A. INTRODUCTION
In this chapter we present sample period caseload and average monthly benefit (AMB) simulations produced by the model, and compare simulated to actual growth. The purpose of the exercise is to provide insights into the size and relative importance of various causes of change in the past. In Section B we present simulations for national caseload and AMB series, and in Section C we present simulations for four selected states: California, Florida, Maryland, and Wisconsin. Concluding comments appear in Section D.
We decompose average annual caseload and AMB growth during selected sample subperiods and the full sample period into growth accounted for by the model and growth not accounted for. Accounted for growth is further decomposed into growth accounted for by five sets of factors: population growth and aging; vital statistics; labor market variables; AFDC program variables; and the miscellaneous other variables included in each model.(1) With one exception, we present separate simulations for the Basic and UP programs; the exception is for Florida, which did not have an UP program for the full sample period.
We do not include the year dummies in the simulated series. If we included them, the simulated series would track the actual series quite closely. We exclude them from the simulations to demonstrate the extent to which variation in the actual series reflects variation in the state-level variables we have included in the model. The year dummies capture national factors, such as changes in federal programs that apply equally to all states (e.g., many aspects of AFDC-related legislation), and the average effects of state-level variables that have not been captured in the model.
The models used in the simulations are the caseload models reported in Exhibits 5.1 and 5.3. The national UP caseload simulations are for the 19 states with UP programs for the full sample period. We also present graphs showing the relationship between actual and predicted caseload growth during the sample period. These show the timing and magnitude of significant departures between the actual and predicted series, and may provide clues about the causes of such departures.
We divide the sample period into four subperiods, corresponding roughly to periods of national economic recession and expansion: the first four years from 1979.4 to 1983.3 include the "double-dip" recession of 1980 and 1981-82; the next period, the six years from 1983.4 through 1989.3, is one of sustained economic growth; the third period, the four years from 1989.4 to 1993.3, encompasses the recession of 1990-91; and the final year, from 1993.4 to 1994.4, is one of substantial expansion.(2) The first subperiod also includes the implementation of OBRA81, while the second includes the implementation of DEFRA84, and the third includes the implementation of FSA88.
B. NATIONAL SIMULATIONS
1. Basic Caseload
1979.4 - 1983.3 Subperiod
For this four-year subperiod, average annual growth of the Basic caseload was essentially zero, but zero growth was the net effect of very large, offsetting changes (Exhibit 6.1, top section). According to our estimates, the labor market variables account for average annual caseload growth of 2.1 percentage points per year and AFDC benefit reductions account for a decline of 4.6 percentage points per year. This probably understates the impact of benefit reductions associated with OBRA81 because the estimated effects of the benefit reductions do not include any effects of OBRA81 that are captured by the calendar year dummies for 1981 and 1982; after adjusting for seasonal factors, the coefficients of these dummies imply reductions of 2.8 and 1.0 percentage points that might also be attributable to OBRA81. In total, the findings imply that OBRA81 reduced the Basic caseload by approximately 20 percent, after controlling for other factors.
Another important, but less widely recognized, feature of this period is that the population in the age group most at-risk for participating in AFDC was growing at a rapid rate as the tail-end of the baby boom generation -- those born in the early 1960s -- was entering the age group; those born in the year usually recognized as the last baby boom year, 1964, turned 16 in 1980. According to our estimates, this growth contributed 2.0 percentage points per year to average annual caseload growth during the period. Changes in the vital statistics variables and in other variables in the model made modest contributions to growth.
1983.4 - 1989.3 Subperiod
During this subperiod the caseload grew at an average annual rate of 1.0 percent. According to our estimates, economic growth reduced the annual growth rate of the caseload by approximately 3.3 percentage points. The AFDC benefit changes captured by the state-level variables made a positive contribution of 0.2 percentage points per year. The effect of AFDC program changes are likely understated in the simulations, however, because the state-level variables do not fully reflect changes mandated by DEFRA84 and other federal legislation that
partially reversed some of the changes of OBRA81. Growth accounted for by population growth and aging declined from 2.0 percentage points in the previous period to 0.5 percentage points as the smaller post-baby boom cohort began entering the at-risk age group. The vital statistics variables contributed 0.7 percentage points per year. The state level factors in the model accounted for nearly all the growth during this period.
Overall, the model predicts an average annual decline of 1.8 percent per year. Given actual average growth of 1.0 percent, growth of 2.8 percentage points was due to other factors not captured in the model.
| Exhibit 6.1 | ||||||||
| Decomposition of National Caseload and Average Monthly Benefit Series | ||||||||
| Average Annual Growth Rate | Annual Growth Rate Accounted for by: | |||||||
| Program, Period, and Model | Actual | Accounted for by Model | Not Accounted for by Model | Population Growth and Aging | Vital Statistics Variables | Labor Market Variables | AFDC Benefits | Other Variables |
| Basic | ||||||||
| 1979.4 - 1983.3 | 0.0% | -0.1% | 0.1% | 2.0% | 0.3% | 2.1% | -4.6% | 0.2% |
| 1983.4 - 1989.3 | 1.0% | -1.8% | 2.8% | 0.5% | 0.7% | -3.3% | 0.2% | 0.1% |
| 1989.4 - 1993.3 | 6.5% | 3.8% | 2.7% | 0.1% | 0.5% | 1.5% | 0.1% | 1.7% |
| 1993.4 - 1994.3 | 0.4% | 0.8% | -0.3% | -0.4% | 0.5% | -0.8% | 1.3% | 0.2% |
| 1979.4 - 1994.3 | 2.2% | 0.4% | 1.8% | 0.8% | 0.5% | -0.4% | -1.0% | 0.5% |
| Unemployed Parent 1 | ||||||||
| 1979.4 - 1983.3 | 25.1% | 12.2% | 12.9% | 1.7% | n.a. | 15.3% | -4.9% | n.a. |
| 1983.4 - 1989.3 | -7.9% | -15.4% | 7.5% | 1.2% | n.a. | -16.6% | 0.6% | n.a. |
| 1989.4 - 1993.3 | 13.1% | 13.0% | 0.1% | 0.7% | n.a. | 12.8% | -0.4% | n.a. |
| 1993.4 - 1994.3 | 0.0% | -1.7% | 1.7% | -0.3% | n.a. | -0.3% | -1.1% | n.a. |
| 1979.4 - 1994.3 | 7.0% | 0.9% | 6.1% | 1.4% | n.a. | 0.9% | -1.4% | n.a. |
| Average Monthly Benefit | ||||||||
| 1980.4 - 1983.3 | -3.3% | 0.8% | -4.1% | n.a. | 0.2% | -0.2% | 1.1% | -0.3% |
| 1983.4 - 1989.3 | -0.2% | 0.5% | -0.7% | n.a. | 0.5% | 0.1% | -0.1% | 0.0% |
| 1989.4 - 1993.3 | -4.1% | -1.7% | -2.3% | n.a. | 0.3% | -0.3% | -1.9% | 0.2% |
| 1980.4 - 1993.3 | -2.0% | 0.0% | -1.9% | n.a. | 0.4% | -0.1% | -0.3% | 0.0% |
| 1. The UP caseload model does not include the vital statistic variables and the AMB model does not include a variable for population growth and aging. The UP caseload simulations are for the 19 states with UP programs throughout the sample period only. | ||||||||
1989.4 - 1993.3 Subperiod
This four-year period is one of very rapid growth in the Basic caseload, at an average rate of 6.5 percent per year. According to the model, the deteriorating economy accounts for 1.5 percentage points of that growth, and the vital statistics variables account for another 0.5 percentage points. The variables in the "other" category contributed a substantial amount (1.7 points per years); this is primarily attributable to the IRCA immigration variable. AFDC benefit changes captured in the state-level variables contributed just 0.1 percentage points to growth. Again, this neglects any effects of federal legislation that might be picked up by the year dummies. The contribution of population growth and aging continues to decline, to 0.1 percentage points per year. The estimated effects of all state-level factors in the model account for 3.8 percentage points of annual growth, combined, leaving 2.7 percentage points attributable to other factors that could not be measured in the model .
1993.4 - 1994.3 Subperiod
Caseload growth decelerated sharply in the last year of the sample period, to just 0.4 percent. The model also predicts decelerated growth, although not as low as actual growth--0.8% compared to 0.4%. Two factors account for the slow growth: an improving economy contributes -0.8 percentage points and a decline in the size of the at-risk population contributes -0.4 percentage points. These factors are offset by growth attributable to the vital statistics variables of 0.5 percentage points and by the AFDC benefit variables of 1.3 percentage points. The latter growth is attributed to the effect that the expansion of the EITC had on the average tax and benefit reduction rate. Additional factors that could not be measured by the model caused slight declines in the caseload, accounting for the difference between 0.4% and 0.8%
Full Period Findings
The average annual growth rate of the Basic caseload over the period was 2.2 percent. Of this growth, 0.4% per year is attributable to state level factors in the model. Thus, 1.8 percentage points per year of growth are due to factors outside of the model. This disguises the fact, however, the state-level factors and the two federal legislation dummies (OBRA81 and DEFRA84) in the model substantially help explain the large cycles in growth during the period. It also hides the fact that some factors in the models made substantial positive contributions to growth over the period and others made offsetting negative contributions.
Positive contributions come from three identified sources. First, population growth and aging contributed an average of 0.8 percentage points per year. Second, declines in marriage and increases in out-of-wedlock births (the vital statistics variables) contributed average growth of 0.5 percentage points per year. Third, other variables -- especially the IRCA immigration variable -- account for average growth of 0.5 percentage points per year.
Offsetting the factors contributing to positive growth were improvements in the labor market and reductions in benefits. According to the model, these latter factors reduced growth by an average of 0.4 and 1.0 percentage points per year, respectively.
Most of the difference in growth between the predicted and actual series occurs between 1983.4 and 1991.4. This is evident in Exhibit 6.2 (top panel) where we plot the two series. As in Exhibit 1.1, we have graphed logarithms of the caseload, so the slope of each series plot represents the growth rate. The predicted series is normalized to equal the actual series in 1989.4. That is, we use 1989.4 as the base period for the predictions and predict forward and backward from that quarter We have also plotted series showing the contributions of the labor market variables and the IRCA variable.(3) It seems likely that the cause of the substantial divergence in the actual and predicted series is the result of several factors not captured in the state-level variables. We discuss this issue further in Section D, after examining the other simulations.
2. Unemployed Parent Caseload
1979.4 - 1983.3 Subperiod
Much of the UP caseload growth came in the first four years of the sample period, in which the average annual growth rate was just over 25 percent (middle section of Exhibit 6.1). The model accounts for just under half of that growth. As with the Basic caseload, growth accounted for by the model during this period is the result of offsetting factors. We estimate that the recession contributed 15.3 percentage points to annual growth, that population growth and aging contributed 1.7 percentage points, and that cuts in benefits reduced average growth by 4.9 percentage points. The last of these figures is almost identical to the corresponding figure for the Basic caseload: -4.6 points.
The exceptionally high growth of the UP caseload relative to predicted growth during this subperiod is a puzzle. We are not aware of any major factors that would account for such rapid expansion other than the double-dip recession. It may be that the estimated effects of labor market factors, although large, substantially understate the recession's impact.
1983.4 - 1989.3 Subperiod
During this period the UP caseload declined at an average annual rate of 7.9 percentage points, but the model predicts an even larger decline, of 15.4 percent. The model's prediction stems from the estimated impact of growth in the economy, -16.6 percentage points, offset by a 1.2 percentage point increase attributed to population growth and aging, and an 0.6 percentage point increase attributed to AFDC benefit expansions.
Exhibit 6.2
Actual and Predicted Caseloads, 1979.4 - 1994.3*
* The predicted caseload series in each graph is normalized to equal the actual series in 1989.4. That is, we use 1989.4 as the base quarter and predict forward and backward from that point. The highest of the "labor market" series above the 1989.4 caseload shows the contribution of labor market factors relative to their contribution in 1989.4. A similar interprediction applies to the IRCA-86 series
1989.4 - 1993.3 Subperiod
During this period the UP caseload again increased very rapidly, an average of 13.1 percent per year. The model predicts growth of 13.0 percent per year -- a surprisingly accurate prediction given the model's performance in the previous nine years. Predicted growth is primarily due to labor market factors (12.8 percentage points), augmented by 0.7 percentage points from population growth and aging and offset by -0.4 percentage points from AFDC benefit changes. Curiously, the estimated effects of AFDC benefit changes on the UP and Basic caseloads during this period and the last year of the full period are opposite in sign. We discuss this further below
1993.4 - 1994.3 Subperiod
There is essentially no growth in the actual UP caseload during the last year of the sample, although the model predicts negative growth of 1.7 percent. The prediction is a combination of negative contributions from three factors; population growth and aging of (-0.3 percentage points), economic recovery (-0.3 percentage points), and changes in the AFDC benefit variables (- 1.1 percentage points).
The large reduction attributed to the AFDC benefit variables is in sharp contrast to the positive 1.3 percentage points that we estimate these same factors contributed to Basic caseload growth during the same year. The reason for this difference is differences in the coefficients of the average tax and benefit reduction rate in the two models. We estimate essentially no long-run effect for the UP caseload, and a negative effect for the Basic caseload. The long-run finding for UP is, however, the result of substantial offsetting coefficients on the ATBRR variable in the first two quarters of the change. The timing of the EITC change is such that the model's predictions for this subperiod reflect the current quarter effect, but not the second quarter effect.
Full Period Findings
In the 19 states with UP programs, the UP caseload grew at an average annual rate of 7.0 percent over the full period, while the predicted caseload grew at an average rate of 0.9 percent. The divergence between the actual and predicted series differs in timing from that for the corresponding Basic series (Exhibit 6.2). Most of the divergence for the Basic series occurs between 1984 and 1991. The greatest divergence for the UP series occurs in the first two years of the sample, through 1981. The actual series continues to grow at a rate that is above that for the predicted series through 1989, but from 1990 on the series move roughly in parallel.
3. Average Monthly Benefits
Due to data constraints the AMB simulations (bottom section of Exhibit 6.1) are for a slightly different period than the caseload simulations -- 1980.4 to 1993.3 instead of 1979.4 to 1994.3. The first of the three subperiods examined differs from the corresponding subperiod for the caseload simulations -- the first year of the four-year period used in the latter is omitted in the former -- and the last one-year subperiod is omitted for lack of data.
1980.4 - 1983.3 Subperiod
This period includes the implementation of OBRA81. The model predicts that implementation of the various provisions of OBRA81 increased AMB substantially; the average annual percentage point increase in this period due to the AFDC benefit variables is 1.1 percent. Yet actual AMB declined, at an average annual rate of 3.3 percent. The labor market and other variables in the model predict small declines, but the model obviously misses the major source of the decline. According to the estimates, changes in the AFDC benefit variables increased, rather than decreased, AMB over the period. These variables, however, do not include the effects of OBRA81 that are captured by the year dummies. The dummy coefficient for 1981 is -.048 (4.8 percent) after adjusting for seasonality. The adjusted coefficient for the previous year, however, is even larger in magnitude (-.063) suggesting that the changes not captured by the model that account for the AMB decline during this period occurred in advance of OBRA81. These could be changes external to the program that affect the composition of the caseload or the benefits they receive, or program changes that are not picked up by the model (e.g., tightening of rules concerning disregards.)
1983.4 - 1989.3 Subperiod
Actual AMB fell slightly during this period, while the model predicts growth of 0.5 percent per year. The source of the predicted growth is primarily the vital statistics variables.
1989.4 - 1993.3 Subperiod
AMB also fell at a rapid rate during this period -- -4.1 percent per year. While the model predicts some decline, the size of the predicted decline (1.7 percent per year) is substantially smaller. The factor driving the model's predictions is changes in the AFDC benefit variables.
Full Period
Average monthly benefits declined substantially over the full period, at an average annual rate of 2.0 percent. This decline is not predicted by the state-level variables in the model, however; instead, they predict no change.
One intriguing finding is that the average annual percent decline in AMB that is not accounted for by the state-level variables is approximately equal to the average annual percent increase in the caseload that is not accounted for by the same variables.(4) The implication of this finding is that the model accounts for essentially all of the long-term growth in total expenditures (i.e., in the product of the caseload and AMB). An analogous finding applies to the subperiod from 1989.4 to 1993.3, but not in other subperiods. While the finding is intriguing, we know of no reason to think that it is other than fortuitous.
C. SIMULATIONS FOR SELECTED STATES
1. California
Basic Caseload
The Basic caseload in California grew at an average annual rate of 3.9 percent over the entire period, almost twice as fast as the national rate. The model predicts average annual growth of 3.3 percent (top section of Exhibit 6.3). According to the model, the main contributors to growth were population growth and aging (1.7 percentage points per year), and the "other" variables -- primarily legalizations under IRCA (1.7 percentage points per year). Changes in the vital statistics and labor market variables made smaller positive contributions, while reductions in AFDC benefits had a substantial offsetting effect (-1.1 percentage points per year).
In most ways, the simulations indicate that California's experience over this period mirrors the experience of the rest of the country, but with differences in the magnitudes of the various effects. The most significant differences are:
| Exhibit 6.3 | ||||||||
| Decomposition of California Caseload and Average Monthly Benefit Series | ||||||||
| Average Annual Growth Rate | Annual Growth Rate Accounted for by: | |||||||
| Program, Period, and Model | Actual | Accounted for by Model | Not Accounted for by Model | Population Growth and Aging | Vital Statistics Variables | Labor Market Variables | AFDC Benefits | Other Variables |
| Basic | ||||||||
| 1979.4 - 1983.3 | 2.1% | 0.8% | 1.2% | 3.2% | 0.2% | 1.8% | -4.5% | 0.2% |
| 1983.4 - 1989.3 | 2.8% | 0.6% | 2.2% | 1.9% | 0.8% | -2.7% | 0.2% | 0.4% |
| 1989.4 - 1993.3 | 7.5% | 9.7% | -2.2% | 0.4% | 0.7% | 3.4% | -0.3% | 5.5% |
| 1993.4 - 1994.3 | 3.2% | 4.1% | -0.9% | -0.8% | 1.1% | 2.2% | 1.1% | 0.4% |
| 1979.4 - 1994.3 | 3.9% | 3.3% | 0.6% | 1.7% | 0.6% | 0.5% | -1.1% | 1.7% |
| Unemployed Parent 1 | ||||||||
| 1979.4 - 1983.3 | 23.1% | 10.1% | 13.0% | 3.6% | n.a. | 11.1% | -4.6% | n.a. |
| 1983.4 - 1989.3 | -3.1% | -10.7% | 7.6% | 2.7% | n.a. | -13.6% | 0.2% | n.a. |
| 1989.4 - 1993.3 | 19.2% | 18.8% | 0.4% | 1.0% | n.a. | 18.7% | -0.8% | n.a. |
| 1993.4 - 1994.3 | 9.1% | 5.2% | 3.9% | -0.5% | n.a. | 7.2% | -1.6% | n.a. |
| 1979.4 - 1994.3 | 10.6% | 3.8% | 6.9% | 2.3% | n.a. | 3.0% | -1.5% | n.a. |
| Average Monthly Benefit | ||||||||
| 1980.4 - 1983.3 | -2.2% | 2.6% | -4.8% | n.a. | 0.4% | -0.2% | 2.4% | 0.0% |
| 1983.4 - 1989.3 | 1.0% | 1.2% | -0.2% | n.a. | 0.6% | 0.1% | 0.4% | 0.0% |
| 1989.4 - 1993.3 | -6.6% | -3.0% | -3.6% | n.a. | 0.5% | -0.4% | -3.7% | -0.7% |
| 1980.4 - 1993.3 | -2.1% | 0.2% | -2.3% | n.a. | 0.5% | -0.1% | -0.4% | 0.2% |
| 1. The UP caseload model does not include the vital statistic variables and the AMB model does not include a variable for population growth and aging. | ||||||||
The contributions and timing of labor market factors and IRCA immigration to growth in California's Basic caseload are illustrated in Exhibit 6.4 (top panel), where we also compare predicted and actual caseload growth for the whole period. The model's state-level factors and federal legislation dummies predict caseload growth that is slower than actual growth over the period from the beginning of the sample through 1989, with the greatest divergence in growth rates occurring from 1984 on -- just as for the national caseload. From 1989 to 1992 the series move closely together. After 1992 predicted growth exceeds actual growth.
UP Caseload
Over the entire sample period, California's UP caseload also grew at a rate substantially faster than that for the nation -- 10.6 percent per year vs. 7.0 percent nationally (Exhibit 6.3, middle section).(5) As with the national caseload, the periods of greatest growth in the California UP caseload were 1979.4 - 1983.3 and 1989.4 - 1993.3; growth during the latter subperiod was especially strong in California (19.2 percent per year). Just as with the national caseload, the model predicts less than half of the growth of the California caseload in the earlier subperiod, predicts a larger decline than actually occurred in the following subperiod, but predicts most of the growth in the 1989.4 - 1993.3 subperiod. The labor market variables dominate the predicted series.
Werner Schink offered the following additional information about the history of the California caseload and suggestions for further analysis:
Exhibit 6.4
Actual and Predicted Caseloads for California, 1979.4 - 1994.3
Michael Wiseman also reviewed our findings for California and compared them to his own findings in a recent time-series analysis of openings and closings. According to Wiseman, his model tracks the California caseload better than our model, as we would expect (see Chapter 2). The primary reason apparently is better measures of demographic variables than are available in other states.(6)
Average Monthly Benefits
The results of the AMB simulation for California (Exhibit 6.3, bottom section) are very similar to those for the country as a whole: actual AMB declines at an average annual rate of 2.1 percent, but the model predicts a small increase; the average rate of decline was 2.2 percent in the first three-year subperiod, but the benefit variables predict a 2.4 percentage points annual increase (again, not reflecting the effects of OBRA81 captured by the year dummies); and recent changes in benefits account for at least part of the decline in AMB during the last four-year subperiod. The AMB decline in the last subperiod was substantially larger in California than in the country as a whole (6.6 percentage points per year vs. 4.1 percentage points) and changes in the benefit variables account for more (3.7 percentage points per year vs. 1.9 percentage points).
2. Florida
Basic Caseload
The Basic caseload in Florida grew at an average annual rate of 6.9 percent over the entire period, more than three times as fast as the national Basic caseload. The model predicts average annual growth of just 2.8 percent (top panel of Exhibit 6.5). The main contributors to growth that are accounted for by the model were population growth and aging (2.8 percentage points per year). Changes in the vital statistics and "other" variables -- primarily IRCA legalizations -- contribute about half a percentage point each to annual growth, while the labor market variables and AFDC benefit variables are estimated to have reduced growth by about a half a percentage point each.
The most notable feature of the Florida simulations is the extraordinarily high average annual growth rate of the actual caseload from 1989.4 to 1993.3, 17.5 percent. The model predicts only 5.7 percent annual growth during that period. As in California and the nation as a whole, the labor market variables and the IRCA immigration variable are the main factors accounting for growth during this period. This finding suggests that we have missed a very important factor for Florida during this period which, if we could identify and model it, might substantially improve the model's ability to predict caseload growth in both Florida and other states.
| Exhibit 6.5 | ||||||||
| Decomposition of Florida Caseload and Average Monthly Benefit Series | ||||||||
| Average Annual Growth Rate | Annual Growth Rate Accounted for by: | |||||||
| Program, Period, and Model | Actual | Accounted for by Model | Not Accounted for by Model | Population Growth and Aging | Vital Statistics Variables | Labor Market Variables | AFDC Benefits | Other Variables |
| Basic | ||||||||
| 1979.4 - 1983.3 | 5.4% | 4.2% | 1.2% | 6.0% | 0.5% | 1.7% | -4.2% | 0.1% |
| 1983.4 - 1989.3 | 2.7% | 0.3% | 2.4% | 2.4% | 0.4% | -3.2% | 0.5% | 0.2% |
| 1989.4 - 1993.3 | 17.5% | 5.7% | 11.8% | 0.9% | 0.4% | 2.3% | 0.3% | 1.8% |
| 1993.4 - 1994.3 | -4.6% | 0.1% | -4.7% | 0.4% | 0.0% | -1.6% | 1.1% | 0.2% |
| 1979.4 - 1994.3 | 6.9% | 2.8% | 4.1% | 2.8% | 0.4% | -0.4% | -0.7% | 0.6% |
| Unemployed Parent 1 | ||||||||
| 1979.4 - 1983.3 | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. |
| 1983.4 - 1989.3 | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. |
| 1989.4 - 1993.3 | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. |
| 1993.4 - 1994.3 | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. |
| 1979.4 - 1994.3 | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. |
| Average Monthly Benefit | ||||||||
| 1980.4 - 1983.3 | -2.3% | 3.5% | -5.8% | n.a. | 0.6% | 0.2% | 3.2% | -0.6% |
| 1983.4 - 1989.3 | 0.5% | -0.8% | -0.3% | n.a. | 0.5% | 0.2% | 0.1% | 0.0% |
| 1989.4 - 1993.3 | -0.9% | -1.1% | 0.3% | n.a. | 0.3% | -0.5% | -1.3% | 0.3% |
| 1980.4 - 1993.3 | -0.6% | 0.8% | -1.4% | n.a. | 0.5% | 0.0% | 0.4% | -0.1% |
| 1. Florida did not have an UP program for the entire sample period and therefore was not included in the sample for the UP model. The AMB model does not include a variable for population growth and aging. | ||||||||
Donald Winstead commented on several aspects of the Florida experience that are not captured in the model:
Florida's Medicaid expansion and outreach efforts, starting in 1988 may have had a substantial impact. These included outposting of over 400 eligibility workers in major public hospitals. This effort in combination with Florida's integrated eligibility process for AFDC, Food Stamps, and Medicaid resulted in the identification of many AFDC eligible families.
Two other features of the Florida simulation are especially noteworthy:
Actual growth from 1993.4 to 1994.3 is -4.6 percent, while the model predicts essentially zero growth. This large positive prediction error may be the reverse side of the large negative prediction errors in the previous four years; i.e., the same factor may explain both. Based on the plotted actual and simulated series (Exhibit 6.6), the reversal in the sign of the prediction error for caseload growth actually occurs in the first half of 1993.
UP Caseload
Florida was one of many states that did not have an UP program for the full sample period, and we have not used the findings from the UP models to simulate its caseload experience over the period of its existence.
Exhibit 6.6
Actual and Predicted Caseload for Florida, 1979.4 - 1994.3
Average Monthly Benefits
The average annual decline in AMB in Florida over the period (exhibit 6.5, bottom section) was less than for the nation (0.6 percent per year vs. 2.0 percent). The model predicts an increase of 0.8 percent per year, implying an unaccounted for decline of 1.4 percentage points per year -- close to the value for the nation. As in California, the pattern of changes predicted by the model is similar to that for the nation. The most substantial difference is that the AFDC benefit variables predict a much larger increase in AMB during the first three-year subperiod (3.2 percentage points per year vs. 1.1 percentage points for the nation).
3. Maryland
Basic Caseload
In contrast to California and Florida, Maryland experienced average Basic caseload growth that was substantially below the national average over the whole period (0.5 percent per year vs. 2.2 percent nationally). The model predicts growth that is just a tenth of a percentage point higher (Exhibit 6.7, top section). Patterns of growth predicted by the state-level variables and federal legislation dummies in the model are remarkably similar to the patterns observed for the nation as a whole (Exhibit 6.1).
| Exhibit 6.7 | ||||||||
| Decomposition of Maryland Caseload and Average Monthly Benefit Series | ||||||||
| Average Annual Growth Rate | Annual Growth Rate Accounted for by: | |||||||
| Program, Period, and Model | Actual | Accounted for by Model | Not Accounted for by Model | Population Growth and Aging | Vital Statistics Variables | Labor Market Variables | AFDC Benefits | Other Variables |
| Basic | ||||||||
| 1979.4 - 1983.3 | -1.6% | -0.2% | -1.4% | 2.2% | 0.6% | 1.2% | -4.4% | 0.2% |
| 1983.4 - 1989.3 | -1.6% | -1.5% | 0.0% | 1.5% | 0.5% | -4.0% | 0.4% | 0.1% |
| 1989.4 - 1993.3 | 5.6% | 4.4% | 1.2% | -0.1% | 0.2% | 3.8% | -0.2% | 0.7% |
| 1993.4 - 1994.3 | 1.1% | 1.0% | 0.0% | -0.6% | 0.7% | -0.6% | 1.3% | 0.2% |
| 1979.4 - 1994.3 | 0.5% | 0.6% | -0.1% | 1.1% | 0.5% | -0.3% | -1.0% | 0.3% |
| Unemployed Parent 1 | ||||||||
| 1979.4 - 1983.3 | 13.2% | 5.4% | 7.8% | 2.1% | n.a. | 7.8% | -4.5% | n.a. |
| 1983.4 - 1989.3 | -18.4% | -14.6% | -3.8% | 2.0% | n.a. | -16.9% | 0.3% | n.a. |
| 1989.4 - 1993.3 | 12.1% | 18.8% | -6.7% | 0.6% | n.a. | 18.8% | -0.5% | n.a. |
| 1993.4 - 1994.3 | -15.1% | -7.9% | -7.2% | 0.2% | n.a. | -7.7% | -0.4% | n.a. |
| 1979.4 - 1994.3 | -1.6% | 0.1% | -1.7% | 1.5% | n.a. | -0.2% | -1.3% | n.a. |
| Average Monthly Benefit | ||||||||
| 1980.4 - 1983.3 | -3.4% | 2.0% | -5.3% | n.a. | 0.5% | -0.1% | 1.5% | 0.1% |
| 1983.4 - 1989.3 | 2.1% | 1.2% | 0.9% | n.a. | 0.4% | 0.0% | 0.9% | -0.1% |
| 1989.4 - 1993.3 | -6.5% | -3.4% | -3.1% | n.a. | 0.0% | -0.3% | -3.2% | 0.0% |
| 1980.4 - 1993.3 | -1.8% | 0.0% | -1.8% | n.a. | 0.3% | -0.1% | -0.2% | 0.0% |
| 1. The UP caseload model does not include the vital statistic variables and the AMB model does not include a variable for population growth and aging. | ||||||||
One distinct difference is that the labor market variables have a much larger estimated impact in Maryland during the 1989.4 - 1993.3 period than for the country as a whole (3.8 percentage points per year vs. 1.5 nationally), while they have a relatively smaller estimated impact in the 1979.4 - 1983.3 period (1.2 percentage points per year vs. 2.1 nationally). Overall, the predicted series tracks the actual series for Maryland quite closely (Exhibit 6.8, top panel).
UP Caseload
Maryland's UP caseload declined over the entire sample period, at an average annual rate of 1.6 percent (Exhibit 6.7, middle section), while the model predicts essentially no change. According to the model, the only factor contributing substantially to the decline is reductions in AFDC benefits, accounting for average annual decline of 1.3 percentage points -- about the same as for the national UP caseload. The model underpredicts growth somewhat during the first four-year subperiod of the sample, underpredicts the decline in the second five-year subperiod, and overpredicts the growth in the third four-year subperiod. In the last year of the sample, the UP caseload declines by almost twice as much (15.1 percent) as the model predicts (7.9 percent). It appears from the plot of the actual and predicted series (Exhibit 6.8, bottom panel) that the model predicts an earlier impact of economic recovery after the 1981-82 recession than actually occurred, but a later impact of recovery after the 1990-91 recession than actually occurred.
Steven Thompson reviewed our findings for Maryland and affirmed that the model provides a reasonably accurate explanation of the behavior of Maryland's caseloads.(8) He thinks that the main reason Maryland's caseload growth over the full period was less than the national average was relatively strong employment growth; from 1980 to 1990 non-agricultural employment and per capita personal income grew at a rate that was almost 50 percent higher than the national average. He also pointed out that the larger impact of the recent recession for Maryland was related to substantial cutbacks in federal spending, a major source of employment in Maryland.
Thompson has developed for Maryland the most advanced time-series model of state caseloads that we have seen. He recently has developed a version of the model with asymmetries in business cycle effects that fits Maryland's experience well, with the effects of recessions on caseload growth being substantially larger and more immediate than effects of recoveries on caseload decline. Although the pooled model tracks Maryland's experience fairly well, introducing asymmetric business cycle effects might substantially improve the fit.
Exhibit 6.8
Actual and Predicted Caseloads for Maryland, 1979.4 - 1994.3
Average Monthly Benefits
The AMB simulation findings for Maryland (bottom section of Exhibit 6.7) are very similar to those for the nation. The average annual rate of decline of AMB during the whole period was 1.8 percent, vs. 2.0 for the nation, and in both cases the model predicts essentially no change. The estimated positive effects of benefit changes captured by the model during the first three-year subperiod are about the same as for the nation. As in California, AMB declined in the last four-year subperiod by substantially more than for the nation (6.5 percent per year vs. 4.1 percent) and the benefit variables account for more decline (3.2 percentage points per year vs. 1.9 percentage points).
4. Wisconsin
Basic Caseload
Wisconsin's Basic caseload at the end of the period was essentially the same as at the beginning, but this masks some sharp changes in the series within the period. According to Tom Corbett, episodes for Wisconsin's caseload differ in timing from the subperiods selected for the simulation analysis (Exhibit 6.9):(9)
The welfare cuts in the middle period were partly in the form of lower maximum monthly benefits, which are captured in the model, and, to a lesser extent, a change in attitude in welfare offices. Cultural change in the welfare system has apparently been a more significant factor recently. Corbett writes that "guarantee levels [i.e., MMB] and benefit reduction rates are being swamped in importance by aggressive client diversion programs and aggressive changes in agency mission and modes of operation."
| Exhibit 6.9 | ||||||||
| Decomposition of Wisconsin Caseload and Average Monthly Benefit Series | ||||||||
| Average Annual Growth Rate | Annual Growth Rate Accounted for by: | |||||||
| Program, Period, and Model | Actual | Accounted for by Model | Not Accounted for by Model | Population Growth and Aging | Vital Statistics Variables | Labor Market Variables | AFDC Benefits | Other Variables |
| Basic | ||||||||
| 1979.4 - 1983.3 | 1.1% | 0.4% | 0.6% | 0.9% | 0.4% | 3.1% | -4.2% | 0.1% |
| 1983.4 - 1989.3 | -0.4% | -3.5% | 3.1% | -0.1% | 0.6% | -4.0% | -0.2% | 0.1% |
| 1989.4 - 1993.3 | 0.2% | 0.2% | 0.0% | 0.0% | 0.7% | -0.7% | 0.0% | 0.3% |
| 1993.4 - 1994.3 | -3.2% | 0.3% | -3.5% | -0.7% | 0.3% | -0.4% | 1.0% | 0.1% |
| 1979.4 - 1994.3 | 0.0% | -1.2% | 1.2% | 0.2% | 0.5% | -1.0% | -1.1% | 0.1% |
| Unemployed Parent 1 | ||||||||
| 1979.4 - 1983.3 | 46.6% | 19.4% | 27.1% | 0.8% | n.a. | 22.8% | -4.2% | n.a. |
| 1983.4 - 1989.3 | -12.2% | -19.3% | 7.0% | 0.3% | n.a. | -19.2% | -0.4% | n.a. |
| 1989.4 - 1993.3 | -2.2% | 0.6% | -2.8% | 0.9% | n.a. | 0.0% | -0.3% | n.a. |
| 1993.4 - 1994.3 | -18.6% | -3.7% | -14.9% | 0.2% | n.a. | -3.2% | -0.7% | n.a. |
| 1979.4 - 1994.3 | 5.7% | -2.6% | 8.3% | 0.6% | n.a. | -1.8% | -1.4% | n.a. |
| Average Monthly Benefit | ||||||||
| 1980.4 - 1983.3 | -0.6% | 2.0% | -2.6% | n.a. | 0.3% | -0.7% | 2.7% | -0.3% |
| 1983.4 - 1989.3 | -3.2% | -1.2% | -2.0% | n.a. | 0.4% | 0.2% | -1.9% | 0.0% |
| 1989.4 - 1993.3 | -1.6% | -1.9% | 0.3% | n.a. | 0.3% | -0.3% | -1.8% | 0.0% |
| 1980.4 - 1993.3 | -2.1% | -0.7% | -1.4% | n.a. | 0.4% | -0.2% | -0.8% | -0.1% |
| 1. The UP caseload model does not include the vital statistic variables and the AMB model does not include a variable for population growth and aging. | ||||||||
The model simulations largely support Corbett's comments and provide some additional insights. According to the model, the strength of Wisconsin's labor market reduced Basic caseload growth by an average of 4.0 percentage points per year from 1983.4 to 1989.4, whereas the improving national labor market reduced nation growth by only 3.3 percentage points. In the following period, through 1993.3, the economy contributed, on average, -0.7 percentage points per year to Wisconsin's caseload growth, compared to 1.5 percentage points for the nation. In the last year of the sample, however, the recovery of the national economy has had a somewhat larger effect on the nation's caseload than on growth in Wisconsin (-0.8 percentage points for the nation vs. -0.4 percentage points for Wisconsin).(10)
The analysis also finds that changes in program parameters have slowed growth in Wisconsin's caseload relative to that of the rest of the country since 1983.4, but the importance of these changes appears secondary to strong economic performance. From 1983.4 to 1989.3, program parameter changes contributed, on average, -0.2 percentage points per year to Wisconsin's caseload growth compared to positive 0.2 percentage points for the nation. From 1989.4 to 1993.3 the difference was small -- 0.0 percentage points for Wisconsin vs. 0.1 percentage points for the nation. In the last year these made a positive contribution to Wisconsin's caseload growth of 1.0 percentage points, according to the model. As explained previously, this is attributed to the expansion of the EITC, and the estimated effect for the nation is even larger, 1.3 percent.
The effects of welfare reforms in Wisconsin may have been substantially greater than the effects that are captured in the model, however. As Corbett points out, the many changes that have occurred are not captured in the key program parameters. The decline in the caseload in the last year of the sample, which is not explained by the model, is especially noteworthy. The actual caseload declined by 3.2 percent in 1993.4 - 1994.3, while the model predicts an increase of 0.3 percent. Corbett and others have attributed much of the recent decline to Wisconsin's aggressive efforts to encourage employment and discourage long-term dependency (see Mead, 1996, and Wiseman, 1996). Our findings are largely consistent with that interpretation for 1994, but factors in the model account for all of Wisconsin's low caseload growth in the previous four years. In fact, from 1987 to 1993 the predicted caseload series tracks the actual series very well (Exhibit 6.10).
The simulations point to two other factors that have contributed to low caseload growth in Wisconsin relative to the nation. First, population growth and aging have contributed less to caseload growth in Wisconsin than in the nation -- 0.2 percentage points per year on average over the entire sample period, vs. 0.8 percentage points for the country. In the last year of the sample, the contribution of this factor in Wisconsin is estimated at -0.7 percentage points, vs. -0.4 percentage points for the country. Second, IRCA legalizations had a much smaller estimated impact in Wisconsin than in other states. This is reflected in the estimated contribution of "other factors" for the period from 1989.4 to 1993.3; the contribution of these factors in Wisconsin is an average 0.3 percentage points per year during this period, compared to 1.7 percentages points for the nation.
Exhibit 6.10
Actual and Predicted Caseloads for Wisconsin, 1979.4 - 1994.3
UP Caseload
The Wisconsin UP caseload grew at an average annual rate of 5.7 percent over the full period, slightly lower than the national rate of growth (7.0 percent), but the model predicts an average annual decline of 2.6 percent (Exhibit 6.9, middle section). As is evident from plots of the predicted and actual series (Exhibit 6.10, bottom panel), most of the positive prediction error comes from the 1979.4 - 1983.3 period when actual growth occurred at an average annual rate of 46.6 percent, compared to predicted growth of 19.4 percent. We do not have an explanation for this exceptionally high rate of growth. Like in California, the model predicts a larger caseload decline than actually occurred in the following five-year subperiod. During the next four years the caseload declines by an average of 2.2 percent per year when the model predicts growth of 0.6 percent, and during the final year of the sample the caseload declines by 18.6 percent while the model predicts a decline of only 3.7 percent. While these larger than expected declines may be due to Wisconsin's work-oriented welfare reforms, the size of the prediction errors that occur in the earlier part of the sample period suggest that this conclusion may be premature.
Average Monthly Benefits
The AMB simulation findings for Wisconsin (bottom section of Exhibit 6.9) depart from those for the nation and the three other states in some interesting ways. While the average annual decline for the full period is about the same as for the nation (2.1 percent per year vs. 2.0 percent), much of the decline occurred in the middle four-year period, the period during which the state began to cut its benefits; most of the decline for the nation and the other states occurred in the first and last subperiods. In Wisconsin, benefit changes captured by the model had a substantial positive impact during the first three-year subperiod (2.7 percentage points per year) and substantial negative impacts in the middle and last four-year subperiods (-1.9 and -1.8 percentage points per year, respectively).
D. SUMMARY AND DISCUSSION
1. Basic Caseload
The model provides several insights into the cycles in caseload growth during the sample period:
Effects of federal legislation that are captured by the coefficients of the calendar dummies in the regression model, but not included in the predicted series. This almost surely explains part of the growth, but how much is difficult to determine. It would certainly not explain the very high growth not accounted for in Florida;
Overestimation of the impact of the recovery from 1983 to 1989, and underestimation of the impact of the recession of 1990-91. It could be that characteristics of the recessions that are not captured in the model's variables explain exceptionally high growth in some states (as per Don Winstead's comments on Florida);
Underestimation of the contribution of immigration. We have not looked at the effects of legal immigration after the three-year waiting period, nor have we modeled the effects of refugees, whom we understand are a high participant group;
Underestimation of the role of changes in household characteristics -- especially the growth in female headed households -- CBO (1993) attributed much of the Basic caseload growth in the 1989 - 1992 period to this factor in their time-series analysis. We could not satisfactorily measure female headed households at the state level, and found that vital statistics variables played a less substantial role in predicting growth during this period;
Increases in the cost of health care and reductions in access to health insurance -- substantial efforts to capture these factors in the model were not very successful. Given the evidence of the importance of these factors from micro data analyses, however, we still believe they may have been important. Don Winstead's comments concerning Medicaid outreach efforts in Florida suggest that unmeasured Medicaid factors could be an important factor in some states. In previous studies of SSI applications and awards, we have found substantial evidence of similar effects in other states for SSI, but also have not been able to quantify them (Lewin-VHI, 1995b, 1995c, and 1995d); and
Declines in job prospects for low-skilled workers that are not reflected in the unemployment rate and/or the trade employment variable. This explanation of the growth not accounted for by the model over the entire period was favored by several welfare researchers when preliminary findings from this project were presented at a recent conference.
2. Unemployed Parent Caseload
The model also provides important insights concerning past cycles in the UP caseload:
3. Average Monthly Benefits
The model predicts very little change in AMB over the period in all states, yet there was a substantial decline. Particularly anomalous is the model's prediction of an increase due to changes in benefit variables in the first three-year subperiod, when a substantial decline actually occurred. While effects of OBRA81 that are captured by the year dummies and not included in the simulation may explain part of the anomaly, the largest negative coefficient on a year dummy is for the previous year. We have devoted relatively little effort to development of the AMB model, and it may be that additional effort would resolve this anomaly.
4. Conclusion
The simulations demonstrate the ability of a pooled analysis to provide insights into the causes of caseload growth. Drawing on the experiences of all states, as the model does, and comparing simulations for individual states to national simulations using a single model, provides information about caseload growth in individual states that might easily be missed in an individual state time-series analysis.
At the same time, however, the simulations for individual states demonstrate limitations of the model, at least as currently constructed. Most of these limitations stem from measurement problems -- factors thought by many to be important determinants of caseload growth at the state level are not adequately measured at the state level. Some improvements can be made in this area, as outlined in Chapter 1, and we expect that making these improvements would add significantly to the model's ability to capture the determinants of caseload growth. One factor that will become increasingly important as we move forward, and that will be especially difficult to measure, is the many detailed and varied changes that states are currently making to their welfare programs.
Other changes to the model might also make it substantially more powerful. Given the interaction between the Basic and UP programs that is known in California, exploring ways to model this interaction might be fruitful. Alternatively, it may be better to simply combine the caseloads and model them together. As Michael Wiseman has pointed out, the big issue is whether or not a mother and her children end up on welfare, not whether there is a second parent in the household.(11) This would also solve the specification problem that arises because of the absence of the UP program in many states for a significant part of the sample.(12) The findings for individual states and the comments on them also suggest that efforts to disaggregate the caseload by characteristics such as race/ethnicity and citizenship would be very useful. It might also be useful to divide the largest states into sub-state areas for the analysis, such as Dade County vs. the rest of Florida and northern vs. southern California.(13) Further consideration of asymmetries in business cycle effects also seems warranted. A number of other ideas have been suggested by reviewers of the model, as discussed at the end of Chapter 1.
Improvements to the model would likely increase the extent to which the state-level factors in the model and dummy variables for federal program changes account for growth and cyclical variation in the national time series. It would be a mistake, however, to focus exclusively on improving the fit of the national series. Significant gains in understanding caseload growth are more likely to be achieved if future efforts focus on the fundamental measurement and structural issues rather than goodness of fit per se. That has been our approach, and we think it has been very worthwhile. The strength of our findings, especially concerning business cycle effects and program changes, have impressed the many experts in welfare research who have reviewed our work and have convinced us that further efforts to improve the model would be rewarding.
1. The calendar year dummies reflect growth not accounted for by the explanatory variables. The quarterly dummies do not play a role in the simulations because the number of quarters in the full period, and each subperiod, is a multiple of four.
2. Each of the two recessionary periods includes approximately one year following the end of the official recession, during which the economy continues to be sluggish.
3. The vertical distance between a point on the labor market line corresponding to a specific quarter and the point corresponding to 1989.4 is the estimated contribution of labor market strength to the caseload for that quarter relative to labor market strength for 1989.4. An analogous interpretation applies to the IRCA series. The IRCA series begins in 1988.1, the first quarter in which there were legalizations under IRCA.
4. For the Basic caseload, growth not accounted for is 1.8 percent, and for the UP caseload in the 19 states it is 6.1 percent. Because the UP caseload is a very small share of the total caseload, total caseload growth not accounted for is only slightly above the 1.8 percent figure -- very close to 2.0.
5. "National" estimates refer only to the 19 states with UP programs for the entire sample period.
6. Personal correspondence, October 1996.
7. Personal correspondence, October 22, 1996.
8. Personal correspondence, October 22, 1996.
9. Personal correspondence, November 4, 1996.
10. Wiseman (1996) also concludes that economic growth has had a greater impact on the national caseload than on the Wisconsin case load in the last two years.
11. Personal correspondence, October 1996.
12. This is the approach taken by Cromwell et al. (1986), although the participation measure was Medicare AFDC enrollees, not the AFDC caseload.
13. Steven Thompson has developed a separate time-series model for Prince George's County because the behavior of the caseload in that county is differs substantially from the behavior of the caseload in the rest of the state.