Lewin Group ReportTemplate

In this chapter we describe the data set we have constructed for this project. Variable definitions and sources are listed in the Appendix. We discuss the dependent variables in Section B and explanatory variables in Section C.

For both the Basic and UP programs, we estimate three participation models: caseload, total recipients, and child recipients. In addition, we estimate an average monthly benefit per family (AMB) for the combined programs. We have defined the dependent variables for each model as follows:

We acquired electronic caseload data from the Administration for Children and Families (ACF) for both the Basic and UP programs, by month, for the period from October 1978 through March 1995. ACF likewise provided us with total recipient data for both the Basic and UP programs for the period from October 1982 through March 1995. ASPE supplemented the ACF data with total recipient data extracted from the database assembled by Grossman (1985) for the period from January 1978 through September 1982. ACF also supplied us with child recipient data from January 1978 through March 1995. We received these data in a combination of electronic and hard copy formats. All participation data used in the model match those published in the ACF publication Quarterly Public Assistance Statistics and its monthly predecessor, Public Assistance Statistics.

The state-level AMB data are for the period from 1980.1 through 1993.4 only, and were obtained from numerous editions of Quarterly Public Assistance Statistics.

We obtained the regional CPIs electronically from the Bureau of Labor Statistics. There are indices for four regions: North East, North Central, Southern, and Western. The regional CPIs appear bimonthly from 1980 through 1986 and monthly from 1986 to the present. We calculate quarterly values from the bimonthly/monthly indices.⁽³⁾

We group demographic variables into two subgroups: population characteristics and vital statistics. Population characteristics include the size of the population by age and sex, while vital statistics (e.g., out-of-wedlock births) refer to the structure of families. Population characteristics appear in all models. We have, however, explored the extent to which changes in economic factors affect AFDC caseloads and spending through their impact on family characteristics by including these characteristics in some models, but not in others

A general description of the methodology we use to capture the effects of population characteristics appears in Chapter Three. Recall that this requires the construction of "expected participation" variables, plus age adjustments to selected economic variables. We provide more details on the construction of expected participation variables here; details of the age-adjustments appear in later discussions of the variables to be adjusted. We also discuss the immigration variables here.

The state-level population characteristic estimates needed for constructing the expected participation variables are available only on an annual basis. Hence, we have constructed an annual average monthly participation series for each program and then expanded the series to a quarterly series, using the methodology described in Chapter Three.

The following is a description of the construction of the expected Basic and UP caseload variables. Calculation of the expected total recipient and child recipient variables follows this same methodology (see the Appendix).

We employ three types of data to construct the expected caseload variables. The first type is AFDC participation by type of family (one-parent versus two-parent) and age of head for 1990, estimated using the 1990 Survey of Income and Program Participation (SIPP).⁽⁴⁾ We use the SIPP data to compute initial national age-specific participation rates in the base period for the two programs. The rate for the Basic program in each specific age group is the proportion of women in an age group who head a one-parent AFDC family. The corresponding initial rate for the UP program is the number of men in the age group who are a parent in a two-parent AFDC family.⁽⁵⁾

The second type of data is national Current Population Estimates by age and sex in 1990 from the Bureau of the Census, and were used to control our initial participation rate estimates to national totals. For the Basic program, we multiply the initial age-specific participation rates by the number of women in the corresponding age group and then add across age groups to get implied estimates of the national Basic caseload in the base period. We then adjust all of the initial age-specific participation rates by the ratio of the actual caseload average monthly caseload for the period (from ACF statistics) to the estimated caseload. We repeat this process for the UP program, but using age-specific estimates for the number of men.

The third type of data is state Current Population Estimates by age and sex, from the Bureau of the Census, used with age-specific participation rates to construct the expected participation series for each state. For the Basic program, we multiply the age-specific participation rates for the base period by the number of women in the corresponding age group for each state and quarter and then add across age groups to obtain the value for the expected Basic caseload in that state for that quarter. We follow this same procedure for the UP program, but using the population estimates for men.

We employ two measures of immigration in our models: the total number of immigrants by state and the number of aliens per thousand population legalized under the Immigration Reform and Control Act (IRCA) of 1986. We obtained fiscal year data for both series from the Immigration and Naturalization Service (INS). INS data for total immigrants cover the period from FY1978 through FY1994 while data for IRCA legalizations cover the period from FY1989 through FY1994; no IRCA legalizations occurred before FY1989. We interpolate quarterly values for each variable from the fiscal year series to obtain the series used in the models. We experimented with several variants of the IRCA variable including lagged and lagged moving average constructions. The final construction included in the models is the movement of the four previous quarters.

The number of female headed households with children under the age of 18 has proven to be a strong predictor of AFDC Basic participation in previous studies. Unfortunately, reliable estimates of the number of female headed households at the state level are unavailable. Therefore, we relied on variables constructed from state-level vital statistics on marriages, divorces, and out-of-wedlock births to proxy for female-headed households in our Basic models.⁽⁶⁾ The quarterly series used in the model are interpolated from the annual data provided by the Natality, Marriage, and Divorce Statistics Branch, National Center for Health Statistics. We also tried a "marriage market" variable in some models.

We expect the changes in the vital statistics variables to result in changes in the number of female headed households and, consequently, have an impact on the Basic caseload. We estimated Basic participation models with and without these data to determine whether the coefficients of business cycle variables are sensitive to their inclusion. It could be that business cycle effects work, in part, through their effects on formation of female-headed households. We also explore the effects of these factors in our UP models because changes in the number of two-parent households are expected to affect UP participation. In addition to the current values of these variables, we experimented with both multiple lag and lagged moving average specifications. In the results reported we use the moving average of the four previous quarters.

At least one previous study of participation in the AFDC has included a measure of "marriage market conditions" as an explanatory variable (Shroder, 1995). The variable used is a mixture of a demographic variable and an economic variable: the log of the number of women between the ages of 15 and 64 in the state divided by the number of employed men in the state. We obtained annual state-level employment data by sex from the Bureau of Labor Statistics (BLS) and annual state population data by age and sex from the Bureau of the Census to create an annual series. The quarterly series included in the models is interpolated from this annual series. We experimented with a few variants of this variable, including lag specifications and the moving average of the last four quarters.

We assess the explanatory power of several labor market variables in our models:

We obtained monthly, state-level unemployment rate data from BLS for the period from January 1976 through December 1994. We converted this series to a quarterly series by averaging the monthly values within a given quarter. The specification used in all models is age-adjusted: the log of the actual value for each quarter divided by an expected value for the quarter.

The expected value is constructed at an annual level first, using national unemployment rates by age and sex for 1990 and state population data by age and sex for each year, then interpolated to get quarterly figures. As discussed in Chapter Two, substantial evidence already exists that increases in the unemployment rate have their full impact on participation only after several quarters have passed, and therefore we experimented with various lag specifications for this variable.

We similarly obtained monthly data series for total employment, employment in trade, and employment in manufacturing from the BLS for the period from January 1976 through December 1994. We converted these monthly series to quarterly series by averaging the monthly values within a given quarter. The total unemployment series was calculated from the total employment and unemployment rate series.⁽⁷⁾ The per capita series were obtained simply by dividing by the state population and then multiplying by 1000. All variables were entered in log form. As with the unemployment rate variable, we experimented with various lag specifications for these variables.

Annual average-weekly wage data at the state level for both the retail and manufacturing industries were obtained from BLS for 1978 through 1994. The quarterly values used in the model are interpolated from the annual series and then divided by the appropriate regional CPI-U. The variable as it appears in the models is equal to the log of the real wage.

Following previous researchers (Chapter Two), we initially used the retail wage variable for the Basic model and the manufacturing wage variable for the UP model. As with the unemployment rate variable, we experimented with various lag specifications of the wage variables.

In Chapter One we presented a stylized, but essentially correct, version of the budget constraint for an AFDC household. Ignoring the value of Medicaid benefits, which we consider separately, the constraint reflects earnings, AFDC cash payments, the value of Food Stamps, the earned income tax credit (EITC) and payroll (FICA) taxes. The stylized constraint is characterized by four parameters: the maximum monthly benefit (MMB), the average tax and benefit reduction rate (ATBRR), the marginal tax and benefit reduction rate (MTBRR), and the gross income limit (CUTGIL). We constructed quarterly series for the first three of these parameters for each state using program data. For CUTGIL, which may be inconsequential in some cases, but binding in others, we constructed a measure of how restrictive the limit is likely to be. One of the four series, MTBRR, varies relatively across states in any given year during the sample period and, therefore, is not used in the models we report.⁽⁸⁾

In addition to these four parameters, we constructed a series of dummy variables to capture the effects of program changes that are not fully reflected in changes to the parameters.

All of the program parameters are for a hypothetical AFDC family of three -- one parent and two children.

The most commonly used program parameter in past studies is the maximum monthly benefit (MMB). The reasons for this may be that: it is intuitively appealing; the data are easily constructed; MMB varies substantially both across states and over time; and the MMB may be highly correlated with other aspects of the program that determine both eligibility and benefit levels.

We define MMB as the typical maximum AFDC benefit for a three-person family during the quarter plus the value of Food Stamps for a family receiving that benefit, deflated by the regional CPI-U.⁽⁹⁾ If a state changes its nominal AFDC payment rate during the quarter, we use the average rate applicable over the three months.

We use several data types and sources to create the MMB variable. ACF provided us with state-level typical maximum monthly payment (MAXPAY) data for a family of three from 1979 through 1994. Quarterly MAXPAY data were not compiled before July 1982; therefore, we estimated quarterly MAXPAY values from 1979.1 to 1982.2 based on historical state fiscal year budgeting patterns.⁽¹⁰⁾ We obtained maximum monthly Food Stamp benefit and standard deduction data by quarter from the Program Reports and Analysis Branch, USDA. The Food Stamp benefit for a three-person family receiving the typical maximum AFDC benefit is equal to the maximum Food Stamp benefit for a three-person family less 30 percent of the difference between MAXPAY and the Food Stamp standard deduction.

For practical reasons, our treatment of other program variables is not symmetric with our treatment of the MMB measure in that we do not assume they are simultaneously determined with participation and average monthly benefits. Instead, we treat them as exogenous, with some risk of reporting findings that are biased estimates of their impact on participation.

The average tax and benefit reduction rate (ATBRR) is the average rate at which disposable income is reduced per each dollar of income, earned or unearned, between zero earnings and the AFDC "earnings cut-off" -- the highest level of gross earnings that a family of three can have and still receive some benefit . Formally:

where Y_c is disposable income at the earnings cut-off, Y₀ is disposable income at zero earnings, and E_c is the AFDC earnings cut-off. We define disposable income as the sum of earnings, EITC, AFDC benefits, Food Stamp benefits, and less FICA where the AFDC benefit is calculated using the earnings disregard for a family that has received AFDC benefits for more than 12 months. Thus, Y₀ is identical to MMB.

ATBRR varies across states as a result of a state's MMB, earnings cutoffs, and other program characteristics. ATBRR also varies over time within states as a result of federal program provisions, most notably OBRA81 and DEFRA84, and changes in the EITC. These latter changes had differential effects across states because of the initial cross-state variation in the variable. This is especially true for OBRA81. We experimented with various lags of ATBRR in the models.⁽¹¹⁾

The marginal tax and benefit reduction rate (MTBRR) is the rate at which disposable income decreases for each additional dollar of earnings; we calculated the MTBRR for the level of earnings just below the AFDC earnings cut-off. Specifically,

where, Y_c is disposable income at the earnings cut-off, and Y_c-20is disposable income at $20 below the AFDC earnings cut-off.⁽¹²⁾ Like ATBRR, MTBRR is sensitive to both state and federal program provisions. We experimented with various lags of MTBRR in the models.

Prior to October 1, 1981, there was no federal provision for a gross income limit at which families became ineligible for AFDC benefits. In some states, families with very substantial earnings could obtain AFDC benefits because of income disregards for employment expenses and child care. OBRA81 imposed a limit on the gross income that a family could have and still received any benefit, at 150 percent of the state's need standard; i.e., a family became ineligible for AFDC benefits if its income exceeded 150 percent of the applicable need standard. DEFRA84 raised the gross income limit to 185 percent of a state's need standard.

The practical effect of a federally mandated gross income limit varies across states and is dependent on the level of the gross income limit in relation to the AFDC earnings cut-off. In fact, the gross income limit enacted under OBRA81 exceeded the AFDC earnings cutoff for our hypothetical family in every state. We have assumed very small disregards however -- the monthly standard allowance after twelve months of receiving benefits and no child care.⁽¹³⁾ If the same family had substantially greater employment or child care expenses, the gross income limit might have been binding in many states after OBRA81, and in fewer states after DEFRA84. The closer a state's gross income limit is to the AFDC earnings cutoff we have computed, the more likely it is to be binding.

To reflect these considerations, our measure of how binding the gross-income limit is in a state, CUTGIL, is equal to the AFDC earnings cutoff relative to the gross income limit. Prior to 1981.4, CUTGIL is equal to zero in all states because the gross income limit during that period was, implicitly, infinity. The larger this ratio is, the more likely the gross income limit is binding for some potential AFDC families.

The introduction of the Unemployed Parent (UP) program in a state may reduce participation in the Basic program. It may also reduce the sensitivity of participation in the Basic program to changes in the labor market -- in the absence of the UP program, the best option for a pair of unemployed parents may be to not marry, or to get divorced, so that the mother and children can obtain Basic benefits.

In addition, the length of UP program benefits may have a significant impact on participation in the both programs. FSA88 requires that states provide benefits for a minimum of sixth months per year; many states, however, provide benefits for the entire year, and states with 12-month programs before October 1, 1990 were required to continue 12-month programs under FSA88.

To gauge the effects of both the existence and the length of UP programs, we include two separate dummy variables in our Basic models: UP12M and UP6M. UP12M, is equal to one in those quarters during which a state administers an UP program with no time-limited eligibility; otherwise, it is equal to zero. Similarly, UP6M is equal to one in those quarters during which a state administers an UP program limiting eligibility to six months out of every twelve months and equal to zero elsewhere.

It is possible that UP programs introduced in 1990 under Federal mandate have different impacts on participation than those introduced voluntarily by states in earlier years, holding the months of coverage constant. States may, for instance, introduce a Federally mandated program in a way that minimizes its fiscal impact on the state's budget, whereas states may place more weight on other objectives in implementing a voluntary program. Hence, we define a third UP dummy, UP90, to distinguish programs that were introduced in the fourth quarter of 1990, under the Federal mandate, from others.

Changes were made in federal AFDC requirements under each of the following acts during the 1980-95 period: the Omnibus Budget Reconciliation Act of 1981 (OBRA81), the Deficit Reduction Act of 1984 (DEFRA84), OBRA87, the Family Support Act of 1988 (FSA88), OBRA90, and OBRA93 (Committee on Ways and Means, 1994).

To some extent, the effects of these changes in Federal requirements are captured through changes in the program parameter variables - especially the ATBRR and CUTGIL. Furthermore, the methodology used to model the UP program also captures one of the major changes mandated by FSA88. Other provisions of these acts, especially the JOBS program and the Medicaid and child care expansions mandated by FSA88, are not, however, captured in these variables. The year dummies may also capture some of the effects of these acts to the extent that the effects are proportional and contemporaneous in all states. Recall, however that the year dummies are constrained to be the same in each quarter of the year, so their ability to capture the proportional, contemporaneous effects of a requirement that is implemented in a specific quarter is limited.

We experimented with nine different dummy variables related to federal AFDC requirements. They are:

4. MEDCCXPN, for effects of transitional Medicaid and child care expansion provisions in FSA88;

6. FSAUP1, for effects of FSA88 provisions requiring states implement UP programs;

7. FSAUP2, for effects of work and educational program requirements in FSA88 for UP families;

8. OBRA90, for the exclusion of any children receiving foster care maintenance or adoption assistance payments from the AFDC assistance unit mandated under OBRA90; and,

Details for each variable appear in the Appendix. With two exceptions, all of these variables change from zero to one in a specific quarter in all states -- the quarter of implementation. The first exception is JOBS which changes from zero to one in the quarter during which a state initiated its JOBS program. The second exception is FSAUP1 which changes from zero to one in 1990.4 for states with no UP program before the federal mandate. FSAUP1 equals zero throughout for those states with UP programs before October 1990. We experimented with lagged specifications for some of these variables, on the assumption that the full impact of implementation on participation or average monthly benefits did not occur in the first quarter of implementation.

Many states have obtained and implemented waivers to federal rules under Section 1115 of the Social Security Act ("1115 Waivers") during the period under investigation, especially during the last few years. Based on descriptions of all waivers granted during this period provided by ACF, we identified those waivers that were both implemented for a large share of the state's population at some point during the sample period and expected to have an impact on participation and/or average monthly payments.⁽¹⁴⁾ We have grouped these waivers into five categories based on the provisions of the waivers, and have created a dummy variable to capture them:

The dummy for a particular waiver equals one in each state that implements the waiver (sometimes just one state) for all quarters from the first quarter of implementation in the state forward, and is zero for all earlier quarters; the dummy's value is zero in all quarters for all other states. This specification assumes a once and for all proportional change in participation and/or average monthly benefits as the result of the waiver. We also experimented with lagged values of the dummies to allow for transitional effects. See the Appendix for further details.

All of the following programs are either substitutes or complements for AFDC: Medicaid, general assistance (GA), Supplemental Security Income (SSI), and unemployment insurance (UI). That is, these programs provide benefits to some members of AFDC families that, in the case of substitutes, are counted against AFDC benefits (GA, SSI, and UI) or that, in the case of complements, are obtained because the family receives AFDC (Medicaid). All of these programs changed substantially in at least some states during the sample period, and we attempt to capture the effects of these changes on AFDC participation and average monthly benefits through a set of program variables. Note that we also take the effects of changes in the Food Stamp program into account via adjustments to the maximum monthly AFDC benefit variable.

We also attempt to estimate the impacts of state laws concerning establishment of paternity; enforcement of child support; and limits on abortions on AFDC participation and/or average monthly benefits. The variables we develop to capture changes in other programs and changes in state laws are described below.

We use three Medicaid variables. The first is an estimate of the value of Medicaid benefits for an AFDC family with an adult and two children. Other things equal, we expect increases in this variable to increase participation. Robert Moffitt has constructed a similar variable valuing the Medicaid benefits for an AFDC family with an adult and three children (Personal correspondence with Robert Moffitt). Following Moffitt's' methodology, our Medicaid benefit for a family of three equals the mean Medicaid expenditures for AFDC mothers in a state plus two times the mean for AFDC children. The raw data for this variable are for fiscal years and come from Form HCFA-2082 Data Tables. We interpolate quarterly values from the fiscal year series and deflate the value by the appropriate regional CPI-U to obtain the series used in the model. We use the logarithm of this variable in the model.

The second Medicaid variable is intended to capture the potential effects of expansions of Medicaid benefits to low-income mothers and children who are not AFDC eligible, under OBRA86, OBRA87, OBRA89 and OBRA90. At our request, Aaron Yelowitz has compiled a measure of the share of children affected by these expansions for the years from 1988 to 1993, using data from the March Current Population Surveys and information about the expansions that he had compiled in the process of analyzing this issue using micro data (see Yelowitz, 1994). The measure is the percent of children made eligible for Medicaid whether or not they are in an AFDC household. We assume that the share is zero in every state before 1988, although a small number of children under the age of two and living in families with incomes below the federal poverty line were eligible in some states. We interpolate quarterly values from the annual series we received from Aaron Yelowitz to obtain the series used in the model. We experimented with several specifications of this variable including lagged specifications and the moving average of the previous four quarters.

The third Medicaid variable is a dummy variable representing restrictions on Medicaid funding for abortions. Because restrictions and the exact wording of Medicaid funding guidelines vary substantially by state and over time, it would be possible to group states into several different categories based on the relative restrictiveness of their guidelines. For this analysis, however, we simply construct a dummy variable identifying the existence of state Medicaid policies that deny payment for "therapeutic" abortions.⁽¹⁵⁾ Medicaid programs not paying for "therapeutic" abortions maintain "substantive restrictions" on funding, permitting Medicaid funding of abortions only to save the life of the mother, to prevent "substantial physical health hazard," in the case of severe fetal deformity, and/or when a pregnancy is a result of rape or incest. We construct this variable based on month specific data obtained from Merz, et al (1995). For each state, the dummy variable is equal to one in those quarters during which the state refused Medicaid funding for therapeutic abortions. Otherwise, the variable is equal to zero.

The directions of the effects of such restrictions on AFDC participation and average monthly benefits are uncertain, in part because the directions of the effect on fertility are uncertain. Recent studies by Hass-Wilson (1996) and Currie, Nixon, and Cole (1995) show a negative relationship between restrictions on Medicaid payments for abortions and the share of pregnancies that are terminated by abortion. However, more restrictive abortion funding policies may also reduce pregnancies by increasing the risk or cost associated with pregnancy. The effect on AFDC participation is also ambiguous because the benefit is of little value to a pregnant mother unless she is already a participant. The effect on average monthly benefits is likely to depend on the effect on fertility -- if the mean number of children increase, the value of the benefit will increase.

In our previous work on SSA's disability programs, we identified 20 instances in which individual states significantly reduced their general assistance (GA) caseloads between 1980 and 1993 (Exhibit IV.E.1 in Lewin-VHI, 1995b). The termination of Michigan's GA program in 1991 resulted in the largest single reduction on a per capita basis, over 15 per thousand population, but five other reductions exceeded seven cases per thousand. Four of these six very large cuts occurred in 1991 or 1992. We also found one instance of a substantial increase in a state's program.

We have constructed a "GA reductions" variable to use in the AFDC model following the same methodology that we used in our disability program work. In each state, the GA change is zero in the first quarter of the sample period. When a GA cut or increase occurs in a state, we measure the size of the cut per capita as the difference between the average monthly GA caseload in the three months following the quarter in which the change occurred and in the three months preceding that quarter, divided by the state's population (Exhibit 4.1). The value of the GA reductions variable is set equal to the resulting number for the quarter in which the change occurred. We also experimented with lagged values of this variable.

State	Year.qtr	GA Caseload Reductions*	Cuts per Thousand**
Delaware	1985.1	1,159	2.98
District of Columbia	1981.2	790	1.88
	1992.1	1,330	3.40
Illinois	1992.3	45,421	6.39
Indiana	1987.1	26,535	7.93
Kansas	1987.2	1,852	1.27
Louisiana	1986.1	1,679	0.64
Maine	1992.3	4,070	5.34
Maryland	1992.4	1,307	0.42
Maryland	1993.4	2,276	0.72
Massachusetts	1992.2	7,216	1.91
Michigan	1992.1	90,946	15.73
Minnesota	1984.3	-9,512	-3.78
Minnesota	1992.1	14,424	5.34
New Hampshire	1981.3	394	0.68
Ohio	1992.2	66,834	9.92
Oklahoma	1986.3	1,247	0.64
Pennsylvania	1983.3	49,774	6.88
Rhode Island	1993.4	4,050	6.59
Virginia	1992.2	2,947	0.71
West Virginia	1987.1	2,146	1.9

* GA caseload reductions are calculated as the reduction in the average monthly GA caseload from the three months before the quarter in which the change occurred to the average in the three months following that quarter. This difference was calculated only for those states which substantially changed their GA caseloads during a year and for which we were able to confirm a program change.

**Cuts per thousand are GA caseload reductions divided by the population (in thousands) age 18 to 64.

We consistently found strong evidence that GA reductions were associated with increases in SSI participation in our earlier analysis (Lewin-VHI, 1995b, 1995d). We expected weaker results for AFDC because GA programs generally serve adults who are not custodians of children. It is also possible that GA reductions are associated with reductions in AFDC caseloads and payments because states that are making programmatic changes to reduce GA spending may be making analogous changes to reduce AFDC spending. If the latter changes are not fully captured in the AFDC program variables, they may be captured by the GA change variable.

It must be recognized that the GA change variable is a crude measure of changes in GA programs, for several reasons. First many states do not have state-wide programs. Although major counties in such states often do have programs, their caseloads are not included in the state statistics used to construct the GA change variable. Second, this variable does not distinguish between the many different methods used to reduce caseloads (e.g., time limits versus "able-bodied" restrictions versus reductions in benefit levels). Presumably the size of any effect of a reduction on AFDC would depend on the nature of the reduction, as well as its size

Parents and children with sufficiently severe disabilities in AFDC families are eligible for SSI. While individuals cannot receive benefits from both programs, families can -- family members who are receiving SSI benefits are not counted in the AFDC family unit. Although SSI benefits are greater than AFDC benefits, AFDC family members who qualify for SSI may not apply for benefits, for a variety of reasons: the SSI application process is difficult and the outcome is uncertain; parents may be unaware that they or their children qualify; or parents may be misinformed about the effect of obtaining SSI benefits on AFDC eligibility and benefits for other family members.

Increases in SSI benefits would presumably increase the number of individuals in AFDC families that obtain SSI benefits, and therefore reduce average monthly payments. In some cases, the whole family may switch, thereby reducing the AFDC caseload. Changes in the determination of disability for SSI could also have an impact on AFDC payments and participation.

The federal SSI payment schedule has not changed in real terms since the inception of the program in 1974. Many states supplement the federal payment, however, and over the period under examination the real value of the sum of the supplement and the federal benefit have changed substantially in some states. In our work on SSA's disability programs, we found that increases in the real value of the maximum SSI payment (federal plus state supplement) relative to average monthly earnings were strongly associated with increases in SSI participation.

Here, we use the log of the maximum payment variable as an explanatory variable. Other things constant, we expect to find a negative, but relatively weak, association between this variable and AFDC caseloads, and a stronger negative relationship between the same variable and recipients, child recipients, and average monthly benefits.

Changes in SSA's criteria for determining whether an individual has a sufficiently severe disability to qualify for SSA may also have an impact on AFDC participation and benefits. In 1990 and 1991 the eligibility criteria for children were expanded substantially as a result of the Supreme Court decision in the case of Sullivan v. Zebley, and subsequent changes to the criteria for determining the severity of mental illness among children. There were large increases in the number of child SSI recipients in all states, and there is anecdotal evidence that many of the new child SSI recipients came from AFDC families. Thus, during this period especially we would expect an increase in child SSI recipients to be associated with a decline in child AFDC recipients, total recipients, average monthly benefits, and perhaps even caseloads.

We use two alternative variables to try to capture any impact of SSI expansions for children on AFDC participation and expenditures. The first is the number of child SSI recipients. We obtained data on SSI child recipients for December of each year from 1977 through 1994 from the Social Security Bulletin, Annual Statistical Supplement. The second variable is the number "Zebley children," children who received an SSI award as direct consequence of the Zebley. Charles Scott of the Social Security Administration provided us with data on SSI Zebley child recipients for June and December of each year from 1991 through 1994. We interpolated both biannual series to construct quarterly series.⁽¹⁶⁾ In the final series, the variables are equal to the natural logarithm of the average monthly number of SSI child recipients in the given quarter and the natural logarithm of the average monthly number of SSI Zebley child recipients in the given quarter. We experimented with several specifications of these variables including lagged specifications and moving averages of the last four quarters.

Between 1977 and 1978, the Social Security Administration's tightening of eligibility criteria for SSDI and SSI resulted in a decrease in initial allowance rates. While SSA mandated the tightening, it was implemented by state Disability Determination Services at different times and with differing intensities (see Parsons, 1991). We have created a variable to gauge the extent to which this administrative tightening may have increased AFDC participation as families denied SSDI or SSI benefits sought alternative sources of income. This variable is equal to the decrease in the SSDI allowance rate in a given state between 1977 and 1978 interacted with the calendar year dummy variable for 1980. Allowance rate data for Alaska, the District of Columbia, and Hawaii were not available, so we created a separate dummy variable for each of these areas equal to one during 1980 and zero in all other years.

If a newly unemployed parent in a potential AFDC family receives unemployment insurance (UI) benefits, the family is less likely to qualify for AFDC benefits than otherwise. We expect, therefore, that the impact of increases in the unemployment rate on AFDC caseloads would be inversely related to the share of unemployed persons who receive UI benefits. We also expect an inverse relationship between the share of unemployed persons with UI benefits and average monthly AFDC payments because UI benefits paid to AFDC family members reduce the size of AFDC benefits dollar for dollar. Because UI benefits are time limited (six months under normal circumstances in almost all states), the strength of these inverse relationships is likely to diminish substantially after two quarters. Extensions of UI benefit periods during severe recessions (usually an additional quarter) may increase the number of quarters over which the relationships are observed -- from one or two to three or four.

While the direction of the effects of an exogenous increase in UI benefits on AFDC participation and AMB seems clear, there is an important reason why we might not obtain findings that are consistent with expectations. UI program changes may mirror AFDC program changes in states. For instance, states that administratively tighten award of UI benefits out of fiscal necessity are likely to do the same for AFDC benefits. If such changes in the AFDC program are not captured in the AFDC program variables, changes UI program variables may proxy for them. Thus, tightening of UI benefits might be associated in the data with reductions in AFDC participation, rather than the hypothesized increase.

Like AFDC, the UI program is a state-federal program. Each state's program must comply with federal regulations and reporting requirements, but states have substantial latitude within those requirements concerning benefit eligibility and levels. Ideally, we would capture the essential program characteristics of state UI programs in one or two explanatory variables. We explored this approach during our work on participation in SSA's disability programs and found it infeasible, both because of the complexity of state programs and because of limitations on data availability. Hence, we adopted a simpler approach: we include the logarithm of the insured unemployment rate divided by the total unemployment rate as an explanatory variable in both the participation and average monthly benefit equations. Like the unemployment rate, the insured unemployment rate is available from the BLS, monthly. We use the average of each quarter's three months to construct the variable. We experimented with both current and lagged values of the constructed variable.

Because the UI measure is based on actual participation, it is especially susceptible to the problem discussed above -- reductions in the share of unemployed persons who are covered by UI may reflect administrative tightening or other factors that have a common effect on both AFDC and UI, inducing a positive relationship between the UI measure and AFDC participation.

Laws that effectively increase child support are likely to reduce AFDC participation and average monthly benefits in two ways. First, as suggested by Gaylin and McLanahan (1995), they may reduce fertility by increasing the cost of fatherhood to men, and thus reduce the number births to unmarried mothers. Second, increases in actual and reported income from other sources for (potential) AFDC families would reduce AFDC payments. Furthermore, a sufficiently large increase would make the family ineligible for AFDC.

Gaylin and McLanahan (1995) analyze the impact of three child support enforcement (CSE) laws on nonmarital birth rates: presumptive guidelines, immediate wage withholding, and paternity long-arm statutes. Their results demonstrate that states with CSE statutes experience lower nonmarital birthrates (Gaylin and McLanahan, 1995). The authors have provided us with data detailing which states have enacted each of these laws and in which year they were enacted. In addition, Mr. Gaylin has given us data on mandatory withholding laws. We created dummy variables for all four types of CSE laws and initially include all four in all of the participation and AMB equations. Because we only know the year in which each statute went into effect, we assume that each became effective on January 1st of the known year.

In addition to restricting Medicaid payments for abortions, many states have enacted laws regulating minors' access to abortion services. Twenty-four states enforced parental consent and/or parental notification laws for at least part of the period from 1980 through 1994. For this analysis, we treat parental consent and parental notification laws collectively as parental involvement laws based on the assumption that both types of laws have very similar impacts on fertility rates. Using data compiled in Merz, et al (1995), we have a constructed dummy variables denoting the enforcement periods of such laws.

As with the effects of Medicaid funding for abortions on the overall fertility rate, the effect of parental consent or notification laws on fertility among minors is uncertain. Haas-Wilson (1996) finds that the enforcement of such laws decreases the number of abortions per live births among minors. However, such laws may also decrease the fertility rate among minors by restricting minors' options and, thus, increasing the costs associated with becoming pregnant.

1. Each month's figure receives a weight equal to the ratio of the number of days in the month to the total number of days in the quarter in computing the average monthly caseload.

2. Initially, we had hoped to obtain separate average monthly benefit data for the Basic and UP programs, but ACF does not collect the data separately. For most quarters in the time series, quarterly data were available from ACF. When only monthly variables were available, quarterly data were computed by taking a weighted average of the AMB in each month of the quarter.

3. From 1980 through 1986, the second and fourth quarter values of the regional CPI are the average of the two bi-monthly values appearing in those quarters. For the first and third quarters, however, the value equals the one bi-monthly estimate in the quarter, February and August, respectively. From 1987 through 1995, is the average of the three monthly values in the given quarter.

4. We identify AFDC Basic units as those families or sub-families reporting having received AFDC benefits with a single reference person who has never been married, who is widowed, divorced, or separated, or whose spouse is absent. Based on conversations with individuals at ASPE, we also flag families headed by a married couples as AFDC Basic units if either parent has a disability or has held a job during the month in question. We classify all remaining families or sub-families receiving AFDC benefits as AFDC UP units.

5. Depending on the source for these initial rates, adjustments may also be made in order to obtain estimates that reflect average monthly caseloads.

6. We unsuccessfully attempted to produce state-level estimates of female headed households (FHHs). The plan required: 1) estimation of a nine-region pooled regression model for FHH using regional FHH estimates from the Current Population Survey (CPS) and vital statistics data aggregated to the regional level; 2) prediction of state values from the estimated model using state vital statistics data; and 3) controlling predicted state series to estimates from the 1980 and 1990 Censuses. However, these efforts failed to produce reliable estimates. The vital statistics variables proved to be very poor predictors of FHHs at the regional level. We attribute this to the erratic behavior of the FHH estimates over time within region. We expected that CPS-based FHH estimates at the regional level would be sufficiently reliable for our purpose, but this is apparently not so.

7. Monthly total unemployment by state were readily available from the BLS back to 1978. However, using this series would have limited the number of lags available for use in model. To obtain the maximum number of lags possible, we calculated and used the alternate total unemployment series described above. The correlation coefficient for the series available from the BLS and the calculated series was .93.

8. Although the value of MTBBR varies within a state over time, changes generally correspond to changes in federal requirements (OBRA-81 and DEFRA-84) and changes in the EITC. Thus, changes usually occur in the same quarter for most, if not all, states and are approximately equal across states. Overall, MTBBR follows a downward trend throughout the period.

9. A given family's maximum AFDC benefit may differ from the state's "typical" benefit as calculated by the ACF due to factors such as: locality, housing arrangements, family composition, or special needs.

10. More specifically, for each state we observed the quarter in which the nominal benefit changed after 1982. For most states, the quarter was the same every year, so we assumed that earlier changes were made in the same quarter in previous years. For states that did not follow a consistent pattern, with Florida being the most notable of these states, we assumed that changes before October 1982 occurred in the same quarter as the first change after October 1982. In addition, we cross-checked ACF data with semi-annual maximum monthly payment data from the Congressional Research Service. When a discrepancy appeared between the two series, an effort was made to explain the discrepancy and include the appropriate data.

11. We explored the use of effective benefit reduction rates estimated by Fraker, et al (1985) and McKinnish, et al (1995). Unfortunately, a series including all 50 states and the District of Columbia began in 1984 and only extended through 1991.

12. Technically, AFDC monthly benefits paid fall to zero as soon as the calculated benefit falls below $10; i.e., there is a notch in the disposable income schedule a few dollars below the earnings cut-off. Our measure smoothes over that notch.

13. The monthly standard allowance has varied over time as a result of federal legislation, in particular OBRA-81, DEFRA-84, and FSA-88.

15. Merz, et al (1995) uses the term "therapeutic" for "medically necessary" and/or "elective" abortions.

16. The Appendix provides a more detailed explanation for the interpolation process used to create each quarterly series.