated Public Use Microdata Series (IPUMS). I used sample data from 1940 to 2010. I used 1% samples for 1940 to 1960, 1990, and 2000. I used the 1% State Form 1 sample for 1970; the 1% Metro sample for 1980, and the 1% American Community Study(ACS) for 2010. The US Census Bureau administers the ACS. I used the variables MARST, NCHILD, EMPSTAT, and INCWAGE for my analysis. The MARST variable gives describes each respondent’s marital status at the time of the census. The possible outcomes for the MARST variable are ‘Married, spouse present’, ‘Married, spouse absent’, ‘Separated’, ‘Divorced’, ‘Widowed’, and ‘Never Married/Single’. The NCHILD variable describes the number of own children living with each individual, including biological children, stepchildren, and adopted children. The EMPSTAT variable indicates whether the respondent is employed, unemployed, or not in the labor force. The INCWAGE variable indicates the total pre-tax wage and salary income for each individual for the previous year; for the census, this was the previous calendar year, for the ACS, this included the previous 12 months.

All of the following analyses are weighted by the PERWT variable for all years excluding 1950, and weighted by the SLWT variable for 1950.

Method:

I began my analysis by excluding respondents who were reported as ‘Never Married/Single’ (MARST==6). Next, in order to simplify my definition of married within the data, I decided to combine respondents who were recorded as ‘Married, spouse absent’ (MARST==2) with those who were recorded as ‘Married, spouse present’ (MARST==1) to make a single category labeled ‘Married’. I decided to limit my analysis to respondents with at least 1 child (NCHILD>=1), and top coded the number of children to 6. I created categories for the NCHILD variable simply labeled ‘1’, ‘2’, ‘3’, ‘4’, ‘5’, ‘6+’. I limited my analysis to individuals reported as employed in the labor force (EMPSTAT == 1). Finally I adjusted the wage incomes reported by the INCWAGE variable for inflation by using the Consumer Price Index (CPI) to find the multiple for each sample year and converting all dollars to the 2010 level. After manipulating the data in these ways, I created a plot comparing the inflation adjusted wage incomes across the relevant marital statuses and sex; a plot comparing the number of children in custody across sex and between divorced and married respondents; and a plot comparing the inflation adjusted wage incomes across sex and between divorced and married respondents. My R code for the analysis can be found here.

Results:

Figure 1:

           Figure 1 shows the median inflation adjusted wage incomes for fathers and mothers across the examined marital statuses. First, it is important to recognize the far gap between the median inflation adjusted incomes for fathers and mothers, representing the consistent gender pay gap present in the United States throughout history. Yet, there is a gradual lessening of the gap as time approaches the present day. Next, seen here is a steep increase in inflation adjusted wage incomes across all cohorts between 1940 and 1970. For fathers, this trend is followed by a drastic plateauing across all marital statuses. For mothers, the subsequent trend differs across the various marital statuses. For divorced mothers, there is a slight decline in the median wage income between 1970 and 1980, followed by a tapering rate of increase between 1980 and 2010. For married mothers, there is a similar slight decrease in median wage incomes between 1970 and 1980, followed by another period of high rates of increase between 1980 and 2010. Widowed mothers in the workforce also experience a slight decrease in median wage income between 1970 and 1980, followed by a lackluster increase between 1980 and 2010. Separated mothers experience a consistent increase in median wage income until 2000, followed by a decrease in 2010.

Possibly the most interesting trend examined in this plot is the difference in differences between divorced and married fathers and mothers. Across all years examined, married fathers have had consistently higher inflation adjusted wage incomes than divorced fathers. However, divorced mothers have experienced notable higher inflation adjusted wage incomes than married mothers, until only recently. In 2010, we see a convergence of the median wage incomes for both married and divorced mothers for the first time, suggesting a possible current reversal in this consistent historical trend.

Figure 2:

Figure 3:

           Figure 2 shows the distributions of number of children across divorced and married mothers and fathers. Seen here are similar trends in the number of children for married men and women as well as divorced men and women. Seen during the baby boom is a general increase in number of children across all cohorts, followed by a steady decrease between 1970 and 2010.

Figure 3 shows a magnified plot of the percentage of divorced and married mothers and fathers with 4, 5, and 6+ children. Again, similar trends are examined across all cohorts. However, it seems there may be a coding error in the percentage of divorced fathers with 5 children in 1950. Furthermore, divorced fathers are examined to have custody over a slightly higher percentage of the high-numbers groups of children.

Figure 4:

            Figure 4 shows the median inflation adjusted wage incomes for divorced and married mothers and fathers across cohorts of number of custodial children. Seen here is a sustained consistency in the difference in median wage incomes between divorced and married mothers and fathers, despite the increase in number of custodial children. There is a perceived gradual increase in the difference in median wage income for divorced men as the number of children increases, until the ‘6+’ category; the high variance in this category may point to a low number of male respondents in the samples who are divorced and have 6 or greater children in their custody.

Furthermore, there seems to be greater downward pressure on the median income of divorced fathers than divorced mothers as the number of children increases. This downward pressure is seen gradually as the number of children increases, but most drastically when the number of children in custody reaches 5. Although there may be slight downward pressure on difference in median wage income between divorced and married women, the magnitude seems to be less so than that for fathers.

Conclusion:

The median wage incomes of divorced fathers with child custody are effected greater as the number of children in custody increases than the median wage incomes for divorced mothers with child custody. Furthermore, median inflation adjusted incomes for divorced mothers have been consistently greater than the median inflation adjusted incomes for married mothers. This trend is contrary to some of the literature, suggesting that, by the metric of wage income, divorced mothers may have an advantage over married mothers. However, this does not disprove a decrease in economic status post divorce (Duncan and Hoffman, 1988), as the median wage incomes for fathers are still far higher than those for mothers. Finally, there has been a recent convergence in the median inflation adjusted incomes for divorced and married mothers across all cohorts, suggesting a reversal of the consistent trend.

Works Cited:

Shapiro, David, and Frank L. Mott. “Long-Term Employment and Earnings of Womenin Relation to Employment Behavior Surrounding the First Birth.” The Journal of Human Resources 29.2 (1994): 248. Web.

Shapiro, David, and Frank L. Mott. “Labor Supply Behavior of Prospective andNew Mothers.” Demography 16.2 (1979): 199. Web.

Tienda, Marta, and Jennifer Glass. “Household Structure and Labor ForceParticipation of Black, Hispanic, and White Mothers.” Demography 22.3(1985): 381. Web.

Hoffman, Saul D., and Greg J. Duncan. “What Are the Economic Consequences of Divorce?” Demography 25.4 (1988): 641. Web.

Bedard, Kelly, and Olivier Deschênes. “Sex Preferences, Marital Dissolution, and the Economic Status of Women.” Journal of Human Resources J. Human Resources XL.2 (2005): 411-34. Web.

This post looks into the female immigrant population during the 20th century in America, specifically their presence in the labor force.  I found in a previous post that the female immigrant population is not as affected by the Immigration Act of 1924 and becomes larger than the male immigrant population by 1960.  Female participation in the labor force has increased over the course of the 20th century for multiple reasons, narrowing the gender gap in the labor force.  In general, education levels, family income, and the presence and age of children have factored into female participation in the labor force (Cotter, 2001, 431).  Furthermore, the rising wage rates from the 20th century causes men to bring more leisure into their lives from the increased income and became an incentive for women to work more (Cotter, 2001, 432).  While there was an increase in the female labor force during this time, there was also a high level of scrutiny regarding immigration.  A large portion of this scrutiny centered on Americans feeling threatened by immigrants taking their jobs (Jacobson, 2008, 69).  This post looks into the participation of female immigrants in the labor force from 1900-1960 in order to relate the rise in female labor participation and the levels of female immigration during this time.

Data

I gathered data from the Integrated Public-Use Microdata Series (IPUMS) for this project.  I used 1% data samples from the 1900-1960 censuses and took the BPL (Birthplace), SEX, PERWT, AGE and OCC1950 variables from these samples.  The PERWT variable represents the sample weight of each individual.  The SEX variable gives the gender of the individual.  The BPL variable indicates the birthplace of the individual.  I grouped the BPL variable into these categories: U.S. Born, Other NA, Central and South America, Northern EU, UK & Ireland, Western EU, Central/Eastern EU, East Asia, Other Asia, and Other.  I grouped the OCC1950 variable into these separate categories created by IPUMS: Professional, Farmers/Farm Labor, Managers/Proprietors, Clerical, Sales, Crafts, Operatives/Laborers, Private Service, Public Service and non-occupational.

Methods

I created multiple graphs in order to research this topic.  To narrow down the population to the working immigrant population I only included age cohorts of ten years starting at 20 years of age and ending at 70 years of age.  I took out the non-occupational category of OCCUPATION in order to select only the working population of female immigrants as well. I also made separate data sets for each census year to better analyze the female immigrant labor force.  I made multiple bar graphs that chart OCCUPATION on the x-axis and the number of people in each occupation on the y-axis.  The BIRTHPLACE variable is used to fill the bars in the graph, and it is faceted by the five created age cohorts and each individual census year.  The code for this project can be found here.

Results

Figure 1:

Figure 1 illustrates the female immigrant labor force in 1900.  Most female immigrants work in either private service or the operatives/laborers categories with a more equal distribution over the other occupation groups.  Initially, private service is almost double the next category of operatives/laborers in the 20-29 years old age cohort, but this difference becomes almost nothing in all the other age cohorts.  Both the private service and operatives/laborers groups are significantly lower following the 20-29 age cohort, indicating both a lower participation from older immigrant women in the labor force.  The largest immigrant groups in the labor force in this graph are the UK & Ireland, and Central/Eastern and Northern Europe.

Figure 2:

Figure 2 displays the female immigrant labor force in 1910.  In this graph all occupational groups except for farmers/farm labor have increased, especially the operatives/laborers category in the 20-29 age cohort.  The operatives/laborers group, contrary to figure 1, is slightly greater than the private service group.  A large influx of European immigrants, especially from Central/Eastern Europe, boosted the operatives/laborers category and marginally increased the private service profession.  Like figure 1, there is a steep decrease in the labor force from the older cohorts, but they also show significantly increased participation in all categories.  This reflects the increased immigrant population as well as a higher participation of immigrants in the labor force.

Figure 3:

Figure 3 shows the female immigrant labor force in 1920.  There is a large decrease in both the private and public service categories in the 1920 census in the 20-29 age cohort with the most significant increase appearing in the clerical profession.  While the operatives/laborers category remains constant in the 20-29 age cohort, it has a large growth in the older age cohorts compared to the 1910 census.  The immigrant groups that most affected this increase in the operatives/laborers category were the Central/Eastern and Western European groups.  Overall, the total female immigrant population in the labor force increased from the 1910 census.

Figure 4:

Figure 4 illustrates the female immigrant labor force in 1930.  It is important to notice that the y-axis on this graph has a smaller scale than that of the first three figures.  This smaller scale can be attributed to decreased number of women in the 20-29 age cohort in the operatives/laborers profession therefore lowering the highest number need on the y-axis. Even with this change, there is still an increase in both the private service and clerical categories.  There is a steep drop in the clerical profession from the 20-29 age cohort to the 30-39 cohort.  In this figure, there is clear downward linear progression in most of the profession categories of participation going from the youngest to oldest age cohorts.  Like the previous figures, the most impactful immigrant groups on these categories are Central/Eastern and Western Europe.

Figure 5:

Figure 5 shows the female immigrant labor force in 1940.  This graph illustrates a completely different image of the female immigrant labor force than the four preceding figures.  Overall the number of female immigrants in the labor force has decreased from the 1930 census and the age cohort distribution has changed drastically as well.  Here the 40-49 cohort reports the most workers, with operatives/laborers holding more than double any other profession.  The 20-29 age cohort surprisingly has only the third most numbers, indicating either a lack of participation from the cohort or a decrease in the population of that cohort.  The Central/Eastern and Western European immigrant groups still maintain a majority on the total female immigrant population in the labor force with this decrease in population.

Figure 6:

Figure 6 displays the female immigrant labor force in 1950.  This graph continues to show that the working population of female immigrants in the U.S. ages and significantly decreases up to this point in the 20th century.  The 40-49 and 50-59 age cohorts illustrate more or less the same population and spread of professions as they did when they were the 20-29 cohorts in 1930 and 1920.  Those cohorts have mainly remained with the same profession for 20-30 years, with only the private service category declining.  The clerical profession sees the most participation out of the younger age cohorts in this figure.  Still, the Central/Eastern and Western European immigrant groups still dominate the 40-49 and 50-59 cohorts as well.  The 20-29 and 30-39 age cohorts in this figure have a significantly lower population and also have much more equal representation from all of the immigrant groups.

Figure 7:

Figure 7 represents the female immigrant labor force in 1960.  This graph depicts a large boom in the female immigrant labor force population.  The largest cohort is the 50-59 age group, but the levels of participation in other age cohorts and professions are much greater than in 1950.  The y-axis numbers returned to the same levels as the first three figures and there is also greater participation across all cohorts.  The clerical and operatives/laborers categories dominate the younger age cohorts whereas the operatives/laborers group increases markedly in the 50-59 cohort.  The public service profession also sees gradual increases each year compared to the clerical category which sharply declines after the 30-39 cohort and again after the 50-59 cohort.  The distribution of immigrant groups remains the same for the older cohorts, dominated by Central/Eastern and Western Europeans, but the younger cohorts become more balanced  and even show a larger percentage of immigrants from other parts of North America and Central America compared to European immigrants.

Conclusions

These graphs illustrate both changes in levels of immigration in the United States during the early to mid 20th century as well as professional biases that U.S. culture has towards female immigrants.  The female immigrant population most gravitated or more likely was pushed towards the profession categories of operatives/laborers, private service, public service, and clerical work.  These jobs, being a housemaid or secretary for example, are not as respected in American society but still essential to the economy.  These categories represent many of the positions that perform the grunt work of society.  Initially I am sure that the female immigrant population was pushed towards these jobs, but later on found comfort and unity in creating their own communities around these mostly service based industries based on the age cohorts that fell under the 20-29 groups in 1920 and 1930 having similar populations and distributions as they aged each decade.  Furthermore, these graphs clearly lay out the effects of the Immigration Act of 1924 on emigration to the U.S.  The drastic fall of the 20-29 age cohort of female immigrants in the labor force in 1940 and 1950 is a direct result of the restrictions placed on immigration by the Act.  By 1960, the flow of immigrants coming into the U.S. seems to have normalized to somewhere between the high of the early 20th century and the immediate decade after the Immigration Act of 1924.  With regards to the female labor force, the restrictions placed on certain countries, mainly in Europe, seem to have allowed larger populations from other countries like Canada and Mexico to become more of a presence in America and its labor force.

Works Cited

Cotter, David A., Joan M. Hermsen, and Reeve Vanneman. “Women’s Work and Working Women: The Demand for Female Labor”. Gender and Society 15.3 (2001): 429–452. Web..

Jacobson, Matthew Frye. Barbarian Virtues: The United States Encounters Foreign Peoples at Home and Abroad, 1876-1917. New York: Hill and Wang, 2000. Print.

I utilize the University of Minnesota’s Integrated Public Microdata Series (IPUMS) census data for all my projects in History 90.01: U.S. History through Census Data (16W). IPUMS census data provides me, and researchers, both snapshot data and longitudinal data on American individuals (since 1850) and households. All my projects use IPUMS data to analyze important trends in American history. I also take advantage of other primary and secondary sources to explain my results. All my projects use R to visualize IPUMS data in the form of charts and graphs. IPUMS data allows researchers to analyze history at a more top-down, macro level.

My projects explore American history through census data. Native Americans and the Census studies how and why Native Americans were enumerated in the census from 1850 to 1950. It also looks at census enumerator instructions and the concepts of race in America. Japanese American Eastward Migration maps Japanese American immigration patterns from the West coast to East coast, Midwest and South. It also pays special attention to the internment and its effects on migration. Vietnam Veterans Wages and Education focuses on comparing income over time and education attainment of veterans and non-veterans by birth year cohorts. Finally, Divorce Rates among Women by Race and Income analyzes divorce rate trends from 1940 to 2010 by income and race and offers explanations on why divorce has rapidly risen.

In the following blog posts, I use United States census data to analyze patterns in U.S. history. I created these as a part of Dartmouth College’s course “U.S. History Through Census Data,” taught by Emily Merchant. The course had four main units: Race, Migration, Work, and Family. To explore these topics, the class used Integrated Public Use Microdata Series (IPUMS) data, which are samples of individual-level data from each available census starting in 1850.

For the “Race” unit, I analyzed how Asian race categories listed in the United States census changed between 1900 and 1970, and explored how these categories reflected the caucasian American attitude toward these races. When the class discussed “Migration,” I used IPUMS data to estimate the percent of foreign-born immigrants in the United States who were born in Mexico between 1950 and 2000. I also looked at where these Mexican immigrants tended to settle, and explored what led to these immigration patterns. In the “Work” unit, I analyzed the changes in female labor force participation between 1880 and 2000. I also observed how these trends differ between white and non-white women. Finally, in the “Family” unit, I estimated the impact of divorce on children between 1940 and 2000. I performed individual-level analysis to see how the number and percent of children living with a divorced parent changes over this time period. The links to these four blog posts can be found by clicking on the unit name.

I have created this portfolio for my US History class using IPUMS data. IPUMS (Integrated Public Use Microdata Series) converts and publishes records of US census data into a consistent format available online for public use. This resource has been particularly useful for my research because it has pre-selected and randomized 1% samples of US census data. Using the pre-made 1% samples I was able able to manipulate census data in order to study historical trends in the United States.

I completed four projects on the topics of race, immigration, work and family in the United states. The first project analyzes the manner in which the asian race was enumerated from 1880 to 1990. The second project analyzes patterns of Mexican immigration from 1910 to 1970 and how american immigration policy may have affected the observed patterns. For the third project I focus on how race and immigration status impact national and regional female employment rate from 1910 to 1960. In the fourth project I research how the living arrangement for elderly men and women (aged 65 and older) change from 1920 to 1970.

The living arrangements of the elderly are largely dependent on their help seeking behaviors and the resources available to them (Coward 1989, 814). Factors such as increased life expectancy, higher fertility rates, cultural shifts, economic changes and industrialization have all been argued to have altered the living arrangements of elderly americans. I will argue that the higher life expectancy of women than men has led to a rise in single elderly female households. Furthermore I will argue that the increasing economic resources available to the elderly have made living away from family a more feasible alternative.

Data:
The data used for this analysis are from the census data available on the Integrated Public-Use Microdata Series (IPUMS). My data set is composed of one percent samples from the 1920 to 1970 United States censuses. In 1970 I use the 1% state form 1 sample. PUMS has randomly selected the one percent samples used for my analysis. I use the The IPUMS organized variables FAMSIZE and GQ for my analysis. The FAMSIZE variable counts the number of one’s own family members that an individual is living with. This variable is imputed by IPUMS based on several other variables that classify relationships within a household. Family members are anyone related by blood, marriage or adoption. The GQ variable classifies the type of housing unit that an individual is living in (specifically it classifies units as households or group quarters). For all censuses prior to 1940 a “household” was classified as any unit with nine or fewer persons unrelated to the head. From 1940 to 1970, the definition of a household was restricted to four or fewer persons unrelated to the head.  All analyses are weighted by PERWT, the sample weight of each individual.

Methods:

I begin my analyses by restricting the data set to the elderly (individuals over 65 years and older). Then I classify the living arrangements of elderly individuals as living alone, with family members or in group quarters. Individuals living alone are identified as living in a household (GQ==1) and not living with any other family members (FAMSIZE==1). Individuals living with family are identified as living in a household (GQ==1) and living with one or more family members (FAMSIZE>1). Individuals living in group quarters are identified as anyone living with over 4 unrelated persons (GQ>=2). I use this definition of group quarters so that data from before 1940 are comparable to data after 1940. However due to the census’ change in the definition of a household, this may be too wide of a definition for what qualifies as group quarters. Prior to 1940 large households with many servants or boarders was common. Therefore in my analysis, there may be too many individuals in 1920 and 1930 identified as living in group quarters.

After identifying three types of living arrangements, I analyze the living situation of elderly men compared to elderly women. First I graph the population of elderly men and women over time with regard to where they live. Then I graph the proportion of the total elderly men living in each identified living arrangement. I do the same for elderly women. Code for my analysis and visualizations is available here.

Results:

Figure 1:

Figure 1 graphs the population of elderly men and women from 1920 to 1970 with respect to their living arrangement. The population of both men and women grows steadily over the time period. The population elderly men and women is similar from 1920 to 1940. From 1950 to 1970 there are always more elderly women than elderly men.

Figure 2:

Figure 2 graphs the proportion of the total elderly population for each sex living alone, in group quarters or with family. For all years and both sexes, the majority of elderly individuals live with family. The proportion of men who live with family decreases very slightly and unsteadily over time. The proportion of women who live with family decreases steadily over time. Living alone is the next largest living arrangement for both men and women. For men, the proportion of individuals living alone increases very slightly over time. For women, the proportion of individuals living alone increases steadily over time. The smallest proportion of elderly men and women live in group quarters. For men, the proportion of individuals living in group quarters decreases slightly over time. For women, the proportion of individuals living in group quarters stays fairly constant over time.

In my analysis the living arrangement of men does not change significantly because my method of categorization does not distinguish between coresidence with children (or other extended family) and living alone with a spouse. Therefore because women typically outlive their male spouses, the trends of autonomous living arrangements is more significantly seen in the visualization of female living arrangements. The increasing trend in women living alone is likely a reflection of the empty-nester phenomenon (married couples living alone) argued by Brian Gratton and Myron Gutmann. The scholars explain that prior to 1940, rising life expectancy and decreasing fertility rates contribute to elderly parents living away from their children. However after 1940 these factors no longer sufficiently explain changes in living arrangements. Instead, after 1940, the significant gains in economic status for both generations (the elderly and their children) are a more accurate explanation for autonomous living arrangements. Increasing affluence and the decline of the agricultural economy meant multigenerational living arrangements were no longer necessary. Moreover by 1950 old age assistance benefits had rapidly expanded. The expectation of a steady income from social security benefits is another strong causal argument for the increase in elderly individuals living away from family  (Gratton 2010, 332-343). Steven Ruggles supports this explanation of the decreasing multigenerational family. Ruggles explains that from 1850 to 1920 high economic status was closely associated with multigenerational families. However from 1940 to 1970 this phenomenon diminished and multigenerational households became more common in households with a low economic status (Ruggles 2003, 159-160).

Conclusion:

Over the course of the twentieth century as healthcare improved and life expectancy increased, elderly americans simultaneously attained greater economic autonomy, therefore making living away from other family members possible. This trend in the living arrangement of the elderly is most obviously evidenced in my analysis of elderly women. Because women had a longer life expectancy than men and elderly men typically lived alone with their spouses in old age, the trends of autonomous living arrangements are more obvious in the visualization of elderly women.

Bibliography:

Coward, R. T., S. J. Cutler, and F. E. Schmidt. “Differences in the Household Composition of Elders by Age, Gender, and Area of Residence.” The Gerontologist 29.6 (1989): 814-21. Web.

Gratton, Brian, and Myron P. Gutmann. “Emptying the Nest: Older Men in the United States, 1880–2000.” Population and Development Review 36.2 (2010): 331-56. Web.

Ruggles, Steven. “Multigenerational Families in Nineteenth-Century America.” Continuity and Change 18.1 (2003): 139-65. Web.

Millions of southern-born Americans immigrated out of the South to Western and Northern regions of the country between the decades of 1910 to 1970. Tolnal et al. has exemplified the racial discrepancies in demographic migration trends between southerners when considering distances traveled from their birthplace during this period. However, demographic migration trends have continued to develop with racial difference after 1970. I was born in Houston, Texas in January of 1994. Although this was well after the accepted end of the great migration, much of my extended family decided to move to the Northeast after my birth. In this study, I am examining demographic differences in the migration trends of Texas-born people, looking specifically at racial differences between settlers in the Northeast and West.

Data:

The data for this analysis are collected from the University of Minnesota’s Integrated Public-Use Microdata Series (IPUMS). I used 1% samples from years 1930 to 2000; for 1970 I used the 1% State Form 1 sample, and for 1980 I used the 1% Metro sample. In contrast to Tolnay et al., who focused on migration trends of men, I included both men and women of all ages in my study. The IPUMS variables I used are AGE, SEX, YEAR, RACE, BPL, REGION, and PERWT. Variables AGE and SEX indicate self-reported age and sex; YEAR indicates the given census year; RACE indicates the race of the individual; BPL indicates the birthplace of the individual by state; REGION indicates the current region that the individual lives in, as delineated by the census; PERWT indicates the sample weight of each individual in the sample. PERWT was used to weight all of the following analyses.

Method:

I contained my analysis of Southern emigration 1) to men and women from Texas and 2) to those that moved to the Northeastern and Western regions of the country, as defined by the Census Bureau. The Northeast region includes Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont, New York, New Jersey, and Pennsylvania. The West region includes Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Wyoming, Alaska, California, Hawaii, Oregon, Washington. I first created age categories to assign to each individual, each spanning 10 years (1-10 years old, 11-20 years old, ect.). I then delineated each individual into race categories for either White or Non-White, in order to look at differences between the two groups. Then, I separated the two groups by those living in the Northeast and those living in the West. I first graphed the decennial numbers of both Whites and Non-Whites living in both the West and Northeast, looking at the ratio between the two race categories. I then made a series of population pyramids for both race categories in both of the regions for the years 1940, 1970, and 2000. According to Tolnay, 1940 and 1970 would encompass what many believe were the first and second waves of the great migration, respectively. The 2000 census would include the decade during which much of my family moved from Texas to the Northeast. My R code can be found here.

Results:

Figure 1:

         Figure 1 shows the decennial numbers of both White and Non-White, Texas-born people living in the West. Here we see a gradual increase in the proportion of Non-White, Texas-born people living in the West throughout the decades examined. It is interesting to note the significant increases between both 1940 and 1950 as well as between 1980 and 1990. The later is a demographic change that would not have been captured in the research that studied the Great Migration. There is both a notable increase in the number of Non-White, Texas-born people in the West, as well as a significant decrease in the number of White, Texas-born people in the West. The later decrease could be both due to factors such as natural mortality and a trend of returning to Texas for White, Texas-born people between the years of 1980 and 1990.

Figure 2:

           Figure 2 shows the decennial numbers of Texas-born migrants living in the Northeast over the decades examined. This plot shows a more consistent increase in the rates of both White and Non-White, Texas-born individuals living in the Northeast than Figure 1. Furthermore, the ratios of White to Non-White persons remains significantly more consistent during the decades examined than in Figure 1. However, it is important to note how few Texas-born individuals there have been living in the Northeast in comparison to the West. Although I have neglected to examine the differences in distances traveled, as done by Tolnay et al., this is a trend that was not found in their data. This would support the assumption that most people who left the south during the Great Migration to live in the Northeast came from the more Eastern zone of the southern region.

It is also interesting to note the difference between Figures 1 and 2 when considering the previously mentioned demographic trend defined between 1980 and 1990 in Figure 1. Texas-born individuals living in the Northeast did not exhibit the same trend.

Figure 3:

          Figure 3 displays a population pyramid showing the age structure of White, Texas-born men and women who were living In the West during the 2000 Census. This plot confirms the decreasing population trend in Figure 1, showing a gradually aging population. This shows the baby-boomers when they were primarily ages 40-49, yet exemplifies a decrease in the populations of the younger cohorts.

Figure 4:

Figure 4 shows a population pyramid for Non-White, Texas-born individuals living in the West during the 2000 census. This is an example of a slightly more “normal” age structure, showing a larger population of individuals between the ages of 30 and 60. However, this pyramid shows the same trend of smaller populations in the younger cohorts, despite the fact that the Western population of Non-White individuals had increased significantly two decades prior. This phenomena in both figures 3 and 4 could be a factor of Tolnay et al.’s discovery that people were more likely to migrate, and at greater distances, had they been without children.

Figure 5:

Figure 5 shows a population pyramid for White, Texas-born individuals living in the Northeast in the year 2000. This plot shows a younger age structure than both figures 3 and 4. Yet, the significant drop in the number of children age 0-9 suggests that there is a similar decrease in the population in the youngest cohort.

Figure 6:

          Figure 6 shows the population age structure for Non-White, Texas-born individuals living in the Northeast in 2000. Somewhat due to the low number of respondents, this plot shows a highly irregular demographic structure. However, the same trend of fewer-than-normal individuals occupying the youngest cohort that is evident in the previous population pyramids is again present in this iteration.

Tolnay et al. found that individuals moving from the South to other regions of the country during the Great Migration were more likely to do so before having children. These plots show a similar trend extending past the Great Migration and into the 21^st century, with little differentiation between the region to which they moved, the race of the individual, or the sex of the individual. Individuals are still more likely to migrate before having children, and it is most likely that their children are not seen in the plots present here.

Conclusion:

Studies of the Great Migration found significant racial differences in migratory trends throughout the period. Although the Great Migration this study has shown that there continue to be racial differences in migratory trends from Texas-born individuals moving to the West. This trend of decreasing migration from the White cohort and increasing migration from the Non-White cohort could be the result of numerous factors that have yet to reveal themselves to historians. It is important to note that this study only considers the migration of Texas-born individuals. Further research may be done into the migration trends of individuals from the greater-South region to the west.

As for my family, it seems we were a relatively unusual bunch. Most migrants from Texas to the Northeaster and Western regions of the country have shown to do so without or before having children. It seems now that it was uncommon for my parents to decide to have me before moving to the Northeast, even after the Great Migration ended.

Works Cited:

Tolnay, Stewart E., Katherine J. Curtis White, Kyle D. Crowder, and Robert M. Adelman. “Distances Traveled during the Great Migration.” Social Science History Soc. Sci. Hist. 29.04 (2005): 523-48. Web.

Ngai, Mae M. “The Architecture of Race in American Immigration Law: A Reexamination of the Immigration Act of 1924.” The Journal of American History 86.1 (1999): 67-92. Web.

The divorce rate among American women has been rising but by how much and when did the divorce rate begin to climb? And which women are more likely to be divorced?  Is divorce more common among certain races? What are the socioeconomic differences between divorced and married women? For my analyses I will looking at the divorce rate by race and income among American born women to help answer these questions.

Data: 

I am using census data from the Integrated Public Use Microdata Series (IPUMS) for my analyses. My samples range from 1940 to 2010. I am using 1% samples for 1940 – 1960 and 1980 – 2000. I am using the 1% State Form 1 sample for 1970 and the 1% American Community Study (ACS) for 2010. The ACS is also administered by the U.S. Census Bureau. All analyses are individually weighted with the PERWT variable. I am not weighting samples by household weight (HHWT) because divorce and marriage are individual variables. My analyses do not include women living in Alaska or Hawaii because the census does not include Alaska or Hawaii in the 1940 and 1950 censuses. Adding them would skew my data in 1960. Starting in 1960 the census was collected by mail back form. The 1940 and 1950 censuses were conducted by door to door enumerators. The 1960 census and all censuses after allow self-racial identification.

I define divorced as women listed as divorced, separated or has an absent spouse. I do not use widowed or never married (single) women in my analyses.

I will be using INCWAGE to calculate the incomes of women. INCWAGE does not record net losses for individuals–zero is the lowest possible income. It only records an income paid by employers and includes commission, tips, and cash bonuses. It does not include non-cash assets or incomes. Non-cash benefits will not appear in INCWAGE or in my analyses.

Method: 

My data to only includes women (SEX=2) born in the United States (BPL<= 56). I assign racial categories to each race but combine Chinese, Japanese and Other Asian to one racial group, “All Asian” because of their small population size. I use the HISPAN and RACE variables to create Hispanic as a racial category. Any race that identifies as Hispanic is assigned exclusively to the Hispanic group. This is to prevent double counting when I calculate rates and numbers for race groups. I chose this Hispanic definition of race because according to Clara Rodriguez, “For many Latinos, race is primarily cultural; multiple identities are a normal state of affairs; and “racial mixture” is subject to many different, sometimes fluctuating, definitions (Rodriguez 2000, 5).” Due to Latinos’ different understanding of race, the HISPAN variable is the most accurate way to locate Latinos in the census. Lastly, I exclude “2 Races” and “3+ Races” from my analyses because there is not enough data to make a meaningful analysis with them.

I then use CPI99 and INCWAGE to calculate a comparable income for all decades in 1999 USD. I multiple the INCWAGE variable by the CPI99 variable. Without CPI99 it would be meaningless to compare wages across decades due to inflation. I then created income categories. The categories range from “No Income” to “$100,000+” by $20,000 increments.

I remove never married (single) and widowed women from my analyses (MARST<=4). For my graphs, I graph by raw numbers and by percent. For percent I divide the number of divorced women by the total number of women.

Here is my code. 

Results: 

Figure 1: Income for divorced women. 1940 – 2010.

Figure 2: Income for married women. 1940 – 2010.

Figure 1 and Figure 2 show us the percent of divorced and married women earning income and how much they earn. Both Figures 1 and 2 include all races. Between 1940 and 2010, more divorced women are working for an income. Additionally, more divorced women earn higher incomes than married women. Both married and divorced women start earning higher incomes ($20,000+) in 1960. Overall, we observe more women earning an income and we see more women earning higher incomes between 1940 and 2010. Emily Merchant’s analysis of white women living alone may shed some light on why women’s, divorced and married, incomes rose. From 1940 to 1960, more white women entered the labor market and more white women were completing high school (E. R. Merchant et all 2012, 10). With better education and a more accommodating workplace, women were able to enter the labor market and earn higher incomes.

Figure 3: Incomes by race for divorced white, Hispanic and black women. 1940 – 2010.

Figure 3 is the breakdown of divorced white, Hispanic and black women by income. I chose to analyze White, Hispanic and Black women because they are the three largest racial groups in the U.S. In 1940, more black and Hispanic women are earning an income compared to white divorced women. In 2010, nearly all three groups have equal representation in the labor market, approximately 75% are earning an income. However, more white women earn higher incomes than black and Hispanic women. There are many variables that can cause this preference, such as racial hiring preferences for better jobs or better education for white women.

The 3 graphs match the patterns in Figures 1 and 2: the “No Income” category is declining over time and women are earning more, even if white women are earning more than black and Hispanic women. All women experience decline in wages and rise in “No Income” in 2010, most likely due to the 2008 recession–a hiccup in the data.

Figure 3: Divorce rates by race. 1940 – 2010.

Figure 4 shows that divorce rates for all women have risen by a large amount since 1940. All Asian women had the smallest change in divorce–rising 6% between 1940 and 2010. Meanwhile, Black women had the largest increase in divorce. Divorce rose by about 30% between 1940 and 2010. Steve Ruggles’ The Origin of African American Family Structure may help explain the high divorce rates among Black women. It “could have been a response to the socioeconomic conditions faced by newly-freed blacks after the Civil War and by free blacks in 1850. Second, the pattern could simply reflect a difference in social norms between blacks and whites, which could have developed either through the experience of slavery or could have its roots in differences between European and African culture. (Ruggles 1994, 147)”

Hispanic, White and All Asian women divorce rates steadily rose after 1960 at similar intervals. Figures 1 to 3 may help explain the patterns in Figure 4. As women entered the labor market and earned more money, they were able to afford divorces and support themselves (and their dependents). From 1960 onwards, more and more women are earning $20,000+, enough to support themselves.

Conclusion: 

A study of divorce rates among men and their races and incomes will tell us more about why divorce rates began to rise in 1960. However, given the evidence I presented, I am inclined to believe it is because more women were earning an income, giving them the opportunity to get a divorce. 1960 was also the first census where a noticeable amount of women were earning ($20,000+).  But there can be other lurking variables such as education and external support networks among specific races. Steven Mintz and Susan Kellog claim that black women are more likely to live without a spouse because they created “their own distinctive Afro-American kinship network, largely free from interference. (Mintz & Kellog, 68)” Unfortunately I was unable to find Hispanic specific explanations to explain the Hispanic divorce rate and income trends. Finally, my data does not account for women who got divorced and then remarried in between censuses.

Works Cited: 

Merchant, Emily R.; Brian Gratton; Myron P. Gutmann. A Sudden Transition: Household Changes for Middle Aged U.S. Women in the Twentieth Century. Popul Res Policy Rev: Springer Science+Business Media. 27 June 2012.

Mintz, Steven; Susan Kellog. Domestic Revolutions: A Social History of American Family Life. (New York: The Free Press).

Rodriguez, Clara E. Changing Race: Latinos, the Census, and the History of Ethnicity in the United States. (New York: NYU Press, 2000). pp. 3-27.

Ruggles, Steve. The Origins of African American Family Structure.  American Sociological Review, Vol. 59, No. 1 (Feb., 1994), pp. 136-151

This investigation primarily focuses on changes in the population of immigrants from the Slavic countries, over the first half of the twentieth century. In order to do so in an interpretable way, I make use of population pyramids and relative frequency kernel plots, identifying differences in age distributions for various cohorts. I pay particular attention to the net effect that each world war had on Slavic immigration and the Slavic-American population, seeking to determine whether the incentives to immigrate or restrictions against those wishing to do so had a greater impact during each period.

Background

Immigration has long benefited the American public. As a result of this country’s acceptance of the world’s “tired, […] poor, […] huddled masses yearning to breathe free” (Lazarus, The New Colossus), America was able to readily capitalize on the technological breakthroughs of the Industrial Revolution. Unfortunately the dearth of census data from this era forces me to look at other time periods if I choose to investigate immigration. Moreover, while the Industrial Revolution increased demand for immigrants, mass migrations are often the result of supply-side factors, such as war or famine. The first half of the twentieth century provides me with the opportunity to consider one migration wave caused by both categories of factors.

The Second Industrial Revolution took place between 1870 and the beginning of the first World War. Before the outbreak of the war, able-bodied men and women from poorer parts of Europe eagerly set sail to America, chasing the promise of secure careers in factory jobs (Jacobson 2000, 63). Jacobson describes the significant differences in the wages that American workers and their counterparts in Europe could expect to receive (ibid.).

The onset of the first World War invariably led to its own set of challenges but its conclusion prompted a flood of migrants fleeing their destroyed homes. In areas where this damage was significant (such as the Slavic countries), communities would emigrate by the village. The demand for foreign workers of specific ethnicities continued into through the mid-20th century. Employers associated proficiency in certain trades with certain countries, and often hired workers of different backgrounds in order to prevent unionization  (Jacobson 2000, 69).

The effect of the 1930’s and 1940’s is more difficult to predict. On one hand, the conclusion of the second World War in 1945 should have led to increase immigration. Conversely, increase scrutiny towards immigrants during the 1940’s, culminating in the passage of the 1952 Immigration and Nationality Act (McCarran-Walter Act), prevented many of those who could have entered in prior decades from starting a life in America. This act set quotas on immigrants of specific backgrounds and allowed for the deportation of suspected communists. Moreover, reverse migration has been observed during the Great Depression years for Mexican migrants and Americans from the South (Gregory 2005, 32). It is plausible that similar effects led to repatriation among Slavic immigrants.

Data and methodology

I turned to the Integrated Public-Use Microdata Series, using census samples from 1900 through 1950. For all years, I used the 1% sample provided by IPUMS. The variables of interest for this investigation include an individual’s age, birthplace, birthplace of their parents, and years since immigration (collected for immigrants). Region and citizenship status were also considered but did not leave me with any results worth noting.

In order to identify the relevant populations, two indicator variables were created. One variable indicates if an individual is a first generation Slavic immigrant. For my purposes, the Slavic countries include all countries commonly classified as belonging to the Central or Easter European subcontinents, with the exception of Austria and Germany. In order for an individual to be classified as a Slavic immigrant for my purposes, both of their parents must also be born in a Slavic country. This restriction aims to exclude the children of expatriates who happened to be born in the region.

The second indicator variable simply checks if an individual was born in US territory and has at least one parent born in a Slavic country. As such, it classifies individuals as second generation Slavic immigrants on the basis of conventionally accepted definitions. Please note that it is not possible to verify that the ‘pre-immigrant’ generation (in this case, grandparents) was born in a Slavic country, which I do for first generation immigrants.

For first generation immigrants (in the 1900 to 1930 censuses), I can estimate the amount of time they have spent in America by considering the difference between the census year and the year of immigration variable. I can also consider the age at immigration by subtracting this difference from an individual’s age.

With these variables in place, I are able to stratify on the basis of either classification, as well as their combinations. By stratifying the age distribution of various groups of immigrants on the census year, I can see how the makeup of the Slavic immigrant population has changed over time. The results of this methodology were plotted in the form of relative frequency kernel plots and population pyramids, to be discussed below in further detail.

Results

Beginning with unrestricted data on first generation immigrants, I plot each age distribution in order to observe aggregate trends, as shown in Figure 1. The mean age of Slavic immigrants has increased by ~8 years between each census from 1910 to 1950. Assuming that immigrants generally arrive at as young, working age adults, this figure seems to imply that not very much immigration is occurring between these years; one would expect a mean increase of ~10 years per census if no immigration was occurring. The notable exception to this is the change from 1900 to 1910, where there is actually a decline in the mean age, implying significant immigration.

Figure 1.

Unfortunately, this chart does not show me the distribution of immigrants that actually immigrated between census years (rather illustrates the distributions of living immigrants arriving before a given year). Fortunately, the variable I constructed that estimates the amount of time spent in America (see Data and methodology) allows me to filter the data for each census to only immigrants that had arrived within the previous decade. Doing so for the 1890’s and 1900’s (using the 1900 and 1910 censuses, respectively), I find that the age makeup of immigrants in the two periods was fairly constant. As shown in figure 2, much more Slavic immigration occurred in the 1900’s than the 1890’s. As a result of immigration by young men in the 1900’s, the mean age of those immigrating in the decade decreased by  ~2 years (see spread between horizontal lines, Figure 2).

Figure 2.

In contrast, I may choose to look at the change in the age of Slavic immigrants arriving in the 1920’s, comparing this to those who had arrived in the 1900’s (Figure 3). As suspected, the mean  age of immigrants shot up by ~12 years (see horizontal lines). However, my suspicion that this was due to decreased immigration was not correct; Slavic immigration has dramatically increased. Instead, I find that the men and women moving to America during these decades were significantly older, implying the migration of full families rather than young single laborers searching for better work.

Figure 3.

Unfortunately the removal of the year of immigration question in the 1940 census means I cannot directly compare the 1920’s boom to the subsequent shock resulting from the Great Depression and WWII. Instead of observing immigrants arriving within a decade, I consider changes in the immigrant population between years, at each age. Figure 4 illustrates precisely this. In contrast to my previous comparison, the ~18 year increase in mean age indeed appears to be due in part to reduced immigration. Note that the population of older individuals increases by far less than the population loss of young immigrants.  Barring a significantly increase in mortality rate, this is consistent with the immigration restrictions and repatriation discussed in the aforementioned research. The 1924 National Origin Act is one restriction that may be most responsible for what we see in Figure 4.

This act, aiming to preserve American racial homogeny, limited the number of people that could immigrate from a given country to 2% of the number of people from that country already in America by 1890. By doing so, President Coolidge and backers of the bill hoped to stem the flow of Southern and Eastern Europeans, among other groups.

Figure 4.

Figure 5 is analogous to Figure 1, except that it considers second generation immigrants by year. Note the flattening of relative frequency curves. Given that older first generation immigrants are likely to have children with a broader range of ages, the flattening of the curves and increase in mean age is expected on the basis of biology and mathematics.

Figure 5.

However, all relative frequency kernel diagrams fail to show me how a total population has changed from year to year. In order to investigate this metric for a span of time (1930-1950) and I create a synthetic cohort of individuals that would be between the ages of 0 and 19 in 1930. By shifting the range of ages included by 10 years for every census, I are able to track the same age cohort across time. I choose to employ a truncated population pyramid in oder to display this population information, as shown in Figure 6.

Figure 6.

Figure 6 supports the conclusions I drew from Figure 4, as is evident in the shrinking of successive pyramids. The ostensible disappearance of first generation immigrants between 1930 and 1950 is likely the result of emigration on a family level, which is why their (probable) children are leaving with them. Alternatively this disappearance may have occurred due to war casualties, or it may have only occurred “on paper” if racist attitudes and McCarthyism simply led to fewer individuals admitting to foreign links on the census (Roediger 2005, 134).

Unfortunately I cannot differentiate between  these “paper losses” and genuine emigration, but I can investigate whether or not the change is due to the war by comparing second generation immigrants to native born Americans of the same age cohorts.

In Figure 7, I do just that. By matching the 0-19 years-of-age cohorts in 1930 to the 20-39 years-of-age cohorts in 1950, I can estimate the proportion of individuals accounted for in both census that were less than 20 years old in 1930. If I stratify this analysis across by second generation Slavic status, I find that for non-Slavs, virtually all people can be accounted for. In contrast, only ~80% of second generation Slavs are captured in the second census. If this ~20% loss was the result of war or death, I would expect similar amounts of native-born Americans to go missing. Consequently, there must be some heterogenous effect, be it reverse migration (as theorized) or an unwillingness to recognize one’s ancestry.

One should note that the questionnaire text did not change radically between these years. In 1930, the census asked:

Place of birth of each person enumerated and of his or her parents. If born in the United States, give the State or Territory. If of foreign birth, give country in which birthplace is now situated. Distinguish Canada-French from Canada-English, and Irish Free State from Northern Ireland.

In 1950, it asked:

25. What country were his father and mother born in? (Enter US or name of Territory, possession, or foreign country)

Clearly, the difference is not due to a change in the nature of what is being asked. Further data on emigration would be incredibly valuable in explaining this fairly dramatic result.

Conclusion

In short, the fifty year story of Slavic immigration and identity in the early 20th century is one with many chapters. Towards the tail end of the Second Industrial Revolution, age distributions behave in precisely the way I expect them, given the literature explored. The conclusion of the first World War prompts massive waves of migration of families searching for a better life. This scenario does not repeat itself at the conclusion of the second World War, likely as a result of the counter-effect of the Great Depression and restrictive immigration policy. It is plausible that McCarthyism and racist attitudes may also have led to the dramatic disappearance of Slavic immigrants after the war.

Sources

Source code for analysis and figures

Gregory, James N. The Southern Diaspora: How the Great Migrations of Black and White Southerners Transformed America. Chapel Hill: University of North Carolina Press. 2005. Print.

Jacobson, Matthew Frye. Barbarian Virtues: The United States Encounters Foreign Peoples at Home and Abroad, 1876-1917. New York: Hill and Wang, 2000. Print.

Roediger, David R. Working toward Whiteness: How America’s Immigrants Became White: The Strange Journey from Ellis Island to the Suburbs. New York: Basic, 2005. Print.

Ruggles, Steven,  Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek.Integrated Public Use Microdata Series: Version 6.0 [Machine-readable database].Minneapolis: University of Minnesota, 2015.