2500+ words Essay

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09-Spousallaborsupply.ppt
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Spousal labor supply

SESS 0042

2nd December 2019

*

Men’s falling, women’s rising LFP

Data

Duration of unemployment Labor force participation rate Table IE-2 Measures of Individual Earnings Inequality for Full-Time,
Ba597 Ba598 Ba343 Ba344 Ba345 Year-Round Workers by Sex: 1967 to 2005 from the Census Bureau
Year Average number of weeks Median number of weeks Year Total Men Women Unemployment rate from bls.gov Income Ratios
1945 1850 89.7 1968 3.7 Men Women
1946 1860 52 88 13.5 3.8 Year C 90/10 50/10 90/10 50/10
1947 1870 53.6 91.5 15.8 3.7 1965
1948 8.6 1880 55.2 92.1 16.9 3.5 1966
1949 10 1900 57.7 91.7 22.1 3.5 1967 12/ 4.2 2.33 4.67 2.67
1950 12.1 1910 60.2 92.1 26 3.7 1968 4.01 2.2 3.99 2.3
1951 9.7 1920 57.8 90.4 24.1 3.7 1969 3.75 2.06 3.3 1.98
1952 8.4 1940 55.6 84.7 27.1 3.5 1970 3.85 2.14 3.41 1.95
1953 8 1950 56 83.2 30.3 3.4 1971 13/ 3.93 2.13 3.39 1.94
1954 11.8 1960 57.6 81.4 36.1 3.4 1972 14/ 4.01 2.16 3.33 1.94
1955 13 1970 56.7 77 42 3.4 1973 3.85 2.09 3.38 1.94
1956 11.3 1980 60.9 75.8 50.5 3.4 1974 16/15/ 3.84 2.15 3.01 1.78
1957 10.5 1990 64.2 75.3 57.5 1969 3.4 1975 16/ 3.83 2.04 3.14 1.84
1958 13.9 3.4 1976 17/ 4.17 2.21 3.11 1.81
1959 14.4 3.4 1977 4.12 2.27 3.27 1.87
1960 12.8 3.4 1978 4.14 2.21 3.2 1.8
1961 15.6 3.4 1979 18/ 4.01 2.18 3.18 1.82
1962 14.7 3.5 1980 4.38 2.28 3.27 1.83
1963 14 3.5 1981 4.29 2.29 3.23 1.85
1964 13.3 3.5 1982 4.44 2.28 3.34 1.87
1965 11.8 3.7 1983 19/ 4.67 2.39 3.57 1.97
1966 10.4 3.7 1984 4.79 2.45 3.61 2.01
1967 8.7 2.3 3.5 1985 20/ 4.8 2.4 3.74 2
1968 8.4 4.5 3.5 1986 5 2.5 3.9 2.08
1969 7.8 4.4 1970 3.9 1987 21/ 4.81 2.4 4 2.11
1970 8.6 4.9 4.2 1988 4.77 2.36 4.14 2.16
1971 11.3 6.3 4.4 1989 4.87 2.35 4.04 2.08
1972 12 6.2 4.6 1990 5.04 2.42 4.07 2.15
1973 10 5.2 4.8 1991 5 2.42 3.94 2.02
1974 9.8 5.2 4.9 1992 22/ 5.12 2.5 4 2.1
1975 14.2 8.4 5 1993 23/ 5.42 2.5 4.2 2.15
1976 15.8 8.2 5.1 1994 24/ 5.67 2.5 4.5 2.2
1977 14.3 7 5.4 1995 25/ 5.31 2.38 4.46 2.23
1978 11.9 5.9 5.5 1996 5.42 2.46 4.36 2.16
1979 10.8 5.4 5.9 1997 5.36 2.38 4.46 2.23
1980 11.9 6.5 6.1 1998 5.31 2.43 4.33 2.08
1981 13.7 6.9 1971 5.9 1999 29/ 5.33 2.39 4.5 2.17
1982 15.6 8.7 5.9 2000 30/ 5.67 2.47 4.67 2.25
1983 20 10.1 6 2001 5.77 2.44 4.62 2.23
1984 18.2 7.9 5.9 2002 5.81 2.44 4.44 2.22
1985 15.6 6.8 5.9 2003 5.93 2.47 4.64 2.14
1986 15 6.9 5.9 2004 35/ 6.13 2.5 4.69 2.14
1987 14.5 6.5 6 2005 5.88 2.35 4.83 2.17
1988 13.5 5.9 6.1
1989 11.9 4.8 6
1990 12 5.3 5.8
1991 13.7 6.8 6
1992 17.7 8.7 6
1993 18 8.3 1972 5.8
1994 18.8 9.2 5.7
1995 16.6 8.3 5.8
1996 16.7 8.3 5.7
1997 15.8 8 5.7
1998 14.5 6.7 5.7
1999 13.4 6.4 5.6
2000 12.6 5.9 5.6
5.5
5.6
5.3
5.2
1973 4.9
5
4.9
5
4.9
4.9
4.8
4.8
4.8
4.6
4.8
4.9
1974 5.1
5.2
5.1
5.1
5.1
5.4
5.5
5.5
5.9
6
6.6
7.2
1975 8.1
8.1
8.6
8.8
9
8.8
8.6
8.4
8.4
8.4
8.3
8.2
1976 7.9
7.7
7.6
7.7
7.4
7.6
7.8
7.8
7.6
7.7
7.8
7.8
1977 7.5
7.6
7.4
7.2
7
7.2
6.9
7
6.8
6.8
6.8
6.4
1978 6.4
6.3
6.3
6.1
6
5.9
6.2
5.9
6
5.8
5.9
6
1979 5.9
5.9
5.8
5.8
5.6
5.7
5.7
6
5.9
6
5.9
6
1980 6.3
6.3
6.3
6.9
7.5
7.6
7.8
7.7
7.5
7.5
7.5
7.2
1981 7.5
7.4
7.4
7.2
7.5
7.5
7.2
7.4
7.6
7.9
8.3
8.5
1982 8.6
8.9
9
9.3
9.4
9.6
9.8
9.8
10.1
10.4
10.8
10.8
1983 10.4
10.4
10.3
10.2
10.1
10.1
9.4
9.5
9.2
8.8
8.5
8.3
1984 8
7.8
7.8
7.7
7.4
7.2
7.5
7.5
7.3
7.4
7.2
7.3
1985 7.3
7.2
7.2
7.3
7.2
7.4
7.4
7.1
7.1
7.1
7
7
1986 6.7
7.2
7.2
7.1
7.2
7.2
7
6.9
7
7
6.9
6.6
1987 6.6
6.6
6.6
6.3
6.3
6.2
6.1
6
5.9
6
5.8
5.7
1988 5.7
5.7
5.7
5.4
5.6
5.4
5.4
5.6
5.4
5.4
5.3
5.3
1989 5.4
5.2
5
5.2
5.2
5.3
5.2
5.2
5.3
5.3
5.4
5.4
1990 5.4
5.3
5.2
5.4
5.4
5.2
5.5
5.7
5.9
5.9
6.2
6.3
1991 6.4
6.6
6.8
6.7
6.9
6.9
6.8
6.9
6.9
7
7
7.3
1992 7.3
7.4
7.4
7.4
7.6
7.8
7.7
7.6
7.6
7.3
7.4
7.4
1993 7.3
7.1
7
7.1
7.1
7
6.9
6.8
6.7
6.8
6.6
6.5
1994 6.6
6.6
6.5
6.4
6.1
6.1
6.1
6
5.9
5.8
5.6
5.5
1995 5.6
5.4
5.4
5.8
5.6
5.6
5.7
5.7
5.6
5.5
5.6
5.6
1996 5.6
5.5
5.5
5.6
5.6
5.3
5.5
5.1
5.2
5.2
5.4
5.4
1997 5.3
5.2
5.2
5.1
4.9
5
4.9
4.8
4.9
4.7
4.6
4.7
1998 4.6
4.6
4.7
4.3
4.4
4.5
4.5
4.5
4.6
4.5
4.4
4.4
1999 4.3
4.4
4.2
4.3
4.2
4.3
4.3
4.2
4.2
4.1
4.1
4
2000 4
4.1
4
3.8
4
4
4
4.1
3.9
3.9
3.9
3.9
2001 4.2
4.2
4.3
4.4
4.3
4.5
4.6
4.9
5
5.3
5.5
5.7
2002 5.7
5.7
5.7
5.9
5.8
5.8
5.8
5.7
5.7
5.7
5.9
6
2003 5.8
5.9
5.9
6
6.1
6.3
6.2
6.1
6.1
6
5.8
5.7
2004 5.7
5.6
5.8
5.6
5.6
5.6
5.5
5.4
5.4
5.5
5.4
5.4
2005 5.2
5.4
5.2
5.1
5.1
5
5
4.9
5.1
5
5
4.8
2006 4.7
4.7
4.7
4.7
4.7
4.6
4.7
4.7
4.5
4.4
4.5
4.4
2007 4.6
4.5
4.4
4.5
4.5
4.6
4.7
4.7
4.7
4.8
4.7
5
2008 4.9
4.8
5.1
5
5.5
5.5
5.7
6.1
6.1
6.5
6.7
2009

G1

1945 1945
1946 1946
1947 1947
1948 1948
1949 1949
1950 1950
1951 1951
1952 1952
1953 1953
1954 1954
1955 1955
1956 1956
1957 1957
1958 1958
1959 1959
1960 1960
1961 1961
1962 1962
1963 1963
1964 1964
1965 1965
1966 1966
1967 1967
1968 1968
1969 1969
1970 1970
1971 1971
1972 1972
1973 1973
1974 1974
1975 1975
1976 1976
1977 1977
1978 1978
1979 1979
1980 1980
1981 1981
1982 1982
1983 1983
1984 1984
1985 1985
1986 1986
1987 1987
1988 1988
1989 1989
1990 1990
1991 1991
1992 1992
1993 1993
1994 1994
1995 1995
1996 1996
1997 1997
1998 1998
1999 1999
2000 2000
Average number of weeks
Median number of weeks
Duration of unemployment
8.6
10
12.1
9.7
8.4
8
11.8
13
11.3
10.5
13.9
14.4
12.8
15.6
14.7
14
13.3
11.8
10.4
8.7
2.3
8.4
4.5
7.8
4.4
8.6
4.9
11.3
6.3
12
6.2
10
5.2
9.8
5.2
14.2
8.4
15.8
8.2
14.3
7
11.9
5.9
10.8
5.4
11.9
6.5
13.7
6.9
15.6
8.7
20
10.1
18.2
7.9
15.6
6.8
15
6.9
14.5
6.5
13.5
5.9
11.9
4.8
12
5.3
13.7
6.8
17.7
8.7
18
8.3
18.8
9.2
16.6
8.3
16.7
8.3
15.8
8
14.5
6.7
13.4
6.4
12.6
5.9

G2a

1850
1860
1870
1880
1900
1910
1920
1940
1950
1960
1970
1980
1990
Total
%
Labor force participation rate
52
53.6
55.2
57.7
60.2
57.8
55.6
56
57.6
56.7
60.9
64.2

G2b

1850 89.7
1860 88 13.5
1870 91.5 15.8
1880 92.1 16.9
1900 91.7 22.1
1910 92.1 26
1920 90.4 24.1
1940 84.7 27.1
1950 83.2 30.3
1960 81.4 36.1
1970 77 42
1980 75.8 50.5
1990 75.3 57.5
Total
Men
Women
%
Labor force participation rate
52
53.6
55.2
57.7
60.2
57.8
55.6
56
57.6
56.7
60.9
64.2

G3a

1998
4.6
4.7
4.3
4.4
4.5
4.5
4.5
4.6
4.5
4.4
4.4
1999
4.4
4.2
4.3
4.2
4.3
4.3
4.2
4.2
4.1
4.1
4
2000
4.1
4
3.8
4
4
4
4.1
3.9
3.9
3.9
3.9
2001
4.2
4.3
4.4
4.3
4.5
4.6
4.9
5
5.3
5.5
5.7
2002
5.7
5.7
5.9
5.8
5.8
5.8
5.7
5.7
5.7
5.9
6
2003
5.9
5.9
6
6.1
6.3
6.2
6.1
6.1
6
5.8
5.7
2004
5.6
5.8
5.6
5.6
5.6
5.5
5.4
5.4
5.5
5.4
5.4
2005
5.4
5.2
5.1
5.1
5
5
4.9
5.1
5
5
4.8
2006
4.7
4.7
4.7
4.7
4.6
4.7
4.7
4.5
4.4
4.5
4.4
2007
4.5
4.4
4.5
4.5
4.6
4.7
4.7
4.7
4.8
4.7
5
2008
4.8
5.1
5
5.5
5.5
5.7
6.1
6.1
6.5
6.7
Unemployment rate
4.6
4.3
4
4.2
5.7
5.8
5.7
5.2
4.7
4.6
4.9

G3b

1968
3.8
3.7
3.5
3.5
3.7
3.7
3.5
3.4
3.4
3.4
3.4
1969
3.4
3.4
3.4
3.4
3.5
3.5
3.5
3.7
3.7
3.5
3.5
1970
4.2
4.4
4.6
4.8
4.9
5
5.1
5.4
5.5
5.9
6.1
1971
5.9
6
5.9
5.9
5.9
6
6.1
6
5.8
6
6
1972
5.7
5.8
5.7
5.7
5.7
5.6
5.6
5.5
5.6
5.3
5.2
1973
5
4.9
5
4.9
4.9
4.8
4.8
4.8
4.6
4.8
4.9
1974
5.2
5.1
5.1
5.1
5.4
5.5
5.5
5.9
6
6.6
7.2
1975
8.1
8.6
8.8
9
8.8
8.6
8.4
8.4
8.4
8.3
8.2
1976
7.7
7.6
7.7
7.4
7.6
7.8
7.8
7.6
7.7
7.8
7.8
1977
7.6
7.4
7.2
7
7.2
6.9
7
6.8
6.8
6.8
6.4
1978
6.3
6.3
6.1
6
5.9
6.2
5.9
6
5.8
5.9
6
1979
5.9
5.8
5.8
5.6
5.7
5.7
6
5.9
6
5.9
6
1980
6.3
6.3
6.9
7.5
7.6
7.8
7.7
7.5
7.5
7.5
7.2
1981
7.4
7.4
7.2
7.5
7.5
7.2
7.4
7.6
7.9
8.3
8.5
1982
8.9
9
9.3
9.4
9.6
9.8
9.8
10.1
10.4
10.8
10.8
1983
10.4
10.3
10.2
10.1
10.1
9.4
9.5
9.2
8.8
8.5
8.3
1984
7.8
7.8
7.7
7.4
7.2
7.5
7.5
7.3
7.4
7.2
7.3
1985
7.2
7.2
7.3
7.2
7.4
7.4
7.1
7.1
7.1
7
7
1986
7.2
7.2
7.1
7.2
7.2
7
6.9
7
7
6.9
6.6
1987
6.6
6.6
6.3
6.3
6.2
6.1
6
5.9
6
5.8
5.7
1988
5.7
5.7
5.4
5.6
5.4
5.4
5.6
5.4
5.4
5.3
5.3
1989
5.2
5
5.2
5.2
5.3
5.2
5.2
5.3
5.3
5.4
5.4
1990
5.3
5.2
5.4
5.4
5.2
5.5
5.7
5.9
5.9
6.2
6.3
1991
6.6
6.8
6.7
6.9
6.9
6.8
6.9
6.9
7
7
7.3
1992
7.4
7.4
7.4
7.6
7.8
7.7
7.6
7.6
7.3
7.4
7.4
1993
7.1
7
7.1
7.1
7
6.9
6.8
6.7
6.8
6.6
6.5
1994
6.6
6.5
6.4
6.1
6.1
6.1
6
5.9
5.8
5.6
5.5
1995
5.4
5.4
5.8
5.6
5.6
5.7
5.7
5.6
5.5
5.6
5.6
1996
5.5
5.5
5.6
5.6
5.3
5.5
5.1
5.2
5.2
5.4
5.4
1997
5.2
5.2
5.1
4.9
5
4.9
4.8
4.9
4.7
4.6
4.7
1998
4.6
4.7
4.3
4.4
4.5
4.5
4.5
4.6
4.5
4.4
4.4
1999
4.4
4.2
4.3
4.2
4.3
4.3
4.2
4.2
4.1
4.1
4
2000
4.1
4
3.8
4
4
4
4.1
3.9
3.9
3.9
3.9
2001
4.2
4.3
4.4
4.3
4.5
4.6
4.9
5
5.3
5.5
5.7
2002
5.7
5.7
5.9
5.8
5.8
5.8
5.7
5.7
5.7
5.9
6
2003
5.9
5.9
6
6.1
6.3
6.2
6.1
6.1
6
5.8
5.7
2004
5.6
5.8
5.6
5.6
5.6
5.5
5.4
5.4
5.5
5.4
5.4
2005
5.4
5.2
5.1
5.1
5
5
4.9
5.1
5
5
4.8
2006
4.7
4.7
4.7
4.7
4.6
4.7
4.7
4.5
4.4
4.5
4.4
2007
4.5
4.4
4.5
4.5
4.6
4.7
4.7
4.7
4.8
4.7
5
2008
4.8
5.1
5
5.5
5.5
5.7
6.1
6.1
6.5
6.7
Unemployment rate
3.7
3.4
3.9
5.9
5.8
4.9
5.1
8.1
7.9
7.5
6.4
5.9
6.3
7.5
8.6
10.4
8
7.3
6.7
6.6
5.7
5.4
5.4
6.4
7.3
7.3
6.6
5.6
5.6
5.3
4.6
4.3
4
4.2
5.7
5.8
5.7
5.2
4.7
4.6
4.9

G4

1965 1965 1965 1965
1966 1966 1966 1966
1967 1967 1967 1967
1968 1968 1968 1968
1969 1969 1969 1969
1970 1970 1970 1970
1971 1971 1971 1971
1972 1972 1972 1972
1973 1973 1973 1973
1974 1974 1974 1974
1975 1975 1975 1975
1976 1976 1976 1976
1977 1977 1977 1977
1978 1978 1978 1978
1979 1979 1979 1979
1980 1980 1980 1980
1981 1981 1981 1981
1982 1982 1982 1982
1983 1983 1983 1983
1984 1984 1984 1984
1985 1985 1985 1985
1986 1986 1986 1986
1987 1987 1987 1987
1988 1988 1988 1988
1989 1989 1989 1989
1990 1990 1990 1990
1991 1991 1991 1991
1992 1992 1992 1992
1993 1993 1993 1993
1994 1994 1994 1994
1995 1995 1995 1995
1996 1996 1996 1996
1997 1997 1997 1997
1998 1998 1998 1998
1999 1999 1999 1999
2000 2000 2000 2000
2001 2001 2001 2001
2002 2002 2002 2002
2003 2003 2003 2003
2004 2004 2004 2004
2005 2005 2005 2005
Men: 90/10
Men: 50/10
Women: 90/10
Women: 50/10
Income ratios
4.2
2.33
4.67
2.67
4.01
2.2
3.99
2.3
3.75
2.06
3.3
1.98
3.85
2.14
3.41
1.95
3.93
2.13
3.39
1.94
4.01
2.16
3.33
1.94
3.85
2.09
3.38
1.94
3.84
2.15
3.01
1.78
3.83
2.04
3.14
1.84
4.17
2.21
3.11
1.81
4.12
2.27
3.27
1.87
4.14
2.21
3.2
1.8
4.01
2.18
3.18
1.82
4.38
2.28
3.27
1.83
4.29
2.29
3.23
1.85
4.44
2.28
3.34
1.87
4.67
2.39
3.57
1.97
4.79
2.45
3.61
2.01
4.8
2.4
3.74
2
5
2.5
3.9
2.08
4.81
2.4
4
2.11
4.77
2.36
4.14
2.16
4.87
2.35
4.04
2.08
5.04
2.42
4.07
2.15
5
2.42
3.94
2.02
5.12
2.5
4
2.1
5.42
2.5
4.2
2.15
5.67
2.5
4.5
2.2
5.31
2.38
4.46
2.23
5.42
2.46
4.36
2.16
5.36
2.38
4.46
2.23
5.31
2.43
4.33
2.08
5.33
2.39
4.5
2.17
5.67
2.47
4.67
2.25
5.77
2.44
4.62
2.23
5.81
2.44
4.44
2.22
5.93
2.47
4.64
2.14
6.13
2.5
4.69
2.14
5.88
2.35
4.83
2.17

*

Allocation of Weekly Hours to Various Activities,
By Gender and Marital Status

Market Work

Household Work

Personal Care

Passive Leisure

Other

*

Labor Force Participation Rates over the Life Cycle in 2005

*

Hours of Work over the Life Cycle, 2005

*

Women & employment: what we know

  • Huge increase in formal employment over the last century
  • Particularly among married women
  • (Singles had been working for some time already)
  • Big gains in educational attainment
  • Sectoral shift from (light) industry to services (white-collar jobs)

*

Our research questions

  • Why have women changed their ways?
  • How do they make choices between fertility and labor market participation?
  • How are the lifecycle decisions made and how have they changed?
  • Career choices, parity choices etc.
  • Birth timing, marriage timing

*

What was the actual historical experience?

  • The “engines-of-liberation” story
  • Wave of gadgets comes around with electricity
  • The gadgets liberate women from house work
  • Women’s LFP increases
  • But: see Table 6 and Figures 1+3 in Ramey (2008)

*

*

*

*

Composition of employment of single women

Proportions employed as: Single women, aged 20-24
1860 1870 1880 1900 1910 1920 1930
LFP Non-metro area 32.46 26.94 31.39 39.91 49.76 51.01 53.48
Metro area 55.03 56.62 57.09 64.13 73.06 79.98 79.14
Professional, clerical and sales Non-metro area 15.50 18.25 21.76 35.40 48.89 63.58 60.02
Metro area 7.06 5.71 12.19 28.49 40.00 62.21 65.94
Craftswomen and operatives Non-metro area 24.44 23.76 25.40 22.96 16.65 13.43 15.10
Metro area 37.62 37.92 44.14 37.18 36.91 25.54 19.19
Service workers Non-metro area 54.41 52.26 45.08 34.68 23.55 14.68 16.41
Metro area 54.12 51.59 40.52 32.16 20.52 9.65 12.30
Other Non-metro area 5.65 5.72 7.75 6.95 10.92 8.31 8.48
Metro area 1.21 4.78 3.16 2.17 2.57 2.60 2.58

*

Look: household gadgets can actually destroy women’s employment opportunities (decline in service worker share).

*

What was the actual historical experience?

  • The “engines-of-liberation” story
  • Wave of gadgets comes around with electricity
  • The gadgets liberate women from house work
  • Women’s LFP increases
  • But: see Table 6 and Figures 1+3 in Ramey (2008)
  • Men and women are specializing less than before
  • Total hrs per person have not changed much
  • Decline in overall hrs per household corresponds to the decline in the size of households
  • The timing issue

*

Graphical representation?

Household goods

Market goods

  • Specialization is less necessary (men and women have become more “interchangeable”)
  • Individual LFP becomes less predictable
  • Overall quantity of consumption has gone up

*

In what sense was this a liberation?

  • Liberation from
  • Dirt (intensity of work)
  • Drudgery
  • Domestic servitude
  • Sharp specialization along gender lines
  • Alternative explanation?
  • A pull story: emergence of office jobs leads to
  • A decline in the relative supply of domestic servants
  • A response in the form of a market for household gadgets
  • Effect of WW2
  • Blame it on the rising relative wages of women

*

Why have women changed their ways?

Proportions employed as: Single US women, aged 20-24
1860 1870 1880 1900 1910 1920 1930
LFP Non-metro area 32.46 26.94 31.39 39.91 49.76 51.01 53.48
Metro area 55.03 56.62 57.09 64.13 73.06 79.98 79.14
Professional, clerical and sales Non-metro area 15.50 18.25 21.76 35.40 48.89 63.58 60.02
Metro area 7.06 5.71 12.19 28.49 40.00 62.21 65.94
Craftswomen and operatives Non-metro area 24.44 23.76 25.40 22.96 16.65 13.43 15.10
Metro area 37.62 37.92 44.14 37.18 36.91 25.54 19.19
Service workers Non-metro area 54.41 52.26 45.08 34.68 23.55 14.68 16.41
Metro area 54.12 51.59 40.52 32.16 20.52 9.65 12.30
Other Non-metro area 5.65 5.72 7.75 6.95 10.92 8.31 8.48
Metro area 1.21 4.78 3.16 2.17 2.57 2.60 2.58

*

Sectoral change

  • Techno change makes brawn more productive
  • It is easier to satisfy demand for brawny stuff
  • Relative price of brawny stuff declines
  • Some reallocation of resources to brainy stuff: sectoral shift in employment
  • Demand for brawn on lab market declines
  • Relative wages of brawn go down
  • Crucial assumption: brawny stuff and brainy stuff are complements in consumption

Brainy stuff

Brawny stuff

*

Note, however:

  • Initial growth in women’s employment did not mean women got careers:
  • Mostly, they just had (less-skilled) jobs
  • Career jobs were available to life-time single women (teaching)
  • Marriage bar in place (first informal, then formal)
  • Attachment to labor market was very weak

*

Source: YouGov

Non-economic changes in the labour market: how it operates, professional interaction between men and women.

*

*

Lifecycle decisions

  • Children are a major disruption in a career
  • Most women (and their male partners) want children at some point
  • It is an issue that affects the dynamic decision-making (i.e. across time)
  • What career to choose
  • What kind of human capital to accumulate
  • When exactly to go ahead with the family
  • How many years to stay away

*

Career and children

  • Lifetime career employment required better control of timing of fertility
  • This did not arrive until 1960s: Enovid
  • “the Pill” is a most effective contraceptive
  • It puts the contraceptive decision entirely in the woman’s hands

*

Bailey (2007) & Stange (2010)

*

https://www.nytimes.com/interactive/2018/08/04/upshot/up-birth-age-gap.html

*

*

https://www.nytimes.com/interactive/2018/08/04/upshot/up-birth-age-gap.html

*

*

Occupational crowding

  • Better timing is great but is only part of the cost
  • Fertility still determines human capital accumulation
  • Leaving job depreciates human capital
  • Leaving job means missing promotions
  • Women are still more likely to choose careers with slower HC depreciation:
  • Teaching, child care etc.
  • NOT finance, NOT IT, NOT anything cutting-edge

*

Data on occupational crowding

*

Salient features of male LFP

  • It is generally higher than women’s
  • But it has come down in the last century
  • Greater variance among men in wages
  • Shift from market place to home

*

Labor force participation rate

*

Careful with statistics!

  • Longevity can account for some of the decline
  • Longer education for another portion
  • Finally, there is the growing similarity between men and women

*

Average hours worked/week, 1900-2005

*

Male labor and marriage

  • Earning potential matters on the marriage market
  • Improved labor market conditions will make men more attractive as partners
  • This will increase marriage rate overall

*

Proportion Never Married/Single by Age 45 – 54; by Census Year (Birth Decade)

*

Mean OCCSCORE of White Men by Age

*

Mean OCCSCORE of Black Men by Age

*

Table 11 - Effect of Explanatory Variables on Imputed Probability of Marriage
  White Black
  Men Women Men Women
Sex ratio 5.4 -0.9 12.6 2.6
Trait 8.5 6.4 7.5 6.9
Variance in number of breadwinners 1.2 0.9 -1.3 -1.0
Variance in number of children -0.5 -0.3 0.9 -1.6
All partner search variables 14.5 3.1 17.2 2.5
Single women's LFP -6.0 -10.4 0.9 -7.0
Married women's LFP 0.9 0.3 0.6 -2.1
Men's LFP 1.2 3.7 1.6 4.2
Own OCCSCORE 20.8   25.7  
Average men's job quality 11.0 9.3
All labor market variables 16.9 2.2 26.1 1.6
All marriage market variables 30.9 5.5 41.4 4.1
Note: The reported values are percentage point changes in the probability of being ever married by each race-sex groups median age (25 for white men, 22 for white women, 23 for black men, 20 for black women) as each variable is varied between its 10th and 90th percentile.

*

Early 20th century

  • Labor market factors pushed for marriage as much as search factors
  • Men were getting good stable jobs
  • This was the heyday of stable mfg jobs
  • Women clearly responded by accepting more marriage proposals
  • Apart from the long term trend, there is also response to cyclical conditions

*

How have things changed?

  • Mfg jobs have decreased in importance
  • Weakening of the unions
  • Career launch takes longer
  • Growing inequality among men
  • Outcome:
  • Average vs variance again
  • Stabler marriages at the upper end of income distribution

*

Marriage and unemployment

  • Marital stability increases with income
  • Income is still frequently man’s significant contribution to relationship gains
  • Unemployment significantly reduces those gains
  • Particularly his end of the bargain
  • Jalovaara (2003) study on unemployment and divorce risk: Finland

*

Jalovaara (2003)

  • Link census information on couples to divorce papers
  • Covers period 1990 – 1993
  • This was a time of severe recession in Finland
  • We get plenty of variation of economic outcomes
  • Measure risk of divorce per marriage-year

*

*

*

  • Clearly, unemployment increases chances of divorce
  • The effect is stronger for men than for women
  • Men and/or women do not adjust well to male unemployment
  • Education gradient shows there may be a long-term problem on the horizon: marriage only for the rich?

*

Determinants of the Male-Female Wage Ratio

  • The wage gap is VERY persistent

*

Wage distribution by gender (earnings ratios of full-time workers)

*

Determinants of the Male-Female Wage Ratio

  • The wage gap is VERY persistent
  • Men and women differ in their labor market histories
  • Human capital is more profitable the longer the payoff period
  • Occupation crowding has segregated women into particular occupations where the return to education is lower
  • Women are better off if they enter occupations in which their skills do not deteriorate during the years they spend in the household sector

*

Source: The Economist

*

Source: The Economist

*

*

*

Discrimination – hard to pin down

  • Selection among women
  • Who works and who does not
  • What kind of women go to work
  • When and how many children they have
  • Long-term investment in human capital
  • Forms of discrimination:
  • Glass-ceiling
  • Different hiring standards
  • Statistical discrimination

*

Goldin (1997) on blind auditions

  • Orchestras need musicians
  • For long, they were predominantly male
  • By 1970s, some start adopting blind auditions
  • Selection committees do not see gender of applicants – that should eliminate bias

*

Goldin (1997) on blind auditions

*

*

*

*

Conclusions

  • Women and men are much samer than they used to be but still different
  • Unlike men, whose labor supply is boring, women face a more complicated decision
  • Lot has changed over the past century
  • More will change in the future
  • It is difficult to disentangle discrimination from other stuff going on in the market
  • Considering all this, some differences between men and women may persist, others may change continually

The rest

  • If there is time

*

What came of it all

  • Changes in women’s lives over 20th century:
  • More LFP
  • Higher wages
  • Better careers
  • Convergence in housework
  • Higher quality of children
  • Improvements in health
  • Are women happier?

Stevenson & Wolfers (2009)

  • Happiness research
  • Beware – it has many critics!
  • Q: Taken all together, how would you say things are these days, would you say that you are very happy, pretty happy or not too happy?
  • Same question over 30 years
  • Asked of men and women of all walks of life
  • Separate questions about specific areas of life

What is going on?

  • Change in reference?
  • Women compare with men
  • Women compare over more domains
  • Expectations rose faster than reality

Conclusion

  • Careful what you wish for (you may get it)
  • The less you expect, the less disappointed you will be
  • Always look on the bright side of life
  • More seriously:
  • Reported happiness is not a direct index of quality of life
  • Most women, even if reporting less happiness, would not want to go back to the life of 1970

Labor force participation rate

0

10

20

30

40

50

60

70

80

90

100

1850186018701880190019101920194019501960197019801990

%

TotalMenWomen

40.2

32.9

16.7

22.2

14.3

12

34.9

23.5

77.6

76.9

78.7

79.4

22.4

24.2

22

23.8

13.5

22

15.7

19.1

0

24

48

72

96

120

144

168

Married Men

Unmarried Men

Married Women

Unmarried Women

30

40

50

60

70

80

90

100

152535455565

Age

Labor force participation rate

Male

Female

500

1,000

1,500

2,000

2,500

152535455565

Age

Annual hours of work

Male

Female

30

35

40

45

50

55

60

1900192019401960198020002020

Year

Weekly hours

67   

 

Notes: Updated version of Figure 7‐2 from Blau, Ferber, and Winkler (2014); for additional information on references, see p. 148.  Workers aged  16 and over from 1979 onward, and 14 and over prior to 1979. 

55

60

65

70

75

80

85

19 55

19 60

19 65

19 70

19 75

19 80

19 85

19 90

19 95

20 00

20 05

20 10

Ea rn in gs  R at io  (P

er ce nt )

Year

Figure 1:  Gender Earnings Ratios of Full‐Time Workers  1955‐2014

Weekly Annual (Full Year)

67 

 

 

Notes: Updated version of Figure 7‐2 from Blau, Ferber, and Winkler (2014); for additional information on references, see p. 148.  Workers aged 

16 and over from 1979 onward, and 14 and over prior to 1979. 

55

60

65

70

75

80

85

1

9

5

5

1

9

6

0

1

9

6

5

1

9

7

0

1

9

7

5

1

9

8

0

1

9

8

5

1

9

9

0

1

9

9

5

2

0

0

0

2

0

0

5

2

0

1

0

E

a

r

n

i

n

g

s

 

R

a

t

i

o

 

(

P

e

r

c

e

n

t

)

Year

Figure 1:  Gender Earnings Ratios of Full‐Time Workers 

1955‐2014

WeeklyAnnual (Full Year)

68   

Source: Authors’ calculations from Panel Study of Income Dynamics (PSID) data.  See text for definitions. 

62.1%

74.0%

77.2%

79.3%

71.1%

81.5% 82.7% 82.1%

79.4%

92.4% 91.4% 91.6%

50.0%

55.0%

60.0%

65.0%

70.0%

75.0%

80.0%

85.0%

90.0%

95.0%

1980 1989 1998 2010

Figure 2:  Female to Male Log Wage Ratio, Unadjusted and Adjusted for  Covariates (PSID)

Unadjusted

Adjusted:  Human Capital Specification

Adjusted:  Full Specification

68 

 

Source: Authors’ calculations from Panel Study of Income Dynamics (PSID) data.  See text for definitions. 

62.1%

74.0%

77.2%

79.3%

71.1%

81.5%

82.7%

82.1%

79.4%

92.4%

91.4%

91.6%

50.0%

55.0%

60.0%

65.0%

70.0%

75.0%

80.0%

85.0%

90.0%

95.0%

1980 1989 1998 2010

Figure 2:  Female to Male Log Wage Ratio, Unadjusted and Adjusted for 

Covariates (PSID)

Unadjusted

Adjusted:  Human Capital Specification

Adjusted:  Full Specification

73   

Table 4:  Decomposition of Gender Wage Gap, 1980 and 2010 (PSID)

1980 2010 Effect of Gender Gap in  Explanatory Variables

Effect of Gender Gap in  Explanatory Variables

Variables Log Points

Percent of  Gender Gap  Explained Log Points

Percent of  Gender Gap  Explained

A. Human Capital Specification

Education Variables 0.0129 2.7% ‐0.0185 ‐7.9% Experience Variables 0.1141 23.9% 0.0370 15.9% Region Variables 0.0019 0.4% 0.0003 0.1% Race Variables 0.0076 1.6% 0.0153 6.6% Total Explained 0.1365 28.6% 0.0342 14.8% Total Unexplained Gap 0.3405 71.4% 0.1972 85.2% Total Pay Gap 0.4770 100.0% 0.2314 100.0%

B.  Full Specification

Education Variables 0.0123 2.6% ‐0.0137 ‐5.9% Experience Variables 0.1005 21.1% 0.0325 14.1% Region Variables 0.0001 0.0% 0.0008 0.3% Race Variables 0.0067 1.4% 0.0099 4.3% Unionization 0.0298 6.2% ‐0.0030 ‐1.3% Industry Variables 0.0457 9.6% 0.0407 17.6% Occupation Variables 0.0509 10.7% 0.0762 32.9% Total Explained 0.2459 51.5% 0.1434 62.0% Total Unexplained Gap 0.2312 48.5% 0.0880 38.0% Total Pay Gap 0.4770 100.0% 0.2314 100.0%

Notes: Sample includes full time nonfarm wage and salary workers age 25‐64 with at least 26 weeks of employment.  Entries are the male‐female differential in the indicated variables multiplied by the current year male log wage coefficients for the corresponding variables.   The total unexplained gap is the mean female residual from the male log wage equation.

73 

 

Table 4:  Decomposition of Gender Wage Gap, 1980 and 2010 (PSID)

1980 2010

Effect of Gender Gap in 

Explanatory Variables

Effect of Gender Gap in 

Explanatory Variables

Variables Log Points

Percent of 

Gender Gap 

ExplainedLog Points

Percent of 

Gender Gap 

Explained

A. Human Capital Specification

Education Variables0.01292.7%‐0.0185‐7.9%

Experience Variables0.114123.9%0.037015.9%

Region Variables0.00190.4%0.00030.1%

Race Variables0.00761.6%0.01536.6%

Total Explained0.136528.6%0.034214.8%

Total Unexplained Gap0.340571.4%0.197285.2%

Total Pay Gap 0.4770100.0%0.2314100.0%

B.  Full Specification

Education Variables0.01232.6%‐0.0137‐5.9%

Experience Variables0.100521.1%0.032514.1%

Region Variables0.00010.0%0.00080.3%

Race Variables0.00671.4%0.00994.3%

Unionization 0.02986.2%‐0.0030‐1.3%

Industry Variables0.04579.6%0.040717.6%

Occupation Variables0.050910.7%0.076232.9%

Total Explained0.245951.5%0.143462.0%

Total Unexplained Gap0.231248.5%0.088038.0%

Total Pay Gap 0.4770100.0%0.2314100.0%

Notes: Sample includes full time nonfarm wage and salary workers age 25‐64 with at least 26

weeks of employment.  Entries are the male‐female differential in the indicated variables

multiplied by the current year male log wage coefficients for the corresponding variables.  

The total unexplained gap is the mean female residual from the male log wage equation.