Describing Same Gender Couples from the 100% Results
An initial examination of the data in Tables 1, 2, and 3 shows some basic patterns. The relative proportions of same gender married and unmarried partner households are reversed for the two sites, with unmarried partners more prevalent than same gender married couples in California, but the reverse in South Carolina (Table 1). This pattern repeatedly emerges both in the total and sample data sets. The similarity in the numbers is coincidental. One possible reason for the reversal is the provision for persons in California to have domestic partnerships, and the fact that the population in California may be more familiar or comfortable with the concept of an "unmarried partner." However, an argument could be made that California has a more active homosexual community and there may be more interest and awareness in the same-gender marriage debate. This is conjecture and something that will not be revealed by this data. Nonetheless we continue to examine patterns of the other covariates by marital status, and by site. If there were significant differences in the meaning of "marriage" by site, it is our hope that site specific tables will illuminate these differences.
Tables 2 and 3 present distributions of basic "short-form" characteristics; age, race, presence and age of children, and number of unrelated adults in the household for same gender couples by gender and type of relationship in California and South Carolina respectively. These results show age distributions that reveal striking differences. For both sites, the proportion of respondents (person 1) over age 50 in married couple households is much greater than in unmarried partner households, indicating a relatively older population for married households. This is true whether one examines male/male or female/female households. The mean age difference between person 1 and their spouse/partner is less than 3 years although there are sizable standard deviations, indicating some spread in individual age differences. A greater proportion of married male couples are in the same age group than are male unmarried partner couples.
The distributions of same gender couple households by the race of person 1 show differences for both the site at which the data was collected and for married versus unmarried partner households. In California (Table 2) less than 50% of the married couple households have a householder identifying only white as their race. Compared to about 80% of the householders in unmarried partner households. This pattern persists in South Carolina, although there is only about a 10-15 percentage point difference between married and unmarried couple households. Overall, South Carolina has less racial diversity than California with approximately 95% of householders either White or Black regardless of couple status; so this finding was not unexpected. For both sites, in both married-couple or unmarried partner households, the majority of partners identify the same racial group as person 1. There are only marginally greater proportions of interracial couple households in California (about 15 to 20 percent) than in South Carolina (5 to 10 percent).
Household composition proved to hold some interesting differences between married and unmarried partner households. Children were coresident in a much greater proportion of married couple households than in unmarried partner households. Between 50 and 60% of married couple households in California and between 45 and 50 % of married couple households in South Carolina have children in the household. This contrasts sharply with the unmarried partner households where only 8 to 20% of households in California, and between 18 and 22% in South Carolina, had coresident children. We also examined the number of coresident unrelated adults, and found very few households (generally less than 5 percent), either married couple or unmarried partner, that had any coresident unrelated adults, other than the respondent and their spouse/partner. This finding was similar by gender of the couple and survey site.
The two columns of "edited" data in tables 2 and 3 indicate the estimated number of same gender unmarried couples which would result after all married couple households were edited into unmarried partner households. For the California site, about 30 percent of unmarried partner households would be comprised of households originally designating themselves as married-couple households. For South Carolina, this percentage would be 70 percent. A combination of these two demographically different household structures into a single edited household type could affect the analyses of same gender households, especially if different areas produce an aggregate total from different proportions of household types.
Table 2. Number of households by household characteristics: Sacramento, California. (6k)
Table 3. Number of households by household characteristics: Columbia, South Carolina (7k)
Describing Same Gender Couples from the Long Form (Sample) Results
Long form distributions for California and South Carolina are presented in tables 4 and 5. These tables present data from the sample "long" form for same gender couples by gender and type of relationship. For this set of tables we include: the respondents' reported marital status, the educational attainment of the respondent and the similarity of this level for the spouse/partner of person 1, the employment status of person 1 and of their spouse/partner in the last week, and an indicator of their residential stability. The frequency distributions in tables 4 and 5 are based on very small numbers of households. The percentages should be considered cautiously. However, it should be noted that when aggregated and analyzed in a logistic regression the relationships cursorily observed in these tables persist. Discussion of the regression results follows the discussion of these tables.
The reported marital status of person 1 is the first of the variables added from the sample forms. This was included to evaluate the responses entered for relationship to household reference person (person 1). The distribution of the marital status item for both men and women, in both data collection sites, and for married couples and unmarried partners, prove to be consistent and validate the responses to the relationship item. The distributions do not reflect perfect agreement, but the overwhelming trend is in a direction that is consistent and distinguishes these two groups of households. In California, 74 percent of partners were reported to be in agreement with the respondent, that they were married spouse present. In South Carolina, between 80 and 85 percent of partners were reported to be in agreement with the respondent, that they are married spouse present in married couple households. The percentage of respondents and partners in unmarried partner households both reported in agreement that they were "currently not married" ranges between 84 and 97 percent of the households for the two sites and gender combinations.
There are greater proportions of respondents in unmarried partner households that have experience in college, and there are correspondingly higher proportions of respondents in married couple households that have high school degrees or less. Current employment status follows a trend consistent with educational status between these two types of couples. For both male and female couple households, a greater proportion of unmarried partner households have both the respondent and spouse/partner currently employed than in married couple households. Residential stability also indicates some differences by type of couple. Higher proportions of married couple households have both the respondent and their spouse reporting that they lived in that house five years ago. These patterns are consistent with the age pattern of the householders shown in tables 2 and 3 which indicate a higher proportion of householders age 50 years and over among the married couples than unmarried partner households. Younger couples have probably benefitted from more recent trends in increasing educational levels, and are now more mobile, and likely to be employed than are people who are older.
Table 4. Number of households by household characteristics, Sample characteristics only: Sacramento, California. (5k)
Table 5. Number of households by household characteristics, Sample characteristics only: Columbia, South Carolina. (5k)
Multivariate Summary of Same Gender Couple Household Results
Table 6 presents the results of simple multivariate regressions on the probability that a same gender household is also a married couple household by various characteristics. It is important to point out that these are probabilities of associations, not cause. The data collection sites are combined in these models, and a dummy variable is added to indicate the collection site. Although we believe that there are interactions present in the underlying relationship, for the purpose of this paper we do not attempt to model them. In model A we include the variables available from the short form (100%) only. The relationship between having children in the household and being married is quite strong. Same gender households with children are 7 times more likely to be married couple households. Households in South Carolina are also more likely to identify as married couple households, 5 times more than those households from Sacramento. Controlling for the relative age of the partner, householders who are 40 years or older are more likely to identify as married couple households (3 times more likely), and partners who are in the same five year age group are more likely to also be married compared to those whose partners are in the adjacent or more distant age groups.
In model B, we add variables from the long form, and correspondingly reduce the sample to long form respondents. The relationships between presence of children, age of the respondent, and data collection site and the likelihood of being married persist in the presence of additional covariates from the sample form. In model B, the variable indicating the presence of other unrelated adults is dropped because it failed to attain significance in the 100 percent models. In addition to these relationships, respondents with any college experience are less likely to be in married couple households 74% less likely than respondents with a high school degree. Respondents in stable households, those where both partners lived at that residence five years ago, are more likely to also be married. Households in which both the respondent and their partner worked last week, are less likely to identify themselves as married couples, 53% less than households where only one of the partners is working.
Models C and D show results for the variables included in model A, but stratified by site. The results in these models only differ from the combined model in two places. First, for the Sacramento site, couples in which the respondent and partner identify the same race category are more likely to also be married. This effect would not be expected to be present at the Columbia, South Carolina site because of the very high overall level of racial homogamy in both married and unmarried partner households. At the Columbia site, female couples are 38% more likely to also be married compared to male couples.
Appendix A shows results for the same four models, only exchanging marriage for the presence of children as a dependent outcome. These models present a consistent picture with that shown for marriage. Some of the interactions present in the data begin to be apparent when these results are examined. Because the presence of children was such a strong correlate for being in a married couple household, this appendix is included as reference.
Table 6. Odds ratios (a) for being married: Census 2000 Dress Rehearsal (4k)