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School and neighborhood: residential location choice of immigrant parents in the Los Angeles Metropolitan area

Abstract

This paper studies how immigrant parents value education for their children in the United States when making residential decisions. Parent valuation of education is examined through the differential effects of school quality on the residential location choices of households with and without children. The analysis relies on data from the 2000 Census and focuses on the Los Angeles Metropolitan Area. The results suggest that immigrant parents place a positive weight on school quality when choosing residences. The weight assigned to school is positively associated with household income and householder’s education. The paper further explores variation across immigrants to get at the potential economic mechanisms for differential valuation of school quality. Number of school-aged children in the household, selective migration, and potential returns to education may explain variation in the emphasis immigrant parents place on school quality in residential location choices.

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Notes

  1. A second-generation immigrant is someone who was born and raised in the destination but either one or both parents were foreign born.

  2. Tiebout (1956) suggests that competition among local jurisdictions would lead to the efficient provision of a series of local public goods, and individuals reveal their preferences by voting with their feet.

  3. API scores are produced by the California Department of Education to evaluate school accountability and the API Reports are publicly available to parents and guardians.

  4. In the U.S. education system, the common high school finishing age is 18.

  5. The density function is f(e)= exp[−e − exp(−e)].

  6. The household characteristics are included in the regression by interacting them with location characteristics. In a conditional logit model, variables such as household characteristics that do not vary across alternatives would be automatically dropped in the regression if included directly. However, their effects can be controlled for by interacting these variables with the characteristics of the alternatives.

  7. A short proof is as follows. Suppose e j is a function of S j . For simplicity, they are assumed to be linearly related, i.e., e j =f(S j )=c S j + u j , where c is a constant, and u j is an error term that is uncorrelated with school quality. Equation 6 could be rearranged as V h j = α 1h S j + α 2 S j c h d h + β h Z j + γ h c S j + γ h u j = (α 1h + γ h c)S j + α 2 S j c h d h + β h Z j + γ h u j . So the estimated main effect of school quality may be biased, but the estimate on the interaction between school quality and having children in the household is not.

  8. More details about the effectiveness of the propensity score trimming approach are discussed in Appendix A.1.

  9. Marital status, family size, and number of families in household may explain the spike in the propensity score distributions among households without children. When excluding the three variables from the regression, the distribution is much smoother and more bell-shaped.

  10. The bandwidth in the figure is 0.04. When narrower bandwidth is employed, all the lines show the same pattern but more noise.

  11. For the Los Angeles Metropolitan Area, the correlation between the district mean APIs (weighted by student enrollments) of elementary schools and those of high schools is .94, and the correlation between the district mean APIs (weighted by student enrollments) of middle schools and those of high schools is .95.

  12. As the APIs prior to 1999 are not available, I compare the district average APIs in the Los Angeles Metropolitan Area in the subsequent five years. The correlation between the district mean APIs (weighted by student enrollment) in 1999 and those in 2004 is over .95, indicating that school quality is quite stable over time.

  13. The Los Angeles Unified School District is very large and covers a number of PUMAs, while other school districts are usually smaller than or of similar size as PUMAs.

  14. Distance to U.S. is calculated as the number of air kilometers between home country’s largest city and the nearest U.S. gateway (Los Angeles, Miami, or New York). The data are from www.timeanddate.com.

  15. The information about nations’ official languages is from http://en.wikipedia.org/wiki/List/_of_official_languages.

  16. The national origin shares are calculated from the 2000 Census.

  17. Data source: www.dhs.gov

  18. I first calculate the ratio of the 90th percentile to the 10th percentile occupational income by year and country. Then I regress the year by country ratio on a set of country dummies and year dummies, using the U.S. and year 1990 as the omitted country and year.

  19. Only national origins that have no less than five observations in the sample of immigrant households with children under 18 are included.

  20. More details about the estimation of ”local” returns to education among immigrants are in the Appendix AA.2.

  21. The household income employed here is the income adjusted by household size. Both household income and householder’s educational attainment are standardized to have mean of 0 and standard deviation of 1 when interacting with location characteristics from now on. Hence, the estimated main effects of location attributes represent the weights assigned by an immigrant household with mean income and mean education.

  22. The income quintiles are formed among all the households in the Los Angeles Metropolitan Area based on the household income adjusted by family equivalent scale. The cutoffs would be same in all the analysis related to income quintiles in this paper.

  23. To balance the preferences toward non-school location characteristics of households with and without children, I first estimate the propensity for having children under 18 among the sample of immigrant and native movers in the manner discussed in the previous section and trim the sample using this propensity score. Then I estimate the propensity score for being an immigrant household and further trim the sample.

  24. More details are discussed in the Appendix A.4.

  25. Immigrant households generally have lower household income than natives. It is important to compare the location choices of the two groups when they face the same budget constraints.

  26. Four groups are considered: 1) both parents are immigrants; 2) the household head is an immigrant but the spouse is not; 3) the household head is native-born, but the spouse is not; and 4) both parents are native-born.

  27. As predicted by the literature, the origin-specific income inequality measure is negatively correlated with both household income and householders’ educational attainment. However, both correlations are low and around 0.1.

  28. To rule out the probability that the regression results are driven by the high share of Mexican immigrants in the Los Angeles Metropolitan Area, I re-estimate all the specification in Tables 10 and 11 excluding immigrant households from Mexico in my sample. The results are very similar.

  29. I use the full model specification in Table 6 to estimate the origin-specific weight placed on school quality, excluding origins with less than five households in the sample.

  30. Similar as the previous section, I run the regressions without Mexican immigrants. The results are not affected.

  31. The correlation between ”local” returns to education in the Los Angeles Metropolitan Area and the ”universal” returns to education estimated by Bratsberg and Terrell (2002) is about 0.7.

  32. Only 3 % of the immigrant households in the sample analyzed in this paper migrated to the U.S. prior to 1965. The majority of the immigrant sample entered the U.S. subject to the Immigration and Nationality Act of 1965.

  33. California’s Proposition 13, passed in 1978, mandates a property tax rate of one percent and limits its growth rate. At the same time, housing prices have increased dramatically in California. Accordingly, households who have owned a house in California for many years have a disincentive to move because of the higher property tax on the new home’s assessed market value they have to pay.

  34. The propensity score is estimated among immigrant households with children aged 12–18 and immigrant households with no children under 23 using the same set of household characteristics presented in Table 2. I drop the observations is the propensity score lower than 0.1 or higher than 0.9.

  35. Possible scores on the SAT range from 400 to 1600 in the 1998–1999 academic year.

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Acknowledgments

I am especially grateful to Julie Cullen for her precious comments and encouragements. I thank Eli Berman, Gordon Dahl, Gordon Hanson, and two anonymous referees for their insightful suggestions. I also thank Erin Wolcott and various seminar participants at the Oikos Young Scholars Economics Academy, the University of California, San Diego, and the University of South Carolina for their helpful discussions. All mistakes are my own.

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Correspondence to Crystal Zhan.

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Appendices

Appendix A.1: Parents v.s. non-parents

The empirical analysis on the weight immigrant parents assign to school quality in residential location choice relies on the assumption that unobserved location attributes affect households with and without children similarly. I employ propensity score trimming to balance the characteristics of the two types of households so that their preferences toward non-school location attributes would align better.

To justify the effectiveness of this approach, I explore how results vary as I change the cutoffs of propensity score trimming based on the model specification in column 5 Table 5. Table 13 reports the estimates on the interaction terms between neighborhood characteristics and the child indicator. Besides the school quality, parents and non-parents display differential weights on a few attributes, including neighborhood age structure, unemployment rate, education level, homeownership, crime rate, metro stations, and number of colleges. Nevertheless, the majority of the differences result from residential sorting and different demographic characteristics of households with and without children. The preferences toward non-school amenities, namely, house features and public goods provision, do not differ much. In general, when trimming the sample to a tighter range of propensity score, the weights assigned to non-school neighborhood attributes by the two types of households converge more. Yet the parent-non-parent difference in the value on school quality is insensitive to the changes in trimming.

Table 13 Residential location choice: parents v.s. non-parents

These evidences help bolster that trimming is a useful way to balance the sample on preferences and reassure the assumption that households with and without children value unobserved neighborhood characteristics is plausible. I use the trimming from 0.1–0.9 in the main text to have my sample more representative for the immigrant population in the Los Angeles Metropolitan Area.

Appendix A.2: Local returns to education

The origin-specific “local” returns to education among immigrants in the Los Angeles Metropolitan Area are estimated in the same manner as Card and Krueger (1992) and Bratsberg and Terrell (2002). The estimation proceeds as follows:

$$ \ln w_{ij}=\theta X_{i}+{\sum}_{j}\eta_{j}D_{ij}\cdot edu_{i}+\varepsilon_{ij} $$
(A1)

where w i j denotes the weekly wage of immigrant i born in country j; X i is a vector of socioeconomic characteristics, including age and its square, English fluency, marital status, health status, year of immigration, and county of residence; D i j is a binary indicator which is equal to one if the immigrant was born in country j and zero otherwise; e d u i is the years of schooling of immigrant i; and ε i j is the stochastic error term. The parameter η j measures the value of the Los Angeles labor market placed on a year of schooling of immigrants who originate from country j. I examine male immigrants aged 35–54 and currently employed in Los Angeles Metropolitan Area. Only countries of origin with at least 10 individuals that satisfy the criteria are included. There are 31,326 immigrants from 95 countries in the sample accordingly. The equation is estimated through weighted linear squares, using the Census person weight as weight.

Appendix A.3: Robustness checks

A.3.1 Choice of private schools

In considering the above results, one concern is whether the value placed on school quality is affected by the omission of private school choices. Private schools serve as a substitute for public schools to households with children, and partly break the strict link between school choice and residential location (Hanushek et al. 2011). It is possible that parents who have sent or plan to send their children to private schools would value public school quality less when deciding where to live.

Therefore, I re-estimate the conditional logit regressions in the previous sections by including the fraction of private school enrollment among households with children in each PUMA and an interaction between this fraction and the child indicator. Since limited information on private schools in the Los Angeles Metropolitan Area, such as their quality and locations, is publicly available, it is hard to incorporate private school choices directly into the analysis. I use the percentage of households who send children to private schools as a proxy for the propensity that households living in a certain area choose private schools over public schools. The correlation between the API and the fraction of households who choose private school is 0.6, so that private schools tend to be located in areas with good public schools.

Table 14 reports the regression result when private school choices are taken into account. Compared to the estimates in Table 5, the coefficient on the API-child interaction term increases slightly, suggesting the availability of private school options may mitigate the importance of public school quality in residential choices.

Table 14 School quality and residential location choices: robustness checks

A.3.2 Mischaracterized choice sets

A.3.2.1 Naturalized citizens

One concern is whether the choice sets of immigrant households have been mischaracterized. Because the Census surveys all the foreign-born individuals in the United States, illegal immigrants and temporary migrants are also included. Due to their immigration status, illegal immigrants have limited access to certain public goods. Temporary migrants, such as those on a student visa, are very likely to relocate back to their home countries after a certain period. Borjas and Bratsberg (1996) find that about one-quarter of the foreign-born population in the U.S. emigrated after 10 years, and argue that return migration may have been planned as part of an optimal life-cycle residential location sequence. A foreseeable tendency to move would alter the calculus in residential location decisions.

Therefore, as a robustness check, I focus solely on naturalized immigrant households in this section. These people may be more comparable to natives and are less likely to leave the country (Hook and Zhang 2011). They may also be better informed in their selection of residential locations. There are 1,511 households with householders being naturalized citizens, making up about 18 % of the trimmed sample of immigrants. On average, these households are wealthier and better educated than other immigrant households, whereas the fraction of households with children is slightly higher. As reported in Table 14, the interaction effect of school quality on naturalized citizens with children is of similar scope.

A.3.2.2 Householders not in school

In the trimmed sample of immigrant households, about 11 % of household heads are still in school. Compared to the sample, these householders are significantly younger, better educated but less wealthy. Among them, 23 % have children under 18 years of age. It is likely that this group is mainly composed by immigrants who migrate to the United States for higher education. For ”student” families, the residential location choice is more restricted by the location of school/college, especially for the households with lower income. It is difficult to disentangle values of education for children or for the parents themselves. As discussed above, the ”student” families may also have a higher propensity to migrate back to their source countries.

Hence, I estimate the parent-non-parent difference in weight assigned to school quality only among households whose heads are no longer in school. The regression produces very similar results.

A.3.3 Differential unobserved preferences

A.3.3.1 Prime-aged householders

The preferences toward location attributes, especially local amenities may be associated with the age of householders. For instance, seniors may have a greater demand for medical care. Yet trimming by the propensity to have children under 18 may not perfectly balance the sample so that households with and without children would have similar views about non-school location characteristics. Accordingly, I restrict the sample to households with household heads aged 30 to 54. This age group is likely to have children, and their preferences toward location attributes other than public schooling are more likely to be homogenous.

Table 14 presents the estimates for households with prime-aged householders only. The estimated interaction effect of school quality on households with children is noticeably larger than the one estimated using households of the whole age range, and stays statistically significant.

A.3.3.2 Potential parents-to-be

The identification of the key parameters in the paper relies on comparison between households with and without children. Yet it is possible that certain immigrant households without children may consider school quality the same way as those with children if they are similarly motivated. One group of potential candidates with similar motivations are households planning on fertility. Even if these households do not have a current demand for schooling services, they may take their future needs into account when deciding where to live. Treating these households the same as other household without children underestimates the value placed on school quality by households with children.

Therefore, I define a group of ”potential” parents-to-be by the householder’s marital status and age. Specifically, married couples in the childbearing age, i.e. 20–40, are considered as the most likely to have children in the near future. I generate a dummy variable for this group, and interact it with the API. Table 14 presents the results when this additional interaction term is included. As expected, parents-to-be place significantly higher value on schools than other households without children. If I allow the weight assigned to school quality by parents to vary by the age of their children, parents-to-be resembles households with children between 6 and 12 in regard to the weight placed on school quality. When the case of parents-to-be is considered, the interaction effect of the API appears to be higher on households with children.

A.3.3.3 Homeowners v.s. renters

When households purchase a house, they are likely to choose locations with good neighborhood schools even if they do not have children for two reasons. First, housing price is closely linked to school quality (Black 1999; Kane et al. 2006). So the value of the properties in good school districts is less likely to depreciate. Second, compared to renters, the cost to move for homeowners would be higher. When choosing residential locations, homebuyers may take their long-term plans into consideration.

The difference between homeowners and renters complicates the interpretation of the previous results. This section thereby examines the renters only. In general, renters have lower income than homeowners. Given lower mobility cost, the residential locations of renters may better reflect their current demands for as well as the trade-offs among local amenities. The regression results are presented in Table 14. The parent-non-parent difference is positive and significant among renters, but smaller compared to the whole sample, which might be a result from the lower income.

A.3.4 Differential mobility

A.3.4.1 Out-of-state movers

The lock-in effect of Proposition 13 in California results in differential incentives to relocate among households who moved within California and those who moved across states.Footnote 33 At the same time, out-of-state movers are more likely to undergo a move-inducing shock (Thomas 2011). Thus, this section examines out-of-state movers who are less likely to have been subject to Proposition 13 lock-in. Looking at out-of-state movers would also address the problem linked with households that move right across the border line of the Los Angeles Metropolitan Area because of some endogenous changes in the preferences over local public goods provision.

This group composes about 83 % of the trimmed sample, and about 80 % of them were abroad one year ago. Estimates from the out-of-state movers are reported in Table 14 and are very similar to those obtained from the sample including within-state movers.

A.3.4.2 Movers within Los Angeles metropolitan area

Since I restrict the sample to households who moved to the Los Angeles Metropolitan Area from outside the area with the past five years, the sample includes a sizable fraction of new immigrants, i.e. immigrants who migrated to the United States in the past five years. One concern is that when the immigrants first arrived in the U.S., their residential choice might be highly restricted by their job locations, family and/or friend ties. Therefore, it may provide more insights to examine immigrant households who migrated within the Los Angeles Metropolitan Area in the past five years because these people have spent some time in the area and gained more information about neighborhoods and school districts.

However, the main problem to study the local movers is that, if the Tiebout sorting is effective in the first place and the local amenities stay the same, people move again only because their preferences over public goods change. For example, households may move to neighborhoods with good school when their children reach school age, whereas the empty-nest movers do the opposite to reduce the exposure to local school spending. It is hard to believe that these two types of movers would value the unobserved location attributes the same.

Accordingly, I focus on immigrant households with children aged 12–18 and use immigrant households with no children under age of 23 as the baseline. Supposedly, neither group moves within Los Angeles because of changes in the demand for schooling services. Therefore, for whatever reason they moved, they are more likely to share a similar view over the unobserved neighborhood characteristics. I trim the sample by the propensity score approach to better balance their preferences.Footnote 34

The results are presented in Table 15. In general, when financially capable, immigrant households with secondary-school-aged children tend to locate in areas with better public schools when moving within the metropolitan area.

Table 15 School quality and residential location choices: local movers

A.3.5 Alternative school quality measure

Another school quality measure that is commonly available to the public is the SAT score. Very often, real estate agencies make the information regarding a school district’s average SAT score available to potential homebuyers. I use the school average SAT score during the 1998–1999 academic year provided by the California Department of Education Policy and Evaluation Division and aggregate the school averages to the PUMA level in the same manner as I aggregate the API. The summery statistics of the SAT scoreFootnote 35 are presented in Table 3. The correlation between the API and SAT score is .88.I employ the SAT score instead of the API in the regression and the results are shown in Table 14. The interaction between the SAT score and the child indicator is positive and significant. The marginal effect calculated according has similar magnitude as the marginal effect reported in Table 5.

Appendix A.4: Immigrants v.s. natives

In order to make the choice sets of immigrant and native households more comparable, I employ the propensity score method, and estimate the propensity score for being an immigrant household from observable household and householder characteristics using a probit model:

$$ \Pr \left( img_{h}=1\right) ={\Phi} \left( \lambda X_{h}+u_{h}\right) . $$
(A2)

X h represents observable household characteristics, including household type, household income, family size, number of families in a household, homeownership, linguistic isolation, whether there are children under 18 householder’s gender, marital status, educational attainment, school attendance, and race. u h is the unobserved characteristics related to being an immigrant. I drop observations with estimated propensities below 0.1 and above 0.9 as before.

Table 16 presents the summary statistics for the natives. Compared to immigrants, native households have higher income, higher education, less children, and smaller families. The two groups have distinct racial compositions: the majority of natives are White, whereas Blacks and Hispanics comprise sizable proportions; yet the majority of immigrants are Hispanics and Asians.

Table 16 Summary statistics for natives vs. immigrants

Figure 4 depicts the distribution of propensity score for being immigrant among both the native and immigrant households. The strongest predictors here are language isolation and race, which may be the culprits for the spike at the right tail of the propensity score distribution of immigrants. Figure 5 compares the distributions of household characteristics that may distinguish immigrants from natives and also affect location decisions by the propensity score to be an immigrant. Three characteristics are examined: adjusted household income, linguistic isolation, and whether there are children under 18 in the household. The propensity score appears to match the three characteristics pretty well.

Fig. 4
figure 4

Propensity score for being an immigrant household

Fig. 5
figure 5

Family characteristics by propensity score

I trim the combined sample of movers by the propensity score for having children under 18 and the propensity score for being immigrant in the household sequentially. The last two columns in Table 16 report the summary statistics for the trimmed sample of natives and immigrants respectively. After trimming, the characteristics of immigrant and native-born households converge to a more or less extent.

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Zhan, C. School and neighborhood: residential location choice of immigrant parents in the Los Angeles Metropolitan area. J Popul Econ 28, 737–783 (2015). https://doi.org/10.1007/s00148-015-0545-0

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Keywords

  • School quality
  • Residential location choice
  • Immigrant household
  • Discrete choice model
  • Selective migration
  • Returns to education

JEL Classification

  • J61
  • I2
  • R2