How Many (White) Men are Getting Married?

Recently, several bloggers have challenged my assertions that fewer and fewer (White) men are getting married. They cited the GSS Data, available here as evidence that I was wrong, that White Men are getting married at the same rates now as before. The Inductivist and also Audacious Epigone have used the Web Tools available at the Berkeley site to conclude that there is no evidence to support that Men are not getting married, or delaying marriage, over time.

Having downloaded the data myself, I can only conclude that so far ... they're wrong.

[For those interested in replicating my results, the following is how I did it. Those uninterested may skip this section.]

Downloading the data, (I used the 1972-2006 data set), with the variable AGE (age of respondent), YEAR (year of survey), MARITAL (marital status, 1=Married, 5=Never Married, etc), Sex (1=Male, 2=Female), RACE (1=White), plus of course the always present CASEID, you get a nice text file you can load into MySQL. To make things simpler, I added a constraint in the file created and downloaded, and specified only White Men (SEX=1, RACE=1). Loading this into MySQL gives you interesting results. [I wanted to look at White Men only, avoiding race and class, and looking at what most observers believe the most stable and unchanging group of men in the US.]

One of the things that struck me (and this is why you CANNOT avoid looking at the data in raw form) is how small sample sizes are for White Men in the Age Ranges. For example, the SQL Query

select year, age, count(*) as Num_Age from gss_marital where year = '1975' group by age with rollup;

gives you this output:



Year	Age	# of Respondents
1975	18	2
1975	19	17
1975	20	13
1975	21	13
1975	22	12
1975	23	11
1975	24	16
1975	25	21
1975	26	9
1975	27	20
1975	28	10
1975	29	14
1975	30	15
1975	31	6
1975	32	13
1975	33	11
1975	34	9
1975	35	11
1975	36	12
1975	37	11
1975	38	14
1975	39	7
1975	40	12
1975	41	11
1975	42	11
1975	43	11
1975	44	7
1975	45	11
1975	46	10
1975	47	6
1975	48	9
1975	49	10
1975	50	13
1975	51	6
1975	52	11
1975	53	7
1975	54	10
1975	55	8
1975	56	9
1975	57	10
1975	58	11
1975	59	6
1975	60	10
1975	61	2
1975	62	9
1975	63	11
1975	64	7
1975	65	7
1975	66	8
1975	67	7
1975	68	7
1975	69	8
1975	70	13
1975	71	4
1975	72	4
1975	73	4
1975	74	6
1975	75	1
1975	76	4
1975	77	7
1975	78	2
1975	79	2
1975	80	5
1975	81	3
1975	82	1
1975	83	3
1975	84	1
1975	85	1
1975	86	2
1975	87	1
1975	88	>1
1975	89	1
1975	\N	598

The last row is of course the rollup row, showing there were indeed 598 White Men interviewed in 1975. That might seem like a lot, but look at the data at a more atomic level. Only 7 53-year olds, and only 10 54-year olds, in 1975 were interviewed. Your data is only as good as your sample size, and for each age, the samples can be appallingly small. The problem of course is randomness. Can we be sure that for the very small sample sizes for each age, survey takers got a truly random sample of White Men? Much less repeating it for every year? Given that the survey ran from 1972-2006 (I did not use the file with incomplete 2007-2008 data).

select year, count(*) as Num_Men from gss_marital group by year;
select year, count(*) as Never_Married from gss_marital where marital = '5' group by year;


Will each get you the total for each year of all men (all ages), by year, and then the total for all men, all ages, who were never married (MARITAL=5). I've dumped that into an Open Office Spreadsheet to get the following:


[click Image to Enlarge]

Wow. Just for laughs I tried the following:


select year, count(*) as Num_Men from gss_marital where age > '34' and age < '41' group by year;

select year, count(*) as Never_Married from gss_marital where marital = '5' and age > '34' and age < '41' group by year;


And got the following graph (once I loaded it into OpenOffice)





Therefore, it certainly looks as if the data suggests that AT LEAST White Men are getting married later, which would certainly make the "Never Married" status stronger in surveys. The data is not inconsistent with that hypothesis, at any rate. How good is the GSS Data? Not particularly good, given questions about small sample sizes for White Men, at each age, and just how random the selection of the survey takers was, but it is one of the few social surveys we do have covering considerable time periods. We certainly see a fairly consistent rise in "Never Married" over the years, which matches the increased cost for a family, given rising housing prices, and the decline of real wages, in terms of house-buying at least, since the 1970's.

Just as important however, may be the changing expectations of women with respect to marriage. Sandra Tsing Loh, the NPR commentator, writer, and performance artist, has a revealing column in the upcoming Atlantic Montly, which I will be posting on later. It certainly seems among professional urban women, marriage has undergone redefinition. Akin to more of a short-term contract offered to NFL free agents, than anything else.
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