《国际政治经济学》文献资料（Political Economy of Migration）Welfare and the Multifaceted Decision to Move

American Political Science Review Vol.99.No.1 February 2005 Welfare and the Multifaceted Decision to Move MICHAEL A.BAILEY Georgetown University hether poor single mothers move in response to welfare benefits has important implications for social policy in a federal system.Many scholars claim that welfare does not affect migration. These claims are not definitive,however,because the models underlying them rely on problem- atic assumptions and do not adequately control for nonwelfare determinants ofmigration.I address these shortcomings with an improved statistical model of individual-level migration.The results indicate that welfare does affect residential choice.Although the effects of welfare are much smaller than the effects of family ties,they are real and have the potential to cause nontrivial changes in welfare populations and welfare expenditures. re poor single mothers more likely to stay in 2000,519)or,more typically,that welfare-induced mi- or move to states with higher welfare benefits? gration is a"myth"(Allard and Danziger 2000;Schram This question has important implications on at and Soss 1999.83).However.we should be cautious least two levels.As a policy matter,the answer will about accepting this emerging conventional wisdom. enlighten us about the effects of welfare on society In a variety of ways,these studies fail to account for and will assist efforts to understand whether states the complexities of migration and consequently run systematically lower benefits in order to avoid becom- the risk of either obscuring the effect of welfare or, ing"welfare-magnets"(Peterson and Rom 1989;Rom, even worse,conflating the effect of welfare with the Peterson,and Scheve 1998:Volden 2002).This answer effects of other unmeasured factors.As I show be- will also help us anticipate how welfare policies may low,this is precisely what has happened,the case in evolve in other areas of the world where it is-or point being the link between welfare benefits granted is becoming-as easy to move across jurisdictional through the Aid to Families with Dependent Children boundaries as in the United States. (AFDC)program in the late 1980s and the residential As a theoretical matter,understanding the relation- choices of poor single mothers,the program's primary ship between welfare and migration can help us bet- constituency. ter understand how the increasing mobility of people, firms,and capital affects governmental capacities to provide welfare and other redistributive benefits.If THREE HOLES IN THE EXISTING generous government benefits prompt people who re- LITERATURE ceive them to flow in and the people who pay for them to flow out,the benefits will become increasingly diffi- Assessing whether welfare affects migration is no sim- cult to sustain.This is true not only for a federal system ple task,as attested by a vast,highly contested liter- such as the United States,but also for the international ature (for a review,see Brueckner 2000).To do this system in which political,social,and economic barriers convincingly,researchers must account for all the fac- to migration have fallen dramatically in recent years tors other than welfare that affect people's decisions to Finding that welfare-induced migration occurs in the move.Researchers have made progress in this respect, United States would enhance concern about govern- but problems persist.Three issues undermine the re- mental capacity for social services;finding no such be- cent wave of research that downplays or dismisses the havior,on the other hand,would make us less inclined effect of welfare on migration. to believe that migration constrains governments in First,many studies risk distorting the effect of wel- other,less likely contexts. fare by inadequately accounting for state attributes that Lately,most scholars researching the question have affect migration.These studies typically consist of sta- found very little or no support for the idea that wel- tistical analyses of either aggregated migration flows fare affects migration in the United States (Allard and or individual migration choices.They control for state- Danziger 2000;Levine and Zimmerman 1999;Schram, level influences on residential choice through variables Nitz,and Krueger 1998;Schram and Soss 1999).They measuring such attributes as state economic perfor- conclude that the evidence is"at best mildly in favor' mance and differences in state climates (e.g.,Allard of the idea that welfare affects migration (Brueckner and Danziger 2000:Frey et al.1996:Schram.Nitz.and Krueger 1998). This seemingly straightforward enterprise is actu- Michael A.Bailey is Associate Professor,ICC.Suite 681,Depart- ally remarkably difficult.Consider,for example,the ment of Government,Georgetown University,Washington,DC variables Schram,Nitz,and Krueger use to charac- 20057(baileyma@georgetown.edu). terize nonwelfare components of state attractiveness. I appreciate helpful comments from Scott Allard,Arik Levinson. Florida-the quintessential high-population growth Bob Lieber,Forrest Maltzman,Dan Nexon,Mark Rom,George state-averaged 5.5%unemployment from 1985 to Shambaugh,Joe Soss,Michele Swers,and anonymous reviewers.I also wish to thank Craig Volden for sharing data.In addition,I grate- 1990;its median income averaged $34,931 in nominal fully acknowledge the support of the Hoover Institution National terms and $29.090 in state cost-of-living adjusted terms. Fellowship Program,Stanford University. Many states that were less attractive to potential 125

American Political Science Review Vol. 99, No. 1 February 2005 Welfare and the Multifaceted Decision to Move MICHAEL A. BAILEY Georgetown University Whether poor single mothers move in response to welfare benefits has important implications for social policy in a federal system. Many scholars claim that welfare does not affect migration. These claims are not definitive, however, because the models underlying them rely on problem￾atic assumptions and do not adequately control for nonwelfare determinants of migration. I address these shortcomings with an improved statistical model of individual-level migration. The results indicate that welfare does affect residential choice. Although the effects of welfare are much smaller than the effects of family ties, they are real and have the potential to cause nontrivial changes in welfare populations and welfare expenditures. Are poor single mothers more likely to stay in or move to states with higher welfare benefits? This question has important implications on at least two levels. As a policy matter, the answer will enlighten us about the effects of welfare on society and will assist efforts to understand whether states systematically lower benefits in order to avoid becom￾ing “welfare-magnets” (Peterson and Rom 1989; Rom, Peterson, and Scheve 1998; Volden 2002). This answer will also help us anticipate how welfare policies may evolve in other areas of the world where it is—–or is becoming—–as easy to move across jurisdictional boundaries as in the United States. As a theoretical matter, understanding the relation￾ship between welfare and migration can help us bet￾ter understand how the increasing mobility of people, firms, and capital affects governmental capacities to provide welfare and other redistributive benefits. If generous government benefits prompt people who re￾ceive them to flow in and the people who pay for them to flow out, the benefits will become increasingly diffi- cult to sustain. This is true not only for a federal system such as the United States, but also for the international system in which political, social, and economic barriers to migration have fallen dramatically in recent years. Finding that welfare-induced migration occurs in the United States would enhance concern about govern￾mental capacity for social services; finding no such be￾havior, on the other hand, would make us less inclined to believe that migration constrains governments in other, less likely contexts. Lately, most scholars researching the question have found very little or no support for the idea that wel￾fare affects migration in the United States (Allard and Danziger 2000; Levine and Zimmerman 1999; Schram, Nitz, and Krueger 1998; Schram and Soss 1999). They conclude that the evidence is “at best mildly in favor” of the idea that welfare affects migration (Brueckner Michael A. Bailey is Associate Professor, ICC, Suite 681, Depart￾ment of Government, Georgetown University, Washington, DC 20057 (baileyma@georgetown.edu). I appreciate helpful comments from Scott Allard, Arik Levinson, Bob Lieber, Forrest Maltzman, Dan Nexon, Mark Rom, George Shambaugh, Joe Soss, Michele Swers, and anonymous reviewers. I also wish to thank Craig Volden for sharing data. In addition, I grate￾fully acknowledge the support of the Hoover Institution National Fellowship Program, Stanford University. 2000, 519) or, more typically, that welfare-induced mi￾gration is a “myth” (Allard and Danziger 2000; Schram and Soss 1999, 83). However, we should be cautious about accepting this emerging conventional wisdom. In a variety of ways, these studies fail to account for the complexities of migration and consequently run the risk of either obscuring the effect of welfare or, even worse, conflating the effect of welfare with the effects of other unmeasured factors. As I show be￾low, this is precisely what has happened, the case in point being the link between welfare benefits granted through the Aid to Families with Dependent Children (AFDC) program in the late 1980s and the residential choices of poor single mothers, the program’s primary constituency. THREE HOLES IN THE EXISTING LITERATURE Assessing whether welfare affects migration is no sim￾ple task, as attested by a vast, highly contested liter￾ature (for a review, see Brueckner 2000). To do this convincingly, researchers must account for all the fac￾tors other than welfare that affect people’s decisions to move. Researchers have made progress in this respect, but problems persist. Three issues undermine the re￾cent wave of research that downplays or dismisses the effect of welfare on migration. First, many studies risk distorting the effect of wel￾fare by inadequately accounting for state attributes that affect migration. These studies typically consist of sta￾tistical analyses of either aggregated migration flows or individual migration choices. They control for state￾level influences on residential choice through variables measuring such attributes as state economic perfor￾mance and differences in state climates (e.g., Allard and Danziger 2000; Frey et al. 1996; Schram, Nitz, and Krueger 1998). This seemingly straightforward enterprise is actu￾ally remarkably difficult. Consider, for example, the variables Schram, Nitz, and Krueger use to charac￾terize nonwelfare components of state attractiveness. Florida—–the quintessential high–population growth state—–averaged 5.5% unemployment from 1985 to 1990; its median income averaged $34,931 in nominal terms and $29,090 in state cost-of-living adjusted terms. Many states that were less attractive to potential 125

Welfare and the Multifaceted Decision to Move February 2005 in-migrants looked similar or better in these terms: The omission of race in many studies raises simi- Rhode Island averaged 3.9%unemployment and lar concerns.Individuals are more likely to move to $38,492 in nominal median income.South Dakota states with larger numbers of racially similar people averaged 4.4%unemployment and $30,460 in cost- (Frey et al.1996).In the data discussed below,there adjusted median income.Of course.one could add are about 60,000 poor white single mothers and about variables(e.g,“average temperature,.”“murder rate") 40,000 poor black single mothers.Of the whites,510 and all manner of nonlinearities and interactions (e.g., lived in North Dakota,South Dakota,or Vermont;two “temperature squared,”“temperature x income"). of the black single mothers lived in those states.If race- Nonetheless,one cannot help but suspect that signifi- specific attraction to states correlates with welfare (as is cant aspects of state attractiveness resist measurement likely if,for example.African Americans are relatively The danger is that studies with inadequate state-level more attracted to low-benefit southern states that have controls will conflate the effect of welfare on migration relatively large African American populations),failure with other factors.Recent demographic trends make to account for such variables may introduce yet another this a particular concern.Americans tend to move source of omitted variable bias that can distort the es- from northern("rust belt")states with relatively high timated effects of welfare on migration. welfare benefits to southern ("sun belt")states with Third,many studies aggregate away important state- relatively low welfare benefits.Failing to account for level differences.Levine and Zimmerman estimate a the complicated mixture of economic and social fac- model in which the dependent variable is whether an tors behind such moves results in analyses in which individual moved out of state.By ignoring whether the states where welfare is high are also the states the person left for a high-benefit state such as where the unmeasured attractiveness of living is low, California or a low-benefit state such as Louisiana, and vice versa.The statistical result is that unmea- this approach limits the ability of the method to as- sured disincentives to migrate to a state get lumped certain the role of welfare.Meyer (2000)estimates in with the observed (and correlated)welfare mea- a model in which migration across regions is the de- sures,leading to estimates in which the effect of wel- pendent variable,thereby treating states as identi- fare appears to be small or inconsequential,even if it cal within regions.Depending on the specification, is not. Meyer assumes that there are two or nine regions in Second,existing research fails to account adequately the entire United States,implying,for example,that for individual-level factors that influence migration. Maryland is identical to West Virginia and that New Many individuals want to move "home"to the state in Hampshire is identical to New York.This assump- which they were born because,that is,where they are tion of intraregional homogeneity creates a chronic more likely to have family and to know the neighbor- error in variables problem that likely will obscure hoods,schools,and industries.Moving home may have relationships between variables such as welfare and a particularly powerful appeal for single mothers,who migration. often depend on the housing,childcare,financial assis- Each of these problems potentially obscures or dis- tance,and psychological support of parents,siblings, torts the estimated effect of welfare on migration.Ev- and friends (Allard and Danziger 2000,358:Schram. ery recent study that dismisses welfare effects suffers Nitz,and Krueger 1998;Vartanian et al.1999).In what from more than one of these problems,meaning that follows,I refer to the attractions of home as "family the true effect of welfare is buried under multiple layers ties";some scholars refer to them as"social capital." of specification error.To get a better sense of the true The data described below bear out these expecta- relationship between welfare and migration,I develop tions.Home is not just another variable:it is a funda- an analytical approach that directly addresses each of mental influence on migration.Fully one-third of all these issues interstate moves by poor single mothers were to the individuals'birth states.For many states,the propor- tion of in-migrants who were born there is extremely A MORE COMPREHENSIVE APPROACH high:54%of poor single mothers moving into Alabama At the heart of the analysis is a random utility model of from out of state had been born in Alabama.The individual-level migration choices.The model charac- comparable numbers were 57%for Louisiana.58% terizes the utility for every individual of living in every for Mississippi,and 51%for West Virginia.(At the single state.Specifically,the utility of living in state s other extreme,only 12%of poor single mothers mov- for person i currently in state j consists of a deter- ing into Florida or Nevada were returning to their state ministic component viis and a stochastic component of birth. E订s Failure to account for the special attractiveness of birth states can lead researchers to understate or even Us=vs+es· (1) reverse the true effect of welfare on migration.The reason is that single mothers were born disproportion- I estimate the model with a conditional logit setup ately in poor,low-benefit states.If we fail to control (Greene 2000.858).In the model.each individual se- for the attraction of home states for these women,we lects the state that offers the highest utility.Assuming may mistake their fairly common moves home with a that the random shocks are independently and identi- complete disregard for the low welfare benefits in their cally distributed Extreme Value Type I random vari- states of birth. ables,the probability that person i living in state j 126

Welfare and the Multifaceted Decision to Move February 2005 in-migrants looked similar or better in these terms: Rhode Island averaged 3.9% unemployment and $38,492 in nominal median income. South Dakota averaged 4.4% unemployment and $30,460 in cost￾adjusted median income. Of course, one could add variables (e.g., “average temperature,” “murder rate”) and all manner of nonlinearities and interactions (e.g., “temperature squared,” “temperature × income”). Nonetheless, one cannot help but suspect that signifi- cant aspects of state attractiveness resist measurement. The danger is that studies with inadequate state-level controls will conflate the effect of welfare on migration with other factors. Recent demographic trends make this a particular concern. Americans tend to move from northern (“rust belt”) states with relatively high welfare benefits to southern (“sun belt”) states with relatively low welfare benefits. Failing to account for the complicated mixture of economic and social fac￾tors behind such moves results in analyses in which the states where welfare is high are also the states where the unmeasured attractiveness of living is low, and vice versa. The statistical result is that unmea￾sured disincentives to migrate to a state get lumped in with the observed (and correlated) welfare mea￾sures, leading to estimates in which the effect of wel￾fare appears to be small or inconsequential, even if it is not. Second, existing research fails to account adequately for individual-level factors that influence migration. Many individuals want to move “home” to the state in which they were born because, that is, where they are more likely to have family and to know the neighbor￾hoods, schools, and industries. Moving home may have a particularly powerful appeal for single mothers, who often depend on the housing, childcare, financial assis￾tance, and psychological support of parents, siblings, and friends (Allard and Danziger 2000, 358; Schram, Nitz, and Krueger 1998; Vartanian et al. 1999). In what follows, I refer to the attractions of home as “family ties”; some scholars refer to them as “social capital.” The data described below bear out these expecta￾tions. Home is not just another variable; it is a funda￾mental influence on migration. Fully one-third of all interstate moves by poor single mothers were to the individuals’ birth states. For many states, the propor￾tion of in-migrants who were born there is extremely high: 54% of poor single mothers moving into Alabama from out of state had been born in Alabama. The comparable numbers were 57% for Louisiana, 58% for Mississippi, and 51% for West Virginia. (At the other extreme, only 12% of poor single mothers mov￾ing into Florida or Nevada were returning to their state of birth.) Failure to account for the special attractiveness of birth states can lead researchers to understate or even reverse the true effect of welfare on migration. The reason is that single mothers were born disproportion￾ately in poor, low-benefit states. If we fail to control for the attraction of home states for these women, we may mistake their fairly common moves home with a complete disregard for the low welfare benefits in their states of birth. The omission of race in many studies raises simi￾lar concerns. Individuals are more likely to move to states with larger numbers of racially similar people (Frey et al. 1996). In the data discussed below, there are about 60,000 poor white single mothers and about 40,000 poor black single mothers. Of the whites, 510 lived in North Dakota, South Dakota, or Vermont; two of the black single mothers lived in those states. If race￾specific attraction to states correlates with welfare (as is likely if, for example, African Americans are relatively more attracted to low-benefit southern states that have relatively large African American populations), failure to account for such variables may introduce yet another source of omitted variable bias that can distort the es￾timated effects of welfare on migration. Third, many studies aggregate away important state￾level differences. Levine and Zimmerman estimate a model in which the dependent variable is whether an individual moved out of state. By ignoring whether the person left for a high-benefit state such as California or a low-benefit state such as Louisiana, this approach limits the ability of the method to as￾certain the role of welfare. Meyer (2000) estimates a model in which migration across regions is the de￾pendent variable, thereby treating states as identi￾cal within regions. Depending on the specification, Meyer assumes that there are two or nine regions in the entire United States, implying, for example, that Maryland is identical to West Virginia and that New Hampshire is identical to New York. This assump￾tion of intraregional homogeneity creates a chronic error in variables problem that likely will obscure relationships between variables such as welfare and migration. Each of these problems potentially obscures or dis￾torts the estimated effect of welfare on migration. Ev￾ery recent study that dismisses welfare effects suffers from more than one of these problems, meaning that the true effect of welfare is buried under multiple layers of specification error. To get a better sense of the true relationship between welfare and migration, I develop an analytical approach that directly addresses each of these issues. A MORE COMPREHENSIVE APPROACH At the heart of the analysis is a random utility model of individual-level migration choices. The model charac￾terizes the utility for every individual of living in every single state. Specifically, the utility of living in state s for person i currently in state j consists of a deter￾ministic component vijs and a stochastic component
ijs : Uijs = vijs +
ijs . (1) I estimate the model with a conditional logit setup (Greene 2000, 858). In the model, each individual se￾lects the state that offers the highest utility. Assuming that the random shocks are independently and identi￾cally distributed Extreme Value Type I random vari￾ables, the probability that person i living in state j 126

American Political Science Review Vol.99.No.1 chooses state s is ner that state unemployment and state climate were above).The state fixed effect will "soak up"the wel- Ps=Prob(Us>UkHk≠s), (2) fare effect and leave it statistically unidentified.I avoid =Prob(∈k-∈s<s-vHk卡S), 3) this problem by using a quasi-experimental research design,sometimes referred to as a comparison group e method (Levine and Zimmerman 1999;Meyer 2000). (4) ∑et This design requires that I include in the sample a "control group"that is not eligible for welfare but oth- where K is the total number of states to which an erwise resembles the"treatment group"of poor single individual can move.The computationally convenient mothers.General state attributes (captured by state form makes estimation conceptually straightforward fixed effects)influence individuals in the control and (even as it is practically difficult,given that a very treatment groups;welfare,however,influences only in- large number of individuals are choosing among a large dividuals in the treatment group.Given the inclusion number of discrete choices).By explicitly modeling all of the control group,the welfare variable is no longer a the state choices,I reduce the possibility that errors in constant for all individuals for any given state (that is, variables obscure the effect of welfare on migration. welfare benefits are zero for individuals in the control I control for state attributes by using state-level fixed group and the measured value for individuals in the effects,i.e.,by using state-level dummy variables to treatment group).The welfare variable now is statisti- control for all state attributes that are the same for all cally identified:it allows us to see whether differences individuals in the analysis.For example,these variables in welfare benefits explain any differences in behavior control for state unemployment and state climate be- by the treatment and control groups. cause for any given state,the values of these variables I also control for,among other factors,the gravi- will be the same when modeling the probability that tational pull of birth states and potential differences any individual will move to the state.2(A variable not in the attraction whites and African Americans have encompassed by fixed effects varies for a given state toward states.Including these variables not only serves across individuals;for example,only some people were important statistical control purposes,but also human- born in New York,meaning that when modeling the izes the analysis by moving beyond the caricature of utility of New York,the state-of-birth variable would welfare recipients as solely motivated by financial gain be one for some individuals and zero for others.)The (see the excellent discussion on this point in Schram, real advantage of fixed effects comes from their ability Nitz.and Krueger 1998). to subsume unmeasured variables and unspecified in- teractions.That is,fixed effects control for any attribute of a state-measurable or not-that affects all individ- DATA uals in the same way.Thus the fixed-effect approach Individual-level data are from the Census Bureau's controls for state-level factors at least as well as-and Public Use Microdata Series (PUMS)1990 5% usually better than-any approach relying on state- sample as accessed via Integrated PUMS (IPUMS) level covariates. (Ruggles et al.1997).This data set provides individual When using fixed effects,one must make special ef- information on age,marital status,number and ages forts to distinguish the effect of welfare on migration- of children,income,race,education,birth state,and if any exists-from the more general attractiveness of state of residence in 1985 and 1990.I work with the states measured by fixed effects.If the sample includes 1990 data for two reasons.First,most studies of welfare only poor single mothers who are all eligible for Aid migration assess AFDCin the late 1980s or early 1990s. to Families with Dependent Children("AFDC"),then I use data from that period in order to ensure that it the welfare generosity of each state will be the same is the methods-and not changes in reality-behind for all individuals in the sample (in the same man- any new results.Second,the highly variable welfare environment from 1996 to 2000 makes it hard to draw inferences about migration based on average levels of 1 The model automatically satisfies the"independence of irrelevant benefits over that time period.In contrast,AFDC was alternatives"(IIA)condition.This condition implies that the ratio of quite stable from 1985 to 1990. probabilities of choosing one option to another is the same,whether or not a third option is included in the choice set.In an appendix The welfare population consists of 110,243 single available upon request,I discuss alternative estimation strategies mothers between 25 and 53 with children between 4 and present results that indicate that the results are very similar in and 18 who had an income less than 125%of the models that do not satisfy the IIA condition. poverty level.3 Of these,8.9%moved across state 2 To see this,first suppose that the utility of a state depends only on a single variable(say"unemployment rate")and that the coefficient on this variable is negative one.For every individual,the utility of living in any given state would be negative one times the unemploymen 3 The earlier literature sometimes focuses on individuals who ac. rate for the state.A state-level fixed effect completely captures this tually receive welfare.Meyer(2000,5)details how doing so biases amount.If we add another state-level variable with a coefficient of the results in favor of the welfare migration hypothesis.For exam- two,say,the utility for all individuals of living in the state would be ple,some of the people who would not receive welfare in a low- negative one times the unemployment rate plus two times the value benefit state could move to a higher-benefit state and receive welfare of the new variable.Again,a state-level fixed effect would capture simply because eligibility is easier in the higher-benefit state.This the utility value of a state.This reasoning directly extends to any dynamic will exaggerate the flow of welfare recipients into high- number of state-level variables. benefit states and nonrecipients out of low-benefit states.This paper 127

American Political Science Review Vol. 99, No. 1 chooses state s is Pijs = Prob(Uijs > Uijk ∀ k = s), (2) = Prob(
ijk −
ijs < vijs − vijk∀ k = s), (3) = evijs
K k evijk , (4) where K is the total number of states to which an individual can move. The computationally convenient form makes estimation conceptually straightforward (even as it is practically difficult, given that a very large number of individuals are choosing among a large number of discrete choices).1 By explicitly modeling all the state choices, I reduce the possibility that errors in variables obscure the effect of welfare on migration. I control for state attributes by using state-level fixed effects, i.e., by using state-level dummy variables to control for all state attributes that are the same for all individuals in the analysis. For example, these variables control for state unemployment and state climate be￾cause for any given state, the values of these variables will be the same when modeling the probability that any individual will move to the state.2 (A variable not encompassed by fixed effects varies for a given state across individuals; for example, only some people were born in New York, meaning that when modeling the utility of New York, the state-of-birth variable would be one for some individuals and zero for others.) The real advantage of fixed effects comes from their ability to subsume unmeasured variables and unspecified in￾teractions. That is, fixed effects control for any attribute of a state—–measurable or not—–that affects all individ￾uals in the same way. Thus the fixed-effect approach controls for state-level factors at least as well as—–and usually better than—–any approach relying on state￾level covariates. When using fixed effects, one must make special ef￾forts to distinguish the effect of welfare on migration—– if any exists—–from the more general attractiveness of states measured by fixed effects. If the sample includes only poor single mothers who are all eligible for Aid to Families with Dependent Children (“AFDC”), then the welfare generosity of each state will be the same for all individuals in the sample (in the same man- 1 The model automatically satisfies the “independence of irrelevant alternatives” (IIA) condition. This condition implies that the ratio of probabilities of choosing one option to another is the same, whether or not a third option is included in the choice set. In an appendix available upon request, I discuss alternative estimation strategies and present results that indicate that the results are very similar in models that do not satisfy the IIA condition. 2 To see this, first suppose that the utility of a state depends only on a single variable (say “unemployment rate”) and that the coefficient on this variable is negative one. For every individual, the utility of living in any given state would be negative one times the unemployment rate for the state. A state-level fixed effect completely captures this amount. If we add another state-level variable with a coefficient of two, say, the utility for all individuals of living in the state would be negative one times the unemployment rate plus two times the value of the new variable. Again, a state-level fixed effect would capture the utility value of a state. This reasoning directly extends to any number of state-level variables. ner that state unemployment and state climate were above). The state fixed effect will “soak up” the wel￾fare effect and leave it statistically unidentified. I avoid this problem by using a quasi-experimental research design, sometimes referred to as a comparison group method (Levine and Zimmerman 1999; Meyer 2000). This design requires that I include in the sample a “control group” that is not eligible for welfare but oth￾erwise resembles the “treatment group” of poor single mothers. General state attributes (captured by state fixed effects) influence individuals in the control and treatment groups; welfare, however, influences only in￾dividuals in the treatment group. Given the inclusion of the control group, the welfare variable is no longer a constant for all individuals for any given state (that is, welfare benefits are zero for individuals in the control group and the measured value for individuals in the treatment group). The welfare variable now is statisti￾cally identified; it allows us to see whether differences in welfare benefits explain any differences in behavior by the treatment and control groups. I also control for, among other factors, the gravi￾tational pull of birth states and potential differences in the attraction whites and African Americans have toward states. Including these variables not only serves important statistical control purposes, but also human￾izes the analysis by moving beyond the caricature of welfare recipients as solely motivated by financial gain (see the excellent discussion on this point in Schram, Nitz, and Krueger 1998). DATA Individual-level data are from the Census Bureau’s Public Use Microdata Series (PUMS) 1990 5% sample as accessed via Integrated PUMS (IPUMS) (Ruggles et al. 1997). This data set provides individual information on age, marital status, number and ages of children, income, race, education, birth state, and state of residence in 1985 and 1990. I work with the 1990 data for two reasons. First, most studies of welfare migration assess AFDC in the late 1980s or early 1990s. I use data from that period in order to ensure that it is the methods—–and not changes in reality—–behind any new results. Second, the highly variable welfare environment from 1996 to 2000 makes it hard to draw inferences about migration based on average levels of benefits over that time period. In contrast, AFDC was quite stable from 1985 to 1990. The welfare population consists of 110,243 single mothers between 25 and 53 with children between 4 and 18 who had an income less than 125% of the poverty level.3 Of these, 8.9% moved across state 3 The earlier literature sometimes focuses on individuals who ac￾tually receive welfare. Meyer (2000, 5) details how doing so biases the results in favor of the welfare migration hypothesis. For exam￾ple, some of the people who would not receive welfare in a low￾benefit state could move to a higher-benefit state and receive welfare simply because eligibility is easier in the higher-benefit state. This dynamic will exaggerate the flow of welfare recipients into high￾benefit states and nonrecipients out of low-benefit states. This paper 127

Welfare and the Multifaceted Decision to Move February 2005 lines between 1985 and 1990.The nonwelfare control averaged across 1985-90.I adjust for cost-of-living dif- groups reasonably match the welfare population in ferences using Meyer's (2000,14)state price index all respects except for eligibility for welfare.Follow- (which focuses on variation in housing costs)and the ing Meyer(2000)and Levine and Zimmerman (1999), national consumer price index. I use three different control groups.The first con- I control for moving costs with several variables.I sists of 69,270 childless single women between 25 and measure the "fixed cost"of moving across state lines 53 years of age who had less than three times the with a dummy variable called move that takes on a poverty income and no college degree.4 The second value of one if js.I measure the "variable cost"of control group consists of 96,684 childless single males moving,which depends on the distance of the move, who had less than three times the poverty income and with a variable called distance.which is the log of dis- no college degree.The third control group consists of tance between state s and state j.Interaction terms 122.681 married women with children with household allow for the possibility that the effect of moving costs incomes greater than three times the poverty level differs between the welfare and the nonwelfare popu- and below the lesser of five times the poverty level or lations. $50,000.No group perfectly matches the welfare pop- I also control for the possibility that the welfare and ulation,but all match in some way the skill profiles and nonwelfare populations respond differently to state economic circumstances of poor single mothers.Using characteristics.For example,individuals in the welfare multiple specifications should increase confidence in population may care less about wages and unemploy- the robustness of the results. ment if they are expecting to rely on government or The focal variable is welfare benefits measured as family assistance.Therefore I include interactions of the sum of maximum AFDC benefits for a family of state-level wage and unemployment variables with an four and Food Stamps for each state.The Food Stamp individual-specific indicator variable for individuals in data are from the U.S.House Committee on Ways and the welfare population.Although the general effects of Means(various years).I restrict the welfare effect to wages and unemployment are not identified (because be zero for the control group by multiplying welfare they are soaked up by the fixed effects),I can estimate benefits times a dummy variable indicating whether the differential effect of these variables on the con- an individual is in the welfare population.This creates trol and treatment populations with these interaction within-state individual-level variation in the welfare terms. variable and allows it to be included in a model with state-level fixed effects.This is the critical variable for the welfare migration hypothesis. RESULTS State wage data are the average retail wages for food stores from the Census Bureau (2000):data from The analysis proceeds in two steps.First,I replicate this sector of the economy reflect the earnings poten- and extend the analysis by Schram,Nitz,and Krueger tial of low-skill women (Berry,Fording,and Hanson (1998)to make two points:(1)that models with no 2003).State unemployment data are from the Bureau or few nonwelfare controls show no welfare effects of Labor Statistics(2001).All state-level variables are and (2)that better accounting for state-level and nonwelfare determinants of migration produces initial evidence of a welfare effect.I then present results for follows Meyer's recommendation(8)of using an "at-risk group(sin the more flexible and powerful random utility model gle mothers or.better yet,low-educated single mothers)."He also of migration. notes that "a substantial fraction of any at-risk group may not be likely welfare recipients,and thus effects on the overall group are likely to be watered down estimates of the effects on likely partic- Revisiting Schram,Nitz,and Krueger ipants."Given the findings of this paper,it is reassuring that the welfare population is identified in a manner that biases against the welfare migration rather than in favor of it.Following the convention Schram,Nitz,and Krueger (1998)model migration of this literature,I include only individuals who started and ended patterns of poor single mothers as a function of wel- up in the continental United States;including Alaska and Hawaii fare,income,and employment differentials.(Allard produces essentially the same results.The limits on children's ages and Danziger (2000,361)provide,among other anal- limit the sample to only those women who had children during the yses,a similar analysis with no controls.)In two of entire period from 1985 to 1990;earlier versions of this paper allowed for younger children and had similar results.The poverty level varies four specifications,Schram,Nitz,and Krueger find a based on number of children in the family and other factors;the significant negative relationship between welfare ben- average poverty threshold in 1989 was S12,674 (IPUMS codebook efits and migration.This odd result suggest either that Ruggles et al.1997],225). high welfare benefits repel poor single mothers(which For all control groups I exclude individuals who have served in the seems unlikely and would constitute a major paradigm military in the last five years,as their mobility may be very different from that of civilians.Also,I exclude disabled individuals from the shift if true)or that nonwelfare factors correlated with control groups,as they may be more eligible for,or more interested welfare benefits have been omitted and are causing a in,welfare than others in the group. spurious negative relationship. This is the standard measure of welfare generosity in the literature. To investigate whether omitted variable bias is the Other aspects of welfare generosity such as eligibility standards are problem,Table 1 revisits Schram,Nitz,and Krueger's correlated,but distinct.See Bailey and Rom 2004 for further discus- sion of the multiple dimensions of welfare generosity.Estimating the model.The dependent variable is Census Bureau data model using a measure of spending per poor person-a measure that on the proportion of poor,single women with chil- taps eligibility as well-produces similar results. dren moving from one state to another between 1985 128

Welfare and the Multifaceted Decision to Move February 2005 lines between 1985 and 1990. The nonwelfare control groups reasonably match the welfare population in all respects except for eligibility for welfare. Follow￾ing Meyer (2000) and Levine and Zimmerman (1999), I use three different control groups. The first con￾sists of 69,270 childless single women between 25 and 53 years of age who had less than three times the poverty income and no college degree.4 The second control group consists of 96,684 childless single males who had less than three times the poverty income and no college degree. The third control group consists of 122,681 married women with children with household incomes greater than three times the poverty level and below the lesser of five times the poverty level or $50,000. No group perfectly matches the welfare pop￾ulation, but all match in some way the skill profiles and economic circumstances of poor single mothers. Using multiple specifications should increase confidence in the robustness of the results. The focal variable is welfare benefits measured as the sum of maximum AFDC benefits for a family of four and Food Stamps for each state.5 The Food Stamp data are from the U.S. House Committee on Ways and Means (various years). I restrict the welfare effect to be zero for the control group by multiplying welfare benefits times a dummy variable indicating whether an individual is in the welfare population. This creates within-state individual-level variation in the welfare variable and allows it to be included in a model with state-level fixed effects. This is the critical variable for the welfare migration hypothesis. State wage data are the average retail wages for food stores from the Census Bureau (2000); data from this sector of the economy reflect the earnings poten￾tial of low-skill women (Berry, Fording, and Hanson 2003). State unemployment data are from the Bureau of Labor Statistics (2001). All state-level variables are follows Meyer’s recommendation (8) of using an “at-risk group (sin￾gle mothers or, better yet, low-educated single mothers).” He also notes that “a substantial fraction of any at-risk group may not be likely welfare recipients, and thus effects on the overall group are likely to be watered down estimates of the effects on likely partic￾ipants.” Given the findings of this paper, it is reassuring that the welfare population is identified in a manner that biases against the welfare migration rather than in favor of it. Following the convention of this literature, I include only individuals who started and ended up in the continental United States; including Alaska and Hawaii produces essentially the same results. The limits on children’s ages limit the sample to only those women who had children during the entire period from 1985 to 1990; earlier versions of this paper allowed for younger children and had similar results. The poverty level varies based on number of children in the family and other factors; the average poverty threshold in 1989 was $12,674 (IPUMS codebook [Ruggles et al. 1997], 225). 4 For all control groups I exclude individuals who have served in the military in the last five years, as their mobility may be very different from that of civilians. Also, I exclude disabled individuals from the control groups, as they may be more eligible for, or more interested in, welfare than others in the group. 5 This is the standard measure of welfare generosity in the literature. Other aspects of welfare generosity such as eligibility standards are correlated, but distinct. See Bailey and Rom 2004 for further discus￾sion of the multiple dimensions of welfare generosity. Estimating the model using a measure of spending per poor person—–a measure that taps eligibility as well—–produces similar results. averaged across 1985–90. I adjust for cost-of-living dif￾ferences using Meyer’s (2000, 14) state price index (which focuses on variation in housing costs) and the national consumer price index. I control for moving costs with several variables. I measure the “fixed cost” of moving across state lines with a dummy variable called move that takes on a value of one if j = s. I measure the “variable cost” of moving, which depends on the distance of the move, with a variable called distance, which is the log of dis￾tance between state s and state j . Interaction terms allow for the possibility that the effect of moving costs differs between the welfare and the nonwelfare popu￾lations. I also control for the possibility that the welfare and nonwelfare populations respond differently to state characteristics. For example, individuals in the welfare population may care less about wages and unemploy￾ment if they are expecting to rely on government or family assistance. Therefore I include interactions of state-level wage and unemployment variables with an individual-specific indicator variable for individuals in the welfare population. Although the general effects of wages and unemployment are not identified (because they are soaked up by the fixed effects), I can estimate the differential effect of these variables on the con￾trol and treatment populations with these interaction terms. RESULTS The analysis proceeds in two steps. First, I replicate and extend the analysis by Schram, Nitz, and Krueger (1998) to make two points: (1) that models with no or few nonwelfare controls show no welfare effects and (2) that better accounting for state-level and nonwelfare determinants of migration produces initial evidence of a welfare effect. I then present results for the more flexible and powerful random utility model of migration. Revisiting Schram, Nitz, and Krueger Schram, Nitz, and Krueger (1998) model migration patterns of poor single mothers as a function of wel￾fare, income, and employment differentials. (Allard and Danziger (2000, 361) provide, among other anal￾yses, a similar analysis with no controls.) In two of four specifications, Schram, Nitz, and Krueger find a significant negative relationship between welfare ben￾efits and migration. This odd result suggest either that high welfare benefits repel poor single mothers (which seems unlikely and would constitute a major paradigm shift if true) or that nonwelfare factors correlated with welfare benefits have been omitted and are causing a spurious negative relationship. To investigate whether omitted variable bias is the problem, Table 1 revisits Schram, Nitz, and Krueger’s model. The dependent variable is Census Bureau data on the proportion of poor, single women with chil￾dren moving from one state to another between 1985 128

American Political Science Review Vol.99,No.1 TABLE 1.Determinants of Interstate Mobility Rates of Poor,Single Mothers All State Dyads Interstate Move Dyads Only 2 Welfare benefits difference 0.00004 0.0001* 0.00004 0.0001* (0.03) 2.21) (0.68) (2.86) Unemployment difference 0.00005 0.0001* 0.00005 0.0001* (0.04) (2.93) (0.97) (3.81) Income difference -0.00003 0.00014 -0.00003 0.0001 (0.03) (3.21) (0.76) (3.69) Nonwelfare migration 0.99+ 0.83* (1306.68) (57.58) Intercept 0.02* 0.0003* 0.002 0.0006* (7.88) (2.56) (22.58) (8.15) Observations 2,304 2.304 2.256 2.256 R2 0.000001 0.999 0.001 0.596 Note:Figures are OLS coefficients for a model in which the dependent variable is the proportion of poor single mothers moving from state j to state k for all continental state pairs;see text for details.t-statistics are in parentheses.*p <0.05;**p <0.01; *p<0.001. and 1990 for all state pairs.The independent variables Conditional Logit Results measure welfare,unemployment,and income differ- ences.Column 1 reports results for a sparse specifica- Tables 2 through 4 present the results for the more tion as in Schram,Nitz,and Krueger.The results echo compelling tests based on the individual-level model theirs:no variable is significant and the R2 is hardly of state choice.I estimate but do not report state fixed measurable.Column 2 reports results when I add con- effects for all specifications. trol for non-welfare state attractiveness with a vari- Table 2 indicates that welfare benefits exert a pos- able measuring the proportion of poor,non-college- itive and highly significant effect on migration.The educated single women without children who moved first specification includes only the distance,move,and from state j to state k.The same economic,social,and welfare variables.The second specification adds birth cultural attributes of states affect these women and the state variables.I proceed in this fashion in order to welfare population with one important exception:the highlight how omitting birth state effects attenuates women without children were not eligible for AFDC. the estimated effect of welfare on migration.Note that Their migration patterns therefore embody(and con- including the birth state variables causes the coeffi- trol for)the nonwelfare attractiveness of states. cient on welfare benefits to almost double.The welfare Including better controls dramatically changes the benefits variable is significant-and hardly changed- results.Most importantly for our purposes,the welfare in the third specification,which adds wage and unem- variable is now positive and significant,as predicted by ployment interactions for the welfare population.In the welfare migration hypothesis.One discordant note the last two specifications,I assess whether welfare is the extremely high R2.This occurs because the both benefit levels interact with birth state and distance the dependent variable and the nonwelfare migration The results indicate that both interactions matter,but variable are close to one for the 48 own-state pairs and that neither substantially changes the results.Col- close to zero for the 2,256 other pairs.Therefore the umn 4 reports the results when birth state and benefits next two columns look only at interstate move dyads variables are allowed to interact.The coefficient on by excluding own-state pairs.Again,the sparsely spec- welfare is higher than in the other specifications for ified model performs abysmally and the model with non-birth states,while the coefficient on welfare for improved controls performs much better.Here again, birth states(the sum of the main effect and the interac- welfare benefits are positively and significantly associ- tion)is essentially zero.This result implies that welfare ated with migration. and family effects are substitutes.not complements Other individual-level variables measure systematic Column 5 reports the results when distance and bene- determinants of individual-specific attraction to or re- fits interact.This tests whether the magnetic effect of pulsion from certain states.An excellent proxy for welfare diminishes across space.The results indicate family ties and social capital is the birth state of an that this is indeed the case.as the interaction is signi- individual.The variable birth state is one if person i was ficant. born in states and zero otherwise.Simply put,this vari- The other noteworthy result in Table 2 is the over- able controls for the possibility that-all else equal-a whelming statistical significance of the birth state vari- person born in Mississippi derives greater utility from ables,which consistently has a t-statistic over 90.The living in Mississippi than someone born in Vermont.I interaction with the indicator variable for poor single also interact this variable with an indicator for individ- mothers is also significant,indicating a stronger birth- uals in the welfare population in case birth state effects state attraction for poor single mothers relative to the differ for the welfare and nonwelfare populations. control group of women without children.Even taking 129

American Political Science Review Vol. 99, No. 1 TABLE 1. Determinants of Interstate Mobility Rates of Poor, Single Mothers All State Dyads Interstate Move Dyads Only 1 2 12 Welfare benefits difference 0.00004 0.0001∗ 0.00004 0.0001∗∗ (0.03) (2.21) (0.68) (2.86) Unemployment difference 0.00005 0.0001∗∗ 0.00005 0.0001∗∗∗ (0.04) (2.93) (0.97) (3.81) Income difference −0.00003 0.0001∗∗∗ −0.00003 0.0001∗∗∗ (0.03) (3.21) (0.76) (3.69) Nonwelfare migration — 0.99∗∗∗ — 0.83∗∗∗ — (1306.68) — (57.58) Intercept 0.02∗∗∗ 0.0003∗∗ 0.002∗∗∗ 0.0006∗∗∗ (7.88) (2.56) (22.58) (8.15) Observations 2,304 2,304 2,256 2,256 R2 0.000001 0.999 0.001 0.596 Note: Figures are OLS coefficients for a model in which the dependent variable is the proportion of poor single mothers moving from state j to state k for all continental state pairs; see text for details. t-statistics are in parentheses. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001. and 1990 for all state pairs. The independent variables measure welfare, unemployment, and income differ￾ences. Column 1 reports results for a sparse specifica￾tion as in Schram, Nitz, and Krueger. The results echo theirs: no variable is significant and the R2 is hardly measurable. Column 2 reports results when I add con￾trol for non–welfare state attractiveness with a vari￾able measuring the proportion of poor, non-college￾educated single women without children who moved from state j to state k. The same economic, social, and cultural attributes of states affect these women and the welfare population with one important exception: the women without children were not eligible for AFDC. Their migration patterns therefore embody (and con￾trol for) the nonwelfare attractiveness of states. Including better controls dramatically changes the results. Most importantly for our purposes, the welfare variable is now positive and significant, as predicted by the welfare migration hypothesis. One discordant note is the extremely high R2. This occurs because the both the dependent variable and the nonwelfare migration variable are close to one for the 48 own-state pairs and close to zero for the 2,256 other pairs. Therefore the next two columns look only at interstate move dyads by excluding own-state pairs. Again, the sparsely spec￾ified model performs abysmally and the model with improved controls performs much better. Here again, welfare benefits are positively and significantly associ￾ated with migration. Other individual-level variables measure systematic determinants of individual-specific attraction to or re￾pulsion from certain states. An excellent proxy for family ties and social capital is the birth state of an individual. The variable birth state is one if person i was born in state s and zero otherwise. Simply put, this vari￾able controls for the possibility that—–all else equal—–a person born in Mississippi derives greater utility from living in Mississippi than someone born in Vermont. I also interact this variable with an indicator for individ￾uals in the welfare population in case birth state effects differ for the welfare and nonwelfare populations. Conditional Logit Results Tables 2 through 4 present the results for the more compelling tests based on the individual-level model of state choice. I estimate but do not report state fixed effects for all specifications. Table 2 indicates that welfare benefits exert a pos￾itive and highly significant effect on migration. The first specification includes only the distance, move, and welfare variables. The second specification adds birth state variables. I proceed in this fashion in order to highlight how omitting birth state effects attenuates the estimated effect of welfare on migration. Note that including the birth state variables causes the coeffi- cient on welfare benefits to almost double. The welfare benefits variable is significant—–and hardly changed—– in the third specification, which adds wage and unem￾ployment interactions for the welfare population. In the last two specifications, I assess whether welfare benefit levels interact with birth state and distance. The results indicate that both interactions matter, but that neither substantially changes the results. Col￾umn 4 reports the results when birth state and benefits variables are allowed to interact. The coefficient on welfare is higher than in the other specifications for non-birth states, while the coefficient on welfare for birth states (the sum of the main effect and the interac￾tion) is essentially zero. This result implies that welfare and family effects are substitutes, not complements. Column 5 reports the results when distance and bene- fits interact. This tests whether the magnetic effect of welfare diminishes across space. The results indicate that this is indeed the case, as the interaction is signi- ficant. The other noteworthy result in Table 2 is the over￾whelming statistical significance of the birth state vari￾ables, which consistently has a t-statistic over 90. The interaction with the indicator variable for poor single mothers is also significant, indicating a stronger birth￾state attraction for poor single mothers relative to the control group of women without children. Even taking 129

Welfare and the Multifaceted Decision to Move February 2005 TABLE 2.Conditional Logit Model of Migration Choice with Poor Single Women without Children as Control Group (1) (2) (3) (4 (5) Welfare benefits poor single mother 0.07* 0.13** 0.13** 0.17* 0.21林 (5.70) (10.17) (10.37) (12.65) (13.40) Log distance -0.60** -0.57* -0.57 -0.57* -0.57** (37.49) (37.34) (37.23) (37.22) (37.22) Log distance poor single mother -0.06* -0.01 -0.01 -0.02 0.12* (3.23) (0.63) (0.71) (0.82) (4.72) Move -1.97* -1.28* -1.29* -1.29* -1.29** (18.60) (12.56) (12.68) (12.67)） (12.72) Move poor single mother 0.48* 0.14 0.15 0.17 0.12 (3.65) (1.06) (1.19) (1.29) (0.94) Birth state 2.20** 2.21** 2.21** 2.21* (94.74) (94.76) (94.80) (94.73) Birth state poor single mother 0.28* 0.27* 1.47* 0.27* (9.72） (9.10) (12.22）) (9.30) Retail wage poor single mother -0.11* -0.11* -0.11* (2.75) (2.78) (2.66) Unemployment poor single mother 0.05* 0.05* 0.05* (5.23) (4.99) (5.45) Welfare birth state poor single mother -0.19* (10.31) Welfare benefits poor single -0.02*林 mother distance (8.62) Observations Treatment group 110,243 110,243 110,243 110,243 110,243 Control group 69,270 69,270 69,270 69,270 69,270 Pseudo-R2 0.844 0.863 0.863 0.863 0.863 Note:Figures are coefficients from a conditional logit estimation.Variables that are the same for all individuals within each state(e.g.. unemployment or average temperature)are controlled for with fixed effects for states(not reported);see text for details.t-statistics are in parentheses.p 0.01;***p <0.001. into account the massive sample size.there can be no dicates that failure to account for family ties in birth doubt that birth state attractiveness matters. states can attenuate or even reverse estimated welfare The control variables perform as expected.The dis- effects. tance variable is negative and significant,implying that Again,the null hypothesis of no birth state effects is the farther away a state is,the less likely an individual is overwhelmingly rejected.Birth states exert a stronger to move there;the effect is the same for both the treat- attraction on poor single mothers,especially when the ment and the control groups in columns 2 through 4. control group is middle-income married mothers.It The move variables indicate a clear fixed cost to moving appears that the family and employment concerns of across state borders that is the same for control and married women's husbands reduce the relative likeli- treatment populations.Wages are less magnetic and hood that these women will move to their birth states. unemployment is less repelling to individuals in the The results also indicate that wages exert a smaller welfare population.Given the availability of govern- effect on the welfare population than on the single male ment support for these individuals,it is not surprising control group.There does not appear to be a difference that market factors play less of a role in migration for in the effect of wages relative to married women,sug- them. gesting that married women with higher incomes and. Table 3 explores the robustness of the results by in many cases,working husbands do not experience presenting results for alternative specifications of the much of a wage effect.Unemployment repels poor model.In columns 2 and 3,the control group is sin- single mothers less than either control group,again gle men without children;in columns 4 and 5,it is consistent with the idea that the welfare population is middle-income married women with children.These more likely to have nonmarket means of support. specifications lead to rejection of the null hypothesis The final column in Table 3 presents results for a that welfare benefits exert no effect as long as birth conditional logit model estimated on a sample con- state is taken into account.That is,in the specifications fined to those who did not reside in the same state that include birth state and other controls(the third and in 1985 and 1990.Limiting the sample in this fashion fifth columns),welfare benefits are positive and statisti- addresses two concerns.First,it is possible that people cally significant.In the specifications that do not include make different calculations when deciding whether to birth state,the coefficient on welfare benefits is 30% move than when deciding where to move once they smaller (when single men are the control group)or have decided to make an interstate move.Second.it is negative and borderline statistically significant (when possible that cultural or behavioral differences across married women are the control group).This again in- states may lead some states to have both high welfare 130

Welfare and the Multifaceted Decision to Move February 2005 TABLE 2. Conditional Logit Model of Migration Choice with Poor Single Women without Children as Control Group (1) (2) (3) (4) (5) Welfare benefits ∗ poor single mother 0.07∗∗∗ 0.13∗∗∗ 0.13∗∗∗ 0.17∗∗∗ 0.21∗∗∗ (5.70) (10.17) (10.37) (12.65) (13.40) Log distance −0.60∗∗∗ −0.57∗∗∗ −0.57∗∗∗ −0.57∗∗∗ −0.57∗∗∗ (37.49) (37.34) (37.23) (37.22) (37.22) Log distance ∗ poor single mother −0.06∗∗∗ −0.01 −0.01 −0.02 0.12∗∗∗ (3.23) (0.63) (0.71) (0.82) (4.72) Move −1.97∗∗∗ −1.28∗∗∗ −1.29∗∗∗ −1.29∗∗∗ −1.29∗∗∗ (18.60) (12.56) (12.68) (12.67) (12.72) Move ∗ poor single mother 0.48∗∗∗ 0.14 0.15 0.17 0.12 (3.65) (1.06) (1.19) (1.29) (0.94) Birth state 2.20∗∗∗ 2.21∗∗∗ 2.21∗∗∗ 2.21∗∗∗ (94.74) (94.76) (94.80) (94.73) Birth state ∗ poor single mother 0.28∗∗∗ 0.27∗∗∗ 1.47∗∗∗ 0.27∗∗∗ (9.72) (9.10) (12.22) (9.30) Retail wage ∗ poor single mother −0.11∗∗ −0.11∗∗ −0.11∗∗∗ (2.75) (2.78) (2.66) Unemployment ∗ poor single mother 0.05∗∗∗ 0.05∗∗∗ 0.05∗∗∗ (5.23) (4.99) (5.45) Welfare ∗ birth state ∗ poor single mother −0.19∗∗∗ (10.31) Welfare benefits ∗ poor single −0.02∗∗∗ mother ∗ distance (8.62) Observations Treatment group 110,243 110,243 110,243 110,243 110,243 Control group 69,270 69,270 69,270 69,270 69,270 Pseudo-R2 0.844 0.863 0.863 0.863 0.863 Note: Figures are coefficients from a conditional logit estimation. Variables that are the same for all individuals within each state (e.g., unemployment or average temperature) are controlled for with fixed effects for states (not reported); see text for details. t-statistics are in parentheses. ∗∗p < 0.01; ∗∗∗p < 0.001. into account the massive sample size, there can be no doubt that birth state attractiveness matters. The control variables perform as expected. The dis￾tance variable is negative and significant, implying that the farther away a state is, the less likely an individual is to move there; the effect is the same for both the treat￾ment and the control groups in columns 2 through 4. The move variables indicate a clear fixed cost to moving across state borders that is the same for control and treatment populations. Wages are less magnetic and unemployment is less repelling to individuals in the welfare population. Given the availability of govern￾ment support for these individuals, it is not surprising that market factors play less of a role in migration for them. Table 3 explores the robustness of the results by presenting results for alternative specifications of the model. In columns 2 and 3, the control group is sin￾gle men without children; in columns 4 and 5, it is middle-income married women with children. These specifications lead to rejection of the null hypothesis that welfare benefits exert no effect as long as birth state is taken into account. That is, in the specifications that include birth state and other controls (the third and fifth columns), welfare benefits are positive and statisti￾cally significant. In the specifications that do not include birth state, the coefficient on welfare benefits is 30% smaller (when single men are the control group) or negative and borderline statistically significant (when married women are the control group). This again in￾dicates that failure to account for family ties in birth states can attenuate or even reverse estimated welfare effects. Again, the null hypothesis of no birth state effects is overwhelmingly rejected. Birth states exert a stronger attraction on poor single mothers, especially when the control group is middle-income married mothers. It appears that the family and employment concerns of married women’s husbands reduce the relative likeli￾hood that these women will move to their birth states. The results also indicate that wages exert a smaller effect on the welfare population than on the single male control group. There does not appear to be a difference in the effect of wages relative to married women, sug￾gesting that married women with higher incomes and, in many cases, working husbands do not experience much of a wage effect. Unemployment repels poor single mothers less than either control group, again consistent with the idea that the welfare population is more likely to have nonmarket means of support. The final column in Table 3 presents results for a conditional logit model estimated on a sample con- fined to those who did not reside in the same state in 1985 and 1990. Limiting the sample in this fashion addresses two concerns. First, it is possible that people make different calculations when deciding whether to move than when deciding where to move once they have decided to make an interstate move. Second, it is possible that cultural or behavioral differences across states may lead some states to have both high welfare 130

American Political Science Review Vol.99,No.1 TABLE 3.Alternative Specifications of the Conditional Logit Model of Migration Choice Control Group Movers Single Men Married Women Only Welfare benefits poor single mother 0.07* 0.10* -0.02* 0.10* 0.19* (5.70) (9.11) (1.80) (9.00) (10.32) Log distance -0.60** -0.58** -1.60* -0.66* -0.95* (37.49) (44.41) (19.86) (54.68) (48.80) Log distance poor single mother -0.06* -0.01 0.09 0.07* -0.104 (3.23) (0.49) (0.78) (4.41) (4.07 Move -197* -1.10* -0.69** -1.07* (18.60) (12.62) (55.79) (13.72) Move poor single mother 0.48* -0.02 0.03 -0.09 (3.65) (0.20) (1.54) (0.83) Birth state 2.39** 1.79* 3.51# (123.22) (96.59) (90.30) Birth state poor single mother 0.09** 0.69* 0.23+ (3.31) (26.71) (4.88) Retail wage poor single mother -0.08* -0.04 -0.01 (2.24) (1.11) (0.20) Unemployment poor single mother 0.04* 0.08* 0.05+ (4.75) (10.15) (3.66) Observations Treatment group 110,243 110,243 110,243 110,243 9,841 Control group 96,684 96,684 122,681 122,681 5,870 Pseudo-R2 0.844 0.863 0.854 0.869 0.328 Note:Figures are coefficients from a conditional logit estimation.Variables that are the same for all individuals within each state (e.g. unemployment and average temperature)are controlled for with fixed effects for states(not reported);see text for details.t-statistics are in parentheses.*p<0.05;***p<0.001. benefits and,say.high rates of out-of-wedlock births. ity that an individual moves from state to state k when This could mean that states with high benefits may have state k is or is not the individual's birth state.To con- a higher proportion of single mothers relative to the serve space,I discuss a representative example rather control group than other states even if welfare did not than provide a comprehensive tally of the simulations. promote or deter migration.I exclude the move vari- Consider a poor single mother living in Illinois in ables from the analysis because they are not identified 1985.If she was born outside the continental United for a sample in which everyone moved.Despite the States,the model predicts that she would have a 93.7% massive reduction in the sample size,the last column in probability of living in Illinois in 1990,a 0.11%proba- Table 3 indicates that welfare and birth state variables bility of living in Alabama in 1990,and a 0.37%prob- remain highly significant. ability of living in Florida in 1990.If she had been Table 4 includes race in the analysis by providing born in Illinois,however,her probability of staying in separate results for whites and African Americans. Illinois through 1990 would rise to 99.4%;if she had This controls for the possibility that state attractiveness been born in Alabama,her predicted probability of varies by race and tests whether the marginal effects moving from Illinois to Alabama by 1990 would be of welfare and other factors vary by race.The results 1.3%.If she had been born in Florida,her predicted indicate that race matters in some ways but not in oth- probability of moving from Illinois to Florida by 1990 ers.Although state-level attractiveness varies across would be 4.3%.In general,women would be about 10 races,the marginal effects of the welfare.birth state. times more likely to move to a given state if they had and other variables are similar for both races.The wel- been born there than if they had been born outside of fare variable is positive and significant even without the the continental United States. birth state variable for both races.What is happening Birth state effects on migration not only are large here is that exclusion of both birth state and racial fac- in an absolute sense,but also are large relative to tors severely attenuates the estimated effect of welfare the effect of welfare on migration.In order to pro- on migration.Including one or both of these factors duce a change in the probability of living in Alabama makes the effect more clearly visible.When both are similar to that produced by changing a 1985 Illinois accounted for (as in the second column for each race), resident's birth state from the noncontinental United the effect of welfare is most clearly apparent. States to Alabama.Alabama would have to increase The results indicate the existence of both birth state its average welfare spending by the implausibly large and welfare effects.But how large are these effects?To amount of $18,000 in 1983 adjusted dollars (its actual illustrate the magnitude of the birth state effect,I use average spending from 1985 to 1990 in 1983 housing specification(3)from Table 2 to simulate the probabil- cost-adiusted dollars was about $4.500) 131

American Political Science Review Vol. 99, No. 1 TABLE 3. Alternative Specifications of the Conditional Logit Model of Migration Choice Control Group Movers Single Men Married Women Only Welfare benefits ∗ poor single mother 0.07∗∗∗ 0.10∗∗∗ −0.02∗ 0.10∗∗∗ 0.19∗∗∗ (5.70) (9.11) (1.80) (9.00) (10.32) Log distance −0.60∗∗∗ −0.58∗∗∗ −1.60∗∗∗ −0.66∗∗∗ −0.95∗∗∗ (37.49) (44.41) (19.86) (54.68) (48.80) Log distance ∗ poor single mother −0.06∗∗∗ −0.01 0.09 0.07∗∗∗ −0.10∗∗∗ (3.23) (0.49) (0.78) (4.41) (4.07) Move −1.97∗∗∗ −1.10∗∗∗ −0.69∗∗∗ −1.07∗∗∗ (18.60) (12.62) (55.79) (13.72) Move ∗ poor single mother 0.48∗∗∗ −0.02 0.03 −0.09 (3.65) (0.20) (1.54) (0.83) Birth state 2.39∗∗∗ 1.79∗∗∗ 3.51∗∗∗ (123.22) (96.59) (90.30) Birth state ∗ poor single mother 0.09∗∗∗ 0.69∗∗∗ 0.23∗∗∗ (3.31) (26.71) (4.88) Retail wage ∗ poor single mother −0.08∗ −0.04 −0.01 (2.24) (1.11) (0.20) Unemployment ∗ poor single mother 0.04∗∗∗ 0.08∗∗∗ 0.05∗∗∗ (4.75) (10.15) (3.66) Observations Treatment group 110,243 110,243 110,243 110,243 9,841 Control group 96,684 96,684 122,681 122,681 5,870 Pseudo-R2 0.844 0.863 0.854 0.869 0.328 Note: Figures are coefficients from a conditional logit estimation. Variables that are the same for all individuals within each state (e.g., unemployment and average temperature) are controlled for with fixed effects for states (not reported); see text for details. t-statistics are in parentheses. ∗p < 0.05; ∗∗∗p < 0.001. benefits and, say, high rates of out-of-wedlock births. This could mean that states with high benefits may have a higher proportion of single mothers relative to the control group than other states even if welfare did not promote or deter migration. I exclude the move vari￾ables from the analysis because they are not identified for a sample in which everyone moved. Despite the massive reduction in the sample size, the last column in Table 3 indicates that welfare and birth state variables remain highly significant. Table 4 includes race in the analysis by providing separate results for whites and African Americans. This controls for the possibility that state attractiveness varies by race and tests whether the marginal effects of welfare and other factors vary by race. The results indicate that race matters in some ways but not in oth￾ers. Although state-level attractiveness varies across races, the marginal effects of the welfare, birth state, and other variables are similar for both races. The wel￾fare variable is positive and significant even without the birth state variable for both races. What is happening here is that exclusion of both birth state and racial fac￾tors severely attenuates the estimated effect of welfare on migration. Including one or both of these factors makes the effect more clearly visible. When both are accounted for (as in the second column for each race), the effect of welfare is most clearly apparent. The results indicate the existence of both birth state and welfare effects. But how large are these effects? To illustrate the magnitude of the birth state effect, I use specification (3) from Table 2 to simulate the probabil￾ity that an individual moves from state j to state kwhen state k is or is not the individual’s birth state. To con￾serve space, I discuss a representative example rather than provide a comprehensive tally of the simulations. Consider a poor single mother living in Illinois in 1985. If she was born outside the continental United States, the model predicts that she would have a 93.7% probability of living in Illinois in 1990, a 0.11% proba￾bility of living in Alabama in 1990, and a 0.37% prob￾ability of living in Florida in 1990. If she had been born in Illinois, however, her probability of staying in Illinois through 1990 would rise to 99.4%; if she had been born in Alabama, her predicted probability of moving from Illinois to Alabama by 1990 would be 1.3%. If she had been born in Florida, her predicted probability of moving from Illinois to Florida by 1990 would be 4.3%. In general, women would be about 10 times more likely to move to a given state if they had been born there than if they had been born outside of the continental United States. Birth state effects on migration not only are large in an absolute sense, but also are large relative to the effect of welfare on migration. In order to pro￾duce a change in the probability of living in Alabama similar to that produced by changing a 1985 Illinois resident’s birth state from the noncontinental United States to Alabama, Alabama would have to increase its average welfare spending by the implausibly large amount of $18,000 in 1983 adjusted dollars (its actual average spending from 1985 to 1990 in 1983 housing cost––adjusted dollars was about $4,500). 131

Welfare and the Multifaceted Decision to Move February 2005 TABLE 4.Conditional Logit Model of Migration Choice by Race Whites African Americans Welfare benefits poor single mother 0.14* 0.16** 0.14** 0.17** (9.87) (10.25) (4.61) (4.99) Log distance -1.88* -0.59** -3.06* -0.42** (15.79) (33.60) (10.04) (9.39) Log distance poor single mother 0.75* -0.03 -0.07 -0.01 (4.90) (1.39) (0.19) (0.10) Move -0.61** -1.10*# -0.48+ -2.56 (33.47) (9.49) (10.19) (8.80) Move poor single mother -0.08* 0.38* 0.012 0.15 (3.54) 2.53) (0.22) (0.43) Birth state 2.17** 2.27* (84.19) (37.41) Birth state poor single mother 0.25** 0.19* (7.32) (2.77) Retail wage poor single mother -0.05 0.03 (1.10) (0.23) Unemployment poor single mother 0.06** 0.03 (5.16) (1.49) Observations Treatment group 58,799 58,799 40,002 40,002 Control group 49,696 49.696 13,779 13,779 Pseudo-R2 0.820 0.841 0.895 0.909 Note:Figures are coefficients from a conditional logit estimation.Variables that are the same for all individuals within each state (e.g.. unemployment and average temperature)are controlled for with fixed effects for states(not reported);see text for details.t-statistics are in parentheses.*p<0.01;***p<0.001. That birth state effects are so large does not mean The columns on the right simulate what would hap- that the welfare effects are negligible.Table 5 uses the pen if a state increased its benefits by one standard results from Table 4 to simulate the effects on mi- deviation of all states while all other states maintained gration and spending of changes in welfare benefits. their benefit levels.The results are,not surprisingly, For each state,I calculate the welfare population by larger.For example,if Alabama increased its benefits computing the predicted probability individuals from by this amount,it would have 1,878 more poor sin- all states move to (or remain in)that state.For each gle mothers.If Wisconsin increased its benefits by this state.I then increase its welfare benefits (holding all amount,it would have 2,039 more poor single mothers. other states constant)and calculate the net change Because TANF has supplanted AFDC,these data are in expected population.I look at two levels of in- not to be taken as predictive;rather,they character- creases.A 10%increase represents the average leg- ize the magnitude of the effect of more substantial islated increase in welfare benefits during the time pe- changes in welfare policy in terms of a well-studied riod covered.The national standard deviation($1,086 program. per year in real,state-adjusted 1982-84 dollars)rep- Table 5 also provides an estimate of the annual resents a more meaningful change.Under AFDC such amount spent on AFDC for the net in-migrants,cal- large changes were rare,but Temporary Assistance for culated as follows.First,I estimated the eligibility per- Needy Families ("TANF")TANF has produced very centage for each state as the actual(unsimulated)num- substantial changes across states(although the changes ber of households receiving welfare divided by the ac- are harder to quantify in terms of benefit levels as the tual number of low-income single mothers.6 Second, changes include changes in eligibility.sanctions,and I multiplied the expenditures per welfare household other nonbenefit aspects of aid). (from the Social Security Bulletin Annual Statistical Columns 2 and 3 in Table 5 simulate the effect Supplement for 1990 [Social Security Administration, on migration of an ordinary increase in benefits.Be- various years)times the net change in poor single cause the simulated increase is in percentage terms mothers times the average eligibility of such individu- the simulated effects are lower for low spending states. als.This amount is the approximate cost per year real- The effects are modest but nontrivial.Alabama(which ized after changing policy and having five years'worth had a low level of benefits,meaning that a 10%in- of migration(since the estimated migration effects are crease would be absolutely small)would see a net based on data spanning five years). inflow of 688 families headed by poor single moth- ers;Wisconsin (which had more generous benefits meaning that a 10%increase would be absolutely large)would see an increase of 1,320 poor single 6To produce conservative cost simulations,I set the eligibility pro- portion at one for states that have more households receiving welfare mothers. than low-income single mothers. 132

Welfare and the Multifaceted Decision to Move February 2005 TABLE 4. Conditional Logit Model of Migration Choice by Race Whites African Americans Welfare benefits ∗ poor single mother 0.14∗∗∗ 0.16∗∗∗ 0.14∗∗∗ 0.17∗∗∗ (9.87) (10.25) (4.61) (4.99) Log distance −1.88∗∗∗ −0.59∗∗∗ −3.06∗∗∗ −0.42∗∗∗ (15.79) (33.60) (10.04) (9.39) Log distance ∗ poor single mother 0.75∗∗∗ −0.03 −0.07 −0.01 (4.90) (1.39) (0.19) (0.10) Move −0.61∗∗∗ −1.10∗∗∗ −0.48∗∗∗ −2.56∗∗∗ (33.47) (9.49) (10.19) (8.80) Move ∗ poor single mother −0.08∗∗∗ 0.38∗∗ 0.012 0.15 (3.54) (2.53) (0.22) (0.43) Birth state 2.17∗∗∗ 2.27∗∗∗ (84.19) (37.41) Birth state ∗ poor single mother 0.25∗∗∗ 0.19∗∗ (7.32) (2.77) Retail wage ∗ poor single mother −0.05 0.03 (1.10) (0.23) Unemployment ∗ poor single mother 0.06∗∗∗ 0.03 (5.16) (1.49) Observations Treatment group 58,799 58,799 40,002 40,002 Control group 49,696 49,696 13,779 13,779 Pseudo-R2 0.820 0.841 0.895 0.909 Note: Figures are coefficients from a conditional logit estimation. Variables that are the same for all individuals within each state (e.g., unemployment and average temperature) are controlled for with fixed effects for states (not reported); see text for details. t-statistics are in parentheses. ∗∗p < 0.01; ∗∗∗p < 0.001. That birth state effects are so large does not mean that the welfare effects are negligible. Table 5 uses the results from Table 4 to simulate the effects on mi￾gration and spending of changes in welfare benefits. For each state, I calculate the welfare population by computing the predicted probability individuals from all states move to (or remain in) that state. For each state, I then increase its welfare benefits (holding all other states constant) and calculate the net change in expected population. I look at two levels of in￾creases. A 10% increase represents the average leg￾islated increase in welfare benefits during the time pe￾riod covered. The national standard deviation ($1,086 per year in real, state-adjusted 1982––84 dollars) rep￾resents a more meaningful change. Under AFDC such large changes were rare, but Temporary Assistance for Needy Families (“TANF”) TANF has produced very substantial changes across states (although the changes are harder to quantify in terms of benefit levels as the changes include changes in eligibility, sanctions, and other nonbenefit aspects of aid). Columns 2 and 3 in Table 5 simulate the effect on migration of an ordinary increase in benefits. Be￾cause the simulated increase is in percentage terms, the simulated effects are lower for low spending states. The effects are modest but nontrivial. Alabama (which had a low level of benefits, meaning that a 10% in￾crease would be absolutely small) would see a net inflow of 688 families headed by poor single moth￾ers; Wisconsin (which had more generous benefits, meaning that a 10% increase would be absolutely large) would see an increase of 1,320 poor single mothers. The columns on the right simulate what would hap￾pen if a state increased its benefits by one standard deviation of all states while all other states maintained their benefit levels. The results are, not surprisingly, larger. For example, if Alabama increased its benefits by this amount, it would have 1,878 more poor sin￾gle mothers. If Wisconsin increased its benefits by this amount, it would have 2,039 more poor single mothers. Because TANF has supplanted AFDC, these data are not to be taken as predictive; rather, they character￾ize the magnitude of the effect of more substantial changes in welfare policy in terms of a well-studied program. Table 5 also provides an estimate of the annual amount spent on AFDC for the net in-migrants, cal￾culated as follows. First, I estimated the eligibility per￾centage for each state as the actual (unsimulated) num￾ber of households receiving welfare divided by the ac￾tual number of low-income single mothers.6 Second, I multiplied the expenditures per welfare household (from the Social Security Bulletin Annual Statistical Supplement for 1990 [Social Security Administration, various years]) times the net change in poor single mothers times the average eligibility of such individu￾als. This amount is the approximate cost per year real￾ized after changing policy and having five years’ worth of migration (since the estimated migration effects are based on data spanning five years). 6 To produce conservative cost simulations, I set the eligibility pro￾portion at one for states that have more households receiving welfare than low-income single mothers. 132

American Political Science Review Vol.99,No.1 TABLE 5.Simulated Effect of Welfare on Migration and Spending Welfare Increased by 10%of Own Welfare Increased by National State Spending Standard Deviation Net Change Annual Migration- Net change Annual Migration- of Poor Induced Change in of Poor Induced Change in Single Percentage AFDC Spending Single Percentage AFDC Spending Mothers Change (in 2000 Dollars) Mothers change (in 2000 Dollars) Alabama 688 0.83% $1,420,609 1,660 2.00% $3,430,833 Arizona 748 1.52% $4,130,727 1,464 2.98% $8,087,585 Arkansas 724 1.59% $2,418,703 1,399 3.08% $4,673,836 California 2.666 0.83% $35,031,702 3,782 1.18% $49,689,734 Colorado 892 2.13% $5,893,835 1,469 3.51% $9,712.292 Connecticut 772 3.21% $9.097.960 1.262 5.24% $14,868.845 Delaware 244 3.56% $1,470,342 468 6.82% $2.817.895 Florida 1.724 1.00% $9,346,809 3,630 2.11% $19.686.960 Georgia 1,191 0.87% $6,507216 2,373 1.74% $12,968,386 Idaho 372 3.30% $1,656,929 612 5.42% $2,723,995 Illinois 1,838 1.20% $12,981,360 3,322 2.17% $23,465,472 Indiana 1.243 1.77% $6,751,476 2,204 3.13% $11,973,241 lowa 716 2.36% $5,476,152 1,036 3.42% $7,924,389 Kansas 795 2.80% $5,446,604 1.205 4.25% $8,255,996 Kentucky 857 1.42% $3,960,985 1,638 2.71% $7,574,044 Louisiana 878 0.69% $3,032,914 1,763 1.39% $6,091,852 Maine 424 2.74% $3,693,688 651 4.21% $5,667,848 Maryland 931 1.85% $7,107,384 1,684 3.35% $12,852,611 Massachusetts 1.290 2.03% $14,810,181 1,929 3.04% $22,146,686 Michigan 2.058 1.36% $19.724044 2.913 1.93% $27.916,721 Minnesota 859 2.07% $9,072,960 1,177 2.85% $12,440.938 Mississippi 690 0.76% $1.710.962 1.649 1.81% $4.089.303 Missouri 1,140 1.56% $6,452.632 2,066 2.83% $11,691,701 Montana 366 3.00% $2,603,514 527 4.32% $3,741.044 Nebraska 485 2.93% $3,357,771 751 4.54% $5,202,919 Nevada 418 3.20% $2,397,503 847 6.47% $4,851,808 New Hampshire 309 4.55% $2,749.967 529 7.78% $4,705,108 New Jersey 1.307 2.03% $9,493,061 2,383 3.69% $17,305,984 New Mexico 570 1.79% $3.181.471 1,038 3.25% $5,788,680 New York 3,335 1.36% $38.102.980 4,768 1.95% $54.475.436 North Carolina 1.243 1.09% $6,090,581 2,394 2.09% $11,730,348 North Dakota 214 3.17% $1,585,323 291 4.32% $2,162,826 Ohio 1.880 1.16% $12,710,548 3,115 1.92% $21,058,295 Oklahoma 1.008 1.97% $5,803.655 1,609 3.15% $9,262,292 Oregon 818 2.24% $6,318.966 1,271 3.48% $9,816.173 Pennsylvania 1.596 1.20% $12,584.212 2,529 1.90% $19.941.384 Rhode Island 458 3.99% $1,371,194 669 5.83% $2,005,138 South Carolina 745 1.04% $2.696.859 1,490 2.08% $5,391,737 South Dakota 240 2.76% $229,244 353 4.07% $337,968 Tennessee 939 1.08% $5,266,028 2,132 2.46% $11,957,675 Texas 1.690 0.60% $6,891,791 3,754 1.33% $15,312,704 Utah 491 2.76% $253.406 758 4.26% $391,456 Vermont 314 5.09% $1,752.887 408 6.62% $2.277.623 Virginia 995 1.32% $5,435.119 2,026 2.69% $11,068,208 Washington 1,148 1.94% $10,698,691 1,650 2.79% $15,382,629 West Virginia 646 2.08% $3,326,057 1,048 3.38% $5,393,940 Wisconsin 1.320 2.58% $12,646,313 1,708 3.33% $16,357,411 Wyoming 265 4.24% $1,713,030 406 6.51% $2,627,578 Wote:Simulation based on specification 3 in Table 2 and assumes that other states do not change policies.See text for details. These financial simulations understate actual costs in-migration of poor single mothers.These expenses in several respects.First,costs would continue indefi- may be large.For example,in 1990,almost four times nitely,accruing every year.Second,the costs would rise as much was spent on Medicaid as on AFDC.Other as in-migration continued over time.Third,the amount social services and education also involve substantial does not include other expenses associated with the sums.Fourth,benefit increases would also have direct 133

American Political Science Review Vol. 99, No. 1 TABLE 5. Simulated Effect of Welfare on Migration and Spending Welfare Increased by 10% of Own Welfare Increased by National State Spending Standard Deviation Net Change Annual Migration- Net change Annual Migration￾of Poor Induced Change in of Poor Induced Change in Single Percentage AFDC Spending Single Percentage AFDC Spending Mothers Change (in 2000 Dollars) Mothers change (in 2000 Dollars) Alabama 688 0.83% $1,420,609 1,660 2.00% $3,430,833 Arizona 748 1.52% $4,130,727 1,464 2.98% $8,087,585 Arkansas 724 1.59% $2,418,703 1,399 3.08% $4,673,836 California 2,666 0.83% $35,031,702 3,782 1.18% $49,689,734 Colorado 892 2.13% $5,893,835 1,469 3.51% $9,712,292 Connecticut 772 3.21% $9,097,960 1,262 5.24% $14,868,845 Delaware 244 3.56% $1,470,342 468 6.82% $2,817,895 Florida 1,724 1.00% $9,346,809 3,630 2.11% $19,686,960 Georgia 1,191 0.87% $6,507,216 2,373 1.74% $12,968,386 Idaho 372 3.30% $1,656,929 612 5.42% $2,723,995 Illinois 1,838 1.20% $12,981,360 3,322 2.17% $23,465,472 Indiana 1,243 1.77% $6,751,476 2,204 3.13% $11,973,241 Iowa 716 2.36% $5,476,152 1,036 3.42% $7,924,389 Kansas 795 2.80% $5,446,604 1,205 4.25% $8,255,996 Kentucky 857 1.42% $3,960,985 1,638 2.71% $7,574,044 Louisiana 878 0.69% $3,032,914 1,763 1.39% $6,091,852 Maine 424 2.74% $3,693,688 651 4.21% $5,667,848 Maryland 931 1.85% $7,107,384 1,684 3.35% $12,852,611 Massachusetts 1,290 2.03% $14,810,181 1,929 3.04% $22,146,686 Michigan 2,058 1.36% $19,724,044 2,913 1.93% $27,916,721 Minnesota 859 2.07% $9,072,960 1,177 2.85% $12,440,938 Mississippi 690 0.76% $1,710,962 1,649 1.81% $4,089,303 Missouri 1,140 1.56% $6,452,632 2,066 2.83% $11,691,701 Montana 366 3.00% $2,603,514 527 4.32% $3,741,044 Nebraska 485 2.93% $3,357,771 751 4.54% $5,202,919 Nevada 418 3.20% $2,397,503 847 6.47% $4,851,808 New Hampshire 309 4.55% $2,749,967 529 7.78% $4,705,108 New Jersey 1,307 2.03% $9,493,061 2,383 3.69% $17,305,984 New Mexico 570 1.79% $3,181,471 1,038 3.25% $5,788,680 New York 3,335 1.36% $38,102,980 4,768 1.95% $54,475,436 North Carolina 1,243 1.09% $6,090,581 2,394 2.09% $11,730,348 North Dakota 214 3.17% $1,585,323 291 4.32% $2,162,826 Ohio 1,880 1.16% $12,710,548 3,115 1.92% $21,058,295 Oklahoma 1,008 1.97% $5,803,655 1,609 3.15% $9,262,292 Oregon 818 2.24% $6,318,966 1,271 3.48% $9,816,173 Pennsylvania 1,596 1.20% $12,584,212 2,529 1.90% $19,941,384 Rhode Island 458 3.99% $1,371,194 669 5.83% $2,005,138 South Carolina 745 1.04% $2,696,859 1,490 2.08% $5,391,737 South Dakota 240 2.76% $229,244 353 4.07% $337,968 Tennessee 939 1.08% $5,266,028 2,132 2.46% $11,957,675 Texas 1,690 0.60% $6,891,791 3,754 1.33% $15,312,704 Utah 491 2.76% $253,406 758 4.26% $391,456 Vermont 314 5.09% $1,752,887 408 6.62% $2,277,623 Virginia 995 1.32% $5,435,119 2,026 2.69% $11,068,208 Washington 1,148 1.94% $10,698,691 1,650 2.79% $15,382,629 West Virginia 646 2.08% $3,326,057 1,048 3.38% $5,393,940 Wisconsin 1,320 2.58% $12,646,313 1,708 3.33% $16,357,411 Wyoming 265 4.24% $1,713,030 406 6.51% $2,627,578 Note: Simulation based on specification 3 in Table 2 and assumes that other states do not change policies. See text for details. These financial simulations understate actual costs in several respects. First, costs would continue indefi- nitely, accruing every year. Second, the costs would rise as in-migration continued over time. Third, the amount does not include other expenses associated with the in-migration of poor single mothers. These expenses may be large. For example, in 1990, almost four times as much was spent on Medicaid as on AFDC. Other social services and education also involve substantial sums. Fourth, benefit increases would also have direct 133

Welfare and the Multifaceted Decision to Move February 2005 costs due to increased spending on in-state residents, spending is desirable or effective;instead,the results some of whom might be more likely to seek welfare imply that migration may discourage states from pro- benefits if benefits were increased. viding high welfare benefits because such generosity Even for these narrowly defined costs,Table 5 in- attracts and retains potential welfare recipients.There- dicates a nontrivial financial effect of welfare migra- fore,if redistributive social spending is desirable.then tion.For example,five years after increasing benefits policymakers need to create institutional structures by one standard deviation,Alabama would be pre- that can support it.They could do this,for example. dicted to spend $3.8 million more per year on the net with federal matching programs designed to offset for in-migration alone.Even increasing benefits by 10% states the costs associated with welfare-induced mi- would cost Alabama $1.1 million a year on the in- gration (see Gramlich 1985 and Inman and Rubinfeld migrants.Cutting costs by these amounts would yield 1997,58).The recent move toward funding welfare commensurate savings.Although these estimates are with block grants,on the other hand,may increase modest relative to overall state budgets,they demon- incentives for politicians to cut funding and may fur- strate how welfare-induced migration can make raising ther undermine the ability of states to formulate social benefits more problematic and cutting benefits more policies independently. tempting for many states. The results also have implications for broader debates about governmental capacities for redistribu- tive social spending.Migration effects can be real but. CONCLUSION nonetheless,obscured by the complexity of model- ing reality.This implies that scholars should continue The analysis presented here is built on three premises to cast a critical eye on empirical results in related First,we should be wary of existing research that is areas such as tax and regulation related migration based on inadequately specified models.Second,state- Substantively,the findings validate the idea that mo- level fixed effects,combined with a quasi-experimental bility constrains governmental activities in at least research design,can effectively control for state at- one important context.To say that such constraints tributes that affect migration.Third,a discrete-choice exist,however.is not to say that they are neces- conditional logit model can effectively control for im- sarily problematic.After all,these constraints could portant individual determinants of migration such as constitute either an undue hindrance on democratic family and race. autonomy or a useful discipline against excessive The results validate prior indications that family redistribution.Therefore,scholars should also con- ties fundamentally influence residential choice.In all tinue the arduous task of normatively and theoreti- specifications,birth state effects are huge.These ef- cally investigating the proper role of government and fects dwarf welfare effects,supporting Schram,Nitz, the institutional mechanisms for enabling government and Krueger's (1998,228)rejection of narratives to play that role in an increasingly integrated-and in which poor single mothers narrow-mindedly and mobile-world. illegitimately-if economically rationally-flock to states with higher benefits.These results imply that poor single mothers are as likely-or even more REFERENCES likely-to move home as everyone else.But more im- Allard,Scott,and Sheldon Danziger.2000."Welfare Magnets:Myth portantly for the vast literature on welfare and mi- or Reality?"Journal of Politics 62 (May):350-68. gration,the results also indicate that welfare benefits Bailey,Michael,and Mark Rom.2004."A Wider Race?Interstate exert a nontrivial effect on state residential choice.This Competition Across Health and Welfare Programs."Journal of Politics 66 (May):326-47. finding stands in contrast to much previous work. Berry,William D.,Richard C.Fording,and Russell L.Hanson.2003. The results do not imply that welfare-induced mi- "Reassessing the 'Race to the Bottom'Thesis:A Spatial Depen- gration will fundamentally remake the demographic dence Model of State Welfare Policy."Journal of Politics 65 (May): profile of the country.However,the welfare migration 327-49. Brueckner,Jan.2000."Welfare Reform and the Race to the Bottom: hypothesis does not require welfare to exert a dom- Theory and Evidence."Southern Economic Journal 66 (January): inant effect,only a real effect.And here,the results 505-25 provide strong,robust indications that the effect is real. Bureau of Labor Statistics.2001."Annual Average Unemploy- For example,simulations indicate that if California in- ment."Available online at bls.gov/data/home.htm.Accessed creased its benefits by a standard deviation,it would summer 2001. Census Bureau 2000."County Business Patterns."Available on- have a net inflow of about 4.289 households headed by line at fisher.lib.virginia.edu/cbp/state.html.Accessed December poor single mothers after five years.These additional 2001 households would add approximately $56 million per Frey,William,Kao-Lee Liaw,Yu Xie,and Marcia Carlson.1996. year indefinitely to AFDC costs,to say nothing of ad- "Interstate Migration of the US Poverty Population:Immigration Pushes and Welfare Magnet Pulls."Population and Environment ditional costs associated with Medicaid,housing,and 17(July):491-535. other services.Such effects are modest relative to state Gramlich,Edward M.1985."Reforming U.S.Fiscal Arrangements." populations and budgets;whether they are modest with In American Domestic Priorities,ed.John Quigley and Daniel regard to the politics of state policymaking is an open Rubinfeld,Berkeley:University of California Press. question. Greene,William.2000.Econometric Analysis.4th ed.New York: Prentice Hall. The results have important policy implications.They Inman,Robert and Daniel Rubinfeld.1997."Rethinking Federal- do not provide guidance whether redistributive social ism."Journal of Economic Perspectives 11 (Fall):43-64. 134

Welfare and the Multifaceted Decision to Move February 2005 costs due to increased spending on in-state residents, some of whom might be more likely to seek welfare benefits if benefits were increased. Even for these narrowly defined costs, Table 5 in￾dicates a nontrivial financial effect of welfare migra￾tion. For example, five years after increasing benefits by one standard deviation, Alabama would be pre￾dicted to spend $3.8 million more per year on the net in-migration alone. Even increasing benefits by 10% would cost Alabama $1.1 million a year on the in￾migrants. Cutting costs by these amounts would yield commensurate savings. Although these estimates are modest relative to overall state budgets, they demon￾strate how welfare-induced migration can make raising benefits more problematic and cutting benefits more tempting for many states. CONCLUSION The analysis presented here is built on three premises. First, we should be wary of existing research that is based on inadequately specified models. Second, state￾level fixed effects, combined with a quasi-experimental research design, can effectively control for state at￾tributes that affect migration. Third, a discrete-choice conditional logit model can effectively control for im￾portant individual determinants of migration such as family and race. The results validate prior indications that family ties fundamentally influence residential choice. In all specifications, birth state effects are huge. These ef￾fects dwarf welfare effects, supporting Schram, Nitz, and Krueger’s (1998, 228) rejection of narratives in which poor single mothers narrow-mindedly and illegitimately—–if economically rationally—–flock to states with higher benefits. These results imply that poor single mothers are as likely—–or even more likely—–to move home as everyone else. But more im￾portantly for the vast literature on welfare and mi￾gration, the results also indicate that welfare benefits exert a nontrivial effect on state residential choice. This finding stands in contrast to much previous work. The results do not imply that welfare-induced mi￾gration will fundamentally remake the demographic profile of the country. However, the welfare migration hypothesis does not require welfare to exert a dom￾inant effect, only a real effect. And here, the results provide strong, robust indications that the effect is real. For example, simulations indicate that if California in￾creased its benefits by a standard deviation, it would have a net inflow of about 4,289 households headed by poor single mothers after five years. These additional households would add approximately $56 million per year indefinitely to AFDC costs, to say nothing of ad￾ditional costs associated with Medicaid, housing, and other services. Such effects are modest relative to state populations and budgets; whether they are modest with regard to the politics of state policymaking is an open question. The results have important policy implications. They do not provide guidance whether redistributive social spending is desirable or effective; instead, the results imply that migration may discourage states from pro￾viding high welfare benefits because such generosity attracts and retains potential welfare recipients. There￾fore, if redistributive social spending is desirable, then policymakers need to create institutional structures that can support it. They could do this, for example, with federal matching programs designed to offset for states the costs associated with welfare-induced mi￾gration (see Gramlich 1985 and Inman and Rubinfeld 1997, 58). The recent move toward funding welfare with block grants, on the other hand, may increase incentives for politicians to cut funding and may fur￾ther undermine the ability of states to formulate social policies independently. The results also have implications for broader debates about governmental capacities for redistribu￾tive social spending. Migration effects can be real but, nonetheless, obscured by the complexity of model￾ing reality. This implies that scholars should continue to cast a critical eye on empirical results in related areas such as tax and regulation related migration. Substantively, the findings validate the idea that mo￾bility constrains governmental activities in at least one important context. To say that such constraints exist, however, is not to say that they are neces￾sarily problematic. After all, these constraints could constitute either an undue hindrance on democratic autonomy or a useful discipline against excessive redistribution. Therefore, scholars should also con￾tinue the arduous task of normatively and theoreti￾cally investigating the proper role of government and the institutional mechanisms for enabling government to play that role in an increasingly integrated—–and mobile—–world. REFERENCES Allard, Scott, and Sheldon Danziger. 2000. “Welfare Magnets: Myth or Reality?” Journal of Politics 62 (May): 350–68. Bailey, Michael, and Mark Rom. 2004. “A Wider Race? Interstate Competition Across Health and Welfare Programs.” Journal of Politics 66 (May): 326–47. Berry, William D., Richard C. Fording, and Russell L. Hanson. 2003. “Reassessing the ‘Race to the Bottom’ Thesis: A Spatial Depen￾dence Model of State Welfare Policy.” Journal of Politics 65 (May): 327–49. Brueckner, Jan. 2000. “Welfare Reform and the Race to the Bottom: Theory and Evidence.” Southern Economic Journal 66 (January): 505–25. Bureau of Labor Statistics. 2001. “Annual Average Unemploy￾ment.” Available online at bls.gov/data/home.htm. Accessed summer 2001. Census Bureau 2000. “County Business Patterns.” Available on￾line at fisher.lib.virginia.edu/cbp/state.html. Accessed December 2001. Frey, William, Kao-Lee Liaw, Yu Xie, and Marcia Carlson. 1996. “Interstate Migration of the US Poverty Population: Immigration Pushes and Welfare Magnet Pulls.” Population and Environment 17 (July): 491–535. Gramlich, Edward M. 1985. “Reforming U.S. Fiscal Arrangements.” In American Domestic Priorities, ed. John Quigley and Daniel Rubinfeld, Berkeley: University of California Press. Greene, William. 2000. Econometric Analysis. 4th ed. New York: Prentice Hall. Inman, Robert and Daniel Rubinfeld. 1997. “Rethinking Federal￾ism.” Journal of Economic Perspectives 11 (Fall): 43–64. 134