POST ABOUT LABOR/WORK AND WIDOWED WOMEN, SHOWING HOW TO USE NAPP TO INVESTIGATE GENDER, WORK, AND MARITAL STATUS.

INITIAL PARAGRAPH LINKING TO PREVIOUS WORK ON NAPP. ALSO, A BIT OF HISTORIOGRAPHY? (JSTOR?)

USE META/DATE DIRECTIVE.

Jobs held by widowed women

Perhaps we are interested in the sorts of jobs widowed women held.

Let us first discover what the top job categories were for women in 1880, according to the microdata.[^fnwarning] There are 24,655,420 women in NAPP's 1880a microdata. Note: the most common listed occupation for women at this time was "Keeping House," which is generally categorized as "No occupation" by the HISCO classifications used below.

SELECT occhisco.desc AS JobType
, count(*) AS Number
FROM data
JOIN occhisco ON data.occhisco = occhisco.id
WHERE sex = 2
GROUP BY occhisco
ORDER BY Number DESC
LIMIT 20;
JobType Number
No occupation/Unknown 20904850
Housekeeper 814863
Servants nfs 631592
Farm workers, specialisation unknow 379871
Labourers nfs 225924
House servants nfs and maids 191547
Dressmakers 160846
Teachers (unspecified) 153755
Textile workers, specialisation unk 137277
Washing and laundry services 114580
Cooks 91543
General farmers and farmers nfs 87805
Seamstresses 67393
Helpers of relative or helping at h 47900
Tailors and tailoresses 46702
Milliners 42593
Nurses nfs 33704
Other garment makers 28907
Ambiguous responses 26304
Dealer, merchant etc. (Wholesale an 22639

Next, let's dig a little deeper to find out specifically about widows. The variable MARST gives the marital status of each person in the data set. Those with MARST = 5 are widows/widowers, so further limiting it by gender (SEX in NAPP) produces results for widowed women who had not remarried. There are 1659566 widowed women counted in the database.

SELECT occhisco.desc AS JobType
, count(*) AS Number
FROM data
JOIN occhisco ON data.occhisco = occhisco.id
WHERE sex = 2 AND marst = 5
GROUP BY occhisco
ORDER BY Number DESC
LIMIT 20;
JobType Number
No occupation/Unknown 1211636
Housekeeper 88894
General farmers and farmers nfs 51886
Servants nfs 45089
Washing and laundry services 39156
Farm workers, specialisation unknow 31754
Labourers nfs 25254
Cooks 20101
Dressmakers 18879
House servants nfs and maids 15931
Seamstresses 12680
Dealer, merchant etc. (Wholesale an 9232
Boarding and lodging house keepers 8550
Nurses nfs 8114
Textile workers, specialisation unk 5690
Teachers (unspecified) 5362
Tailors and tailoresses 4955
Milliners 4081
Helpers of relative or helping at h 3318
Other garment makers 2414

[^fnwarning]: Warning: these queries will probably take a while to run. Having a fast disk (such as an SSD) helps, as they are I/O limited, at least on my machine.