Center for Studies in Demography 
and Ecology

The Effects of Sons and Daughters on Men’s Labor Supply and Wages

by

Shelly Lundberg
University of Washington

Elaina Rose
University of Washington

UNIVERSITY OF WASHINGTON			           CSDE Working Paper No. 00-01

The Effects of Sons and Daughters

On Men’s Labor Supply and Wages* 

Shelly Lundberg

Elaina Rose

Department of Economics
University of Washington
Mail Code 353330
Seattle, WA  98195

October 1999

ABSTRACT

	In this paper we estimate the effects of children and the differential effects of sons and daughters on men’s labor supply and hourly wage rates. The responses to fatherhood of two cohorts of men from the PSID sample– men born in and before 1950 and men born after 1950– are examined separately, and we use fixed effects estimation to control for unobserved heterogeneity.  We find that fatherhood significantly increases the hourly wage rates and annual hours of work for men from both cohorts, and that it is important to allow for heterogeneity and non-linearity in estimating these effects.  Most notably, men’s labor supply and wage rates increase significantly more in response to the births of sons than to the births of daughters. (JEL:  J23, J16, J22, J24)
I.  Introduction	

The impact of fatherhood on men’s labor market outcomes has received little attention from economists, in contrast to the central role played by children in studies of women’s labor supply.  Though child care has traditionally been viewed as the wives’ responsibility, we would expect parenthood to affect men’s earnings and labor supply if the labor market decisions of husbands and wives are interdependent.  Since women’s roles in the labor market and the family have changed dramatically in recent decades, we would also expect to see a shift in the relationship between children and men’s labor market behavior.
In this paper we estimate the effect of children on men’s labor supply and hourly wages using data from the Panel Study of Income Dynamics (PSID). Our fixed effects estimates indicate that, on average, a child increases hourly earnings by 4.5 percent and annual hours of work by 40 hours per year. However, the effects of children are highly non-linear and non-monotonic, with significant positive incremental effects limited to the first two children.  Comparison of OLS and fixed effects estimates suggest that there is substantial heterogeneity bias in conventional cross-section estimates of the effect of fatherhood on men’s outcomes. We also break our sample into two cohorts: men born in and before 1950, and men born after 1950.  We find that the relationship between children and men’s labor supply and wages has shifted over time.
Our most notable results relate to the effects of child gender on men’s labor market outcomes. Sons increase men’s annual hours of work and wage rates significantly more than do daughters.  Fathers of both cohorts respond differently to sons and daughters, though the gender effects are more pronounced in the hours worked of the late cohort and the hourly wage rates of the early cohort.   We find little evidence of an effect of child gender on the labor market outcomes of mothers, and are unable to explain our results in terms of differences in the expected pecuniary returns to boys and girls in the United States.  Our results are consistent with a model in which the gender composition of a couple’s offspring affects the returns to marriage, and this has implications for future research.
Section II is a review of the literature on marriage, parenthood, and men’s labor market outcomes.  Section III describes the data.   Section IV outlines the estimation procedures and presents the results with respect to the effects of marriage and children on men’s hours and wages.  Section V presents and discusses the estimation and results for the gender-specific effects of children on the outcomes.  Section VI is concludes. 

II.  Literature 
 	Most research on the relationship between household roles and men’s labor market outcomes has focused on the effect of marriage on hourly earnings.  Married men earn more than single men with the same education and experience, but it has not been clear whether marriage makes men more productive, or more productive men select into marriage.  Korenman and Neumark [1991] estimate this marriage premium using fixed effects and find that married men earn approximately 6 percent more than single men and that the premium accrues gradually over the course of the marriage.  Their analyses of data from one firm’s records on reviews, earnings, and personal characteristics of professionals and managers indicate that the effect of marriage arises through promotions rather than through a premium for married men within a job category.  Taken together, their findings suggest that much of the marriage premium can be attributed to increased productivity of married men, perhaps due to returns to specialization within the household.  Gray [1997] finds that the marriage wage premium has fallen over time and attributes this to declining specialization of husbands and wives.  
The large literature on motherhood and women’s labor market behavior finds that children substantially reduce women’s labor supply and earnings.  The fall in women’s labor supply is attributed to the increased value of women’s home time after having a child (Becker [1985]), and the decline in wage rates to a fall in market productivity due to reduced time and effort on the job.  If husbands’ and wives’ labor market outcomes are interdependent, then we would expect this reallocation of mothers’ time to be accompanied by some labor market response among fathers. For women, our a priori expectations regarding the direction of the effects of parenthood are unambiguous:  children reduce labor supply and hourly wages.  However, for men our predictions are not so straightforward.  In Lundberg and Rose [1999], we present a model in which parenthood has two effects: an increase in specialization and an increase in the home-intensity of a household.   The increase in specialization depends upon the gender gap in market wages, and typically takes the form of wives’ increasing their focus on home production while husbands concentrate more on the labor market.  The increase in overall home- (relative to market- ) intensity is a result of the increased value of parental home time as inputs into child care. For wives, both the specialization and the home-intensity effects on earnings are negative; for men, the first is generally positive and the second is negative. The greater the extent to which fathers share in parenting responsibilities, the more likely it is that the home-intensity effect will dominate the specialization effect and fatherhood, as well as motherhood, will lead to a fall in earnings.  On the other hand, if the specialization effect dominates, having children increases men’s earnings and reduces women’s earnings.  
The empirical estimates in Lundberg and Rose [1999] indicate that specialization does in fact increase with the birth of the first child. However, these findings are also limited as they only apply to the initial transition into parenthood.  Since the marginal changes in specialization and home intensity are likely to vary by parity, the incremental effects of each child on men’s labor supply and earnings may be non-linear or even non-monotonic.  For instance, we might expect large specialization effects associated with the first one or two children leading to increases in men’s labor supply and wages, as there are substantial declines in women’s labor supply at these points.  However, if the specialization effects are exhausted after the first one or two children, births of subsequent children will reduce men’s labor market productivity.  Therefore, it is important to allow for non-linearities in estimating the effects of children on men’s earnings.
 There have been only a few attempts to measure the effect of parenthood on men’s labor supply and earnings.  Pencavel [1986] finds that young children are associated with longer work hours for men in the 1980 U.S. Census, and Waldfogel [1998] reports that the wages of young men in 1980 and 1991 NLS samples are higher if they have two or more children.  However, both of these studies use cross-section data and do not correct for endogeneity.  To the extent that fathering children is endogenous with respect to labor market outcomes or correlated with unobservables in the wage or labor supply equations, the estimated effects of fatherhood will be subject to bias.
There are essentially two approaches to correcting for endogeneity and heterogeneity:  instrumental variables and fixed effects.  Angrist and Evans [1998] use instrumental variables to estimate the effect of the birth of a third child on the labor supply of men and women, and find no significant effect of this birth on men’s labor supply. Their approach, however, cannot be used to estimate the effects of earlier children, which may be different from those of the third child.   In Lundberg and Rose [1998] we estimate age-hours and age-wage profiles for husbands and wives with and without children under fixed effects. We find that the transition to parenthood is associated with higher wages and labor supply for men, but do not address the effects of subsequent births.   A comprehensive picture of the effect of children on men’s labor market outcomes requires that we address the issues of heterogeneity and endogeneity and allow for parity-specific effects.

III. 	Data
	We examined the effects of both marital status and parenthood on work hours and wages, using a sample of men drawn from the Panel Study of Income Dynamics (PSID).  The dependent variables are annual hours of work and the (log of the) real hourly wage rate.  The wage rate was computed as total annual labor income divided by annual hours of work, and deflated to 1983 dollars by the Consumer Price Index. 
	Marital status was measured as a dummy variable indicating that the individual reported having been married in a particular year.   Fertility measures were constructed from the fertility histories and include all children ever born, whether currently living with the father or not.  In addition to the total number of offspring, we calculated the number of children by gender, whether the man had at least one son or daughter, and whether the man had a first child that was a son or a daughter.  
Additional regressors used as controls in all empirical models were age, education, and year of the observation, all of which are entered as a series of dummy variables to allow for non-linearities.  In some analyses, we control for the length of the marriage using values calculated from the marital history.
Our raw PSID sample consisted of 26809 observations on 2304 white male heads of household who were born in 1943 or later, and for whom fertility and marital histories exist.  Observations were deleted for the following reasons: the man was under age 18 or over age 60 (5 observations), education was missing (30 observations), the marriage history indicates that the man was in two marriages simultaneously (44 observations), the man had a child but did not report its gender (77 observations), hours worked was missing (448 observations).  The final sample consisted of 26205 observations on 2243 individuals.  
To examine changes in household responses to children over time, we divided the sample into two cohorts - men born in or before 1950 and men born after 1950.  Means and standard deviations of the variables used in the analysis are reported in Table 1.
We note that the ratio of total sons born to total daughters born is approximately 1.14 (.87/.76) for the early cohort, and approximately 1.02 (.58/.57) for the later cohort.  The latter figure is comparable to the expected biological ratio of boys born relative to girls of about 1.05, and the expected number of boys surviving until age 5  relative to girls of about 1.0 (in the U.S.).   The apparent overreporting of sons relative to daughters by the early cohort is quite striking, since it is generally believed that bias in favor of male children is relatively mild in the U.S. and other developed countries.  We interpret this preponderance of sons as evidence that men in the early cohort are more likely to recall the birth of a child if it is a son relative to a daughter, particularly if the birth is nonmarital or from a prior marriage.  It is possible that gender is endogenous here in the sense that the likelihood a father reports having a son relative to a daughter is correlated with unobservables in the estimating equations. 
Table 2 reports the frequency distribution of children by parity for each cohort.  89 percent of the men in the early cohort and 66 percent of the men in the later cohort have had at least one child.  This difference is due to age effects, as the average age is 34 for the early cohort and 28 for the later cohort, as well as cohort effects. Very few men have more than four children (less than 1 percent for each cohort).  Therefore, in our empirical analysis, we focus on the effects of the first few children, and include a separate dummy variable for observations with more than four children.  Table 3 reports frequency counts for number of children by gender and reveals the apparent undercounting of daughters by the early cohort.

IV.  The Effects of Children on Wages and Hours: Estimation and Results 
The base specification for the analysis is: 
         (1)
where the subscript “i” indicates individual and “t’ indicates time. Y is the outcome of interest (the log of the real hourly wage rate, or annual hours of work), MARR  is a dummy variable indicating whether the individual is married, DAge is a series of dummy variables for each year of age of the individual, DYear  is a series of dummy variables representing the year of the observation,  and DEduc is a series of dummy variables indicating the number of years of education.  Since both age and education are included as regressors, an estimate of Mincerian experience is implicitly included in these estimates. 
We introduce children into the analysis in two ways.  In a linear specification, we include the variable NKID04, which is the number of children if the man has four children or less and zero otherwise, and a dummy variable for five or more children (DKID5).  In a non-linear specification we include instead a series of dummy variables DKID1 though DKID4 indicating whether the man has exactly that number of children.   We only examine the effects for the first four children because there are so few observations for men with five or more children (See Table 2).
	These models are estimated two ways.  First, we estimate OLS equations to obtain estimates which are, more or less, comparable to what would be found in a conventional cross-section analysis.  Second, we estimate fixed effects to allow for unobserved heterogeneity. In this specification, the intercept  varies by individual.  To the extent that marriage and fatherhood are correlated with unobservables in the estimating equations that are constant over time – such as the effects of preferences or labor market endowments – the OLS estimates will suffer from omitted variable bias, but this bias will “difference out” under fixed effects.
	There are two reasons that even the fixed effects estimates may suffer from endogeneity or omitted variable bias.  First, timing of marriage and parenthood may be caused by, or correlated with, actual or expected shocks to the outcome.  For instance, men may time marriage or childbirth at a time when they expect to receive a promotion and a raise.  Second, men with higher growth rates of earnings may be more likely to get married or have more children. In principle, instrumental variables estimation could be used to correct for these potential problems, but a valid instrument for this application is unlikely.

Results:  The Effect of Children on (Log Real Hourly) Wage Rate (Table 4)
Table 4 presents the results regarding the effects of marriage and children on hourly wage rates. Table 4a reports results for the entire sample, and Table 4b reports the results by cohorts. Columns (1) through (3) contain the OLS estimates, and columns (4) through (6) contain the fixed effects estimates.  Columns (1) and (4) present the base specifications without children, columns (2) and (5) are the linear child specifications and columns (3) and (6) are the non-linear specifications.   The estimated incremental effect of each child, and the p-values associated with the test that the incremental effects are zero are reported in the shaded regions of column (3) and (6).
The fixed effects results for the base specification reported in column (4) indicate that married men earn approximately 6.2  percent more than single men, holding constant age, education, race, and year of observation.  Adding NKID04 and DKID5 into the regression in column (5) reduces the estimate of the marriage premium slightly to 5.7 percent.  The coefficient on NKID4 is .045 and statistically significant.  This means that each additional child is associated with an increase in hourly earnings of approximately 4.5 percent. The results in column (6) indicate that the relationship between number of children and hourly wages is highly non-linear.  The first child increases earnings by 7.3 percent (t=6.4), the second by an additional 5.8 percent (p=.00), and the incremental effects of the third and fourth child are small and insignificant.
 The OLS results in columns (1)-(3) indicate a somewhat larger marriage premium (10 percent rather than 6 percent) and a substantially smaller effect of children (1.7 percent per child rather than 4.5 percent).  The fall in the marriage coefficient when we move from OLS to fixed effect estimates indicates that one reason that married men earn more than single men is positive selection: men with higher levels of the unobservables affecting wages are more likely to get married.  This positive selection effect in terms of marriage is consistent with the findings of Korenman and Neumark [1991] and Gray [1997].  However, the implied selection into fatherhood is negative.  The estimated effects of children are higher under fixed effects relative to OLS in both the linear specification in column (5) and for each parity in the non-linear specification in column (6).  This means that, although fatherhood itself increases hourly earnings, having children is associated with lower levels of unobservables in the wage equation.  
The effect of heterogeneity can be seen graphically in Figure 1a, which plots the OLS coefficients (solid line) and the fixed effects coefficients (dashed  line) against the number of children.  For each parity the fixed effects coefficient is greater than the respective OLS coefficient.  The difference at parity 4 is particularly striking: the OLS estimates suggest that having a fourth child relative to a third reduces hourly earnings substantially, but the fixed effect estimate indicates that this drop is due entirely to heterogeneity.  
The analyses in Table 4a are repeated by cohort in Table 4b.   For both cohorts, we find positive marriage premia and evidence of positive selection into marriage.  We find, as does Gray, that the marriage premium has fallen over time:  our fixed effects estimates indicate that it has been reduced by half. For both cohorts, there is evidence that fatherhood increases earnings and that negative selection into fatherhood is present.  In the linear specification for the early cohort, the selection effect apparently nearly outweighs the true effect and the estimated OLS relationship between the number of children and wages is small and insignificant.  
The effects of children on men’s wages appear to have changed over time.  The incremental effects of the first two children are about half as large for the later cohort (5.5 percent vs. 10 percent for the first child, and 4.4 percent vs. 8.6 percent for the second child.) The incremental effect of the third child is significantly negative for the early cohort, and positive but not highly significant for the later cohort.  For the early cohort, the effects of children are highly non-linear and non-monotonic; for the later cohort the effects are monotonic and approximately linear (see Figures 1b and 1c).  

Results:  The Effect of Children on Annual Hours Worked (Table 5)
	The analyses reported in the previous section are repeated for annual hours of work in this section.  The formats of the tables and figures are identical.  The results for the entire sample reported in Table 5a indicate that men work more hours per year after marriage, in addition to earning more per hour.    The OLS estimates indicate that married men work approximately 201 hours per year more than single men, the comparable fixed effects estimate is 116 hours per year.  As with the results for hourly earnings, there is evidence of positive selection into marriage, as the fixed effects estimates are approximately half the magnitudes of the OLS estimates for the entire sample, and for each cohort individually.  Comparing the estimates for the two cohorts indicates that the marriage “premium” in terms of hours of work has increased somewhat over time. 
	Having children significantly increases men’s annual hours of work.  For the sample as a whole, the linear OLS estimate of the effect of children is 46 hours per year per child and the comparable fixed effects estimate is 41 hours per child.  The non-linear fixed effects estimates reported in column (6) indicate that men work approximately 85 hours per year more (t=5.7) after the birth of the first child and 29 hours per year more (p=.035) after the second child.  The incremental effects of the subsequent children are not statistically significant.
Interpretation of the non-linear estimates by cohort is facilitated by examining Figures 2b and 2c.  For the early cohort, the fixed effects coefficients are less than the OLS coefficients for each parity, and they indicate a step-function relationship between children and men’s labor supply.  The effect of the first child is positive and significant, but the effects of subsequent children are all small.  For the later cohort, however, the effects of each child on hours of work are positive and significant. 
	In summary, men work more hours and earn more per hour after becoming fathers, although the incremental effects of children are non-linear.  For the early cohort the relationship is non-monotonic.   The first two children increase wages, but subsequent children reduce them. For hours of work, the relationship is a step function, with the first child leading to higher labor supply, and no effect of children at higher parities. In terms of the framework in Lundberg and Rose [1999] and discussed in Section II, the specialization effect outweighs the market intensity effect for the first one or two children, but the market intensity effect dominates the specialization effect for higher parities.  For the late cohort, in contrast, the positive effect of the first four children on hours and wages is approximately linear.
	
V.  The Effects of Sons vs. Daughters on Wages and Hours: Estimation and Results 
	In order to estimate the effects of sons relative to daughters on wages and hours work we estimate several variants of Equation (1).   
First, we examine the differential effect of the number of boys and girls.  We measure the number of boys and girls as NBOY03 and NGIRL03, which refer to the number of boys and girls if there are less than three.  Observations in which there are more than three boys or girls are dummied out with the variable GIRBOYG3.  The first specification of the model used to estimate gender-specific effects, then, is:
            
                            	       (2)                        
Second, we note that Morgan, Lye, and Condron’s [1988] finding on the effect of sons relative to daughters on marital survival probabilities pertained to whether there was at least one son or at least one daughter, and Butcher and Case’s [1993] finding on the effect of brothers on girls’ education relates to the presence of at least one brother.  Therefore, in the second specification we include the variables IFBOY and IFGIRL indicating whether the man has at least one son or daughter; i.e., 
            
                             (3)    
Third, we include the dummy variables FIRBOY and FIRGIR indicating that the man has had at least one child and the first child was a boy or girl, respectively; i.e., 
      
                 (4)     
	Finally, in the non-linear specification, we include two sets of dummy variables corresponding the gender specific parities; i.e., 
                   
                                  	  (5)                               
	All of these equations are estimated under individual fixed effects.  To the extent that the gender composition of a man’s offspring is essentially random, the issues of endogeneity and heterogeneity with respect to actual or expected shocks to hours or wages are not of concern.
The results for the gender-specific effects on hourly wage rates and hours worked are reported in Tables 6 and 7, respectively.  In each, the results for the entire sample are reported in column (1), for men born in or before 1950 in column (2), and for men born subsequent to 1950 in column (3).  The differential effects of sons vs. daughters, and the p-values for the tests that these differences are significant, are reported in the shaded portions of the tables.

Results:  The Effects of Sons vs. Daughters on (Log Real Hourly) Wage Rate (Table 6)
	We first turn to the estimates for wage rates, and for the sample as a whole. There is a significant effect of the gender of the man’s offspring in only one case:  for the third child (p=.035).  However, when we disaggregate by cohort, more striking patterns emerge.  
	For men in the early cohort, we find significantly higher wages for fathers of sons relative to daughters in most of the specifications.  Each son raises earnings by approximately 2.9 percent more than each daughter, and this difference is significant (p=.063).  Men with at least one son earn 3.1 percent more than men with at least one daughter, although this effect is not significant (p=.24).  However, men whose first child was a son earn approximately 7.9 percent more per hour than men whose first child was a daughter and this is statistically significant (p=.079).  The non-linear specification at the bottom of column (2) indicates that for each gender-specific parity men earn more after having sons relative to daughters, but these results are statistically significant only for the third boy or girl (p=.01).There are no significant gender-specific effects on wages for men born after 1950.

Results:  The Effects of Sons vs. Daughters on Annual Hours Worked (Table 7)
	The gender effects on for men’s hours of work reported in Table 7 are striking.  For the full sample, we find that men work significantly more if they have at least one son vs. at least one daughter  (53 hours per year, p=.01) or if their first child was a boy rather than a girl (66 hours per year, p=.007).  In the non-linear estimates we again find hours are significantly higher if the first child is a boy rather than a girl (64 hours per year, p=.003), but no significant effect for subsequent children.
	We find significant effects of child gender on labor supply for both cohorts, though only the effects for the later cohort are substantial and pervasive. Men in the early cohort work 63 hours more when their first son is born than when their first daughter is born.  For men born after 1950, we find statistically and quantitatively significant positive effects of sons relative to daughters in every specification.  The linear specification indicates that each son increases his father’s labor supply by 40 hours per year more than (or about 2.5 times as much as) each daughter (p=.026).  Having at least one son leads to about 73 more hours of work per year than having at least one daughter (p=.008), and having a son as a first child leads to an increase in labor supply of about 73 hours per year more than a daughter (p=.02). Thus the incremental effect of having a son rather than a daughter amounts to more than 3 percent of total male labor supply.  In the non-linear specification, we find increases in labor supply for each of the gender-specific parities.
	In summary, having sons vs. daughters leads to higher hourly wages and higher labor supply for fathers.  The labor supply effect is particularly striking, as we find significant effects for both the early and late cohorts and for a variety of specifications of the gender composition of a man’s offspring.

Discussion 
Our results indicate that men work more and/or harder after having sons relative to daughters.  Furthermore, when we estimated the same specifications reported in Tables 4-7 for women as well as men, we found little evidence that children’s sex affects women’s hourly earnings and no evidence of an effect on labor supply.
What type of economic model would explain these findings? First, we consider models in which having sons relative to daughters shifts parents’ constraints.  If the returns to educating sons are greater than the returns to educating daughters, parents may work more if they have sons relative to daughters in order to finance their education. While there is limited evidence to suggest that parents spend more on sons’ education than on daughters’ education, the magnitudes would be too small to explain the difference in earnings of parents of boys relative to girls (Taubman [1990]).  
	Parents’ lifetime constraint sets may also differ by child gender if they expect more old age support from daughters relative to sons.  It is often observed that women are more likely to care for elderly parents than are men, perhaps because the opportunity cost of women’s time at the age at which parents need care is lower than the opportunity cost of men’s time.  However, McGarry [1998] finds that men are less likely to care for elderly parents only if they have sisters, and that men with only male siblings are no less likely to care for parents than are women from female-only families.  This implies that the labor supply effects of  “at least one son” vs. “at least one daughter” would not be due to anticipated differences in old-age support.
	The alternative to a constraint explanation for fathers’ responses to child gender is a preference explanation.  If men prefer sons to daughters or value the time spent with sons more highly, then the value of marriage (or at least co-residence) with the child’s mother will be higher for fathers of sons. Morgan et al [1988] find that the birth of a son relative to a daughter increases the likelihood that a marriage will survive by approximately 7 percentage points using data from the U.S. Census.  Reduced probability of marital dissolution will increase the returns to marriage-specific investments, and we would expect this to lead to greater specialization within the marriage.  This is consistent with our finding that husbands to work more in the labor market after a son is born relative to a daughter, but we do not find greater specialization in home production by mothers of sons.
We can also analyze the effects of child gender in the context of a bargaining model with a divorce threat point in which husbands and wives each allocate their resources to the production of household public goods and to private goods.   If men prefer sons and divorce causes a reduction in the child services that fathers receive, they will contribute more to household public goods and less to their private consumption of leisure in a marriage with sons.  Our labor supply results are consistent with this story, but the bargaining framework implies that child gender should affect the intrahousehold distribution of goods and time more generally.  Yeung et al’s [1999] finding that boys spend more time with fathers than do girls suggests that the increased in work intensity of men with sons is not at the expense of their contribution to the child-care component of household production.

VI.  Conclusion
	In this paper we have estimated the effects of children, both total and by gender, on men’s labor supply and hourly wages. We find that fatherhood results in significantly higher wages and labor supply, and that these effects are significantly greater for fathers of sons relative to daughters. We also find that the relationship between children and labor market outcomes for fathers has changed;  men born after 1950 have larger labor supply responses to children than do men from earlier cohorts.  Finally, we find that child effects are non-linear, with positive incremental effects on men’s hours and wages limited to the first two children. 
There are several implications of our findings.  First, although the role of children is typically ignored in studies of male labor supply and wage determination, fatherhood has quantitatively and statistically significant effects on both outcomes.  Second, since we observe increases in both hourly wages and annual hours of work for fathers, increased specialization of husbands and wives in response to parenthood is the dominant pattern for both early and late cohorts.  Third, the increase in men’s hourly wage rates suggests that additional  research into the source of this “fatherhood premium” and its relationship to human capital investments, job changes, or promotions is warranted.
Finally, the increased commitment to the labor market that men demonstrate after having sons relative to daughters provides surprising evidence of the significance of child gender for families in the United States. Since we did not find evidence of gender effects on mother’s labor supply, it appears that the “first round” effects on household outcomes arise through the behavior of fathers, not mothers.  In conjunction with other research on the effects of child gender on divorce and father’s time with children, our results suggest that sons increase the value of marriage and family life for men.

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Rendall, M.S., L. Clarke, H.E. Peters, and G. Verrropoulou, “Incomplete Reporting of Men’s Fertility in the U.S. and Britain:  A Research Note”, Demography, February 1999 36:1, 135-44.

Rose, Elaina, “Consumption Smoothing and Excess Female Mortality in Rural India”, Review of Economics and Statistics, 81:1, February 1999, 41-49.

Rose, Elaina, “Gender Bias, Credit Constraints and Time Allocation in Rural India”, forthcoming, The Economic Journal.

Rosenzweig, Mark and T. Paul Schultz, “Market Opportunities, Genetic Endowments, and Intrafamily Resource Distribution: Child Survival in Rural India”, American Economic Review, 72:4, September 1982, 803-15.

Taubman, Paul, “Discrimination within the Family:  The Treatment of Daughters and Sons”, in Essays on the Economics of Discrimination, ed. Emily P. Hoffman, Kalamazoo:  W.E. Upjohn Institute for Employment Research, 1990, 25-42.

Teachman, Jay D. and Paul T. Schollaert, “Gender of Children and Birth Timing”, Demography, August 1989, 26:3, 411-423.

Waldfogel, Jane, “Understanding the ‘Family Gap’ in Pay for Women with Children,” Journal of Economic Perspectives, Winter 1998, 12:1, 137-156.

Yeung, W. Jean, et al, “Children’s Time with Fathers in Intact Families”, mimeo, University of Michigan, March 1999.

Table 1: Means (Standard Deviations) of Key Variables

	Early Cohort
(Men Born In or Before 1950)	Late Cohort
(Men Born After 1950)	
Log (Real Hourly Wage)	2.36*
(0.61)	2.14**
(0.63)	
Annual Hours Worked	2248.07
(703.15)	2168.85
(717.41)	
Years of Education	13.93
(2.42)	13.24
(2.08)	
Age	34.18
(6.69)	28.44
(5.03)	
Married?	0.895	0.806	
Length of Marriage	9.07
(7.29)	4.85
(4.79)	
Length of Marriage (If Married)	10.13
(6.97)	6.01
(4.64)	
Number of Children	1.63
(1.17)	1.15
(1.15)	
Number of Sons	0.87
(0.90)	0.58
(0.78)	
Number of Daughters	0.76
(0.86)	0.57
(0.78)	
After First Child Born (Son)	0.43	0.32	
After First Child Born (Daughter)	0.36	0.30	
If at Least One Son	0.58	0.43	
If at Least One Daughter	0.53	0.41	
Number of Observations	11248	14957	

*Based on 11090 observations
**Based on 14665 observations

Table 2
Frequency Distribution:  Number of Children
Number of Observations
(Percent of Sample)

	By Individual*Time	By Individual
(Maximum Number per Individual)	
	Early Cohort	Late Cohort	Early Cohort	Late Cohort	
No Children	2342
(20.82)	5749
(38.44)	70
(11.65)	560
(34.10)	
One Child	2501
(22.24)	3548
(23.72)	86
(14.31)	317
(19.31)	
Two Children	4062
(36.11)	3823
(25.56)	259
(43.09)	488
(29.72)	
Three Children	1761
(15.66)	1398
(9.35)	129
(21.46)	207
(12.61)	
Four Children	455
(4.05)	339
(2.27)	42
(6.99)	53
(3.23)	
Five Children	109
(0.97)	82
(0.55)	12
(2.00)	15
(0.91)	
Six Children	11
(0.10)	17
(0.11)	1
(0.17)	1
(0.06)	
Seven Children	7
(0.06)	1
(0.01)	2
(0.33)	1
(0.06)	
	
  Total 	11248
(100)	14957
(100)	601
(100)	1642
(100)	

Table 3
Frequency Distribution of Sons and Daughters
Number of Observations
(Percent of  Sample)

	Early Cohort	Late Cohort		Early Cohort	Late Cohort	
No Sons	4763
(42.35)	8533
(57.05)	No Daughters	5266
(46.82)	8756
(58.54)	
One Son	3789
(33.69)	4460
(29.82)	One Daughter	3797
(33.76)	4241
(28.35)	
Two Sons	2131
(18.95)	1671
(11.17)	Two Daughters	1819
(16.17)	1620
(10.83)	
Three Sons	522
(4.64)	244
(1.63)	Three Daughters	311
(2.76)	312
(2.09)	
Four Sons	43
(0.38)	45
(0.30)	Four Daughters	45
(0.40)	28
(0.19)	
Five Sons	0	4
(0.03)	Five Daughters	9
(0.08)	0	
Six Sons	0	0	Six Daughters	1
(0.01)	0	
  Total 	11248
(100)	14957
(100)	  Total 	11248
(100)	14957
(100)	

Table 4a: The Effect of Marriage and Children on (Log Real Hourly) Wage
(Entire Sample)
 (Additional regressors include: dummy variables for: year of observation, years of education, age)
(T-statistics in parentheses, unless otherwise noted)
 (N =  25755 )

	(1)
OLS	(2)
OLS	(3)
OLS	(4)
FE	(5)
FE	(6)
FE	
Married 
	0.10
(4.30)	0.086
(3.73)	0.078
(3.38)	0.062
(4.84)	0.057
(4.48)	0.051
(3.96)	
Number of Children		0.017
(1.65)			0.045
(7.91)		
More than 4 Children		-0.157
(-1.14)	-0.141
(-1.03)		0.096
(1.95)	0.095
(1.93)	
One Child
			0.020
(0.95)			0.073
(6.36)	
Two Children
			0.070
(2.72)			0.135
(9.84)	
Three Children
			0.073
(2.18)			0.130
(6.83)	
Four Children
			-0.04
(-0.61)			0.127
(4.47)	
 Two Children
- One Child (p)			0.05
(0.021)			0.062
(0.00)	
 Three Children
 - Two Children (p)			0.003
(0.925)			-0.005
(0.699)	
 Four Children
- Three Children (p)			-0.113
(0.071)			-0.003
(0.911)	
R-squared	0.16	0.17	0.17	0.59	0.60	0.60	

Table 4b:  The Effect of Marriage and Children on (Log Real Hourly) Wage
(By Cohort)
 (Additional regressors include: dummy variables for: year of observation, years of education, age)
(T-statistics in parentheses, unless otherwise noted)

Cohort
(N)		(1)
OLS	(2)
OLS	(3)
OLS	(4)
FE	(5)
FE	(6)
FE	
Early
(11090)	Married	0.153
(3.15)	0.146
(3.09)	0.134
(2.84)	0.084
(4.19)	0.077
(3.82)	0.068
(3.36)	
	Number of Children		0.008
(0.42)			0.044
(5.20)		
	More than 4 Children	
	0.040
(0.29)	0.072
(0.53)		0.127
(1.87)	0.138
(2.03)	
	One Child
			0.019
(0.44)			0.100
(5.40)	
	Two Children
			0.076
(1.58)			0.186
(8.74)	
	Three Children
			0.064
(1.13)			0.139
(4.86)	
	Four Children
			-0.105
(-1.03)			0.092
(2.30)	
	 Two Children
- One Child (p)			0.057
(0.114)			0.086
(0.00)	
	 Three Children
 – Two Children (p)			-0.012
(0.792)			-0.047
(0.022)	
	 Four Children
- Three Children (p)			-0.169
(0.064)			-0.047
(0.144)	
	R-squared	0.13	0.13	0.13	0.56	0.56	0.56	
Late
(14665)	Married 
	0.070
(2.78)	0.048
(1.88)	0.044
(1.70)	0.041
(2.49)	0.038
(2.30)	0.035
(2.12)	
	Number of Children		0.028
(2.46)			0.044
(5.77)		
	More than 4 Children	
	-0.438
(-2.32)	-0.431
(-2.30)		0.045
(0.63)	0.048
(0.65)	
	One Child
			0.030
(1.27)			0.055
(3.77)	
	Two Children
			0.075
(2.58)			0.099
(5.47)	
	Three Children
			0.089
(2.21)			0.125
(4.90)	
	Four Children
			0.057
(0.77)			0.176
(4.29)	
	 Two Children
- One Child (p)			0.045
(0.074)			0.044
(0.002)	
	 Three Children
 – Two Children (p)			0.014
(0.687)			0.026
(0.162)	
	 Four Children
- Three Children (p)			-0.032
(0.665)			0.051
(0.156)	
	R-squared	0.16	0.16	0.16	0.60	0.61	0.61	

Table 5a: The Effect of Marriage and Children on Annual Hours Worked
(Entire Sample)
 (Additional regressors include: dummy variables for: year of observation, years of education, age)
(T-statistics in parentheses, unless otherwise noted)
 (N =  26205)

	(1)
OLS	(2)
OLS	(3)
OLS	(4)
FE	(5)
FE	(6)
FE	
Married 
	200.679
(8.17)	160.945
(6.53)	148.516
(5.97)	116.382
(7.12)	112.294
(6.87)	104.594
(6.35)	
Number of Children		45.86
(4.48)			40.58
(5.59)		
More than 4 Children		-57.497
(-0.43)	-34.916
(-0.26)		44.736
(0.72)	56.573
(0.91)	
One Child
			68.297
(2.97)			84.809
(5.72)	
Two Children
			138.562
(5.41)			113.383
(6.40)	
Three Children
			138.922
(4.04)			118.553
(4.84)	
Four Children
			126.268
(1.90)			162.384
(4.45)	
 Two Children
- One Child (p)			70.265
(0.004)			28.572
(0.035)	
 Three Children
 – Two Children (p)			0.360
(0.991)			5.17
(0.772)	
 Four Children
- Three Children (p)			-12.654
(0.845)			43.831
(0.158)	
R-squared	0.04	0.04	0.04	0.46	0.46	0.46	

Table 5b:  The Effect of Marriage and Children on Annual Hours Worked
(By Cohort)
 (Additional regressors include: dummy variables for: year of observation, years of education, age)
(T-statistics in parentheses, unless otherwise noted)

Cohort
(N)		(1)
OLS	(2)
OLS	(3)
OLS	(4)
FE	(5)
FE	(6)
FE	
Early
(11248)	Married 	173.946
(4.49)	132.761
(3.38)	110.868
(2.82)	88.394
(3.37)	84.299
(3.21)	74.933
(2.84)	
	Number of Children		45.183
(2.73)			26.836
(2.44)		
	More than 4 Children		19.678
(0.11)	70.617
(0.38)		10.509
(0.12)	34.631
(0.40)	
	One Child
			111.941
(2.80)			106.112
(4.37)	
	Two Children
			174.566
(4.19)			107.794
(3.89)	
	Three Children
			137.205
(2.60)			102.353
(2.74)	
	Four Children
			184.275
(1.80)			97.529
(1.88)	
	 Two Children
- One Child (p)			62.625
(0.110)			1.682
(0.936)	
	 Three Children
 – Two Children (p)			-37.361
(0.415)			-5.491
(0.837)	
	 Four Children
- Three Children (p)			47.07
(0.629)			-4.824
(0.909)	
	R-squared	0.03	0.03	0.04	0.42	0.42	0.42	
Late
(14957)	Married 
	219.089
(7.03)	179.695
(5.80)	175.617
(5.53)	130.513
(6.21)	126.949
(6.05)	122.641
(5.77)	
	Number of Children		46.971
(3.66)			53.417
(5.48)		
	More than 4 Children		-184.608
(-0.90)	-177.162
(-0.86)		76.21
(0.85)	85.33
(0.95)	
	One Child
			41.381
(1.50)			73.610
(3.92)	
	Two Children
			120.189
(3.79)			124.077
(5.36)	
	Three Children
			157.128
(3.46)			139.981
(4.27)	
	Four Children
			78.916
(1.01)			243.733
(4.64)	
	 Two Children
- One Child (p)			78.08
(0.007)			50.467
(0.004)	
	 Three Children
 – Two Children (p)			36.929
(0.350)			15.904
(0.512)	
	 Four Children
- Three Children (p)			-78.212
(0.309)			103.752
(0.024)	
	R-squared	0.04	0.05	0.05	0.48	0.49	0.49	

Table 6:  The Effect of Sons vs. Daughters on (Log Real Hourly) Wage

 (Additional regressors include: dummy variables for: marital status, year, years of education, age)
(T-statistics in parentheses, unless otherwise noted)

	(1)
Full Sample	(2)
Early Cohort	(3)
Late Cohort	
Number of Boys
	0.039
(5.16)	0.049
(4.39)	0.030
(2.99)	
Number of Girls
	0.032
(4.27)	0.020
(1.77)	0.041
(4.01)	
Number of Boys
- Number of Girls (p)	0.007
(0.544)	0.029
(0.063)	-0.011
(0.447)	
If at Least One Boy
	0.054
(4.70)	0.087
(4.79)	0.035
(2.40)	
If at Least One
Girl	0.057
(5.03)	0.056
(3.12)	0.056
(3.77)	
If at Least One Boy
- If at Least One Girl (p)	-0.003
(0.834)	0.031
(0.242)	-0.021
(0.345)	
After First Child, Boy
	0.095
(6.60)	0.155
(6.71)	0.056
(3.06)	
After First Child, Girl
	0.081
(5.44)	0.101
(4.24)	0.067
(3.51)	
After First Child Boy
- After First Child Girl (p)	0.014
(0.454)	0.054
(0.079)	-0.011
(0.669)	
One Boy
	0.053
(4.62)	0.078
(4.28)	0.038
(2.54)	
One Girl
	0.051
(4.46)	0.054
(2.97)	0.049
(3.33)	
One Boy 
– One Girl (p)	0.002
(0.925)	0.024
(0.37)	-0.011
(0.579)	
Two Boys
	0.104
(6.23)	0.122
(4.93)	0.089
(3.91)	
Two Girls
	0.092
(5.37)	0.078
(3.04)	0.098
(4.23)	
Two Boys
 – Two Girls (p)	0.012
(0.605)	0.044
(0.203)	-0.009
(0.774)	
Three Boys
	0.119
(4.03)	0.103
(2.57)	0.147
(3.27)	
Three Girls
	0.035
(1.17)	-0.033
(-0.77)	0.103
(2.49)	
Three Boys 
– Three Girls (p)	0.084
(0.035)	0.136
(0.014)	0.044
(0.456)	

Table 7:  The Effect of Sons vs. Daughters on Annual Hours Worked

 (Additional regressors include: dummy variables for: marital status, year, years of education, age)
(T-statistics in parentheses, unless otherwise noted)

	(1)
Full Sample	(2)
Early Cohort	(3)
Late Cohort	
Number of Boys
	37.296
(3.87)	4.223
(0.29)	66.616
(5.13)	
Number of Girls
	28.016
(2.87)	32.332
(2.20)	26.58
(2.02)	
Number of Boys
- Number of Girls (p)	9.280
(0.491)	-28.109
(0.167)	40.036
(0.026)	
If at Least One Boy
	83.975
(5.70)	70.82
(2.98)	95.647
(5.06)	
If at Least One
Girl	31.219
(2.13)	44.234
(1.89)	23.028
(1.22)	
If at Least One Boy
- If at Least One Girl (p)	52.756
(0.014)	26.586
(0.443)	72.619
(0.008)	
After First Child, Boy
	121.915
(6.59)	133.021
(4.44)	114.759
(4.87)	
After First Child, Girl
	56.349
(2.94)	79.551
(2.55)	42.698
(1.75)	
After First Child Boy
- After First Child Girl (p)	65.566
(0.007)	53.47
(0.175)	72.061
(0.020)	
One Boy
	88.641
(5.99)	88.47
(3.70)	93.53
(4.93)	
One Girl
	24.487
(1.65)	25.944
(1.09)	25.742
(1.35)	
One Boy 
– One Girl (p)	64.154
(0.003)	62.526
(0.075)	67.788
(0.014)	
Two Boys
	83.575
(3.91)	53.357
(1.67)	116.429
(4.00)	
Two Girls
	61.394
(2.79)	71.206
(2.14)	55.999
(1.89)	
Two Boys
 – Two Girls (p)	22.181
(0.451)	-17.849
(0.69)	60.430
(0.125)	
Three Boys
	102.004
(2.69)	16.115
(0.31)	208.243
(3.64)	
Three Girls
	74.460
(1.93)	75.767
(1.35)	84.595
(1.59)	
Three Boys 
– Three Girls (p)	27.544
(0.592)	-59.652
(0.410)	123.648
(0.099)	

Appendix Table A.1.1:  The Effect of Children on (Log Real Hourly) Wage
 (Additional regressors include: dummy variables for: year of observation, years of education, age, if married)
(T-statistics in parentheses, unless otherwise noted)

Cohort
(N)		(1)
OLS	(2)
OLS	(3)
FE	(4)
FE	
All
(25755)	Number of Children
	0.01
(0.92)		0.039
(7.16)		
	One Child
		0.020
(0.95)		0.073
(6.37)	
	Two Children
		0.070
(2.72)		0.135
(9.84)	
	Three Children
		0.073
(2.18)		0.131
(6.87)	
	Four Children
		-0.040
(-0.61)		0.127
(4.47)	
	Five Children
		-0.071
(-0.57)		0.173
(3.25)	
	Six Children
		-0.484
(-0.95)		-0.192
(-1.77)	
	Seven Children
		-0.685
(-1.83)		-0.444
(-2.26)	
	
Early
(11090)	Number of Children
	0.007
(0.40)		0.038
(4.70)		
	One Child
		0.019
(0.44)		0.101
(5.43)	
	Two Children
		0.076
(1.58)		0.186
(8.76)	
	Three Children
		0.064
(1.13)		0.141
(4.92)	
	Four Children
		-0.105
(-1.03)		0.095
(2.37)	
	Five Children
		0.123
(0.89)		0.196
(2.69)	
	Six Children
		0.014
(0.05)		0.052
(0.33)	
	Seven Children
		-0.687
(-1.81)		-0.373
(-1.88)	
	
Late
(14665)	Number of Children
	0.014
(1.09)		0.039
(5.22)		
	One Child
		0.030
(1.27)		0.055
(3.74)	
	Two Children
		0.074
(2.57)		0.098
(5.43)	
	Three Children
		0.088
(2.2)		0.125
(4.89)	
	Four Children
		0.056
(0.77)		0.171
(4.14)	
	Five Children
		-0.354
(-2.60)		0.14
(1.78)	
	Six Children
		-0.798
(-1.12)		-0.370
(-2.45)	
	Seven Children
		0.0		0.0	

Appendix Table A.1.2:  The Effect of Children on Annual Hours Worked
 (Additional regressors include: dummy variables for: year of observation, years of education, age, if married)
(T-statistics in parentheses, unless otherwise noted)

Cohort
(N)		(1)
OLS	(2)
OLS	(3)
FE	(4)
FE	
All
(26205)	Number of Children
	37.321
(3.54)		34.932
(4.96)		
	One Child
		68.431
(2.98)		84.78
(5.72)	
	Two Children
		138.784
(5.42)		113.239
(6.39)	
	Three Children
		139.198
(4.05)		118.768
(4.84)	
	Four Children
		126.569
(1.90)		161.567
(4.42)	
	Five Children
		-28.354
(-0.22)		96.976
(1.46)	
	Six Children
		-241.498
(-0.62)		-148.888
(-1.06)	
	Seven Children
		542.48
(1.22)		-87.469
(-0.39)	
	
Early
(11248)	Number of Children
	39.084
(2.38)		22.203
(2.11)		
	One Child
		111.625
(2.79)		106.021
(4.37)	
	Two Children
		174.402
(4.19)		107.730
(3.89)	
	Three Children
		136.768
(2.59)		102.037
(2.73)	
	Four Children
		184.436
(1.80)		96.954
(1.87)	
	Five Children
		-11.553
(-0.060)		25.325
(0.27)	
	Six Children
		320.331
(0.93)		28.656
(0.140)	
	Seven Children
		927.751
(8.52)		140.335
(0.57)	
	
Late
(14957)	Number of Children
	35.494
(2.59)		47.384
(4.94)		
	One Child
		41.198
(1.49)		72.927
(3.88)	
	Two Children
		119.703
(3.78)		122.866
(5.31)	
	Three Children
		156.065
(3.44)		138.844
(4.24)	
	Four Children
		78.182
(1.00)		236.031
(4.49)	
	Five Children
		-80.628
(-0.37)		179.794
(1.87)	
	Six Children
		-565.50
(-1.26)		-347.987
(-1.79)	
	Seven Children
		-1903.692
(-15.97)		-1442.796
(-2.48)	

Appendix Table A.2.1:  The Effect of Sons vs. Daughters on (Log Real Hourly) Wage
(Additional regressors include: dummy variables for  year of observation, years of education, age, if married)
(T-statistics in parentheses, unless otherwise noted)

Cohort		(1)
FE	(2)
FE	(3)
FE	(4)
FE	
All
(25755)	Length of Marriage	0.011
(5.48)	0.011
(5.69)	0.012
(5.84)	0.010
(4.89)	
	(Length of Marriage)2	-0.001
(-7.70)	-0.001
(-7.65)	-0.001
(-7.60)	-0.001
(-7.10)	
	Number of Boys
- Number of Girls (p)	0.007
(0.503)				
	If at Least One Boy
- If at Least One Girl (p)		-0.002
(0.876)			
	After First Child Boy
- After First Child Girl (p)			0.014
(0.454)		
	One Boy 
– One Girl (p)				0.003
(0.889)	
	Two Boys
 – Two Girls(p)				0.010
(0.649)	
	Three Boys 
– Three Girls (p)				0.088
(0.026)	
Early
(11090)	Length of Marriage	0.013
(5.1)	0.013
(4.98)	0.012
(4.91)	0.013
(4.83)	
	(Length of Marriage)2a	-0.001
(-6.2)	-0.001
(-5.89)	-0.001
(-5.7)	-0.001
(-5.76)	
	Number of Boys
- Number of Girls (p)	0.030
(0.048)				
	If at Least One Boy
- If at Least One Girl (p)		0.034
(0.202)			
	After First Child Boy
- After First Child Girl (p)			0.054
(0.071)		
	One Boy 
– One Girl (p)				0.026
(0.331)	
	Two Boys
 – Two Girls(p)				0.042
(0.218)	
	Three Boys 
– Three Girls (p)				0.147
(0.008)	
Late
(14665)	Length of Marriage	0.014
(3.71)	0.015
(4.06)	0.016
(4.34)	0.012
(3.25)	
	(Length of Marriage)2	-0.001
(-5.98)	-0.001
(-6.14)	-0.001
(-6.19)	-0.001
(-5.67)	
	Number of Boys
- Number of Girls (p)	-0.011
(0.444)				
	If at Least One Boy
- If at Least One Girl (p)		-0.021
(0.328)			
	After First Child Boy
- After First Child Girl (p)			-0.012
(0.614)		
`	One Boy 
– One Girl (p)				-0.013
(0.557)	
	Two Boys
 – Two Girls(p)				-0.006
(0.855)	
	Three Boys 
– Three Girls (p)				0.043
(0.461)	

Appendix Table A.2.2:  The Effect of Sons vs. Daughters on Annual Hours of Work
(Additional regressors include: dummy variables for: year of observation, years of education, age, if married)
(T-statistics in parentheses, unless otherwise noted)

Cohort		(1)
FE	(2)
FE	(3)
FE	(4)
FE	
All
(26205)	Length of Marriage	3.277
(1.25)	3.12
(1.21)	3.378
(1.33)	2.604
(0.99)	
	(Length of Marriage)2	-0.188
(-1.69)	-0.166
(-1.50)	-0.160
(-1.46)	-0.152
(-1.36)	
	Number of Boys
- Number of Girls (p)	9.434
(0.483)				
	If at Least One Boy
- If at Least One Girl (p)		52.943
(0.014)			
	After First Child Boy
- After First Child Girl (p)			65.317
(0.007)		
	One Boy 
– One Girl (p)				64.358
(0.003)	
	Two Boys
 – Two Girls(p)				21.745
(0.460)	
	Three Boys 
– Three Girls (p)				28.742
(0.576)	
Early
(11248)	Length of Marriage	4.809
(1.41)	3.474
(1.04)	3.224
(0.97)	3.729
(1.09)	
	(Length of Marriage)2	-0.242
(-1.85)	-0.187
(-1.44)	-0.169
(-1.31)	-0.195
(-1.48)	
	Number of Boys
- Number of Girls (p)	-27.323
(0.18)				
	If at Least One Boy
- If at Least One Girl (p)		27.618
(0.426)			
	After First Child Boy
- After First Child Girl (p)			54.24
(0.169)		
	One Boy 
– One Girl (p)				63.387
(0.071)	
	Two Boys
 – Two Girls(p)				-18.491
(0.679)	
	Three Boys 
– Three Girls (p)				-55.83
(0.441)	
Late
(14957)	Length of Marriage	3.184
(0.67)	4.538
(0.97)	5.016
(1.08)	2.935
(0.62)	
	(Length of Marriage)2	-0.158
(-0.60)	-0.176
(-0.67)	-0.157
(-0.60)	-0.138
(-0.52)	
	Number of Boys
- Number of Girls (p)	39.83
(0.027)				
	If at Least One Boy
- If at Least One Girl (p)		72.373
(0.008)			
	After First Child Boy
- After First Child Girl (p)			70.995
(0.022)		
	One Boy 
– One Girl (p)				67.771
(0.014)	
	Two Boys
 – Two Girls(p)				60.089
(0.127)	
	Three Boys 
– Three Girls (p)				122.921
(0.101)	

Appendix Table A.3.1:  The Effect of Sons vs. Daughters on (Log Real Hourly) Wage
Controlling for Number of Children

(Additional regressors include: dummy variables for: year of observation, years of education, age)
(T-statistics in parentheses, unless otherwise noted)

	(1)
FE	(2)
FE	(3)
FE	(4)
FE	(5)
FE	(6)
FE	
Cohort	All	All	Early	Early	Late	Late	
If at Least One Boy
	0.007
(0.49)		0.087
(4.79)		-0.010
(-0.51)		
If at Least One
Girl	0.010
(0.65)		0.056
(3.12)		0.011
(0.55)		
If at Least One Boy
- If at Least One Girl (p)	-0.003
(0.881)		0.031
(0.242)		-0.021
(0.330)		
After First Child, Boy
		0.052
(3.03)		0.114
(4.14)		0.015
(0.71)	
After First Child, Girl
		0.038
(2.19)		0.061
(2.22)		0.024
(1.08)	
After First Child Boy
- After First Child Girl (p)		0.014
(0.475)		0.053
(0.085)		-0.009
(0.719)	
Number of Children, if < 4 Children (else  =  0)	0.046
(4.87)	0.036
(4.75)	0.043
(3.03)	0.031
(2.69)	0.044
(3.47)	0.038
(3.71)	
If 4 or More Children
	0.101
(2.91)	0.082
(2.77)	0.039
(0.77)	0.017
(0.42)	0.155
(3.17)	0.139
(3.30)	

Appendix Table A.3.2:  The Effect of Sons vs. Daughters on Annual Hours of Work
Controlling for Number of Children

(Additional regressors include: dummy variables for: year of observation, years of education, age)
(T-statistics in parentheses, unless otherwise noted)

	(1)
FE	(2)
FE	(3)
FE	(4)
FE	(5)
FE	(6)
FE	
Cohort	All	All	Early	Early	Late	Late	
If at Least One Boy
	60.876
(3.15)		71.051
(2.34)		51.216
(2.03)		
If at Least One
Girl	7.752
(0.40)		46.236
(1.52)		-19.524
(-0.77)		
If at Least One Boy
- If at Least One Girl (p)	53.124
(0.013)		24.815
(0.475)		70.740
(0.010)		
After First Child, Boy
		99.18
(4.52)		134.950
(3.77)		74.29
(2.66)	
After First Child, Girl
		34.276
(1.53)		80.643
(2.24)		0.387
(0.010)	
After First Child Boy
- After First Child Girl (p)		64.904
(0.008)		54.307
(0.169)		73.903
(0.017)	
Number of Children, if < 4 Children (else  =  0)	22.557
(1.84)	19.864
(2.02)	-0.042
(0.00)	-1.790
(-0.12)	42.732
(2.59)	39.064
(2.98)	
If 4 or More Children
	72.608
(1.62)	78.531
(2.08)	-36.453
(-0.56)	-23.652
(-0.43)	179.067
(2.87)	184.333
(3.44)	

	

Increase in (Log Real)
Hourly Wage

Increase in (Log Real)
Hourly Wage

Increase in (Log Real)
Hourly Wage

       OLS              FE        Sig. Diff. From 0      Sig. Diff. From Previous

       OLS              FE        Sig. Diff. From 0      Sig. Diff. From Previous

Increase in Annual Hours Worked

Increase in Annual Hours Worked

Increase in Annual Hours Worked