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    • To verify the paradox within the longitudinal data

    2018-11-05

    To verify the paradox within the longitudinal data, I estimate a linear probability model that relies on a comprehensive set of individual and contextual controls to study the conditional differences in birth outcomes between immigrants and natives. Formally, I consider the following model:where the subscripts G1 and G2 represent the first and second generations, respectively. The parameter is the birth outcome (such as birth weight, incidence of low birth weight, etc.) of the second-generation child i (for both females and males), whose mother resided (or delivered) in zip code z at time t. The variable is a dummy equal to one when the first-generation woman delivering between 1970 and 1985 was born in Cuba, Mexico, or Puerto Rico. The set of individual socio-demographic characteristics of the first-generation mothers is delineated in , including education (high school dropout, high school graduate, some college, and college or more), marital status, parity, race, age dummies (in Florida, the mother age is not available for the apexbio dilution 1970--1985), an index of the adequacy of prenatal care based on the month in which prenatal care began, father age (quadratic), father education (high school dropout, high school graduate, some college, and college or more), child gender, and type of birth (singleton vs. multiple birth). Information on parental education, age, marital status and parity is not available in all years under analysis (see Table 1). To retain the sample size, I assign a specific value to individuals with missing information and include indicators for missing information on parental education and age, marital status, and parity. Note that as in the empirical analysis I control for education using four educational dummies, a woman with missing education receives zero for all education dummies and then has a dummy variable equal to one for missing information on education. Finally, I control for both time and zip code fixed effects. Next, I turn to the analysis of the linked sample and analyze whether these differences persist over time and whether they are transferred to the children of third-generation immigrants. Formally, I estimate the following model:where the subscripts G1, G2, and G3 represent the first, second, and third generations, respectively. The parameter is a birth outcome of the third-generation child (for both females and males), whose mother resided (or delivered) in zip code z at time t and all variables are as previously defined.
    Results Table 2 presents the matching rates for the main racial and ethnic groups in the sample. Despite the high rate of matching, the linked sample is not representative of women (men) born between 1970 and 1985. The final sample includes 1,355,896 (46%) of the 2,952,909 female children born between 1970 and 1985 in California and Florida. This reflects the reality that not all the women born in California and Florida between 1970 and 1985 were still living in those states between 1989 and 2009 and that not all these women became mothers before 2009. These results are consistent with the mobility and fertility patterns found using other datasets. In particular, the Natality Detail Data, which contain information on the mother state of birth and the state of birth of the child, indicate that approximately 13.2% of women born in California and Florida between 1970 and 1985 had a child in a different U.S. state before 2004 (the last year for which both the information on the state of birth of the mother and the state of birth of the child are available in this database). According to the American Community Survey (2010), we know that approximately 37% of women born in California and Florida between 1970 and 1985 had not had a child by 2009. Data problems such as misspelled or missing information account for the remaining attrition. Although these descriptive statistics provide evidence of selection on sociodemographic characteristics (see column 3), the differences in initial health endowments between linked and non-linked observations, if anything, suggest that the linked sample has a slightly lower incidence of low birth weight. A 100-g increase in birth weight only increases the probability of a subsequent observation by 0.6%. However, if the mother was born with a weight below the 2500 gram threshold, hydrostatic skeleton is 15% less likely to be linked. The lower incidence of low birth weight (LBW) in the linked sample can be explained by higher rates of infant mortality, higher probabilities of returning to the family country of origin (“salmon bias”), or by a lower probability of having a child among those children born with poor health outcomes (Abraido-Lanza, Dohrenwend, Ng-Mak, & Turner, 1999).