In this paper, we examine the relationship between women’s age at marriage and their own and their spouses’ labor market outcomes using nationally representative household data from India. We …nd evidence of positive e¤ects of age at marriage of women on their own as well their spouses’ labor market outcomes. To examine whether these e¤ects are causal (i.e., the e¤ect arises due to more schooling as a result of marriage delay for example) or the e¤ects arise due to selection into marriage, we use an IV based empirical strategy that utilizes variation in age at menarche to obtain exogenous variation in the age at marriage.
Our results indicate that the positive e¤ects of age at marriage of women on their own as
well their spouses’ labor market outcomes arise due to selection into marriage. Our …ndings are robust to (1) dropping women from our sample who are not likely to be a¤ected by our instrument, and (2) addressing biases due to nonrandom selection into labor force. Our
…ndings might appear to be somewhat puzzling since it has been documented in the previous literature that a delay in women’s marriage leads to more schooling in developing countries (see for e.g. Field and Ambrus 2008). In fact, for our analytical sample as well, we show that there exists a positive link between marriage delay and women’s schooling (see Table A2 in the Appendix). As such, at least due to the formal schooling hypothesis, one would have expected to …nd a causal e¤ect of a women’s age at marriage on labor market outcomes.
We argue that one potential explanation for this apparently puzzling …nding could be as follows. It has been noted by Kingdon (1998), Kingdon and Unni (2001), and Kanjilal-Bhaduri and Pastore (2017) among many others that in India, labor market returns to education is low and insigni…cant for women with relatively low education. This could be due to: (1) low quality of primary education in India (Pratham 2011),18 and/or (2) for labor market success, a threshold level of education might be necessary (for instance, completing college or having a vocational degree); below that, an extra year of schooling might not lead to better labor market outcomes. As it turns out, 72% women in our sample have completed at most primary education (i.e., …ve years of formal schooling) and more than 90% have completed only secondary schooling (i.e., 10 years of formal schooling). Thus, although women in our sample might complete more formal schooling due to a delay in marriage by a year, this might not be su¢ciently productive to get translated into better labor market outcomes since most women in our sample would still belong to the lower end of the education distribution.
Our …ndings thus suggest that complementing policies that seek to delay marriages of women in developing countries with educational policies that would augment the quality of primary schooling is likely to be useful. If this could be achieved, even a delay in marriage
18Pratham (2011) notes that 48% of Indian children in grade 5 could not read at grade 2 level, and nearly 58% could not solve a simple division problem.
by a year that might allow a woman to attain only one more year of primary schooling might be useful for her in the labor market. Additionally, policymakers perhaps might also think of designing policies that would incentivize parents to delay their daughters’ marriages by such an extent that they are able to complete higher education (for e.g. complete college or …nish 15 years of formal schooling), since our suggest that a marriage-delay policy that would cause women to complete an extra year of education is unlikely to be meaningful in terms of getting translated into better labor market prospects for women who only complete primary or secondary schooling.
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0 .05 .1.15 .2
5 10 15 20 25 30 35 40 45
Age at marriage (in years)
Women's Sample
0
.05 .1.15 .2
Density
5 10 15 20 25 30 35 40 45
Age at marriage (in years)
Spousal Sample Figure 1. Distribution of women’s age at marriage for the two samples
0.1.2.3
9 11 13 15 17 19 21
Age first started menarche (in years)
Women's Sample
0.1.2.3
Density
9 11 13 15 17 19 21
Age first started menarche (in years)
Spousal Sample Figure 2. Distribution of age at menarche for the two samples
0 .05 .1.15
5 10 15 20 25 30 35 40 45
Age at marriage (in years)
Early Menarche Late Menarche
Women's Sample
0
.05 .1.15
Density
0 10 20 30 40 50
Age at marriage (in years)
Early Menarche Late Menarche
Spousal Sample
Figure 3. Distribution of women’s age at marriage by age at menarche group for the two samples
150152154156158
9 11 13 15 17 19 21
Age first started menarche (in years)
Women's Sample
150152154156158
Average height (in cm)
9 11 13 15 17 19 21
Age first started menarche (in years)
Spousal Sample
Figure 4. Relationship between women’s average height and age at menarche for the two samples
0123456
9 11 13 15 17 19 21
Age first started menarche (in years)
Women's Sample
0123456
Average years of education completed
9 11 13 15 17 19 21
Age first started menarche (in years)
Spousal Sample
Figure 5. Relationship between women’s average years of schooling and age at menarche group for the two samples
0 .05 .1.15 .2.25
0 5 10 15 20
Years of education completed First Tercile Second Tercile Third Tercile
Women's Sample
0
.05 .1.15 .2.25
Density
0 5 10 15 20
Years of Education Completed First Tercile Second Tercile Third Tercile
Spousal Sample
Figure 6. Kernel density estimates of women’s years of schooling by terciles of age at menarche
Table 1. Summary Statistics
Women’s Sample Spousal Sample
Mean SD Mean SD
Women’s Labor Market Outcomes
Hourly earnings (in Rs.) 18.25 24.40
Annual wage earnings (in Rs.) 23977.56 50282.33
Work days per year 205.29 103.85
Spousal Labor Market Outcomes
Hourly earnings (in Rs.) 33.12 41.16
Annual wage earnings (in Rs.) 66885.67 88398.96
Work days per year 273.22 82.13
Women's characteristics
Age at marriage 17.23 3.76 17.93 3.62
Age at Menarche 13.88 1.40 13.85 1.39
Age 37.64 8.90 35.14 9.32
Spousal age 42.06 9.72 40.25 10.10
Height (in cm) 151.32 6.55 151.73 6.56
Father's years of schooling 2.09 3.76 3.11 4.34
Mother's years of schooling 0.85 2.42 1.36 2.94
Number of Siblings 3.77 1.97 3.81 1.98
Place of Residence (=1 if Urban) 0.21 0.41 0.34 0.47
N 10,511 21,718
Notes: In subsequent regressions, women (spousal) sample is used for examining the impact of women’s age at marriage on women’s (spousal) labor market outcomes. Women (Spousal) sample consists of working as well as non-working spouses (women). As such we do not report the mean and standard deviations of labor market outcomes of spouses (women) in the women (spousal) sample. In the women sample, spousal age is available for 9,262 observations, and hence the mean and standard deviation of spousal age is computed based on these observations. We, however, do not use this variable in subsequent regressions based on the women sample.
Table 2. OLS estimates of the effect of age at menarche on women’s age at marriage
Panel A: Women’s Sample
[1] [2] [3] [4] [5]
Age at Menarche 0.243*** 0.233*** 0.220*** 0.215*** 0.446***
(0.063) (0.060) (0.060) (0.052) (0.039)
F-statistic 14.87 32.06 30.69 84.00 130.00
R2 0.008 0.053 0.059 0.149 0.361
Observations 10,511 10,511 10,511 10,511 10,511 Panel B: Spousal Sample
[1] [2] [3] [4] [5]
Age at Menarche 0.147*** 0.164*** 0.151*** 0.154*** 0.366***
(0.048) (0.043) (0.043) (0.037) (0.024)
F-statistic 9.40 33.03 33.62 96.35 225.03
R2 0.003 0.043 0.050 0.144 0.335
Observations 21,718 21,718 21,718 21,718 21,718 Notes: Estimation via OLS. The outcome variable is women’s age at marriage.
Regressions reported in columns (1) of Panels A and B, do not include any controls. In column (2) regressions we include women’s age and caste affiliation as controls. In column (3) regressions the control variables are women’s age, caste affiliation, and height. In column (4), controls include women’s age, caste affiliation, height, father’s years of schooling, mother’s years of schooling, and number of siblings. In column (5), we include district fixed effects in addition to all controls used. For regressions reported in columns (2) through (5) in Panel B, we also include spousal age as an additional control. Standard errors reported in the parentheses are clustered at the district level. ***p < 0.01, **p <0.05, *p
< 0.1.
Table 3. OLS estimates of the effect of age at menarche and women’s age at marriage on women’s years of schooling Panel A: Women's sample
[1] [2]
Age at Menarche 0.104*** -0.012
(0.028) (0.027)
Age at Marriage 0.260***
(0.016)
R2 0.539 0.567
Observations 10,511 10,511
Panel B: Spousal sample
[1] [2]
Age at Menarche 0.116*** 0.011
(0.023) (0.022)
Age at Marriage 0.286***
(0.012)
R2 0.535 0.566
Observations 21,718 21,718
Notes: Estimation via OLS. The outcome variable is women’s years of schooling. The regressions reported in Panel A control for women’s age, caste affiliation, height, father’s years of schooling, mother’s years of schooling, number of siblings, and district fixed effects. The regressions reported in Panel B include control for spousal age in addition to all the controls used in the regressions reported in Panel A. Standard errors reported in the parentheses are clustered at the district level. ***p < 0.01, **p
<0.05, *p < 0.1.
Table 4. OLS estimates of the effect of women’s age at marriage on own and spousal labor market outcomes
Panel A: Women’s Regression
[1] [2] [3]
Hourly Earnings Annual Wage Earnings Work Days Per Year
Age at Marriage 0.017*** 0.033*** 1.655***
(0.002) (0.004) (0.352)
R2 0.350 0.436 0.278
Observations 10,511 10,511 10,511
Panel B: Spousal Regression
[1] [2] [3]
Hourly Earnings Annual Wage Earnings Work Days Per Year
Age at Marriage 0.011*** 0.020*** 0.873***
(0.002) (0.003) (0.181)
R2 0.372 0.449 0.240
Observations 21,718 21,718 21,718
Notes: Estimation via OLS. The regressions reported in Panel A control for women’s age, caste affiliation, height, father’s years of schooling, mother’s years of schooling, number of siblings, and district fixed effects. The regressions reported in Panel B include control for spousal age in addition to all the controls used in the regressions reported in Panel A. Standard errors reported in the parentheses are clustered at the district level. ***p < 0.01, **p <0.05, *p < 0.1.
Table 5. IV estimates of the effect of women’s age at marriage on own and spousal labor market outcomes
Panel A: Women’s Regression
[1] [2] [3]
Hourly Earnings Annual Wage Earnings Work Days Per Year
Age at Marriage 0.005 0.015 -0.288
(0.010) (0.020) (1.677)
R2 0.347 0.434 0.275
First Stage F-statistic 130.00 130.00 130.00
[p=0.000] [p=0.000] [p=0.000]
Kleibergen Paap rK LM statistic 65.07 65.07 65.07
[p=0.000] [p=0.000] [p=0.000]
Observations 10,511 10,511 10,511
Panel B: Spousal Regression
[1] [2] [3]
Hourly Earnings Annual Wage Earnings Work Days Per Year
Age at Marriage -0.005 -0.007 -0.262
(0.011) (0.019) (1.481)
R2 0.368 0.445 0.239
First Stage F-statistic 225.03 225.03 225.03
[p=0.000] [p=0.000] [p=0.000]
Kleibergen Paap rK LM statistic 109.13 109.13 109.13
[p=0.000] [p=0.000] [p=0.000]
Observations 21,718 21,718 21,718
Notes: Estimation via TSLS. The regressions reported in Panel A control for women’s age, caste affiliation, height, father’s years of schooling, mother’s years of schooling, number of siblings, and district fixed effects. The regressions reported in Panel B include control for spousal age in addition to all the controls used in the regressions reported in Panel A. Standard errors reported in the parentheses are clustered at the district level. ***p < 0.01, **p <0.05, *p < 0.1.
Table 6. OLS estimates of the effect of age at marriage on labor market outcomes, Complier Subsample
Panel A: Women’s Regression
[1] [2] [3]
Hourly Earnings Annual Wage Earnings Work Days Per Year
Age at Marriage 0.023*** 0.040*** 2.070***
(0.003) (0.005) (0.415)
R2 0.364 0.438 0.283
Observations 9,362 9,362 9,362
Panel B: Spousal Regression
[1] [2] [3]
Hourly Earnings Annual Wage Earnings Work Days Per Year
Age at Marriage 0.011*** 0.020*** 0.938***
(0.002) (0.003) (0.199)
R2 0.370 0.446 0.248
Observations 20,102 20,102 20,102
Notes: Estimation via OLS. The regressions reported in Panel A control for women’s age, caste affiliation, height, father’s years of schooling, mother’s years of schooling, number of siblings, and district fixed effects. The regressions reported in Panel B include control for spousal age in addition to all the controls used in the regressions reported in Panel A.
Standard errors reported in the parentheses are clustered at the district level. ***p < 0.01,
**p <0.05, *p < 0.1.
Table 7. IV estimates of the effect of age at marriage on labor market outcomes, Complier subsample
Panel A: Women's Regression
[1] [2] [3]
Hourly Earnings Annual Wage Earnings Work Days Per Year
Age at Marriage 0.005 0.021 0.072
(0.009) (0.017) (1.429)
R2 0.358 0.436 0.281
First stage F statistic 267.65 267.65 267.65
[p=0.000] [p=0.000] [p=0.000]
Kleibergen Paap rK LM statistic 91.42 91.42 91.42
[p=0.000] [p=0.000] [p=0.000]
Observations 9,362 9,362 9,362
Panel B: Spousal Regression
[1] [2] [3]
Hourly Earnings Annual Wage Earnings Work Days Per Year
Age at Marriage -0.001 -0.004 -0.07
(0.010) (0.016) (1.281)
R2 0.368 0.443 0.247
First stage F statistic 372.73 372.73 372.73
[p=0.000] [p=0.000] [p=0.000]
Kleibergen Paap rK LM statistic 136.15 136.15 136.15
[p=0.000] [p=0.000] [p=0.000]
Observations 20,102 20,102 20,102
Notes: Estimation via TSLS. The regressions reported in Panel A control for women’s age, caste affiliation, height, father’s years of schooling, mother’s years of schooling, number of siblings, and district fixed effects. The regressions reported in Panel B include control for spousal age in addition to all the controls used in the regressions reported in Panel A. Standard errors reported in the parentheses are clustered at the district level. ***p < 0.01, **p <0.05, *p < 0.1.
Table 8. Validity tests of instruments in selection models
Panel A: Women's Regression
Full Sample Compliers
[1] [2] [3] [4]
Marginal effects Validity test Marginal effects Validity test
Husband's income -0.122*** -3.547 -0.118*** -3.587
(0.010) [p=1.000] (0.010) [p=1.000]
Panel B: Spousal Regression
[1] [2] [1] [2]
Marginal effects Validity test Marginal effects Validity test
Wife's income -0.030*** -3.248 -0.033*** -3.257
(0.003) [p=1.000] (0.003) [p=1.000]
Notes: The regressions reported in Panel A control for women’s age, caste affiliation, height, father’s years of schooling, mother’s years of schooling, number of siblings, and district fixed effects. The regressions reported in Panel B include control for spousal age in addition to all the controls used in the regressions reported in Panel A. Standard errors reported in the parentheses are clustered at the district level. ***p < 0.01, **p <0.05, *p <
0.1. The validity test of the IV is developed in Huber and Mellace (2011). The null hypothesis is that the IV is valid.
Table 9. Selection-bias corrected IV estimates of the effect of age at marriage on labor market outcomes
First Stage F-statistic 130.05 130.05 130.05 268.41 268.41 268.41
[p=0.000] [p=0.000] [p=0.000] [p=0.000] [p=0.000] [p=0.000]
First Stage F-statistic 227.69 227.69 227.69 372.57 372.57 372.57
[p=0.000] [p=0.000] [p=0.000] [p=0.000] [p=0.000] [p=0.000]
Kleibergen Paap rK LM statistic 109.38 109.38 109.38 135.87 135.87 135.87
[p=0.000] [p=0.000] [p=0.000] [p=0.000] [p=0.000] [p=0.000]
Observations 21,718 21,718 21,718 20,102 20,102 20,102
Notes: Estimation via TSLS. The regressions reported in Panel A control for women’s age, caste affiliation, height, father’s years of schooling, mother’s years of schooling, number of siblings, and district fixed effects. The regressions reported in Panel B include control for spousal age in addition to all the controls used in the regressions reported in Panel A. Standard errors reported in the parentheses are clustered at the district level. ***p < 0.01, **p <0.05, *p < 0.1.
Appendix
Table A1. OLS estimates of the effect of age at marriage on labor market outcomes, Alternative Complier Subsample
Panel A: Women's Regression
[1] [2] [3]
Hourly Earnings Annual Wage Earnings Work Days Per Year
Age at Marriage 0.016*** 0.028*** 1.676***
(0.003) (0.005) (0.466)
R2 0.329 0.420 0.276
Observations 8,861 8,861 8,861
Panel B: Spousal Regression
[1] [2] [3]
Hourly Earnings Annual Wage Earnings Work Days Per Year
Age at Marriage 0.013*** 0.021*** 1.267***
(0.002) (0.004) (0.236)
R2 0.355 0.434 0.242
Observations 18,910 18,910 18,910
Notes: Estimation via OLS. The regressions reported in Panel A control for women’s age, caste affiliation, height, father’s years of schooling, mother’s years of schooling, number of siblings, and district fixed effects. The regressions reported in Panel B include control for spousal age in addition to all the controls used in the regressions reported in Panel A. Standard errors reported in the
parentheses are clustered at the district level. ***p < 0.01, **p <0.05, *p < 0.1.
Table A2. IV estimates of the effect of age at marriage on labor market outcomes, Alternative complier subsample
Panel A: Women's Regression
[1] [2] [3]
Hourly Earnings Annual Wage Earnings Work Days Per Year
Age at Marriage 0.005 0.021 0.157
(0.008) (0.016) (1.395)
R2 0.327 0.419 0.275
First stage F statistic 417.13 417.13 417.13
[p=0.000] [p=0.000] [p=0.000]
Kleibergen Paap rK LM statistic 102.11 102.11 102.11
[p=0.000] [p=0.000] [p=0.000]
Observations 8,861 8,861 8,861
Panel B: Spousal Regression
[1] [2] [3]
Hourly Earnings Annual Wage Earnings Work Days Per Year
Age at Marriage 0.002 0.001 0.206
(0.008) (0.014) (1.140)
R2 0.354 0.432 0.242
First stage F statistic 747.88 747.88 747.88
[p=0.000] [p=0.000] [p=0.000]
Kleibergen Paap rK LM statistic 155.86 155.86 155.86
[p=0.000] [p=0.000] [p=0.000]
Observations 18,910 18,910 18,910
Notes: Estimation via TSLS. The regressions reported in Panel A control for women’s age, caste
affiliation, height, father’s years of schooling, mother’s years of schooling, number of siblings, and district fixed effects. The regressions reported in Panel B include control for spousal age in addition to all the controls used in the regressions reported in Panel A. Standard errors reported in the parentheses are clustered at the district level. ***p < 0.01, **p <0.05, *p < 0.1.
Table A3. IV estimates of the effect of age at marriage on women’s years of
First Stage F-statistic 130.00 267.65
[p=0.000] [p=0.000]
Kleibergen Paap rK LM statistic 65.07 91.42
[p=0.000] [p=0.000]
First Stage F-statistic 230.33 383.46
[p=0.000] [p=0.000]
Kleibergen Paap rK LM statistic 110.57 137.69 [p=0.000] [p=0.000]
Observations 21,718 20,102
Notes: Estimation via TSLS. The regressions reported in Panel A control for women’s age, caste affiliation, height, father’s years of schooling, mother’s years of schooling, number of siblings, and district fixed effects. The regressions reported in Panel B include control for spousal age in addition to all the controls used in the regressions reported in Panel A. Standard errors reported in the parentheses are clustered at the district level. ***p < 0.01, **p <0.05, *p < 0.1.