This study investigates the behavior of the labor supply of Canadian women at different composite hourly wages of all paid jobs in 2009. Since it is widely demonstrated in the literature that variables such as age and levels of education, as well as the demographic, social, and financial characteristics of the household influence a woman’s decision to join the labor market, such variables are included in the study.
Although there have been a few studies on the women’s labor supply in both developed and developing countries, there are no recent studies as of 2009 that focus on the “backward-bending” labor supply of Canadian women. While some studies have focused on labor supply and its elasticities by considering the aspect of poverty in the labor supply analysis (El-Hamidi 2003, Sharif 1991, Dasgupta & Goldar 2005), and others have focused on testing the hypotheses advanced by the Nakamuras (1981) of finding a backward-bending supply curve for females similar to that of males (Robinson and Tomes 1985), this study tests the hypothesis that a backward-bending labor supply exists for Canadian women by using cross-sectional data from 2009. The results of this study offer strong support to the conclusions reached by the Nakamuras (1981), and Robinson and Tomes (1985).
Focusing on the female labor supply in Canada is motivated by two simple facts: first, in 2006, Canada’s population consisted of 49% males (15.5 million) and 51% females (16.1 million), a sex ratio of 96 males per hundred females. Females outnumbered males in every province except for Alberta and the three territories
(The Atlas of Canada). However, labor force participation rates are generally higher among men than women in Canada. In 2006, the participation rate for Canadian men was 72.5% while it was only 62.1% for Canadian women (
CCSD Facts & Stats).
The second motivating issue is of gender discrimination affecting Canadian women. Because of gender discrimination, women who perform the same tasks as men are often paid less and receive fewer benefits from their work. Even in developed countries like Canada, women earn only 70.4% of what men earn – a percentage lower today than in the 1990s [Gender Discrimination in Canada]. By understanding female participation behavior, policy makers will be in a position to assess the likelihood of tackling this issue, and provide effective policy prescriptions.
The analysis in this paper focuses on Canadian women in ten provinces aged 24 to 60. The objective of this paper is to test the assumption that the canonical model of labor supply is backward-bending for Canadian women. The second objective is to test if factors that determine labor supply decisions differ according to the economic well-being of the household where the female worker lives. However, this paper does not provide any policy prescriptions regarding the nature of the labor supply for Canadian women.
According to Robinson and Tomes (1985), given that men typically work more hours and receive higher wages than women, the larger income effect was expected to dominate the substitution effect for men, resulting in a backward-bending labor supply curve. On the other hand, for women the dominant substitution effect generates a positively sloped labor supply curve consistent with the upward trend in female labor force participation. In the United States, studies by Hall (1973) and Boskin (1973) provide empirical support for these arguments. In addition, the results for Canada reported by Carliner et al. (1980) for Canadian women are also congruent with this view.
However, this consensus has been challenged in a series of influential papers by Alice and Masao Nakamura (Nakamura, Nakamura, and Cullen, 1979; Nakamura and Nakamura, 1981, 1983) and also later challenged by Robinson and Tomes (1985) by affirming the conclusions reached by the Nakamuras with more recent and different data. Their results show that the estimated labor supply elasticities are predominantly negative, implying a backward-bending supply curve, and are broadly consistent with values typically reported for men. Hence the results of the Nakamuras and Robinson and Tomes suggest that there is no significant disparity between the labor supply elasticities of working men and women.
The results of the Nakamuras were of controversial nature at the time, and Nakamuras’ hypothesis was subject to independent tests especially by Robinson and Tomes (1985) and others. According to Robinson and Tomes (1985), past studies used census data that suffer from several drawbacks. They described that the first problem was that the actual hours of work were not recorded and instead intervals were used. The use of intervals is particularly problematic for females, because more females tend to supply hours outside the “normal” range and hence fall in much wider intervals than the males. The second problem was that an actual wage was not recorded. The wage has to be computed by dividing observed annual earnings by computed annual hours which results in a problem of division bias [Borjas (1980)]. Similar to the study of Robinson and Tomes (1985), this study deals with these problems but in a different manner. By using cross-sectional data of the Survey of labor and Income Dynamics (SLID) from Statistics Canada, a direct measure of the hourly wage rate and the direct hours of work for a subset of women in Canada were obtained.
At the beginning of the 20th century, the average factory worker worked almost 60 hours per week, the equivalent of six 10-hour days (Borjas, 1996). It was not until the 1960s that the average hours in manufacturing conformed to the more familiar 40-hour workweek (eight hours per day, for five days). Figure 1.1 below illustrates the apparent degree of flexibility that Canadian workers have in the number of hours they work per week. The work patterns of women are quite varied. Men are more likely to work the typical 40-hour week. However, even for working men, less than half work 40 to 49 hours per week. Almost a quarter work more than 50 hours, and an equal proportion work fewer than 40 hours. Women, on the other hand, are more likely to work part-time than 40 hours (Ibid).
Figure 1.1: Distribution of Hours Worked per Week by Gender, 1996
Table 1.1 below traces the standard work week in Canadian manufacturing and shows a pronounced and continuous decline over time in hours of work. Between 1901 and 1981 the standard work week declined from almost 60 hours to less than 40 hours. The decline slowed down in the depression years of the 1930s, and the war years of the 1940s, and it appears to be slower in the postwar period. However, as the last column illustrates, when vacations and holidays are considered the decline in average working hours is more noticeable. In essence, in recent years the work force has reduced its working hours more in the form of increased vacations and holidays rather than a reduction in hours worked per week. The decline in net weekly hours in the postwar period and in standard hours prior to World War II give a long-run trend reduction of about two hours per decade (Borjas, 1996).
Table 1.1: Standard Weekly Hours in Manufacturing, Canada, 1901-1981
|
Year
|
Standard Weekly Hours
|
Hrs Net Vacation & Holidays
|
|
1901
|
58.6
|
N/A
|
|
1911
|
56.5
|
N/A
|
|
1921
|
50.3
|
N/A
|
|
1931
|
49.6
|
N/A
|
|
1941
|
49.0
|
N/A
|
|
1951
|
42.6
|
40.7
|
|
1961
|
40.4
|
38.1
|
|
1971
|
39.8
|
36.7
|
|
1981
|
39.2
|
34.8
|
Since real wages have risen over the century, this long-run decline in hours worked appears inconsistent with an upward-sloping labor supply function (Borjas, 1996). Instead, it suggests an independent effect of increased wages: as people become wealthier, they need not toil as hard, and can afford to take more time off. Consequently, the classical theory will clarify the ways in which wages can affect labor supply.
A plausible explanation of the declining hours of work is the classical theory of labor supply. The classical theory of labor supply states that at low levels of income the substitution effect dominates which results in a positive elasticity of labor supply (raising wages raises hours of work). On the other hand, at high wage levels, the income effect dominates resulting in a negative elasticity (raising wages reduces hours of work). In other words, the income effect of higher wages means workers will reduce the amount of hours they work, because they can maintain a target level of income through less work. On the other hand, the substitution effect of higher wages means workers will give up leisure to do more hours of work because more work leads to higher rewards. As a result, the labor supply schedule forms a backward-bending shape for an individual (Robins, 1930). Since Canada is a developed country, this type of a backward-bending labor supply schedule for the majority of Canadians is expected in this study.Continued on Next Page »
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Endnotes
1.) The author would like to thank Professor Craig Brett for his invaluable suggestions and comments.
2.) Carliner et al. (1980) in their analysis of 1971 Canadian census data employ three measures of labor supply: labor force participation, hours per week, and weeks per year. Using education as a proxy for potential market wages they found that “greater education of the wife is associated with significantly increased labor supply for all three measures. This suggests that the … substitution effects of an increase in wf [the wife’s wage] … outweigh the income effect.”
3.) The emphasis of the three papers is quite different. Nakamura, Nakamura, and Cullen (1979) report estimates for Canadian women using the 1971 Canadian census. Nakamura and Nakamura (1981) analyze both Canadian and U.S. census data emphasizing the role of taxes. Nakamura and Nakamura (1983) using these same data sets, distinguish further between full-time and part-time workers.
4.) Robinson and Tomes (1985) used data from 1979 Quality of Life Survey, which is a survey conducted by the Institute for Behavioural Research, York University, to deal against the problems of using census data for their study. The survey contained a direct measure of the hourly wage rate and also presented hours of work directly rather than in intervals for a subset of Canadian women.
5.) Source: http://highered.mcgraw-hill.com/sites/dl/free/0070891540/43156/benjamin5_sample_chap02.pdf.
6.) Source: See http://highered.mcgraw-hill.com/sites/dl/free/0070891540/43156/benjamin5_sample_chap02.pdf for the original table.
7.) Standard hours are usually determined by collective agreements or company policies, and they are the hours beyond which overtime rates are paid. The data apply to non-office worker.
8.) Standard hours minus the average hours per week spent on holidays and vacations.
9.) This supply curve shows how the change in real wage rate affects the amount of hours worked by employees. Source: http://en.wikipedia.org/wiki/Backward_bending_supply_curve_of_labor. See the appendix section.
10.) Although the Heckman sample selection model is written in terms of hours of work H, the same equations
apply equally as well to the wage W.
11.) All the steps of the Heckit method is borrowed from lecture notes: Cross-Section Regression Estimates of labor Supply Elasticities: Procedures and Problems.
12.) See http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=3889&lang=en&db=imdb&adm=8&dis=2 for more details on the Survey of labor and Income Dynamics (SLID).
13.) Census data is not used as the limitations of Census data in labor economics is well documented [Killingsworth (1983); Angrist and Krueger (1999)]. Income variables are based on respondents’ memory and willingness to disclose this information that is mostly underreported in the Census.
14.) To check for educational assortative mating, the husband’s education variable was added to the actual data that contains only females. After running a single regression of husband’s education on female’s education, a positive correlation for each level of education was found. Hence, husband’s education was added to the model to see how it affects the results. However, it must be noted that adding husband’s education to the model did not change the Heckit results that much. Most importantly, since adding husband's education to the model still results in a positive coefficient of non-female income in the Heckit, the sorting is not on education even though there is a positive correlation among husband's and wife's education. Therefore, the Heckit results with the inclusion of husband’s education to the model are not reported in this paper. Moreover, the existing literature of labor supply of women doesn't include this kind of variable.
15.) It has been mentioned by Adkins and Hill (2004) that “Donald (1995) has studied this problem and suggested a semiparametric estimator that is consistent in heteroscedastic selectivity models. Chen & Khan (2003) has also proposed a semiparametric estimator of this model. More recently, Lewbel (2003) has proposed an alternative that is both easy to implement and robust to heteroskedastic misspecification of unknown form.” The authors themselves proposed a “simple estimator that is easily computed using standard regression software,” and studied the performance of the estimator in a small set of Monte Carlo simulations.
Appendix
Table 1.2: Variable Descriptions
|
hours
|
total hours paid all jobs during 2009
|
|
wage
|
composite hourly wage all paid jobs in 2009
|
|
wagesqrd
|
the square of composite hourly wage all paid jobs
|
|
age
|
female's age, 2009, external cross-sec file
|
|
agesqrd
|
the square of female's age
|
|
marst
|
marital status of female as of December 31 of 2009
1 – female is married
2 – female is in a common-law relationship
3 – female is separated
4 – female is divorced
5 – female is widowed
6 – female is single (never married)
|
|
fslsp
|
female is living with spouse in 2009
1 – Yes
2 - No
|
|
province
|
Province of residence group, household, December 31, 2009
10 - Newfoundland and Labrador
11 – Prince Edward Island
12 – Nova Scotia
13 – New Brunswick
24 – Quebec
35 – Ontario
46 – Manitoba
47 – Saskatchewan
48 – Alberta
59 – British Columbia
|
|
exper
|
number of years of work experience, full-year full-time
|
|
expersqrd
|
the square of number of years of work experience, full-year full-time
|
|
alimo
|
Support payments received
|
|
educ
|
Highest level of education of female, 1st grouping
1 - Never attended school
2 - 1-4 years of elementary school
3 - 5-8 years of elementary school
4 - 9-10 years of elementary and
secondary school
5 - 11-13 years of elementary and
secondary school (but did not
graduate)
6 - Graduated high school
7 - Some non-university postsecondary (no certificate)
8 - Some university (no certificate)
9 - Non-university postsecondary
certificate
10 - University certificate below
Bachelor's
11 - Bachelor's degree
12 - University certificate above
Bachelor's, Master's, First
professional degree in law, Degree
in medicine, dentistry, veterinary
medicine or optometry, Doctorate
(PhD)
|
|
nonfemaleincome
|
income of non-female in the household
|
|
kidslt6
|
female with a child less than six years old
|
|
working
|
total hours paid all jobs greater than zero
|
| |
|
|
|
|
Table 1.3: Summary Statistics of Canadian women
|
Variable
|
Observations
|
Mean
|
Standard Deviation
|
Minimum
|
Maximum
|
|
puchid25(id)
|
32065
|
4012858
|
7414.513
|
4000001
|
4025693
|
|
province
|
31819
|
33.74845
|
14.69714
|
10
|
59
|
|
agyfm
|
32065
|
38.72475
|
25.07988
|
0
|
80
|
|
agyfmg46
|
32065
|
5.924965
|
2.56457
|
1
|
9
|
|
|
|
|
|
|
|
alimo46
|
32065
|
263.0711
|
1860.065
|
0
|
45000
|
|
earng46
|
31745
|
51132.91
|
63660.3
|
0
|
1387250
|
|
age
|
17042
|
43.26998
|
10.50669
|
24
|
60
|
|
marst
|
28264
|
2.8629
|
2.118468
|
1
|
6
|
|
fslac
|
28325
|
1.907326
|
.2899806
|
1
|
2
|
|
|
|
|
|
|
|
fslsp
|
28325
|
1.406884
|
.4912616
|
1
|
2
|
|
hours
|
24009
|
1129.835
|
922.4242
|
0
|
5200
|
|
wage
|
16371
|
19.89017
|
11.81493
|
6
|
142
|
|
exper
|
24864
|
14.9928
|
13.18434
|
0
|
50
|
|
|
|
|
|
|
|
alimo
|
28325
|
249.0071
|
1825.297
|
0
|
45000
|
|
earng42
|
28108
|
20899.72
|
28372.56
|
0
|
539000
|
|
mtinc42
|
28179
|
25065.66
|
30446.84
|
0
|
680000
|
|
oas42
|
28325
|
1210.796
|
2430.963
|
0
|
7750
|
|
ogovtr42
|
28325
|
33.60018
|
181.1052
|
0
|
2400
|
|
|
|
|
|
|
|
ottxm42
|
28325
|
561.278
|
4202.446
|
0
|
120000
|
|
prpen42
|
28325
|
2120.96
|
7977.688
|
0
|
185000
|
|
sapis42
|
28325
|
406.2242
|
2022.65
|
0
|
25000
|
|
uccb42
|
28325
|
139.9682
|
495.2109
|
0
|
7800
|
|
uiben42
|
28325
|
757.8279
|
2789.844
|
0
|
31000
|
|
|
|
|
|
|
|
wgsal42
|
28325
|
19643.28
|
27591.65
|
0
|
525000
|
|
wkrcp42
|
28325
|
130.5137
|
1279.867
|
0
|
32000
|
|
educ
|
28204
|
7.580946
|
2.599754
|
1
|
12
|
|
totalfemincome
|
28179
|
50174.48
|
56331.64
|
0
|
1110900
|
|
nonfemincome
|
28067
|
29301.02
|
32393.06
|
0
|
680000
|
|
|
|
|
|
|
|
wagesqrd
|
16371
|
535.2028
|
918.9231
|
36
|
20164
|
|
agesqrd
|
28325
|
2642.723
|
1801.337
|
256
|
6400
|
|
expersqrd
|
24864
|
398.6038
|
528.935
|
0
|
2500
|
|
|
|
|
|
|
|
kidslt6
|
32065
|
.0902542
|
.28655
|
0
|
1
|
|
working
|
32065
|
.8051458
|
.3960946
|
0
|
1
|
Table 1.4: Marital Status of Canadian women
|
Marital Status
|
Frequency
|
Percent
|
Cumulative
|
|
1 – female is married
|
13,841
|
48.97
|
48.97
|
|
2 – female is in a common-law relationship
|
2,485
|
8.79
|
57.76
|
|
3 – female is separated
|
982
|
3.47
|
61.24
|
|
4 – female is divorced
|
1,900
|
6.72
|
67.96
|
|
5 – female is widowed
|
2,776
|
9.82
|
77.78
|
|
6 – female is single (never married)
|
6,280
|
22.22
|
100.00
|
|
Total
|
28,264
|
100.00
|
|
Table 1.5: Canadian women living with spouse or not
|
Living with spouse or not
|
Frequency
|
Percent
|
Cumulative
|
|
1 - Yes
|
16,800
|
59.31
|
59.31
|
|
2 - No
|
11,525
|
40.69
|
100.00
|
|
Total
|
28,325
|
100.00
|
|
Table 1.6: Residence of Canadian women
|
Province
|
Frequency
|
Percent
|
Cumulative
|
|
10 - Newfoundland and Labrador
|
1,390
|
4.37
|
4.37
|
|
11 – Prince Edward Island
|
870
|
2.73
|
7.10
|
|
12 – Nova Scotia
|
1,877
|
5.90
|
13.00
|
|
13 – New Brunswick
|
1,849
|
5.81
|
18.81
|
|
24 – Quebec
|
6,136
|
19.28
|
38.10
|
|
35 – Ontario
|
8,976
|
28.21
|
66.31
|
|
46 – Manitoba
|
2,124
|
6.68
|
72.98
|
|
47 – Saskatchewan
|
2,304
|
7.24
|
80.22
|
|
48 – Alberta
|
3,172
|
9.97
|
90.19
|
|
59 – British Columbia
|
3,121
|
9.81
|
100.00
|
|
Total
|
31,819
|
100.00
|
|
Table 1.7: Highest level of education attained by Canadian women
|
Highest level of education
|
Frequency
|
Percent
|
Cumulative
|
|
1 - Never attended school
|
111
|
0.39
|
0.39
|
|
2 - 1-4 years of elementary school
|
227
|
0.80
|
1.20
|
|
3 - 5-8 years of elementary school
|
2,025
|
7.18
|
8.38
|
|
4 - 9-10 years of elementary and
secondary school
|
2,037
|
7.22
|
15.60
|
|
5 - 11-13 years of elementary and
secondary school (but did not
graduate)
|
1,869
|
6.63
|
22.23
|
|
6 - Graduated high school
|
4,449
|
15.77
|
38.00
|
|
7- Some non-university postsecondary (no certificate)
|
2,037
|
7.22
|
45.22
|
|
8 - Some university (no certificate)
|
1,584
|
5.62
|
50.84
|
|
9 - Non-university postsecondary
certificate
|
8,548
|
30.31
|
81.15
|
|
10 - University certificate below
Bachelor's
|
617
|
2.19
|
83.34
|
|
11 - Bachelor's degree
|
3,447
|
12.22
|
95.56
|
|
12 - University certificate above
Bachelor's, Master's, First
professional degree in law, Degree
in medicine, dentistry, veterinary
medicine or optometry, Doctorate
(PhD)
|
1,253
|
4.44
|
100.00
|
|
Total
|
28,204
|
100.00
|
|
Table 1.8: Canadian women with or without a child less than six years old
|
Child less than six years old or not
|
Frequency
|
Percent
|
Cumulative
|
|
1 - Yes
|
29,171
|
90.97
|
90.97
|
|
2 - No
|
2,894
|
9.03
|
100.00
|
|
Total
|
32,065
|
100.00
|
|
Table 2.9: OLS Estimates for Canadian Women
|
Dependent Variable: hours of work
|
|
Independent Variables
|
Coefficient
|
|
composite hourly wage of all paid jobs
|
-1.42
[2.70]
|
|
the square of composite hourly wage of all paid jobs
|
-.065**
[.0327]
|
|
female's age
|
39.23***
[5.01]
|
|
the square of female's age
|
-.49***
[.06]
|
|
1 - Never attended school (base group)
|
---
|
|
2 - 1-4 years of elementary school
|
-104.7
[201.5]
|
|
3 - 5-8 years of elementary school
|
65.4
[115.8]
|
|
4 - 9-10 years of elementary and
secondary school
|
90.5
[110.1]
|
|
5 - 11-13 years of elementary and
secondary school (but did not
graduate)
|
21.25
[111]
|
|
6 - Graduated high school
|
117.3
[105.5]
|
|
7 - Some non-university postsecondary (no certificate)
|
4.36
[106.9]
|
|
8 - Some university (no certificate)
|
-14.6
[107.8]
|
|
9 - Non-university postsecondary
certificate
|
85.8
[105.2]
|
|
10 - University certificate below
Bachelor's
|
61.8
[109.1]
|
|
11 - Bachelor's degree
|
48
[106.2]
|
|
12 - University certificate above
Bachelor's, Master's, First
professional degree in law, Degree
in medicine, dentistry, veterinary
medicine or optometry, Doctorate
(PhD)
|
51.84
[107.8]
|
|
female is living with spouse (base group)
|
---
|
|
female is not living with spouse
|
77.14 ***
[28]
|
|
income of non-female in the household
|
.008***
[.0007]
|
|
1 – female is married (base group)
|
---
|
|
2 – female is in a common-law relationship
|
29.3
[15.98]
|
|
3 – female is separated
|
18.8
[35.14]
|
|
4 – female is divorced
|
20.65
[33.11]
|
|
5 – female is widowed
|
-78.7
[54.71]
|
|
6 – female is single (never married)
|
-23.2
[29.9]
|
|
Support payments received
|
-.013***
[.003]
|
|
female without a child less than six years old (base group)
|
---
|
|
female with a child less than six years old
|
-193.2***
[17.73]
|
|
constant
|
606.7
[149.9]
|
|
Sample size
|
12469
|
|
R-squared
|
0.143
|
* Statistical significance at the 90% level
** Statistical significance at the 95% level
*** Statistical significance at the 99% level
[ ] Heteroskedasticity-robust standard error
Table 2.0: Probit Estimates for Canadian women
|
Independent Variables
|
Coefficient
|
∆P(working) per unit ∆independent variable
|
|
female's age
|
.041***
(.012)
|
.0164
|
|
the square of female's age
|
-.001***
(.0001)
|
-.0004
|
|
number of years of work experience, full-year full-time
|
.09***
(.004)
|
.036
|
|
the square of number of years of work experience, full-year full-time
|
-.001***
(.0001)
|
-.0004
|
|
1 - Never attended school (base group)
|
---
|
---
|
|
2 - 1-4 years of elementary school
|
.61
(.441)
|
.244
|
|
3 - 5-8 years of elementary school
|
.87**
(.35)
|
.348
|
|
4 - 9-10 years of elementary and
secondary school
|
1.11***
(.351)
|
.444
|
|
5 - 11-13 years of elementary and
secondary school (but did not
graduate)
|
1.16***
(.354)
|
.464
|
|
6 - Graduated high school
|
1.36***
(.35)
|
.544
|
|
7 - Some non-university postsecondary (no certificate)
|
1.25***
(.35)
|
.5
|
|
8 - Some university (no certificate)
|
1.34***
(.35)
|
.536
|
|
9 - Non-university postsecondary
certificate
|
1.56***
(.35)
|
.624
|
|
10 - University certificate below
Bachelor's
|
1.64***
(.36)
|
.656
|
|
11 - Bachelor's degree
|
1.8***
(.35)
|
.72
|
|
12 - University certificate above
Bachelor's, Master's, First
professional degree in law, Degree
in medicine, dentistry, veterinary
medicine or optometry, Doctorate
(PhD)
|
1.96***
(.353)
|
.784
|
|
10 - Newfoundland and Labrador (base group)
|
---
|
---
|
|
11 – Prince Edward Island
|
.301***
(.108)
|
.1204
|
|
12 – Nova Scotia
|
-.1
(.082)
|
-.04
|
|
13 – New Brunswick
|
-.001
(.083)
|
-.0004
|
|
24 – Quebec
|
-.08
(.07)
|
-.032
|
|
35 – Ontario
|
-.146
(.07)
|
-.0584
|
|
46 – Manitoba
|
.044
(.081)
|
.0176
|
|
47 – Saskatchewan
|
.0454
(.081)
|
.0182
|
|
48 – Alberta
|
.052
(.076)
|
.0208
|
|
59 – British Columbia
|
-.106
(.076)
|
-.0424
|
|
constant
|
-1.22
(.427)
|
---
|
|
Pseudo R-squared
|
0.17
|
---
|
|
Proportion of women who worked
|
0.42
|
---
|
|
Final value of log of likelihood function
|
-5637.7
|
---
|
* Statistical significance at the 90% level
** Statistical significance at the 95% level
*** Statistical significance at the 99% level
( ) Usual standard error
Table 2.1: Heckit Estimates for Canadian Women
|
Dependent Variable: hours of work
|
|
Independent Variables
|
Coefficient
|
|
composite hourly wage of all paid jobs
|
-9.1***
(1.41)
|
|
the square of composite hourly wage of all paid jobs
|
-.006
(.015)
|
|
female's age
|
19.8***
(5.13)
|
|
the square of female's age
|
-.235***
(.061)
|
|
1 - Never attended school (base group)
|
---
|
|
2 - 1-4 years of elementary school
|
-84.8
(334.3)
|
|
3 - 5-8 years of elementary school
|
-18.02
(280)
|
|
4 - 9-10 years of elementary and
secondary school
|
-50.34
(278.3)
|
|
5 - 11-13 years of elementary and
secondary school (but did not
graduate)
|
-148.6
(279.2)
|
|
6 - Graduated high school
|
-67.43
(277.2)
|
|
7 - Some non-university postsecondary (no certificate)
|
-188.9
(277.7)
|
|
8 - Some university (no certificate)
|
-202.9
(278.2)
|
|
9 - Non-university postsecondary
certificate
|
-125.9
(277.1)
|
|
10 - University certificate below
Bachelor's
|
-183.8
(279.1)
|
|
11 - Bachelor's degree
|
-171.1
(277.4)
|
|
12 - University certificate above
Bachelor's, Master's, First
professional degree in law, Degree
in medicine, dentistry, veterinary
medicine or optometry, Doctorate
(PhD)
|
-174.3
(278.1)
|
|
female is living with spouse (base group)
|
---
|
|
female is not living with spouse
|
60.9**
(25.6)
|
|
income of non-female in the household
|
.01***
(.0003)
|
|
1 – female is married (base group)
|
---
|
|
2 – female is in a common-law relationship
|
19.5
(17)
|
|
3 – female is separated
|
24.6
(34.52)
|
|
4 – female is divorced
|
20.3
(31.3)
|
|
5 – female is widowed
|
-73.3
(50.9)
|
|
6 – female is single (never married)
|
-7.3
(27.8)
|
|
Support payments received
|
-.013***
(.003)
|
|
female without a child less than six years old (base group)
|
---
|
|
female with a child less than six years old
|
-172.2***
(17.3)
|
|
constant
|
1292.3
(299.2)
|
|
(Selectivity bias)
|
-314.8
(18.12)
|
|
Sample size
|
13515
|
* Statistical significance at the 90% level
** Statistical significance at the 95% level
*** Statistical significance at the 99% level
( ) Usual standard error
