State sanctions and the decline in welfare caseloads.
Much of the scholarship analyzing fluctuations in welfare caseloads focuses on such factors as the strength of the economy and the generosity of welfare benefits. However, with the passage of the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) in 1996, states obtained significantly more control over welfare policy. Despite this shift, there has been relatively little academic research on the role of state policy variation in welfare caseload fluctuations. This article provides solid evidence that strength of state sanctioning policies, which give caseworkers the ability to restrict the benefits of welfare recipients, has played a very significant role in recent welfare caseload declines. A comprehensive regression analysis of welfare caseloads from all 50 states from every year from 1996 to 2002, finds that strong state sanctioning policies are highly correlated with both large welfare caseload declines and low caseload levels.Welfare Reform in 1996
In 1996, President Bill Clinton signed landmark welfare reform legislation into law. While previous attempts at reform resulted in relatively cosmetic changes, the Personal Responsibility and Work Opportunity Reconciliation Act has had a meaningful and lasting impact on federal welfare policy. PRWORA ended the entitlement status of Aid to Families with Dependent Children (AFDC) and replaced it with a time-limited assistance and work requirement program called Temporary Assistance to Needy Families (TANF).
The most important policy change brought about by PRWORA was the fact that it gave states more control over welfare policy. Under PRWORA, states receive federal block grant allocations. These allocations allow states to use TANF funding in any manner reasonably calculated to accomplish the purposes of TANF, as long as the states maintain historical levels of spending agreed to in "maintenance of effort" plans. To continue receiving their full federal TANF allocations, states must also conform to specific requirements regarding current recipients' work participation rates and length of time on the rolls (Blank 2002).
Although PRWORA passed by wide margins in both the House and Senate, it was still politically controversial. The Senate minority leader at the time, Tom Daschle (D-SD), opposed the bill, saying that "When it comes to kids this bill is too punitive" (Vobejda and Dewar 1996). Likewise, then House minority leader Richard Gephardt (D-MO) voted against the bill, saying, "It could put a million children into a difficult situation" (Dine 1996). Senator Daniel Patrick Moynihan (D-NY) was even more strident, declaring that the new law was "the most brutal act of social policy since reconstruction" (Moynihan 1997: 58). He predicted, "Those involved will take this disgrace to their graves" (Welch 1996).
Between the passage of PRWORA and 2002, welfare caseloads declined by approximately 60 percent (U.S. Dept. of Health and Human Services 2003). (1) This result has generated a considerable amount of debate in both the academic and policy communities. Many conservatives supported welfare reform in 1996 and argue that the caseload declines provide evidence that welfare reform is working (National Review Online 2006). Additionally, some onetime opponents of PRWORA have become supportive. Wendell Primus, former deputy assistant secretary in the Department of Health and Human Services (HHS), who resigned in protest after President Clinton signed the welfare reform bill, remarked in 2001, "In many ways welfare reform is working better than I thought it would." He added, "Whatever we have been doing during the past five years, we ought to keep doing" (Harden 2001).
However, others credit the 1990s economic boom for the welfare caseload decline. Donna Shalala, who as secretary of HHS opposed the welfare reform bill, said, "What happened on welfare reform was this combination of an economic boom and a political push to get people off the welfare rolls" (Wall Street Journal 2001). Others who argued that the economy deserved most of the credit for the decline in caseloads included Marian Wright Edelman of the Children's Defense Fund (Gray 1996).
Literature Review
Since the passage of welfare reform, some states have experienced considerably larger caseload declines than others. As such, an analysis of the states might provide insights as to which factors were most responsible for the overall decline in welfare caseloads. Indeed, since 1996, states have experienced differing rates of economic growth. States have differed in the generosity of benefits they offer to welfare recipients. Also, since PRWORA gave states greater control over welfare policy, states have pursued varying welfare reforms since that time.
Prior the passage of PRWORA, most studies that have analyzed fluctuations in welfare caseloads primarily focused on factors like the strength of the economy and the generosity of welfare benefits. A number of academic studies find statistically significant evidence that the caseloads rise during times of high unemployment and fall during times of low unemployment (Moffit 1999, CEA 1997, Hoynes 2000, Blank 2000, Blank 2001, Figlio and Ziliak 1999, Levine and Whitmore 1998, Wallace and Blank 1999, Ziliak et al. 2000, Bartik and Eberts 1999). Black, McKinnish, and Sanders (2003) also find that the strength of the economy has a robust and statistically significant effect on welfare caseloads.
Similarly, a number of studies have found that more generous welfare benefits are correlated with higher welfare caseloads (Blank 2000, Blank and Wallace 1999, Bartik and Eberts 1999, CEA 1997, Niskanen 1996). In her paper, Blank (2001) argues that the expansion in government public assistance programs was a factor behind the sharp increases in AFDC caseloads during the 1960s. However, she and other authors (Moffit 1987) also argue that other factors contributed to this caseload increase. These include a Supreme Court decision, King v. Smith (1968), that allowed single mothers to continue to receive AFDC benefits even if they were residing with a man, and a subsequent decision, Dandridge v. Williams (1970), that gave "employable mothers" equal rights to AFDC benefits (Blank 2001).
Furthermore, a number of static models of welfare participation also find that higher levels of welfare benefits increase the likelihood that a female head of household will receive AFDC (Willis 1980, Barr and Hall 1981, Moffitt 1983, Moffitt 1986, Robins 1986, Robins 1987, Blank 1989). Likewise, dynamic models of welfare participation, which track single women over a number of years, also find that increases in cash welfare benefits raise the likelihood of receiving welfare (Hutchens 1981, Plotnick 1983). These models also find that high benefit levels reduce the likelihood that AFDC recipients will cease receiving benefits (Hutchens 1981, Plotnick 1983, O'Neil et al. 1984, Blank 1989, Ellwood 1986, Fitzgerald 1991).
In recent years, there has been a shift in the scholarship about welfare caseloads. A number of scholars have authored studies to explain the decline in welfare rolls that took place during the 1990s. In the early and mid 1990s, a number of states applied for and received waivers from the U.S. Department of Health and Human Services. These waivers granted states greater latitude to set welfare policy (Blank 2002). Overall, a total of 43 states received waivers between 1993 and 1996 (CEA 1997).
While some studies have found that these waivers are correlated with reductions in welfare caseloads (CEA 1997, Levine and Whitmore 1998, O'Neil and Hill 2001, Moffit 1999) other researchers contend that other factors, like the economy, have had a considerably greater effect (Figlio and Ziliak 1996, Ziliak et al. 2000). However, critics of these studies argue that many states applied for waivers at around the same time to pursue policies to move welfare recipients to work. As such, the economy could be masking the effects of the waivers in these studies.
Among those studies that analyze data after 1996, there exists a broad consensus that the enactment of PRWORA resulted in caseload declines (CEA 1999, Grogger 2000, O'Neil and Hill 2001, Schoeni and Blank 2000, Wallace and Blank 1999). A number of studies also find that the strong economy played a statistically significant role in caseload declines after 1996. However, many of these studies find that the economy had a larger effect on welfare caseload fluctuations prior to 1996 than after 1996 (CEA 1999, Wallace and Blank 1999, Schoeni and Blank 2000, O'Neil and Hill 2001). More important, all of these studies agree that welfare reform had a substantially larger effect than the economy on the late 1990s caseload decline.
Shortcomings in the Academic Literature
Overall, much of the academic literature is insightful. However, there are some shortcomings in many of the studies. First, the dependent variable is nearly always caseload levels. While this variable provides some insights, it would also be worthwhile for researchers to examine the magnitude of the caseload declines as a dependent variable.
This modification would be useful because there has been considerable variation in state welfare caseload declines since the passage of PRWORA. For instance, between August 1996 and August 2002, Wyoming reduced its welfare caseload by over 93 percent. Conversely, Indiana's caseload actually increased by 3 percent over the same period. (U.S. Dept. of Health and Human Services 2003). (2) Furthermore, when aggregate caseloads are being analyzed, the regression models may place too much emphasis on the characteristics of states with consistently low caseloads, both before and after welfare reform. As such, an examination of caseload declines might reveal new insights.
Second, even though a considerable amount of academic literature on welfare considers the generosity of welfare benefits, many of the recent studies analyzing welfare caseload declines pay little attention to the benefit levels. Now it is true that most states did not dramatically change the cash benefits they offered to welfare recipients after the passage of PRWORA (U.S. House Ways and Means Committee 2004). (3) However, it is certainly possible that interaction of benefit levels and the new welfare policies may have had an effect on caseloads. While some policy studies have examined this aspect (New 2002), it has gone largely unexplored in the academic literature
Finally, there is little consideration of the variation within state welfare reform policies. Many studies consider the effect of welfare reform by including an indicator variable that denotes the time periods after a state's TANF plan went into effect. (4) However, state specific policy components are typically ignored. This omission is unfortunate because considerable variation exists in the welfare reform policies that states implemented after the passage of PRWORA.
For instance, PRWORA gave states the ability to terminate the cash benefits of welfare recipients who were not complying with required work activities. Some states adopted tough sanctions that required benefits to be rescinded at the first instance of noncompliance. However, other states adopted weaker sanctions that allowed welfare recipients to keep receiving benefits after repeated infractions. It seems likely that the strength of these sanctioning policies might play a large role in caseload declines, because the persistent threat of losing benefits might encourage current TANF recipients to leave welfare. Additionally, strong sanctions might discourage others from applying for welfare benefits.
Furthermore, some policy studies have found that the strength of sanctioning policies is strongly correlated with caseload declines (Rector and Youssef 1999). This finding, which has been largely ignored in the academic literature, is worth revisiting--especially since many states have changed their sanctioning policies in recent years, resulting in a richer dataset (U.S. Dept. of Health and Human Services 1999, Government Accounting Office 2000, State Policy Documentation Project 2000).
In my analysis of welfare caseloads, I will attempt to correct for these shortcomings in the academic literature. I will analyze both caseload declines and caseload levels. Furthermore, I will analyze the role of welfare benefits in caseload declines. Finally, I will examine the strength of state sanctioning policies in my analysis. More details about my methodological approach can be found in the next section.
Methodology
Previous and current research has identified three major factors that affect fluctuations in welfare caseloads: the strength of sanctions, the performance of the economy, and the level of benefits. Regression analysis is well suited for this type of research because it allows us to simultaneously examine the effects of these factors on the central concern of this article, welfare caseload fluctuations.
First Regression Analysis: Percentage Caseload Decline 1996-2002
The first set of regressions examines why some states have experienced larger welfare caseload declines than others since the enactment of welfare reform. Nationally, the number of families receiving TANF has declined substantially, falling by approximately 60 percent between 1996 and 2002 (U.S. Dept. of Health and Human Services 2003). However, some states have experienced considerably larger caseload declines than others. The TANF caseloads of Wyoming, Idaho, and several Midwestern states all declined by over 80 percent since 1996. Conversely, Indiana's TANF caseload actually increased slightly after passage of welfare reform. Similarly, Hawaii's caseload increased during the late 1990s until more stringent sanctioning policies were put in place (U.S Department of Health and Human Services 2003).
The question remains: Why did states like Wyoming and Idaho experience larger caseload declines than other states? This first set of regressions provides some insights by analyzing the three factors identified in the academic and policy literature: the performance of the economy, the strength of sanctions on welfare recipients, and the generosity of welfare benefits.
Dependent Variables
The regressions were run on two separate dependent variables: (1) the percentage decline in the number of individuals receiving TANF between August 1996 and August 2002, and (2) the percentage decline in the number of families receiving TANF between August 1996 and August 2002. (5)
Independent Variables
The regressions analyzed the effects of five different independent variables on state welfare caseloads between 1996 and 2002. The first two variables--Full Sanction and Graduated Sanction--examine the strength of the sanctions that states imposed on welfare recipients who were not complying with required work activities. After passage of the welfare reform in 1996, every state adopted one of three types of sanctioning policies: full family sanctioning, graduated sanctioning, and partial sanctioning (Table 1). Some states sanction the entire TANF check at the first instance of nonperformance of required work or other activities (Full Sanction). Other states do not sanction the entire TANF check at the first instance of nonperformance, but do sanction the full TANF check after multiple infractions (Graduated Sanction). Finally, some states sanction only file adult portion of the TANF check, even after repeated infractions (Partial Sanction). This type of sanction enables recipients to retain the bulk of their TANF benefits, even if they fail to perform world: are or other required activities. Only the first two types of sanctions are included as independent variables because the use of "partial sanction" suffered from multicollinearity.
The third independent variable included in the model is Income Growth, which measures the real growth of state per capita personal income between 1996 and 2002. (6) The fourth variable of interest is TANF Benefits, which measures the average level of TANF cash benefits available to a single mother with two children from 1996 to 2002, as a percentage of state per capita income. (7)
The final independent variable is Caseload 1996, which measures the percentage of the state population receiving AFDC in August 1996. (8) It seems likely that states with a higher percentage of people on welfare could reduce their caseloads more easily than states with relatively few people on welfare. A generalized least squares model is used and the data are weighted by state population. The regression results are presented in Table 2. (9)
The results demonstrate that the welfare policies adopted by states are a key determinant in the size of their welfare caseload declines. In both regressions, states with full sanctions experienced the largest caseload declines. For every year between 1996 and 2002 that a state had a full sanction in place, the regression model predicts that its welfare caseload will decline by 3.38 percentage points more than a state with a partial sanction for that period of time. That means that a state with a full sanction for all six years would see its caseload decline by more than 20 percentage points compared to a state with a partial sanction during the same time period. This finding is statistically significant.
Furthermore, for every year that a state had a graduated sanction in place, the regression model predicts that its caseload will decline 2.29 percentage points more than a state with a partial sanction. Over six years, this works out to approximately a 14 percentage point difference between a state with a graduated sanction and a state with a partial sanction for all six years. This finding also achieves conventional standards of statistical significance.
The only other variable in this set of regressions that reaches statistical significance is the percentage of the population that received AFDC in 1996. States with a relatively high percentage of AFDC recipients in 1996 enjoyed more success in reducing their caseloads than did states with a low percentage of AFDC recipients. This is unsurprising. A state with a low caseload might already have had success in lowering its welfare rolls prior to 1996, and those remaining on the welfare rolls might be those who would encounter more difficulty making the transition from welfare to work. Conversely, if a state has a high welfare caseload, it seems likely that it has more welfare recipients who could be more easily persuaded to leave welfare and obtain employment.
Finally, the regression results indicate that states with strong economic growth between 1996 and 2002 experienced larger caseload declines, but this finding fails to achieve statistical significance. States with low TANF benefits between 1996 and 2002 also experienced larger caseload declines than states with high TANF benefits. However, the coefficient is also small and fails to meet conventional standards of statistical significance. Overall, these results provide further evidence that welfare reform--and not the strength of the economy--was the most important factor in the 1990s caseload decline.
Second Regression Analysis: Caseload Levels 1996-2002
To further this analysis, another set of regressions was run. In this case, the dependent variables measure caseload levels rather than caseload declines. This analysis will provide insights into why some states have smaller percentages of people receiving TANF than others. For instance, in 2002, only 0.18 percent of Idaho residents were receiving TANF as compared to over 7 percent of the residents of Washington, D.C. (U.S. Dept. of Health and Human Services 2003). Overall, analyzing the percentage of people receiving TANF should provide additional insights into the factors most responsible for welfare caseload fluctuations.
Furthermore, this analysis of caseload levels should nicely complement this article's earlier analysis of caseload declines for several reasons. First, simply analyzing caseload declines could be misleading. Some states could have experienced small caseload declines simply because they had relatively few welfare recipients prior to the passage of PRWORA. Similarly, states with large welfare caseloads in 1996 might have experienced large declines, but still have caseload levels that are considerably higher than those of other states.
Analyzing caseload levels offers additional advantages. We have seven years of data on caseload levels after the passage of welfare reform, so analyzing data from each year grants us considerably more data points. Furthermore, analyzing caseload levels might grant additional insights into the effects of sanctions, benefits, and the economy on maintaining low caseloads.
In this analysis, two sets of regressions were run. In the first regression, the dependent variable is the percentage of each state's population that was receiving TANF benefits. In the second regression, the dependent variable is the percentage of each state's families that was receiving TANF benefits.
The independent variables are similar to the ones used in the first set of regressions. Full Sanction, an indicator variable, equals one if a state has implemented a full family sanction that year and zero otherwise. Similarly, Graduated Sanction is scored a one if a state has implemented a graduated sanction that year and zero otherwise. Income Growth measures the real growth in state per capita personal income for that year. TANF Benefit measures the cash benefits welfare available to a single mother with two children as a percentage of state per capita income. Finally, state and year fixed effects are included as well.
Corrections to the Regression Model
The time-series, cross-sectional data used in this regression pose a unique set of problems. Some panels may have greater error variance than others. Additionally, it is possible that the errors of one panel may be contemporaneously correlated with those of another panel. Furthermore, it is possible that autocorrelation within the panels may bias the regression results.
As a result, the best solution is running a regression with panel-corrected standard errors and a correction for autocorrelation. The panel-corrected standard errors will correct for both differing error variance within the panels and contemporary correlation of errors across panels. The autocorrelation correction will ensure that autocorrelation does not bias the results.
It should be also noted that these regression results are not sensitive to the type of corrections performed. Models run using OLS, OLS with robust standard errors, GLS with a correction for autocorrelation, and models with panel-corrected standard errors and no correction for autocorrelation "all produce similar outcomes. The regression results are presented in Table 3.
This set of regression results provides further evidence that strong sanctioning policies result in low caseloads. The findings indicate that states with stronger sanctioning policies have a lower percentage of individuals and families receiving welfare than states with weaker sanctions. These findings achieve statistical significance.
There is also statistically significant evidence that welfare caseloads fluctuate with the strength of the economy. Unsurprisingly, caseloads fall during times of strong economic growth and rise when the economy slows. Finally, there is statistically significant evidence that states with low cash TANF benefits have a lower percentage of people receiving welfare than states with high cash TANF benefits. These results are consistent with previous scholarly research.
Overall, it appears that welfare benefit levels and economic growth rates had relatively little to do with the large decline in welfare caseloads since 1996. However, this analysis and other academic studies find that they do affect year-to-year fluctuations in welfare caseloads. This finding has relevance for future policy debates over welfare reform.
Conclusion
Welfare reform was one of the leading public policy stories of the 1990s. Between 1996 and 2002, the number of people receiving welfare fell by nearly 60 percent (U.S. Dept. of Health and Human Services 2003). This decline in welfare caseloads has attracted a great deal of attention from scholars and policy analysts. Since some states experienced considerably larger caseload declines than others, many studies analyzing welfare reform have paid close attention to these state caseload fluctuations.
Many of those studies have presented a number of important insights into why welfare caseloads declined so sharply after welfare reform. However, shortcomings are evident in some of the research. Prior analyses of welfare caseloads indicate that three factors influence welfare caseload fluctuations: the strength of sanctions, the level of benefits, and the strength of the economy. However, many studies omit one or more of these factors from their analysis. In particular, academic studies have paid little attention to state policy variations, including the strength of state level sanctions. Furthermore, since many studies consider caseload declines over a limited period of time, they are unable to distinguish between policies that cause short-term fluctuations and those that lead to long-term declines.
This article breaks new ground in several ways. First, the use of multivariate regression analysis makes it possible to consider the effects of the economy, sanctions, and TANF benefits simultaneously and to determine which factors have had the greatest effect. Second, although many other studies consider caseload declines for a short period of time after reform, this article tracks caseload declines for six years. Using a longer time frame increases the certainty that the various factors are having a long-term impact on caseloads and are not simply causing a temporary decline. Finally, this article analyzes both caseload levels and caseload declines. As such, it offers insights into the ability of sanctions to both maintain and preserve low caseload levels.
The most important finding is that the strength of state sanctioning policies had the largest impact on both caseload declines and caseload levels between 1996 and 2002. The other variables that were considered, including the strength of the economy and TANF benefit levels, had some effect on year-to-year caseload levels, but played only a minor role in the large decline in welfare caseloads between 1996 and 2002. Overall, this research provides solid evidence that the policies that states enacted in the aftermath of welfare reform played a substantial role in America's recent welfare caseload decline.
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(1) The last year for which the Department of Health and Human Services released TANF caseload data was 2002. Calculation by author.
(2) Calculations by author.
(3) Between 1996 and 2002, only three states saw their average cash monthly TANF benefit change by more than 30 percent. On average, states saw their average TANF benefits increase by 0.7 percent. Calculations by author.
(4) Even though PRWORA was passed in 1996, states took varying amounts of time to implement all the welfare reform policies.
(5) Caseload data that are exactly six years apart are used to ensure that regional seasonal variation in caseloads does not bias the findings.
(6) Most studies of welfare reform use changes in the unemployment rate to measure the strength of the economy. However, real per capita income growth was used since there exists greater variation in per capita personal income growth during the six years that were analyzed.
(7) This variable is in the form of a ratio of TANF benefits to average state per capita personal income to account for the differences in the cost of living between states.
(8) When the dependent variable is the percentage decline in families receiving TANF, Caseload 1996 measures the percentage of families receiving AFDC. Likewise, when the dependent variable is the percentage decline in individuals receiving TANF, Caseload 1996 measures the percentage of individuals receiving AFDC.
(9) Data on state welfare caseloads were obtained from the U.S. Department of Health and Human Services. Data on state per capita personal income growth were obtained from the Bureau of Economic Analysis. Data on state TANF benefits were obtained from various editions of The Green Book, published by the House Ways and Means Committee. Finally, data on state sanctioning policies were gathered from the U.S. Department of Health and Human Services, the General Accounting Office, and the State Policy Documentation Project.
Michael J. New is Assistant Professor of Political Science at the University of Alabama, Visiting Fellow at the Witherspoon Institute, and an Adjunct Scholar at the Cato Institute.
TABLE 1 STATE SANCTIONING POLICES, l996-2002 Full Family Graduated Partial Sanction Sanction Sanction Alabama 2000-2002 1996-2000 Alaska 1996-2002 Arizona 1996-2002 Arkansas 1996-1998 1998-2002 California 1996-2002 Colorado 1996-2002 Connecticut 1996-2002 Delaware 1996-2002 Florida 1996-2002 Georgia 1996-2002 Hawaii 1998-2002 1996-1998 Idaho 1996-2002 Illinois 1996-2002 Indiana 1996-2002 Iowa 1998-2002 1996-1998 Kansas 1996-2002 Kentucky 1996-2002 Louisiana 1996-2002 Maine 1996-2002 Maryland 1996-2002 Massachusetts 1996-2002 Michigan 1996-2002 Minnesota 1996-2002 Mississippi 1996-2000 2000-2002 Missouri 1996-2002 Montana 1996-2002 Nebraska 1996-2002 Nevada 1996-2002 New Hampshire 1996-2002 New Jersey 1996-2002 New Mexico 1996-2002 New York 1996-2002 North Carolina 1998-2002 1996-1998 North Dakota 1996-2002 Ohio 1996-2002 Oklahoma 1996-2002 Oregon 1996-2002 Pennsylvania 1996-2002 Rhode Island 1996-2002 South Carolina 1996-2002 South Dakota 1996-2002 Tennessee 1996-2002 Texas 1996-2002 Utah 1996-2002 Vermont 1996-1999 2000-2002 Virginia 1996-2002 Washington 1996-2002 West Virginia l996-2002 Wisconsin 1996-2000 2000-2002 Wyoming 1998-2002 1996-1998 SOURCE: U.S. Department of Health and Human Services (1999: Table W-3), U.S. General Accounting Office (2000: 44-47), and State Policy Documentation Project (2000). TABLE 2 ANALYZING THE DECLINE IN STATE WELFARE CASELOADS, 1996-2002 Model 1 Model 2 Percent Decline Percent Decline in Individuals in Families Receiving TANF Receiving TANF Years with Full -3.38 *** -3.16 *** Sanction (1.06) (1.04) Years with -2.29 ** -2.14 ** Graduated Sanction (0.88) (0.86) Income Growth -0.29 -0.28 (1996-2002) (0.29) (0.29) Average TANF 1.28 1.11 Benefit (4.50) (4.41) Percent of -3.56 ** -3.60 ** Population (1.57) (1.67) receiving AFDC in 1996 Number of Cases 50 50 Number of States 50 50 R squared .249 .234 NOTES: Both regressions were estimated with a generalized least squares model; data are weighted by state population; standard errors are in parentheses; ** significant at the 5 percent level, *** significant at the 1 percent level. TABLE 3 ANALYZING THE PERCENTAGE OF STATE RESIDENTS RECEIVING TANF, 1996-2002 Model 1 Model 2 Percentage of Percentage of Individuals on TANF Families on TANF Full Sanction -0.48 *** -0.47 *** (0.13) (0.13) Graduated Sanction -0.36 *** -0.32 *** (0.12) (0.11) Personal Income -0.09 ** -0.09 ** Growth (0.04) (0.03) TANF Benefit 0.72 *** 0.59 *** (0.11) (0.11) Number of States 50 50 Number of Years 7 7 Number of 350 350 Observations R squared .402 .349 NOTES: Both regressions were estimated with a generalized least squares model with state and year indicator variables, panel corrected standard errors, and a correction for AR1 autocorrelation; data are weighted by state population; standard errors are in parentheses; ** significant at the 5 percent level, *** significant at the 1 percent level.
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| Author: | New, Michael J. |
|---|---|
| Publication: | The Cato Journal |
| Article Type: | Report |
| Geographic Code: | 1USA |
| Date: | Sep 22, 2008 |
| Words: | 5864 |
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