Immigration, community composition, and local public goods*

Immigration, community composition, and local public goods∗ Alix Peterson Zwane†

David L. Sunding‡

March 28, 2006

Abstract The paper uses county-level data from California to test whether community composition is systematically related to spending on local public goods. The focus of the paper is the Immigration Reform and Control Act (IRCA) of 1986, which brought roughly 400,000 new immigrants to California. This reform created exogenous variation in the ethnicity and legal status of residents of California counties and had the greatest impact in counties with the most demand for agricultural labor. In a similar fashion, IRCA changed the share of revenues from property taxes in these counties since farm workers typically pay less in property taxes than sales taxes. Differences in IRCA treatment intensity across counties allow us to address concerns that Tiebout sorting may be the cause of observed change in spending on local public goods. The econometric estimates indicate that the IRCA reforms resulted in an eight percent decline in local public good spending per capita on average, which is about equal to county average spending on public health services and a similar fall in the fraction of revenue from property taxes. Using an instrumental variables strategy, a one percent decline in the largest ethnic group’s population share is estimated to result in a five percent decline in per capita spending on local public goods, controlling for income. JEL classification: H41, J43, J15 Key words: local public goods, ethnic diversity, immigration, California ∗

We thank Bill Easterly, Ethan Ligon, Phillip Martin, Jeff Perloff, Elisabeth Sadoulet, seminar participants at UC Berkeley and the 2004 AAEA meetings, and especially Ted Miguel for helpful comments and suggestions. Amrita Nangina provided able research assistance. Support from the Giannini Foundation for Agricultural Economics is gratefully acknowledged. All remaining errors are our own. † University of California Berkeley. Department of Agricultural and Resource Economics. 207 Giannini Hall #3310. Berkeley, CA 94110, (510) 642-7628 (phone), (510) 643-8911 (fax), [email protected]. ‡ University of California Berkeley. Department of Agricultural and Resource Economics. 207 Giannini Hall #3310. Berkeley, CA 94110, (510) 642-8229 (phone), (510) 643-8911 (fax), [email protected]. Corresponding author.

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Introduction

Low levels of spending on local public goods may directly affect community welfare and prospects for long-run economic development. There is a growing body of empirical work that investigates whether local public good spending is related to measures of community composition such as income or asset inequality and ethnic diversity (Miguel & Gugerty forthcoming, Vigdor 2004, Alesina, Baqir & Easterly 1999, Cutler & Glaeser 1997, Poterba 1996, Glaeser, Scheinkman & Schliefer 1995, Cutler, Elmdorf & Zeckhauser 1993). In the U.S., nearly all of this work is motivated by adverse outcomes in racially polarized cities and suggests that public good provision is lower in more diverse or polarized communities.1 Despite the literature’s attention to ethnicity and public good outcomes in urban areas, rural communities in many U.S. states are at least as heterogeneous as inner cities and also can have difficulty providing public goods. For example, the Central Valley region of California has been characterized as a region of “poverty amid prosperity” (Martin & Taylor 1998). The percentage of persons living in poverty in Fresno and Tulare counties is comparable to that in metro Baltimore or Philadelphia. A larger fraction of families live in poverty in these counties than in Mississippi, Louisiana, or West Virginia (U.S. Department of Commerce, Bureau of the Census 2000). Just as Alesina, Baqir & Easterly (1999) note that popular discussions often compare American cities to “Third World countries,” similar language can be found in the popular press when discussing California’s Central Valley (Kasler 2000). Both poverty rates in the Central Valley and the region’s ethnic diversity are closely related to the region’s concentration in agriculture. Three of the four poorest counties in California, as 1

In the context of developing countries, ethnic diversity and measures of relatively low social capital appear to be correlated with adverse economic and institutional outcomes in cross-country data (Easterly & Levine 1997, Knack & Keefer 1997, Mauro 1995).

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measured by the fraction of families living in poverty, are in the top ten nationally by value of agricultural production per square mile (U.S. Department of Agriculture/ National Agricultural Statistical Service (USDA/NASS), California Statistical Office 2000, U.S. Department of Commerce, Bureau of the Census 2000). About 800,000 farmworkers and their families, who make up much of the Valley’s poor, are largely foreign-born Latinos (about 77 percent of farmworkers in California are foreign-born), many of whom are not authorized to be employed in the U.S. (approximately 53 percent of farmworkers lack work authorization) (U.S. Department of Labor 2005). Concerns about reverse causality due to Tiebout (1958) sorting or omitted variable bias in which an unmeasured third factor affects both community composition and observed spending levels mean that it is often difficult to interpret empirical evidence on the question of whether ethnic diversity or other kinds of community heterogeneity reduce spending on local public goods. In this paper, we propose a novel identification strategy to address these potential problems. The Immigration Reform and Control Act of 1986 (IRCA) created an exogenous increase in the number of Hispanic agricultural workers in California and allowed previously illegal residents to regularize their status and potentially assume a higher profile within their communities. We examine local public good expenditure before and after this policy was implemented across counties with different levels of demand for agricultural labor.2 Because of the particular nature of the immigration reform, this corresponds to examining how expenditure differed across counties with varying levels of IRCA "treatment" intensity. We are able to rely on the short-run reduced-form relationship between 2

Card (1990) and Saiz (2003) also study impacts of immigration shocks, estimating the impact of the Mariel Boatlift on labor and housing market outcomes in Miami. The effect of IRCA on farm labor market outcomes and, indirectly, agricultural commodity prices has been studied by Tran & Perloff (2002), Gramlich, Jarrett & Duffield (1992) and Taylor & Thilmany (1993). This work is largely motivated by testing whether the stated goal of IRCA– creating a smaller, more legal farmworker labor force–was achieved. We discuss briefly assessments of IRCA in this respect later in this paper. Our identification strategy is related to that used by Bansak & Raphael (2001) to investigate whether the penalties imposed on firms for hiring illegal immigrants as a result of IRCA resulted in discrimination against Latino workers. They use that fact that penalties for hiring undocumented workers were phased-in at different rates in the agricultural and non-agricultural sectors to create differential treatment groups.

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IRCA program intensity, by county, and public good expenditure to draw causal inferences about the impact of the program. We also present instrumental variables results in which we instrument for the population share of the largest ethnic group in a county (a linear measure of ethnic diversity) with IRCA program intensity and then estimate the causal relationship between this measure of community composition and local public good expenditure. Just as IRCA affected the ethnic composition of California counties, it may have made agricultural counties poorer, leading to lower spending on local pubic goods. In order to attribute the impacts of IRCA on local public good expenditure to ethnic composition effects as opposed to income effects, we must control for income in our regression analysis However, the same logic that makes the IRCA treatment intensity an appropriate instrument for county ethnic composition implies that the IRCA variables are also correlated with current income levels. In order for our instrumental variables strategy to be valid, we must have a sufficient number of excluded exogenous variables to account for this fact. By expanding our set of excluded exogenous variables to include an additional variable correlated with income per capita (the average income of neighboring counties), we are able to control for income when estimating the impact of ethnic diversity on local public goods spending. We estimate that in California counties the IRCA reforms resulted in an eight percent decline in local public good spending per capita on average. In dollar terms this is about equal to county average spending per capita on public health services. In findings that are similar to those identified in urban contexts, we estimate that more heterogenous communities choose to supply lower levels of local public goods. The impact of a decline in the population share of the largest group on local public goods spending is economically significant; we estimate that a one percent decline in the largest group’s share, which may easily occur over period of a few years in this context, results in

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a five percent decline in per capita spending. A strength of the identification strategy used in this paper is that endogenous residential sorting on the basis of demand for public goods is unlikely to be the source of the short-run effects that we estimate. IRCA immigrants in the agricultural sector did not choose their location on the basis of the bundle of local public goods available, but rather on the basis of agricultural labor demand. Relocation decisions by native-born residents, which would affect the composition of the electorate, are likely to have happened only after a period of delay, as IRCA was largely unanticipated. The fact that IRCA beneficiaries were not eligible to vote until at least 1994 is also important for interpreting our results. While it is certainly possible that these new residents had systematically different tastes for local public goods than existing residents of California, or that, as migrants with short time horizons, they had lower demand for services, these preferences could not be translated into policy via the expression of preferences at the ballot box. Our results instead shed light on the preferences of pre-existing voters for funding goods that benefit other groups besides their own. The observed negative "diversity effects" that we estimate cannot be a result of the mechanism proposed by Alesina, Baqir & Easterly (1999) in which voters’ differing preferences, by group, drive declining overall spending as communities become increasingly heterogeneous. Rather, our findings are consistent with a model in which voters exhibit "within group affinity" (Vigdor 2004, Vigdor 2002) or "discriminatory community preferences" (Cutler, Elmdorf & Zeckhauser 1993) and vary their desired levels of public spending in response to changes in community composition. Our ability to differentiate between these mechanisms is another contribution of this paper. Besides ethnicity, another dimension along which SAW program participants differed systematically from other residents of California is their housing choices. Farmworkers are relatively likely to live in substandard units (i.e., mobile homes or units that are lacking major appliances) and

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crowded conditions (Housing Assistance Council 2001). They are also disproportionately likely to live in employer-provided housing, with rent deducted from the agricultural wage (U.S. Department of Labor 2005). Thus, even after controlling for income, we expect that SAW program participants paid a greater fraction of their tax bill through sales taxes than existing county residents. Elected representatives chosen by citizens continue to determine the use of the discretionary portion of these revenues. This distinction between non-voting, non-property-owning SAW participants and other county residents suggests a second channel by which IRCA may have affected local public good expenditures that we can identify econometrically and which is relatively under-explored in existing research. "Homevoter" citizens may choose tax burdens and spending packages that increase their private benefits rather than the entire community’s well-being or wealth (Fischel 2001). Our econometric results suggest that the greater the fraction of county revenue from property taxes, which was affected by IRCA in the same way as ethnic composition, the greater the level of public good expenditure per capita. That is, property tax revenues (generated disproportionately from citizen property-owners) are more likely to be used to fund productive public goods, which presumably affect property values, relative to other taxes and fees paid by property owner voters and non-owners/ non-citizens alike. The effect we identify here is small relative to that identified for ethnic diversity and more imprecisely estimated, but it remains economically significant; we estimate that the elasticity of local public good expenditure with respect to the fraction of county revenue collected from property taxes is about 0.4. While the main focus of this work is the relationship between community composition and public good spending, our findings also bear on some unintended consequence of immigration reforms like IRCA that include an amnesty component. Amnesties for immigrants that result in new residents may impact public service provision, with potential long-run implications. We show, for example,

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that residents of counties that experienced the greatest intensity of IRCA were also more likely to increase their vote share for the Republican party in the 1988 and 1992 presidential elections, relative to the previous election. We discuss briefly the implications of our findings for future immigration policy in the conclusion of the paper. The remainder of this paper is organized as follows: Section 2 reviews several models that predict a correlation between community composition and local public good provision. We discuss which of these models our econometric estimation is able to test. Section 3 outlines the history of IRCA. Section 4 presents our econometric strategy and the data we use for estimation. Results are presented in section 5. Section 6 concludes.

2

Models of community composition and local public goods

Several alternative models have been proposed as a means of understanding how community demographic composition may affect local public good outcomes. In particular, these models have been used to motivate the econometric estimation of equations in which measures of ethnic diversity explain expenditure levels.3 We review these models briefly in this section, and discuss how an evaluation of the impact of the IRCA immigration reforms can be used to test these models. Community composition may affect local public good provision if voters have different preferences over public services (Alesina, Baqir & Easterly 1999, Alesina & La Ferrara 2000). When these preferences are correlated with demographic characteristics like ethnicity or citizenship status, then increasing demographic diversity implies reduced provision of local public goods. Individuals with divergent preferences, anticipating that spending packages chosen by the median voter will differ P In this context, ethnic diversity may be measured as a fractionalization index (calculated as 1 − i (Racei )2 and defined as the probability that two people picked at random in a community will be of different ethnicities) or as the population share of the largest ethnic group (Miguel & Gugerty forthcoming), a linear measure of homogeneity. 3

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relatively substantially from their own preferred outcome, chose lower levels of taxation and fewer services. Divergent preferences over certain classes of public goods and services may be expected, especially if service provision involves choices about social or linguistic factors (e.g., the language of instruction in schools or policing models). Alesina, Baqir & Easterly (1999) emphasize that many other public services may have a non-obvious ethnic dimension, if, for example, choices must be made about the route that roads or public transportation will follow. However, there are certainly classes of public services for which it is difficult to understand why ethnic groups’ preferences would significantly diverge, such as water and sewerage infrastructure. Even with identical tastes for public services, diverse communities may choose lower levels of public good expenditure if voters exhibit "within group affinity" (Vigdor 2004, Vigdor 2002) or "discriminatory community preferences" (Cutler, Elmdorf & Zeckhauser 1993). In this class of models, individual utility is a function of consumption, public goods, and community welfare, where the welfare of members of different groups within the community may be weighted differently (Luttmer 2001). When individuals prefer that benefits accrue to members of their own group, they choose lower levels of public services when they find themselves in relatively diverse communities. In the event that the enforcement of social sanctions is less costly within groups than across groups, and such sanctions are important for determining the quality of public services, more diverse communities may choose lower levels of public good expenditure even when preferences are identical and within-group affinity is absent. Miguel & Gugerty (forthcoming) show that in a rural African setting, social sanctions are critical for overcoming collective action problems; thus networks and norms that exist within ethnic groups, but which may be lower or missing across groups, result in higher levels of spending on public goods in less diverse communities. Similar models may be

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appropriate in certain contexts in developed countries, such as participation in local school boards or parent-teacher associations. Community composition may also be linked to local public good outcomes because community demographics result in differential benefits across groups. In developed countries, a salient dimension along which the benefits of spending, and thus participation in collective action, may differ is homeownership. DiPasquale & Glaeser (1999) develop a model in which homeowners are disproportionately willing to invest in local amenities because the benefits from these investments are capitalized into home values. In addition, ownership creates a barrier to mobility, so the benefits of investments are consumed over a longer period time by owners, relative to renters. This implies that in communities with more homeowners, amenity provision, either privately supplied or supplied through taxation, may be higher. Models of "homevoters," in which voters make taxation decisions on the basis of expected impacts on property values, have similar implications (Fischel 2001). When the benefits of some public services are capitalized in home values but taxation can also be used to redistribute wealth from owners to non-owners, levels of service may be higher in communities where more voters are owners as voters’ interests are more closely aligned.4 Differential benefits from public services are one reason why minority groups may participate less in political processes that determine public good expenditure. Differential participation rates may also arise for legal reasons, however; non-citizens have limited scope for influencing public budgets to reflect their preferences. In communities with relatively large numbers of non-voters, citizens may vote for services that increase their private benefits rather than the entire community’s 4

In a developing country context Khwaja (2002) makes a similar point. Public projects like irrigation schemes are better maintained when project returns are more evenly distributed among commmunity members.

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well-being or wealth. When these voters are homeowners, private benefits are likely to coincide with local property values (Fischel 2001). This is similar to the finding of the corporate control literature (in which differential participation is effectively created by the use of one-share one-vote decisionmaking rules) that majority shareholders may vote for the firm to take actions that increase large holders’ wealth disproportionately. In either context, observed investment or spending decisions may be linked to the composition of the electorate and the distribution of voting power (Grossman & Hart 1988, DeMarzo 1993, Banerjee, Mookherjee, Munshi & Ray 2001). In this paper, we study a reform that changed the ethnic composition of communities and also increased the number of non-citizen residents in these communities. Existing California residents at the time of IRCA may have responded to the increase in SAW program participant residents by changing the level of public good provision either in their capacity as "homevoters" or as a result of within-group affinity. The policy change that we study allows us to test the implications of Vigdor’s (2002, 2004) model directly because SAW program participants were virtually all Mexican-born. Since SAW program participants were given residency but were not eligible to vote, and because they paid more of their tax bill in sales rather than property taxes compared to existing residents, we are also able to test whether voters responded to the change in the fraction of government revenue coming from property taxes following IRCA by adjusting public good provision. This would be consistent with the homevoter/ corporate control models outlined above.

3

The Immigration Reform and Control Act

IRCA gave legal amnesty to people who had worked as agricultural workers for at least 90 days between May 1, 1985 and May 1, 1986 or for a total of 90 days during the period May 1, 1983

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and May 1, 1986.5 Under the SAW provisions, a total of about one million applications for legal permanent resident status were granted and approximately 700,000 of these were in California (U.S. Department of Labor 1993). The vast majority of beneficiaries were of Mexican origin. Applications far exceeded predicted levels (about 300,000 in total), leading observers to conclude that the program gave residence to many people living in Mexico in 1986 who may not have fulfilled the requirements for amnesty (Orrenius & Zavodny 2001, Martin 2003). The time line of the IRCA SAW reforms is summarized in table 2. In the short run, IRCA had a limited impact on the composition of the electorate relative to the number of people who received amnesty under the program. While temporary residency permits were provided in 1987-1988, and employer sanctions for hiring illegal workers were in place in 1989, citizenship through naturalization became possible for participants in 1994 (Citizenship and Immigration Services 2003). Changes in the composition of the electorate, by county, prior to 1994 that can be ascribed to IRCA must be a result of endogenous sorting by citizen residents. On this basis, we restrict our main analysis to the period 1985-1989, as moving is less likely to occur immediately following the reform.6 IRCA also had a limited impact on the California housing market relative to the number of new residents in the state as a result of the program. Unlike, for example, the Cuban immigrants that increased the demand for low-quality rental housing in Miami and thus the price of this housing following the Mariel Boatlift (Card 1990), SAW program participants that worked in agriculture had a relatively limited effect on the local demand for rental units because of their sector of employment. 5 The stated intention of the amnesty was to act as a cushion against potential labor shortages as a result of new penalties for employers that were found to be “knowingly” employing undocumented workers (Tran & Perloff 2002, GAO (General Accounting Office) 1989). 6 Incentives to avoid or delay moving are particularly strong in California relative to other states because of the incentives created by the property tax provisions of Proposition 13.

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Farmworkers are relatively likely to live in substandard units (i.e., mobile homes or units that are lacking major appliances) and crowded conditions. (Housing Assistance Council 2001) They are also disproportionately likely to live in employer-provided housing, the rent for which is deducted from the agricultural wage (U.S. Department of Labor 2005). As a result, SAW program participants paid a greater fraction of their tax bill through sales taxes as opposed to property taxes than existing county residents. Our econometric identification strategy relies on the maintained hypothesis that the greater the fraction of land devoted to agriculture in a county, and the more labor intensive agriculture is in a county, then the greater the change in the county’s composition as a result of the SAW program. In order for this to be true, it must be the case that a sizeable fraction of amnesty recipients, if only initially, did work in agriculture in California, rather than in the service industry or other sectors. The more SAW program participants who worked in services, say, rather than agriculture, the weaker the relationship between agricultural labor demand and IRCA “treatment” intensity. Evidence in favor of the claim the SAW program increased the number of farmworkers in California is shown in Figure 1, which reports Hispanic employment in agriculture and in the unskilled services sector in California for the period 1983-1998 as measured by the Bureau of Labor Statistics (BLS). There is a 43 percent increase (70,000 people) in agricultural employment between 1986 (pre-IRCA) and 1987 (after IRCA), which is not evident in the service sector employment series (where employment levels increased by five percent, or 50,000) This is suggestive evidence that, at least in the years immediately following reform, an important fraction of amnesty recipients did work in agriculture.7 However, as more SAW participants move out of agriculture, there will 7

An alternative way of looking at this question is to note that from 1986 to 1989 agricultural employment increased by 61 percent, or about 100,000 people. Services employment increased by 26 percent, or 282,000 people. If all these people were new-resident SAW program participants, about one-third of the total did work in agriculture in the years immediately following IRCA.

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be less correlation between the ethnic diversity of county residents and agricultural labor demand.8 This is another arguement for restricting the period of our analysis to the years immediately before and after the reform.

4

Empirical strategy and data

4.1

Empirical strategy

In order to use IRCA to identify the impacts of community composition on spending, our strategy is to estimate equations in which the explanatory variable of interest is the interaction of the variables “after IRCA” and the fraction of a county’s land devoted to harvested agriculture, weighted by a county-specific measure of agricultural labor intensity. The coefficients on this variable and associated first-order interaction terms will identify the impact of IRCA on public good expenditure in the reduced form and on community composition in a first-stage regression of a two-stage least squares estimation. While we focus on ethnic composition and homevoter status as observable and salient measures of heterogeneity impacted by IRCA, and account for the fact that IRCA affected average income levels across counties, we do not rule out that other characteristics of SAW program participants could drive the "within group affinity" effects that we find. The reduced-form equation that we estimate using ordinary least squares (OLS) is: 8 Using the Labor Department survey data, Tran & Perloff (2002) find that SAW participants are somewhat more likely than undocumented workers to remain in agriculture than other immigrants. This suggests that the identification strategy we use in this paper will be valid for the period around the time of the reform. The regression results that we report in this paper are generally qualitatively unchanged if we include 1990 data in our analysis. Long-run impacts of the SAW program do appear to differ from the short run effects of the program. Surveys conducted in 1989-91 found that 10 percent of the farmworkers in California were undocumented and about 60 percent of farmworkers were SAW program participants (U.S. Department of Labor 1993). By 1998 however, Department of Labor surveys found that about 50 percent of farmworkers were undocumented (U.S. Department of Labor 2000), which is consistent with the contention that SAW program participants gradually moved out of agriculture and were replaced with new undocumented workers (Taylor & Thilmany 1993, Martin 2003).

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Yit = µi + aAGAREAit + bAF T ERt + cLAB_Iit + d(AF T ER ∗ AGAREA)it +f (AF T ER ∗ LAB_I)it + g(AGAREA ∗ LAB_I)it

(1)

+h(AF T ER ∗ AGAREA ∗ LAB_I)it + γmADJit + X0it δ + vit .

The outcome measure Yit is defined as local public good expenditure per capita in county i at time t. The dependent variables for which we estimate this equation include total local public goods expenditure per capita, police expenditure per capita (as an example of a service that may have an ethnic dimension to demand), and water and sanitation expenditure per capita (a service for which it seems less likely that demand differs across ethnic groups, and that is difficult to provide privately). The variable AGAREA is measured as thousands of harvested acres (excluding forestry) per square mile, and we allow for a direct relationship between this variable and Yit as well as an effect that is weighted by a (time-invariant) measure of the labor intensity of a county’s agricultural production (LAB_I) described in section 4.2. The variable AF T ERt is an indicator variable that takes the value “1” if the year is after the IRCA reforms (this period begins in 1987 because this was the first year that residency was awarded), and Xit is a vector of county demographic and financial control variables (log of total population, transfers per capita from other levels of government, the fraction of the population living in unincorporated areas). The variable mADJ is the (log of) average income per capita in adjacent counties, and it is included here as a variable that is correlated with county income but not directly correlated with Yit , as explained further below. In all specifications we control for county fixed effects, (µi ) to account for time-invariant county characteristics that may affect local public good expenditure levels (e.g., topography in the case of

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roads), and thus potential time-invariant controls are excluded from the X vector. The disturbance term vit is allowed to be correlated across years for the same county. This equation can be interpreted as a “differences” regression in which differential “treatment” groups are created depending on the fraction of land devoted to labor-intensity weighted agriculture. The interpretation of the coefficient estimates in equation 1 is as follows: The coefficients a and c measure the direct correlation between the agricultural land base and the labor intensity of agriculture, respectively, and local public goods expenditure. The coefficient b reflects the change in the level of local public good expenditure following the IRCA reform. We expect this coefficient to be positive given the upward trend in the data that we discuss below. In practice, we control P for time fixed effects ( Tt −1 τ t Y EARt ), in which case the variables AF T ER and LAB_I are

not included as regressors. The coefficient d measures the difference in expenditure after IRCA in counties with relatively more land devoted to agriculture, and the coefficient f measures the difference in expenditure after IRCA in counties where the agriculture is more labor intensive. We expect both of these coefficients to be negative, based on the predictions of the "within group affinity" models presented in section 2. The coefficient h, which measures differences in spending in counties with more land in agriculture and more labor intensive agriculture, may be positive or negative. A priori, it is possible that an ethnic composition "tipping point" is reached more quickly in counties that have are heavily agricultural and where the agriculture is relatively labor intensive. In that case, h could be positive. The models presented in section 2 that we test predict a positive relationship between the homogeneity of county residents and local public goods expenditure per capita. That is, the models predict β > 0 in the estimation of this equation:

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Yit = µ2i + COMP0it β + b2 AF T ERt + X0it δ 2 + eit ,

(2)

where COMP is a vector of relevant measures of community composition, e is an error term, and all other variables are defined as above. We consider three measures of community composition: the population share of the largest ethnic group (a linear measure of ethnic diversity), the share of county revenue from property taxes (a linear measure of the relative importance of sales taxes and fees, paid by all residents, and property taxes, paid disproportionately by residents who own property and are citizens), and log income per capita. We are primarily interested in the coefficients on the first of these two variables, controlling for income. Equation 2 estimated using OLS produces biased estimates of β because the composition of a particular community is endogenously related to local public good expenditure. Instrumental variables (IV) techniques can be used to address the endogenous relationship between community composition and local public good expenditure. Our IV identification strategy relies on the maintained hypothesis that all variables in equation 1 that are interactions with AF T ER are not directly correlated with local public good expenditure, but are correlated with COMP, as a result of the IRCA policy reform. This is a valid approach if (1) IRCA did not directly affect local public good expenditure, (2) the policy reform was an exogenous source of variation in county ethnic composition and county revenue composition, (3) the intensity of the program varied systematically across counties with labor-intensive agricultural land use, and (4) we have sufficient excluded exogenous variables to account for the fact that the variables that are interactions with AF T ER are correlated with income per capita as well as the measures of community composition in which we are interested. The first-stage equations we estimate are of the form:

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COM Pit = µ1i + a1 AGAREAit + b1 AF T ERt + c1 LAB_Iit + d1 (AF T ER ∗ AGAREA)it +f1 (AF T ER ∗ LAB_I)it + g1 (AGAREA ∗ LAB_I)it

(3)

+h1 (AF T ER ∗ AGAREA ∗ LAB_I)it + γ 1 mADJit + X0it δ 1 + uit ,

where the excluded exogenous variables are: AF T ER ∗ LAB_I, AF T ER ∗ AGAREA, AF T ER ∗ AGAREA ∗ LAB_I and mADJ and the coefficients on the independent variables are allowed to vary across measures of community composition. The coefficient numerical subscripts are included to emphasize that this is a first-stage regression and uit is an error term. The variable mADJ is included as an additional exogenous correlate of income per capita. A potential threat to the validity of our econometric strategy is the possibility that IRCA was an endogenous policy response to changes in ethnic diversity in agricultural regions. Interactions of immigration with existing ethnic residential preferences have been identified in other contexts (Cutler, Glaeser & Vigdor 1999) and sorting in anticipation of IRCA could affect both ethnic heterogeneity by county and housing prices. The stated goal of IRCA was to reduce reliance on a continuous supply of new immigrants for labor in the agricultural sector (GAO (General Accounting Office) 1989). Importantly for our identification strategy however, there appears to have been little expectation that the program would change the ethnic composition of the farm labor force; it was hoped that the SAW program participants, overwhelmingly Mexican in origin, would continue to work in agriculture. On the other hand, following IRCA, it is reasonable to expect that residents of California responded to the immigration reform by choosing new residential locations; thus for our identification strategy to be appropriate, it is important that we focus on the years immediately

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following the reform. The validity of our estimation strategy is also impacted if IRCA was anticipated by amnesty applicants or current residents. For example, suppose that people elected to work in agriculture in 1985 or moved to the U.S. in 1985 because they anticipated it would increase their chances of receiving amnesty. Alternatively, if California residents anticipated future immigrants, this might have caused them to reduce local public good spending because of anticipated free-riding by future residents (Schultz & Sjostrom 2001). In either case we will underestimate the effect of IRCA on our measures of homogeneity by looking at changes in outcome variables before and after 1986. In practice, the exact timing of IRCA passage appears to have been unexpected. Legislation to reform immigration policy that failed to become law was regularly before both the House of Representatives and the Senate prior to 1986 (Orrenius & Zavodny 2001). Perhaps the most important threat to validity is the possibility of omitted interactions that make drawing inferences difficult (Meyer 1995). The data in figure 2 reflect a noticeable downward trend in the population share of the largest ethnic group across California counties in the period 1979-1999 and ethnic diversity could vary systematically across counties over time by agricultural land use for reasons unrelated to IRCA. To determine if any pattern in the ethnic homogeneity data remains after controlling for a linear trends, in figure 3 we show the path in ethnic homogeneity for the period 1979-99, after controlling for county fixed effects and county-specific time trends. The residual, or unexplained, homogeneity is low in the years before IRCA, high in the period 198790 immediately after the reform, and then declines again. The ethnic composition of California counties appears to vary systematically around the time of the policy change even after controlling for trends. Trends in local public good expenditure that we may incorrectly ascribe to IRCA are also a

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potential concern. However, as we show in figure 4, after controlling for county fixed effects average local public good expenditure is not strictly falling after 1986. In fact, there is a significant increase in average spending from 1986 (defined as before IRCA in our ecometric analysis) to 1987 (defined as after IRCA in our econometric analysis). There is also a slightly upward trend over the entire period for which data is readily available, 1985 onwards. Finally, if there were other policy changes at the time of IRCA that affect local public good expenditure in agricultural counties disproportionately, our identification strategy would be inappropriate. There are at least two possible policy changes that occurred in this period that may affect public good expenditure; however neither seem to affect communities differently depending on the importance of agriculture in the communities. One candidate policy change comes from IRCA itself. Non-agricultural workers also received amnesty as part of IRCA. People unlawfully living in the U.S. since 1982 were granted permanent residency regardless of sector of employment. About 1.5 million people received permanent residency as a result of this element of IRCA. These immigrants lived throughout the U.S.; perhaps 38,000 of them worked in agriculture in California (U.S. Department of Labor 2000). Most so-called pre-1982 immigrants worked in low-skilled labor or service sectors at the time of the amnesty and at the time they received permanent residence (Rytina 2002), and because service sector jobs are generally considered preferable to agricultural work there is little reason to believe that amnesty would induce these people to work in agriculture. A state ballot measure called Proposition 99 raised cigarette taxes in 1989 in California with the revenue earmarked for spending on public health by counties additional to 1988 levels (California Legislative Analyst’s Office 1995). The impact of this policy would vary across counties according to the volume of transactions eligible to be taxed. Thus, Proposition 99 does not disproportionately

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affect agricultural counties.9 In light of this policy change however, our econometric analysis does not consider public health programs and we focus on expenditure per capita rather than local public goods spending as a fraction of total expenditure because counties in California received a new revenue source in 1988 as a result of the passage of Proposition 99.

4.2

Data

The data used in this paper are a set of county-level statistics for 57 counties in California for the period 1985-1989.10 Table 1 lists the counties included in the sample and a representative sample of the crops grown in each county. The most labor-intensive crops are generally fruits and vegetables, followed by tree crops, and then row crops (e.g., cotton or corn) (U.S. Department of Labor, Occupational Safety and Health Administration 2003). Hay and pasture are the least laborintensive products. Summary statistics for other key county financial and demographic variables are shown in table 3 for the two periods of pre-IRCA and post-IRCA.11 County financial data comes from the California Institute for County Government (CICG) for the period 1985-1995. Local public goods for which CICG provides expenditure data include: fire protection, police services, public transport, transportation terminals, roads, mental health services, water, sanitation, and public health and medical services. The figures in table 3 for total local public goods reflect the sum of all these types of services, except for public health and medical services. We exclude these classes of local public goods from our analysis for the reasons discussed in section 9

We test this proposition by calculating the correlation between the fraction of land devoted to agriculture in 1988 and the change in public health expenditure per capita between 1988 and 1990. The Pearson correlation coefficient is 0.0091, with a significance level of 0.95. Over this period, public health expenditure per capita increased by 10 percent on average. 10 San Francisco county is excluded from the analysis as it is a city as well as a county and revenue and expenditure data are not directly comparable to other county data. 11 All monetary data is deflated to constant 1995 dollars using the Los Angeles regional Consumer Price Index as reported by the BLS.

19

4.1. Real average expenditure on local public goods has remained roughly constant over time. Of the categories of public services that we consider, the highest levels of spending per capita are in the categories of roads, police, mental health services, and fire protection with average spending levels for the entire period of $122 per capita, $71 per capita, $27 per capita, and $10 per capita respectively. We report average expenditure per capita on police services and water and sanitation services as equations 1 and 2 are estimated for each of these classes of spending alone. The composition of revenues available to counties for spending varies fairly widely. Transfers from state and federal government, largely earmarked for particular purposes, exceed local public good expenditures on a per capita basis and vary significantly across counties. To understand these figures, note that, while for historical reasons the fraction of property tax revenue kept by counties in California (as opposed to being transferred to the state or other levels of government) is orthogonal to the preferences of current county residents, all property tax revenue that is returned to counties may be allocated at the discretion of county government (Hill 1998).12 In contrast, only about 20 percent of sales tax revenue collected by counties is available for their discretionary spending (Hill 1998). In total, counties freely allocate 30 to 40 percent of their resources from property taxes, sales taxes, other fees, and transfers over the period under consideration (Shires 1999). On average, the population share of the largest ethnic group is fairly constant over time in California counties.13 However, pre-IRCA data may understate the fraction of a county’s population that is Hispanic as many illegal residents of California are Spanish-speaking and California 12

In California, the distribution of property taxes among local governments (i.e., counties, school districts, cities) is done on the basis of the allocation that was in place prior to Proposition 13, which transferred the authority to allocate property taxes from local government to the state in 1978. 13 In most counties, the largest ethnic group is whites, with the exception of Imperial County where Hispanics are the majority group. Across all counties, the mean share of the population that is Hispanic is 0.16 pre-IRCA and 0.17 after IRCA. Alameda County, which includes the city of Oakland, is an outlier in this data with a relatively large African-American population.

20

law requires proof of U.S. legal residency to obtain a driver’s license because in non-census years California Department of Finance (DOF) population estimates are generated by analyzing driver’s license data and birth and death certificates.14 Thus, since IRCA legalized a fraction of these existing residents as well as allowing new residents to come to California from Mexico, we must interpret our findings about the impact of IRCA to be the result of the combined effect of increased visibility for previously illegal residents and actual changes to a county’s population as a result of net immigration. The coefficient estimates we report when we estimate equation 3 overestimate the effect of IRCA on actual changes in ethnic composition of residents. The wages and salaries component included in this calculation of personal income is generated from data supplied to Bureau of Economic Analysis (BEA) from state employment security agencies (ESAs) that summarizes quarterly state unemployment insurance contribution reports filed with ESAs by employers. For California, these estimates include farmworkers because state law requires that farmworkers be provided with unemployment insurance (in contrast to all other states except Arizona) (BEA (Bureau of Economic Analysis) 2001). Thus, this data is relatively less likely to be subject to the measurement problems we encounter with the population data. Personal income per capita in constant prices is highest in Marin County in the time period we study, and lowest in Yuba County (in the Central Valley) and relatively remote Lassen County, though it is fairly constant over time on average. In table 3 we also summarize key agricultural statistics by county as reported by the California Agricultural Statistical Service (CASS). The fraction of land devoted to agriculture varies quite widely across counties in the sample and declines slightly over time within counties on average. 14

Unlike the U.S. Census data, “Hispanic” is included as an explicit category in the DOF’s rdemographic data. The categories included are: white, black, Asian, Hispanic, and American Indian. These categories sum to the total population figures.

21

Though not shown here, crop choice by county is also quite constant. Agroecological constraints, irrigation infrastructure, and limited water rights markets generally preclude major shifts across crop categories (e.g., from row crops to tree crops). We develop a measure of the labor intensity of agriculture by county using information on a county’s production mix by crop in 1985 as reported by CASS, and classifications of production labor intensity from the Office of Health and Safety Administration (OSHA). OSHA categorizes broad crop categories (mechanized crops, bush and tree crops, livestock and row fruits and vegetables) by labor intensity at the time of harvest and labor intensity while growing. We classify row fruits and vegetables and nursery crops (including cut flowers) as having the highest overall labor intensity because OSHA categorizes these crops has high intensity during both harvest and growing. Mechanized crops are classified as having the lowest labor intensity, as OSHA categorizes these crops as low intensity during both harvest and growing. Crops that have high intensity by OSHA standards during either harvest (livestock) or while growing (bush and tree crops) are classified by us as medium intensity. We assign the high intensity crops a value of two, the lowest intensity crops a value of zero, and medium intensity crops a value of one. Using 1985 production values by crop as weights, we develop a time-invariant measure of the weighted-average labor intensity of agricultural production by county that is exogenous to labor policy in the period we study. As might be expected by reviewing the crops listed in table 1, San Diego County and San Luis Obispo have high values of this variable (2 and 1.92, respectively), while Trinity County, where pasture and cattle are important crops, has the lowest value (0.02).

22

5

Results

5.1

The impact of IRCA on local public goods expenditure

We begin by estimating the relationship between local public good expenditure per capita and IRCA. The results of the estimation of equation 1, for three classes of local public goods expenditure, total spending, police spending, and water and sanitation spending, are shown in table 4. All specifications include both time and county fixed effects. For each type of spending, we present results that include the level of AGAREA and the level and square of AGAREA. Consider the coefficient estimates for total local public good expenditure first (columns 1 and 2). The coefficient estimates on most of the control variables in the X vector are insignificant, with the exception of transfers per capita (greater transfers are correlated with higher levels of spending), as is the coefficient on the variable that measures log average income in neighboring counties. Results are very similar if this variable is replaced with own-county lagged or current income. The coefficient estimates on the variables that are interactions with AF T ER are statistically significant. Similar results exist if a time trend or a simple indicator variable for AF T ER are used instead of time fixed effects. Based on the coefficient estimates in column 2, we predict total spending of $238 per capita before IRCA and $220 per capita following IRCA, assuming that all variables take their sample average values. This $18 average decline in per capita spending is economically meaningful; this is about equal to per capita spending on public health, for example. At the greatest levels of agricultural labor intensity and average agricultural land use levels, the predicted decline is even higher (about $20 per capita). In counties where agriculture is not labor intensive (LAB_I = 0), the predicted change in spending is actually positive, and about $8 per capita.

23

Note that while the coefficient estimates on both AF T ER ∗ AGAREA and AF T ER ∗ LAB_I are negative, as predicted, the coefficient estimate on AF T ER ∗ AGAREA ∗ LAB_I is positive and significant. Given the magnitude of the coefficient estimates, this implies that, while there is a strictly decreasing relationship between agricultural labor intensity and predicted local public good spending, for the highest values of AGAREA (assuming sample average values of LAB_I and other variables) there is a positive relationship between local public good expenditure and land devoted to agriculture. This relationship is shown graphically in figure 5. While about 70 percent of the sample county-year observations have less than 34 percent of land devoted to agriculture, there are some cases in which increasing land under agriculture is correlated with higher spending. The turning point occurs at lower values of AGAREA, the more labor intensive a county’s agricultural production is. If one mechanism that links agricultural land use and expenditure is ethnic homogeneity, as we have postulated, then this is to be expected. Each additional acre of land devoted to agriculture implies more farm workers, who are overwhelmingly Mexican in origin. The point at which Hispanics may become the largest ethnic group, and thus at the margin contribute to an increasingly homogeneous population rather than an increasingly diverse community, will be reached more quickly in counties where agriculture is most labor intensive. The results in columns 2 and 3 for police spending per capita are qualitatively similar to those for total local public goods spending. Police spending is higher in counties with a greater fraction of the population living in unincorporated areas. This is to be expected as policing services are only provided by counties to populations that do not live in (incorporated) towns or cities. As with total spending, we find that spending on police services declined after the IRCA reform in counties with a greater fraction of land in agriculture and in counties where agriculture is relatively labor intensive. However, the spending decline is not dramatic and on average is close to zero; in

24

the counties with the most land in agriculture the predicted decline is equal to only about $5 per capita. These results are robust to the inclusion of controls for violent or property crime rates. Our findings are consistent with the predictions of the “within group affinity” model, which predicts a correlation between measures of homogeneity and local public good outcomes for all types of goods. The results are also consistent with a model in which within group affinity is stronger for goods that may have an ethnic component to them; police services may well be a case in which expenditures may cater to different groups more or less effectively (e.g., if more officers are bilingual). In contrast to police services, water and sanitation services are a class of local public goods for which it is difficult imagining divergent preferences across ethnic groups (controlling for income and other variables). In columns 5 and 6, we show that there is much less evidence of an effect of IRCA on water and sanitation spending than for either of the other two classes of spending that we consider. Finally, these results are also consistent with the homevoter models we discussed; policing, to the extent that it reduces crime across all residential areas, may not be fully capitalized into housing values. Owner-citizens thus may choose to privately supply substitutes for police services after an influx of new non-owner/citizen residents. Water and sanitation services are more difficult to supply privately and may be more fully capitalized into housing values as a result.

5.2

The impact of IRCA on community composition

We have identified two channels in addition to changes in average income that may explain the observed reduced form relationship between IRCA intensity and local public good expenditure, a ethnic homogeneity channel and a homevoter channel. In this section, we present the results of regressions in which we predict the share of the largest ethnic group and the fraction of county

25

revenue from property taxes using the variables that measure IRCA intensity. We also report the result of regressions in which we predict average income per capita using the IRCA variables. These regressions constitute the first-stage of two stage least squares estimates of the relationship between the measures of community homogeneity we study and local public goods spending. In columns 1 and 2 of table 5 we identify a negative relationship between both the fraction of land devoted to agriculture and the production weighted index of agricultural labor intensity, and the share of the largest ethnic group. This is consistent with the description of the IRCA program in section 3. The F-statistics for the variables that are interactions with AF T ER and the income of neighboring counties (the excluded exogenous variables) are somewhat small in these regressions; they are equal to 3.14 (4, 56 degrees of freedom) and 3.53 (5, 56 degrees of freedom), respectively. This suggests that our IV estimates may be subject to a weak instruments problem and biased towards OLS (Staiger & Stock 1997). However, a Wald test allows us to reject the null hypothesis that the coefficients on these variables are jointly equal to zero with p-values less than 0.02. The coefficient estimates in columns 1 and 2 of table 5 imply that the population share of the largest ethnic group fell in California counties after IRCA by about two percent (or 1.7 percentage points). Across counties, the importance of IRCA is mostly determined by the labor intensity of agriculture in a county, rather than the fraction of land devoted to agriculture. The magnitude of the coefficient estimates implies that increasing the labor intensity of agriculture from the sample average (0.9) to one standard deviation above the average (1.4) results in a 0.4 percent decline in the share of the largest ethnic group. We find a similarly sized effect for the fraction of land in agriculture, but this is much more imprecisely estimated. In columns 3 and 4 of table 5 we report the results of regressions in which we estimate the relationship between IRCA intensity and the fraction of county revenue from property taxes. In

26

this case, the F-statistics of the excluded exogenous variables are somewhat smaller than in the regressions where the dependent variable measures ethnic homogeneity (2.59 and 2.06, respectively). In the case of county revenue composition, agricultural land use seems to be an equally important determinant of the impact of IRCA as the labor intensity of agriculture. In particular, there is a statistically significant relationship between the variable AF T ER ∗AGAREA and the share of revenue from property taxes. The coefficient estimates on the triple interaction term (AF T ER ∗AGAREA ∗ LAB_I) is also significant (at the 10 percent level) in both columns 3 and 4. We predict a strictly decreasing relationship between the property tax revenue share and the fraction of land devoted to agriculture for almost all values of AGAREA observed in the sample (see figure 6) and estimate than on average the share of revenue from property taxes declined by almost nine percent (or 2.4 percentage points) following IRCA.15 This is consistent with our description of IRCA; the reform brought new residents to California who paid sales tax and other fees but who had relatively little impact on the demand for housing and thus the revenue base on which property taxes are calculated. The final two columns of table 5 report the coefficients for the estimation of equation 3 when the dependent variable is income per capita. While this is not our focus, as discussed previously it is important that we account for the fact that IRCA may have affected average incomes just as it did other demographic characteristics. In practice, we find little relationship between IRCA and average income. Even when income in neighboring counties is included, the F-statistics for the excluded exogenous variables are below 1.0. Our failure to find a relationship between IRCA and average incomes is likely to be in spite of the fact that the new residents were relatively poor. 15

These results are robust to controlling for either the level of or changes in public health expenditure per capita, a potential concern because the passage of Proposition 99 occurred at the same time as IRCA.

27

One possible explanation for this finding is that the producer surplus associated with the output generated by these agricultural workers also raised incomes of farm owners and managers.

5.3

Local public goods expenditure and community composition

Using the first-stage regressions presented in the previous sub-section, we can estimate the relationship between the measures of community composition and local public good expenditure. In order to establish a baseline against which compare the IV estimates, we begin by presenting the results of the OLS estimation of equation 2 in table 6 (columns 1 through 3). Because of the endogenous relationship between community characteristics and local public good expenditure, the coefficient estimates in these regressions are biased. The OLS regression results in column 1 suggest a positive relationship between the population share of the largest ethnic group and total local public good spending (though the relationship is statistically insignificant), controlling for income. The relationship between the share of revenues from property taxes and spending is also positive. We predicted a positive relationship between each of these measures of community homogeneity and spending because of within group affinity, in the case of ethnic composition, and homevoter behavior by owner/ citizens, in the case of revenue composition. We find qualitatively similar results for police spending, but not for water and sanitation. We find little evidence of a relationship between average income and spending levels for any of the classes of public goods; the coefficient on log income per capita is small and imprecisely estimated. The IV estimates in table 6 (columns 4 through 6) are consistent with the OLS results. The precision of our estimates of the ethnic homogeneity effect is improved, in particular. For both total spending and police spending, we find a statistically significant relationship between the population

28

share of the largest group and spending per capita. Because of our identification strategy, these coefficient estimates have a causal interpretation. The estimated elasticity of local public good spending with respect to the share of the largest group is fairly large; it is equal to 5.1 (though note that we cannot reject the hypothesis that the IV coefficient estimate is equal to the smaller OLS coefficient estimate).16 The elasticity estimate for police spending is also economically significant; we estimate that a one percent increase in the share of the largest ethnic group in a county’s population results in a nearly eight percent increase in police spending. This is a public service that is likely to have an ethnic component, which may explain the relatively larger elasticity estimate. For the service that we identified as less likely to have an ethnic component, water and sanitation services, we do not find an effect between the share of the largest group and spending levels. This is consistent with our reduced-form results, and suggests that in this context at least, within group affinity may vary across categories of local public goods. Our IV estimates of the relationship between the share of revenues from property taxes and local public good spending levels are qualitatively similar to the OLS estimates. The coefficient is imprecisely estimated in the IV specification however, likely as a result of the relative weakness of the instruments for this variable. The point estimates in both the OLS and the IV specifications do suggest that counties with a greater fraction of revenue from property taxes are more likely to supply greater levels of productive public goods, as we hypothesized in section 2. We estimate that the elasticity of total local public goods spending with respect to the fraction of revenues from property taxes is equal to 0.4. While this is small compared to our estimate of the elasticity with respect to ethnic homogeneity, this alternative channel that we have identified as a way that IRCA may have affected local public good outcomes remains economically important. Moreover, 16

These results are robust to controlling for the local crime rate and the fraction of the population over age 65.

29

the homevoter/corporate-control hypothesis that we used to explain how revenue composition may have been affected by IRCA is amenable to policy in a way that the within group affinity channel is not. Providing citizenship to SAW program participants more rapidly would have allowed them to express their preferences through voting in a way that they were unable to in practice. Affecting preferences may be a much more difficult policy challenge.17

6

Conclusions

This paper uses county-level data from California before and after immigration reform to test whether ethnic homogeneity and the composition of county revenues are systematically related to spending on productive local public goods. We estimate that the IRCA immigration reform resulted in a $18 average decline in per capita public goods spending. There are at least two channels, other than via average incomes, by which the changes in community composition caused by IRCA may have caused this result and that we are able to test. First, when immigrants that receive amnesties are ethnically homogeneous, their entry and/or higher profile in local communities following the legalization of their status may result in lower local public good spending as a result of within group affinity. Using an instrumental variables strategy, we estimate that a one percent decline in the population share of the largest ethnic group, which may easily occur over period of a few years in this sample, results in a five percent reduction in per capita spending. Second, amnesty recipients are unable to vote in elections that determine the allocation of local public budgets, and, in the case of IRCA farmworkers, have relatively little impact on local property tax revenues. They do contribute to sales tax revenues. So-called homevoters may elect to spend 17 We tested for an interaction effect between the share of the largest ethnic group and the fraction of revenue from property taxes and find no indication of this effect in addition to the direct effects of these variables on local public good spending discussed here.

30

tax revenues in ways that increase the values of their properties, rather than overall community welfare. Thus, we find that property taxes as a share of total revenue declined more in counties with a greater predicted influx of IRCA amnesty recipients, and that this is in turn correlated with lower spending on those goods for which it is difficult to purchase private substitutes and thus are capitalized into housing values. These results are weaker than those relating to ethnic composition. The impact of IRCA on local public goods expenditure through the channels that we have identified may have been amplified by election results following the reform. Cross-sectional regression results suggest that counties where we estimate that the intensity of IRCA treatment was greatest were more likely to increase their vote share for the Republican Party in the 1988 and 1992 presidential elections, relative to the previous election, controlling for education, income, and unemployment rates.18 While a causal interpretation of the results in table 7 is inappropriate, these findings are further suggestive evidence for reduced demand for spending on public services by citizens in agricultural counties following IRCA and increased demand for lower taxes instead. Had SAW program participants received the right to vote following their residency in the U.S., immigrants’ preferences for local public goods spending could have altered the identity of the median voter and thus service provision, potentially counteracting the within group affinity effects we have estimated. This suggests that future immigration policy reform may be designed to provide citizenship more quickly if policymakers prefer to reduce the consequences for local public good spending. Of course, this may make political consensus in favor of reform more difficult to achieve ex ante. 18

The magnitude of these coefficient estimates suggests that a 10 percent increase in the fraction of land devoted to agriculture at the time of IRCA results in a 0.25 percent increase in votes for the Republican party (column 2) relative to the previous election and assuming average values for all other independent variables. The elasticity estimate is about three times as large in 1992 (column 4).

31

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Figures and tables Figure 1: Increasing Hispanic agricultural employment followed IRCA immigration reform 2,000

400

1,800

350

1,400 250

1,200 1,000

200

800

150

600 100 400 50 0 1983

200

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

CA Hispanic agricultural employment (Annual average, '1000s) CA Hispanic Semi-skilled and unskilled services employment (Annual average, '1000s)

37

1995

1996

1997

0 1998

Services employment

Agriculture employmen

1,600 300

Figure 2: Trends in ethnic composition of California counties 1979-99

.8

.6

year

38

20 00

95 19

19 90

19

85

.4 19 80

Population share of largest ethnic group

1

.05

0

20 00

19 95

19 90

19 85

-.05 19 80

Population share of largest ethnic group, residuals

Figure 3: Trends in ethnic composition of California counties (residual) 1979-1999

year

Notes: Graph is residual population of share of largest ethnic group, controlling for county effects and county-specific time trends.

39

30

20

10

0

-10

19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99

19 88

19 87

19 86

-20

19 85

Average local public good spending per capita , residuals

Figure 4: Average local public good expenditure per capita in California counties (residual) 19851999

year

Notes: Graph is annual residual local public good expenditure per capita ($1995), controlling for county effects. The period after IRCA is the years 1987 onwards, inlcusive of 1987.

40

260 240 220 200 180

Expenditure per capita ($1995)

280

Figure 5: Reduced form relationship between agricultural land use and local public goods expenditure

0

.2 .4 Fraction of land in agriculture (1000 acres/square mile)

.6

Predicted local public good expenditure per capita ($1995) 95% confidence interval

.25 .2 .15

Re ve nue share

.3

Figure 6: Reduced form relationship between property tax revenue share and agricultural land use

0

.2 .4 .6 Fraction of land in agric ulture ( 1000 acres/square mile) Predicted share of revenue from prope rty taxes 95% confidence interval

41

.8

Table 1: California counties by value of agricultural production and poverty prevalence, 2000 County Leading crops Value of ag production Poverty ($1,000/mile2) % families in Rank in CA San Joaquin Grapes, milk, tomatoes 964.1 13.5 14 Monterey Lettuce, broccoli, strawberries 880.0 9.7 31 Stanislaus Milk, almonds, chickens 800.9 12.3 21 Merced Milk, chickens, tomatoes 797.6 16.9 4 Santa Cruz Strawberries, raspberries, lettuce 757.7 6.7 45 Kings Milk, cotton, cattle 636.7 15.8 10 Tulare Milk, oranges, grapes 635.7 18.8 2 Fresno Grapes, poultry, cotton 573.3 17.6 3 Sutter Rice, dried plums, peaches 569.6 12.1 23 Ventura Lemons, celery, strawberries 567.2 6.4 46 Orange Nursery stock, cut flowers, strawberries 463.8 7.0 42 Napa Wine grapes, nursery crops 456.2 5.6 50 San Mateo Nursery products, mushrooms 432.4 3.5 58 Sonoma Wine grapes, milk, livestock 371.2 4.7 55 Madera Grapes, milk, almonds 350.0 15.9 9 Colusa Rice, processing tomatoes, almonds 300.6 13 15 Yolo Processing tomatoes, wine grapes, rice 299.1 9.5 32 San Diego Flowers and foliage, nursery plants 298.2 8.9 36 Sacramento Wine grapes, milk, nursery stock 295.6 10.3 30 Kern Grapes, citrus, cotton 271.3 16.8 5 Santa Barbara Broccoli, wine grapes, strawberries 269.6 8.5 38 Yuba Rice, dried plums, peaches 235.1 16.3 8 Santa Clara Nursery crops, mushrooms, cut flowers 233.1 4.9 54 Solano Nursery stock, tomatoes, wine grapes 223.6 6.0 49 Imperial Cattle, alfalfa, lettuce 220.3 19.4 1 Glenn Paddy rice, dairy products, almonds 213.6 12.5 20 Butte Rice, almonds, walnuts 177.6 12.2 22 San Benito Greens, nursery products, wine grapes 150.4 6.7 44 San Luis Obispo Wine grapes, broccoli, lettuce 147.6 6.8 43 Riverside Milk, nursery products, grapes 145.5 10.7 28 Contra Costa Nursery plants, grapes, cattle 128.6 5.4 52 Marin Milk, cattle, pasture 92.7 3.7 57 Los Angeles Nursery plants, root vegetables, onions 66.0 14.4 11 Alameda Nursery products, wine grapes, cattle 43.2 7.7 41 Placer Milling rice, cattle, nursery products 43.1 3.9 56 San Francisco Vegetables, cut flowers 42.8 7.8 40 Lake Wine grapes, pears, cattle 42.6 12.9 18 Amador Grapes, cattle, pasture 38.3 6.1 48 Tehama Dried plums, walnuts, milk 37.5 13.0 16 Mendocino Wine grapes, pears, cattle 36.6 10.9 27 San Bernardino Milk, cattle, eggs 30.9 12.6 19 Del Norte Milk, nursery products, cattle 30.5 16.4 6 Humboldt Nursery products, milk, cattle 27.1 12.9 17 Siskiyou Alfalfa hay, strawberries, cattle 18.9 14.0 12 Calaveras Cattle, wine grapes, poultry 17.3 8.7 37

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County

Leading crops

Value of ag production Poverty ($ 1,000/mile2) % families in Rank in CA Modoc Alfalfa hay, cattle, potatoes 14.4 16.4 7 El Dorado Apples, wine grapes, pasture 14.1 5.0 53 Shasta Cattle, strawberry plants, alfalfa 13.2 11.3 25 Mariposa Cattle, pasture, livestock 12.8 10.5 29 Lassen All hay, strawberry plants, cattle 11.6 11.1 26 Tuolumne Cattle , pasture, firewood 7.5 8.1 39 Nevada Cattle pasture, wine grapes 7.5 5.5 51 Mono Alfalfa, carrots, cattle 6.9 6.3 47 Plumas Cattle , all hay, pasture 6.9 9.0 34 Sierra Cattle, pasture, all hay 6.6 9.0 35 Inyo Turf, cattle , alfalfa 1.4 9.3 33 Trinity Cattle, pasture, wine grapes 0.7 14 13 Alpine None 0.0 12 24 Source: USDA/NASS, California Agricultural Statistics Service; U.S. Department of Commerce, Bureau of the Census. Excludes revenues earned from timber. Table 2 Special Agricultural Worker program time line Year 1986

Events Immigration Reform and Control Act signed.

1987

Amnesty application filing begins June 1. Amnesty and temporary residence granted to persons who demonstrate evidence of having worked on perishable crops (specifically, in "seasonal agricultural services") for at least "90 person days" between May, 1985 and May, 1986. Applicants could apply from outside the US.

1988

Application filing window closes November 30. 1.3 million applications received; 700,000 in California. Agricultural employers face penalties for employing illegal immigrants beginning in December.

1989

SAW applicants become eligible for permanent residency December 1.

1990

57,000 SAW program persons granted permanent residence.

1991

920,000 SAW program persons granted permanent residence.

1992 117,000 SAW program persons granted permanent residence. Source: Citizenship and Immigration Services (2003), Ryntina (2002).

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Table 3: Summary statistics Pre- IRCA summary statistics (1985-1986) Obs

Mean

Min

Max

235.89 79.83 8.15 254.63 0.09

68.89 20.07 0.00 144.81 0.10

1607.85 606.16 49.35 1729.41 0.60

0.76 0.50 456.05 20,346

0.13 0.25 1,154.48 4,548

0.45 0.07 1.09 14,561

0.94 1.03 8,373.12 39,361

0.23 0.90

0.16 0.49

0.00 0.02

0.63 2.00

County revenue and expenditure ($1995) Local public goods expenditure per capita Police expenditure per capita Water and sanitation expenditure per capita Transfers to county government per capita Property tax revenue as a fraction of county revenue

114 114 114 114 114

235.96 71.99 3.23 396.18 0.29

County demographics Population share of largest ethnic group Fraction of population in unincorporated areas Total population (1,000s) Income per capita ($1995)

114 114 114 114

Agricultural characteristics Harvested acres (1,000s) / mile2 1985 Production-weighted OSHA labor intensity

114 114

Std. Dev.

Post- IRCA summary statistics (1987-1989) Obs

Mean

County revenue and expenditure ($1995) Local public goods expenditure per capita Police expenditure per capita Water and sanitation expenditure per capita Transfers to county government per capita Property tax revenue as a fraction of county revenue

170 170 170 170 170

252.75 72.87 4.04 455.60 0.27

County demographics Population share of largest ethnic group Fraction of population in unincorporated areas Total population (1,000s) Income per capita ($1995)

170 170 170 170

0.75 0.49 486.51 20,916

Std. Dev.

Min

Max

289.78 78.50 12.18 358.92 0.09

59.57 12.98 0.00 171.18 0.08

2,132.36 592.51 102.49 2,556.52 0.48

0.13 0.26 1,212.38 4,744

0.42 0.06 1.07 14,329

0.94 1.06 8,768.69 40,942

Agricultural characteristics 170 0.24 0.16 0.00 Harvested acres (1,000s) / mile2 Notes: Local public goods are: fire protection, police services, public transport, roads, transport terminals, mental health services, water, and sanitation. Excludes medical and public health expenditures. Excludes San Francisco county.

0.57

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Table 4: IRCA and local public goods (Reduced form) Dependent variable: alternative measures of local public goods expenditure (LPG) per capita ($1995) in California counties 1985-1989 Water and Police Police Water and Total LPG Total LPG spending per spending per sanitation sanitation spending per spending capita spending per spending per capita per capita capita capita ($1995) capita ($1995) ($1995) ($1995) ($1995) ($1995) (1) (2) (3) (4) (5) (6) OLS OLS OLS OLS OLS OLS 1000 harvested acres/mile2 (AGAREA) 222.080 224.330 22.069 22.186 -19.766 -19.521 [135.177] [118.486]* [46.928] [47.197] [16.255] [15.190] AGAREA2 -471.002 -483.645 -35.843 -36.504 -3.221 -4.601 [246.692]* [202.149]** [72.861] [74.528] [25.464] [25.155] AGAREA * LAB_I 22.285 61.495 -19.941 -17.891 18.190 22.470 [143.923] [137.392] [29.228] [29.784] [22.417] [21.554] AFTER IRCA * AGAREA -147.039 -408.160 -41.127 -54.780 3.626 -24.877 [52.579]*** [167.929]** [18.188]** [28.776]* [13.150] [17.990] AFTER IRCA * AGAREA2 507.332 26.527 55.379 [279.962]* [42.879] [29.369]* AFTER IRCA * AGAREA * LAB_I 116.155 117.809 30.666 30.753 -12.753 -12.573 [47.547]** [54.080]** [18.307]* [18.270]* [14.946] [14.546] AFTER IRCA * LAB_I -29.434 -30.908 -10.136 -10.213 6.700 6.539 [14.029]** [16.298]* [5.012]** [4.991]** [5.230] [5.110] Log income per capita neighbor counties 109.005 69.648 -69.840 -71.898 55.887 51.590 [212.206] [212.320] [87.897] [86.490] [122.968] [121.025] Log of total population -121.924 -130.754 -49.215 -49.677 -14.367 -15.331 [116.608] [122.627] [35.026] [34.880] [23.699] [23.539] Government transfers per capita 0.283 0.263 -0.020 -0.022 -0.001 -0.004 [0.115]** [0.105]** [0.008]** [0.008]*** [0.004] [0.004] Fraction of pop. in unincorporated areas 173.105 198.950 62.671 64.022 5.976 8.797 [217.602] [224.776] [26.227]** [25.666]** [18.909] [18.957] County fixed effects? YES YES YES YES YES YES Year fixed effects? YES YES YES YES YES YES Observations 284 284 284 284 284 284 R-squared 0.99 0.99 0.99 0.99 0.84 0.85 Root MSE 34.48 34.00 8.61 8.62 4.90 4.86 Notes: Huber robust standard errors in parentheses. Regression disturbance terms clustered at the county level. Significantly different from zero at 90% (*), 95% (**), 99% (***) confidence. AFTER IRCA takes the value of one for the years after 1986. Excludes San Francisco county. LAB_I is value-weighted average labor intensity of agricultural production at the beginning of the period. Local public goods included in dependent variable in columns (1) and (2) are: fire protection, police services, public transport, roads, transport terminals, mental health services, water, and sanitation. Excludes medical and public health expenditures. Excludes San Francisco county.

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Table 5: Community composition and IRCA (First-stage) Dependent variable: Community characteristics impacted by IRCA Pop. share Pop. share Share of Share of Log income Log income of largest of largest revenue from revenue from per per ethnic group ethnic group property taxes property taxes capita ($1995) capita ($1995) (1) (2) (3) (4) (5) (6) OLS OLS OLS OLS OLS OLS 1000 harvested acres/mile2 (AGAREA) 0.081 0.081 -0.005 -0.005 -0.021 -0.022 [0.036]** [0.033]** [0.116] [0.117] [0.096] [0.097] AGAREA2 -0.046 -0.044 0.138 0.139 0.304 0.307 [0.051] [0.048] [0.164] [0.162] [0.129]** [0.122]** AGAREA * LAB_I -0.073 -0.079 0.023 0.020 -0.139 -0.149 [0.026]*** [0.025]*** [0.077] [0.080] [0.071]* [0.073]** AFTER IRCA * AGAREA -0.034 0.006 -0.134 -0.115 -0.035 0.030 [0.017]* [0.029] [0.054]** [0.083] [0.053] [0.093] AFTER IRCA * AGAREA2 -0.076 -0.038 -0.126 [0.042]* [0.157] [0.151] AFTER IRCA * AGAREA * LAB_I 0.018 0.017 0.079 0.079 0.027 0.026 [0.015] [0.015] [0.046]* [0.046]* [0.048] [0.047] AFTER IRCA * LAB_I -0.010 -0.010 -0.004 -0.004 -0.003 -0.003 [0.004]*** [0.004]*** [0.008] [0.008] [0.014] [0.014] Log income per capita neighboring counties 0.011 0.017 0.007 0.010 0.254 0.263 [0.051] [0.051] [0.122] [0.124] [0.171] [0.168] Log of total population 0.005 0.007 -0.054 -0.053 -0.240 -0.237 [0.030] [0.030] [0.071] [0.070] [0.086]*** [0.088]*** Government transfers per capita -0.000 -0.000 -0.000 -0.000 0.000 0.000 [0.000] [0.000] [0.000]*** [0.000]*** [0.000] [0.000] Fraction of pop. in unincorporated areas 0.045 0.041 -0.077 -0.079 -0.110 -0.117 [0.050] [0.050] [0.069] [0.070] [0.119] [0.119] County fixed effects? YES YES YES YES YES YES Year fixed effects? YES YES YES YES YES YES Observations 284 284 283 283 284 284 R-squared 0.99 0.99 0.97 0.97 0.99 0.99 Root MSE 0.01 0.01 0.02 0.02 0.02 0.02 Notes: Huber robust standard errors in parentheses. Regression disturbance terms clustered at the county level. Significantly different from zero at 90% (*), 95% (**), 99% (***) confidence. AFTER IRCA takes the value of one for the years after 1986. Excludes San Francisco county. LAB_I is value-weighted average labor intensity of agricultural production at the beginning of the period. Ethnic groups are: white, Hispanic, black, Asian and Pacific Islander, and Native American and shares sum to one.

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Table 6: Community composition and local public goods Dependent variable: Alternative measures of local public goods expenditure (LPG) per capita ($1995) in California counties 1985-1989 Total LPG Police Water and Total LPG Police Water and spending per spending per Sanitation spending per spending per sanitation capita ($1995) capita ($1995) spending per capita ($1995) capita ($1995) spending per capita ($1995) capita ($1995) (1) (2) (3) (4) (5) (6) OLS OLS OLS IV-2SLS IV-2SLS IV-2SLS Population share of largest group 877.309 455.794 6.212 1,666.547 729.705 -370.862 [715.965] [156.989]*** [70.416] [891.819]* [361.790]** [363.643] Fraction county revenue from property taxes 300.701 45.877 4.887 333.902 124.292 110.274 [164.025]* [52.001] [21.392] [287.444] [156.259] [118.917] Log income per capita 27.714 -28.870 32.816 235.512 -348.423 279.988 [184.298] [23.612] [27.708] [803.299] [316.763] [474.840] 1000 harvested acres/mile2 (AGAREA) 110.025 -29.499 -29.392 90.096 -44.954 18.414 [99.990] [36.357] [11.016]*** [122.440] [55.122] [42.285] AGAREA2 -460.807 -5.305 -0.815 -533.378 79.520 -111.839 [205.376]** [59.222] [26.144] [318.272]* [110.581] [155.182] AGAREA * LAB_I 120.177 18.056 27.520 180.082 -14.298 23.002 [138.321] [24.670] [22.764] [156.340] [57.862] [69.643] Log of total population -95.039 -60.601 0.590 -47.156 -126.541 56.857 [92.622] [37.414] [18.082] [161.941] [80.481] [97.547] Government transfers per capita 0.340 -0.003 -0.005 0.329 0.029 -0.014 [0.146]** [0.007] [0.006] [0.148]** [0.041] [0.049] Fraction of pop. in unincorporated areas 189.301 41.858 25.876 156.676 3.294 59.097 [167.926] [27.426] [35.377] [187.008] [51.573] [75.111] County fixed effects? YES YES YES YES YES YES Year fixed effects? YES YES YES YES YES YES Observations 283 283 283 283 283 283 Root MSE 30.20 9.58 7.29 R-squared 0.99 0.99 0.83 Notes: Huber robust standard errors in parentheses. Regression disturbance terms clustered at the county level. Significantly different from zero at 90% (*), 95% (**), 99% (***) confidence. AFTER IRCA takes the value of one for the years after 1986. Excludes San Francisco county. LAB_I is value-weighted average labor intensity of agricultural production at the beginning of the period. Ethnic groups are: white, Hispanic, black, Asian and Pacific Islander, and Native American and shares sum to one. Local public goods included in dependent variable in columns (1) and (3) are: fire protection, police services, public transport, roads, transport terminals, mental health services, water, and sanitation. Excludes medical and public health expenditures. The instrumental variables for population share of the largest group, fraction of revenue from property taxes and log income per capita in columns (4)-(6) are: interaction of indicator variable for AFTER IRCA and (1) fraction of county land in agriculture, (2) LAB_I, and (3) 1000 harvested acres/mile2 (AGAREA)* LAB_I.

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Table 7: IRCA intensity and subsequent votes for Republican Presidential candidates Dependent variable: Fraction presidential votes for Republican Party (1) (2) (3) (4) OLS OLS OLS OLS 1988 election 1992 election 1987 1000 harvested acres/mile2 (AGAREA) 0.139 0.106 0.269 0.156 [0.169] [0.038]*** [0.128]** [0.060]** 1987 AGAREA * LAB_I -0.184 -0.056 -0.210 -0.072 [0.140] [0.028]* [0.097]** [0.053] 1987 LAB_I 0.051 0.009 0.060 0.020 [0.036] [0.009] [0.026]** [0.016] 1987 Log income per capita 0.233 -0.028 0.268 0.103 [0.114]** [0.043] [0.089]*** [0.064] 1987 Log of total population -0.006 0.013 0.004 0.007 [0.009] [0.003]*** [0.007] [0.004]* 1987 Government transfers per capita -0.000 0.000 0.000 0.000 [0.000] [0.000]*** [0.000] [0.000] 1987 Fraction of pop. in unincorporated areas 0.078 0.050 0.093 0.049 [0.063] [0.019]** [0.043]** [0.027]* 1987 Unemployment rate -0.881 0.064 -0.326 0.299 [0.494]* [0.145] [0.382] [0.235] Fraction population below poverty line 1989 0.200 -0.200 0.493 0.313 [0.329] [0.111]* [0.261]* [0.204] Fraction population with college degree 1990 -0.939 -0.038 -0.950 -0.231 [0.252]*** [0.109] [0.226]*** [0.161] Fraction of pop. over age 18 voting -0.179 -0.038 0.016 0.063 [0.207] [0.077] [0.128] [0.084] Fraction of previous election votes for GOP 1.093 0.715 [0.040]*** [0.068]*** Constant -1.487 -0.017 -2.357 -1.261 [1.071] [0.447] [0.849]*** [0.610]** Average value of dep. var. (std. error) 0.53 (.08) 0.53 (.08) 0.35 (0.07) 0.35 (0.07) Observations 57 57 57 57 R-squared 0.37 0.96 0.41 0.86 Notes: Huber robust standard errors in parentheses. Significantly different from zero at 90% (*), 95% (**), 99% (***) confidence. Excludes San Francisco county. LAB_I is value-weighted average labor intensity of agricultural production at the beginning of the period.

48