Assessing the impact of welfare reform on single mothers.

Fang, Hanming ; Keane, Michael P.

THE PERSONAL RESPONSIBILITY and Work Opportunity Reconciliation Act (PRWORA), signed into law in 1996, transformed the U.S. welfare system. PRWORA replaced the Aid to Families with Dependent Children (AFDC) program with Temporary Assistance for Needy Families (TANF). Since its inception in 1935 as part of the Social Security Act, AFDC had been the main welfare program providing assistance to low-income single mothers. But a number of factors, particularly the rapid growth in the never-married single-mother population and a resumption of growth in caseloads in the early 1990s (following the surge of the late 1960s and early 1970s; figure 1), rendered the program unpopular. (1) Under the new TANF program, welfare participation among single mothers has dropped dramatically, from 25 percent in 1996 to 9 percent today. At the same time, the fraction of single mothers who work has increased from 74 percent in 1996 to 79 percent today. The goal of this paper is to ascertain what features of welfare reform, if any, have been most responsible for this decline in welfare participation and increase in work among single mothers.

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Two factors complicate our task. First, a key feature of PRWORA was that it reduced federal authority over welfare policy, giving the states much greater leeway in the design of their own individual TANF programs. A great deal of program heterogeneity has emerged across states, making it difficult to develop a set of variables that comprehensively characterize the different state TANF programs. Second, a number of other recent developments may also have contributed to the changes in welfare and work participation since 1996. These factors, such as the strong U.S. economy of 1996-2000 and the significant expansion of the earned income tax credit (EITC) after 1993, must be controlled for in order to isolate the impact of particular elements of state TANF policies.

One important fact lends credence to the idea that factors other than PRWORA may account for the lion's share of recent caseload declines: the dramatic drop in welfare participation (and the dramatic increase in work) among single mothers actually began in 1993-94, before PRWORA's enactment (figure 2). From 1993 to 1996 AFDC participation fell from 32 percent to 25 percent. On the other hand, beginning around 1993, many states began to obtain federal waivers allowing them to adopt TANF-like reforms of their AFDC programs. Such reforms included work requirements, time limits on benefits, sanctions for failure to meet work requirements, and family caps. These changes may have contributed substantially to caseload declines even before PRWORA.

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At the same time that PRWORA delegated greater control of welfare policy to the states, it also mandated nationwide many of the popular features introduced under state waivers, such as time limits and work requirements. To understand the sense in which the federal law "mandates" certain features of state TANF programs, one must understand how federal TANF funds are distributed to the states. Under AFDC, states received federal matching funds based on their AFDC expenditures. PRWORA converted these matching funds to block grants. The block grant for a state was fixed at a level related to federal funding of AFDC benefits and other related programs in the year when that funding had been highest in that state. States were given substantial leeway in how the block grant funds could be used: for example, they may use it to support child care (an important postreform development to which we will return). However, to avoid fiscal penalties on the federal block grant, states must adhere to a "maintenance of effort" (MOE) rule: states must maintain their spending on assistance for needy families at no less than 75 to 80 percent of their pre-1996 level. (2)

PRWORA requires that state TANF programs set a five-year lifetime limit for any individual receiving federally funded aid, although states may exempt up to 20 percent of their caseload from the limit. States may elect to set shorter time limits, and many have. However, any assistance provided to recipients beyond the five-year limit must be financed solely out of state funds. Three states (Michigan, New York, and Vermont) have effectively decided not to enforce the five-year limit. And many states (such as California) do not terminate but only reduce benefits when the time limit is reached. PRWORA also requires that a specific and rising percentage of states' TANF recipients either work or engage in work-related activities (such as job search or training), and that states impose a work requirement on any recipient who receives TANF for more than two years. Again, states may set a shorter work requirement time limit, and many have done so. States also vary greatly in the sorts of exemptions from work requirements that they allow and in the penalties they impose if work requirements are not satisfied.

Roughly contemporaneously with the changes implemented by PRWORA, the U.S. economy experienced one of its longest postwar expansions. The national unemployment rate remained below 5 percent from 1997 to 2001 and dropped as low as 4 percent in 2000 (figure 2). At about the same time, the EITC was dramatically expanded in terms of both the number of recipients and the generosity of the credit. Figure 3 shows that the number of federal EITC recipient families increased from about 7 million in 1980 to 19.6 million in 2001. The federal EITC phase-in rate for a single mother with one child increased from 10 percent in 1980 to 34 percent in 2002. (3) Moreover, many states have enacted additional EITC programs of their own (for more details of the EITC expansion, see the discussion of the EITC under "Data" below). Other contemporaneous policy changes include the expansion of Medicaid under the Omnibus Budget Reconciliation Act of 1989 (OBRA 1989), which dramatically expanded health insurance coverage for low-income women and children who had not been receiving cash welfare benefits. Moreover, expenditure on the Child Care and Development Fund (CCDF) increased from $1.4 billion in 1992 to $7.9 billion in 2001 (figure 4). In fact, the value of child care subsidies and other noncash benefits now exceeds cash assistance in total federal and state spending under TANF programs. The federal and state governments have also substantially increased expenditure for child support enforcement (figure 4). Naturally, all of these changes in the economic and policy environment could affect the incentives of single mothers to participate in welfare or work.

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The changes in average yearly AFDC/TANF caseloads over the past several decades, depicted in figure 1, can be summarized as follows:

--A steep increase in AFDC caseloads occurred in the late 1960s and early 1970s, which were a time of enormous expansion in government public assistance programs, including the establishment of the food stamp and Medicaid programs. Moreover, between 1968 and 1971 the Supreme Court abolished the absent father rule, the residency requirement, and regulations that denied aid to families with "employable mothers." These rulings increased the welfare take-up rate substantially.

--AFDC caseloads were almost flat from the early 1970s until 1990, with a mild increase in the early 1980s due to the back-to-back recessions of 1980 and 1981-82. The increase in the benefit reduction rate (the "tax" on wages earned while on welfare) from two-thirds to 100 percent during President Ronald Reagan's first term quickly stopped that uptick.

--A dramatic increase in the caseload occurred from 1990 to 1994. This increase is puzzling because the 1990-91 recession was quite mild, and the 1988 Family Support Act had recently mandated that "work eligible" AFDC recipients participate in welfare-to-work programs. Nor did the welfare participation rate of single mothers exhibit a steep increase (figure 2). We discuss various explanations for this phenomenon in our review of the literature below.

--Welfare caseloads dropped spectacularly after the peak in 1994. The total caseload fell more than 60 percent from the peak of 1994 to 2002, a period roughly contemporaneous with the sustained economic expansion of 1992-2000. The recession that began in March 2001 did increase welfare caseloads in some states, but only slightly, and the national caseload showed a further slight decrease.

How did the different components of welfare reform and other contemporaneous economic and policy changes contribute to the spectacular drops, both in the welfare participation rate of single mothers and in welfare caseloads, that have occurred since 1993? What were the relative contributions of time limits, work requirements, the EITC, child care subsidies, and the strong macroeconomy? These are questions of immense importance for both policymakers and researchers. The answers matter for the design of improved welfare policies and for understanding how welfare policies should respond to macroeconomic conditions.

Much research has already been devoted to these questions, and we review some of the key contributions to this literature in the next section. All of these have focused on only one or a few of the policy and economic variables of interest. Thus they are unable to measure the separate contributions of each of the elements mentioned above. Furthermore, we would argue, studies that focus on only a few policy variables may yield biased estimates of the effects of the policies in question, because they exclude other important policy and environmental factors.

One of the main contributions of this paper is the construction of a detailed data set that includes measures of all the key economic and policy elements described above, on a state-by-state and year-by-year basis, for the entire 1980-2002 period. One concern in incorporating so many features in one grand analysis was the possible collinearity among the policies, (4) many of which were implemented roughly contemporaneously. We deal with this problem by exploiting both cross-state variation in the timing and form of particular policies as well as cross-sectional variation in how individuals with different characteristics are affected differently by seemingly collinear policies. We discuss in detail the sources of variation that we use to identify the effects of each variable of interest.

The individual-level data that we use, in conjunction with the economic and policy variables we compiled ourselves, are those in the Annual Demographics Supplement to the March Current Population Survey of the U.S. Bureau of the Census (March CPS). (5) From the 1981-2003 supplements (which cover the period 1980-2002), we extracted data on all single mothers with dependent children, or, more specifically, women who were not living with a spouse at the time of the interview and who had at least one dependent child age 17 or younger. These women may be divorced, widowed, separated, or never married, and the children may be their biological, step-, or adopted children as long as the mother could claim them as her dependents. Single-mother families are not necessarily single-adult families, since single mothers may be living with other adults, including, for example, their parents or their unmarried partners or other related or unrelated individuals. (6)

We achieve two main goals in this paper. First, we show that, with a comprehensive list of control variables that include demographic, economic, and policy variables and a rich set of interaction terms, we are able to develop a model that rather successfully explains both the levels of and changes in welfare and work participation rates among single mothers across states, time, and various demographic groups for the whole 1980-2002 period. Second, using simulations of the model, we estimate the contributions of the various components of welfare reform and other contemporaneous economic and policy changes to welfare and work participation rates. Of course, our confidence in our counterfactual decomposition relies, to a large degree, on the success of our empirical model in fitting the historical data on work and welfare participation rates.

Our main findings can be summarized as follows:

--The key economic and policy variables that contribute to the overall 23-percentage-point decrease in the welfare participation rate among single mothers from 1993 to 2002 are, in order of relative importance, work requirements (accounting for 57 percent of the decrease), the EITC (26 percent), time limits (11 percent), and changes in the macroeconomy (7 percent). This ranking holds for all years since 1997, although the contributions of the different factors differ by demographic group.

--The key economic and policy variables that contribute to the overall 11.3-percentage-point increase in the work participation rate among single mothers from 1993 to 2002 are, in order of relative importance, the EITC (33 percent), macroeconomic changes (25 percent), work requirements (17 percent), and time limits (10 percent). However, we find interesting differences in the relative importance of these variables across demographic subgroups and by time period.

These findings have important policy implications. It seems that although work requirements are highly effective at getting single mothers off welfare, they are not as effective at getting them to work. Indeed, whether single mothers work or not after leaving welfare depends crucially on conditions in the macroeconomy. One big success in public policy has been the expansion of the EITC, which contributes significantly to both getting single mothers off welfare and getting them to work. Our research highlights the crucial difference between "leaving welfare" and "working." Indeed, we document the somewhat troubling development that nearly one-quarter of welfare leavers actually did not start work.

The paper is organized as follows. We begin with a selective critical review of some influential earlier studies. We then describe both the individual-level data from the March CPS and the economic and policy variables that we use in our empirical analysis. Next we give some descriptive statistics that emphasize the rich interactions between the economic and policy variables and the demographic characteristics of single mothers, and we use these to motivate our empirical model. Following a description of our empirical specification, we present and interpret our empirical estimates, discuss the fit of our empirical model, and use the model to decompose the contributions of different economic and policy variables to changes in welfare and work participation rates. Finally, we draw conclusions and suggest directions for future research.

A Selective Review of the Welfare Reform Literature

In this section we discuss critically some of the key papers in the relevant literature and highlight the differences between their approaches and ours. (7)

Studies on the Effects of Time Limits

The aspect of the 1996 welfare reform that has received the greatest attention is the elimination of the entitlement status of welfare, and in particular the imposition of time limits on welfare receipt. PRWORA created a five-year lifetime limit on TANF receipt, in the sense that, except in limited special circumstances, states may not use federal funds to pay TANF benefits to any adult for more than a total of sixty months during that person's lifetime. But time limits did not originate with PRWORA. Many states had already instituted time limits on welfare receipt under federal waivers. Given the perceived centrality of time limits to the reform strategy, many studies have attempted to estimate the effects of time limits on welfare participation and other aspects of behavior.

Notable studies of time limits include those of Jeffrey Grogger and Charles Michalopoulos. (8) These papers exploit the fact that, under both AFDC and TANF rules, only families with children under 18 are eligible for benefits. Thus time limits should have no (direct) impact on the behavior of single mothers whose children would reach the age of 18 before the limit could come into play. (9) Therefore, in a before-and-after design, any change in welfare participation among mothers with older children should be due solely to other time-varying factors besides the imposition of time limits (such as changes in general economic conditions or in other components of welfare reform). The change in participation rates for mothers with older children thus provides a baseline estimate of the impact of all these other factors. These mothers can therefore serve as a "control group" in estimating the effect of time limits. Under the assumption that all other time-varying factors affect the behavior of mothers with older and younger children in the same way, any incremental participation rate change among mothers with younger children isolates the effect of time limits.

Table 1, which is adapted from one of Grogger's tables, illustrates this idea. (10) A five-year time limit should not have affected the behavior of single mothers whose youngest child was between 13 and 17 years old. Thus the drop in their participation rate from 16 percent to 11 percent should be attributable entirely to other time-varying factors, such as work requirements or macroeconomic conditions. Next consider single mothers whose youngest child is 6 years old or less. These women are potentially affected by time limits, since they could use up the maximum five years of benefits long before their youngest child reaches age 18. Welfare participation dropped a much larger 17.5 percentage points among this group. Using these figures, we can estimate the impact of time limits using a difference-in-differences (DD) approach. Of the 17.5-percentage-point drop in participation for single mothers with young children, we attribute 5 percentage points to the other factors besides time limits, since that is the change we observe for the control group. This leaves 12.5 percentage points as the drop in welfare participation attributable to time limits. This is a very substantial effect. It implies that 71 percent of the drop in welfare participation among mothers with young children was due to time limits.

As Grogger hastens to point out, however, this estimate relies on a number of strong assumptions. (11) Most critically, it supposes that all factors other than time limits have the same impact on single mothers whether their children are older or younger. This is a very strong assumption, since mothers with younger children differ from mothers with older children in important ways. To see this, note that table 1 also shows that, both before and after time limits were imposed, welfare participation rates were much higher among single mothers with younger children (41 percent before time limits) than among those with older children (16 percent). This alone illustrates the dramatic difference between the two groups and calls into serious question the assumption that they would be affected in the same way by other aspects of welfare reform or by the business cycle.

The fact that the baseline participation rates differ so greatly between the two groups creates another serious problem for the simple DD approach. Even if unmeasured time-varying factors did have a common impact across groups, to use a DD approach we need to know whether the "common impact" applies when we measure impacts in levels or in percentages. This point is also illustrated in table 1. The last column shows the percentage change in participation rates for each group following the imposition of time limits. The single mothers with older children had a 31 percent decline in welfare participation, whereas those with younger children had a 42 percent decline. So, if one assumes that the unmeasured factors have a common percentage-change effect across groups, the DD estimate of the effect of time limits on mothers with younger children is 11 percentage points. This implies that only 26 percent of the drop in welfare participation among this group of mothers was due to time limits. Thus time limits seem much less important when impacts are measured in percentages rather than levels. (12)

We contend that there is only one way around this problem, and that is to do the hard work of trying to measure and control for a rich set of time-varying factors that may have affected people with different characteristics differently, and to allow for interactions between these factors and personal characteristics in constructing our model. The DD approach is not a panacea for dealing with unmeasured time-varying factors when the treatment and control groups are different, especially when they have different baseline participation rates. (13)

Recognizing this, Grogger extends the simple DD analysis described above to control for four specific time-varying factors that he believed might have different effects on women with younger children than on those with older children. Those time-varying factors are the unemployment rate, the minimum wage, the real level of welfare benefits (all measured at the state level), and a dummy variable for "any statewide welfare reform." (14) When these factors are controlled for, and state dummy variables and state-specific quadratic time trends are included, the estimated impact of time limits on welfare participation for single mothers with children age 6 and under drops to 8.6 percentage points. (15) This is still 49 percent of the overall 17.5-percentage-point drop in participation for this group.

Thus Grogger's results imply that time limits were a major factor driving down caseloads. His estimates of state unemployment rate effects are all insignificant, implying that the strong economy over the period did not play a significant role. His estimates do imply that falling real AFDC/ TANF benefits had a significant impact on mothers with younger children. Interestingly, neither the time limit dummy nor the general reform dummy nor the unemployment rate nor any of his other controls are significant for the single mothers with older children. Thus Grogger's results apparently attribute the 31 percent drop in welfare participation for this group to the state-specific time trends. These may be picking up the effect of the EITC expansion, a general change in "culture," or some other factor not controlled for in the model, indeed, in a later paper that controlled for EITC expansion, Grogger found an even smaller effect of time limits on welfare participation: they now accounted for only about one-eighth of the decline in welfare use and about 7 percent of the rise in the employment rate since 1993. (16) This is rather close to our own estimates, presented below, of 11 percent and 10 percent for the contributions of time limits to changes in welfare and work participation, respectively.

An important limitation of Grogger's approach is that all other aspects of welfare reform are summarized in his "any statewide welfare reform" dummy variable. This precludes him from estimating the effects of other specific policy changes. Furthermore, it will not adequately control for omitted factors if other reforms affect different demographic groups differently. As an example, one specific feature of welfare reform that Grogger omits, and which could lead to upward bias in his estimates of time limit effects, is the massive expansion of subsidized day care for low-income families that occurred largely as a result of PRWORA (figure 4). Under CCDF rules, funds may not be used to subsidize day care for children over 12 except in very rare instances (for example, for children with special needs). Hence the day care expansion should not have affected single mothers whose youngest child is 13 to 17 years old. And, obviously, subsidized day care could have a bigger effect on mothers with pre-school-aged children. That is, the effects of other contemporaneous reforms omitted from the analysis could indeed be age dependent. We note, somewhat facetiously, that if we chose to ignore time limits rather than day care, we could use table 1 to obtain a DD estimate of the effect of expanded day care spending. (17)

The later analysis of Grogger and Michalopoulos is less subject to these sorts of criticisms. (18) They estimate the effect of time limits using data from a randomized experiment, the Florida Family Transition Program. This was a fairly small experiment in which welfare recipients in Escambia County, Florida, were randomly assigned to either a treatment group that was subject to a two- or a three-year time limit or a control group that was not. (19) They estimate that the two-year time limit reduced welfare participation rates among single mothers with youngest children ages 3 to 5 by 7.4 percentage points (from a base rate of 40.3 percent) during the first two years after the time limit was imposed. This estimate implies significant effects of time limits, but it is difficult to translate it into a prediction for the aggregate welfare caseload, for two reasons: first, the estimate is based on a two-year limit, whereas most states have longer limits; and second, it conditions on a sample of women who had applied for welfare in the first place. Thus it tells us nothing about how time limits would affect entry into welfare.

Furthermore, we do not think it is possible to generalize the significant effects of time limits in the Florida context to the broader national context. Dan Bloom, Mary Farrell, Barbara Fink, and Diana Adams-Ciardullo (BFFA) provide an excellent discussion of how time limits have been implemented in practice in many states. They state that "as a relatively small pilot program ... [the Florida program] was generously funded and heavily staffed," and thus, "With small caseloads, workers were able to have frequent contact with participants." (20) They go on to point out that "Recipients who came within six months of reaching their time limit and who were not employed were referred to specialized staff known as 'transitional job developers,' who worked intensively to help these individuals find jobs. The transitional job developers sometimes met with recipients several times a week, and they offered employers generous subsidies to hire their clients." Finally, BFFA note that "... nearly all of those who reached the time limit had their benefits fully cancelled. Very few extensions were granted; only a handful of cases retained the child's portion of the grant; and no one was given a post-time limit subsidized job." (21)

This combination of intensive case management and strict enforcement of the time limit is wildly at variance with the norms under TANF. In fact, BFFA describe a system where, in practice, time limits are only sporadically enforced because extensions and exemptions are so common. They note that roughly 44 percent of the caseload reside in states such as Michigan, New York, and Vermont, which do not have time limits, or California, Maryland, and Washington, which only reduce (rather than terminate) benefits when the time limit is reached. Furthermore, several states, such as Oregon, stop the welfare time clock if a recipient is participating in required work or work-related activities, and many states, such as Connecticut, provide liberal extensions of the time limit if recipients have made a "good faith effort," which basically means meeting the requirements of the state TANF plan with respect to work, job search and training, and avoiding sanctions.

Thus, in many states, time limits are practically irrelevant. A typical comment is that of the U.S. General Accounting Office: "In Oregon, months count toward the time limit only if the family fails to cooperate, and the State has graduated sanctions resulting in a full family sanction for failure to participate [in required work activities]. Officials told us they do not expect any families to ever reach the State time limits in Oregon because, if families are cooperating, they can expect to receive cash assistance indefinitely (funded by the State after the waiver expires in the year 2002); if families are not cooperating, their grants will be terminated long before the time limit is reached." (22) BFFA describe data on 54,148 TANF recipients who had reached the federal five-year time limit by December 2001. The bulk of these were in Michigan and New York, since these states implemented TANF relatively early on. But these states do not impose the federal limit. Of 5,143 recipients in the other states that did nominally impose time limits, BFFA report that 51 percent continued to receive TANF benefits under some sort of extension. The most common extension criteria were "good faith effort" (in Connecticut, South Carolina, and Tennessee), "disabled or caring for disabled family member" (in Georgia, Louisiana, and Utah), "to complete education or training" (in Georgia), "high unemployment" (in Texas), and "other" (in Ohio).

Studies of Other TANF and TANF-Like Reforms

A number of previous studies have attempted to look more broadly at the whole range of factors that might drive caseloads. A paper by Rebecca Blank was a pioneering effort in this direction. (23) She examined the evolution of welfare caseloads by state and by year over the period 1977-95. Although her data were entirely from the pre-TANF period, a number of states had already instituted waivers in the early 1990s, making it possible to examine the impact of a number of TANF-like reforms.

The details of Blank's specification are worth describing, because they guide much of the subsequent work in this area. Her dependent variable is the log ratio of a state's AFDC caseload to the female population ages 15 to 44. Given that most AFDC recipients are in this age range, the dependent variable can be taken to approximate the percentage of women in this age group who participate in AFDC. This variable ranged from 6 to 8 percent over the sample period and was 7.4 percent in 1994. The policy variables include the state-specific AFDC "grant" for a family of three (that is, the benefit for a family with no earnings or outside income) and dummy variables for whether the state had been granted a waiver and, if so, whether the policies adopted under the waiver included time limits, enhanced work requirements, fewer exemptions from (or more severe sanctions for) failure to meet work requirements, or family caps. (A family cap is a policy whereby AFDC benefits are not increased by the usual per-child increment if a woman has an additional child while already on AFDC.) Controls for aggregate economic conditions were the state unemployment rate (and two lags of this variable), the median wage, and the 20th percentile wage. Blank also controlled for state demographics such as average educational attainment, the share of the population that were black, the share that were elderly, the share that were recent immigrants, and the share of households headed by single females.

Blank's results imply that caseloads are mildly sensitive to the unemployment rate: the estimated elasticity of the welfare participation rate with respect to a sustained increase in the unemployment rate is roughly 0.25. (24) This means that a 3-percentage-point increase in the unemployment rate would raise the participation rate by about 11 percent after three years. Her results also imply that participation is quite sensitive to benefit levels: the estimated elasticity of the participation rate with respect to the benefit level is 0.56.

Blank's study has a few notable shortcomings. First, a salient feature of the data (figure 1) is that the AFDC caseload was quite flat from 1977 through 1989 (in the range of 3.5 million to 3.9 million families). But it rose sharply in the 1990-93 period (from 3.8 million in 1989 to 5.0 million in 1993), peaked in March 1994 at 5.1 million families, and then began to drop sharply in mid-1994. One might suspect that the bulge was due to the mild recession of the early 1990s. Before 1990, however, AFDC caseloads had never exhibited much cyclical sensitivity. In fact, Blank shows that half of the caseload increase in 1990-94 was due to increases in child-only and AFDC-UP cases. (25) Thus her dependent variable exaggerates the increase in the AFDC participation rate among single females age 15 to 44 during that period. Presumably, an ordinary least squares (OLS) estimate would attribute this exaggerated increase to the recession, leading to an overestimate of the effect of unemployment. Despite this, Blank notes that her model still does not succeed in explaining the increase in caseloads in 1990-94.

Second, Blank obtains very puzzling results for the effects of specific reform features. The coefficient on the "any major state welfare waiver" dummy implies that a waiver reduces the participation rate by roughly 11 percent. However, when this is broken down into a set of dummies for different aspects of waivers, the dummy for whether a state imposed time limits is insignificant (and has the wrong sign), and work requirements are insignificant as well. The dummy indicating that a state imposes harsher sanctions for failure to satisfy work requirements is estimated to have a significant positive effect on caseloads. The variables estimated to significantly reduce caseloads are dummies for reduced JOBS exemptions and for whether the state imposed a family cap. The latter policy is estimated to reduce the caseload by roughly 18 percent, which seems highly implausible. As Blank states, "the impact of family caps on the caseload in the short run should be minimal. It merely holds benefits constant for women who are already on the caseload, it does not remove anyone from the rolls." (6)

The Council of Economic Advisers (CEA) conducted a similar exercise using state-level data from 1976 to 1996, updated through 1998 in a second paper. (27) These papers use much sparser sets of controls than does Blank's 1997 paper. The only nonwelfare factors included in the models are the current and lagged unemployment rates (along with state and year dummies). In the 1997 paper, specifications that include only a portmanteau dummy variable for "any statewide welfare waiver" imply that a waiver reduces a state's caseload by roughly 5 percent. (28) When dummies for specific policies are included instead, the estimates are rather imprecise. The only clearly significant policy is stricter work requirement sanctions, which are predicted to reduce the caseload by roughly 10 percent.

It should be stressed that a fairly small amount of data underlies these estimates. For instance, according to Gil Crouse, (29) only five states had implemented benefit time limits by early 1996, with two more doing so in the second half of 1996. Two states implemented work requirement time limits in 1994, four more in 1995, and two more in 1996. Stricter work requirement sanctions were more common. Six states implemented these before 1995, five more in 1995, and eight more in 1996. Thus it was only in 1995-96 that a substantial number of states began to implement TANF-like policies. (30)

The 1997 CEA report notes that a one-year lead of the waiver dummy is significant. The estimates imply that a waiver reduces the caseload by roughly 6 percent in the year before it is implemented. The report points out that this could be an anticipatory effect: the knowledge that welfare policies will become stricter may deter women from welfare participation even before the waiver is implemented. But another explanation is based on policy endogeneity. It is widely accepted that the increase in welfare caseloads in 1990-93, and the increase in program costs that this induced, helped create the political momentum that led to implementation of waivers and ultimately TANF itself. (31) However, by the time many states had implemented waiver policies in 1995-96, and certainly by the time that most had begun to implement TANF policies in 1997, a rapid decrease in the caseload had already begun. (32) Any misspecified model that fails to capture the sharp decline in welfare caseloads beginning around 1995--before the implementation of most TANF-like policies--will tend to attribute these changes to the TANF and waiver dummy variables. The reason is simply that the model will produce large serially correlated residuals in the post-1995 period, and any variable that "turns on" in that period will help absorb those residuals. Thus what the CEA calls a "policy endogeneity" problem we prefer to call a misspecification or omitted variables problem. (33) The best way to deal with this problem is to look for additional control variables that can successfully explain caseload evolution in the prereform period. This is the approach we take here. (34)

It is interesting to note that, in a model with state fixed effects, our approach would not work. Consistency of OLS requires only that the covariates and the errors be contemporaneously uncorrelated (that is, that the policy variables be "predetermined"), whereas fixed effects estimators rely on "strict exogeneity" (that is, a lack of correlation at all leads and lags). Thus policy endogeneity would lead to inconsistent estimates in fixed effects models even if the residuals were serially independent. This is a strong argument for not including state fixed effects if we believe that policy endogeneity is present.

The CEA models certainly fail to explain both the increase in caseloads in 1990-93 and the decline beginning in 1995. Unemployment rate changes over this period--the only non-welfare-related explanatory factor in the CEA models--seem inadequate to explain the phenomenon, given the history of insensitivity of caseloads to unemployment. The 1997 CEA paper notes that "for the 1989-1993 period that saw a tremendous increase in the rate of welfare receipt ... changes in unemployment can only explain about 30 percent of the rise ... that leaves roughly 70 percent of the rise unexplained by this statistical analysis." (35) Their model also attributes 34 percent of the decline in caseloads in 1994-96 to "other unidentified factors." Thus a key challenge is to develop a model that can better account for caseload movements over time, particularly the pre-TANF decline in caseloads beginning in 1995. Unless a model can fit this pattern, any effects that it attributes to waiver and TANF policies may be spurious.

Robert Moffitt argues that the cyclical sensitivity of AFDC caseloads might have increased over time.36 Thus, unless one takes a stand on the cyclical sensitivity of the caseload and how it has evolved over time, one cannot decide how much of the drop in welfare participation after 1994 was due to welfare reform and how much to the strong economy. If only aggregate data were available, these would leave one with a hopeless identification problem. However, Moffitt also pointed out that that cross-state variation in unemployment rates can, in principle, be used to resolve this problem. One could ask whether caseloads fell more or less in states where unemployment fell more or less, and one could even identify how the cyclical sensitivity of caseloads has varied over time, provided one assumes that it varies in the same way in all states. We today are in a much stronger position than previous researchers to identify these cyclical effects, because we can include data from the recession of 2001-02.

Studies of Non-TANF-Related Reform Policies

Other important policy changes that may have influenced the welfare and work decisions of single mothers in recent years are the expansions of Medicaid eligibility for low-income families not on AFDC and the expansion of the EITC. As Keane and Moffitt note, (37) the fact that single mothers would tend to lose Medicaid eligibility if they left AFDC created an important work disincentive before 1987. But a series of Medicaid eligibility expansions in 1987-2002 may have reduced this disincentive, by allowing single mothers with income above the AFDC/TANF eligibility threshold to continue to receive Medicaid benefits. Often eligibility for Medicaid expansions depended on the age of a woman's children.

Aaron Yelowitz attempted to quantify the effect of Medicaid expansions on work. (38) He measured the extent of eligibility expansion by a single variable, which he called GAIN%, defined as the difference between the Medicaid income eligibility threshold under the expansion and the AFDC income eligibility threshold before the expansion. Identification of Medicaid expansion effects came from the variation in GAIN% across states, over time, and across individuals. He used March CPS data from 1989 through 1992 to estimate a probit model for work participation as a function of GAIN%. To control for other factors that might vary across states and time, he also included year and state dummies. Yelowitz's estimates imply that the Medicaid expansion of 1989-92 led to a 1.2-percentage-point decrease in welfare participation and a 0.9-percentage-point increase in labor force participation among single mothers with at least one child under 15. However, as discussed earlier, for such a strategy to provide a consistent estimate of the effect of the policy variable in question, one has to make the strong and likely implausible assumption that all other time-varying factors, including all omitted policy variables, impact all single mothers in the same way, regardless of the ages of their children or their state of residence. Furthermore, we must know a priori whether the omitted time-varying factors affect the work participation of the "control" and "treatment" groups in terms of levels or percentages. Only then will the difference-in-differences methodology work.

Bruce Meyer and Dan Rosenbaum have undertaken a more comprehensive study of the effects of a wide range of factors on the work decisions of single mothers, but their focus is on the EITC. (39) They use CPS data for 1984-96 and incorporate changes in the EITC and other tax rates, AFDC and food stamp benefit levels, welfare time limits (under waivers), Medicaid expansion, and child care and training expenditures. Meyer and Rosenbaum's paper represented a significant advance over previous studies in that it controlled for a wide range of factors. Their empirical specification, however, did not control for other key TANF-like reforms under waivers, such as work requirements. Moreover, because their study used data only up to 1996, they do not address the separate contributions of various components of the 1996 welfare reform to the subsequent drop in caseloads. Meyer and Rosenbaum's estimates imply that changes in the EITC and other tax policies explain more than 60 percent of the increase in work among single mothers relative to childless single women in 1984-96. Somewhat unexpectedly, their estimates also imply that Medicaid expansions had a nonnegligible and negative effect on work participation.

We conclude with two general observations about all the studies we have described. First, they all use only dummy variables (such as whether or not a state has implemented a time limit) to capture policy effects. This is a problem because a time limit or other policy change will most likely affect rates of entry and exit from welfare, rather than simply inducing an immediate shift in the level of participation. The effect of such a policy thus builds gradually over time. In contrast, we explicitly construct measures of the time elapsed since particular policy changes might have begun to affect each single mother (based on her state of residence and demographics), thus allowing policy effects to develop gradually.

Second, all the studies we have described include state dummies to control for differences in welfare and work participation across states that the model leaves unexplained. As already mentioned, one reason for not using state fixed effects is that consistency of the fixed effect estimator requires the assumption of strict exogeneity, which we believe is invalid regarding policy changes. Furthermore, Keane and Kenneth Wolpin show how the use of state fixed effects can lead to seriously biased estimates of policy effects in a dynamic model. (40) For example, in a dynamic framework, a person decides whether to go on welfare or work or invest in human capital today based not just on benefits today but on expected future benefits as well. Suppose that each state has a typical level of benefit generosity that is persistent over time (for example, that Minnesota always has higher benefits than Alabama), but that benefits in both states fluctuate from year to year. These transitory fluctuations in benefits may have little effect on work and welfare participation decisions, which instead will be primarily driven by the permanent component of benefits. Hence a state fixed effects estimator may lead one to underestimate the effect of benefit levels. Using simulations of a dynamic model, Keane and Wolpin show that this problem can be severe. (41)

For these reasons we choose not to include state fixed effects in our models. Of course, this may create a problem if our control variables fail to explain the persistent differences in levels of welfare participation across states, and instead generate serially correlated residuals by state. If states with persistently negative residuals for welfare participation tended to adopt certain policies under TANF, one might falsely infer that these policies reduced participation. As we show later in the paper, our models do a reasonably good job of explaining the persistent differences in levels of welfare and work participation across states, so that we are not too concerned about this issue.

To summarize, we feel that previous studies of welfare reform suffer from a number of important limitations. Typically, they examine only a subset of the many policy and economic environment variables that might affect welfare and work decisions. They often use state and time dummies to control for omitted time- and state-varying factors. This procedure is valid only under the assumption that such omitted factors affect all demographic groups equivalently and, even if this is true, that the analyst knows whether the equivalence holds in terms of levels or in terms of percentages. On the other hand, those studies that omit explicit year effects have not developed models that succeed in explaining the evolution of welfare participation over time at the national level, let alone broken down by state and demographic group.

Data

The data set used in this paper combines individual-level data from the March CPS with data on a rich set of economic and policy variables. In describing these data, we will also detail the sources of variation that we exploit to identify the effects of key economic and policy variables.

Individual Data

Our main data source is the series of March supplements to the Current Population Survey fielded between 1981 and 2003, covering activities in 1980-2002. (42) The CPS is designed to provide a nationally representative sample by interviewing approximately 60,000 households. The sample size was increased in 2001 and 2002 to improve estimates of children's health insurance coverage by state, for the purpose of allocating federal funds under the State Children's Health Insurance Program (SCHIP) established in 1997. The CPS asks retrospective questions about demographics, work activities, and income. Questions about demographic variables, such as age, refer to the week before the interview; those about income variables refer to the previous calendar year; and those about work activity, such as hours worked and major occupation, refer to both periods.

Our unit of analysis is families headed by single mothers. Since we condition on single-motherhood, we take marital status and the presence of children as exogenous. Of course, changes in welfare rules could affect marriage and fertility, but existing empirical work suggests that these effects are small. (43)

For purposes of constructing a data set on single mothers, it is important to note that the CPS is organized around households defined by a unique address, for example a house or an apartment. A household may contain more than one family, with the person who rents or owns the house considered the head of the household. We select female-headed families or subfamilies as the unit of analysis. (44) We then count the number of dependents in each female-headed family or subfamily. Note that the dependent children are not necessarily the woman's biological children. Stepchildren or adopted children, grandchildren, and other unrelated children whom the woman lists as dependents are also counted.

The CPS survey asks the respondent to provide detailed demographic information (including age, race, education, and marital status) for every household member. We construct the age composition of the woman's children by counting the number of dependent children at each age. This is an important step because, as we discuss below, whether a woman is subject to particular welfare rules (such as work requirements) or eligible for particular benefits (such as child care subsidies) often depends on the precise ages of her children.

We construct our welfare utilization measures from the family's reported sources of income over the previous calendar year, and we analyze work participation decisions based on the average hours worked in that year. Specifically, we consider a single woman a welfare recipient if her income from public assistance (Unicon recode variable incpa) is positive. (45) The employment variables come directly from the CPS, which includes the "hours worked per week last year" (hrslyr). We recorded a woman as working full-time if she works for thirty-two hours or more a week, and part-time if she works between eight and thirty-two hours a week.

Policy Data

COMPONENTS OF WELFARE REFORM. An important contribution of the paper is the comprehensive documentation of the many welfare policy changes that occurred at the state level over the 1980-2002 period. We collected detailed information about states' policies from many different sources. (46) The rest of this section describes the different policy components in detail.

Time Limits. PRWORA prohibits states from using federal TANF funds to provide benefits to adults beyond a sixty-month lifetime time limit (except that 20 percent of a state's caseload may be exempted). Many states have opted for shorter time limits, whereas others have opted to use their own funds to provide benefits beyond the federal limit. Some states implemented their own time limits under waivers before PRWORA was enacted. (47)

To understand the set of variables we use to capture the possible effects of time limits, it is useful to examine the theory of how time limits can affect behavior. A key point is that time limits may have both anticipatory and direct effects. The direct effect arises simply from the fact that a person who reaches the time limit becomes ineligible for further benefits (assuming the limit is enforced). The anticipatory effect is subtler. The basic idea is that a forward-looking person faced with time-limited welfare benefits should try to conserve (or "bank") her months of eligibility and use them only when truly necessary.

Consider a simple framework where a woman decides each month whether to receive welfare or go to work. A myopic person who maximizes current income would choose to participate in welfare so long as it generated one dollar more in income than she could earn by working (net of the cost of working). But a forward-looking person would choose welfare over work only if the gap between benefits and earnings were substantial. Why use up a month of welfare eligibility just to get a few extra dollars? In some future month she may confront a situation where only very low paying jobs are available, so that welfare benefits far exceed her potential earnings. It is therefore best to conserve her months of welfare eligibility for such circumstances.

Stated more formally (see appendix A), in a dynamic framework, such a woman should make welfare participation decisions by comparing the value of current-period welfare benefits with the value of current-period potential earnings plus the option value of conserving a month of benefit eligibility. As Grogger and Michalopoulos point out, this option value is, ceteris paribus, an increasing function of the time horizon over which benefits may be used (that is, the number of years until the woman's youngest child reaches 18). (48) It is also, ceteris paribus, a decreasing function of the stock of remaining months of eligibility (that is, the option value of preserving a month of eligibility is greater when one has only one month left than when one has sixty).

Our empirical models include several variables designed to capture both the direct and the anticipatory effects of time limits--both those created under TANF and those created earlier under AFDC waivers. These variables and others used in the study are defined in table C1 in appendix C. Each variable has up to three subscripts: i for individual, s for state, and t for year. Thus the subscripts enable one to see whether each variable varies across states, across people, or both.

At the most basic level, we include a dummy variable for whether a state imposed a time limit in a given year ([DTL.sub.st]), as well as a dummy for whether the time limit could have been binding for a particular woman ([DTL_HIT.sub.ist]), given the ages of her children. A woman whose oldest child is x years old cannot have received welfare for more than x years. The time limit cannot bind for this woman unless x exceeds the limit, regardless of how many years ago her state implemented time limits. Thus the year in which time limits may first bind varies across women in the same state.

Note that [DTL.sub.st] captures an anticipatory effect of time limits, and [DTL_HIT.sub.ist] a direct effect. We also include variables that allow the anticipatory and direct effects of time limits on welfare and work decisions to develop gradually over time. First, we construct a variable called "months elapsed since the implementation of time limits" (MONTH_SINCE_ [TL_START.sub.st]). Second, we construct for each single mother a variable called "months elapsed since the time limits could first potentially bind" ([MONTH_SINCE_TL_HIT.sub.ist]).

To evaluate the importance of the anticipatory effect of time limits, we construct two more variables motivated by the theory presented in appendix A. First, the option value of banking welfare eligibility increases with the time horizon over which a woman will be categorically eligible for benefits. This is the remaining time until her youngest child will reach age 18. We call this variable [REMAINING_CHILD_ELIG.sub.ist]. Second, the option value of banking welfare eligibility decreases with the stock of eligible months that a woman currently possesses. We call this variable [REMAINING_TL_ELIG.sub.ist]. To construct this measure, we first calculate the maximum number of months that a woman could have received welfare since her state started her "clock." Subtracting this from the state time limit tells us the minimum stock of months that the woman possesses.

At this point it is worth commenting on our overall strategy in constructing covariates. We assume that a woman's demographics, the welfare policy rules she faces, and the economic environment in her state are all exogenous. Thus, to maintain a true reduced-form specification, every covariate we use as a determinant of welfare or work participation should be a function of these demographic, policy, and economic environment variables. One can see the effect of this strategy quite clearly by looking at how we constructed covariates to measure the effects of time limits. For instance, we do not want to use a woman's actual welfare participation history to construct the remaining months on her time limit clock, because actual participation decisions are endogenous. Similarly, in the construction of [REMAINING_CHILD_ELIG.sub.ist], we ignore the fact that a woman can always extend her months of categorical eligibility by having another child. [REMAINING_CHILD_ELIG.sub.ist] is a function only of a woman's current demographics and state policy variables, and so it is certainly an exogenous variable driving current decisions.

A key point is that Michigan, New York, and Vermont have chosen to use state funds to provide benefits to families beyond the sixty-month federal limit. (49) In other words, these states do not have effective time limits. (50) This is a key source of variation in the data that helps identify the effect of time limits on welfare and work participation. To preview our finding that time limits have had small effects on welfare participation, we note that in Michigan the number of families on welfare dropped by 58 percent from August 1996 to June 2002, while the number of individual recipients dropped by 62 percent. Over the same period the number of families on welfare in New York dropped by 63 percent, while the number of recipients dropped 68 percent. These declines are close to the national average, suggesting that time limits are not the main factor underlying the dramatic drop in welfare participation since 1996.

Another important source of variation across states is the penalty that is imposed when a time limit is reached. Among states with effective time limits, six (Arizona, California, Indiana, Maine, Maryland, and Rhode Island) continue to provide the child portion of benefits to families even after the time limit is reached. As we discuss in appendix A, this substantially reduces the impact of time limits. Therefore we constructed a measure for each state of how benefits are reduced when the time limit is reached.

Work Requirements and Exemptions. Under PRWORA, states must require parents who receive TANF assistance to participate in "work activities" after a maximum of twenty-four months. (51) Many states have chosen to adopt shorter work requirement time limit clocks. States adopted their first TANF plans over the period from October 1996 through January 1998 and adopted revised TANF plans roughly two years later. Under the initial TANF plans, twenty states required benefit recipients to start participating in work activities immediately. Under the revised TANF plans, twenty-five states required immediate work participation. Most states that do not impose an immediate work requirement have adhered to the twenty-four-month maximum allowed under the federal law. The requirement that recipients participate in work activities may increase the disutility of welfare participation, leading to reductions in welfare caseloads and increased work among single mothers.

Section 407, paragraph (b)(5), of PRWORA gives states the option to exempt single parents with a child up to 1 year of age from work requirements. However, many states, such as California, have chosen to exempt only those single mothers with children under 3 or 6 months of age, and a few have granted longer exemptions. Thus there is considerable variation in the variable we call "age of child exemption from work requirements" ([CHILD_EXEMPT_AGE.sub.st]). We use this variable, in conjunction with the state-specific work requirement time limit and the age of the woman's youngest child, to construct an indicator for whether a woman could be subject to a work requirement. We call this variable [SWR.sub.ist].

Thus we have two key sources for the identification of the effects of work requirement time limits. First, because of the variation in when states implemented their TANF plans and in the length of their work requirement time limit clocks, there is substantial variation across states in how early a single mother could have been subject to binding work requirements. For instance, under AFDC waivers, work requirements could have come into force as early as mid-1994 in Iowa, October 1995 in Michigan, and mid-1996 in Wisconsin. TANF work requirements could have been binding as early as the fall of 1996 in Alabama, Connecticut, Florida, Indiana, Kansas, Nebraska, New Hampshire, Oklahoma, Oregon, and Utah. On the other hand, work requirements were not binding until December 1998 in New York, January 1999 in Louisiana, February 1999 in New Jersey, March 1999 in Pennsylvania, and July 1999 in Illinois, Minnesota, and Missouri. (52)

Second, as already noted, we can exploit individual variation based on children's ages. For example, assume that two otherwise similar women living in different states have both been on TANF long enough to have reached their state's work requirement time limit. Suppose that each has a youngest child who is 9 months old. Suppose further that their states have similar policies, except that one state exempts women with children under 12 months old and the other exempts only women with children under 6 months old. Then only the woman in the first state is exempt from the work requirement, and any difference in welfare participation and work behavior between these women will provide additional evidence on the effects of work requirements. Similarly, take two otherwise similar women living in different states, each of whom has just one child, who is 18 months old. Suppose their states have similar policies, except that one imposes an immediate work requirement whereas the other imposes a work requirement only after twenty-four months on welfare. The woman in the first state may be subject to a work requirement, but the woman in the second cannot be. Since her only child is only 18 months old, she cannot yet have been on welfare for twenty-four months. (53)

Besides the exemption based on age of youngest child, many states allow other exemptions from work requirements under TANF. These include exemptions for single parents with children under age 6 who are unable to obtain child care, and for recipients who are disabled or have a disabled household member. (54) We call the total number of these exemptions [N_WR_EXEMPTION.sub.st]. States also differ as to whether they impose a full or a partial benefit sanction if a recipient does not satisfy the work requirement. A "partial" sanction generally means that only the adult portion of benefits, and not the children's portion, is denied. In 1996 nine states imposed a full sanction. That number increased to twenty-three in 1997 and stayed close to thirty from 1998 onward. We call the dummy variable indicating imposition of a full sanction [DFULLSANCTION.sub.st]. (55) We view both the sanction variable and the exemption variable as indicators of the strictness with which a state enforces its work requirement time limit, and we interact the work requirement variables with these measures of strictness. (56)

Finally, work requirements can, in theory, have anticipatory effects just as time limits do. If a state adopts a work requirement with a twenty-four-month time limit before the requirement is triggered, this creates an incentive to avoid welfare participation even before the twenty-four months are used up. One reason is to conserve time on the clock. Another reason is that, since the time limit reduces expected future welfare participation, it increases the value of human capital investment today. Thus we also include in our models a dummy for whether a state has a time limit in effect ([DWORKREQ.sub.st]).

Benefit Reduction Rates and Earnings Disregards. The AFDC program always imposed a "tax" on a recipient's earnings while on welfare, called the benefit reduction rate (BRR). Allowance was made for deductions for work and child care expenses, and over the history of the AFDC program the amounts of these work expense deductions were changed several times, as was the BRR itself. Notably, the BRR was decreased from 100 percent to 67 percent in 1967, but it was raised back to 100 percent in 1982. Starting that year the work expense deduction was set at $90 a month, and an additional child care expense deduction was introduced.

In addition, in an effort to encourage work among participants, the AFDC program at various times in its history included "earnings disregards." That is, for a specified time after an AFDC recipient started a job, a part of her earnings (above and beyond the work and child care expense deductions) would not be subject to the BRR. In general, this earnings disregard consisted of a fixed component (for example, the first $30 of monthly earnings) and a variable component (for example, one-third of earnings beyond the first $30) and applied only during the first several months of work. (57) Starting in late 1992, again in an effort to encourage work, many states used waivers to enhance their earnings disregards. PRWORA did not mandate specific disregard policies, and, as a result, substantial heterogeneity has emerged in how states set disregards. Many states have expanded disregards and allowed them to apply indefinitely. For instance, under its TANF plan implemented in January 1998, California set the fixed portion of the monthly disregard at $225 and the variable portion at 50 percent, with no phase-out over time. Since the variable part of the disregard is not phased out, it acts just like a BRR of 50 percent, and this is in fact how we code it. Across states, as of 2002, fixed disregard amounts varied from zero to $252, and variable disregards ranged from zero to 100 percent.

Obviously, earnings disregards, the BRR, and work expense deductions directly affect a woman's incentive to work by altering her effective after-tax wage rate. A lower effective tax rate makes welfare receipt more attractive. Furthermore, as we discuss in appendix A, effective tax rates also affect the incentive to bank months of eligibility when time limits are present. The higher the effective tax rate, the greater the incentive to forgo participating in welfare in a month when work can be found.

Diversion Programs. Under TANF many states have developed "diversion" programs under which new TANF applicants can receive a few months' worth of benefits up front if they agree not to participate in TANF for some stated period of time. A typical program may offer three months of benefits up front to a person who agrees to stay off "welfare" for three months. We view this as largely an accounting device to make TANF caseloads appear smaller, and so we code such diversion payment recipients as welfare recipients. Eight states, however, have introduced what we regard as genuine diversion programs, whereby TANF applicants agree to stay off welfare for an extended period in return for short-term cash payments (or loans) whose value is well below the maximum value of the forgone benefits. (58) In the empirical analysis we simply introduce a dummy variable to indicate whether the woman lives in a state with a genuine diversion program.

Child Support Enforcement and Treatment of Child Support Income. Although nonpayment is widespread, child support is an important source of income for single women with dependent children (see table 4 below). Under AFDC, recipients were required to assign child support collections to the welfare agency. States were then required to pass through the first $50 of monthly child support payments to the family. This pass-through income was disregarded for purposes of benefit calculation. Between January 1993 and August 1996, states requested and received waivers of a number of AFDC provisions related to child support enforcement. These waivers sometimes involved changing the pass-through amount or allowing single mothers to keep child support payments, in which case they would be subject to certain disregards just like earned income. Under TANF, all states have discretion to set their own policy in terms of pass-through or disregard of child support payments.

The Child Support Enforcement and Paternity Establishment (CSE) program was enacted in 1975 to address the problem of nonpayment of child support owed by noncustodial parents. CSE has programs to help locate absent parents and establish paternity. The CSE administrative expenditure is an important indication of how likely it is that a single woman will be able to collect child support. Figure 4 showed the large increase in CSE expenditure, from $2.92 billion in 1996 to $5.14 billion in 2002, a 76 percent jump. To measure state-level CSE activity, we take state-level CSE expenditure and divide it by the state population of single mothers (excluding widows). (59) This, combined with variation in CSE spending across states and over time, provides the three key sources of variation that identify the effect of child support enforcement expenditure on welfare and work participation.

In terms of the incentives created, there are important interactions between CSE expenditure and the pass-through and disregard rules. Since child support payments are heavily taxed under TANF rules in many states, enhanced child support collections make welfare less appealing. On the other hand, enhanced pass-throughs or disregards may reduce this incentive.

Child Care Subsidies and the Child Care and Development Fund. In the late 1980s several new programs expanded federal support for child care. The Family Support Act of 1988 created two programs, AFDC Child Care and Transitional Child Care. AFDC Child Care was designed as an entitlement for single parents on AFDC who were working or enrolled in job training or education programs. Transitional Child Care provided a temporary child care subsidy to single parents with young children for twelve months after they left AFDC to start working. Both programs used AFDC participation as an eligibility criterion. The Omnibus Budget Reconciliation Act of 1990 (OBRA 1990) created the Child Care and Development Block Grant and the At-Risk Child Care program. These programs gave states funds with which to subsidize child care for low-income working families who were not on AFDC. However, unlike AFDC Child Care and Transitional Child Care, these benefits were not an entitlement. PRWORA consolidated these four preexisting programs into the Child Care and Development Fund. The CCDF provides federal funds to the states to use in providing child care subsidies to low-income working families, whether or not these families are current or former TANF recipients. Under the CCDF a great deal of heterogeneity has emerged in the design of states' child care subsidy programs. In particular, many states ration benefits, and states differ in terms of whether they give priority to low-income families who are on TANF or to those just transitioning off TANF.

We use state CCDF expenditure per single mother as a measure of the availability and generosity of child care subsidies in a state. A key factor identifying the effect of these subsidies is that they are essentially irrelevant for women whose children are older than 12, since they are not eligible for subsidies except in rare instances (for example, for children with special needs). Also, the effect of child care subsidies is presumably stronger for women whose children are not yet of school age.

As we discuss in appendix B, an important aspect of PRWORA is the maintenance-of-effort requirement, which requires each state to maintain spending on assistance for needy families at a minimum of 75 percent of its pre-1996 level in order to receive the full TANF block grant. The MOE requirement interacts with the CCDF in an important way. The CCDF funding system is rather complex, consisting of federal funds to which states are entitled, plus federal matching funds that require state contributions, plus discretionary state contributions, including a certain level of funds that states are allowed to transfer out of the TANF block grant. But the key point is that the state part of CCDF spending counts as MOE spending. Thus, when welfare caseloads began to drop unexpectedly rapidly after 1996, causing state spending on TANF cash assistance to drop, the states shifted substantial resources into the CCDF as one way to achieve the MOE requirement. This dynamic was partly responsible for the rapid growth in total expenditure on CCDF from 1996 to 2002 (figure 4).

An alternative to using CCDF expenditure per single mother as a measure of the generosity of a state's child care program would be to use detailed program parameters, such as the monthly income limit for eligibility and the co-payment rate, which are state-specific and have varied over time within states. We choose not to use this approach because of the problems created by rationing. A state with a seemingly generous program (for example, a high income eligibility threshold and a low co-payment) will tend to have a longer waiting list. Thus program generosity is more accurately measured by the state's actual expenditure per case than by the income eligibility threshold and co-payment rates.

CONTEMPORANEOUS POLICY CHANGES. Our data set also contains detailed information about state policies other than those directly related to AFDC and TANF.

Earned Income Tax Credit. The EITC, enacted in 1975, is a refundable federal income tax credit that supplements wages for low-income working families. Major expansions of the EITC occurred in 1986, 1991, and 1994-96. Because of these expansions, the number of families receiving EITC increased from 6.2 million in 1975 to 19.5 million in 2000, and total EITC payments increased from $1.25 billion to more than $31 billion (figure 3). (60)

The EITC rules specify four parameters: a phase-in rate, a phase-out rate, a phase-in income range, and a phase-out income range. These parameters depend on family size. After the expansions of the mid-1990s, the EITC became a sizable wage subsidy to low- and moderate-income families. Thus it may provide an important work incentive. For example, in 2003 the phase-in and phase-out rates for a family with one child were 34 percent and 15.98 percent, respectively. The phase-in annual income range is from zero to $7,490, and the phase-out range is from $13,730 to $29,666. Thus a single mother with one child with taxable income between $7,490 and $13,730 would receive an EITC of $2,547. The EITC phase-in rate is even higher (40 percent in 2003) for families with two or more children.

As of 2003, seventeen states had enacted their own EITCs that supplement the federal credit. Most of these were enacted in the 1990s. Our econometric analysis combines the federal and state EITC programs and characterizes them by two parameters: the phase-in rate and the maximum credit amount. (61) Many sources of variation help identify the effects of the combined EITC. One source is variation across time. For example, the federal EITC phase-in rate for families with one child increased from 10 percent in 1980-84 to 14 percent in 1987-90, 16.7 percent in 1991, 17.6 percent in 1992, 18.5 percent in 1993, 26.3 percent in 1994, and 34 percent in 1995, where it has remained since. Second, since 1991 a different EITC phase-in rate and maximum credit have applied to families with one child than to families with two or more children, thus introducing variation across individuals. Third, the implementation of state EITC programs at different times and with different parameters has introduced variation across states.

Food Stamps. The food stamp program provides coupons that can be exchanged for food at participating stores. The value of the coupons to which a family is entitled depends on a grant level, which depends on family size, and a benefit reduction rate, which is applied to income. Unlike AFDC/TANF benefits, food stamp benefit levels are set at the federal level, and the same rules apply in all states except Alaska and Hawaii.

We collect the food stamp program parameters directly from the U.S. Department of Agriculture. Currently, the food stamp benefit reduction rate is 30 percent.

Medicaid and SCHIP. AFDC/TANF participants have had health insurance coverage provided by Medicaid since the inception of the Medicaid program in 1965. Since 1987 a number of expansions of Medicaid eligibility have enabled single mothers, under various circumstances, to leave AFDC/TANF while maintaining Medicaid coverage. Between 1987 and 1990 several legislative options and mandates were enacted to expand Medicaid eligibility for pregnant women, infants, and children. OBRA 1989 required states to cover all pregnant women, as well as all children below age 6, living in families with income at or below 133 percent of the federal poverty line. OBRA 1990 required states to phase in coverage of children born after September 30, 1983, and living in families with income below the poverty line, until all children through age 18 were covered. As of October 1, 1997, children 14 years of age and younger were covered in all states, and the upper age limit of 18 was reached in all states in October 2002.

The States Children's Health Insurance Program (SCHIP), established under the Balanced Budget Act of 1997, appropriated roughly $24 billion in federal grants over five years for states to use to provide health insurance to uninsured children under age 19 in families with incomes below 200 percent of the federal poverty line. SCHIP covers approximately 5.3 million children nationwide. States are using this new grant money to expand Medicaid, to develop new programs or expand existing programs that provide health insurance, or both.

We collected Medicaid rules for each state since 1987 (and SCHIP rules since 1997) from the annual Maternal and Child Health updates of the National Governors Association. (62) These updates provide detailed information on the age limits of children covered by Medicaid (independent of welfare status) and the age-specific income eligibility thresholds (as a percentage of the poverty line). We combine these rules with the ages of the children of each single mother to obtain the variable [MEDICID_PCT.sub.ist], which measures the percentage of children who would be covered by Medicaid if their mother left welfare but earned less than the income threshold for Medicaid eligibility, which is coded by the variable [MEDICAID_FPL.sub.ist]. Since the income threshold varies by age of the child, we used the threshold applicable to the woman's youngest eligible child as a percentage of the federal poverty line in constructing [MEDICID_FPL.sub.ist.]

MACROECONOMIC VARIABLES. We include several variables in our model to control for state and national economic conditions. We obtain state unemployment rates from the Bureau of Labor Statistics. From the Urban Institute-Brookings Tax Policy Center we obtain information on personal and standard income tax deductions (deflated by the consumer price index) and the federal income tax rate for the lowest bracket. Data on minimum wage rates are collected from the Department of Labor website. Finally, we construct the 20th percentile wage rate for each state (deflated by the consumer price index) from CPS data.

Descriptive Statistics on Single Mothers

Our data set contains 127,119 observations on single mothers 18 years and older over 1980-2002. Here we provide descriptive statistics about the single-mother population and their welfare and work participation over that period.

Demographics

Table 2 summarizes basic demographic information about single mothers. The racial composition of single mothers has been very stable over time, with about 62 to 65 percent white and 32 to 35 percent black. On the other hand, there has been a dramatic and steady increase in the proportion of never-married single mothers, from 15.6 percent in 1980 to 41.3 percent in 2002. In fact, in 1997 "never married" overtook "divorced" as the most common marital status among single mothers. The fact that the proportion of never-married single mothers continued to increase after 1996 is interesting, as an explicit objective of PRWORA was to lower the incidence of out-of-wedlock births.

Table 2 also shows a slow downward trend in the average size of families headed by single mothers. The proportion of single mothers with only one child increased from 48.3 percent in 1980 to 54.5 percent in 2002. The share of single mothers with four or more children decreased from 7.7 percent in 1980 to 4.7 percent in 2002 (not shown). On average, single mothers have about 1.7 to 1.8 children.

Finally, table 2 summarizes single mothers' educational attainment. The share of single mothers who are high school dropouts declined from 34.5 percent in 1980 to 19.3 percent in 2002. At the same time, the share with at least some college increased from 26.5 percent to 45.3 percent. However, the bulk of this rather substantial increase in educational attainment occurred before 1996.

An important message of table 2 is that shifts in the demographics of single mothers since 1996 have been rather gradual. The largest shift over this period was the increase in never-married single mothers, and this shift is not favorable regarding work. Thus demographic shifts alone will be unable to account for much of the drop in welfare caseloads since 1996.

Welfare Participation Rates

The solid lines in figure 5 show welfare and work participation rates for single mothers from 1980 to 2002. (63) In contrast to the trend in the total AFDC/TANF caseload (figure 1), the welfare participation rate was much more stable before 1994, hovering around 30 percent, with a peak of 32.2 percent in 1993. Since 1993, however, welfare participation has dropped spectacularly, all the way to 9.0 percent in 2002, or by 72 percent. (64)

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Figure 6 reports welfare participation rates for eight large states. Clearly, both levels and trends in participation rates differ substantially from state to state. The participation rate peaked in California in 1993, and in Texas and Florida in 1992; all these observations are roughly consistent with the national trend. But in Michigan the participation rate has trended down since 1983, and in Illinois it has trended down since 1987. The peak year in Pennsylvania was 1984, but a second run-up followed, which peaked in 1992. Peak years in New York and North Carolina were 1990 and 1991, respectively-a bit earlier than the national peak.

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The left-hand panels of figures 7 though 11 show how the welfare participation rates of single mothers vary with their demographic characteristics. Of course, such differences are not surprising. What is more interesting is that the trends in participation rates also differ in important ways across demographic groups. For instance, the left-hand panels of figure 7 show that welfare participation rates differ substantially by educational attainment, as one would expect. In 1994 the participation rate was 47.7 percent among single mothers who were high school dropouts, 26.9 percent among those who were high school graduates without a college degree, and 5.8 percent among those with at least a college degree. (65) More interesting, however, is the fact that, as a percentage, participation has dropped less (62 percent) for the least educated group; the participation rate declines since 1994 for the other two groups were 71 and 80 percent, respectively.

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The left-hand panels of figure 8 show that the welfare participation rates of single mothers also differ substantially by marital status. The participation rate of the never-married mothers has historically been the highest (44.1 percent in 1994), followed in that same year by separated (33.7 percent), divorced (18.8 percent), and widowed mothers (12.3 percent). Interestingly, the percentage drops since 1994 for these four groups also differed, at 71, 67, 74, and 52 percent, respectively. Because of the relatively large drop in their participation rate, divorced single mothers are now the least likely to be on welfare.

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As the left-hand panels of figure 9 show, welfare participation rates have historically been much higher for black than for white single mothers. However, the participation rate for whites was fairly stable at roughly 25 percent from 1980 to 1994, while the rate for blacks fell from 42.6 percent to 37.0 percent. Thus in 1994 the participation rate for blacks was 47 percent higher than that for whites. Since the welfare reform of 1996, racial differences in participation rates have narrowed further: in 2002 the rates were 8 and 10.5 percent for whites and blacks, respectively, so that the rate for blacks was only 31 percent higher. Thus the decline in welfare participation rates has been much greater for blacks than for whites and started much earlier.

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The left-hand panels of figure 10 show that participation rates are much higher for single mothers with younger children, as already discussed. Interestingly, the drop in participation from 1994 to 2002 is larger for mothers whose youngest child is 6 to 12 years old (70 percent) than for those whose youngest child is less than 6 years old (68 percent) or those whose youngest child is 13 to 17 years old (63 percent). The same pattern is found for specific ages at the low end of these ranges: 76, 62, and 47 percent for mothers whose youngest child is 6, 1, and 13 years old, respectively; not shown. Thus the notion of a pure anticipatory time limit effect implies a monotonically decreasing rate of decline as the age of the youngest child increases, ceteris paribus. These figures seem somewhat inconsistent with that story.

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Finally, the left-hand panels of figure 11 show that single mothers with more than one child are more likely to be on welfare than are single mothers with only one child. However, the percentage drop in welfare participation from 1994 to 2002 was similar for single women with one, two, three, or four or more children (69, 71, 65, and 66 percent, respectively; not shown).

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