trends in african-american marriage patterns steven ruggles and catherine fitch minnesota population...
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Trends in African-American Marriage Patterns
Steven Ruggles and Catherine Fitch
Minnesota Population Center
Funded by the National Science Foundation and the National Institutes of Health
We have three big questions:
1. Why was there no marriage boom among blacks?
2. Why did black marriage age rise so rapidly after 1970?
3. Why did the traditional gender pattern of marriage age reverse among blacks after 1990?
Figure 1. Median age at first marriage: Native-born whites and blacks by sex, 1880 - 2000
White men
White women
Black men
Black women
19
20
21
22
23
24
25
26
27
28
29
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Year
Ag
e
Figure 1. Median age at first marriage: Native-born whites and blacks by sex, 1880 - 2000
White men
White women
Black men
Black women
19
20
21
22
23
24
25
26
27
28
29
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Year
Ag
e
Figure 1. Median age at first marriage: Native-born whites and blacks by sex, 1880 - 2000
White men
White women
Black men
Black women
19
20
21
22
23
24
25
26
27
28
29
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Year
Ag
e
Figure 1. Median age at first marriage: Native-born whites and blacks by sex, 1880 - 2000
White men
White women
Black men
Black women
19
20
21
22
23
24
25
26
27
28
29
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Year
Ag
e
Figure 1. Median age at first marriage: Native-born whites and blacks by sex, 1880 - 2000
White men
White women
Black men
Black women
19
20
21
22
23
24
25
26
27
28
29
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Year
Ag
e
Figure 1. Median age at first marriage: Native-born whites and blacks by sex, 1880 - 2000
White men
White women
Black men
Black women
19
20
21
22
23
24
25
26
27
28
29
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Year
Ag
e
Data: Integrated Public Use Microdata Series (IPUMS-USA)
Harmonized census microdata spanning the period from 1850 to 2000 with user-friendly access, integrated comprehensive hypertext documentation makes analysis of long run change easy
http://ipums.org
Although we have three nice questions, we have fewer answers.
Absence of a black marriage boom: –we have that one covered.
Rise of black marriage age 1970-1990: –I will briefly summarize our pending proposal
Reversal of traditional gender pattern –some preliminary results
1. Why was there no black marriage boom?
Marriage age distribution: No marriage boom for black men
Figure 2. Age at which 10, 25, 50 and 75 Percent of Black Men Had Married, 1870-1990
10%
25%
50%
75%
15
18
21
24
27
30
33
36
1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990
Year
Ag
e
Virtually no marriage boom for black women
Figure 3. Age at which 10, 25, 50 and 75 Percent of Black Women Had Married, 1870-1990
10%
25%50%
75%
15
18
21
24
27
30
33
36
1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990
Year
Ag
e
To investigate differentials, we must shift our measures from median marriage age and marriage age distribution to percent of young people never married.
The indirect median age at marriage is unreliable in periods of rapid change.
It also doesn’t allow us to look at differentials between most population subgroups, since people change their characteristics as they age.
Here is how the indirect median is calculated:
Figure 4. Calculating the median age at first marriage: Percent ever-married at each age
0
10
20
30
40
50
60
70
80
90
100
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
Age
Per
cen
t
95% ever married
47.5% ever married
20.2 years
Calculation of median age at first marriage:1) Percent ever-married = 95 %2) Half of all women who will marry = 95/2 = 47.5%3) Age at which 47.5% of women have married = 20.2 years4) Add six months = 20.2 + .5 = 20.7 years
The indirect median has been the principal measure of marriage age in the U.S. for a century, but it is now unreliable.
With the rapid change in marriage patterns we cannot predict how many people will eventually marry, so estimates are increasingly biased upwards.
Also, indirect median is no good for studying differentials in characteristics that change over the life course, like socioeconomic status.
So, forget about marriage age: we will focus on percent of young people never-married.
Note: SMAM is even worse.
Trend in percent never married is closely similar to trend in marriage age, but there is a slight bump in marriage age for black men from 1950 to 1970
Figure 5. Percent Never-married: Black and Native-born White Men ages 22-27, 1850-1990
Black
White
20%
30%
40%
50%
60%
70%
80%
90%
1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990
Year
Figure 6. Percent Never-married: Native-born White Men Ages 22-27, by Occupational Group, 1850-1990
20%
30%
40%
50%
60%
70%
80%
90%
1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990
Year
No occupation
Lower income (non-farm)
Middle income (non-farm)
Higher income (non-farm)
Farm occupations
Among white men, there was a marriage boom in every occupational group.
Figure 7. Percent Never-married: Native-born Black Men Ages 22-27, by Occupational Group, 1850-1990
20%
30%
40%
50%
60%
70%
80%
90%
1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990
Year
No occupation
Lower income (non-farm)
Middle income (non-farm)
Higher income (non-farm)
Farm occupations
Among black men, there was a marriage boom in every occupational group except for farming.
Figure 8. Occupational Distribution of Black Men Ages 22-27, 1880-1990
Farm occupations
Lower income non-farm occupations
Middle income non-farm occupations
Higher income non-farm occupations
No occupation
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990
Year
Conclusion 1:
After the war, blacks were forced off southern farms by mechanization and consolidation of sharecropping farms.
This resulted in massive dislocation and a rise of young men with no occupation.
Without the shift from farming into no occupation, there would have been a marriage boom.
There was no marriage boom for blacks because there was no economic boom for blacks.
2. What caused the extraordinary rise of
black marriage age after 1970?
Hypotheses: 1. Male opportunity
Marriage boom resulted from rising prosperity, job security, optimism (Glick and Carter 1958); declining male opportunities in 1970s and 1980s, especially among blacks, reversed the trend (Wilson 1987 and many others)
Increasing economic uncertainty (Oppenheimer 1988) and inequality (Gould and Paserman 2003) compounded the problem.
Hypotheses 2. Rising female opportunity
Growing economic opportunities for women increased marriage ageDecreased dependence on a spouse, opened
alternatives to marriage (Cherlin 1980)Undermined sex-role specialization and
reduced the value of marriage (Becker 1981)
Hypotheses, continued
These theories predict a positive association between male economic opportunity and early marriage, and an inverse association for female opportunity.
Historically, these relationships have been strong, but recent evidence that the relationship may have reversed for women (e.g. Oppenheimer and Lew 1995)
Hypotheses-continued
Or, maybe it is cultural change
McLanahan 2004: the New Feminism
Past studies that attempted to assess relationship between economic opportunities for men and women at the local level on marriage formation ran into data limitations, especially for blacks
Fitch and Ruggles Research Proposal:
Use internal long-form data
Success of IPUMS-USA
User friendly access, harmonized codes, and integrated comprehensive hypertext documentation led to flood of census-based research:
12,000 users, 75,000 extractions
1,000 publications and working papers
IPUMS-based research is concentrated in the top U.S. journals: the most common venues are Demography, American Economic Review, Journal of Political Economy, American Sociological Review, Social Forces, and Quarterly Review of Economics
Census microdata is now the most widely used source in U.S. demographic research
Other Public-Use Census Microdata
Canada 1971, 1976, 1981, 1986, 1991, 1996: varying designs, densities 1996: Data Liberation Initiative led to an explosion in of usage in
research and teaching
United Kingdom 1991: 2% individuals, 0.5% households
hundreds of publications, thousands of users 2001: double the densities because confidentiality assessments
were too conservative.
Cross-National Harmonization:National Academy of Science recommendations
“National and international funding agencies should establish mechanisms that facilitate the harmonization of data collected in different countries.”
“Cross national studies conducted within a framework of comparable measurement can be a substantially more useful tool for policy analysis than studies of single countries.”
“The scientific community, broadly construed, should have widespread and unconstrained access to the data.”
Source: Preparing for an Aging World: The Case for Cross-National Research (National Academy, 2001)
International Census Microdata Harmonization
1959-1976: Omuece (Latin America) 19 countries, censuses from 1960s and 1970s Goal was standardized tabulations, but microdata was a
byproduct Lowest common denominator approach Preserved extraordinary body of data and documentation
1992-2003: PAU (Europe and North America) 24 countries, 1990s and 2000s Focus on the aging population Complex variables not harmonized
IPUMS-International goals
Follow the model of IPUMS-USA to produce harmonized data and documentation for multiple countries over the 1960-2004 period
Learn from successes and limitations of OMUECE and PAU Lose no information, except when necessary to ensure
confidentiality Harmonize complex variables using a composite coding system Document comparability issues thoroughly Provide user-friendly web-based data access tools Ensure confidentially through non-disclosure agreements and
statistical protections
Countries participating in IPUMS-International
Region Country
Africa Ghana, Kenya, Madagascar, Uganda
Americas Argentina, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Venezuela, USA
Asia China, Tajikistan, Turkmenistan, Vietnam, Mongolia
Europe Austria, Belarus, Bulgaria, Czech Republic, France, Germany, Greece, Hungary, Netherlands, Portugal, Romania, Russia, Slovenia, Spain, the United Kingdom
Middle East Israel, Palestinian Authority
Ipums-International Countries
IPUMS-Latin AmericaCensuses included in Round I (1999-2004)
INEGI-Mexico 1960, 1970, 1990, 2000
DANE-Colombia 1964, 1972, 1985, 1993
IBGE-Brazil 1960, 1970, 1980, 1991, 2000
Censuses included in Round II (2003-2008)
Argentina 1960, 1970, 1980, 1991, 2001
Bolivia 1976, 1992, 2001
Chile 1960, 1970, 1982, 1992, 2002
Costa Rica 1963, 1973, 1984, 2000
Dominican Republic 1960, 1970, 1981, 1993, 2004
Ecuador 1962, 1974, 1982, 1990, 2001
El Salvador 1961, 1971, 1992, 2002
Guatemala 1964, 1973, 1981, 1994, 2002
Honduras 1961, 1974, 1988, 2001
Nicaragua 1971, 1995
Panama 1960, 1970, 1980, 1990, 2000
Paraguay 1962, 1972, 1982, 1992, 2002
Peru 1981, 1993, 2003
Puerto Rico 1960, 1970, 1980, 1990, 2000
Venezuela 1961, 1971, 1981, 1990, 2001
Confidentiality Issues
The USA and Mexican census microdata are completely public, and may be freely downloaded from the web.
Even though these data are entirely public and the U.S. data have been available for forty years, there has not been a single instance of a breach of confidentiality
IPUMS-International is restricted microdata, requiring researchers to commit to a non-disclosure agreement.
IPUMS-International also incorporates statistical disclosure controls (swapping, blurring, top-coding, etc.) to minimize risk to confidentiality.
Two major points:
Disclosure controls work: no one has ever been identified in 40 years of experience
Reducing barriers to access leads to widespread use and quality research
Statistical disclosure control is effective
“For a user of an outside database, attempting this sort of match with no opportunity for verification would prove fruitless. In the first place, the small degree of expected overlap would be a considerable deterrent to an intruder. However, if a match between the two files was attempted the large number of apparent matches would be highly confusing as an intruder would have no way of checking correct identification.”
--Angela Dale and Mark Elliott, Journal of the Royal Statistical Society
Easy access encourages use
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1995 1996 1997 1998 1999 2000 2001 2002 2003
Number of IPUMS-USA Registered Users since 1995
Additional information at http://ipums.org
Steven Ruggles
http://ipums.org
Thank you.