Can immigrants help women "have it all"? Immigrant labor and women's joint fertility and labor supply decisions

Furtado, Delia Article Can immigrants help women "have it all"? Immigrant labor and women's joint fertility and labor supply decisions IZA Journal of Migration Provided in Cooperation with: IZA – Institute of Labor Economics Suggested Citation: Furtado, Delia (2015) : Can immigrants help women "have it all"? Immigrant labor and women's joint fertility and labor supply decisions, IZA Journal of Migration, ISSN 2193-9039, Springer, Heidelberg, Vol. 4, pp. 1-19, https://doi.org/10.1186/s40176-015-0043-x This Version is available at: https://hdl.handle.net/10419/149442 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Immigrant labor and women’s joint fertility and labor supply decisions Delia Furtado 1,2 Correspondence: [email protected] 1 Department of Economics, University of Connecticut, 365 Fairfield Way, Unit 1063, Storrs, CT 06269-1063, USA 2 IZA, Bonn, Germany Abstract This paper explores how inflows of low-skilled immigrants impact the tradeoffs women face when making joint fertility and labor supply decisions. I find increases in fertility and decreases in labor force participation rates among high-skilled US-born women in cities that have experienced larger immigrant inflows. Most interestingly, these changes have been accompanied by decreases in the strength of the negative correlation between childbearing and labor force participation, an often-used measure of the difficulty with which women combine motherhood and labor market work. Using a structured statistical model, I show that the immigrant-induced attenuation of this negative correlation can explain about 24% of the immigrant-induced increases in the joint likelihood of childbearing and labor force participation in the US between the years 1980 and 2000. JEL codes: D10, F22, J13, J22, R23 Keywords: Child care; Fertility; Labor force participation; Immigration; Tetrachoric correlation 1. Introduction The highly time-intensive nature of childrearing implies a tradeoff between fertility and labor supply, particularly for women given their traditional role of performing household work (Becker 1985; Willis 1973). Within-country empirical analyses indicate a consistently negative association between fertility and female labor force participation. However, Engelhardt et al. (2004) find that this relationship has weakened substantially since the 1960s, particularly in the United States, suggesting that women are finding it easier to combine their roles as workers and mothers. Cross-country studies reveal a positive correlation between fertility and labor force participation since the 1980s (Adsera 2004; Brewster and Rindfuss 2000). There is evidence suggesting that high unemployment and unstable employment contracts in Southern Europe combined with the generous maternity benefits in Scandinavia can at least partially explain why countries with the highest fertility rates also have the highest female labor force participation rates (Adsera 2004). Cross-country differences in the cost, availability, and qualityofchildcare,drivenmostlybychildcare subsidies, might also explain why women in certain countries can more easily combine motherhood with labor force participation © 2015 Furtado. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Furtado IZA Journal of Migration (2015) 4:19 DOI 10.1186/s40176-015-0043-x (Brewster and Rindfuss 1996). Formal child care is typically provided by the private market in the US, and any childcare subsidies are targeted to single mothers at the margin of receiving welfare payments (Blau and Tekin 2007). However, in light of the evidence that immigrant inflows are associated with decreases in the price (Furtado 2015) and likely improvements in the convenience of child care (Cortés and Pan 2013), this paper examines whether and how the increased immigration to the US between 1980 and 2000 has contributed to the decreasing tradeoffs women face when making joint work and fertility decisions. Consistent with the notion that combining mother and worker roles has become easier over time, fertility has been increasing for college-educated women (Shang and Weinberg 2013), the very women with the highest labor force participation rates. Although home appliances, such as microwave ovens, and time-saving products, such as frozen foods, have been found to play a role in explaining increased female labor force participation rates (Greenwood et al. 2005; Coen-Pirani et al. 2010), most of the diffusion of these technologies seems to have occurred before 1980 (Greenwood et al. 2005). Hazan and Zoabi (2014) present evidence of a U-shaped relationship between education and fertility among the newest cohorts of US women. They explain this with a model showing that high wage women substitute housekeeping and babysitting services for their own time in household production thereby allowing them to increase fertility without sacrificing their careers. The authors’empirical results suggest that this new pattern is attributable to the change in the cost of child care relative to high-skilled women’swages. Another line of research investigates the relationship between childcare costs and the likelihood that mothers work (Blau and Robins 1988; Connelly 1992; Kimmel 1998; Kornstad and Thoresen 2007). In a survey article, Blau and Currie (2006) highlight the wide-ranging estimated elasticities of employment with respect to child care costs and attribute crossstudy discrepancies largely to variation in biases from different estimation strategies. Newer studies estimate mothers’labor supply decisions by exploiting cross-time and regional variation in the price or availability of care resulting from differences in the implementation of childcare-related public policies (Baker et al. 2008; Cascio 2009; Lefebvre and Merrigan 2008; Lundin et al. 2008; Havnes and Mogstad 2011; Bauernschuster and Schlotter 2015; Nollenberger and Rodríguez-Planas 2015). While these studies generally find that decreased childcare costs increase maternal labor supply, the estimates differ quite substantially. In fact, some studies find little to no causal impact of subsidized child care on mother’s employment (Cascio 2009; Havnes and Mogstad 2011). It may be that subsidized child care simply crowds out informal childcare arrangements (Havnes and Mogstad 2011), but it is also possible that women respond to less expensive child care by having an additional child which then depresses labor supply, perhaps temporarily. Exploiting variation generated from a Swedish childcare subsidy reform, Mörk et al. (2013) find that lower childcare costs lead to more childbearing. Another potential source of variation in the price, convenience, and quality of child care comes from cross-city differences in the availability of low-skilled immigrant labor. My analysis complements a growing literature, pioneered by Cortés and Tessada (2011), examining the relationship between immigration and labor supply decisions of highly skilled native women. Cortés and Tessada find that low-skilled immigration to large US metropolitan areas led to increases in the number of hours worked by women at the top of the wage distribution. Similar conclusions have been drawn for Spain (Farré et al. Furtado IZA Journal of Migration (2015) 4:19 Page 2 of 19 2011), Italy (Barone and Mocetti 2011), and Hong Kong (Cortés and Pan 2013). Using data from several countries, Forlani et al. (2015) find similar results and show that effects are larger in countries with less-supportive family policies. Fertility rates of high-skilled US-born women have also been shown to increase in response to immigrant inflows (Furtado 2015). Consistent with the labor supply impacts of immigration literature, Furtado (2015) finds increased likelihoods of women working 50 or more hours a week in response to immigration, but perhaps paradoxically, she also finds decreases in labor force participation rates, a result potentially attributable to women temporarily leaving the labor force upon giving birth but then working long hours upon returning. 1 Regardless of whether women respond to immigrant-induced decreases in childrearing costs by increasing labor supply and decreasing fertility, increasing fertility and decreasing labor supply, or even increasing both, lower childcare costs should make it less important to leave the labor force after giving birth. Thus, we should expect that in cities with larger immigrant inflows, the negative correlation between childbearing and labor force participation should become less strong. This is precisely the result in Furtado and Hock (2010). However, the problem with using correlations to measure the difficulty with which women combine their roles as worker and mother is that correlations cannot directly quantify changes in observable outcomes of individuals. In this paper, I start by considering the direct impact of immigration on the likelihood that a woman chooses to both give birth and participate in the labor market shortly thereafter, certainly a more tangible measure of mother-worker role compatibility. Next, I estimate the impact of immigrant inflows on the likelihood of childbearing, the likelihood of participating in the labor force, and the correlation between the two. The structure of the model allows me to then combine these estimates to construct an indirect measure of the impact of immigration on the joint likelihood of childbearing and labor force participation. I then decompose this effect into three components: the effect on childbearing, the effect on labor force participation, and the effect on the correlation between the two. In so doing, I can then determine how much of the increase in the joint likelihood of childbearing and working is attributable to a decrease in the tradeoffs women face when making fertility and labor supply decisions. The difficulty with any empirical analysis of immigration that exploits cross-city variation in foreign-born concentrations is that immigrants do not choose where to live randomly. They may be more likely to reside in areas with an industry mix more (or less) conducive to combining work and family for highly skilled women. It is also plausible that immigrants tend to move to areas with more demand for childcare services precisely because women in these areas want to both have larger families and to work. Following other studies in this literature, I take an enclave-based instrumental variables approach pioneered by Card (2001) which exploits historical settlement patterns of immigrants from different countries. Using US Census data from 1980 to 2000 in conjunction with 1970 data to construct the enclave-based instrument, I find that inflows of low-skilled immigrants between 1980 and 2000 resulted in a higher joint likelihood of childbearing and labor force participation among high-skilled women. These increases were a result of increases in fertility that were accompanied by reductions in labor force participation (LFP) rates. I find that that about 24% of the impact of immigration on the joint likelihood of childbearing and working was attributable to a weakening of the negative fertility-work correlation. This suggests that Furtado IZA Journal of Migration (2015) 4:19 Page 3 of 19 indeed low skilled immigration has made it easier for women to combine work and family responsibilities. Additional analyses suggest that these results are quite robust and are unlikely to be driven by omitted variable or selection bias. The remainder of the paper is organized as follows. In Section 2, I describe the model of decision making that underpins my investigation of childbearing and labor force participation patterns. A brief description of the data used in the analysis follows in Section 2. After presenting the main results in Section 3, I discuss how the estimated parameters may be interpreted. I also conduct specification checks concerning the validity of the estimation method and the extent to which geographic selection might affect the results. Finally, Section 4 provides additional discussion and concluding remarks. 2. Fertility and labor force participation decisions 2.1 The model Female employment and fertility decisions can be described using the standard bivariate probit model framework: C igmt ¼β1LSImt þω ′ 1vigmt þεC igmt ð1Þ L igmt ¼β2LSImt þω ′ 2vigmt þεL igmt;ð2Þ where C igmt and L igmt describe the desirability of childbearing and labor force participation for woman iwho is a member of age group gand living in metropolitan area min year t.As will be discussed in more detail below, we divide the sample into groups predominantly because correlations can only be calculated at some level of aggregation. Although we use age to define groups in our analysis, this model could be applied to groups based on other exogenous determinants of childbearing and labor force participation. The associated binary outcomes are C igmt and L igmt ,whereC igmt = 1 is observed if C igmt >0 and likewise for labor force participation. The presence of a child below the age of 1 in the household is used to measure childbearing. 2 There is no generally applicable exclusion restriction to identify the effect of childbearing on employment or vice-versa. Consequently, both equations have the same right-hand-side variables and yield estimates of the net effects of these variables on work and fertility outcomes. The vector of controls, v igmt , includes city, region-year, and age group fixed effects as well as other demographic variables. The two error terms, εC igmt and εL igmt , are distributed according to a joint normal distribution with a mean of zero. Even under the assumption that low-skilled immigrant inflows only affect childbearing and labor force participation decisions of high-skilled women by decreasing childcare costs, theory does not provide clear predictions for β 1 and β 2 . As described in detail in Blau and Robins (1988), women may respond to lower childcare costs by having an additional child given the reduction in the price of childrearing. Because of the highly time-intensive nature of caring for young children, however, labor force participation may, at least initially, decrease if women respond to lower childcare costs by having an additional child. On the other hand, women may also respond to decreased childcare costs by working more hours given that their net take-home pay increases when childcare costs decrease. However, if upon entering the labor force (as a result of decreased childcare costs) women start valuing their roles as breadwinners and become more ambitious in their careers, women who initially planned on having a second or third child may stop at one. In the end, the net Furtado IZA Journal of Migration (2015) 4:19 Page 4 of 19 impact of childcare costs, and hence low-skilled immigration, on fertility and labor supply decisions is an empirical question. While theory does not provide guidance on the expected impact of immigrant inflows on fertility and employment outcomes separately, it does suggest that immigrant inflows should make combining work and family life easier for women. If childcare is less expensive or more convenient, women should find it less important to leave the labor force upon giving birth, and women who join the labor force should find it less important to downwardly adjust future fertility plans. In addition to examining the propensities to work and to bear children, the standard bivariate probit model presented above enables an analysis of the tradeoffs women face when making fertility and labor supply decisions and how these necessary tradeoffs respond to immigrant inflows. If the error terms in equations (1) and (2) follow a bivariate normal distribution, ρgmt ¼corr εC igmt ;εL igmt  is, by definition, the tetrachoric correlation. The tetrachoric correlation, in this model, can be understood as the degree to which changes in childbearing—that are not a result of immigration and the other variables in the model—translate into changes in labor force participation (alternatively, the degree to which exogenous changes in labor force participation translate into changes in childbearing). Hence the tetrachoric correlation would, for example, determine the effect of an unintended pregnancy on desired labor supply or the effect of an increase in the local demand for high-skilled labor on the desirability of childbearing. Thus, ρshould be negative as is almost universally the case in the sample. Although the use of bivariate models to study behaviors is certainly not new, an innovation of this paper is to explore how the correlation is affected by low-skilled immigration. I use the parameterization ρgmt ¼β3LSImt þω ′ 3vgmt þegmt;ð3Þ where v gmt is a vector of characteristics of women in group gin metro min year t,ande gmt represents the un-modeled determinants of ρ. If an increase in low-skilled immigration results in less expensive, more convenient, or better quality market-based childcare services, β 3 should be unambiguously positive. That is, low-skilled immigration should dampen the negative latent correlation between childbearing and labor supply. In areas with more immigrants, women who exogenously have additional children should become less likely to leave the labor force, and women who exogenously enter the labor force should become less likely to decrease desired fertility. 2.2 Grouped estimation with instrumental variables Although I start with the individual-level data, the analyses are all conducted with metropolitan area-year-age group cells as the unit of observation. The reasons for creating these groups are twofold. First, the group-level model, which is based on a slight generalization of Amemiya’s (1974) grouped bivariate probit specification, allows for a straightforward application of instrumental variables. More importantly, grouping is necessary in order to calculate the tetrachoric correlation, which, like any other correlation, is not defined at the individual level. Linking the effect of immigration on the correlation to its effect on the joint probability of work and childbearing, an innovation of this study, would not be possible without the grouped model. Furtado IZA Journal of Migration (2015) 4:19 Page 5 of 19 The coefficients in the individual-level model presented in the previous section can be estimated by analyzing sample proportions and using group-level explanatory variables (v gmt ). Given the bivariate normal distribution of the error terms, the expected rates of childbearing and LFP follow univariate normal distributions: πC gmt ¼Φβ 1LSImt þω ′ 1vgmt  and πL gmt ¼Φβ 2LSImt þω ′ 2vgmt  ð4Þ Let pC gmt;pL gmt;and pCL gmt denote the observed proportions of the women in group gin metropolitan area min year tthat bear children, participate in the labor force, and do both, respectively. A first-order Taylor expansion around the expected values of the sample proportions results in the linear equations: cgmt ¼β1LSImt þω ′ 1vgmt þu1 gmt;ð5Þ ℓgmt ¼β2LSImt þω ′ 1vgmt þu2 gmt;ð6Þ where cgmt ¼Φ−1pC gmt  and lgmt ¼Φ−1pL gmt  denote the inverse standard normal cumulative distribution (normit) function applied to the observed rates of childbearing and LFP. Thus, we can estimate these equations with linear univariate models despite the bivariate structure of the overall model. Moreover, based on equation (3), the expression for the empirical analogue of the population tetrachoric correlation obtained from the data can be expressed as rgmt ¼β3LSImt þω ′ 3vgmt þu3 gmt:ð7Þ Equations (5)–(7) correspond to Amemiya’s (1974) equations (4.11)–(4.13), with the expression in (7) additionally relying on the parameterization of the tetrachoric correlation described above. The empirical analog of the tetrachoric correlation is significantly less straightforward to compute than the analogues of the expected rates of childbearing and labor force participation. It is calculated based on the population relationship: πCL gmt ¼FΦ−1πC gmt  ;Φ−1πL gmt  ;ρgmt  ≡GπC gmt;πL gmt;ρgmt  ;ð8Þ where πCL gmt represents the expected share of women who simultaneously bear children and participate in the labor force, and F(⋅) denotes the standard bivariate normal distribution function. Using the observed proportions (p C ,p L ,andp CL ) as analogues of the expected values in equation (8) allows me to calculate the empirical tetrachoric correlation, r gmt , based on the sample of outcomes. Although there is no closed-form solution for r gmt ,since F(⋅) is monotonic in the third argument (Tihansky 1972), we can apply a recursive binary chop algorithm to search for the value of r gmt that solves pCL gmt−Gp C gmt;pL gmt;rgmt  <ξ; where ξrepresents a pre-defined level of precision, which we set to 2 −50 . 3 Note that monotonicity of F(⋅) also implies that a higher value of ρwill, ceteris paribus, translate into a higher joint likelihood of childbearing and labor force participation. If there are any groups in which any of the dependent variables take on the same value across its members, the data become uninformative and it is not possible to estimate the empirical tetrachoric correlation. Consequently, I divide the sample of college-graduate Furtado IZA Journal of Migration (2015) 4:19 Page 6 of 19 women into only two broad age groups, women ages 22–32 and women ages 33–43, and include measures of the average characteristics of the group as explanatory variables. Even with these broadly specified categories, there were a few MSA-year-age group cells with all ones or all zeros. In these cases, I replace zeros with 0.001 and ones with 0.999 when calculating the normits and estimating ρ. The model described by equations (5) through (7) is estimated using three separate linear equations of the form ygmt ¼βLSImt þμmþμkt þμgþλmtIncControlmt þθ ′ xgmt þugmt;ð9Þ where yis one of the three dependent variables (c, ℓ,r). The low-skilled immigrant share of the overall working-age population immigration is denoted LSI. Metropolitan area-specific intercepts are indicated by μ m ,whileμ kt represents time fixed effects specific to the kth Census region. The variable IncControl denotes the log of income per capita among working-age male college graduates. Added to these variables are age-group fixed effects (μ g ) and a vector of demographic controls (x gmt ). The vector x gmt includes the share of women in each MSA-year-age group cell who are married and the corresponding proportions of the group that self-identify as being black and that self-identify as being a member of another non-white, non-Hispanic race. Each group-MSA-year cell is weighted by the estimated population of women represented by the cell. Robust standard errors are clustered by MSA. In the following sections, I describe the data as well as the instrumental variables strategy and some baseline results relating immigrant inflows to the likelihood that women have both recently given birth and are participating in the labor market. 4 I then come back to the model just presented to gain insight into the mechanisms whereby low-skilled immigration may exert its effects. Specifically, the model will allow me to separately estimate the impact of low-skilled immigration on: (i) the likelihood of bearing children, (ii) the likelihood of participating in the labor market, and (iii) the correlation between the two. Most importantly, however, the model will be used to indirectly calculate the effect of low-skilled immigration on the joint likelihood of working and giving birth, decomposing the total effect into changes in the marginal likelihoods and changes in the correlation between fertility and work. 3 Data In order to provide readers with a basic sense of what has been happening to women’s work and fertility decisions over time, I start by plotting the proportion of women of child-bearing age that both work and have given birth in the previous year (a 3-year moving average is plotted to smooth out year-to-year fluctuations). 5 As shown in Fig. 1, the joint rate of childbearing and labor force participation is small in absolute terms, which reflects the relative infrequency of childbirth. However, the joint rate among women ages 18–39 almost doubled between 1970 and 2000. Among college graduates it more than doubled, increasing from approximately 2.5 to 4.9%. My main analysis relies on data from the 1980–2000 Integrated Public Use Microdata Series (IPUMS) (Ruggles et al. 2010) of the US Census. 6 Additionally, the 1970 Census is used to construct the instrument for immigrant share. The sample consists of US-born non-Hispanic college-educated women between the ages of 22 and 42, inclusively. I focus Furtado IZA Journal of Migration (2015) 4:19 Page 7 of 19 on college-educated women both because they are more likely to use market-provided child care and because their labor market opportunities are less likely to be directly affected by low-skilled immigrant inflows. I do not include Hispanic women in the sample given their greater likelihood of being impacted by the mostly Hispanic low-skilled immigrant inflows for reasons unrelated to childcare markets. A potential difficulty in exploiting within Metropolitan Statistical Area (MSA), crossdecade variation in immigrant share is that MSA boundaries change over time. Some of these changes reflect seemingly arbitrary decisions of the U.S. Office of Management and Budget (OMB), but others are due to natural expansions or contractions of economic activity in the outskirts of an MSA. If immigrants are more likely to settle in expanding cities and women living in outer suburbs make systematically different fertility and labor supply decisions, the changing MSA boundaries may result in biased estimated immigrant share coefficients. To at least partially address this issue, I follow Cortés and Tessada (2011) and use only MSAs with the same codes in the IPUMS between 1970 and 2000. This does not perfectly resolve the problem in that counties represented in MSAs listed with the same code may still have changed over the years. However, because these counties tend to have small populations, they are unlikely to severely bias results. Table 1 presents descriptive statistics of the main variables used in the study. Given the nature of the analysis, all descriptive statistics in the table are constructed from the already grouped variables. The percent of women in the sample who have given birth in the previous year and participate in the labor market is 3.8. Given that 6.4% of women in the sample recently gave birth, we can conclude that 59.4% of the women who have recently given birth are employed. The number of high-skilled women in the population represented by each of the MSAyear-age group cells are used as weights when calculating the statistics in the table. In constructing the immigrant share variable, I divide the number of immigrants aged 18–64 0 0.01 0.02 0.03 0.04 0.05 0.06 Proportion College Graduates Full Sample Fig. 1 Proportion of women bearing children and participating in the labor force. Notes: The figure draws on data from the March Current Population Surveys (CPS), 1969–2001 (King et al. 2010). The sample is comprised of women ages 18–39. We define “childbirth”and “recent motherhood”“notes”based on the presence of an own-child less than or equal to 1 year old in the household. Each series of data has been plotted after applying a 3-year moving average to smooth out year-to-year fluctuations Furtado IZA Journal of Migration (2015) 4:19 Page 8 of 19 Table 4 Robustness checks of the IV estimates of the effects of low-skilled immigration on fertility and work outcomes Panel A Drop California Normit, birth rate Normit, LFP rate Tetrachoric, birth and LFP Normit, joint likelihood of birth and LFP Direct Indirect 12345 Share working age low-skilled immigrant (LSI) 3.438** −3.269*** 1.767** 3.601** (1.613) (1.099) (0.683) (1.493) Mean of Underlying Dependent Variable, 2000 0.0712 0.834 −0.331 0.0458 0.0458 Effect of Average Change in LSI, 1980–2000 0.0116 −0.0220 0.0482 0.00796 0.00775 Proportion Explained by Weakened Correlation 0.3 Number of Observations 648 648 648 648 Panel B Three age groups Normit, birth rate Normit, LFP rate Tetrachoric, birth and LFP Normit, joint likelihood of birth and LFP Direct Indirect 678910 Share Working Age Low-Skilled Immigrant (LSI) 2.278** −0.912* 0.930* 1.989** (0.902) (0.470) (0.516) (0.953) Mean of Underlying Dependent Variable, 2000 0.0701 0.834 −0.312 0.0451 0.0451 Effect of Average Change in LSI, 1980–2000 0.00494 0.00640 Proportion Explained by Weakened Correlation 0.213 Number of Observations 1062 1062 1062 1062 Panel C Marriage and LFP Normit, marriage rate Normit, LFP rate Tetrachoric, marriage and LFP Normit, joint likelihood of marriage and LFP Direct Indirect 11 12 13 14 15 Share Working Age Low-Skilled Immigrant (LSI) 0.109 −1.756*** 0.349 −0.732 (0.536) (0.578) (0.282) (0.673) Mean of Underlying Dependent Variable, 2000 0.578 0.834 −0.368 0.446 0.446 Effect of Average Change in LSI, 1980–2000 0.00129 −0.0138 0.0111 −0.00899 −0.00839 Proportion Explained by Weakened Correlation −0.104 Number of Observations 708 708 708 708 Estimates in Panel A were constructed without California. Panel B uses the full sample but instead of two age groups (22–32, 33–43), MSA-year-age groups are constructed using three age groups (22–28, 29–35, 36–43). Panel C reverts to the two age groups but replaces the incidence of childbearing with marriage rates. Results reported in the first three columns were estimated using one regression run on stacked data for each of the three outcomes. All models include time-varying region fixed effects, MSA fixed effects, age-group fixed effects, the log of income per male college graduate, and the following group-level characteristics: the proportion black, the proportion who are of another non-white race, and the proportion married. Each of the observation-cells is weighted by the population of women represented by the cell, and the robust standard errors in parentheses are clustered by MSA. Reported effects are the change in the underlying dependent variable that would be caused by the change in the share of working age low-skilled immigrants experienced by the average member of the sample. *p< .10; **p< .05; ***p< .01 Furtado IZA Journal of Migration (2015) 4:19 Page 15 of 19 traditional family values. This seems unlikely given that high-skilled women have higher fertility rates in cities with fewer immigrants (Table 1), but if it were true, my estimation strategy would falsely attribute changes in fertility and work outcomes to the rising share of low-skilled immigrants in the labor market. To explore whether this is likely to be problematic, I examine the relationship between marriage patterns and low-skilled immigration. If there happens to be stronger family norms in cities receiving more immigrants as a result of historical ethnic enclaves, then my analysis should generate a positive relationship between immigration and marriage rates along with an increase in the joint probability of marriage and labor force participation. This cannot be considered a pure placebo test given the possibility that committed couples respond to immigrant-induced decreases in childcare costs by having a baby but get married before childbirth. It turns out, however, that my estimation strategy does not yield any statistically or economically significant relationships between immigrant inflows and marriage. As can be seen in Panel C of Table 4, the IV estimate on marriage rates is statistically insignificant (p> 0.83) and trivial. The estimated effect on the joint likelihood of marriage and labor force participation is not only statistically insignificant (p> 0.27) but has a negative sign. Finally, I examine whether high-skilled native-born women move to cities with large foreign-born populations, perhaps to purchase less expensive household services. I test for this type of selection by estimating the following equation: lnNgmt ¼γLSImt þμmþμkt þμgþλIncControlmt þχlnTgmt þεgmt;ð12Þ where N gmt denotes the number of non-Hispanic female college graduates in age group g in metro area min year t. All of the right hand variables have been previously defined, with the exception of T gmt , which denotes the total number of non-Hispanic native females in the age-MSA-year cell. Equation (12) essentially tests for growth in the population of high-skilled females relative to the overall population of same-age females. Although these results are not shown in tables, instrumental variables regression analysis indicates an IV estimate of γof −2.36 with a corresponding pvalue of 0.26. This suggests that composition bias due to selective migration is not likely to be a problem for this analysis. 5 Conclusions The results in this paper suggest that immigration to the US between 1980 and 2000 increased the fertility of US-born college graduates of childbearing age. This rise in childbearing was accompanied by an increase in exits from the labor force. Most interestingly, low-skilled immigration resulted in a weakening of the negative correlation between fertility and work and a sizeable increase in the joint likelihood of childbearing and labor force participation. The structure of the model allows me to determine that about a quarter of the increase in the joint likelihood can be attributed to a weakening of the negative correlation between childbearing and labor force participation. Taken together, these findings indicate that low-skilled immigration substantially reduces the work-fertility tradeoff faced by educated urban American women. Kremer and Watt (2008) argues that migration of foreign domestic household workers may raise wages of low-skilled native workers relative to high-skilled workers and can create large fiscal gains for receiving countries. His model implies that if high-skill women respond Furtado IZA Journal of Migration (2015) 4:19 Page 16 of 19 to immigrant-induced better childcare options by entering the labor force or working more hours, the ratio of skilled to unskilled workers decreases, thereby driving down skilled wages relative to unskilled wages. Given that home production is not taxed and high-skilled women are often married to high wage men, these women will typically pay a high marginal tax rate when entering the labor market, thus generating large fiscal gains. Several analyses have concluded that high-skilled women do tend to work longer hours in response to immigrant inflows (Cortés and Tessada 2011; Barone and Mocetti 2011; Farré et al. 2011; Cortés and Pan 2013; Furtado 2015). This paper contributes to this literature by additionally showing that some women respond to better childcare options with increases in fertility and decreases in labor force participation. While the (most likely temporary) decreases in labor force participation imply smaller decreases in inequality and smaller fiscal gains than what is implied by Kremer and Watt's model, the increases in fertility might help sustain pension programs especially in countries with below-replacement fertility rates. Regardless of whether women choose to respond to immigrant inflows by increasing fertility or labor supply, this paper’s finding that tradeoffs decrease in response to immigrant inflows implies welfare gains for the women making work-life decisions. Endnotes 1 Cortés and Tessada (2011) estimates of the impact of immigration on labor force participation of high-skilled women in the US are also negative, but statistically insignificant. Barone and Mocetti (2011) estimate positive but insignificant effects in their study of Italy, while Farré et al. (2011) and Cortés and Pan (2013) find positive and statistically significant relationships for high-skilled females in Spain and Hong Kong, respectively. My results may differ from all of these given that my sample consists only of women of childbearing age. 2 Using older children to measure childbearing would be problematic because parameters of interest will be identified off of within-MSA differences in the size of the immigrant population across decades. Decisions to have older children are not likely to be affected by relatively recent changes in the immigrant population. 3 Stata code for this calculation is available upon request. 4 My measure of having given birth in the previous year is the presence of a child under the age of one in the household. Some of these children may have been adopted. Moreover, women who have given birth but are not residing with the child will not be counted in my measure of childbearing. 5 The figure draws on yearly data from the March Current Population Surveys (CPS), 1969–2001 (King et al. 2010). These data cannot be used for the main analysis because sample sizes are smaller and questions about immigration were only asked starting in 1994. 6 I do not use the more recent American Community Survey (ACS) data for several reasons. First, the ACS is conducted every year, but it is only a 1% sample of the population. Many researchers merge several years of ACS data to increase sample size, but this is problematic for the purposes of this paper because of my measures of fertility and foreign-born share. For example, if I were to use the 2007–2011 5 year sample, I would at least partially be using the foreign born population in 2011 to predict pregnancy decisions in 2006. Also, the Great Recession may have induced a fair amount of noise into women’s fertility and labor force participation decisions, making it difficult to estimate parameters precisely despite the increase in sample size. Furtado IZA Journal of Migration (2015) 4:19 Page 17 of 19 7 I use male income instead of female income because it is less likely to be affected by gender norms and childcare costs. 8 Just as in the previous section, I compute the APE for the marginal likelihoods of childbearing and labor force participation as the weighted average of the partial derivatives of (4) across the sample, with the share of high-skilled women represented by each age-MSA-year cell used as weights. Scaling the APE by the change in LSI experienced by the representative woman in our sample between 1980 and 2000 yields the reported effects. 9 For convenience, I have included this derivative in Appendix 1. Appendix 1 ⌢ F1¼ϕ ^ β1LSImt þ ^ ω′ 1vgmt  Φ ^ β2LSImt þ ^ ω′ 2vgmt  − ^ β3LSImt þ ^ ω′ 3vgmt  ^ β1LSImt þ ^ ω′ 1vgmt  ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1− ^ β3LSImt þ ^ ω′ 3vgmt  2 r 0 B B @ 1 C C A ⌢ F2¼ϕ ^ β2LSImt þ ^ ω′ 2vgmt  Φ ^ β1LSImt þ ^ ω′ 1vgmt  − ^ β3LSImt þ ^ ω′ 3vgmt  ^ β2LSImt þ ^ ω′ 2vgmt  ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1− ^ β3LSImt þ ^ ω′ 3vgmt  2 r 0 B B @ 1 C C A ⌢ F3¼1 2πffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1− ^ β3LSImt þ ^ ω′ 3vgmt  2 r exp −:5h^ β1LSImt þ ^ ω′ 1vgmt  2þ ^ β2LSImt þ ^ ω′ 2vgmt  2 −2β1LSImt þω ′ 1vgmt  ^ β2LSImt þ ^ ω′ 2vgmt  ^ β3LSImt þ ^ ω′ 3vgmt  1−β3LSImt þω′ 3vgmt  2 0 B @1 C A Competing interests The IZA Journal of Migration is committed to the IZA Guiding Principles of Research Integrity. The author declares that she has observed these principles. Acknowledgments This paper draws heavily from a working paper entitled, “Female Work and Fertility in the United States: Effects of Low-Skilled Immigrant Labor,”coauthored with Heinrich Hock. I am grateful to him for the many insights he provided especially with regard to the statistical model used in the paper. I would also like to thank Amelie Constant and three anonymous referees for their helpful comments. Responsible editor: Amelie Constant. Received: 26 May 2015 Accepted: 31 July 2015 References Adsera A (2004) Changing fertility rates in developed countries. The impact of labor market institutions. J Popul Econ 17(1):17–43 Amemiya T (1974) Bivariate probit analysis: minimum Chi-Square methods. J Am Stat Assoc 69(348):940–944 Baker M, Gruber J, Milligan K (2008) Universal child care, maternal labor supply, and family well-being. 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