Demographic Structure and the Political Economy of State

Demographic Structure and the Political Economy of State-Level
Anti-Immigrant Legislation
Qinping Feng∗
March 29, 2015
Abstract
There has been a considerable increase in state-level anti-immigrant legislation in the past
decade. In this paper, I study the driving forces of state anti-immigrant legislation passed
since 2005, with a focus on a district’s demographic structure. To accomplish this, I compile
a novel dataset of legislative district level characteristics and match it with votes on individual
bills from State House of Representatives and Senate. I show that districts with more established immigrant population and higher fraction of African Americans tend to vote against antiimmigrant legislation. In contrast to the previous finding, however, districts with large fraction
of Hispanic population tend to vote for anti-immigrant legislation. Further analysis also suggests a non-linearity between foreign-born population and attitudes toward immigrants.
JEL-Classification: K37, H72, H75
Keywords: State Immigration Enforcement, Nativism, Ethnic Diversity
∗
University of Illinois, Urbana-Champaign, qfeng4@illinois.edu
1
1
Introduction:
In response to federal government’s failure to enact meaningful reform in the past decade, there
has been a considerable increase in state-level legislation that aims to restrict the number of undocumented immigrants in different dimensions. In 2013, 184 bills were passed in 45 states, a
five times increase compared to the 37 bills passed in 2005. Yet, because states’ effort to limit
the undocumented immigrants was a relatively new phenomena, we do not know much about the
political process and motivation of these legislation.
In this paper, I study the driving forces of state-level immigration-related legislation since
2005. In particular, I focus on how demographic structure in a neighborhood affects a legislative
district representative’s voting decision on the welfare-related anti-immigrant bills. I show that
districts with more established immigrant population and higher fraction of non-Hispanic black
population tend to vote against anti-immigrant legislation, while districts with large fraction of
Hispanic population tend to vote for anti-immigrant legislation.
While there has been a few studies that look at state-level immigrant legislation, my approach
has two advantages in that it exploits the variation in a smaller geography level, the legislative
district-level, and my study focuses on welfare-related bills. Instead of using the state-level variation in other studies (Boushey and Luedtke, 2011 and Rivera, 2014), I compile the novel dataset
on a wealth of legislative district level characteristics. Then, I match it with votes in individual
bills from state House of Representatives and Senate. By observing the district level characteristics, I establish a relationship between a representative’s home district characteristics and his/her
voting behavior in each individual bill. Kanazawa (2005) employs a similar approach to study the
anti-Chinese immigration legislation in California in the 1850s. But this paper focuses on the bills
within a different time frame. Furthermore, previous works (for instance, Boushey and Luedtke
2011) commonly rely on the number of bills enacted to gauge the anti-immigrant activities. However, my study only focus on one type of bills: bills related to immigrants’ access to public benefit.
The structure of the remaining paper is as follows: it begins with an introduction of statelevel welfare-related immigration legislation. Next, I discuss the data and how I compile the state
2
legislative-district level data. Then, the paper presents the econometric specification that provides
the basis for this study and several specifications are explored. Finally, a brief conclusion suggests
several limitations of the current analysis.
2
An Overview of State Immigration Legislation on Immigrant
Welfare Access, 2005-2011
In this section, I will provide a brief chronology of state-level immigration legislation and an
overview of their most common provisions. In this paper, I focus on welfare-related immigration
legislation.
Although with limited jurisdiction over immigration-related issues, states have long been active
in immigration enforcement by passing state bills not only in the area of law enforcement but in
public benefits, employment, driver’s licenses, etc. In 2013, 184 bills were passed in 45 states, a
five times increase compared to the 37 bills passed in 2005. Table 1 lists the state-level welfarerelated immigration legislation passed since 2005. The welfare-related immigration legislation
typically includes the following provisions: limiting the undocumented immigrants’ access to only
federally required public benefits, providing strict and explicit requirements to ensure the limited
access to public benefits for immigrants, and requiring the reports of fraudulent welfare claims
from immigrants.
3
3.1
Empirical Analysis
Empirical Framework
To establish the link between a district representative’s vote with her/his home district’s demographic structure, I estimate the following linear probability model:
3
V oteist = α + βDemographicsist + σs + γt + it
Where the dependent variable, V oteist is a dichotomous variable. V oteist = 1 if a representative of legislative district i from state s votes “Yes” on a given bill in year t, and V oteist = 0 if the
representative votes “No”. In all cases, therefore, a “Yes” vote is interpreted as a vote in favor of a
bill restricting immigrants’ access to public benefit at year t.
I pool data from all states and districts together to conserve degrees of freedom, thus providing
1993 observations. I include state fixed effect σs to account for any differences across states that
do not change over time. For example, the differences in political institutions, ideology, and the
degree of party competition across state legislatures would be absorbed in the state fixed effect.
Therefore, I exploit the variation across legislative districts within states. I use year fixed effect γt
to account for the differences between bills passed in different years.
3.2
Endogeneity Issues
When relating demographics and attitudes towards immigrant welfare access in a legislative district
level, there are two main sources of endogeneity. First, the household location choice is endogenous. For example, Dustmann and Preston (2001) show that individual location decision is partly
driven by their attitudes towards ethnic minorities. When a neighborhood experiences an immigrant inflow, the native flight may happen due to various reasons. Cascio and Lewis (2012) show
the native flights to other school districts due to inflow of immigrant children. Therefore, people
who choose to reside in an area with big immigrant population tend to be generous of immigrants’
access to public benefit. Second, the boundary of a legislative district is also endogenous. A
legislative district is drawn for voting purposes.
To tackle the endogeneity issues, I use a higher level of geography aggregation, the county level
rather than the tract level, to obtain the demographic information in the legislative district level.
Each representative in a state legislative district represents a district of at least some population.
4
For the states analyzed in this paper, the total population of the lower level legislative has a mean
population of 50,000 for each district, ranging from 35,000 of Utah to 208,000 of Arizona. The
boundaries of counties and districts often straddle with each other. I convert the county-level characteristics to the district-level characteristics by 2010 population weight from MABLE/Geocorr12:
Geographic Correspondence Engine. When counties are large enough to have multiple district representatives, the average voting outcomes within a county are linked with the county-level characteristics. When a county is too small, a district representative may represent multiple counties with
small population. In this case, a representative’s vote is linked with the characteristics weighted by
county population. Intuitively, a representative would put a higher weight on a county with larger
population in her/his home district in the voting decision. To the extent that there would be some
level of measurement error in measuring a representative’s home district characteristic, my results
are under-estimated.
3.3
State Legislative District-Level Data and Summary Statistics
The unit of analysis is the legislative district. A state legislature in the United States is the legislative body of any of the 50 U.S. states. Every state except Nebraska has a bicameral legislature,
meaning that the legislature consists of two separate legislative chambers or houses, usually House
and Senate.
The outcome variable of interest is votes on immigration bills by state House of Representatives
and Senate. I use the data from Project Vote Smart (PVS).1 The data includes the representative’s
name, party affiliation, and his/her legislative district and vote on each single bill. I supplement
these data with recorded votes from state legislature websites when the voting information is not
available in PVS.
The key demographic variables of interest are the fraction of non-Hispanic black, Hispanic,
1
Project Vote Smart is a non-profit, non-partisan research organization that collects and distributes information
on candidates for public office in the United States. It covers candidates and elected officials in six basic areas:
background information, issue positions (via the Political Courage Test), voting records, campaign finances, interest
group ratings, and speeches and public statements.
5
foreign-born, and old population in total population. Since the time period I focus on spans 2005
to 2010, I use 2006-2010 5-year American Community Survey for two reasons. First, unlike other
single-year or 3-year ACS data which only have selective counties (usually counties with large population) identified, 5-year ACS data identify all counties. Second, it also provides a large enough
sample size. The variables will be less prone to measurement errors. Other demographic characteristics data include percent of population with at least college degree and percent of population
living in urban areas. Percent of population with at least college degree measures the percentage
of the population over 25 with at least a college degree.
The economics characteristics data include median household income level, poverty rate, unemployment rate, and inequality. These variables also come from 2006-2010 5-year ACS. The
inequality is measured as the ratio of mean to median household income. Median family income
and inequality, are two main variables that affect the demand for redistribution. Legislators from
wealthier districts (higher median household income) are expected to exhibit less favorable attitudes towards immigrants’ welfare access. Meltzer and Richard (1981) shows that increase in
inequality (the gap between the mean and median household income), leads to stronger support for
redistribution. However, a priori, it is not clear how it affects the preference for redistribution for
immigrants.
I then convert the county-level characteristics to the district-level characteristics by 2010 population weight from MABLE/Geocorr12: Geographic Correspondence Engine.
Table 2 provides summary statistics for the variables described above. A few points are worth
highlighting. First, 34% of the representatives voted against the bill. To the extent that multiple
bills are lopsided votes, the party plays a less important in affecting the voting decision. Second,
about 65% of the legislators are republicans, implying that the states who passed the bills are more
conservative states. Third, these states have large share of non-Hispanic black population.
The main goal of the paper is to investigate whether a systematic relationship exists between
a representative’s voting behavior on immigration bills and the demographic structure of his/her
home district.
6
3.4
Results
This main results in Table 3 demonstrate that districts with established immigrant population and
large fraction of non-Hispanic black population tend to vote against anti-immigrant bills, while
districts with higher fraction of Hispanic population tend to vote for the anti-immigrant bills. The
first three columns present the estimate without including the representative’s party affiliation.
I first estimate the model without additional covariates (column 1). The pro-immigrant attitude
from districts with more established immigrant population is consistent with the finding in the
literature: for example, Goldin (1993) finds that longstanding contact with immigrants will reduce
the pressures to pass anti-immigrant legislation. The negative sign for fraction of non-Hispanic
black population suggests non-Hispanic blacks tend to view immigrant issues in the minority right
framework. In contrast, districts with large fraction of Hispanic population are estimated to vote for
anti-immigrant legislation. Districts with higher percentage of old population are estimated to be
against the generosity towards immigrant welfare state. This is likely due to the fear of threat from
immigrants for competing limited government resources. The sign on median household income
is consistent with the finding in the literature, although not significant (Meltzer and Richard, 1981
and Facchini and Mayda, 2009): wealthier area are less supportive of immigrants’ welfare access.
The effect of unemployment rate does not reveal a clear pattern across specifications.
The inclusion of a representative’s party affiliation (the last three columns) attenuates the effect of the percentage of foreign-born and the percentage of non-Hispanic black population. This
suggests districts with higher percentage of foreign-born and non-Hispanic black population tend
to elect a Democratic representative. Adding the party variable also attenuates the effect of Hispanic population. However, this suggests the opposite: Republican representatives are more likely
to be elected in districts with higher proportion of Hispanic population. The signs of these three
key demographic variables are consistently estimated across different specifications, although the
effect of foreign-born population and Hispanic population become insignificant when the share of
college graduate is added, possibly due to multicollinearity.
The estimate on party shows that Republicans are significantly more likely to vote in favor
7
of strict immigration bills. This result is in line with earlier findings by Gimpel and Edwards Jr
(1998), who conclude that “recorded votes on immigration policy have become more partisan over
time, even after controlling for alternative influences on congressional decision making such as
region and constituency characteristics.”
3.5
Heterogeneous Analysis on Hispanic Effects
Table 4 presents the heterogeneous analysis to show where the Hispanic effect stems from. Three
possible mechanisms may explain why districts with higher fraction of Hispanic shows more antiimmigrant sentiment. First, Hispanic communities may not have enough political power due to
their lower voter turnout or higher percentage of undocumented immigrants who are not eligible to
vote. Second, it might also suggest Hispanic communities have more displacement threat from incoming immigrants for competing scarce public benefit. Lastly, it may simply due to the perception
that Hispanics tend to be undocumented immigrants and take up public benefits when a community has a larger contact with Hispanic population. Column 1 and 2 show the analysis replacing
the percentage of Hispanic population as the percentage of US-born Hispanic and foreign-born
Hispanic in separate estimation. Since the correlation between the shares of US-born Hispanic
and foreign-born Hispanic is 0.75, I estimate the Hispanic effect by nativity separately. The effect
of foreign-born Hispanic is not significant and the effect of US-born Hispanic is marginally significant. The anti-immigrant sentiment is therefore more powerfully explained by the percentage
of US-born Hispanic. This suggests perception or displacement threat plays a larger role in antiimmigrant sentiment. Column 3 to Column 5 show the analysis by household income quantile.
The low income areas tend to be in favor of strict immigrant legislation for immigrant welfare.
This suggests the anti-immigrant legislation is more driven by the fear of displacement from lowincome districts.
8
3.6
Nonlinearity
Table 5 shows some interesting nonlinear pattern. I estimate the main specification by the quantile
of foreign-born percentage and Hispanic percentage. In the lower quantile foreign-born percentage
sample, more foreign-born population tend to show a large and significant anti-immigrant attitude.
This suggests that the coalition or political power of foreign-born population do not form until it
reaches some threshold. I do not find such non-linearity between Hispanic percentage and voting
outcomes.
4
Conclusions
To conclude, I show that districts with more established immigrant population and higher fraction
of African Americans tend to vote against anti-immigrant legislation. In contrast to the previous finding, however, districts with large fraction of Hispanic population tend to vote for antiimmigrant legislation. Heterogeneous analysis suggests the anti-immigrant legislation is more
driven by the fear of displacement or the perception about Hispanics. Further analysis also suggests a non-linearity between foreign-born population and attitudes toward immigrants.
References
Boushey, G. and Luedtke, A. (2011). Immigrants across the u.s. federal laboratory: Explaining
state-level innovation in immigration policy. State Politics and Policy Quarterly, 11:390–414.
Cascio, E. U. and Lewis, E. G. (2012). Cracks in the melting pot: Immigration school choice, and
segregation. American Economic Journal: Economic Policy, 4(3):91–117.
Dustmann, C. and Preston, I. (2001). Attitudes to ethnic minorities, ethnic context and location
decisions. The Economic Journal, 111:353–373.
9
Facchini, G. and Mayda, A. M. (2009). Does the welfare state affect individual attitudes toward
immigrants? evidence across countries. Review of Economics and Statistics, 91:295–314.
Gimpel, J. G. and Edwards Jr, J. R. (1998). The congressional politics of immigration reform.
Longman.
Goldin, C. (1993). The political economy of immigration restriction in the united states, 1890 to
1921. NBER Working Paper.
Meltzer, A. H. and Richard, S. F. (1981). A rational theory of the size of government. Journal of
Political Economy, 89:914–927.
10
11
b
a
AZ HB2448/SB2738
CO HB1023
GA SB529∗
(CO HB1314)
ID SB1157
OK HB1804∗
UT SB81
MO HB1549
SC HB4400
GA HB2
NE LB 403
AZ SB1070∗
UT HB497∗
GA HB87∗
AL HB56∗
SC SB20∗
IN SB590∗
04/24/2006
07/31/2006
04/17/2006
03/01/2007
03/30/2007
05/08/2007
03/13/2008
07/07/2008
06/04/2008
05/11/2009
04/08/2009
04/23/2010
03/15/2011
05/13/2011
06/09/2011
06/27/2011
07/01/2011
Eligibility For Services
Public Benefits for Undocumented Immigrants
Georgia Security and Immigration Compliance Act
Extended Rule For Public Benefit
Restrictions On Public Benefits
Oklahoma Taxpayer and Citizen Protection Act
Expanding Immigration Enforcement
Undocumented Immigrants
Illegal Immigration Reform Act
Security and Immigration Compliance
Lawful Presence
Undocumented Immigration Enforcement
Immigration Enforcement Act
Immigrant Enforcement Act
Immigration Enforcement
Immigration Enforcement
Illegal Immigration Matters
Bill Title
27
22
39
29
29
(41
24
27
N.A.a
38
N.A.b
17
22
37
25
37
35
yes
1
13
16
4
6
6)
4
7
N.A.
16
N.A.
11
5
19
7
0
15
no
37
(48
119
64
47
84
56
136
94
121
N.A.
35
59
112
67
69
68
yes
no
20
15)
49
0
21
14
15
12
16
47
N.A.
21
15
59
29
43
31
House
No recorded vote
Nebraska’s Legislature is unicameral and nonpartisan.
Sources: Project Vote Smart and state legislative websites from various sessions
Note: Date signed is the date in which the bill was signed by the governor. Yes/no show the total number of ”Yea/Nay”
votes. Omnibus immigration legislation (comprehensive measures) are marked with an asterisk(∗). It covers a wide
variety of topics, including employment verification, human trafficking, public benefits, identification, tax withholdings,
state enforcement of federal immigration and ethics for immigration assistance services. “HB” is the bill initiated from
state House of Representatives. “SB” is the bill initiated from Senate. SC HB4400 was read third time and returned to
House with amendments
Bill
Date Signed
Senate
Table 1: State Anti-Immigrant Legislation Chronology (2005-2011):
Votes on Bills Related to Public Benefits to Immigrants
Table 2: Summary Statistics: Legislative-Level Measures
VARIABLES
Vote
Party
% Foreign-Born 2006-2010
% Hispanic 2006-2010
% African-American 2006-2010
% College 2006-2010
% Old 2006-2010
Median Household Income 2006-2010
Unemployment Rate 2006-2010
Inequality 2006-2010
N
Mean
S.D.
Minimum
Maximum
1,993
1,993
1,993
1,993
1,993
1,993
1,993
1,993
1,993
1,993
0.744
0.357
0.0740
0.0926
0.184
0.257
0.118
48,541
0.0654
1.311
0.437
0.479
0.0542
0.0829
0.175
0.0959
0.0291
9,650
0.0162
0.0864
0
0
0.00331
0.00453
0.000305
0.0811
0.0594
24,191
0.0255
1.085
1
1
0.255
0.562
0.687
0.570
0.233
99,198
0.138
1.590
NOTE: Vote and party affiliation of the representatives are in the legislative district level. Party
is a dummy variable coded as 1 if the representative of the district belongs to the Democratic
Party. All other variables are weighted to legislative-level. Percent of population with at least
college degree measures the percentage of the population over 25 with at least a college degree.
The unemployment rate as the ratio of the number of people unemployed over the total number
of people for 16 years and over in the labor force. Median household income measures the
median household income within a district. Inequality measures the ratio between mean and
median household income within a district. All monetary values are measured in 2010 dollars.
12
Table 3: Linear Probability Estimates of Representatives’ Votes on Anti-Immigrant Bills
Dependent Variable: Vote on Welfare Only Bills
Variables
% Foreign-Born
% Hispanic
% African-American
(1)
(2)
(3)
(4)
(5)
(6)
-1.389***
(0.369)
0.690**
(0.342)
-1.266***
(0.0958)
-1.397***
(0.422)
0.692**
(0.346)
-1.267***
(0.0967)
-0.0231
(0.550)
-1.356**
(0.526)
0.766**
(0.388)
-1.044***
(0.129)
0.876
(0.631)
0.119
(0.0943)
-0.481***
(0.163)
-0.140
(1.080)
-1.089***
(0.306)
0.732***
(0.283)
-0.506***
(0.0860)
-0.709
(0.435)
0.592*
(0.321)
-0.412***
(0.110)
0.997*
(0.521)
0.0542
(0.0780)
-0.408***
(0.135)
1.354
(0.895)
-0.479***
(0.0209)
-0.481***
(0.0209)
-0.654
(0.471)
0.550
(0.349)
-0.411***
(0.110)
0.945*
(0.549)
0.0782
(0.111)
-0.360*
(0.207)
1.298
(0.914)
-0.0856
(0.280)
-0.480***
(0.0210)
1,156
0.449
1,156
0.458
1,156
0.458
% Old
log(Median Income)
Inequality
Unemployment Rate
% College
Party
Observations
R-squared
1,156
0.196
1,156
0.196
1,156
0.205
NOTE: The total number of observations is 1156. Dependent variable Vote=1 if the legislator vote
yes on an anti-immigrant bill. State fixed effect and year fixed effect are included in all specifications.
The last column is clustered at the state level. The control variables include the percentage of the
population over 25 with at least a bachelor degree, log of median household income, poverty rate,
unemployment rate, and percentage of population living in urban areas.
Standard deviation is reported in parentheses.
* significant at the 10% level; **significant at the 5% level;***significant at the 1% level.
13
14
yes
0.46
1156
Party
R2
observations
1156
0.45
yes
yes
1.31
(0.92)
405
0.39
yes
yes
1.31∗
(0.75)
453
0.58
yes
yes
-0.48
(1.11)
298
0.44
yes
yes
-1.71
(1.39)
By Household Income Quantile
1st Quantile 2nd Quantile 3rd Quantile
NOTE: State fixed effect and year fixed effect are included in all specifications. The control variables include the percentage of
foreign-born population, the percentage of non-Hispanic black population, the percentage of the population over 25 with at least a
bachelor degree, log(median household income), poverty rate, and percentage of population living in urban areas.
Standard deviation is reported in parentheses.
* significant at the 10% level; **significant at the 5% level;***significant at the 1% level.
yes
0.76∗
(0.43)
By Hispanic Nativity
US-born Hispanic Foreign-born Hispanic
Controls
%Foreign-born Hispanic
%US-born Hispanic
%Hispanic
Variables
Dependent Variable: Vote on Anti-Immigrant Bills
Table 4: Heterogeneous Analysis
Table 5: Linear Probability Estimates of Representatives’ Votes on Anti-Immigrant Bills:
Nonlinearity
VARIABLES
%Foreign-Born
%Hispanic
%African-American
log(Median Income)
%College
Unemployment Rate
Party
Observations
R-squared
By Percentage of Foreign-Born
By Percentage of Hispanic
1st Quantile
2nd Quantile
1st Quantile
2nd Quantile
2.374**
(1.140)
-0.489
(0.566)
-0.431***
(0.130)
0.122
(0.0969)
-0.782***
(0.298)
2.807**
(1.093)
-0.428***
(0.0273)
-0.454
(0.699)
0.610
(0.631)
-0.440*
(0.225)
0.319**
(0.144)
-0.363
(0.297)
0.174
(2.216)
-0.538***
(0.0340)
-2.336*
(1.374)
1.718*
(1.008)
-0.400***
(0.137)
0.101
(0.104)
-0.0531
(0.345)
3.041**
(1.181)
-0.477***
(0.0280)
-0.675
(0.642)
0.936*
(0.542)
-0.439**
(0.201)
0.209*
(0.127)
-0.526*
(0.288)
-0.408
(1.801)
-0.469***
(0.0337)
648
0.403
508
0.512
633
0.457
523
0.473
NOTE: State fixed effect and year fixed effect are included in all specifications.
Standard deviation is reported in parentheses.
* significant at the 10% level; **significant at the 5% level;***significant at the 1% level.
15