Unemployment Insurance, Welfare and

Federal-State Fiscal Interrelations:

Final Report

by

Wayne Vroman*

November 1996

* Economist, the Urban Institute, Washington, D.C.. Support for this

paper was provided by the U.S. Department of Labor under a Task Order

with the Urban Institute. Karen MaGuire assisted in much of the

state-level analysis. Margaret Moore and Mary Lee performed the

simulations with the Urban Institute’s TRIM2 model whose output is

reported in Section IV. Helpful comments were received from Joseph

Hight, Mike Miller, William Rogers, Sande Schifferes, Steve Wandner

and Ronald Wilus. Opinions expressed in the report are those of the

author and do not necessarily reflect the opinions of the U.S.

Department of Labor or the Urban Institute.
Executive Summary

This report examines the hypothesis that unemployment insurance

(UI) claimants have been shifted from the UI program to federallyfinanced welfare programs in order to reduce the costs of statefinanced UI benefits. The investigation is divided into four main

sections. Section I introduces the cost shifting hypothesis. Some

alternative ways that a negative association between the receipt of

UI and the receipt of welfare can arise are identified and discussed.

Section I also conducts a literature review, and it notes specific

welfare programs where unemployed workers may seek benefits. 

Section II examines national time series data on the receipt of

UI benefits and the receipt of welfare benefits. A state-level

analysis of UI recipiency is undertaken in Section III. The analysis

identifies states where the receipt of UI has declined the most.

Section IV then examines state-level data on the receipt of welfare

for three major programs: AFDC, Food Stamps and Medicaid. The

objective is to determine if receipt of welfare has increased most in

states where receipt of UI benefits has decreased the most. This

analysis draws upon simulation results from the Urban Institute’s

TRIM2 model. A summary of findings is then given in Section V.

The cost shifting hypothesis that motivated this study asserts

that a part of UI costs has been shifted to welfare programs through

reduced availability of UI benefits. The driving force behind cost

shifting could be either deliberate (or inadvertent) state actions or

evolutionary economic and demographic developments affecting UI and

welfare caseloads in opposite directions. This cost shifting purports

to explain much of the decline in UI recipiency observed over the

past twenty-five years.

Following an analysis that covers both a literature review and

new research, the principal finding can be simply stated: The cost

shifting hypothesis is not supported. 

The cost shifting hypothesis can be criticized from three

distinct perspectives. 1) From the standpoint of state government

fiscal calculus, the hypothesis is incomplete. Shifting potential UI

claimants to Food Stamps would clearly save a state money since Food

Stamps are fully federally financed. However, welfare recipients

typically receive benefits from three programs: AFDC and Medicaid as

well as Food Stamps. AFDC and Medicaid are partly state financed. The

growth in state-level Medicaid costs dominates all of the others (UI

and welfare) program costs under consideration in the report. Because

Medicaid costs are so large and grow so rapidly, it would not reduce

state-level costs to move UI claimants onto welfare.

2) The main empirical evidence supporting the cost shifting

hypothesis is work by the staff of the recent Advisory Council on

Unemployment Compensation. This analysis concluded that 64 percent of

the decline in UI claims activity between 1971 and 1993 can be

explained by growth in welfare. The principal empirical variable used
in a pooled regression analysis was annual per capita Food Stamp

expenditures. Section I reviews this study and raises several

criticisms regarding its logic and the specification of the analysis.

At a minimum, the evidence adduced to support the cost shifting

hypothesis is unpersuasive. 

3) New analysis of state-level data on reductions in UI claims

and increased utilization of welfare did not support the cost

shifting hypothesis. The states where UI claims decreased the most

did not exhibit above-average increases in utilization of welfare.

This analysis was based partly on the Urban Institute’s TRIM2

microsimulation model and covered the years 1979 to 1993. Welfare

recipiency and benefit payments were examined for the three programs:

AFDC, Food Stamps and Medicaid. The most rapid growth in welfare

caseloads was observed in states and regions where welfare

participation rates had been lowest during 1979-1981 and where

population growth was the most rapid. In many specific instances,

rapid growth in welfare caseloads occurred in states in the South and

West, states where the IUTU ratio (a principal indicator of UI

claims) declined less than or about the same as the national average

decline.

These three criticisms of the cost shifting hypothesis are

quite persuasive. It seems more likely that the states have not

attempted to shift potential UI claimants onto welfare. Other readers

may draw a more agnostic conclusion. This could provide a reason for

undertaking more research. The place to start any additional work,

however, is with an explicit formulation of the cost shifting

hypothesis that has testable implications.

The report had other findings that should be noted. 1) A recent

Canadian empirical study of the unemployment-UI-welfare interrelation

(summarized in Section I) tracked UI claimants longitudinally. It

documented the size of the interface between UI and welfare for job

leavers during a period when access to UI was restricted. After UI

eligibility was restricted, the fraction of job leavers who received

welfare did increase, but the increase was rather modest. While the

Canadian study provides important evidence, the federal-provincial

fiscal relationship and associated financial incentives differ from

those in the U.S., e.g., UI is federally financed in Canada. This

study’s relevance lies mainly in its methodology, i.e., the

longitudinal tracking of the unemployed, rather than demonstrating

the effects of intergovernmental fiscal incentives. 

2) Section II documented the time periods when decreases in UI

claims activity and increases in welfare caseloads occurred during

the past forty years. UI claims (as reflected in IUTU ratios)

declined most during two periods: the decade of the 1960s and the

early 1980s with larger declines taking place during the 1960s.

Growth in caseloads and total benefit payments were also traced for

AFDC, Food Stamps and Medicaid. Relative to the size of the poverty

population, AFDC caseload growth was most rapid between the mid 1960s
and the early 1970s while Food Stamp caseload growth was most rapid

between the mid 1960s and the mid 1970s. Growth in Medicaid caseloads

could not be traced as far back in time as for AFDC and Food Stamps.

Caseloads for all three welfare programs grew noticeably after 1989,

but this was a period when UI caseloads were, if anything, higher

(not lower) than anticipated based on IUTU ratios from the 1980s.

3) A regression analysis conducted in Section II examined

decreases in UI claims. The estimated size of the reduction was found

to be sensitive to the estimation period, inclusion of state-level

weights as controls and the choice of the dependent variable.

Comparing 1981-1994 with the earlier 1967-1980 period, the receipt of

UI benefits was estimated to be 8.3-8.7 percent lower during 1981-

1994.

4) Section III used descriptive data and regressions to

characterize the size of the decrease in UI claims for each state. A

wide range of state-level decreases was documented. For the fifteen

states with the largest decreases, the IUTU ratio declined by an

average of 0.111, i.e., by slightly more than one-tenth of average

unemployment. For the fifteen with the smallest decreases, the change

in IUTU averaged almost exactly zero. Section III also examined

whether UI monetary eligibility requirements had increased more in

states with the largest decreases in UI claims and/or in states which

experienced the largest UI financing problems during the early 1980s.

5) A state-level analysis of AFDC, Food Stamps and Medicaid

recipiency was undertaken for the period 1979 to 1993. Detailed

results of this analysis are presented in Section IV and in Appendix

A. For all three welfare programs, similar findings were observed on

the relation between changes in the receipt of UI benefits and the

receipt of welfare. The group of 15 states where UI recipiency

declined the most (as reflected in IUTU ratios) had the smallest

increases in welfare caseloads and associated costs. In contrast, the

fifteen states where IUTU ratios decreased the least had the largest

increases in welfare caseloads. Details for individual states were

displayed in Tables 10, 11 and 12 with supporting detail in Tables

A1-A5 of Appendix A. An unpublished version of this report also

includes in Appendix A a state by state graphical display of welfare

caseloads for the 1979-1993 period. 
Table of Contents

Page

 Introduction 1

I. The Cost Shifting Hypothesis 2

A Taxonomy 3

Case 1. Deliberate Shifting 3

Case 2. Inadvertent Shifting 4

Case 3. Apparent Shifting 5

Previous Literature 8

Other Considerations 18

Potentially affected programs 18

Contrasts in demographic and economic characteristics 21 

II. National Trends in the Receipt of Benefits 22

Receipt of UI benefits 22

Regression analysis of the decline in UI recipiency 28 

Receipt of welfare benefits 31

III. Analysis of State UI Data 38

Changes in state-level receipt of UI benefits 39

Monetary eligibility for UI benefits 41

Monetary eligibility and changes in IUTU ratios 46

UI financing problems and changes in IUTU ratios 48

IV. Receipt of Welfare by State 51

The TRIM2 model and state-level estimates 52

Welfare benefits in three programs 54

Summary 59

V. Summary and Conclusions 60

Tables 1-12

Appendix A. Welfare Programs by State: 

 Participation and Benefits 64

Appendix Tables A1-A5
Introduction 

Federal-state fiscal relations have been the subject of

extensive past research by public finance economists and political

scientists. In the present fiscal environment there are increased

pressures on the fiscal authorities at all levels of government to

economize on total expenditures across a wide range of public

programs. Into this general background, ideas have been advanced

recently that purport to explain why recipiency in the state-financed

unemployment insurance (UI) programs has declined. One hypothesis

asserts that UI claimants have been shifted to federally-financed

welfare programs in order to reduce the costs of state-financed UI

benefits. The present paper investigates this cost shifting

hypothesis. 

The investigation is divided into four main sections. Section I

introduces the cost shifting hypothesis. Some alternative ways that a

negative association between the receipt of UI and the receipt of

welfare can arise are identified and discussed. Section I also

conducts a literature review, and it identifies specific welfare

programs where unemployed workers may seek benefits. Section II

examines national time series data on the receipt of UI benefits and

the receipt of welfare benefits. State level analysis of UI

recipiency is undertaken in Section III. The analysis identifies

states where receipt of UI has declined the most. Section IV then

examines state-level data on the receipt of welfare for three major

programs: AFDC, Food Stamps and Medicaid. It analyzes data to

determine if receipt of welfare has increased most in states where

receipt of UI benefits has decreased the most. This analysis draws

upon simulation results from the Urban Institute’s TRIM2 model. A

summary of findings is then given in Section V.

The main findings can be stated briefly. 1) The cost shifting

hypothesis which seems plausible when one considers shifting UI

claimants to just AFDC and Food Stamps appears to be incomplete

because it also needs to consider Medicaid. Growth in actual and

prospective Medicaid costs would make a state reluctant to try to

shift potential UI claimants onto welfare. 2) The evidence in the

U.S. empirical literature that purports to support the cost shifting
2

1 Table 10-17 of the 1994 Green Book published by the Ways and

Means Committee of the U.S. House of Representatives shows matching

rates for 54 individual jurisdictions; the 50 states plus Guam, the

District of Columbia, Puerto Rico and the Virgin Islands. The twelve

largest states accounted for slightly more than half of the total

AFDC caseload. In five of these states (California, Illinois,

Massachusetts, New Jersey, New York) the federal matching rate during

fiscal year 1995 was 50 percent. In the remaining seven the

percentage matching rates were as follows: Florida - 56.28 ,Georgia -

62.23, Michigan - 56.84, North Carolina - 64.71, Ohio - 60.69,

Pennsylvania - 54.27, Texas - 63.31. Twelve smaller jurisdictions had

matching rates of at least 70.00 percent with the highest being 78.58

in Mississippi. Table 10-17 of the 1994 Green Book also shows that

matching rates in individual states were quite stable from 1984 to

1995. Nationwide, federal matching has accounted for about 53-55

percent of outlays for both AFDC and Medicaid in recent years. 

hypothesis can be questioned. 3) In the state-level data examined for

this report, there is no important support for the cost shifting

hypothesis. Welfare caseloads have not grown especially rapidly in

states where receipt of UI benefits has declined the most. 4) While

the cost shifting hypothesis is interesting to consider, the

empirical evidence of the present report does not support it. 

I. The Cost Shifting Hypothesis

The cost shifting hypothesis to be examined in this report

refers to actions by state governments to shift costs of statefinanced unemployment insurance (UI) to the federal government. The

idea that one level of government would try to shift program costs to

another level of government has strong intuitive appeal, particularly

in a fiscal environment where all levels of government are under

increasing pressures to reduce expenditures. Unemployment insurance

benefits are state-financed through payroll taxes on UI covered

employers. Food Stamps are fully federally financed while AFDC and

Medicaid have joint state-federal financing with the federal share

ranging from 50 percent to almost 80 percent.1 If a state

successfully shifted a UI claimant onto Food Stamps this would save

the most, but shifting to AFDC could also entail substantial savings. 
3

The hypothesis has not been subjected to much empirical

analysis. Before reviewing previous literature and other issues,

however, it will be useful to consider three formulations of the

hypothesis and the evidence needed to support the hypothesis and to

distinguish among the different forms of the hypothesis. 

A Taxonomy

Three forms of the cost shifting hypothesis can be

distinguished: deliberate shifting, inadvertent (or unintended)

shifting and apparent shifting. The third situation results from

causal factors affecting both UI and welfare program caseloads but in

opposite directions. For example, job leavers typically do not

qualify for UI benefits. If there is an increase in unemployment

among job leavers who are family heads, there would be a tendency for

receipt of UI to decline and receipt of welfare to increase at a

given level of aggregate unemployment. 

Case 1. Deliberate Shifting

Deliberate shifting would involve the following temporal

sequence. A state realizes it can save money by restricting UI

eligibility and inducing previously eligible UI claimants to seek

benefits from welfare programs. The state enacts restrictive monetary

and/or nonmonetary eligibility statutes and/or initiates restrictive

administrative provisions, e.g., requiring increased evidence of

active work search. 

Following these restrictions, UI caseloads decline. Associated

with the decline are short run increases in rates of adverse

determinations affecting claimants, i.e., higher denial rates from

monetary and/or nonmonetary determinations or more severe penalties,

e.g., durational disqualifications for quits.

The longer run response of denial rates may be more muted as

claimants gain information on the new, more restrictive eligibility

criteria and adjust downward their UI application rates. This pattern

is suggested in research findings where lower denial rates for
4

2 In the basic AFDC program, for example, eligibility is

restricted to families headed by a woman whose income falls below

size-related thresholds. There are limitations on the value of

household possessions such as liquid assets and automobiles. The

income and asset limits are state-level determinations. 

3 In annual data the three events might appear to occur at the

same time rather than in sequence.

separation issues are associated with lower (not higher) UI claims

activity. 

The unemployed persons who no longer file for UI benefits may

then increase their rate of filing for welfare benefits. Absent UI

benefits, family income would be lower leading to increased

eligibility for welfare programs such as AFDC and Food Stamps. These

latter programs, however, have other eligibility criteria such as

demographic screens and asset tests.2 Thus not all persons made

ineligible by the UI restrictions would be eligible for welfare

benefits.

The temporal sequence of key measurable events has three

elements: 1) statutory and administrative restrictions on UI

eligibility criteria, 2) reductions in measures of UI receipt such as

the ratio of UI claims to total unemployment and/or the ratio of UI

beneficiaries to total unemployment and 3) increases in applications

for and subsequent receipt of welfare benefits. This three step

sequence might not be apparent in annual data, but it would be

apparent in monthly or quarterly data.3 With appropriate time series

data, deliberate shifting could be tested with two equations.

Equation 1 would have UI recipiency depend negatively on UI

eligibility criteria. Equation 2 would have the receipt of welfare

depend positively on the nonreceipt of UI. 

 

Case 2. Inadvertent Shifting

The temporal sequence of events described in Case 1 could also

occur but without any conscious motivations extending beyond the UI

program itself. If a UI program experienced a financing problem,

signaled by a low or negative trust fund balance, it might enact
5

4 Three kinds of financial incentives were provided: lower

interest rates on trust fund loans, deferral of loan repayments and

limitations on future increases in FUTA tax rates.

5 Some forms of UI benefit restrictions would not reduce UI

caseloads or lead to an increase in welfare caseloads. A common form

of benefit reduction during the early 1980s, for example, was to

freeze the UI weekly benefit maximum.

legislation to improve solvency through increased employer taxes

and/or benefit restrictions. The latter could take the form of higher

eligibility criteria or reduced levels of payments or both. In fact,

solvency legislation with tax and benefit components was common

during the early 1980s when many UI programs had serious funding

problems evidenced by large negative trust fund balances. Added

motivation for such legislation was provided by the 1983 Social

Security amendments, i.e., strong financial rewards for enacting

solvency packages that included both benefit reductions and tax

increases.4

If unemployed workers newly ineligible for UI then applied for

and received welfare benefits, the three step sequence outlined for

Case 1 would also be observed for Case 2. Restrictions on UI

eligibility come first, then reductions in UI receipt and finally

increases in welfare receipt. Factors that might help to distinguish

Cases 1 and 2 are the timing and identity of the states enacting UI

restrictions. Under Case 2 the restrictions would be recessionrelated and largest in states with the most severe UI funding

problems.5

Case 3. Apparent Shifting 

Another pattern would be for UI and welfare caseloads to change

in response to major evolutionary developments in the economy or an

increase in the generosity of welfare benefits. An important change

in the U.S. economy since the early 1970s, for example, has been the

increased dispersion of wage rates and earnings for individual
6

6This is but one of several evolutionary developments that might

be relevant. Others would be increased female headship of families,

decreased unionization, reduced importance of manufacturing

employment and reduced stigma for receipt of welfare benefits. All of

these changes could have effects on UI recipiency and/or welfare

recipiency. 

workers within the overall earnings distribution.6 The change could

result in fewer unemployed persons claiming benefits due to accurate

perceptions of reduced monetary eligibility. Monetary eligibility

criteria are commonly indexed to the statewide average wage. This

average has continued to advance in recent years because it is

strongly influenced by wage increases among high wage workers even

when there has been stagnation in wage growth among low wage workers.

If those no longer eligible for UI then apply for welfare, decreases

in UI recipiency and increases in welfare recipiency would occur.

Another possible situation could occur if welfare benefits were

more fully indexed to inflation than UI benefits. Increased

generosity of welfare benefits might induce some to start to claim

welfare rather than UI. In practice, average UI benefits have kept up

with wage inflation in nearly all states. Among welfare programs,

Food Stamps are fully indexed to price inflation but AFDC needs

standards and benefit levels have tended to lag behind inflation over

the past twenty years. Thus, the actual importance of this

possibility seems limited. 

In the preceding hypotheticals no change in UI eligibility

criteria need occur. However, changes in the receipt of UI and

welfare benefits would occur and the changes would be in opposite

directions. Changes in benefit recipiency would take place

simultaneously, but UI would not be the causal factor behind the

change. The change would be either an unmeasured third factor

elsewhere in the economy or increased welfare generosity.

Unique identification of these three cases might not be

possible. Cases 1 and 2 as described could appear to be identical

within the confines of a single state. If, for example, restrictions

on UI eligibility provided the initial impulse, it would be important
7

to distinguish legislative intent. Were the restrictions intended to

shift claimants onto welfare (Case 1) or to improve UI program

solvency (Case 2) or both? Achieving the appropriate identification

of the state’s motives would be especially difficult if UI

restrictions were implemented when the trust fund was not in

immediate danger of insolvency. 

Case 3 has two identifying features: 1) there is no state

action restricting UI eligibility, and 2) the changes in UI

recipiency and welfare recipiency are simultaneous and not sequential

with reduced UI recipiency occurring first.

Some other considerations might help to distinguish Case 2 from

Case 1. The first is the permanence of the changes in UI eligibility

criteria. Solvency legislation as enacted by several states in the

early 1980s often had time-limited features on the benefit

reductions and tax increases. Provisions such temporary surtaxes and

freezes/reductions in maximum weekly benefits were specified to end

within a few years, i.e., after the trust fund had been replenished.

Enacting temporary solvency features would suggest Case 2. Second,

under Case 2 the restrictions would occur mainly during recessionary

periods when UI trust funds are most depleted. Third, since the

motivation for the restrictions in UI eligibility under Case 2 is a

state’s solvency situation, the states with the most serious funding

problems would be the ones where benefit restrictions should be the

largest. In contrast, under Case 1 the restrictions in UI eligibility

could occur at any phase of the business cycle and could be enacted

even when a state had a comparatively large trust fund balance. 

To summarize, the three variants of the cost shifting

hypothesis just discussed share a number of empirical predictions.

Thus accurate differentiation among the three would not be easy even

though the motivations behind the three cases are quite distinct.

Under Case 1 one would expect to observe quite large changes in

(statutory and other) UI eligibility criteria, and these changes

should precede a reduction in receipt of UI benefits. One would also

expect to observe above-average increases in receipt of welfare
8

7 Two citations for this work are as follows. There is a

freestanding research paper by Laurie Bassi, Amy Chasanov, Eileen

Cubanski, Stacey Grundman and Daniel McMurrer, “The Evolution of

Unemployment Insurance,” (August 1995) as well as Chapter 4 in

Advisory Council on Unemployment Compensation, Defining Federal and

State Roles in Unemployment Insurance, (Washington, D.C.: Advisory

Council on Unemployment Compensation, 1996). 

benefits in these same situations. Geographic detail seems to be

crucial to the hypotheses as it predicts larger increases in welfare

caseloads in states where above-average decreases in UI recipiency

have taken place. The latter, in turn, may be caused by major

restrictions on UI eligibility.

Previous Literature

Because the cost shifting hypothesis has gained prominence

quite recently, it has been the focus of only a few empirical

investigations. The work of Laurie Bassi and coauthors undertaken for

the Advisory Council on Unemployment Compensation (ACUC) is the most

notable of the U.S. research completed to date.7 It provides the

starting point for this review.

The ACUC analysis examines state-level data on the ratio of UI

claimants to total unemployment within a multiple regression

framework. Their dependent variable is the ratio of claimants in

regular UI programs (insured unemployment or IU) to total

unemployment as measured in the monthly household survey of the labor

force (TU). This variable is commonly referred to as the IUTU ratio.

Among the regressors were variables representing the condition of

individual state labor markets, indicators of UI tax and benefit

levels, interstate UI tax competition variables and three variables

representing availability and generosity of welfare benefits. The

three were: 1) per capita Food Stamp benefits, 2) the AFDC matching

rate and 3) the federal per capita share of AFDC benefits. The
9

8 The five are: AFDC; Food Stamps; Emergency Assistance; Women,

Infants and Children Nutrition Program; and General Assistance. In

1994 expenditures across the five totaled $55.5 billion with AFDC and

Food Stamps accounting for $47.0 billion or 84.7 percent.

welfare variables were lagged one year. Regressions were fitted to

state-level data for the 48 contiguous states covering the years 1979

to 1990. They found welfare variables contributed significantly in

explaining changes in state-level IUTU ratios. Specifications with

variables measured in levels and changes were both tested, but

greatest reliance was placed on results from the change formulations. 

A visual display related to the authors’ argument is provided

by a graph showing two aggregate annual time series: the IUTU ratio

and the federal share of means tested public assistance expenditures.

For the years 1947 to 1990 the graph shows the IUTU ratio declined

from roughly 0.500 to 0.320. Over the same years the federal share of

public assistance grew from about 0.400 to about 0.650. Five

individual welfare programs were included in their grouping, but the

two large programs are AFDC and Food Stamps.8 Medicaid is not

included.

The regression coefficients were then used to simulate the

pattern of change in the IUTU ratio between 1971 and 1993. They found

that 64 percent of the decline in the IUTU ratio over these 23 years

was explained by cost shifting from the states to the federal

government. The cost shifting variables that accounted for the change

in IUTU were the three welfare variables noted in the previous

paragraph.

Several aspects of the analysis warrant comments. 1) The

estimation period is unusually short. The authors argue that 1979 was

selected as the start date because of limitations on data
10

9 Walter Corson and Walter Nicholson, “An Examination of

Declining UI Claims During the 1980s,” Unemployment Insurance

Occasional Paper 88-3, (Washington, D.C.: U.S. Department of Labor,

1988).

10 Section II presents national data on two ratios: weeks

claimed to total unemployment (IUTU) and weeks compensated to total

unemployment (AWKTU). 

11 In Section II this variable is termed AWKTU.

availability. Other analyses, e.g., Corson and Nicholson9, have

extended the data period back as far as 1971. Even if 1979 is the

earliest possible start date, there is no apparent reason for not

extending the estimation period through 1993. Since the authors

subsequently undertake simulations for the 1971-1993 period, it would

seem they have most if not all of the requisite variables to estimate

for the full twenty-three years. It would be sensible to test for the

sensitivity of coefficients to choice of estimation period,

particularly to note changes in the size and significance of the

coefficients on the welfare variables.

2) The use of the IUTU ratio as the dependent variable follows

the practice of many previous investigations. A unique feature of

this study, however, is the heavy reliance placed on financial

motivations for state actions. The IUTU ratio includes many weeks

claimed for which no benefits were actually paid (about 10 percent of

total weeks claimed).10 Closer in concept to the cost variable

motivating state behavior under the cost shifting hypothesis would be

the ratio of the weekly number of beneficiaries to total

unemployment.11 The authors could have investigated the sensitivity

of the findings using this alternative formulation of the dependent

variable. 

3) The welfare variable that yielded the strongest empirical

results was per capita Food Stamp expenditures. This is the product

of average benefits per recipient (measured in current dollars) and

recipients as a proportion of the state’s population. Thus, an
11

12 Over the estimation period used in the regressions, current

dollar Food Stamp expenditures per capita increased most rapidly

during 1979-1983, precisely the period when IUTU declined most.

However this explanatory variable also increased sharply during 1970,

1974-75 and most recently during 1988-1991. These other three periods

which lie outside the estimation period for their regressions, are

periods when the IUTU ratio increased. Table 1 in Section II shows

annual time series on the IUTU ratio.

13 These national totals were built-up from state data.

explanatory variable measured in current dollars is used to explain a

real labor market variable, the IUTU ratio. Some of the variation in

this explanatory variable arises simply from price inflation.12 Use

of explanatory variables measured in real terms would seem more

appropriate. Since the dependent variable (IUTU) is a ratio based on

measures of persons, some readers would want to know how the results

would differ if per capita Food Stamp recipiency was the explanatory

variable rather than per capita Food Stamp expenditures. Since Food

Stamps recipiency rates vary across states there is an argument for

using this variable rather than a national variable that takes on the

same value across all states for a given year.

4) Questions about the timing of the variables can also be

raised. In the regressions, lagged increases in welfare variables

lead to reductions in the IUTU ratio. However the motivation for the

state under the cost shifting hypothesis is to shift UI claimants

onto welfare. The reduction in the IUTU ratio should precede, rather

than follow, the increase in welfare recipiency. 

5) Absent from their list of welfare programs is Medicaid. This

has a joint federal-state financing arrangement essentially the same

as for AFDC, but Medicaid involves much larger dollar amounts.

Medicaid expenditures totaled $135.5 billion in 1994 and the state

share was $58.2 billion.13 Once Medicaid is considered, it becomes

much more difficult to argue that a state will realize any budgetary

savings by shifting UI claimants onto welfare. 
12

14 Steven Craig and Michael Palumbo, “The Interaction between

Unemployment Insurance and Income Redistribution Programs,” in

Advisory Council on Unemployment Compensation: Background Papers,

Volume I, (July 1995), pages C1-C51.

15 The percentage breakdown of aggregate spending for combined

welfare programs was 23 percent for AFDC, 56 percent for Medicaid and

21 percent for the “all other” category. See Table 1 in their report. 

16 The measure of the UI recipiency rate was initial claims per

covered employee. Average benefits across all these programs were

measured in real Alabama dollars of 1989 purchasing power, i.e.,

current dollar averages deflated by an index of average earnings in

Alabama (the UI average weekly wage) in 1989. Because counts of

recipients for Medicaid and the combined welfare program were first

available only in 1976, their recipiency rates and average real

benefits were measured only from 1976.

These five issues are important enough to call into question

the validity of the assertion that about two-thirds of the decrease

in UI recipiency between 1971 and 1993 can be attributed to cost

shifting from the states to the federal government. Other research is

needed to test the robustness of their findings. In-depth

investigations of a few states could yield useful insights. 

Craig and Palumbo14 also examined possible interrelations

between UI benefits and spending on welfare programs. They conducted

several regression analyses with pooled data for the 48 contiguous

states based on a full sample period of 1969-1989 (1008 observations)

and a restricted period of 1976-1989 (672 observations) when earlier

data were not available. Three welfare programs were studied: AFDC,

Medicaid and total state and local welfare which combined AFDC and

Medicaid with other (predominantly in-kind) state administered

programs.15 For each of four programs (UI and the three welfare

programs), they created three variables: per capita expenditures, per

capita beneficiaries and average real expenditures per beneficiary.16

Their descriptive analysis showed substantial interstate

variation in most variables with the smallest variation observed in

average real UI benefits per recipient. They found state-level
13

unemployment was a major determinant of total UI expenditures and UI

recipiency but not so for the welfare programs.

Several regressions were fitted using pooled data with UI

variables and the individual welfare program variables first entered

as independent variables and then as dependent variables. Many other

controls for labor market conditions, demographics and federal-state

financial aid parameters were utilized in specifications that also

included fixed effects by state and year. The signs and significance

of the coefficients on the welfare and UI variables were then

examined to make inferences as to partial effects of UI on welfare

and the effects of welfare on UI. Negative coefficients were

interpreted as evidence that the programs interrelated as substitutes

(more spending on one was associated with less spending on the other)

and positive coefficients were interpreted as evidence of program

complementarities. Complementary interrelations would be expected if

the overall liberality (restrictiveness) of state’s political

environment led to high (low) spending across social programs

generally and these four programs in particular.

The preponderance of the evidence was consistent with the

interpretation that UI and AFDC interrelated as substitutes. The

regression coefficients suggested a $1.00 increase in UI spending

reduces AFDC spending by $0.19 while a $1.00 increase in AFDC

spending reduces UI spending by $0.25. The partial interrelations

with Medicaid and total welfare spending, however, were generally

positive, consistent with the interprogram complementarity

interpretation. Further, since AFDC was included within total welfare

spending, the positive association for aggregate welfare spending

becomes a stronger finding. In general, states that spend more on UI

tend to spend more on welfare programs.

Questions regarding technical aspects of the estimation and

interpretation can be raised. 1) There is evidence of strong

multicollinearity among the explanatory variables. First, the overall

R

2s are generally high, but individual variables have quite low t
14

17 For example the average R2 is 0.87 in Tables 7-9, but the sum

of the partial R2s is less than 0.2 for the first regression in Table

8. The remainder of explained variation cannot be attributed to

individual variables, an indication of positive collinearity among

explanatory variables. In this same table the coefficients vary

considerably between the first and third equations even though the

specifications are identical except for the one recipiency rate

variable is different. 

ratios. Second, small changes in specifications lead to large changes

in some coefficients.17 Both patterns are indicators of collinearity.

2) Identifying directions of causation in the regressions is not

obvious. 3) The authors assert that specific variables are associated

with constituency groups for individual welfare programs, e.g., a

higher percentage of elderly in a state raises Medicaid spending, but

their causal interpretations can be questioned. 

It should be noted that the focus of Craig-Palumbo research is

state-level decision making. The analysis does not directly address

questions of federal-state fiscal interrelations. The Food Stamps

program does not directly enter into the analysis (except in devising

a measure of the total welfare beneficiary population). AFDC and

Medicaid spending are measured as statewide totals with no

distinction made between state-financed shares versus federal shares.

No explicit attention is given to fully state-financed welfare

programs that are included within total state welfare expenditures,

the third of their three welfare programs. Presumably substitution

between such programs and UI would be smaller than between UI and

AFDC or between UI and Medicaid simply because the former are fully

state-financed.

Three final comments about the Craig and Palumbo analysis

should be made. 1) They find the predominant direction of association

between UI spending and welfare program spending at the state level

is positive. 2) Evidence of interprogram substitution in spending was

found only between UI and AFDC. The magnitude of the implied offset

was 19-25 percent, much less than a dollar for dollar offset even for
15

18 Martin Browning, Stephen Jones and Peter Kuhn, “Studies of

the Interaction of UI and Welfare Using the COEP Dataset,” Human

Resources Development Canada, Unemployment Insurance Evaluation

Series, August 1995.

the state-financed share of AFDC spending. Since AFDC accounts for

only 23 percent of total welfare spending, the implied offset has a

small aggregate effect on state welfare spending. 3) Their analysis

is not mainly directed towards federal-state fiscal interrelations.

Thus, it does not address the cost shifting hypothesis in the direct

manner of the Bassi, et.al., analysis.

A recent study by Browning, Jones and Kuhn18 of the UI-welfare

interrelation in Canada yielded findings that should be noted. In

April 1993 Canada changed the disqualification penalty for UI

claimants who quit or were discharged without a good personal reason.

The penalty increased from a seven-to-twelve week disqualification to

a durational disqualification coupled with a requirement of twelveto-twenty weeks of subsequent employment to reestablish eligibility.

Their analysis compared the experiences of two 1993 job-separation

cohorts: a January 31-March 13 cohort (Cohort 1) and an April 25-June

5 cohort (Cohort 2), i.e., just before and just after the change.

These two cohorts’ experiences with UI and welfare benefits were

compared using data from administrative records and follow-up

interviews conducted 25, 40 and 60 weeks after their job separations.

Canada’s UI program is governed by a national statute whereas

welfare is mainly a provincial responsibility. Thus, the new penalty

was national in application. Compared to their U.S. counterparts the

Canadian UI and welfare programs are more generous, both their

average benefits and the sizes of caseloads relative to the target
16

19 For a comparison of welfare programs in the two countries see

Rebecca Blank and Maria Hanratty, “Responding to Need: A Comparison

of Social Safety Nets in the United States and Canada,” in Richard

Freeman and David Card, eds., Small Differences that Matter: Labor

Market and Income Maintenance in Canada and the United States,

(Chicago, Ill.: University of Chicago Press for NBER, 1993).

20 See Tables 1 and 2 in Browning, Smith and Kuhn, op. cit.. 

populations (unemployed persons and poor households respectively).19

Given the greater availability of benefits from both programs, a

larger overlap between UI and welfare would be expected in Canada.

Chart 1 displays key data from the Canadian study. Of the 

Chart 1. Receipt of UI and Welfare in Canada20

Sample 

Size

Proportion 

with UI Ben

Proportion with

Welfare

Cohort 1

Non-VQs 3804 0.505 0.066

VQs 678 0.259 0.132

Total 4482 0.468 0.076

Cohort 2

Non-VQs 3951 0.484 0.079

VQs 493 0.177 0.151

Total 4444 0.450 0.087

Total, but with

Cohort 1 weights

0.438 0.090

two kinds of job separations most affected by the statutory change,

voluntary quits could be clearly identified but not discharges for

misconduct. The chart shows data for voluntary quits (VQs) and all
17

21 These proportions (not shown in Chart 1) were 0.180 for

Cohort 1 and 0.286 for Cohort 2. The change of 0.106 roughly matches

the reduction in the beneficiary proportion from 0.405 in Cohort 1 to

0.302 in Cohort 2.

others (non-VQs). Note that VQs constitute only about 15 percent of

the claimant total in Cohort 1 and 11 percent in Cohort 2. Overall,

the beneficiary proportions are 0.468 for Cohort 1 and 0.450 for

Cohort 2. Among VQs there is a large reduction in the UI beneficiary

proportion, from 0.259 in Cohort 1 to 0.177 in Cohort 2. Reductions

occurred among two subgroups of VQs: persons reemployed and those

still not employed 25 weeks after their separations. The aggregate

beneficiary proportion for Cohort 2 reflects a different composition

of the sample as well as reduced UI recipiency among VQs. When Cohort

1 subsample weights are applied to the Cohort 2 beneficiary

proportions, the aggregate proportion for Cohort 2 becomes 0.438 (the

bottom line in Chart 1) or 0.030 lower than the actual Cohort 1

proportion.

Probably the most interesting aspect of Chart 1 is the low

proportions who received welfare benefits. The aggregate proportions

for both cohorts are less than 0.100. Among VQs the proportions are

generally higher and particularly so for those not reemployed 25

weeks following their separations.21 Overall, the UI beneficiary

proportion for VQs was 0.082 lower for Cohort 2 while the welfare

beneficiary proportion was 0.019 higher.

To summarize, four observations about the UI-welfare interface

in Canada can be offered. 1) Among two recent cohorts of job

separations, somewhat fewer than half collected UI benefits and fewer

than 10 percent collected welfare. A substantial minority of job

terminations, approaching half, did not collect either type of

benefit. 2) Among VQs the recent restrictions in UI eligibility

appear to have reduced recipiency by almost one third (from 0.259 to

0.177). For this same group, however, the welfare recipiency

proportion only increased from 0.132 to 0.151. 3) A close connection

between reduced UI recipiency and increased welfare recipiency was

observed among VQs who were not yet reemployed 25 weeks after their
18

22 These proportion are suggested as orders of magnitude

calculations. One-half comes from the comparison of the change in the

UI beneficiary proportion of -.030 (0.468 from Cohort 1 with 0.438

from Cohort 2 using Cohort 1 sub-sample weights) with the change in

the welfare beneficiary proportion of 0.014 (0.0756 from Cohort 1

with 0.090 from Cohort 2 using Cohort 1 sub-sample weights). Onequarter comes from the reduction in VQ UI recipiency of 0.082 (0.259

in Cohort 1 and 0.177 in Cohort 2) with the increase in VQ welfare

recipiency of 0.019 (0.132 in Cohort 1 and 0.151 in Cohort 2). The

width of this range is an indication of the degree of uncertainty in

the calculation. 

job separations. 4) Recall that UI and welfare are both more

accessible in Canada than the U.S.. If the longitudinal overlap

(nonreceipt of UI followed by receipt of welfare) falls into the onefourth to one-half range in Canada,22 the analogous fraction in the

U.S. would be considerably smaller. 

No other literature with explicit tests of the cost shifting

hypothesis has been encountered. There is a related popular

discussion of the “race to the bottom” which focuses on interstate

competition presumably manifested by the sensitivity of each state’s

benefit levels and tax rates to those of adjacent states. There is

also a discussion of federal-state fiscal interrelations by political

scientists such as Kent Weaver at the Brookings Institution. However,

formal testing of cost shifting is absent from this literature.

Other Considerations

Two other issues related to the cost shifting hypothesis merit

some discussion. These are: 1) the identity of the transfer payment

programs involved and 2) inter-program differences in the personal

and economic characteristics of claimants.

Potentially affected programs

This report’s analysis of cost shifting has singled out the

AFDC, Food Stamps and Medicaid programs for prime consideration. The

empirical analysis to be conducted will place more emphasis on AFDC
19

23 Estimates for 1994 appear in the January/February 1996 issue

of the Survey of Current Business, Table 3.12.

24 Pension and disability payments for Social Security (OASDI),

Federal civilian and military retirees, State and local retirement

systems, railroad retirees, veterans’ pensions and disability, State

temporary disability, federal and state Workers’ Compensation and

Black Lung totaled $477.9 billion in 1994. Health expenditures,

mainly Medicare and Medicaid, totaled $310.0 billion. The combined

share across all these programs was 84.3 percent of the $933.8

billion. 

and Food Stamps than Medicaid, partly because of better data

availability and partly because TRIM2 simulations of Medicaid are not

possible for as many earlier years as for AFDC and Food Stamps.

Several other programs also make cash or in-kind transfers to persons

and families. When the individual programs and their eligibility

criteria are reviewed, however, none seem likely to be quantitatively

important candidates for state-to-federal governmental cost shifting.

Our starting point is to note all federal and state programs

that make transfer payments to individuals as identified in the

National Income and Product Accounts (NIPA). During 1994, for

example, transfer payments to persons totaled $933.8 billion.23

Government payments for pensions, permanent disability and medical

expenses constitute the bulk of this spending, more than 80 percent

of the total.24 Recipients of retirement pensions and permanent

disability benefits exhibit little attachment to the labor force.

General Assistance (GA) and Supplemental Security Income (SSI)

are targeted on the low income population. GA is fully state financed

while SSI is about 90 percent federally financed. Shifting UI

claimants to another state-financed program would not be expected

under the cost shifting hypothesis unless benefit levels for the

latter were much lower than for UI. In fact, GA has been subjected to

eligibility restrictions in recent years, e.g., time limits of 26

weeks in a given annual period. While most of SSI is federally

financed, nearly all recipients are aged, disabled or blind. Thus,
20

SSI is not a likely destination for unsuccessful UI claimants. 

The conclusion of this brief review is that AFDC and Food

Stamps represent two likely programs to examine for evidence of cost

shifting. Since most AFDC and Food Stamps beneficiaries are eligible

for Medicaid, the latter program is also important to acknowledge. In

fact, potential eligibility for Medicaid is one reason why an

unemployed person could be attracted to welfare. 

One other aspect of state-to-federal UI cost shifting should be

noted. During the later phases of the temporary Federal Emergency

Unemployment Compensation (EUC) program of 1991-1994, the states

encouraged UI claimants to file for EUC even though these individuals

were experiencing new spells of unemployment, spells beginning well

past the end of the benefit year from which they previously had

exhausted regular UI benefit eligibility. This “optional” EUC was

fully federally financed as was the rest of EUC. Many claimants found

it to be an attractive option as the weekly benefit was generally

higher than under regular UI and no reduction of the regular UI

entitlement was implied if optional EUC was received. For claimants

who received EUC under this optional feature, the states shifted

costs from state-financed regular UI to the federally-financed EUC

program. 

To the extent that optional EUC was utilized, it had the effect

of reducing the IUTU ratio for the regular UI program. Thus, the IUTU

ratio would have been somewhat higher from August 1992 to April 1994

had optional EUC not been available. 

Optional EUC provides a concrete example of state-to-federal

cost shifting, but the affected persons continued to collect UI

benefits, albeit federally-financed benefits. Since EUC was a

temporary program that expired in early 1994, the relevance of

optional EUC was limited to this one earlier time period. No

permanent shifting of UI financial costs to the federal government

was implied. However it does illustrate that knowledgeable

participants in the UI program were aware of the financial
21

25 The UI programs of twelve states plus the District of

Columbia pay dependents’ benefits. These benefits are represent a

small part of total benefit costs in all thirteen jurisdictions.

26 In 1979 0.868 million of 3.950 million (22.0 percent) units

eligible for AFDC had an adult with earnings. For Food Stamps 5.077

million of 12.963 million (39.2 percent) eligible units in 1981 had

an adult with earnings. The corresponding percentages in 1989, a

strong labor market year, were 17.5 percent and 36.5 percent. Thus

the percentages do not seem responsive to the business cycle. All

estimates for 1979, 1989 and 1993 are based on the Urban Institute’s

TRIM2 model.

consequences of increasing the federal share of UI financing, albeit

during this a short and temporary episode. 

Contrasts in demographic and economic characteristics 

Demographic and economic contrasts between UI recipients and

welfare recipients are pronounced. The UI claimant-beneficiary in the

vast majority of instances is an individual with substantial recent

attachment to the labor force. Benefits are targeted on the

individual claimant whose potential entitlement depends on previous

(or base period) earnings.25 In contrast, most recipient units for

Food Stamps and AFDC are families, with average sizes of 2.5 and 2.8

respectively during 1993. Most of the other family members are

children. Low family income is a primary criterion for welfare

program eligibility. 

Average labor force attachment among the adult welfare

recipients is not strong. Of the 6.006 million units eligible for the

AFDC program in 1993, only 1.157 million (19.3 percent) had a working

adult member. Only 6.046 million of 16.527 million (36.6 percent)

units eligible for Food Stamps during 1993 had an adult with

earnings. Further, between 1979 and 1993 the degree of labor force

attachment among welfare units did not increase.26 

Thus while there could have been shifting of formerly eligible

UI claimants onto welfare, there has been no obvious change in the
22

27 Potential benefits in most states may be received for a

variable period with 26 weeks as the maximum in all states except

Massachusetts and Washington where the maximum is 30 weeks.

degree of labor force attachment of adult welfare recipients. Adult

recipients of Food Stamps and AFDC exhibited consistently low labor

force attachment between 1979 and 1993.

Sections II-IV undertake additional analysis of the cost

shifting hypothesis.

II. National Trends in the Receipt of Benefits

This section presents the major background “facts” on the

receipt of UI benefits and welfare benefits. National developments

are the primary concern. One objective is to pinpoint the exact

periods of time when changes in patterns of benefit receipt took

place. Since the motivating changes behind the cost shifting

hypothesis are restrictions in state-financed UI benefits, this

program is examined first.

Receipt of UI benefits

Unemployment insurance (UI) cash benefits are received through

three distinct programs. The regular UI program provides up to 26

weeks of potential compensation27 to eligible claimants who satisfy 

monetary and nonmonetary eligibility criteria. The second tier, the

Federal-State Extended Benefits (EB) program, may pay up to an

additional thirteen weeks if the program is triggered “On” in a

state. The third tier is emergency benefits that are available in

certain recessionary periods as a result of federal legislation.

Regular UI is fully state financed while EB financing is shared

equally by the states and the federal government. Emergency benefits

are fully federally financed. Since the EB program is rarely

activated, the regular UI program will be the focus of the ensuing

discussion.

Total benefit payouts from the regular state UI programs are
23

28 For example, the national ratio of the average weekly benefit

to the average weekly wage was 0.352 in 1960 and 0.357 in 1994. 

29 The annual average of TU was 7.992 million while the average

monthly number unemployed fewer than five weeks was 2.727 million.

Multiplying the latter by 12 produces an estimate of 32.720 million

new unemployment spells for the full year.

the product of the weekly benefit amount times weeks compensated.

Because weekly UI benefit levels have not changed much relative to

average weekly wages over the past 40 years,28 attention will center

on the number of recipients and weeks compensated. 

The most commonly used metric of benefit receipt is the ratio

of UI claimants (insured unemployment or IU) to the total number

unemployed (total unemployment or TU). The latter, measured by the

monthly household labor force survey of 55,000 households, counts all

persons 16 and older who are actively seeking work. National

estimates of TU for those 16 and older extend back to 1947. Universe

counts of IU are available back to the inception of unemployment

insurance in the late 1930s. However, not all UI claimants receive

benefits. In recent years the average weekly number of beneficiaries

in regular UI programs (AWK) when expressed as a proportion of IU has

averaged from 0.87 to 0.89. Nonrecipients counted in IU but excluded

from AWK include those serving a one week waiting period and certain

claimants serving fixed length disqualifications. Claimants who have

exhausted their entitlements to regular UI benefits are excluded from

both IU and AWK. 

All three unemployment measures TU, IU and AWK are weekly

averages for an unemployed population that is subject to frequent

turnover. In 1994, for example, while the weekly average of TU was

roughly 8.0 million persons the number of new unemployment spells

that began sometime during the year was almost 33.0 million.29 The

average length of a new spell of unemployment was about 13 weeks.

Table 1 displays annual data on TU, IU and AWK for the years

1956 to 1994. (All data exclude Puerto Rico and the Virgin Islands.)

Also shown is a business cycle indicator, the annual unemployment

rate (the TUR, measured as a percent of the civilian labor and, like
24

30During recessions the mix of unemployment shifts to include an

increased proportion of job losers, the group most likely to claim

and receive UI benefits, causing IUTU to rise. In the later stages of

recessions IUTU tends to decline as beneficiaries exhaust their

entitlements.

31 Since 1947 is the first year for which there are estimates of

total unemployment for persons 16 and older, 1956 is the first year

for which the five year difference in the five year averages of IUTU

can be computed. The 1952-1956 average was 0.499 and the 1947-1951

average was 0.470. 

TU, based on the monthly household labor force survey). All three

measures increase sharply in the recession years 1958, 1961, 1975,

1980, and 1991. The numbers of unemployed and of UI claimants are

highly variable over the business cycle.

There is no data source that measures the number of persons

eligible for UI benefits. The most commonly used measure of benefit

receipt is the ratio of insured unemployment to total unemployment

(IUTU). A second, closely related measure of the rate of benefit

receipt is the ratio of beneficiaries to total unemployment (AWKTU).

Both IUTU and AWKTU appear in Table 1. Finally, to show the

proportion of UI claimants who receive benefits, the ratio of

beneficiaries to claimants also appears in the table (AWKIU). The

IUTU and AWKTU ratios both display considerable year to year

variability with increases occurring in recession years when the TUR

rises.30 In contrast, the AWKIU ratio has much greater year-to-year

stability. 

 A main reason for displaying these aggregate data is to trace the

recent history of UI receipt by the unemployed. To reduce the effects

of cyclical factors on the annual ratios, the final columns show

information arranged as five year averages of the IUTU ratio. The

observation of 0.499 for 1956, for example, is the average for the

years 1952 through 1956, and the five-year change of 0.029 (the final

column) is the difference between the averages for 1952-1956 and

1947-1951.31

The final column of Table 1 is especially useful for

documenting changes in the IUTU ratio. Most of the entries are
25

32 Food Stamp benefits totaled only $300 million in 1969

compared to $2.1 billion for regular UI benefits.

negative, 26 of 39, indicating a downward trend in the average IUTU

ratio. Note also that the negative and positive entries are bunched

for adjacent years, e.g., all negative from 1958 to 1972 and again

from 1980 to 1990, but all positive from 1973 to 1979 and again from

1991 to 1994. The historic decline in the IUTU ratio is far from an

automatic phenomenon equally applicable to all past periods. 

Table 1 highlights two periods when the IUTU ratio declined

especially rapidly. The five year changes in the five year averages

exceeded -.040 in every year between 1965 and 1970 (the final column

in Table 1) and again in every year between 1984 and 1989. If states

were consciously or unconsciously reducing UI eligibility to move

people out of UI program and onto welfare programs, these are the two

time periods when one might expect to observe the terms of UI

eligibility to have changed most noticeably in a restrictive

direction. 

These two periods when IUTU decreased differ considerably in

the availability of benefits from welfare programs to which the low

income unemployed could potentially be shifted. While regular AFDC is

a longstanding benefits program, the Food Stamps program was founded

in 1966 and remained small during the late 1960s.32 The AFDC-UP

program also was not important in the mid to late 1960s. Thus the

shifting of unemployed persons to welfare programs would have wider

possibilities in the mid to late 1980s than earlier due to the

existence and the larger scale of Food Stamps and AFDC-UP in the

1980s. Later sections of this report will emphasize developments

during the 1980s and 1990s.

The two periods of major decreases in the IUTU ratio have

contrasting causal explanations. The decrease of the 1960s is

generally understood to be a demographic phenomenon. This period

witnessed a rapid growth in the labor force with the initial entry of

the post World War II baby boom generation plus continued increases

in female labor force participation. These entrants changed the age-
26

33 See Corson and Nicholson, op.cit., for an analysis that

decomposes the explanation for the decline in IUTU of the early

1980s. Restrictions on UI benefit availability resulting from statelevel legislation figures prominently in their explanation. One

detailed analysis of state level legislative changes is given in

Chapter 2 Wayne Vroman, The Funding Crisis in State Unemployment

Insurance, (Kalamazoo, MI: W.E. Upjohn Institute, 1986).

gender mix of unemployment toward younger persons and women, groups

less likely to file for and receive UI benefits compared to adult

men. This decline is an example of Case 3 from the taxonomy offered

in Section I, i.e., developments in the economy affecting UI and

(possibly) welfare recipiency in opposite directions.

The decline in IUTU of the 1980s, in contrast, is generally

understood to be related to UI financing issues. Low levels of UI

trust funds, increased costs of borrowing and legislation to improve

trust fund solvency all characterized the situation of UI programs

during early to mid 1980s.33 Individual state UI programs had

differing financial experiences during these years. To the extent

that financial considerations motivated state UI program changes of

this period, it should be reflected in differential decreases in IUTU

ratios associated with differential restrictions on UI eligibility by

state, i.e., both changing most in the states with the biggest

financing problems. This would be an example of Case 2 from the

taxonomy of Section I: restrictions in UI eligibility caused by UI

financing problems leading to reduced IUTU ratios and to increased

receipt of welfare. This possibility is investigated in Section III. 

The bottom three rows of Table 1 display important summary

information on rates of benefit receipt in the regular UI 

program. There exists a substantial literature on the decline in
27

34 Analysis of this phenomenon includes the following papers.

Gary Burtless and Daniel Saks, “The Decline in Insured Unemployment

During the 1980s,” (Washington, D.C.: The Brookings Institution,

1984), Corson and Nicholson(1988), op.cit., Rebecca Blank and David

Card, “Recent Trends in Insured and Uninsured Unemployment: Is There

an Explanation?” Quarterly Journal of Economics, Vol. 106, (February

1991), pp. 1157-1189, Wayne Vroman, “The Decline in Unemployment

Insurance Claims Activity in the 1980s,” Unemployment Insurance

Occasional Paper 91-2, (Washington, D.C.: U.S. Department of Labor,

1991) and Bassi, et. al.(1995), op.cit.. 

UI claims in the 1980s.34 The Table 1 averages focus on two fourteenyear periods, 1967-1980 and 1981-1994, as well as the entire twentyeight years 1967 to 1994. The choice of 1967 as the starting date for

measuring these averages is related to the measurement of TU. In 1967

the monthly household survey sample was expanded, and the U.S. Labor

Department started to collect and publish labor force and

unemployment data for the nine “Census” regions and for the ten

largest states. State-level detail was increased to 27 states in 1970

and then to all states in 1976. Since state-level analysis is central

to this report, the averages are measured from 1967 when state-level

detail on TU is first available.

The fourteen-year averages at the bottom of Table 1 show that

UI availability declined noticeably after 1981. The 1981-1994 average

IUTU ratio is only 82.2 percent of the ratio for the 1967-1980 period

(0.329 versus 0.400). The corresponding comparison for the AWKTU

ratio is 85.5 percent (0.290 in 1981-1994 versus 0.339 in 1967-1980).

There is an important point regarding the contrasting percentage

reductions in the IUTU and AWKTU ratios. Actual receipt of UI

benefits declined somewhat less than the decline in UI claims

activity, e.g., 14.5 percent in recipiency but 17.8 percent in

claims. 

This point is further emphasized in the averages of the AWKIU

ratios for the two periods. The ratio was 0.883 during 1981-1994

compared to 0.846 during 1967-1980. Note also in Table 1 that the

AWKIU ratio fell below 0.860 in every year from 1967 to 1980 but

exceeded 0.860 in every year after 1980. Since 1981 there has been a
28

systematic increase in the proportion of UI weeks claimed that have

been compensated.

If UI programs have been restricting eligibility, the pattern

of aggregate claims data in Table 1 could suggest that restrictive

activities were concentrated in the early to mid 1980s. Further, from

the behavior of the recipiency ratios since 1990, it does not appear

that benefit availability declined during the last five years covered

by these data. 

Regression analysis of the decline in UI recipiency

This section summarizes a regression analysis of changes in UI

benefit recipiency. Multiple regressions based on time series data

are fitted with the objective of estimating the size of the decrease.

State-level data as well as national data are examined. Attention

focuses on decreases that occurred after 1980 relative to earlier

years. Actual receipt of benefits (AWKTU) as well as regular UI

claims (IUTU) are both studied.

The multiple regression specification to explain time series

changes in IUTU and AWKTU ratios utilizes three explanatory

variables. First, the unemployment rate (TUR) is used to control for

the effects of the business cycle on UI claims. Higher unemployment

raises IUTU due to the change in the mix of unemployment during

recessions, i.e., a larger share of job losers who are more likely to

be eligible. Second, lagged unemployment is used to control for the

effects of exhaustions. Increased exhaustions reduce UI claims, hence

TUR lagged is expected to have a negative coefficient. The third

explanatory variable is a dummy variable which equals zero through

1980 and then 1.0 in later years. Its coefficient is expected to be

negative and provide a point estimate of the size of the post-1980

downward shift in UI claims and benefit recipiency. Multiple

regressions have been fitted to annual data spanning the period from

1967 to 1994.

Table 2 displays the regression results. Two time periods are

utilized, 1967-1989 and then 1967-1994, in order to highlight the

effects of adding the 1990-1994 period. Equation 1 utilizes national
29

35 The t ratio needed for significance at the 0.05 level under a

one sided t test is 1.7. All three t ratios exceed 10.0.

data on the IUTU ratio for the 1967-1989 period. Note that all three

coefficients have expected signs and all are highly significant.35

The dummy variable’s coefficient of -0.0707 represents 0.191 of the

mean for the 1967-1989 period. This regression suggests that holding

other factors constant, i.e., TUR and TUR lagged, regular UI claims

shifted downward by 19.1 percent after 1980. 

Equation 2 is fitted using a fixed weight index of state-level

IUTU ratios as the dependent variable. Each state’s share of national

unemployment for the years 1967-1989 (a single number) served as

weights to be combined with annual IUTU ratios by state in deriving a

national time series. In effect, weighting removes the consequences

of faster labor force growth by states in the South and the West

relative to states in the North East and Midwest. Since IUTU ratios

are generally lower in the faster growing geographic areas, there is

a measurable difference in the results. Specifically the D1981

coefficient is now -0.0558 (as opposed to -0.0707, 21 percent

smaller) and the elasticity of the estimated post-1980 downward shift

is 15.1 percent rather than the 19.1 percent of equation 1. Use of

state-level data in equation 3 yields very similar results as to the

estimated size of the post-1980 downward shift.

Equations 4-6 repeat equations 1-3 respectively but with the 

weeks compensated ratio (AWKTU) as the dependent variable. All

coefficients for the TUR, the TUR lagged and the D1981 dummy 

variable are significant. Note in equations 5 and 6 that the D1981

coefficients are now even smaller, -0.0308 and -0.0311 respectively,

and the estimated elasticities of the post-1980 downward shift are

both about 10 percent. The combined effects of using fixed weights by

state and explaining the actual receipt of benefits (as opposed to

claims) cause the estimated downward shift in equations 5 and 6 to be

roughly half the size of the estimate from equation 1.

Equations 7 and 8 are included mainly to reemphasize the

earlier observation that the ratio of weeks of UI benefits actually
30

paid to weeks claimed increased after 1980. The D1981 coefficient is

positive, highly significant and suggests the ratio increased by 4.0-

4.5 percent after 1980.

The second set of eight equations repeats the specifications of

equations 1-8 but with the data extended through 1994. The addition

of these five years has a consistent effect on the estimated size of

the post-1980 downward shift in both claims and the receipt of

benefits. All D1981 coefficients in equations 9-14 are smaller than

their counterparts from equations 1-6. When pairs of estimates are

compared, e.g., equations 1 and 9, the D1981 coefficients for the

1967-1994 period are uniformly about 10 percent smaller than for the

1967-1989 period. In contrast, the D1981 coefficients in the AWKIU

regressions are about 10 percent larger for the longer data period. 

The important point here is that one’s estimate of the size of

the post-1980 downward shift in UI claims and the associated receipt

of benefits is sensitive to the choice of data period. Adding data

from 1990-1994 causes the estimated size of the downward shift, i.e.,

the D1981 coefficient, to be smaller, not larger. This finding might

surprise those who view the downward trend in the receipt of UI

benefits as an inexorable phenomenon.

To summarize, all regressions in Table 2 consistently indicate

that UI benefit recipiency shifted downward after 1980. The estimated

size of the downward shift, however, was sensitive to several

identifiable factors: the UI program variable being explained (weeks

claimed or weeks compensated), controlling for regional shifts in

labor force composition and the choice of estimation period. Using

equations 13 and 14, actual weeks compensated are estimated to be

about 8.5 percent lower after 1980 compared to the 1967-1980 period.

The estimated size of the downward shift is less than half of what

would be estimated from a regression based just on the national IUTU

ratio through 1989, i.e., equation 1 of Table 2.

Two points about the estimated downward shift in AWKTU should be

stressed. First, while an 8.5 percent reduction is measurable, it

accounts for a modest reduction in the number of beneficiaries. In

1994 total unemployment was about 8.0 million, the AWKTU ratio was
31

36 These are offered merely as illustrative estimates. Average

benefit duration of 15.5 weeks implies a multiplier of about 3.3 to

convert the weekly average to the total number who received benefits

sometime during the year. The estimate of added outlays is 8.5

percent of the $20.4 billion total for 1994. 

37 During 1993, for example, the 1.509 million AFDC-UP

recipients represented 10.9 percent of all AFDC beneficiaries. 

0.291 and the average weekly number of beneficiaries was 2.323

million (Table 1). Adding 8.5 percent to this weekly average would

bring it up to 2.520 million. Since the average duration in benefit

status during 1994 was 15.5 weeks, the difference in these two

weekly averages of 0.197 million probably implies 0.650 million fewer

recipients during the year and savings of $1.7 billion in benefit

outlays.36 Second, the 8.5 percent reduction is a nationwide average.

When the experiences of individual states are reviewed, several had

much larger post-1980 downward shifts in recipiency. A later section

identifies these states and examines their experiences. 

Receipt of welfare benefits

Total expenditures on welfare programs have expanded sharply

since the 1950s. The following paragraphs provide an overview of the

growth in total benefit payments, caseloads and average payments to

recipients. Three programs are examined: AFDC, Food Stamps and

Medicaid with the AFDC encompassing both AFDC-Basic and AFDC-UP.

Despite the recent growth in AFDC-UP, this component only accounts

for about 10 percent of all AFDC recipients.37

Table 3 displays annual time series data from 1950 to 1994 for

the three welfare programs. AFDC was founded in the 1930s as part of

the Social Security Act and has been continuously operative in all

later years. States could offer AFDC to two parent families starting

in 1961, but full national implementation of AFDC-UP commenced only

in 1990. Food Stamps were first available in the early 1960s, but the

full scale national program dates from 1969-1970. Medicaid was

founded in 1966 and has become the main program providing in-kind
32

38 The state shares of AFDC and Medicaid were estimated as 44

percent of the national total. This percentage is an average of

results from state-level analysis of expenditures and matching

formulas for the two years 1984 and 1994.

medical services to low income families. AFDC and Medicaid have

eligibility criteria that are largely state-determined, and both

programs operate with joint federal-state funding. The federal share

has ranged from 50 to 80 percent in most years. 

Aggregate annual expenditures on the three welfare programs have

grown sharply. To help place this growth into a comparative

perspective relevant to this report, Table 3 also shows annual

benefit payments for the state-financed component of UI, i.e., all of

regular UI plus the state share of EB. Rapid growth in aggregate Food

Stamp benefits occurred during 1969-1975, 1979-1981 and 1989-1992.

Growth in total AFDC benefits has been more continuous since the mid

1950s, but accelerations are apparent during 1967-1972 and 1990-1992. 

Compared to the other benefit series, however, the growth in

Medicaid is of a completely different order of magnitude. While the

other three programs fall into the $20-$24 billion range in 1994, for

example, Medicaid benefits totaled $143.5 billion. Medicaid benefits

are more than two times the total for the other three programs

combined. Note that total Medicaid expenditures have been the largest

of the four programs shown in Table 3 in every year since 1966. The

implications of Medicaid for state budgets, while much larger in

recent years than in the past, is not a recent problem. 

To help focus on the fiscal burdens that UI and welfare pose to

states, Table 3 displays two averages for the 1981-1994 period: total

benefits and state-financed benefits. Over these fourteen years the

states financed an average of $17.3 billion for UI, $7.9 billion for

AFDC and $31.0 billion for Medicaid.38 Medicaid represents by far the

largest burden for the states even though more than half of Medicaid

costs are federally financed.

Introducing Medicaid into the analysis dramatically alters the
33

terms of debate over state-level financial incentives. Intake into

welfare programs is often integrated with claimants informed about

rights for all three programs: AFDC, Food Stamps and Medicaid. In

many situations a person who qualifies for one program qualifies for

all three. In typical situations a person who qualifies for AFDC

automatically qualifies for Medicaid. The overlap between Food Stamps

and Medicaid is much less complete, but joint receipt of these

benefits is also common. Generally, states cannot offer AFDC and

Food Stamps but exclude Medicaid.

The counts of recipients in the three welfare programs show time

series patterns that parallel the patterns for total expenditures.

Between 1950 and 1994 AFDC recipient families increased from 0.6

million to 5.0 million while persons on AFDC increased from 2.2

million to 14.2 million. AFDC caseloads (both families and

recipients) doubled during 1967-1971 and also grew noticeably from

1989 to 1992. 

Food Stamp recipients increased from 3.6 million in 1969 to 17.8

million in 1975. Per-capita Food Stamp expenditures also rose sharply

during these years. From the averages of real benefits per-recipient

it is also clear that Food Stamp benefit levels increased sharply

between 1969 and 1970. Noticeable increases in Food Stamp caseloads

occurred during 1979-1981 and 1990-1993, but were much smaller than

the 1969-1975 increases.

Because data on Medicaid recipients are first available in the

early 1970s, caseload growth for earlier years cannot be documented.

The numbers of recipients was stable in the 20-23 million range

between 1973 and 1989 but then increased sharply after 1989, reaching

35.1 million in 1994. The recent growth in Medicaid caseloads has

surpassed Food Stamps caseload growth. In all recent years many more

persons have collected benefits from Food Stamps and Medicaid than

from AFDC. 

Between 1989 and 1992 per capita Food Stamp benefits increased

by 67 percent (from $49.73 to $83.00) and by more than 10 percent per
34

39 The AFDC per-recipient data show the combined federal and

state components of benefits.

recipient in real terms (from $520 to $586). Per capita Medicaid

benefits have followed a strong upward trend, roughly tripling during

the 1980s and almost doubling between 1990 and 1994. In 1994 real

Medicaid benefits per recipient were roughly 2.4 times the level for

AFDC ($2762 versus $1153) and roughly five times real Food Stamps per

recipient. 

While large increases in per capita AFDC benefits have also

occurred, these increases have been much more modest than for Food

Stamps and Medicaid.39 In fact, real AFDC benefits per recipient

peaked during 1976-1977 and then declined measurably. The 1994

average of $1153 represents a cumulative reduction of 27 percent from

1976-1977. Comparisons of real benefits per recipient in 1962 and

1994 for two programs are instructive. Whereas real Food Stamp

benefits per recipient roughly doubled (from $285 to $560), the 1994

real AFDC benefit was almost identical to that of 1962 ($1153 versus

$1171). 

In the most recent years aggregate outlays for the AFDC and Food

Stamps have been about equal. Over the past thirty years more rapid

caseload growth and faster growth in benefits per recipient have

caused Food Stamp benefits to grow much more rapidly than AFDC

benefits. At present Food Stamps serves roughly twice as many as

AFDC, but its per recipient benefit levels are about half of AFDC

benefit levels. 

AFDC, Food Stamps and Medicaid are targeted on the population of

low income individuals and families. For both AFDC and Food Stamps,

families typically participate for several consecutive months in the

receipt of benefits. In contrast, participation by Medicaid eligibles

is more episodic, dependent upon the health conditions of individual

family members. Also, as noted, summary data on the numbers of

Medicaid beneficiaries are available for a much more restricted time
35

40 A later section of this report examines eligibility for Food

Stamps, AFDC-Basic and AFDC-UP in state-level data using CPS income

data and Urban Institute’s TRIM2 model.

period when compared to the other two welfare programs. Consequently,

the receipt of Medicaid is not emphasized in the following

discussion.

Table 4 helps to place the growth of AFDC and Food Stamps into a

broader income distribution context by showing their size relative to

the poverty population. While eligibility criteria for Food Stamps

and AFDC differ and both consider assets as well as income in

determining eligibility, both programs are designed to serve the

poor. The two programs differ in at least three important respects.

Food Stamps has uniform federal eligibility criteria, its benefits

are indexed to inflation and benefits are fully federally financed.

AFDC has a large role for the states in setting eligibility standards

and benefit levels, joint state-federal financing and its benefits

are not indexed. For both programs, however, the poverty population

is a convenient aggregate proxy for the eligible population.40

Poverty in the U.S. has been measured continuously since 1959.

The first two columns of data in Table 4 show respectively poverty

rates and poverty counts. Poverty declined sharply during the 1960s,

but has been resistant to further sustained reductions since about

1970. In fact, the 1994 poverty rate was almost two full percentage

points higher than the poverty rate of 1970 (14.5 percent versus 12.6

percent). Note also that since 1980 the poverty rate was at least

13.0 percent in every year but 1989.

Counts of Food Stamp and AFDC recipients are then displayed

(repeating information from Table 3) along with caseloads measured as

a proportion the poverty population. By 1994 beneficiaries from the

two programs represented 0.372 and 0.722 of the poverty population

respectively. Again the more rapid growth of Food Stamps is apparent

as the program only began in 1962 whereas AFDC recipients already
36

41 For example the 1968 change of .045 for Food Stamps-topoverty in the final column is the difference between the 1964-1968

average (0.047) and the 1959-1963 average (0.002).

represented 9.6 percent of the poverty population in that year.

The four right-hand columns of Table 4 then trace the growth of

the two programs by measuring five year averages and five year

changes in five year averages of recipient-to-poverty ratios.41 The

five-year average for Food Stamps recipients first exceeded the AFDC

five year average in 1974 (0.466 versus 0.424), and the differential

then increased substantially in subsequent years.

To help pinpoint the periods when the two welfare programs grew

most relative to the poverty population, the final two columns of

Table 4 show five year changes in the five year averages of

recipients-to-poverty. The choice of five year periods is deliberate

so that comparisons with earlier results from Table 1 can be made. 

For Food Stamps the largest changes in these averages occurred

between 1972 and 1979 when the changes exceeded 0.200 in all eight

years and the largest changes occurred in 1975 and 1976 (0.425 and

0.416 respectively). Observe that the five year changes were then

negative from 1983 through 1990. The decreases in the averages

exceeded -0.050 in the years 1985 through 1989. Finally, note that

the changes became positive in the 1990s indicating renewed growth in

Food Stamp recipiency relative to the poverty population.

For AFDC the five-year changes in the five-year averages exhibit

a similar pattern. All changes were positive from 1968 to 1979 and

they consistently exceeded 0.100 between 1970 and 1977. The 1973-1975

changes all exceeded 0.200. As with Food Stamps, changes in the AFDCto-poverty average ratios were then negative for several consecutive

years in the 1980s with each change exceeding -0.050 between 1981 and

1986. Finally, growth AFDC recipiency relative to poverty is

indicated after 1989, but the changes were much smaller in 1990-1994

than during 1968-1978.
37

Comparing the timing of the changes in the recipient-to-poverty

average ratios with the averages of IUTU ratios from Table 1 is

instructive. During the 1970s all three sets of five year changes in

five year averages were generally positive (1973- 1979 for the IUTU

ratio, 1968-1979 for the AFDC-poverty ratio and 1968-1982 for the

Food Stamp-poverty ratio). Then the changes turned generally negative

in the 1980s (1980-1990 for the IUTU ratio, 1980-1988 for the AFDCpoverty ratio and 1983-1990 for the Food Stamps-poverty ratio).

Finally, the changes in the five year averages have been positive in

the 1990s (1991-1994 for the IUTU ratio, 1989-1994 for the AFDCpoverty ratio and 1991-1994 for the Food Stamps-poverty ratio). 

Comparisons of aggregate time series of the type just made can

be criticized because they do not hold constant potentially relevant

factors affecting the individual data series. However, the phenomenon

of interest (shifting unemployed workers from UI to welfare programs)

should have aggregate manifestations if it is truly important. 

Instead, the national historic record seems to suggest the

following four summary statements. 1) The regular UI program shrank

relative to total unemployment during the 1960s with negative five

year changes in five year averages indicated for every year between

1958 and 1972. During the latter part of this period the welfare

programs grew rapidly relative to the size of the poverty population.

2) During the 1970s the five year changes in five year averages were

generally positive across all three programs under study. In terms of

orders of magnitude, however, the increases in the Food Stamps-topoverty ratios were by far the largest while the increases in the

IUTU ratios were the smallest across the three programs. 3) During

the 1980s the changes in the five-year-average ratios were generally

negative for all three programs. 4) During the first half of the

1990s the changes in the average ratios have generally been positive

for these programs. The timing of the positive changes of the 1970s
38

and 1990s and the negative changes of the 1980s was not exactly

coincident across the three programs, but strong similarities were

apparent. The fact that the changes have been generally positive

during the 1990s is especially interesting since state budgets have

probably been under greater stress during these years than in earlier

decades.

The overall conclusion suggested by the preceding analysis of

national time series is that in periods when the IUTU ratio decreased

so did measures of the receipt of welfare benefits. Conversely, when

IUTU increased there was a clear tendency for welfare recipiency to

increase relative to the poverty population. These patterns are

opposite of what would be expected if cost shifting from UI to

welfare programs had large national manifestations. The next two

sections move from national aggregates to state data to further

examine the cost shifting hypothesis. Many readers may find this a

more appropriate geographic unit of analysis. 

III. Analysis of UI Data by State

The present report has relied heavily on analyses that use

individual states as units of observation. This section focuses on

the receipt of UI benefits (as reflected in IUTU ratios) and UI

eligibility criteria by state. It examines four topics relevant to

the possible linkages between changes in UI provisions and decreases

in IUTU ratios. First, it notes changes in the probability of

receiving UI benefits by state. Second, changes in UI eligibility

provisions are reviewed. Third, the association between decreased

receipt of UI benefits and state-level eligibility restrictions is

explored. Fourth, it examines the possible linkage between UI

financing problems of the early 1980s and decreases in IUTU ratios.
39

42 Under a one sided test, the t ratio required for significance

at the 0.05 level is 1.71. 

Changes in state-level receipt of UI benefits

Analyses of IUTU ratios and AWKTU ratios by state were

undertaken for the period 1967 to 1994. Time series multiple

regressions were fitted to explain variation in IUTU ratios. The

explanatory variables were the same three as used in the regressions

of Table 2. The unemployment rate (TUR) and the TUR lagged were

entered to control for the change in the composition of unemployment

and associated UI claims at different stages of the business cycle

and for the effects of benefit exhaustions respectively. Also,

included was a dummy variable to test for a downward shift during the

1981-1994 period relative to 1967-1980. Coefficients were expected to

be positive for the TUR and negative for both the TUR lagged and the

D1981 dummy. 

Table 5 shows the results with coefficients, t ratios (in

parentheses), adjusted R2s and other summary measures. The states are

arranged into the nine Census divisions so that similarities by

geographic area can be noted. The equations generally conform to

expectations, but with rather poor fits in several states. 

Across the fifty-one states the TUR displays the expected

positive sign in 46 equations with 27 coefficients significant at the

0.05 level.42 Just one of the negative TUR coefficients is

significant. Forty-five of the lagged TUR coefficients have the

expected negative signs and thirty-one are significant. 

For most states the regressions give evidence of a downward

shift in the IUTU ratio with forty-six D1981 dummy coefficients

negative and twenty-eight significant. As in the aggregate regression

results of Table 2, state-level data provide strong support for the

hypothesis that relative to total unemployment UI claims were lower

during 1981-1994 than during 1967-1980.

The overall fits of many regressions are disappointing. In

nineteen states the adjusted R2 falls below 0.30. Also note that the

standard error exceeds 0.05 in twenty-five states. There is
40

43 BLS only started to publish annual CPS-based estimates of the

labor force, employment and unemployment for all states in 1976.

However, detail for ten large states and all nine Census divisions

extends back to 1967, and state level detail is available from 1970

for 27 states and from 1973 for 29 states. The author’s own estimates

of TU and the TUR back to 1967 were utilized where CPS data were

unavailable. The constructed state-level estimates were informed by

published divisional and national totals from the CPS. They also

utilized information on UI claims and Decennial Census information.

For every year the state estimates of TU, employment and the civilian

labor force summed to CPS national totals. 

44 The size of the negative D1981 coefficient was larger in a

negative direction for 38 of 51 states when the equations were fitted

through 1989 rather than through 1994. This finding is consistent

with results reported earlier in Table 2. The post-1981 downward

shift in the IUTU ratio is larger when data from 1990-1994 are not

included in the analysis.

substantial “noise” in these state-level relationships.43 

The size of the negative coefficients for the D1981 dummy

variables is one indicator of the extent of the downward shift in UI

claims. Eight coefficients are more negative than -0.100, six are

positive but just one of the positive coefficients (Wyoming) is

significant. The median of the fifty-one D1981 coefficients is -

0.0527. For most states, somewhat larger negative coefficients for

D1981 were obtained when the data period ended in 1989.44

Two important points emerge from these state-level regression

results. First, the preponderance of evidence is that IUTU ratios

shifted downward after 1981. Second, and more important for this

analysis, there was a broad distribution of estimated downward shifts

in state-level IUTU ratios. 

Table 6 displays the states arranged by the size of the downward

shift in IUTU. Two kinds of information are combined to characterize

the size of the downward shift by state. The three left-hand columns

show fourteen year averages (1967-1980 and 1981-1994) and the change.

Ten of these changes exceed -0.100, nine are positive and eight are

negative but fall between 0.000 and -0.020. The adjacent column in

Table 6 repeats the D1981 regression coefficients from Table 5.
41

45 The top fifteen are the group starting with New Hampshire and

ending with Massachusetts in Table 6. The intermediate states run

from Iowa to Vermont and the low-change states are from Hawaii to

Wyoming. The corresponding changes in the AWKTU ratios for the three

groups were -0.083, -0.020 and 0.018 respectively.

The next column then shows the average of two estimates of the

IUTU changes: 1) the change between 1967-1980 and 1981-1994 and 2)

the regression coefficient from Table 5. The table is sorted by this

third average. New Hampshire’s average is the most negative (-0.1687)

while Wyoming’s is the most positive (0.0809). Ten of these averages

exceed -0.100 while seven are positive.

The final column of Table 6 then shows the change between 1967-

1980 and 1981-1994 in the fraction of the unemployed who received

regular UI benefits (AWKTU). Generally, states with the largest

decreases in the IUTU ratio also had the largest decreases in the

AWKTU ratio. However, only three AWKTU changes exceeded -0.100 while

fourteen were positive. This repeats the earlier finding from Section

II that AWKTU ratios decreased less than IUTU ratios after 1980,

i.e., the actual receipt of regular UI benefits declined somewhat

less than UI claims activity. 

The changes in IUTU ratios shown in Table 6 were then used to

sort the states into three groups: the fifteen with the largest

decreases, twenty-one with intermediate sized decreases and the

fifteen with the smallest decreases. The simple averages of the

changes in the IUTU ratios between 1967-1980 and 1981-1994 for the

three groups of states were -0.111, -0.047 and 0.005.45 On average,

the decrease in UI claims represented more than 10 percent of

unemployment for the top group while the average change for the third

group was almost exactly zero. 

Monetary eligibility for UI benefits

State UI programs use both monetary and nonmonetary criteria in

making eligibility determinations. This section examines several

indicators of monetary eligibility criteria for evidence of change

during recent years. 
42

46 See Blank and Card (1991), op.cit., and Laurie Bassi and Amy

Chasanov, “Low Wage Workers and the Unemployment Insurance System,”

in The American Woman 1996-1997, (New York, NY: W.W. Norton, 1996),

forthcoming. 

47 The UI Service monthly publication Unemployment Insurance

Statistics was available until 1980.

It should be noted that two previous investigations of this

topic reached similar findings, namely monetary eligibility

requirements did increase somewhat, but the changes were modest.46

Blank and Card studied the periods 1977-1978 and 1986-1987 in a CPSbased analysis that compared state UI earnings requirements with

earnings as reported to interviewers. Quarterly earnings patterns had

to be simulated because quarterly information is not reported in the

March CPS annual income supplement. They also had to make inferences

as to the base periods for individual workers. Bassi and Chasanov

compared monetary eligibility in 1979 with 1990 using the Survey of

Income and Program Participation (SIPP). Unlike Blank and Card, their

SIPP-based analysis could use reported quarterly earnings, but

information on actual base periods was not available from SIPP. Both

analyses concluded there had been only modest increases in the

earnings requirements for monetary eligibility.

The present analysis traced selected indicators of monetary

eligibility by state from 1967 to 1994. Seven different indicators of

eligibility were examined: 1) the proportion of claimants deemed

monetarily eligible, 2) the base period earnings needed for

eligibility (measured two ways), 3) base period earnings needed for

26 weeks of eligibility at the maximum weekly benefit amount

(measured two ways), 4) indexation of the maximum weekly benefit and

5) the ratio of the maximum weekly benefit to the average weekly wage

(UI mean AWW) of the preceding year. Some discussion of these

requirements may be useful.

The proportion of claimants who are monetarily eligible have

been reported by state for more than thirty years. Since 1971,

quarterly data from the ETA-218 Report are available. Earlier data

are available from hard copy publications.47 
43

Base period eligibility in most states is determined from

covered earnings during the earliest four of the five past completed

quarters. Eligibility is typically calculated using a threshold

dollar amount for the four quarters of the base period and a second

threshold for the quarter of highest earnings. Other earnings

requirements are also present in several states. The measures

developed here use the minimum dollar amount for the base period but

expressed as a ratio to the average weekly wage.

Two measures of the average weekly wage (AWW) were used to

construct base period minimum earnings requirements. The AWW as

measured by the UI reporting system is a mean, and it is based on a

concept of full-person years of employment. The annual AWW for a

state is measured as total UI covered earnings for the year divided

by average employment in the twelve months, and that ratio is divided

by 52. It is appropriately viewed as an average for weeks employed,

and those with high wages exert a very large influence on this

average.

The second measure is the median AWW for persons as calculated

from earnings data reported in the CPS. This CPS-based measure is

less than the UI measure for two reasons. First, it is person-based

as opposed to a full year equivalent (52 weeks per employee)

employment measure. Second, it is a median as opposed to a mean so

that it reflects the weekly earnings of the typical worker. With the

growing disparity in earnings between high wage and low wage workers,

this median shows slower growth than the mean weekly earnings. 

Consequently, base period minimum earnings requirements using

the CPS median AWW implies more weeks of employment to achieve

eligibility than earnings measured by the mean AWW from the UI

reporting system. Across all 51 states, the simple average of the two

ratios for the 1967-1994 period was 3.43 weeks for the UI mean AWW
44

48 The CPS median AWW was created in two steps. First, annual

median/mean ratios based on state-level CPS data were taken from

regressions fitted over the 1964-1988 period. Second, ratios of CPS

means to UI means for the AWW were multiplied by regression-based

projections of median/mean ratios.

49 The maximum duration is 30 weeks in Massachusetts and

Washington. For these two states our measures calculated earnings

needed to be eligible for 26 weeks at the maximum weekly benefit.

but 4.73 weeks for the CPS median AWW.48

Each state limits the amount of benefits potentially collected

during a benefit year. The maximum potential benefit is the product

of the maximum weekly benefit amount (WBA) times twenty-six, the

maximum potential benefit duration in all but two states.49 In most

states eligibility for this maximum dollar amount requires a stated

amount of base period earnings. For example, in Minnesota during 1994

the maximum WBA was $305 and maximum potential benefits were $7930.

Minnesota limits potential entitlements to one third of base period

earnings. To be eligible for $7930 in 1994, a claimant needed base

period earnings of $23,790. Earnings requirements for maximum

potential entitlements were expressed in terms of weeks of employment

at the UI mean AWW and weeks at the CPS median AWW. For Minnesota in

1994 these two measures were respectively 47.21 weeks and 64.76

weeks. Since the base period can have only 52 weeks of employment, a

1994 claimant in Minnesota earning the statewide median could not

potentially collect 26 weeks of benefits at the maximum WBA.

Two other measures of monetary eligibility are the presence of

an indexed maximum WBA and the maximum WBA expressed as a percentage

of the average weekly wage over the past year. 

Note that of the seven monetary eligibility measures to be

reviewed, six (all but the proportion monetarily eligible) include

explicit statutory monetary eligibility provisions in their

construction. Thus if a state tried to restrict eligibility to

economize on UI benefit outlays, these measures should move in an

obvious direction. The requirements for minimum eligibility 

and maximum potential eligibility (both expressed as weeks of
45

employment required in the base period) should increase. The

prevalence indexed maximum WBAs and the maximum WBA expressed as a

ratio to the lagged AWW should decline. 

Table 7 summarizes these monetary eligibility provisions for the

single years 1967 through 1994 and averages for 1967-1994, 1967-1980

and 1981-1994. All entries are simple averages of state-level detail.

When the averages for the two fourteen-year subperiods are compared,

there are some obvious changes. While weeks of employment for minimum

monetary eligibility increases only slightly between 1967-1980 and

1981-1994, weeks for maximum eligibility increased more noticeably.

The increase was 4.66 weeks of employment (from 31.56 to 36.22) using

the UI mean AWW but 8.57 weeks (from 42.73 to 51.30) using the CPS

median AWW. 

The other three measures show increased monetary eligibility

during the 1981-1994 period. The increases in the proportion

monetarily eligible and the ratio of the maximum WBA to the lagged

AWW increased only modestly over the averages for 1967-1980. However,

indexation of the maximum WBA was noticeably more prevalent in the

latter period. In fact, thirty-four or more states were indexed in

every year between 1977 and 1994. Increases in the number of states

with indexed maximum WBAs occurred mainly between 1969 and 1977.

A few other patterns are apparent in these data. Decreases in

the proportion monetarily eligible follow recession years, e.g.,

1972, 1976, 1983-84, and 1992. The widening spread of the earnings

distribution has caused much of the increase in base period weeks of

employment needed for maximum potential benefits.

Overall, most monetary eligibility measures summarized in Table

7 suggest stability in requirements over the 1967-1994 period. The

average for minimum requirements increased slightly while the ratio

of the maximum WBA to the AWW and the proportion monetarily eligible

suggested small increases in accessibility. The largest change shown

in Table 7 is a measurable increase in weeks of work at the average

weekly wage needed to qualify for the maximum benefit entitlement.

This increase is especially noticeable when the CPS median is

utilized to measure the AWW. 
46

The stability of most monetary earnings requirements covered by

Table 7 (except the requirements for the maximum benefit entitlement)

contrasts with what would be expected under the cost shifting

hypothesis. On average, the requirements for monetary eligibility

were no more difficult to meet at the end of the data period than

they had been ten or twenty years previously. The next pages continue

this analysis by focusing more directly on groupings of states where

increases in eligibility requirements might be especially likely. 

 

Monetary eligibility and changes in IUTU ratios

Table 8 brings together summary information on changes IUTU

ratios with information on changes in monetary eligibility. The same

seven monetary eligibility measures from Table 7 are displayed

separately for the three groupings of states identified previously in

Table 6. If states were deliberately trying to reduce UI claims

loads, the fifteen with the largest decreases in IUTU ratios should

be particularly interesting. Recall that their average IUTU ratio

decreased by 0.111 between 1967-1980 and 1981-1994. Table 8 displays

averages for these same two periods as well as some individual year

data from 1981-1994. As in Table 7, all entries in Table 8 are simple

averages where each state-year observation is given equal weight. 

The top panel in Table 8 (the states with the largest decreases

in IUTU ratios) does not reveal dramatic changes. The proportion

monetarily eligible is essentially unchanged. Weeks for minimum

eligibility show nearly identical averages for the two fourteen-year

periods. Note that weeks for minimum eligibility are higher in 1994

than in 1981. The change occurred between 1981 and 1985 with little

change after 1985. 

Weeks for maximum eligibility do show an increase between 1967-

1980 and 1981-1994 and also within the 1981-1994 period. However, the

increases between 1981 and 1994 are only 10 percent using the UIbased mean AWW (increasing from 30.24 to 33.35) and 15 percent using

the CPS-based median AWW (increasing from 40.55 to 46.74)

respectively. Indexation was more prevalent among this group of

states during 1981-1994 compared to the earlier period and
47

practically unchanged during 1981-1994. Note that the ratio of the

maximum WBA to the lagged AWW was higher during 1981-1994 and that it

increased modestly during these years.

For this group of states the largest change in a restrictive

direction is the requirement for weeks of earnings needed for the

maximum potential entitlement. Weeks of employment needed for the

maximum entitlement increased using both measures of the AWW and the

increases occurred mainly between 1981 and 1989. When these changes

are compared with changes in the two other sets of states, however,

nothing unusual is present in the top panel of Table 8. The increase

in the weeks requirements between 1967-1980 and 1981-1994 were about

the same in the states where IUTU decreased the least (by 6.04 weeks

from 47.49 to 53.53 weeks using the CPS median AWW). This requirement

increased even more in the twenty-one states with intermediate

decreases in their IUTU ratios (by 12.10 weeks using the CPS median

AWW).

A series of multiple regressions was fitted to explicitly test

for post-1981 changes in monetary eligibility requirements. The

patterns of the post-1981 dummy variable regression coefficients

mirrored the patterns of the average eligibility requirements as

shown in Table 8, i.e., the largest changes occurred in the

requirements for maximum weeks of eligibility. The regression

coefficients for the states with the largest decreases in IUTU ratios

did not demonstrate above-average increases in requirements. In fact,

the states with the largest decreases in IUTU ratios had belowaverage increases in more than half of the regressions. These

findings were present using both unweighted data, i.e., each state

weighted equally as in Table 8, and in regressions where states were
48

50One consistent result was that for states with the smallest

decreases in IUTU ratios, the weighted results changed markedly when

California was excluded from the data. Because of its size and

because its requirements became easier to satisfy in more recent

years, the exclusion of California made the increases in the

requirements for this third group larger than when California was

included. 

51 The thirty-two included 30 states plus the District of

Columbia and the Virgin Islands. One analysis of the financing

problem is given in Chapters 1 and 2 of Vroman (1986), op.cit..

weighted by size.50 

For the UI eligibility requirements examined here, there is

nothing to suggest that above-average decreases in IUTU ratios

reflect state-level restrictions on monetary eligibility. For five of

the seven measures there was very little change between 1967-1980 and

1981-1994. The earnings requirements needed for twenty-six weeks of

benefits (measured in two different ways) did increase during these

years. Much of the increased difficulty in qualifying for the maximum

entitlement, however, is due to the increased dispersion in the

earnings distribution. Although this area of monetary eligibility

tightened measurably during 1981-1994, it happened for all three

groupings of states not just for the fifteen where the IUTU ratio

decreased the most. More important, the underlying cause for this

increase does not reflect restrictive UI legislative actions. 

UI financing problems and changes in IUTU ratios

Widespread problems of UI program financing were experienced in

the early 1980s and several states with depleted trust funds had to

borrow from the U.S. Treasury in order to make benefit payments.

Between 1980 and 1988 a total of 32 programs needed loans and

borrowing totaled $24.2 billion.51 Since an aspect of the hypothesis

under investigation is that decreases in IUTU ratios reflect

conscious actions to reduce UI eligibility (Cases 1 and 2 from

Section I), the pressures for restrictions were probably greatest in

states with the largest financing problems. 
49

52 The national average decreased from 0.386 to 0.335.

Information on UI claims and monetary eligibility has been

investigated with attention to state-level differences in funding

problems. The states were divided into three groups. The fifteen with

the most serious problems were identified on the basis of having the

highest borrowing-to-wages ratios for 1980-1988. Those with the least

serious funding problems include the thirteen that did not borrow at

all during the 1970s, 1980s or 1990s plus two that had the smallest

loans-to-wages ratios during the 1970s. Fifteen of remaining 21

borrowed lesser amounts during the 1980s while the other six borrowed

during the 1970s. 

Table 9 displays summary information for the three groups of

states with attention to two periods (1967-1980 and 1981-1994) and

the individual years 1981, 1985, 1989 and 1994. The table shows

averages for IUTU ratios and the seven monetary eligibility variables

from Table 8. The fifteen with the most serious financing problems

are of particular interest. They might be expected to show aboveaverage reductions in IUTU ratios and above-average increases in

monetary eligibility requirements.

The decrease in the average IUTU ratio for these fifteen states

almost exactly matches the national average decrease between 1967-

1980 and 1981-1994. Their decrease was 0.049 whereas the national

average decrease was 0.051.52 Note that IUTU ratios also decreased for

the other two groups of states although somewhat less for those with

the least serious financing problems (-.034) and more for the

intermediate group (-.064). Within the 1981-1994 period the IUTU

ratios follow a broadly similar pattern for the three groups of

states: declining between 1981 and 1985 and then recovering somewhat

after 1985. Only for the states with the least serious financing

problems does the IUTU ratio fail to recover noticeably after 1985.

When monetary eligibility criteria are examined, strong

parallels are found for two groups of states: those with the most
50

serious and those with the least serious financing problems. These

two groups of states experienced larger increases in eligibility

requirements than the group of 21 states with intermediate-sized

financing problems. Weeks of earnings required for minimum

eligibility and for 26 weeks of eligibility were higher during 1981-

1994 than during 1967-1980. Within the 1981-1994 period the

requirements for minimum eligibility increased between 1981 and 1985,

decreased between 1989 and 1994, but 1994 levels still exceeded 1981

levels. Earnings needed for 26 weeks of eligibility increased

throughout 1981-1994, and the changes were larger for these two

groupings than for the 21 states with intermediate-sized financing

problems. 

Several indicators in Table 9 suggest that benefits have been

consistently less accessible and less generous in the group of states

with the least serious financing problems. 1) The IUTU ratio is

consistently lowest for multi year periods. 2) The proportion

monetarily eligible is lowest. 3) Indexation of the maximum weekly

benefit is much less prevalent. 4) The maximum weekly benefit is the

lowest percentage of the average weekly wage. Thus there is a

suggestion that their avoidance of borrowing during the 1972-1994

period could be partially a consequence of deliberate decisions to

limit the availability and generosity of benefits. 

Conversely, the states with the largest financing problems have

above-average indicators for the proportion monetarily eligible, the

number with indexation (14 of 15 during most of 1981-1994) and the

level of the maximum WBA relative to the lagged average weekly wage.

Recall, however, that their IUTU ratio is somewhat below the national

average. Their financing problems of the early 1980s appear to be

associated with high UI payment levels but not with unusually large

caseloads relative to total unemployment. Finally, the presence of

financing problems in these states is not associated with unusually

large restrictions on monetary eligibility, at least not for the

eligibility indicators examined in Table 9. For most indicators the
51

53 This result held both when California was included and

excluded from the states with the least serious funding problems.

patterns of change were quite similar for the states with the most

serious and least serious financing problems.

Multiple regression analysis generally confirmed the patterns of

change as described in Table 9. However, one contrast between the

tabular display and the regressions merits notice. For the two

minimum eligibility requirements, the post-1981 increases were

consistently largest for the group of states with the most serious

financing problems when data were weighted by state size.53 Otherwise,

the regression coefficients for post-1981 changes showed that

requirements increased by about the same amount for the states with

the most serious and the least serious financing problems, more than

for the intermediate group. 

Under Cases 1 and 2 of the cost shifting hypothesis (deliberate

and inadvertent shifting respectively) the states with the largest UI

financing problems would be expected to make the largest restrictive

changes in UI eligibility and generosity. For the monetary

eligibility requirements examined here, only one set of patterns were

observed for these fifteen states that suggested unusually large

increases in requirements, i.e., minimum eligibility requirements in

regressions with weighted data. Finally, recall that these 15 states

did not exhibit above-average decreases in IUTU ratios. 

 

IV. Receipt of Welfare by State

If cost shifting has occurred, state-level data on the receipt

of welfare benefits could provide persuasive evidence. States where

IUTU ratios have decreased the most may have experienced aboveaverage increases in welfare caseloads. The Urban Institute’s TRIM2

model was utilized to examine caseloads and participation rates for
52

54 AFDC-Basic pays cash benefits mainly to families headed by

women and accounts for roughly 90 percent of AFDC caseloads. AFDC-UP

is available to two parent families where at least one adult

demonstrates recent labor market attachment.

55 See Linda Giannarelli, “An Analyst’s Guide to TRIM2: The

Transfer Income Model, Version 2,” (Washington, D.C.: The Urban

Institute, 1992) for a description of the capabilities of TRIM2,

details of the individual sections (modules) and model use.

three welfare programs: AFDC-Basic, AFDC-UP (unemployed parent)54 and

Food Stamps. Simulations of eligibility and receipt of welfare

benefits were conducted for the years 1979 to 1993. 

The TRIM2 model and state-level estimates

TRIM2 is a static microsimulation model developed at the Urban

Institute during the 1980s based on earlier modelling extending back

to the late 1960s.55 The model operates with micro data from the

Current Population Survey (CPS) to simulate details of individual

cash and in-kind transfer payment programs. Three modules used in

this report simulate eligibility for the AFDC-Basic, AFDC-UP and Food

Stamp programs. Eligibility is simulated by combining TRIM2's

detailed state-level rules on program eligibility with income and

family composition data as reported in the CPS. The ratio of actual

recipients to TRIM2 simulated eligibles yields estimated

participation rates. 

The AFDC-Basic, AFDC-UP and Food Stamps programs are

particularly relevant because a measurable minority of recipient

families has at least one member actively engaged in the labor

market. For the fifteen years 1979 to 1993 the average proportion of

households with at least one working member were as follows: AFDCBasic - 0.160, AFDC-UP - 0.381 and Food Stamps - 0.360. Thus an

unemployed worker ineligible for UI might apply for and receive

benefits from one or perhaps two of these welfare programs. The AFDCUP and Food Stamps programs would seem to be especially relevant

given the higher likelihood of labor market attachment among

recipient households. TRIM2 was examined to count eligible families,
53

56 The particular measures to be emphasized here are calendar

year monthly averages of families and persons and total annual dollar

amounts of benefits.

57 A specific example would be an unmarried adult daughter

living with parents and mother of a young child. Previously the women

and child were grouped with other family members and not recognized

as a sub-family.

persons and the dollar amounts of benefit entitlements.56

To undertake the TRIM2-based analysis, a number of problems had

to be addressed. Program data for certain years were not available.

Specifically, dollars of AFDC-UP benefit payments were not available

prior to 1985, and the number of Food Stamp households was not known

for 1979.

Since the CPS sample has 55,000 households nationwide, the

sample sizes by state were often quite small. Small samples produce

considerable statistical “noise” in state-level estimates. This noise

problem is most severe for the smallest states and for the AFDC-UP

program in all states. In 1993 when AFDC-UP was present in all 51

“states” (including the District of Columbia), the total number of

simulated eligible households was only 766 or an average of 15 per

state. To help address this noise problem, the analysis emphasizes

results based on three consecutive years of data. Even with

averaging, however, the state-level estimates of eligibility still

contain a sizeable random component.

A technical problem related to the processing of CPS data in the

early 1980s should also be noted. Starting in 1981, the coding of

families and households was improved to recognize sub-families

previously included within larger household units but not

differentiated.57 By 1982 these changes added about 1.0 million subfamilies, bringing the total to about 2.0 million, and roughly half

of the added units were eligible for AFDC-Basic benefits. The TRIM2

estimates of eligibility for 1979, 1980 and 1981 were adjusted to
54

58 The adjustments which increased the number of eligible units

by 14.1 percent in 1979 and 1980 and by 3.4 percent in 1981 were

based on a report by Patricia Ruggles and Richard Michel,

“Participation Rates in the Aid to Families with Dependent Children

Program: Trends for 1967 Through 1984,” The Urban Institute,(1987).

Their report discusses the change in the CPS data handling and

evaluates its effect on estimated eligibility and participation rates

in the AFDC-Basic program.

59 See Chapter I of LaDonna Pavetti, “The Dynamics of Welfare

and Work: Exploring the Process by Which Women Work their Way Off

Welfare,” Ph.D. Thesis, Kennedy School of Government, Harvard

University (1993).

60 The number of state-level programs increased from 28 to 51 in

October 1990. For an analysis of the effects on caseloads see Gregory

Acs and Linda Giannarelli, “An Evaluation of the Aid to Families with

Dependent Children Unemployed Parent (AFDC-UP) Program,” draft

report, (Washington, D.C.: The Urban Institute, October, 1995).

account for this change in CPS data handling.58

In considering the 1979-1993 time series for welfare programs,

two important legislative changes should also be noted. 1) After

Ronald Reagan’s election of 1980 there was national legislation that

restricted eligibility for both AFDC and Food Stamp benefits. The

changes became effective during 1982 but then were partially reversed

during the next two years. The number of recipients of AFDC-Basic and

Food Stamp benefits declined in 1982 even though unemployment was

higher than in 1981. These changes had the largest eligibilityreducing effects on AFDC families where one or more members worked.59

2) In October 1990 the AFDC-UP program became mandatory for all

states and caused an increase in AFDC-UP caseloads starting in 1991.

For states that introduced AFDC-UP in 1991, however, participation

rates have consistently been much lower than in states that

previously offered AFDC-UP.60

Welfare benefits in three programs

Tables 10, 11 and 12 summarize information on the receipt of

benefits from the AFDC, Food Stamps and Medicaid programs
55

61 Appendix A in an unpublished version of this report presents

summary detail for every state on total unemployment, insured

unemployment and welfare caseloads. Tables A1-A5 of the present

report shows three year averages of participation and benefit

payments for AFDC, Food Stamps and Medicaid.

respectively. Comparisons of beneficiaries, participation rates and

total benefits are made for three year periods: 1979-1981 to 1991-

1993 for AFDC and Food Stamps and 1984-1986 to 1991-1993 for

Medicaid. The tables summarize the experiences of eighteen big states

and three aggregations of states: by Census Division, size of

decrease in IUTU ratios and the size of UI debts. The latter are the

three-way groupings previously analyzed in Section III. The eighteen

states represented 70.9 percent of UI taxable covered employment in

1994. Their experiences dominate the nationwide U.S. totals for UI

and welfare programs.61

After examining data on AFDC-UP caseloads and participation

rates it was decided to exclude this component of AFDC from the

present analysis. The reason was that the underlying data have very

high levels of statistical “noise” due to small sample sizes.

Interested readers can examine Table A2 of Appendix A for summary

data on this element of the AFDC program. 

Each table demonstrates that growth in welfare caseloads and

expenditures varied widely over the period from 1979 to 1993. Table

10 shows that AFDC-Basic caseloads and participation rates increased

the most in the West South Central and Mountain divisions while

little change occurred in the four divisions of the North East and

Midwest. Thus while the national participation rate for AFDC-Basic

increased only modestly between 1979-1981 and 1991-1993 (from 0.806

to 0.834 or by 3.5 percent) the average participation rate increased

by 20.2 percent in the West South Central states (from 0.635 to

0.763) and by 30.2 percent in Mountain states (from 0.719 to 0.936).

Above-average population growth and increased participation rates

both contributed to the growth in caseloads and total benefit

payments in these areas. 

AFDC-Basic caseload growth over this period can be summarized as
56

62 Recall that Table 6 displays states arranged according to the

size of the decrease in IUTU between 1967-1980 and 1981-1994. For the

top fifteen states the average decrease was -0.111 compared to -0.047

for the middle 21 and +0.005 for the bottom fifteen. For these three

groupings the decreases in the simple averages of IUTU from 1979-1981

to 1991-1993 were -0.071, -0.036 and -0.020 respectively, i.e., the

same rankings. 

a process whereby states and regions with the lowest participation

rates experienced the largest proportional increases. Six of the

eighteen states had participation rates below 0.700 in 1979-1981, but

that only one (Indiana) had a similarly low participation rate during

1991-1993.

A major reason for examining state-level data was to observe

possible differences across states ranked by the size of the decrease

in the IUTU ratio. Note in Table 10 that all of the AFDC-Basic

caseload growth has occurred in states other than the fifteen where

IUTU decreased the most.62 When participation rates are examined,

states in all three groupings show modest increases. 

For all six states from the top group note that participation

rates in AFDC-Basic were quite stable over the period, decreasing for

four and increasing for two. The largest change was the 7.4 percent

reduction in New Jersey.

Perhaps the most dramatic finding is the comparative increases

in total AFDC benefit payments for the three groups of states. Those

where IUTU decreased the most had total benefits increases of 43.4

percent compared to 89.4 percent for the middle twenty-one and 139.7

percent for the bottom fifteen. On average, the states where AFDC

costs have increased the most have been those where IUTU decreased

the least. 

The bottom rows of Table 10 summarize experiences for states

grouped by the size of UI debts from the 1980s. Here the increases in

AFDC participation were largest in states where UI debt problems were

the least serious. Their average participation rates in AFDC-Basic

increased by 10.6 percent whereas the changes for the other two

groups were both close to zero. Average participation rates for all
57

three groups are quite similar in 1991-1993. Note also that the

percentage change in total AFDC benefit payments was largest for the

states with the least serious UI debt problems and smallest in states

with the most serious UI debt problems. The increases were 153.0 and

43.0 percent for the two groups respectively. 

From this examination of AFDC it appears the explanation for

growth in caseloads and costs is related to a “catching up” phenomena

whereby participation has increased most in states and areas where

participation was the lowest during 1979-1981. When participation

rates for individual states and groups of states are examined, they

are much more similar in 1991-1993 than they were during 1979-1981.

Table 11 summarizes experiences with the Food Stamps program.

Nationwide, the participation rate increased from 0.537 in 1979-1981

to 0.607 in 1991-1993. In this program three Census Divisions were

characterized by low participation during 1979-1981: the West North

Central, West South Central and Mountain divisions. Respectively

their average participation rates were 0.358, 0.458 and 0.403 in

these years. Note how participation rates for these three divisions

increased the most so that by 1991-1993 all stood within 0.038 of the

national average. 

When states are ranked by the size of the decline in IUTU all

three groups show increases in participation, but the largest

increases are for the “Middle 21" grouping (20.8 percent). The top

and bottom grouping of 15 experienced similar average percentage

increases in Food Stamps participation. 

State-level detail shows that Food Stamps participation rates

were relatively stable in the “Top 15" group with three of the

group’s six largest states having lower participation rates in 1991-

1993 than in 1979-1981. Participation rates increased the most in

Washington, Virginia and Texas, three states where participation in

AFDC-Basic (Table 10) also increased sharply.

The aggregate budget implication of growth in Food Stamps

caseloads per se is not an issue because benefits are fully federally

financed. Note that the growth in Food Stamps caseloads and in total

benefits were largest in the fifteen states where IUTU decreased the
58

least (52.4 percent and 183.1 percent respectively) and smallest in

the fifteen where IUTU decreased the most (14.1 percent and 122.6

percent respectively).

The bottom of Table 11 summarizes the experiences of states

grouped by the size of UI debts from the early 1980s. Here the

obvious pattern is the similarity of the “Top 15" and the “Bottom 15"

in caseload growth, growth in participation rates and growth in total

benefits. The middle group experienced smaller increases across all

three welfare indicators. Earlier experiences with UI debts from the

early 1980s have no obvious link to growth in Food Stamps utilization

for the time period covered by Table 11.

The Medicaid experiences of the states as summarized in Table 12

parallel earlier findings from Tables 10 and 11. The largest growth

in caseloads occurred in the South Atlantic, East South Central, West

South Central and Mountain divisions. For all states and groupings of

states, however, Medicaid growth has had major fiscal implications.

National average growth in total benefits was 173.9 percent between

1984-1986 and 1991-1993. For the eighteen individual states, growth

in benefit payouts ranged from a low of 90.0 percent (Minnesota) to a

high of 335.7 percent (Florida). The absolute levels of the outlays

and the attendant state shares are very large.

When the growth in caseloads and benefits are examined for

states according to the decrease in IUTU, the growth was somewhat

lower in states where IUTU decreased the most. However, the three

averages for total benefit increases are not that different: 165.4

percent, 181.9 percent and 180.8 percent for the Top 15, Middle 21

and Bottom 15 respectively. Except for New Jersey, all states where

growth exceeded 200.0 percent were in the middle and bottom groups of

decreased IUTU ratios.

The bottom three rows of Table 12 summarize experiences of the

states with UI indebtedness of differing severity. As with AFDC, the

largest growth in Medicaid costs occurred in states with the least

serious UI debt problems and the smallest growth was among states

with the largest UI debt problems. Again, however, the differences in

Table 12 are comparatively modest ranging from 164.2 percent to 180.6
59

63 This is a calculation by the author using the averages in

Table 12 ($39.996 billion and $199.468 billion) assuming a state

share of 44 percent for both periods. 

percent. All three groups witnessed major increases in Medicaid

costs. 

While the data in Table 12 show growth in total Medicaid costs

(as was the data on AFDC in Table 10), there clearly are major fiscal

implications for the states for their shares of Medicaid costs.

Between 1979-1981 and 1991-1993 the state share of Medicaid grew from

$17.6 billion to $48.2 billion, or by $30.6 billion.63 Compared to the

$1.7 billion of state savings on UI costs nationwide caused by the

reduction in the IUTU ratio (as estimated earlier, page 29), the

contrast in orders of magnitude is sobering. It is difficult to argue

that inducing UI claimants onto welfare roles would save money for a

state once Medicaid costs are factored into the calculation.

Summary

The preceding analysis of welfare caseloads and benefit outlays

emphasized state-level experiences. Data for individual states and

groups of states show a wide diversity of experiences. However, no

association was found between decreased availability of UI benefits

(as signaled by a reduction in the IUTU ratio) and increased

utilization of welfare. In fact, welfare caseloads and participation

rates have been most stable in states where IUTU ratios have

decreased the most. Welfare participation rates have changed with

bigger increases observed for Food Stamps than for AFDC. However, a

major factor driving growth in welfare caseloads appeared to be a

leveling-up of participation rates among those eligible for welfare,

i.e., the largest increases occurred in states where participation

rates were the lowest in 1979-1981.

Two factors appear to lie behind growth in welfare caseloads in

states where growth has been most rapid: 1) population growth and 2)

increased participation rates among eligibles. States like Florida,

Texas, Virginia and Washington experienced especially rapid increases

in caseloads. 
60

Of the welfare programs examined here, the budget implications

of Medicaid growth are of much greater importance to states than

growth in AFDC. All states have come under fiscal pressures due to

growth in Medicaid costs. Nationwide, the growth in state-financed

Medicaid costs exceeded $30 billion between 1979-1981 and 1991-1993.

For the group of 18 large states examined here, total Medicaid costs

grew by at least 90 percent over this period and for eight states the

growth exceeded 200 percent. Shifting UI claimants to welfare when

Medicaid is an element of welfare costs is not rational. Because the

cost shifting hypothesis has not explicitly considered Medicaid, it

has omitted the most important element of state welfare costs.

V. Summary and Conclusions

Because the report is rather long it will be useful to review

the findings and conclusions from the individual sections. Before

descending to the details, however, the principal finding of the

report should be stated. The cost shifting hypothesis that motivated

this study maintains that a part of UI costs has been shifted to

welfare programs through reduced availability of UI benefits. The

driving force behind the shifting could be either state actions

(deliberate or inadvertent) or evolutionary changes affecting UI and

welfare caseloads in opposite directions. 

This report conducted three analyses of the cost shifting

hypothesis and found that the hypothesis is not supported. 1) From

the standpoint of state government fiscal calculus the hypothesis is

incomplete. Shifting potential UI claimants to Food Stamps would

clearly save a state money since Food Stamps are fully federally

financed. However, welfare recipients often receive benefits from

three programs: AFDC and Medicaid as well as Food Stamps. AFDC and

Medicaid are partly state financed. The growth in the state-level

Medicaid costs dominates all of the other (UI and welfare) program

costs under consideration in the report. Because Medicaid costs have

been so large and growing so rapidly, it does not seem rational for a
61

state to move UI claimants onto welfare.

2) The main empirical evidence supporting the cost shifting

hypothesis is work undertaken by the staff of the recent Advisory

Council on Unemployment Compensation. This analysis concluded that 64

percent of the decline in UI claims activity between 1971 and 1993

can be explained by growth in welfare. The principal empirical

variable used in a pooled regression analysis was annual per capita

Food Stamp expenditures. Chapter I reviewed this study and made

criticisms of the logic and the specification of the analysis. At a

minimum, this evidence adduced to support the cost shifting

hypothesis is not persuasive. 

3) New analysis of state-level data on reductions in UI claims

and increases in the utilization of welfare did not support the cost

shifting hypothesis. The states where UI claims decreased the most

did not exhibit above-average increases in utilization of welfare. 

The state-level analysis was based partly on the Urban

Institute’s TRIM2 microsimulation model and covered the years 1979 to

1993. Welfare recipiency and benefit payments were examined for the

three programs: AFDC, Food Stamps and Medicaid. The most rapid growth

in welfare caseloads was observed in states and regions where

participation rates had been lowest during 1979-1981 and where

population growth was the most rapid. In many specific instances,

rapid growth in welfare caseloads occurred in states in the South and

West, states where the IUTU ratio (a principal indicator of UI

claims) declined less than or about the same as the national average

decline.

To this author these three criticisms of the cost shifting

hypothesis are persuasive. It seems more likely that the states have

not shifted potential UI claimants onto welfare. Other readers may

draw a more agnostic conclusion. This could provide a reason for

undertaking more research. The place to start any additional work is

with an explicit formulation of the cost shifting hypothesis that has

testable implications.

The report had other findings that should be noted. 1) A recent

Canadian empirical study on the unemployment-UI-welfare interrelation
62

(summarized in Section I) tracked UI claimants longitudinally. It

documented the size of the UI-welfare interface for job leavers

during a period when access to UI was restricted. After UI

eligibility was restricted, the proportion of job leavers who

received welfare did increase, but the increase was rather modest.

While the Canadian study provides important evidence, the federalprovincial fiscal relationship and related financial incentives are

different from those in the U.S., e.g., UI is federally financed in

Canada. This study’s relevance lies mainly in its methodology, i.e.,

the longitudinal tracking of the unemployed, rather than

demonstrating the effects of intergovernmental fiscal incentives. 

2) Section II documented the time periods when decreases in UI

claims activity and increases in welfare caseloads occurred during

the past forty years. UI claims (represented by IUTU ratios) declined

most during two periods: the decade of the 1960s and the early 1980s.

The declines were larger during the 1960s. Growth in caseloads and

total benefit payments were also traced for AFDC, Food Stamps and

Medicaid. Growth in welfare program caseloads was not unusually rapid

in periods when IUTU declined the most. Caseloads for all three

welfare programs grew rapidly after 1989, but this was a period when

UI caseloads were, if anything, higher (not lower) than expected

based on caseloads from earlier in the 1980s. 

3) A regression analysis was conducted in Section II to examine

decreases in UI claims. The estimated size of the reduction was found

to be sensitive to the estimation period, inclusion of state-level

weights as controls and the choice of the dependent variable.

Comparing 1981-1994 with the earlier 1967-1980 period, receipt of UI

benefits was estimated to be 8.3-8.7 percent lower during 1981-1994.

4) Section III used descriptive data and regressions to

characterize the size of the decrease in UI claims for each state. A

wide range of state-level decreases was identified. For the fifteen

states with the largest decreases, the IUTU ratio declined by an

average of 0.111. For the fifteen with the smallest decreases, the

change in IUTU averaged almost exactly zero. Section III also

examined whether UI monetary eligibility requirements had increased
63

more in states with the largest decreases in UI claims and/or in

states which experienced the largest UI financing problems during the

early 1980s. Monetary eligibility requirements did not exhibit

unusually large increases in either grouping of states. 

5) A state-level analysis of AFDC, Food Stamps and Medicaid

recipiency was undertaken for the period 1979 to 1993. Detailed

results of this analysis are presented in Section IV and in Appendix

A. For all three welfare programs, similar findings were observed on

the relation between changes in the receipt of UI benefits and the

receipt of welfare. The group of 15 states where UI recipiency

declined the most (as reflected in IUTU ratios) had the smallest

increases in welfare caseloads and associated costs. In contrast, the

fifteen states where IUTU ratios decreased the least had the largest

increases in welfare caseloads. Details for individual states were

displayed in Tables 10, 11 and 12 and with supporting Tables A1-A5 in

Appendix A. An unpublished version of this report also presents a

state by state graphical analysis of unemployment and welfare

caseload growth between 1979 and 1993. 

 
Table 1. National Data on Unemployment and UI Claims, 1956-1994.

Year TUR TU-a IU AWK IUTU AWKTU AWKIU IUTU IUTU

5YrAvg 5YrChng

5YrAvg

1956 4.1 2750 1212 1022 0.441 0.372 0.843 0.499 0.029

1957 4.3 2859 1447 1235 0.506 0.432 0.853 0.491 0.000

1958 6.8 4602 2513 2226 0.546 0.484 0.886 0.492 -0.020

1959 5.5 3740 1665 1464 0.445 0.391 0.879 0.485 -0.033

1960 5.5 3852 1903 1647 0.494 0.428 0.866 0.486 -0.019

1961 6.7 4714 2271 1994 0.482 0.423 0.878 0.495 -0.004

1962 5.5 3911 1765 1516 0.451 0.388 0.859 0.484 -0.008

1963 5.7 4070 1772 1531 0.435 0.376 0.864 0.462 -0.031

1964 5.2 3786 1571 1362 0.415 0.360 0.867 0.455 -0.029

1965 4.5 3366 1293 1119 0.384 0.332 0.865 0.433 -0.053

1966 3.8 2875 1029 884 0.358 0.307 0.859 0.409 -0.086

1967 3.8 2975 1171 1004 0.394 0.337 0.857 0.397 -0.086

1968 3.6 2817 1079 922 0.383 0.327 0.854 0.387 -0.075

1969 3.5 2832 1065 905 0.376 0.320 0.850 0.379 -0.077

1970 4.9 4093 1762 1495 0.430 0.365 0.848 0.388 -0.045

1971 5.9 5016 2102 1806 0.419 0.360 0.859 0.400 -0.008

1972 5.6 4882 1800 1529 0.369 0.313 0.849 0.395 -0.002

1973 4.9 4365 1578 1337 0.362 0.306 0.847 0.391 0.004

1974 5.6 5156 2202 1843 0.427 0.357 0.837 0.401 0.022

1975 8.5 7929 3900 3325 0.492 0.419 0.853 0.414 0.025

1976 7.7 7406 2922 2411 0.395 0.326 0.825 0.409 0.008

1977 7.1 6991 2584 2141 0.370 0.306 0.829 0.409 0.013

1978 6.1 6202 2302 1905 0.371 0.307 0.828 0.411 0.020

1979 5.8 6137 2372 2004 0.387 0.327 0.845 0.403 0.001

1980 7.1 7637 3305 2831 0.433 0.371 0.857 0.391 -0.023

1981 7.6 8273 2989 2580 0.361 0.312 0.863 0.384 -0.024

1982 9.7 10678 3998 3533 0.374 0.331 0.884 0.385 -0.024

1983 9.6 10717 3347 2969 0.312 0.277 0.887 0.373 -0.037

1984 7.5 8539 2434 2118 0.285 0.248 0.870 0.353 -0.050

1985 7.2 8312 2561 2260 0.308 0.272 0.882 0.328 -0.063

1986 7.0 8237 2607 2307 0.316 0.280 0.885 0.319 -0.065

1987 6.2 7425 2265 1998 0.305 0.269 0.882 0.305 -0.080

1988 5.5 6701 2048 1787 0.306 0.267 0.873 0.304 -0.069

1989 5.3 6528 2118 1848 0.324 0.283 0.872 0.312 -0.041

1990 5.5 6918 2479 2197 0.358 0.318 0.887 0.322 -0.006

1991 6.7 8482 3291 2943 0.388 0.347 0.894 0.336 0.017

1992 7.4 9452 3190 2845 0.337 0.301 0.892 0.343 0.037

1993 6.8 8788 2640 2370 0.300 0.270 0.898 0.342 0.038

1994 6.1 7996 2608 2323 0.326 0.290 0.891 0.342 0.030

Avg.

1967-94 6.4 6839 2454 2126 0.365 0.315 0.864

1967-80 5.7 5317 2153 1818 0.400 0.339 0.846

1981-94 7.0 8360 2755 2434 0.329 0.290 0.883

Source: U.S. Department of Labor, Bureau of Labor Statistics and Unemployment Insurance Service.

 a - The 1990-1994 estimates of TU incorporate 1990 Census-based population controls.
Table 2. Regressions Explaining UI Claims and Beneficiary Rates, 1967-1994

Equation and Constant TUR TUR D1981 Sample Adj R2 Std Mean Elast

Dep Var Lagged Size Error DepVar D1981

Data Period 1967-1989

1. IUTU Macro 0.3812 0.0293 -0.0271 -0.0707 23 0.946 0.0119 0.3695 -0.191

(35.3) (11.8) (10.5) (11.4)

2. IUTU Macro, 0.3799 0.0269 -0.0254 -0.0558 23 0.890 0.0150 0.3695 -0.151

 Fixed Wt (28.0) (8.6) (7.8) (7.2)

3. IUTU State, 0.4040 0.0185 -0.0183 -0.0492 1173 0.899 0.1544 0.3695 -0.133

 Fixed Wt (40.2) (7.7) (7.5) (7.5)

4. AWKTU Macro 0.3273 0.0263 -0.0254 -0.0471 23 0.954 0.0087 0.3166 -0.149

(41.5) (14.5) (13.5) (10.5)

5. AWKTU Macro 0.3252 0.0242 -0.0240 -0.0308 23 0.891 0.0115 0.3156 -0.098

 Fixed Wt (31.3) (10.1) (9.7) (5.2)

6. AWKTU State, 0.3452 0.0176 -0.0173 -0.0311 1173 0.878 0.1504 0.3156 -0.099

 Fixed Wt (35.3) (7.5) (7.3) (4.9)

7. AWKIU Macro 0.8586 0.0030 -0.0055 0.0384 23 0.769 0.0091 0.8581 0.045

(104.1) (1.6) (2.8) (8.1)

8. AWKIU State 0.8253 0.0065 -0.0034 0.0339 1173 0.992 0.0906 0.8459 0.040

 Fixed Wt (139.8) (4.6) (2.4) (8.8)

Data Period 1967-1994

9. IUTU Macro 0.3917 0.0294 -0.0291 -0.0630 28 0.920 0.0139 0.3646 -0.173

(32.8) (10.6) (10.5) (10.4)

10. IUTU Macro, 0.3896 0.0271 -0.0273 -0.0495 28 0.867 0.0158 0.3656 -0.135

 Fixed Wt (28.6) (8.5) (8.6) (7.2)

11. IUTU State, 0.4096 0.0199 -0.0207 -0.0466 1428 0.905 0.1483 0.3656 -0.127

 Fixed Wt (44.3) (9.2) (9.6) (8.5)

12. AWKTU Macro 0.3374 0.0264 -0.0273 -0.0388 28 0.915 0.0112 0.3145 -0.123

(34.9) (11.7) (12.1) (8.0)

13. AWKTU Macro, 0.3334 0.0242 -0.0256 -0.0261 28 0.857 0.0128 0.3137 -0.083

 Fixed Wt (30.3) (9.5) (10.0) (4.7)

14. AWKTU State, 0.3488 0.0194 -0.0198 -0.0274 1428 0.884 0.1456 0.3137 -0.087

 Fixed Wt (38.4) (9.1) (9.3) (5.1)

15. AWKIU Macro 0.8607 0.0028 -0.0056 0.0427 28 0.812 0.0094 0.8642 0.049

(106.0) (1.5) (3.0) (10.4)

16. AWKIU State 0.8221 0.0078 -0.0041 0.0374 1428 0.991 0.0950 0.8503 0.044

 Fixed Wt (138.7) (5.6) (3.0) (10.7)

Source: Regressions based on annual (national and state-level) data. Beneath each coefficient

is the absolute value of its t ratio. Weights are state shares of covered employment from

1967 to 1989.
Table 3. National Data on Expenditures and Recipients of AFDC, Food Stamps and Medicaid, 1950-1994.

Aggregate Benefits: Number of Recipients: Per Capita Benefits: Real Benefits

State AFDC Food Medic- AFDC AFDC FS MAid AFDC FS MAid per Recipient

Year UI Stamps aid Fam. Pers. Pers. Pers. AFDC FS MAid

($billions) ($millions) ($dollars) (1982-1984 dollars)

1950 1.4 0.6 NA NA 0.6 2.2 NA NA 3.9 NA NA 1129 NA NA

1951 0.8 0.6 NA NA 0.6 2.1 NA NA 3.9 NA NA 1080 NA NA

1952 1.0 0.5 NA 0.1 0.6 2.0 NA NA 3.2 NA 0.6 936 NA NA

1953 1.0 0.5 NA 0.1 0.6 2.0 NA NA 3.1 NA 0.6 953 NA NA

1954 2.0 0.6 NA 0.2 0.6 2.1 NA NA 3.7 NA 1.2 1084 NA NA

1955 1.4 0.6 NA 0.2 0.6 2.2 NA NA 3.6 NA 1.2 1011 NA NA

1956 1.4 0.6 NA 0.2 0.6 2.2 NA NA 3.6 NA 1.2 989 NA NA

1957 1.7 0.7 NA 0.2 0.6 2.4 NA NA 4.1 NA 1.2 1045 NA NA

1958 3.5 0.8 NA 0.3 0.7 2.5 NA NA 4.6 NA 1.7 1111 NA NA

1959 2.3 0.9 NA 0.5 0.8 2.7 NA NA 5.1 NA 2.8 1139 NA NA

1960 2.7 1.0 NA 0.5 0.8 3.0 NA NA 5.5 NA 2.8 1124 NA NA

1961 3.4 1.1 NA 0.7 0.9 3.4 NA NA 6.0 NA 3.8 1097 NA NA

1962 2.7 1.3 0.0 0.9 0.9 3.7 0.2 NA 7.0 0.1 4.8 1171 285 NA

1963 2.8 1.4 0.0 1.1 0.9 3.9 0.3 NA 7.4 0.1 5.8 1180 261 NA

1964 2.5 1.5 0.0 1.3 1.0 4.1 0.4 NA 7.8 0.2 6.8 1175 249 NA

1965 2.1 1.7 0.0 1.5 1.0 4.3 0.6 NA 8.7 0.3 7.7 1247 240 NA

1966 1.8 1.9 0.1 2.0 1.1 4.5 1.2 NA 9.7 0.5 10.2 1299 267 NA

1967 2.1 2.3 0.1 2.7 1.2 5.0 1.8 NA 11.6 0.5 13.6 1373 164 NA

1968 2.0 2.8 0.2 4.0 1.4 5.7 2.5 NA 14.0 1.0 19.9 1410 226 NA

1969 2.1 3.5 0.3 4.5 1.7 6.7 3.6 NA 17.3 1.5 22.2 1422 227 NA

1970 3.8 4.8 1.1 5.4 2.2 8.5 6.9 NA 23.4 5.4 26.3 1461 414 NA

1971 5.2 6.2 1.7 6.7 2.8 10.2 10.2 NA 29.9 8.2 32.3 1495 410 NA

1972 4.7 6.9 2.0 8.2 3.0 10.9 11.6 18.6 32.9 9.5 39.1 1508 411 1054

1973 4.0 7.2 2.2 9.6 3.1 10.9 12.5 20.5 34.0 10.4 45.3 1481 395 1053

1974 6.2 7.9 3.4 11.3 3.2 10.9 15.0 21.7 36.9 15.9 52.8 1475 460 1055

1975 12.9 9.2 4.6 14.0 3.5 11.3 17.8 22.4 42.6 21.3 64.8 1507 480 1161

1976 10.0 10.1 4.6 15.6 3.6 11.3 17.8 22.8 46.3 21.1 71.5 1570 454 1201

1977 9.1 10.6 4.4 17.1 3.6 11.1 16.8 22.6 48.1 20.0 77.6 1583 432 1248

1978 8.0 10.7 4.6 19.0 3.5 10.6 16.5 21.9 48.1 20.7 85.4 1553 429 1333

1979 8.6 11.0 6.3 21.5 3.5 10.3 18.6 21.5 48.9 28.0 95.5 1469 468 1375

1980 14.5 12.4 8.2 24.6 3.7 10.8 21.4 21.7 54.5 36.0 108.0 1397 465 1376

1981 13.8 13.0 10.1 28.8 3.8 11.1 22.3 21.9 56.5 43.9 125.2 1291 499 1448

1982 21.8 13.3 9.9 31.7 3.5 10.3 21.7 21.6 57.3 42.6 136.5 1344 473 1521

1983 18.6 14.2 11.1 35.5 3.7 10.8 21.4 21.6 60.6 47.4 151.5 1325 520 1653

1984 12.5 14.8 10.7 38.3 3.7 10.8 20.6 21.7 62.6 45.3 162.0 1315 499 1702

1985 14.0 15.4 10.7 41.8 3.7 10.9 19.8 22.0 64.6 44.9 175.3 1318 503 1767

1986 15.4 16.4 10.6 46.3 3.8 11.0 19.3 22.7 68.1 44.0 192.4 1356 500 1864

1987 13.6 16.7 10.6 50.7 3.8 11.0 19.0 23.1 68.8 43.7 208.8 1333 491 1936

1988 12.5 17.3 11.2 55.9 3.7 10.9 18.7 23.1 70.6 45.7 228.1 1340 507 2049

1989 13.5 18.0 12.3 63.8 3.8 11.0 19.1 23.4 72.8 49.7 257.9 1320 520 2194

1990 17.2 19.8 14.7 76.7 4.1 11.7 20.7 24.5 79.2 58.8 306.9 1295 544 2394

1991 24.5 22.0 18.2 100.1 4.5 12.9 23.3 28.9 87.1 72.0 396.3 1249 573 2540

1992 23.8 23.3 21.2 119.7 4.8 13.8 25.8 31.6 91.2 83.0 468.7 1206 586 2704

1993 20.5 23.9 22.2 132.5 5.0 14.2 27.1 33.8 92.6 86.0 513.3 1164 567 2710

1994 20.3 24.2 22.8 143.5 5.0 14.2 27.5 35.1 92.8 87.5 550.5 1153 560 2762

Averages for 1981-1994

Total 17.3 18.0 14.0 69.0 4.1 11.8 21.9 25.3 73.2 56.8 276.7 1286 524 2089

State- 17.3 7.9 0.0 31.0

Financed

Source: Data on UI benefits from U.S. Department of Labor, Unemployment Insurance Financial Data. Estimates include state share of Extended

Benefits. National expenditures on welfare programs from the National Income Accounts, Table 3.12. Small amounts of other medical assistance

included in the Medicaid category. Counts of AFDC, Food Stamps and Medicaid recipients are monthly averages from program data taken from the

Social Security Bulletin, Annual Statistical Supplement, various issues. Population data for per capita expenditure estimates taken from the 1995

Economic Report of the President, Table B-33. Real per recipient estimates derived using the all-items CPI, 1982-1984 = 1.000.
Table 4. National Data on Poverty and the Receipt of AFDC and Food Stamps, 1956-1994.

Year Pov- Pov- AFDC FS AFDC FS AFDC FS AFDC FS

erty erty Recip- Recip- Recip/ Recip/ Recip/ Recip/ Recip/ Recip/

Rate Pop. ients ients Pov. Pov. Pov. Pov. Pov. Pov.

(Pct.) (Mill.) (Mill.) (Mill.) Pop. Pop. Pop. Pop. Pop. Pop.

5YrAv 5YrAv 5YrCh 5YrCh

5YrAv 5YrAv

1956 NA NA 2.2 NA

1957 NA NA 2.4 NA

1958 NA NA 2.5 NA

1959 22.4 39.5 2.7 NA 0.069 0.000

1960 22.2 39.9 3.0 NA 0.075 0.000

1961 21.9 39.6 3.4 NA 0.085 0.000

1962 21.0 38.6 3.7 0.2 0.095 0.005

1963 19.5 36.4 3.9 0.3 0.106 0.008 0.086 0.002

1964 19.0 36.1 4.1 0.4 0.114 0.011 0.095 0.004

1965 17.3 33.2 4.3 0.6 0.130 0.019 0.106 0.008

1966 14.7 28.5 4.5 1.2 0.158 0.041 0.120 0.016

1967 14.2 27.8 5.0 1.8 0.181 0.066 0.137 0.028

1968 12.8 25.4 5.7 2.5 0.225 0.100 0.161 0.047 0.076 0.045

1969 12.1 24.1 6.7 3.6 0.278 0.149 0.194 0.075 0.099 0.070

1970 12.6 25.4 8.5 6.9 0.333 0.270 0.234 0.125 0.129 0.116

1971 12.5 25.6 10.2 10.2 0.401 0.400 0.283 0.197 0.162 0.180

1972 11.9 24.5 10.9 11.6 0.448 0.476 0.336 0.279 0.199 0.250

1973 11.1 23.0 10.9 12.5 0.477 0.546 0.387 0.368 0.225 0.321

1974 11.2 23.4 10.9 15.0 0.465 0.641 0.424 0.466 0.230 0.392

1975 12.3 25.9 11.3 17.8 0.438 0.688 0.445 0.550 0.211 0.425

1976 11.8 25.0 11.3 17.8 0.453 0.713 0.456 0.612 0.173 0.416

1977 11.6 24.7 11.1 16.8 0.447 0.680 0.455 0.653 0.119 0.374

1978 11.4 24.5 10.6 16.5 0.431 0.672 0.446 0.678 0.060 0.310

1979 11.7 26.1 10.3 18.6 0.396 0.712 0.433 0.692 0.008 0.226

1980 13.0 29.3 10.8 21.4 0.368 0.732 0.418 0.701 -0.027 0.151

1981 14.0 31.8 11.1 22.3 0.348 0.699 0.398 0.698 -0.058 0.086

1982 15.0 34.4 10.3 21.7 0.298 0.631 0.368 0.689 -0.088 0.035

1983 15.2 35.3 10.8 21.4 0.305 0.607 0.342 0.676 -0.104 -0.003

1984 14.4 33.7 10.8 20.6 0.321 0.612 0.328 0.656 -0.105 -0.037

1985 14.0 33.1 10.9 19.8 0.328 0.599 0.320 0.629 -0.099 -0.072

1986 13.6 32.4 11.0 19.3 0.341 0.598 0.318 0.609 -0.079 -0.090

1987 13.4 32.2 11.0 19.0 0.342 0.590 0.327 0.600 -0.041 -0.088

1988 13.0 31.7 10.9 18.7 0.344 0.588 0.335 0.597 -0.008 -0.079

1989 12.8 31.5 11.0 19.1 0.349 0.605 0.340 0.595 0.013 -0.060

1990 13.5 33.6 11.7 20.7 0.348 0.616 0.344 0.599 0.025 -0.030

1991 14.2 35.7 12.9 23.3 0.362 0.653 0.349 0.610 0.030 0.001

1992 14.8 38.0 13.8 25.8 0.362 0.679 0.353 0.628 0.025 0.027

1993 15.1 39.3 14.2 27.1 0.362 0.690 0.356 0.648 0.021 0.051

1994 14.5 38.1 14.2 27.5 0.372 0.722 0.361 0.671 0.021 0.076

Source: Poverty estimates based on the Current Population Survey. Counts of Food Stamps and AFDC recipients are

monthly averages from program data as reported in the Social Security Bulletin, Annual Statistical Supplement, various

issues.
 Table 5. Time Series Regressions Explaining IUTU Ratios, 1967 to 1994

State Constant TUR TURLag D1981 AdjR2 S.E. D.W. Mean Elast81

Connecticut .576(11.5) .0188(1.7) -.0288(2.7) -.1103(3.9) 0.433 0.073 0.97 0.4673 -0.236

Maine .621(15.6) .0043(0.4) -.0250(2.7) -.0642(3.0) 0.477 0.057 1.62 0.4625 -0.139

Massachusetts .814(30.5) -.0021(0.4) -.0406(7.0) -.0894(5.5) 0.848 0.042 1.30 0.5189 -0.172

New Hampshire .463(9.3) .0304(1.9) -.0414(2.6) -.1572(3.9) 0.503 0.098 1.70 0.3409 -0.461

Rhode Island .912(25.2) -.0245(3.6) -.0174(2.6) -.0995(4.3) 0.757 0.060 2.03 0.5990 -0.166

Vermont .513(15.9) .0177(2.1) -.0215(2.7) -.0158(0.9) 0.178 0.045 2.28 0.4860 -0.033

New Jersey .740(31.7) .0012(0.2) -.0310(5.6) -.1120(8.6) 0.858 0.034 1.93 0.4987 -0.225

New York .694(35.1) .0008(0.2) -.0300(6.4) -.0991(9.6) 0.900 0.027 2.09 0.4539 -0.218

Pennsylvania .495(16.6) .0294(3.7) -.0263(3.3) -.0803(4.1) 0.572 0.046 0.86 0.4786 -0.168

Illinois .346(8.2) .0298(2.9) -.0134(1.2) -.1502(4.2) 0.519 0.062 1.17 0.3792 -0.396

Indiana .254(15.1) .0243(5.5) -.0184(4.5) -.0579(4.1) 0.700 0.031 1.81 0.2676 -0.216

Michigan .460(17.9) .0186(4.2) -.0216(4.6) -.1078(5.7) 0.782 0.042 1.74 0.3828 -0.282

Ohio .279(10.1) .0316(5.2) -.0254(4.1) -.0211(1.0) 0.495 0.044 0.87 0.3122 -0.068

Wisconsin .448(11.5) .0257(2.5) -.0233(2.1) -.0600(2.1) 0.290 0.063 1.24 0.4323 -0.139

Iowa .387(20.1) .0181(2.1) -.0170(1.8) -.0781(3.7) 0.575 0.037 1.40 0.3545 -0.220

Kansas .350(7.0) .0386(2.6) -.0340(2.3) -.0107(0.4) 0.166 0.050 1.44 0.3668 -0.029

Minnesota .387(9.8) .0151(1.4) -.0079(0.7) -.0745(3.2) 0.309 0.050 1.51 0.3850 -0.194

Missouri .555(15.7) .0115(1.3) -.0278(2.8) -.1088(4.3) 0.743 0.048 2.45 0.4107 -0.265

Nebraska .312(9.7) -.0000(0.0) .0069(0.6) -.0041(0.2) -0.097 0.049 0.95 0.3338 -0.012

North Dakota .157(1.6) .0198(0.9) .0325(1.4) -.0659(2.0) 0.087 0.068 0.67 0.3621 -0.182

South Dakota .198(4.6) .0103(0.6) .0196(1.0) -.1280(5.1) 0.518 0.045 0.84 0.2443 -0.524

Delaware .385(11.3) .0037(0.4) -.0079(0.9) -.0277(1.5) 0.021 0.048 1.87 0.3461 -0.080

Dist. of Col. .386(8.2) .0067(0.6) -.0085(0.7) .0174(0.6) -0.091 0.071 1.52 0.3823 0.046

Florida .171(8.4) .0172(3.9) -.0047(1.1) -.0527(5.0) 0.576 0.027 1.33 0.2238 -0.235

Georgia .183(4.4) .0351(3.5) -.0191(1.9) -.0203(1.0) 0.276 0.051 2.19 0.2635 -0.077

Maryland .348(10.1) .0292(2.7) -.0286(2.6) -.0221(1.2) 0.227 0.046 1.48 0.3418 -0.065

North Carolina .272(7.7) .0345(4.5) -.0262(3.4) .0040(0.2) 0.401 0.049 1.34 0.3184 0.013

South Carolina .225(4.8) .0398(4.2) -.0260(2.7) -.0193(0.8) 0.349 0.060 1.82 0.3032 -0.064

Virginia .081(3.1) .0355(4.5) -.0094(1.2) -.0265(1.9) 0.483 0.033 1.74 0.1926 -0.138
West Virginia .351(6.2) .0198(2.3) -.0189(2.1) -.0862(2.0) 0.334 0.069 0.51 0.3177 -0.271

Alabama .335(11.8) .0089(1.4) -.0068(1.0) -.0987(4.1) 0.516 0.046 1.58 0.3016 -0.327

Kentucky .371(7.9) .0182(1.9) -0200.(1.9) -.0813(2.2) 0.418 0.060 0.90 0.3190 -0.255

Mississippi .235(6.9) .0280(3.8) -.0228(3.0) -.0156(0.5) 0.301 0.047 1.16 0.2676 -0.058

Tennessee .458(14.6) .0176(2.3) -.0264(3.3) -.0822(3.4) 0.614 0.051 1.31 0.3637 -0.226

Arkansas .313(5.1) .0222(2.2) -.0158(1.4) -.0279(1.0) 0.098 0.054 0.69 0.3445 -0.081

Louisiana .228(5.0) .0196(2.2) -.0090(1.0) -.0595(2.3) 0.160 0.049 1.08 0.2857 -0.208

Oklahoma .395(7.5) .0009(0.1) -.0175(1.7) -.0533(1.6) 0.356 0.059 1.33 0.2795 -0.191

Texas .120(4.3) .0217(3.5) -.0067(1.1) -.0161(0.9) 0.358 0.025 1.64 0.1981 -0.081

Arizona .304(8.1) .0144(2.2) -.0163(2.5) -.0300(1.6) 0.232 0.048 1.91 0.2775 -0.108

Colorado .111(3.3) .0175(2.3) .0069(0.8) .0155(0.9) 0.466 0.037 1.26 0.2477 0.063

Idaho .378(7.0) .0088(0.9) -.0078(0.7) .0220(0.9) -0.044 0.052 0.80 0.3958 0.056

Montana .350(4.5) -.0139(0.9) .0176(1.1) -.0424(1.5) 0.009 0.065 0.50 0.3522 -0.120

Nevada .527(10.7) .0069(0.8) -.0164(1.9) -.1067(5.2) 0.526 0.054 1.74 0.4104 -0.260

New Mexico .317(6.4) .0066(0.8) -.0084(1.0) -.0520(2.6) 0.271 0.043 1.80 0.2785 -0.187

Utah .264(5.3) .0089(0.9) .0081(0.8) -.0788(4.0) 0.368 0.051 0.80 0.3204 -0.246

Wyoming .301(6.9) .0216(1.8) -.0358(3.2) .0967(2.9) 0.386 0.056 1.16 0.2783 0.347

Alaska .864(6.0) -.0101(0.6) -.0253(1.5) -.0002(0.0) 0.114 0.104 0.43 0.5302 0.000

California .536(26.1) .0056(1.6) -.0220(6.3) -.0038(0.5) 0.660 0.020 1.28 0.4150 -0.009

Hawaii .467(11.4) .0069(0.7) -.0124(1.3) -.0221(1.0) -0.024 0.054 1.71 0.4274 -0.052

Oregon .510(13.6) .0084(1.1) -.0184(2.4) -.0131(0.7) 0.204 0.047 1.70 0.4302 -0.030

Washington .519(9.8) .0086(0.9) -.0156(1.7) -.0465(1.9) 0.169 0.063 1.12 0.4425 -0.105
Table 6. Changes in IUTU Ratios by State, Regression Results and Raw Averages, 1967 to 1994

State Average IU/TU Ratio Table 5: Average: Change

1967- 1981- Change D81 Change AWK/TU

1980 1994 Coeff. & Coeff. Ratio

New Hampshire 0.4310 0.2508 -0.1802 -0.1572 -0.1687 -0.1310

Missouri 0.4883 0.3332 -0.1551 -0.1088 -0.1320 -0.0928

Illinois 0.4343 0.3242 -0.1101 -0.1502 -0.1301 -0.0793

Michigan 0.4518 0.3138 -0.1380 -0.1078 -0.1229 -0.0772

New Jersey 0.5560 0.4414 -0.1146 -0.1120 -0.1133 -0.1084

Rhode Island 0.6653 0.5427 -0.1226 -0.0995 -0.1111 -0.1042

Connecticut 0.5221 0.4126 -0.1095 -0.1103 -0.1099 -0.0912

South Dakota 0.2888 0.1998 -0.0890 -0.1280 -0.1085 -0.0663

Nevada 0.4653 0.3556 -0.1097 -0.1067 -0.1082 -0.0959

New York 0.5112 0.3967 -0.1145 -0.0991 -0.1068 -0.0896

Tennessee 0.4206 0.3068 -0.1138 -0.0822 -0.0980 -0.0622

Alabama 0.3497 0.2536 -0.0961 -0.0987 -0.0974 -0.0670

West Virginia 0.3602 0.2751 -0.0851 -0.0862 -0.0857 -0.0583

Pennsylvania 0.5219 0.4354 -0.0865 -0.0803 -0.0834 -0.0611

Massachusetts 0.5573 0.4804 -0.0769 -0.0894 -0.0832 -0.0545

Iowa 0.3959 0.3132 -0.0827 -0.0781 -0.0804 -0.0579

Utah 0.3577 0.2832 -0.0745 -0.0788 -0.0766 -0.0433

Minnesota 0.4194 0.3507 -0.0687 -0.0745 -0.0716 -0.0338

Maine 0.5006 0.4243 -0.0763 -0.0642 -0.0703 -0.0609

Oklahoma 0.3226 0.2364 -0.0862 -0.0533 -0.0698 -0.0547

Indiana 0.3026 0.2327 -0.0699 -0.0579 -0.0639 -0.0672

Wisconsin 0.4655 0.3991 -0.0664 -0.0600 -0.0632 -0.0104

New Mexico 0.3069 0.2502 -0.0567 -0.0520 -0.0544 -0.0274

Washington 0.4697 0.4154 -0.0543 -0.0465 -0.0504 -0.0117

Florida 0.2461 0.2015 -0.0446 -0.0527 -0.0486 -0.0043

Louisiana 0.3011 0.2703 -0.0308 -0.0595 -0.0452 -0.0220

North Dakota 0.3711 0.3531 -0.0180 -0.0659 -0.0420 0.0088

Montana 0.3696 0.3349 -0.0347 -0.0424 -0.0386 -0.0205

Kentucky 0.3672 0.3763 0.0091 -0.0813 -0.0361 0.0233

Arizona 0.2949 0.2600 -0.0349 -0.0300 -0.0325 -0.0103

Delaware 0.3600 0.3323 -0.0277 -0.0277 -0.0277 -0.0185

Maryland 0.3572 0.3264 -0.0308 -0.0221 -0.0265 -0.0280

Arkansas 0.3557 0.3333 -0.0224 -0.0279 -0.0251 0.0028

Ohio 0.3238 0.3007 -0.0231 -0.0211 -0.0221 -0.0056

Oregon 0.4450 0.4154 -0.0296 -0.0131 -0.0214 0.0168

Vermont 0.4971 0.4749 -0.0222 -0.0158 -0.0190 -0.0019

Hawaii 0.4337 0.4209 -0.0128 -0.0221 -0.0175 0.0112

Georgia 0.2686 0.2585 -0.0101 -0.0203 -0.0152 0.0210

Virginia 0.1933 0.1920 -0.0013 -0.0265 -0.0139 0.0169

South Carolina 0.3072 0.2992 -0.0080 -0.0193 -0.0136 -0.0048

Mississippi 0.2692 0.2660 -0.0032 -0.0156 -0.0094 0.0043

Kansas 0.3704 0.3633 -0.0071 -0.0107 -0.0089 0.0156

California 0.4185 0.4114 -0.0071 -0.0038 -0.0055 -0.0030

Nebraska 0.3324 0.3353 0.0029 -0.0041 -0.0006 -0.0082

North Carolina 0.3178 0.3190 0.0012 0.0040 0.0026 -0.0035

Texas 0.1872 0.2089 0.0217 -0.0161 0.0028 0.0391

Alaska 0.5201 0.5403 0.0202 -0.0002 0.0100 -0.0121

Dist. of Col. 0.3759 0.3887 0.0128 0.0174 0.0151 0.0060

Idaho 0.3858 0.4058 0.0200 0.0220 0.0210 0.0376

Colorado 0.2220 0.2733 0.0513 0.0155 0.0334 0.0601

Wyoming 0.2458 0.3108 0.0650 0.0967 0.0809 0.0913

U.S. Total 0.3861 0.3352 -0.0509 -0.0255 -0.0288
Table 7. Annual Summary of Monetary Eligibility, Unweighted State Averages, 1967-1994.

Year Prop. MinElg: MinElg: 26WksElg: 26WksElg: States Avg.

Mon. WksEarn WksEarn WksEarn WksEarn with Ratio,

Elig. @UIMean @CPSMed. @UIMean @CPSMed Indexed MAXWBA/

AWW AWW AWW AWW MAXWBA AWWLag

1967 0.858 4.12 5.46 29.70 39.41 16 0.425

1968 0.855 3.98 5.30 29.82 39.66 19 0.432

1969 0.854 3.85 5.14 28.86 38.52 19 0.427

1970 0.854 3.71 4.97 29.30 39.23 21 0.429

1971 0.832 3.60 4.83 29.91 40.16 23 0.440

1972 0.811 3.58 4.81 32.21 43.37 25 0.469

1973 0.840 3.41 4.61 32.10 43.37 25 0.477

1974 0.850 3.24 4.39 32.01 43.36 28 0.490

1975 0.822 3.05 4.14 32.03 43.51 29 0.497

1976 0.787 2.99 4.07 33.34 45.43 32 0.515

1977 0.800 2.88 3.93 33.86 46.29 34 0.518

1978 0.822 2.86 3.91 33.89 46.48 35 0.520

1979 0.857 2.80 3.84 32.69 44.98 35 0.512

1980 0.867 2.85 3.92 32.17 44.40 35 0.504

1981 0.853 2.83 3.91 31.98 44.29 35 0.494

1982 0.848 3.08 4.26 33.61 46.69 36 0.496

1983 0.805 3.37 4.67 34.85 48.58 36 0.497

1984 0.827 3.60 5.02 35.67 49.89 36 0.496

1985 0.845 3.62 5.07 36.02 50.55 36 0.498

1986 0.859 3.77 5.29 37.12 52.29 35 0.503

1987 0.861 3.75 5.29 36.97 52.25 35 0.504

1988 0.859 3.71 5.24 36.79 52.18 34 0.502

1989 0.867 3.74 5.31 37.17 52.89 34 0.500

1990 0.869 3.62 5.15 37.28 53.23 34 0.510

1991 0.865 3.55 5.08 37.23 53.34 34 0.507

1992 0.848 3.45 4.95 36.83 52.93 34 0.514

1993 0.863 3.45 4.97 37.43 53.98 34 0.506

1994 0.870 3.48 5.02 38.09 55.11 34 0.519

Averages

 1967-94 0.845 3.43 4.73 33.89 47.01 30.8 0.489

 1967-80 0.836 3.35 4.52 31.56 42.73 26.9 0.475

 1981-94 0.853 3.50 4.94 36.22 51.30 34.8 0.503

Source: Data compiled at the Urban Institute based on UI Service publications.

All entries are simple averages for 51 UI programs: the fifty states plus D.C..
Table 8. Summary of Changes in Monetary Eligibility, 1967 to 1994,

States Grouped by Size of Decreases in IUTU Ratio

Time Prop. MinElig: MinElig: 26WksElig: 26WksElig: States Avg.

Period Mon. WksEarn WksEarn WksEarn WksEarn with Ratio,

Elig. @UIMean @CPSMed. @UIMean @CPSMed Indexed MAXWBA/

 AWW AWW AWW AWW MAXWBA AWWLag

Fifteen states with largest decreases in IUTU

1967-80 0.865 3.47 4.64 30.24 40.55 6.6 0.468

1981-94 0.868 3.41 4.77 33.35 46.74 9.9 0.492

Change 0.003 -0.06 0.13 3.10 6.19 3.3 0.024

1981 0.870 2.88 3.94 28.86 39.59 9 0.478

1985 0.856 3.48 4.82 33.80 46.92 10 0.492

1989 0.875 3.60 5.07 34.26 48.23 10 0.487

1994 0.883 3.18 4.55 35.15 50.36 10 0.510

Twenty-one states with intermediate decreases in IUTU

1967-80 0.823 3.34 4.51 30.10 40.87 12.2 0.472

1981-94 0.849 3.87 5.48 37.29 52.96 16.0 0.516

Change 0.026 0.53 0.97 7.19 12.10 3.8 0.045

1981 0.840 3.15 4.37 32.52 45.12 17 0.508

1985 0.841 4.00 5.61 36.45 51.28 17 0.509

1989 0.867 4.18 5.95 38.30 54.66 15 0.511

1994 0.874 3.94 5.69 39.80 57.17 15 0.538

Fifteen states with smallest decreases in IUTU

1967-80 0.826 3.26 4.43 34.92 47.49 8.0 0.488

1981-94 0.843 3.08 4.37 37.58 53.53 8.9 0.496

Change 0.017 -0.18 -0.05 2.67 6.04 0.9 0.009

1981 0.853 2.33 3.23 34.33 47.82 9 0.491

1985 0.840 3.24 4.56 37.63 53.14 9 0.490

1989 0.859 3.27 4.67 38.49 55.07 9 0.498

1994 0.852 3.13 4.54 38.63 56.19 9 0.502

Source: Data compiled at the Urban Institute based on UI Service publications.

 All entries are simple averages across subsets of 51 programs.

 Decreases in IUTU ratios by state appear in Table 6.
Table 9. Summary of Changes in Monetary Eligibility, 1967 to 1994,

States Grouped by Extent of UI Financing Problems in Early 1980s

Time IUTU Prop. MinElig: MinElig: 26WksElig: 26WksElig: States Avg.

Period Mon. WksEarn WksEarn WksEarn WksEarn with Ratio,

Elig. @UIMean @CPSMed. @UIMean @CPSMed Indexed MAXWBA/

 AWW AWW AWW AWW MAXWBA AWWLag

Fifteen states with largest financing problems

1967-80 0.370 0.838 2.98 4.00 32.47 43.86 10.1 0.488

1981-94 0.321 0.862 3.47 4.89 38.63 54.76 14.3 0.545

Change -0.049 0.024 0.49 0.89 6.16 10.91 4.1 0.057

1981 0.359 0.864 2.45 3.38 34.31 47.52 14 0.541

1985 0.293 0.854 3.70 5.16 37.33 52.43 15 0.536

1989 0.312 0.866 3.76 5.34 39.79 56.66 14 0.536

1994 0.319 0.884 3.39 4.90 40.50 58.71 14 0.552

Twenty-one states with intermediate financing problems

1967-80 0.425 0.848 3.51 4.74 29.07 39.26 11.7 0.482

1981-94 0.360 0.861 3.31 4.63 32.28 45.37 13.5 0.487

Change -0.064 0.013 -0.21 -0.10 3.21 6.11 1.8 0.006

1981 0.382 0.857 3.06 4.20 28.77 39.60 14 0.480

1985 0.347 0.847 3.36 4.67 32.94 45.92 14 0.485

1989 0.364 0.884 3.47 4.88 32.74 46.22 13 0.485

1994 0.361 0.875 3.40 4.84 34.45 49.34 13 0.513

Fifteen states with smallest financing problems

1967-80 0.348 0.818 3.50 4.75 34.14 46.43 5.0 0.454

1981-94 0.314 0.832 3.81 5.43 39.31 56.13 7.0 0.484

Change -0.034 0.014 0.31 0.69 5.17 9.70 2.0 0.030

1981 0.356 0.835 2.88 4.03 34.13 47.61 7 0.468

1985 0.300 0.834 3.92 5.53 39.01 55.13 7 0.480

1989 0.305 0.844 4.12 5.90 40.75 58.45 7 0.486

1994 0.293 0.850 3.68 5.37 40.77 59.58 7 0.495

Source: Data compiled at the Urban Institute based on UI Service publications. All entries are simple

averages across subsets of 51 programs. States with largest financing problems had the largest loans

relative to covered wages during 1980-1988. States with smallest financing problems include thirteen

that never have borrowed plus two with the smallest loans relative to covered wages during the 1970s.
Table 10. AFDC Program Participation and Expenditures, 1979-1981 to 1991-1993.

AFDC-Basic, Participants AFDC-Basic, Part. Rate: AFDC-Total, Total Benefits:

1979- 1991- Percent 1979- 1991- Percent 1979- 1991- Percent

1981 1993 Change 1981 1993 Change 1981 1993 Change

U.S. Total 9840 12014 22.1 0.806 0.834 3.5 12037 21859 81.6

Census Divisions

New England 102 100 -2.2 0.953 0.883 -7.3 137 244 78.2

Middle Atlantic 695 650 -6.5 0.955 0.903 -5.4 961 1395 45.2

E. N. Central 410 442 7.7 0.818 0.876 7.1 557 743 33.4

W. N. Central 79 90 14.3 0.907 0.941 3.7 97 151 55.3

South Atlantic 154 217 41.5 0.892 0.866 -2.9 122 265 117.5

E. S. Central 170 192 13.3 0.875 0.741 -15.3 89 148 66.4

W. S. Central 173 298 72.2 0.635 0.763 20.2 97 231 137.2

Mountain 34 65 90.1 0.719 0.936 30.2 33 90 168.6

Pacific 303 438 44.6 0.873 0.881 0.9 552 1364 147.2

States Ranked in Decline in IUTU

Top 15 299 296 -1.0 0.854 0.892 4.4 386 554 43.4

Middle 21 122 165 35.3 0.840 0.872 3.8 134 253 89.4

Bottom 15 186 274 47.1 0.850 0.867 2.0 229 549 139.7

Big States by Decline in IUTU

6 Biggest of Top 15:

Illinois 651 642 -1.4 0.805 0.855 6.2 735 907 23.4

Michigan 587 562 -4.2 0.866 0.898 3.7 1019 1178 15.5

New Jersey 437 329 -24.7 1.044 0.967 -7.4 537 514 -4.3

New York 1058 1071 1.3 0.771 0.762 -1.2 1594 2770 73.8

Pennsylvania 591 551 -6.8 1.051 0.980 -6.8 751 902 20.1

Massachusetts 325 291 -10.6 1.022 0.983 -3.8 434 729 67.9

6 Biggest of Middle 21

Minnesota 127 156 22.8 1.319 0.723 -45.2 210 384 82.4

Wisconsin 197 201 2.1 0.866 0.904 4.4 345 448 29.7

Indiana 159 150 -6.1 0.692 0.664 -4.0 133 210 57.5

Washington 133 213 59.4 0.618 0.961 55.5 227 583 157.5

Florida 258 575 123.2 0.509 0.782 53.6 191 710 271.6

Ohio 456 617 35.3 0.863 1.059 22.7 551 972 76.4

6 Biggest of Bottom 15

Georgia 224 377 68.6 0.958 1.019 6.4 138 415 202.0

Virginia 167 181 8.4 0.697 1.029 47.6 159 222 39.6

California 1214 1806 48.7 0.819 0.772 -5.7 2280 5815 155.1

North Carolina 196 303 54.3 0.684 0.774 13.2 151 335 121.9

Texas 309 722 134.0 0.551 0.913 65.7 130 514 294.7

Colorado 73 117 60.0 1.020 1.140 11.8 81 160 97.2

States Ranked by UI Debts

Top 15 247 290 17.3 0.900 0.895 -0.6 295 422 43.0

Middle 21 171 178 4.0 0.862 0.878 1.9 203 337 65.7

Bottom 15 169 262 54.7 0.773 0.855 10.6 223 564 153.0

Source: Program data and simulations with the TRIM2 model as described in the text. Participants are monthly

averages measured in thousands while benefits are in millions of dollars. Estimates for groups of states are simple

averages for the indicated groups.
Table 11. Food Stamps Program Participation and Expenditures, 1979-1981 to 1991-1993

Program Participants: Participation Rate: Total Benefits

1979- 1991- Percent 1979- 1991- Percent 1979- 1991- Percent

1981 1993 Change 1981 1993 Change 1981 1993 Change

U.S. Total 19051 25475 33.7 0.537 0.607 13.0 8173 20574 151.8

Census Divisions

New England 155 160 3.1 0.672 0.668 -0.6 62 115 84.7

Middle Atlantic 1119 1187 6.0 0.635 0.634 -0.2 464 985 112.1

E. N. Central 642 836 30.2 0.555 0.630 13.5 276 710 156.9

W. N. Central 126 205 63.1 0.358 0.569 58.9 53 156 196.8

South Atlantic 394 485 23.1 0.604 0.600 -0.7 175 405 131.4

E. S. Central 535 578 8.0 0.612 0.633 3.4 240 457 90.4

W. S. Central 544 969 78.2 0.458 0.599 30.8 236 803 240.2

Mountain 90 165 83.7 0.403 0.572 41.9 43 133 209.6

Pacific 418 693 65.6 0.565 0.631 11.7 166 507 205.0

States Ranked in Decline in IUTU

Top 15 511 583 14.1 0.563 0.623 10.7 216 480 122.6

Middle 21 269 381 41.4 0.514 0.621 20.8 121 313 159.2

Bottom 15 382 582 52.4 0.516 0.581 12.6 160 453 183.1

Big States by Decline in IUTU

6 Biggest of Top 15:

Illinois 926 1155 24.7 0.571 0.624 9.3 429 1042 143.1

Michigan 828 1005 21.3 0.646 0.693 7.3 302 838 177.3

New Jersey 583 498 -14.5 0.626 0.569 -9.1 240 434 80.6

New York 1776 1921 8.2 0.626 0.617 -1.4 744 1613 116.7

Pennsylvania 1000 1141 14.2 0.653 0.716 9.6 409 909 122.2

Massachusetts 449 429 -4.6 0.682 0.642 -5.9 179 310 72.9

6 Biggest of Middle 21

Minnesota 177 307 74.0 0.441 0.513 16.3 67 225 235.0

Wisconsin 231 326 41.3 0.544 0.616 13.2 77 224 191.8

Indiana 347 453 30.6 0.403 0.462 14.6 161 371 130.9

Washington 249 434 74.5 0.468 0.786 67.9 106 340 220.9

Florida 887 1359 53.3 0.474 0.537 13.3 426 1217 185.5

Ohio 878 1241 41.4 0.609 0.756 24.1 413 1074 160.0

6 Biggest of Bottom 15

Georgia 606 755 24.6 0.613 0.593 -3.3 263 615 133.6

Virginia 379 494 30.3 0.504 0.686 36.1 163 400 145.8

California 1509 2628 74.1 0.484 0.494 2.1 541 1807 233.9

North Carolina 570 594 4.2 0.498 0.490 -1.6 237 452 90.9

Texas 1139 2482 117.8 0.412 0.705 71.1 511 2088 308.6

Colorado 161 261 62.1 0.566 0.656 15.9 73 215 193.6

States Ranked by UI Debts

Top 15 476 674 41.8 0.547 0.641 17.2 205 558 172.9

Middle 21 322 366 13.8 0.554 0.617 11.4 139 298 114.2

Bottom 15 344 511 48.7 0.475 0.569 19.8 145 396 172.3

Source: Program data and simulations with the TRIM2 model as described in the text. Participants are monthly averages

measured in thousands while benefits are in millions of dollars. Estimates for groups of states are simple averages for the

indicated groups.
Table 12. Medicaid Participation and Expenditures, 1984-1986 to 1991-1993.

Program Participants: Total Benefits:

1984- 1991- Percent 1984- 1991- Percent

1986 1993 Change 1986 1993 Change

U.S. Total 21188 29398 38.7 39966 109468 173.9

Census Divisions

New England 178 247 39.0 472 1432 203.2

Middle Atlantic 1364 1502 10.1 3568 8929 150.2

E. N. Central 862 948 9.9 1417 3335 135.4

W. N. Central 181 248 37.3 371 951 156.4

South Atlantic 304 549 80.2 504 1730 243.0

E. S. Central 367 581 58.0 479 1625 239.6

W. S. Central 416 835 100.7 791 2696 240.8

Mountain 69 124 78.7 129 403 211.7

Pacific 818 1093 33.6 1245 3017 142.3

States Ranked by Decline in IUTU

Top 15 576 696 20.7 1243 3299 165.4

Middle 20 289 421 45.7 525 1480 181.9

Bottom 15 451 702 55.8 721 2025 180.8

Big States by Decline in IUTU

6 Biggest of Top 15:

Illinois 1058 1284 21.4 1744 3927 125.2

Michigan 1287 1137 -11.7 1741 3836 120.4

New Jersey 610 698 14.4 1172 3996 240.9

New York 2308 2586 12.0 7579 17563 131.8

Pennsylvania 1176 1221 3.9 1954 5228 167.5

Massachusetts 512 686 34.0 1498 4270 185.1

6 Biggest of Middle 20

Minnesota 348 413 18.7 1020 1937 90.0

Wisconsin 574 441 -23.2 1007 1952 93.9

Indiana 303 492 62.3 757 2362 211.9

Washington 358 569 59.2 585 1953 233.5

Florida 574 1495 160.4 948 4128 335.7

Ohio 1091 1387 27.2 1837 4600 150.4

6 Biggest of Bottom 15

Georgia 503 854 69.7 753 2420 221.1

Virginia 306 511 66.8 569 1535 169.5

California 3459 4446 28.6 5168 11794 128.2

North Carolina 354 784 121.4 684 2482 262.7

Texas 796 2014 153.1 1591 5910 271.5

Colorado 173 254 46.5 314 944 200.6

States Ranked by UI Debts

Top 15 544 698 28.3 924 2441 164.2

Middle 21 331 447 35.0 790 2196 178.1

Bottom 14 435 682 56.9 681 1910 180.6

Source: Program data after editing at the Urban Institute. Participants

are counts of persons ever-on measured in thousands while benefits are in

millions of dollars. Estimates for groups of states are simple averages

for the indicated groups. Arizona not included in Medicaid data.
64

Appendix A: Welfare Programs by State: Participation and Benefits

This appendix presents summary data on state-level participation

and benefit payments for four welfare programs: AFDC-Basic, AFDC-UP,

Food Stamps and Medicaid. Three measures are emphasized: numbers of

participants, participation rates among eligibles and total benefit

payments. The information was derived from annual data files covering

the years 1979 to 1993. For three of the programs (AFDC-Basic, AFDCUP and Food Stamps) the Urban Institute’s TRIM2 model was utilized to

derive state-level estimates of eligibility. The analysis using TRIM2

developed time series for eligibility and participation for families,

persons and benefit entitlements. Thus, the summaries to be discussed

here represent only a portion of the state-level detail generated by

the TRIM2 simulations undertaken for this report.

Two forms of summary information were prepared. First, five

tables provide summaries of the individual welfare programs

emphasizing participation and expenditures for three-year periods:

1979-1981, 1987-1989 and 1991-1993. Tables A1-A5 underlie the tabular

summaries appearing in Section IV (Tables 10, 11 and 12) of the

report. Second, an unpublished version of this report has graphic

displays that show numbers of unemployed and welfare beneficiaries by

state for the years 1979 to 1993. 

Tables A1-A5 display summaries for the three programs AFDC, Food

Stamps and Medicaid. Three tables are used for the AFDC program so

that a clear distinction is made between AFDC-Basic (Table A1), AFDCUP (Table A2) and the two combined (Table A3). As noted, expenditure

detail for AFDC-UP is only available from 1985. Thus Table A3 shows

expenditures for the combined program, the only consistent series

available back to 1979.

Each table follows the same format with individual state detail

for three year periods. Participants, participation rates and total

benefits are three year averages for the three-year periods 1979-

1981, 1987-1989 and 1991-1993. Where states are grouped: e.g., by

Census Division, decline in IUTU and UI debt, the displays are all

simple averages. 
65

Throughout these tables greatest volatility is observed for the

participation rates. TRIM2-based estimates of eligibility, the

denominator of estimated participation rates, exhibit sampling

variability due to small sample sizes. This variability is greatest

where small numbers of micro records underlie the state-year

estimates. Across programs, this sampling variability is smallest for

Food Stamps, intermediate for AFDC-Basic and greatest for AFDC-UP.

Because the Food Stamps program has the largest eligible population

there are more micro records in the CPS upon which to base estimates

of eligibility. Within a given welfare program, of course,

variability in the eligibility estimates is greatest for the 

smallest states. 

The most obvious manifestations of variability in the estimates

of eligibility are in Table A2 which pertains to AFDC-UP. Eleven of

the three year averages of estimated participation rates exceed 1.5.

This is the smallest welfare program and several of the highest

averages are for small states, e.g., Iowa, Nebraska and Vermont.

However, there are also high average ratios for the middle sized

states of Minnesota, Washington and Wisconsin. After examining these

series in several states it was decided to exclude AFDC-UP from the

discussion of Section IV in the report. Table A2 is displayed to show

the results of the TRIM2-based simulations.

Finally, note that the treatment of Medicaid in Table A5 differs

from the other programs. All the information in this table was taken

directly from program data. Estimates of eligibility and Medicaid

program participation rates were not made for this report. Partly

this reflects an early decision to emphasize AFDC and Medicaid as in

the analysis by Bassi, et.al.. It should also be noted, however, that

TRIM2's Medicaid module does not extend back to 1979. Thus, a fully

parallel analysis of this program extending from 1979 is not

possible.

Also, note that the earliest three-year average for the Medicaid

program refers to 1984-1986, not 1979-1981. While program data by

state do extend back to the mid-1970s, they were not obtained for

this report. The data from 1984 were already resident at the Urban
66

64 See David Liska, Karen Obermaier, Barbara Lyons and Peter

Long, “Medicaid Expenditures and Beneficiaries: National and State

Profiles and Trends, 1984-1993,” (Washington, D.C.: Kaiser Commission

on the Future of Medicaid, July 1995).

65 There are only five exceptions across the fifty-one states:

Alaska - 1984-1986, Idaho - 1986, Nevada - 1984-1987, New Hampshire -

1990 and Wyoming - 1986. In all instances total unemployment exceeds

the number of recipients of Food Stamps and/or Medicaid. Note four

situations pertain to small western states during the mid 1980s while

the fifth is New Hampshire that experienced an especially large

increase in unemployment between 1988 and 1991.

Institute and had been edited for errors. “Clean” data for years

before are not available. Readers can note the national time series

for Medicaid recipiency in Table 3 and in the graph from this

appendix entitled “Total US.” Both displays show that Medicaid

caseloads were stable between 1979 and 1986. It should be emphasized,

of course, that aggregate caseload stability does not necessarily

reflect stability in individual states. Thus errors could be made in

assuming that state-level growth in caseloads from 1979-1981 to 1987-

1989 and 1991-1993 would be the same as growth from 1984-1986. 

Each graph in the unpublished report (not shown in Appendix A of

this report) has six time series: 1) total unemployment, 2) insured

unemployment, 3) AFDC-Basic, 4) AFDC-UP, 5) Food Stamps and 6)

Medicaid. For all but Medicaid the graphs show counts of persons

measured as annual averages. Medicaid data start from 1984 and the

estimates refer to persons ever-on during the year. Ever-on counts

are inherently larger than monthly averages, probably 20 to 30

percent larger. The ever-on estimates were derived from other

research at the Urban Institute and have been edited for errors.64

There exist state-level Medicaid recipiency data for earlier years

but they could not be obtained with the resources available for the

present project. 

The graphs show a number of consistent patterns. The counts of

beneficiaries are largest for Food Stamps and Medicaid compared to

the other four series for practically every state-year observation.65

Food Stamps recipiency moves similarly to total unemployment in most
67

66 For most states except those along the Atlantic coast and

California total unemployment is highest either in the early 1980s or

the mid 1980s. Participation in AFDC-Basic generally increases very

little in the years of highest unemployment. 

67 These percentages are based on Tables A1, A4 and A5 below.

states. After 1988, Food Stamps and Medicaid caseloads both exhibit

rapid growth. 

The two mid-level series in practically all states for all years

are total unemployment and AFDC-Basic average monthly recipiency. The

AFDC-Basic series is the more stable of the pair, and does not

increase much in years when total unemployment increases.66 Also note

that while AFDC-Basic caseloads increase in the 1990s, the increases

are absolutely and proportionately much smaller than for Food Stamps

and Medicaid. Between 1987-1989 and 1991-1993 AFDC-Basic caseloads

grew by 21.8 percent, about two-thirds the growth in Food Stamps and

Medicaid caseloads (35.2 percent and 33.1 percent respectively).67

The two bottom series in the graphs are always insured

unemployment and AFDC-UP average monthly recipiency. Generally,

insured unemployment is two to three times the level of AFDC-UP.

During 1991-1993 AFDC-UP caseloads averaged 1.370 million or 45.1

percent of insured unemployment which averaged 3.040 million. For

this three-year period AFDC-UP averaged half or more of insured

unemployment in just eleven states: California, Alaska, Kentucky,

Maine, Michigan, Minnesota, Montana, Nebraska, Ohio, Vermont,

Washington, West Virginia and Wisconsin. West Virginia is the only

state where AFDC-UP is the higher of the two series.

The “Total US” graph in the unpublished report vividly

illustrates all of the preceding points. The most cyclical series are

total unemployment, insured unemployment and Food Stamps. AFDC-Basic

was much less cyclical in the downturns of the early 1980s and 1990-

1992 and during the sustained expansion of 1984-1989. Again, the high

levels and sharp post-1988 growth in Food Stamps and Medicaid

recipiency are two very pronounced patterns in the “Total US” summary

graph.
Table A1. AFDC BASIC: Participation Data 

 

Participants Participation Rates

STATE GROUPINGS 79-81 87-89 91-93 79-81 87-89 91-93

TOTAL US 9,840,085 9,867,768 12,014,463 0.806 0.787 0.834

CENSUS DIVISIONS

1 New England 102,133 76,625 99,932 0.953 1.029 0.883

2 Middle Atlantic 695,175 589,873 650,263 0.955 0.970 0.903

3 East North Central 410,049 409,480 441,758 0.818 0.984 0.876

4 West North Central 78,711 77,916 89,992 0.907 0.897 0.941

5 South Atlantic 153,688 148,565 217,466 0.892 0.720 0.866

6 East South Central 169,655 164,335 192,188 0.875 0.715 0.741

7 West South Central 172,943 239,886 297,809 0.635 0.617 0.763

8 Mountain 33,952 45,902 64,667 0.719 0.789 0.936

9 Pacific 303,068 344,881 438,168 0.873 0.867 0.881

DECLINE IN IUTU, RANK

Top 15 States 299,215 262,152 296,250 0.854 0.902 0.892

Middle 21 States 121,950 132,923 165,048 0.840 0.832 0.872

Bottom 15 States 186,060 209,607 273,648 0.850 0.782 0.867

UI DEBT, RANK

Top 15 States 247,219 257,807 290,095 0.900 0.885 0.895

Middle 21 States 171,181 150,064 177,987 0.862 0.863 0.878

Bottom 15 States 169,134 189,955 261,687 0.773 0.755 0.855

FIPS CODE

1 Alabama 174,412 131,769 138,166 0.653 0.619 0.730

2 Alaska 15,276 19,271 24,840 0.910 0.912 1.025

4 Arizona 55,117 97,331 173,977 0.673 0.672 1.120

5 Arkansas 84,389 68,599 72,616 0.645 0.448 0.630

6 California 1,214,440 1,404,962 1,805,767 0.819 0.793 0.772

8 Colorado 73,291 94,941 117,281 1.020 0.639 1.140

9 Connecticut 134,833 106,046 147,506 1.005 1.712 0.894

10 Delaware 30,614 19,587 25,824 1.116 0.803 0.937

11 Dist. of Columbia 82,839 48,997 61,837 0.999 1.097 0.938

12 Florida 257,649 313,826 574,994 0.509 0.564 0.782

13 Georgia 223,870 257,444 377,498 0.958 0.643 1.019

15 Hawaii 55,872 39,215 47,823 1.408 0.828 0.729

16 Idaho 20,118 17,190 18,737 0.645 0.587 0.644

17 Illinois 651,315 615,999 642,291 0.805 0.942 0.855

18 Indiana 159,469 149,813 186,476 0.692 0.768 0.664

19 Iowa 93,903 90,427 92,433 0.933 0.936 1.064

20 Kansas 65,526 65,075 74,910 0.813 1.014 0.904

21 Kentucky 167,858 157,549 189,742 1.231 0.857 0.671

22 Louisiana 210,844 271,922 266,883 0.602 0.690 0.726

23 Maine 58,572 47,993 56,416 1.071 0.826 0.824

24 Maryland 207,645 174,444 213,199 1.323 0.819 0.933

25 Massachusetts 325,428 233,368 290,850 1.022 0.802 0.983

26 Michigan 586,605 535,081 561,917 0.866 0.938 0.898

27 Minnesota 126,825 131,914 155,785 1.319 0.674 0.723

28 Mississippi 173,353 178,480 174,239 0.989 0.695 0.818

29 Missouri 196,568 186,480 228,703 0.903 0.823 1.165

30 Montana 17,630 23,689 27,853 0.857 0.810 1.066

31 Nebraska 35,682 38,039 42,079 0.676 0.874 0.915

32 Nevada 12,343 18,439 30,998 0.536 0.537 0.870

33 New Hampshire 22,336 11,974 25,507 0.941 0.776 0.548

34 New Jersey 436,915 302,699 328,939 1.044 1.033 0.967

35 New Mexico 53,701 58,341 80,702 0.830 1.085 0.963

36 New York 1,057,858 967,140 1,071,270 0.771 0.766 0.762

37 North Carolina 196,127 188,998 302,712 0.684 0.625 0.774

38 North Dakota 13,015 14,718 16,100 0.914 1.163 0.981

39 Ohio 456,070 534,864 617,261 0.863 0.951 1.059

40 Oklahoma 87,952 101,570 129,578 0.743 0.623 0.782

41 Oregon 96,401 78,070 99,831 0.610 0.665 0.917

42 Pennsylvania 590,751 499,779 550,578 1.051 1.111 0.980

44 Rhode Island 51,208 41,993 56,784 0.962 1.176 1.198

45 South Carolina 151,796 114,178 135,932 0.899 0.596 0.446

46 South Dakota 19,457 18,759 19,932 0.791 0.799 0.839

47 Tennessee 162,998 189,541 266,605 0.627 0.687 0.745

48 Texas 308,586 517,451 722,161 0.551 0.705 0.913

49 Utah 32,746 43,812 50,381 0.502 0.786 0.749

50 Vermont 20,421 18,377 22,528 0.718 0.882 0.849

51 Virginia 167,451 146,395 181,494 0.697 0.523 1.029

53 Washington 133,348 182,885 212,577 0.618 1.138 0.961

54 West Virginia 65,201 73,215 83,699 0.840 0.809 0.940

55 Wisconsin 196,787 211,645 200,845 0.866 1.318 0.904

56 Wyoming 6,673 13,474 17,407 0.686 1.200 0.935
Table A2. AFDC-UP: Participation Data

 

 Participation Participation Rates

STATE GROUPINGS 79-81 87-89 91-93 79-81 87-89 91-93

TOTAL US 704,011 921,642 1,370,267 0.448 0.840 0.616

CENSUS DIVISIONS

1 New England 4,636 2,398 8,180 0.209 0.903 0.860

2 Middle Atlantic 38,014 29,219 41,631 0.385 0.572 0.576

3 East North Central 51,039 64,084 64,592 0.404 0.986 1.077

4 West North Central 4,231 10,578 11,438 0.120 1.314 2.112

5 South Atlantic 2,290 4,873 8,228 0.137 0.381 0.322

6 East South Central - - 11,316 - - 0.320

7 West South Central - - 9,770 - - 0.203

8 Mountain 1,521 564 3,040 0.078 0.065 0.331

9 Pacific 48,912 75,353 122,109 0.279 0.807 1.067

DECLINE IN IUTU, RANK

Top 15 States 20,480 20,660 26,042 0.220 0.395 0.577

Middle 21 States 7,692 12,327 17,785 0.153 0.887 1.029

Bottom 15 States 15,686 23,525 40,411 0.133 0.312 0.651

UI DEBT, RANK

Top 15 States 22,545 29,290 33,587 0.249 0.978 1.143

Middle 21 States 6,551 6,096 12,203 0.178 0.540 0.601

Bottom 15 States 15,218 23,618 40,680 0.069 0.215 0.685

FIPS CODE

1 Alabama - - 1,252 - - 0.117

2 Alaska - - 7,494 - - 1.325

4 Arizona - - 5,542 - - 0.199

5 Arkansas - - 1,511 - - 0.189

6 California 220,974 334,416 523,436 0.654 1.091 0.662

8 Colorado 4,926 - 2,999 0.247 - 0.426

9 Connecticut 3,691 2,202 8,385 0.318 0.389 0.660

10 Delaware 1,485 189 440 0.318 0.146 0.336

11 Dist. of Columbia 1,044 452 537 0.436 1.355 0.238

12 Florida - - 16,842 - - 0.418

13 Georgia - - 3,972 - - 0.163

15 Hawaii 4,308 3,110 3,643 0.369 0.535 0.417

16 Idaho - - 1,381 - - 0.168

17 Illinois 37,117 49,654 45,476 0.328 0.607 0.649

18 Indiana - - 12,874 - - 0.436

19 Iowa 8,171 12,711 9,389 0.199 5.282 6.858

20 Kansas 3,066 7,737 9,753 0.198 1.060 0.526

21 Kentucky - - 34,435 - - 0.766

22 Louisiana - - 3,541 - - 0.163

23 Maine - 4,333 10,733 - 0.504 0.992

24 Maryland 5,760 3,171 4,050 0.247 0.881 0.275

25 Massachusetts 20,489 5,353 19,438 0.410 0.277 0.562

26 Michigan 112,649 109,535 121,595 0.586 1.221 1.240

27 Minnesota 11,810 31,458 32,596 0.267 1.742 0.975

28 Mississippi - - 567 - - 0.031

29 Missouri 5,608 17,168 21,023 0.081 0.566 1.269

30 Montana 1,554 4,511 4,892 0.151 0.516 0.631

31 Nebraska 965 4,974 5,338 0.092 0.550 4.268

32 Nevada - - 1,148 - - 0.180

33 New Hampshire - - 2,259 - - 0.572

34 New Jersey 23,010 10,732 18,732 0.517 0.847 0.710

35 New Mexico - - 6,884 - - 0.372

36 New York 46,333 40,467 64,053 0.306 0.463 0.448

37 North Carolina - 368 8,964 - 0.020 0.268

38 North Dakota - - 1,723 - - 0.781

39 Ohio 81,005 108,601 103,328 0.536 1.196 1.224

40 Oklahoma - - 2,532 - - 0.079

41 Oregon 3,269 6,780 14,985 0.097 0.503 1.114

42 Pennsylvania 44,699 36,459 42,108 0.333 0.407 0.570

44 Rhode Island 1,285 475 2,507 0.189 0.194 0.357

45 South Carolina - 1,816 2,912 - 0.071 0.125

46 South Dakota - - 249 - - 0.105

47 Tennessee - - 9,011 - - 0.365

48 Texas - - 31,496 - - 0.381

49 Utah 5,686 - 746 0.230 - 0.130

50 Vermont 2,352 2,025 5,759 0.335 4.055 2.019

51 Virginia - - 2,944 - - 0.230

53 Washington 16,009 32,458 60,985 0.273 1.905 1.819

54 West Virginia 12,324 37,858 33,389 0.235 0.954 0.844

55 Wisconsin 24,424 52,629 39,689 0.572 1.907 1.834

56 Wyoming - - 731 - - 0.543
Table A3. AFDC TOTAL: Benefits Data

 

 Annual Dollars (in 1,000's)

STATE GROUPINGS 79-81 87-89 91-93

TOTAL US 12,036,546 16,814,196 21,858,734

CENSUS DIVISIONS

1 New England 137,083 171,010 244,221

2 Middle Atlantic 960,775 1,113,408 1,395,307

3 East North Central 556,720 702,710 742,694

4 West North Central 97,222 130,488 151,019

5 South Atlantic 121,954 171,893 265,264

6 East South Central 88,815 106,639 147,753

7 West South Central 97,191 176,483 230,538

8 Mountain 33,355 56,526 89,588

9 Pacific 551,822 977,843 1,363,928

DECLINE IN IUTU, RANK

Top 15 States 386,230 456,098 553,847

Middle 21 States 133,722 192,493 253,287

Bottom 15 States 228,996 395,359 548,800

UI DEBT, RANK

Top 15 States 295,147 377,512 422,154

Middle 21 States 203,221 254,796 336,808

Bottom 15 States 222,781 386,719 563,563

FIPS CODE

1 Alabama 80,284 62,338 84,348

2 Alaska 28,515 53,322 96,352

4 Arizona 39,013 108,004 236,677

5 Arkansas 49,767 53,421 60,182

6 California 2,279,727 4,215,269 5,815,039

8 Colorado 81,176 126,245 160,056

9 Connecticut 200,935 230,934 373,603

10 Delaware 31,065 24,708 37,010

11 Dist. of Columbia 90,659 77,443 106,701

12 Florida 191,070 331,631 710,010

13 Georgia 137,581 271,895 415,467

15 Hawaii 90,786 80,591 128,520

16 Idaho 22,933 19,286 25,538

17 Illinois 734,592 816,732 906,567

18 Indiana 133,137 160,561 209,627

19 Iowa 136,090 155,343 163,620

20 Kansas 81,979 100,442 118,964

21 Kentucky 132,941 148,350 209,771

22 Louisiana 120,195 181,525 182,215

23 Maine 58,478 83,863 116,340

24 Maryland 207,874 258,134 325,621

25 Massachusetts 434,254 564,193 729,238

26 Michigan 1,019,444 1,221,253 1,177,529

27 Minnesota 210,419 339,285 383,905

28 Mississippi 59,902 84,205 87,754

29 Missouri 175,642 216,961 274,498

30 Montana 18,126 40,869 45,220

31 Nebraska 42,749 57,420 64,378

32 Nevada 10,748 21,159 40,424

33 New Hampshire 25,603 22,010 53,621

34 New Jersey 537,191 456,740 514,242

35 New Mexico 41,232 55,840 107,205

36 New York 1,594,030 2,138,070 2,769,854

37 North Carolina 151,044 210,076 335,222

38 North Dakota 15,487 22,616 26,977

39 Ohio 550,965 816,507 971,770

40 Oklahoma 88,600 119,762 165,850

41 Oregon 133,572 130,275 196,572

42 Pennsylvania 751,105 745,414 901,825

44 Rhode Island 70,596 83,824 128,704

45 South Carolina 70,616 94,471 116,163

46 South Dakota 18,191 21,349 24,796

47 Tennessee 82,134 131,662 209,139

48 Texas 130,201 351,224 513,907

49 Utah 45,522 62,163 75,501

50 Vermont 32,633 41,237 63,819

51 Virginia 158,981 169,849 221,861

53 Washington 226,509 409,758 583,157

54 West Virginia 58,699 108,831 119,317

55 Wisconsin 345,461 498,496 447,975

56 Wyoming 8,092 18,640 26,084
Table A4. Food Stamps: Participation and Benefits Data 

Participants Participation Rates Annual Dollars (in 1,000's)

STATE GROUPINGS 79-81 87-89 91-93 79-81 87-89 91-93 79-81 87-89 91-93

TOTAL US 19,051,145 18,846,492 25,475,354 0.537 0.514 0.607 8,172,515 11,370,645 20,574,379

CENSUS DIVISIONS

1 New England 155,358 103,795 160,167 0.672 0.574 0.668 62,472 51,844 115,365

2 Middle Atlantic 1,119,312 946,041 1,186,771 0.635 0.588 0.634 464,439 568,128 985,146

3 East North Central 642,055 718,185 836,178 0.555 0.636 0.630 276,283 466,560 709,762

4 West North Central 125,849 160,706 205,364 0.358 0.470 0.569 52,607 92,357 156,162

5 South Atlantic 393,838 304,048 484,963 0.604 0.482 0.600 175,059 189,546 405,173

6 East South Central 534,722 476,823 577,517 0.612 0.546 0.633 240,211 296,203 457,260

7 West South Central 543,668 700,473 968,567 0.458 0.480 0.599 235,933 446,631 802,726

8 Mountain 89,959 109,400 165,280 0.403 0.430 0.572 42,963 69,696 133,010

9 Pacific 418,414 469,782 692,872 0.565 0.537 0.631 166,178 228,214 506,845

DECLINE IN IUTU, RANK

Top 15 States 511,421 461,920 583,330 0.563 0.530 0.623 215,601 282,298 479,948

Middle 21 States 269,326 283,602 380,934 0.514 0.529 0.621 120,824 178,520 313,224

Bottom 15 States 381,598 397,470 581,720 0.516 0.479 0.581 160,080 225,817 453,164

UI DEBT, RANK

Top 15 States 475,767 547,374 674,430 0.547 0.582 0.641 204,615 346,110 558,462

Middle 21 States 321,745 268,626 366,069 0.554 0.520 0.617 139,202 163,076 298,162

Bottom 15 States 343,866 332,983 511,431 0.475 0.440 0.569 145,337 183,627 395,736

FIPS CODE

1 Alabama 567,059 442,271 544,749 0.511 0.434 0.568 251,658 271,499 441,329

2 Alaska 28,587 28,239 38,469 0.440 0.387 0.563 25,015 25,640 41,501

4 Arizona 184,444 239,220 457,569 0.462 0.442 0.710 99,029 156,442 370,815

5 Arkansas 292,471 231,095 276,399 0.487 0.388 0.487 121,874 128,415 202,728

6 California 1,509,236 1,713,789 2,627,957 0.484 0.460 0.494 541,321 710,588 1,807,308

8 Colorado 160,968 205,406 260,849 0.566 0.445 0.656 73,161 127,690 214,803

9 Connecticut 168,905 112,744 201,819 0.710 0.742 0.675 61,356 51,354 129,696

10 Delaware 50,742 29,394 51,693 0.717 0.450 0.646 22,536 18,338 41,829

11 Dist. of Columbia 100,133 59,120 82,234 0.906 0.656 0.662 41,890 34,619 71,344

12 Florida 886,845 642,726 1,359,106 0.474 0.341 0.537 426,403 425,940 1,217,204

13 Georgia 606,229 480,840 755,388 0.613 0.453 0.593 263,104 286,557 614,515

15 Hawaii 100,246 80,110 95,965 0.802 0.575 0.634 60,035 78,659 121,775

16 Idaho 58,146 61,258 74,045 0.375 0.397 0.437 28,800 37,470 53,556

17 Illinois 926,318 1,024,689 1,155,053 0.571 0.605 0.624 428,812 729,097 1,042,399

18 Indiana 347,020 303,069 453,375 0.403 0.385 0.462 160,539 192,082 370,627

19 Iowa 144,210 180,013 191,273 0.426 0.485 0.611 59,354 102,031 140,017

20 Kansas 93,009 124,576 176,514 0.336 0.423 0.549 41,042 76,647 132,154

21 Kentucky 470,186 469,430 522,803 0.629 0.610 0.614 220,247 303,446 416,941

22 Louisiana 545,035 725,721 770,152 0.533 0.626 0.700 239,221 466,658 648,070

23 Maine 134,417 88,583 131,611 0.713 0.558 0.719 60,511 48,938 104,483

24 Maryland 322,255 247,675 348,593 0.704 0.538 0.655 146,008 170,239 312,672

25 Massachusetts 449,326 308,909 428,551 0.682 0.513 0.642 179,421 149,750 310,211

26 Michigan 828,417 877,509 1,005,177 0.646 0.692 0.693 302,208 517,335 837,944

27 Minnesota 176,685 240,005 307,479 0.441 0.474 0.513 67,121 124,211 224,860

28 Mississippi 489,125 497,301 532,605 0.696 0.660 0.686 204,401 308,505 411,338

29 Missouri 333,845 395,129 554,320 0.432 0.484 0.635 147,689 240,926 444,873

30 Montana 42,247 57,857 66,881 0.350 0.427 0.504 18,571 35,291 51,700

31 Nebraska 66,233 95,851 107,922 0.297 0.436 0.566 25,554 51,091 76,338

32 Nevada 32,169 38,697 82,184 0.327 0.306 0.509 16,298 27,979 75,210

33 New Hampshire 49,399 20,406 56,785 0.569 0.277 0.519 22,452 11,033 43,323

34 New Jersey 582,769 361,831 498,067 0.626 0.567 0.569 240,113 228,479 433,694

35 New Mexico 172,537 153,397 223,556 0.560 0.436 0.672 78,368 98,871 179,186

36 New York 1,775,592 1,537,709 1,921,031 0.626 0.572 0.617 744,283 927,060 1,613,153

37 North Carolina 569,906 399,880 593,853 0.498 0.399 0.490 236,635 225,716 451,679

38 North Dakota 25,462 37,648 45,507 0.282 0.540 0.572 9,799 20,496 33,535

39 Ohio 877,982 1,075,136 1,241,421 0.609 0.728 0.756 413,151 737,662 1,074,058

40 Oklahoma 197,788 271,660 345,680 0.402 0.438 0.504 71,614 163,066 272,009

41 Oregon 205,008 213,883 267,487 0.633 0.594 0.677 98,621 145,738 223,763

42 Pennsylvania 999,575 938,584 1,141,216 0.653 0.623 0.716 408,920 548,844 908,593

44 Rhode Island 84,780 58,010 87,570 0.690 0.573 0.690 32,831 32,521 68,252

45 South Carolina 410,487 285,696 371,262 0.519 0.405 0.444 180,684 181,121 288,047

46 South Dakota 41,498 51,719 54,533 0.293 0.450 0.540 17,691 31,098 41,358

47 Tennessee 612,517 498,290 709,910 0.610 0.480 0.666 284,539 301,360 559,432

48 Texas 1,139,376 1,573,418 2,482,035 0.412 0.466 0.705 511,023 1,028,385 2,088,096

49 Utah 55,664 91,593 124,015 0.291 0.428 0.536 23,477 56,520 93,314

50 Vermont 45,319 34,119 54,664 0.669 0.782 0.764 18,260 17,469 36,226

51 Virginia 378,792 328,793 493,556 0.504 0.471 0.686 162,531 197,252 399,511

53 Washington 248,992 312,889 434,483 0.468 0.668 0.786 105,898 180,446 339,877

54 West Virginia 219,150 262,308 308,981 0.506 0.626 0.690 95,743 166,136 249,757

55 Wisconsin 230,539 310,522 325,865 0.544 0.770 0.616 76,702 156,626 223,780

56 Wyoming 13,499 27,774 33,144 0.290 0.557 0.555 5,998 17,309 25,496
Table A5. Medicaid: Participation and Benefits Data 

 (excludes Arizona)

Participants (Ever On) Annual Dollars (In 1,000's)

STATE GROUPINGS 84-86 87-89 91-93 84-86 87-89 91-93

TOTAL US 21,188,136 22,081,394 29,397,927 39,966,215 53,273,957 109,468,413

CENSUS DIVISIONS

1 New England 177,708 182,000 246,959 472,138 654,810 1,431,668

2 Middle Atlantic 1,364,470 1,345,333 1,501,744 3,568,221 4,672,002 8,929,079

3 East North Central 862,457 817,256 948,038 1,417,000 1,767,901 3,335,397

4 West North Central 180,682 187,785 248,119 370,756 482,032 950,673

5 South Atlantic 304,496 334,830 548,608 504,313 752,331 1,729,957

6 East South Central 367,345 401,876 580,583 478,661 678,948 1,625,493

7 West South Central 416,153 511,476 835,042 791,116 1,044,247 2,696,260

8 Mountain 69,344 79,094 123,888 129,212 187,506 402,758

9 Pacific 818,312 866,417 1,093,346 1,245,204 1,627,814 3,016,707

DECLINE IN IUTU, RANK 576,343 573,396 695,886 1,243,254 1,623,224 3,299,373

Top 15 States 289,118 306,156 421,241 525,132 722,724 1,480,364

Middle 20 States 450,709 490,490 702,320 720,985 964,740 2,024,702

Bottom 15 States

UI DEBT, RANK

Top 15 States 543,623 556,333 697,676 923,577 1,173,945 2,440,524

Middle 21 States 330,826 336,931 446,749 789,672 1,073,827 2,196,371

Bottom 14 States 434,746 475,775 682,219 680,675 936,744 1,909,768

FIPS CODE

1 Alabama 333,101 305,288 463,904 424,335 485,504 1,397,701

2 Alaska 24,804 32,539 57,943 69,650 112,961 213,964

5 Arkansas 199,102 226,726 305,090 383,321 459,712 898,119

6 California 3,458,533 3,563,456 4,446,217 5,167,743 6,574,634 11,793,887

8 Colorado 173,303 189,813 253,805 314,000 459,550 943,771

9 Connecticut 218,058 219,415 300,767 625,483 892,818 1,968,977

10 Delaware 44,430 40,371 60,047 74,327 103,985 217,972

11 Dist. of Columbia 100,211 97,239 109,232 310,133 376,404 595,723

12 Florida 573,895 761,211 1,494,604 947,572 1,606,461 4,128,399

13 Georgia 502,906 541,497 853,560 753,423 1,136,633 2,419,613

15 Hawaii 98,084 99,334 98,628 140,703 167,704 316,228

16 Idaho 38,564 44,420 85,500 74,344 114,385 257,507

17 Illinois 1,057,684 1,042,858 1,284,006 1,743,750 1,964,261 3,926,844

18 Indiana 302,931 322,288 491,660 757,243 1,067,976 2,361,801

19 Iowa 232,770 228,242 275,570 365,718 490,937 893,926

20 Kansas 160,027 173,079 225,653 264,879 338,139 765,951

21 Kentucky 461,424 442,638 575,135 545,278 737,642 1,734,122

22 Louisiana 414,987 474,497 679,669 746,749 980,508 2,980,130

23 Maine 137,601 126,846 160,622 242,368 337,493 729,543

24 Maryland 325,428 317,591 392,040 663,956 915,545 1,761,763

25 Massachusetts 512,059 559,582 685,963 1,497,633 2,080,719 4,269,891

26 Michigan 1,287,115 1,124,435 1,136,712 1,740,600 2,064,965 3,836,455

27 Minnesota 347,658 335,161 412,642 1,019,680 1,217,490 1,937,180

28 Mississippi 307,499 371,094 486,988 316,209 452,555 1,032,484

29 Missouri 357,556 384,729 555,723 557,784 754,702 2,090,891

30 Montana 55,965 58,445 70,617 106,594 157,072 276,132

31 Nebraska 94,128 107,037 146,481 172,110 248,936 481,979

32 Nevada 31,114 39,650 75,083 72,041 98,338 327,227

33 New Hampshire 42,832 34,164 68,734 121,658 170,145 637,847

34 New Jersey 609,633 537,322 697,689 1,171,941 1,764,732 3,995,638

35 New Mexico 87,401 105,143 202,241 154,485 225,819 483,045

36 New York 2,308,162 2,257,892 2,586,193 7,578,569 9,767,511 17,563,400

37 North Carolina 353,980 428,086 783,554 684,345 1,019,777 2,482,381

38 North Dakota 38,294 44,379 57,223 118,241 193,316 248,851

39 Ohio 1,090,941 1,127,845 1,387,189 1,836,642 2,522,168 4,599,700

40 Oklahoma 254,921 267,242 341,433 443,661 613,505 996,895

41 Oregon 152,614 190,013 294,619 262,491 369,764 806,870

42 Pennsylvania 1,175,614 1,240,784 1,221,348 1,954,153 2,483,762 5,228,199

44 Rhode Island 104,488 100,382 189,411 257,261 335,058 751,171

45 South Carolina 327,371 273,495 420,688 357,787 509,006 1,506,385

46 South Dakota 34,341 41,869 63,540 96,881 130,701 235,930

47 Tennessee 367,358 488,482 796,307 628,821 1,040,092 2,337,667

48 Texas 795,601 1,077,441 2,013,977 1,590,732 2,123,261 5,909,897

49 Utah 79,642 87,270 138,223 153,684 208,327 415,479

50 Vermont 51,207 51,610 76,256 88,425 112,624 232,582

51 Virginia 306,210 329,897 510,833 569,380 788,109 1,534,622

53 Washington 357,527 446,744 569,323 585,434 914,006 1,952,587

54 West Virginia 206,029 224,080 312,913 177,894 315,058 922,757

55 Wisconsin 573,615 468,855 440,624 1,006,766 1,220,136 1,952,187

56 Wyoming 19,417 28,919 41,748 29,339 49,049 116,142