The impact of state economic differentials on household welfare and labor force behavior

Journal

of Public

Economics

28 (1985) 25-58.

North-Holland

THE IMPACT OF STATE ECONOMIC DIFFERENTIALS ON HOUSEHOLD WELFARE AND LABOR FORCE BEHAVIOR Rebecca M. BLANK* Woodrow Wilson School, Princeton University, Princeton, NJ 08544, USA Received July 1984, revised version

received

March

1985

This paper calculates state-specific income expectations for low-income households, using a simultaneous model of household labor force and welfare participation decisions. A variety of simulations indicate the interlocking effects of existing state differences in welfare, wages and taxes on work/welfare choices and income. Significant differences among similar households in different states occur. Equalization of welfare benefits eliminates some but not all of these differences, but generates potentially serious policy problems for the states.

1. Introduction The current major U.S. welfare program available to low-income households, Aid to Families with Dependent Children (AFDC), varies greatly between states. The highest-benefit state pays more than four times what the lowest-benefit state pays in maximum possible benefits to a family of four. The impact of these differences are much discussed. Concerned groups in low-benefit states protest inadequate support, while the states claim they cannot afford higher payment levels because they cannot support the large number of additional participants which higher benefits would produce. Meanwhile, state officials in high-benefit states worry about large caseloads, low work incentives, and potential in-migration of poorer households. Finally, there is a continuing debate that focuses on the issue of intracountry equity: is it fair if the same household faces very different welfare options in different parts of the country? Yet, information on welfare benefit differentials alone may predict very little about interstate differences in welfare recipiency, labor force participation or income. In particular, high wage rates and/or low tax rates might *The author expresses deep appreciation to Henry Farber for his advice during the writing of this paper. Additional useful comments were also made by Jerry Hausman, Lester Thurow, and two anonymous referees. An earlier version of this paper was presented in seminars at MIT, Princeton, Cornell, University of Maryland, Northwestern, Wesleyan, University of Wisconsin and Columbia. The author appreciates the comments made by participants at these sessions. Of course, all remaining errors are the responsibility of the author. Support funding for this work was provided by the Sloan Foundation and the National Science Foundation. 0047-2727/85/$3.30

0

1985, Elsevier

Science Publishers

B.V. (North-Holland)

26

R.M. Blank, Household welfare and labor force behavior

induce significant labor force participation and low welfare recipiency even in the presence of high benefit levels. This research focuses on estimating the total effect of all state economic differences. To the extent that differences in state benefit levels and AFDC tax rates are offset by differences in state cost of living, or in wage rates or non-AFDC tax rates, then the state AFDC differences are less worrisome. Alternatively, to the extent that the differences in the AFDC parameters do not seem to produce significant effects on behavior (incentive effects are small or elasticities are low) then the state welfare differences are again of less concern. This research investigates the range of interstate economic differences facing households potentially eligible for welfare and asks how the existing wage, welfare and tax differentials affect welfare participation, labor force involvement and overall income expectations. In addition, this paper investigates the interactions between these state-level economic variables and household demographic characteristics. Previous research on this subject can be grouped into three categories. Early studies investigated the relation between aggregate state welfare or labor force participation rates and average welfare payments, along with other aggregate state-level measures. ’ More recent micro-data studies have looked at the welfare participation decision without considering labor supply, or have looked at discrete labor supply decisions, without attention to the welfare participation decision. 2 The one exception to this is Moflitt (1983) who estimates joint labor supply/welfare decisions. However, his research, like the others, does not focus on cross-state differences, nor take into account the range of economic variation between states beyond welfare differentials. A third area of research has analyzed the results of the Negative Income Tax (NIT) experiments of the 1970~.~ These studies typically analyze the elasticity of labor supply to the presence of a particular NIT plan. While these results indicate something about the sensitivity of the labor force to transfers, they say little about the comparative incentive/disincentive effects of the existing patchwork of state AFDC programs in combination with other state economic differentials. In contrast with previous research, the focus of this paper is on the effect of the enormous state-level differences in the AFDC program that exist in the United States, and the interaction between these differences and other regional economic disparities. This is not a subject which previous research has directly addressed. A methodology is developed which allows us to jointly estimate the parameters of a discrete welfare participation choice and a continuous labor supply choice for individuals scattered across 48 different states. The crucial variables of the model will be the state welfare, wage and

‘See Brehm and Saving (1964) or Gartinkel and Orr (1974) ‘See Barr and Hall (1981), Hosek (1980), or Levy (1979). %ee Hausman (1980) or Ashenfelter (1983).

R.M. Blank, Household welfare and labor force behavior

21

tax levels specific to each household, along with information on individual household characteristics. With the resulting coefficients, we can calculate the probability that any household in our sample would participate in welfare in any given state and their expected labor supply in that state, either as a welfare participant or as a nonparticipant. We can also go a step further and calculate expected household income in each state as a function of the welfare/labor force choices and household characteristics. This leads to a variety of simulations which determine the extent to which the differing wage and welfare rates among states induce significant behavioral and income differences among similar households. The primary welfare program this study investigates is the AFDC program. The low-income group of interest is those female-headed households which contain children and whose ‘other’ (i.e. non-labor, non-public assistance) income is limited. These are the households for whom AFDC is most readily available.4 We shall find that significant differences exist between states in the economic incentives facing low-income households. As a result, similar households may make very different welfare/labor force choices in different locations. This also translates into significant interstate income differentials. Both wage and welfare differences between states significantly impact labor force and welfare participation. Wage and tax differences between states do not offset the AFDC differences (and in fact they often reinforce them). Equalization of welfare benefits would probably create more uniform labor supply and welfare participation behavior, but could generate potentially serious policy problems for the states. Household characteristics also have a strong influence on welfare participation probabilities and work behavior, yet even among relatively similar households, a large variance occurs in the ‘taste’ for welfare. Section 2 of this paper discusses the data set used here. Section 3 presents a set of estimation techniques. In particular, for each household in the sample, expected wages and tax rates must be estimated, along with potential welfare payments. These inputs feed into a simultaneous model of the labor supply/welfare participation decision. The parameters of this estimation can be used to calculate expected behavior and household income across all states. In section 4 these results are used to estimate a variety of simulations which answer the questions outlined above. Finally, section 5 summarizes results and ends with comments on the limits of this study.

“AFDC is of course equally available to single-parent male-headed families and in 25 states in 1979 it was also available to intact two-parent families who have experienced extensive unemployment. However, in 1979 adult males were present in less than 10 percent of the AFDCrecipient households. Certainly the major population who use the program and for whom it has been historically targetted are single-parent, female-headed households.

28

R.M. Blank, Household welfare and labor force behavior

2. The data

The data for the empirical work in this paper come from the Current Population Survey’s March Supplement in 1979.5 The March supplement contains the regular monthly CPS questionnaire on labor force status along with a variety of additional questions regarding income sources and amounts. This indicates not only who is receiving AFDC money and how much they receive, but also what the amounts and source of their nonAFDC income are. In addition, there is information on labor force status, wage rates and hours (for workers), and a variety of personal characteristics. There are 66275 observations in the survey in total. Most of these were eliminated on the basis of several selection criteria. All household heads who lived outside the continental United States or were under age 18 were omitted. The data was then sorted to include only household heads with children present in the household whose non-labor, non-welfare (‘other’) income was less than $5000.6 Taking just female-headed households provided a sample of 2459 observations. All of these sorts are on variables exogenous to the study and should not induce selection bias. This sample is composed of 1058 AFDC recipients of whom 42 percent work and 1401 non-welfare recipients of whom 93 percent work. The variables used in various parts of this study are defined in table 1. The mean characteristics of the sample are shown in table 2. Table 2 presents a variety Table

1

Data definitions. AGE BEN CH<6 # DEP EDUC

Age in years. AFDC benefits in dollars per month. Number of children in household less than 6 years old. Number of total dependents in household. Average years of education completed.

Dummy variables for years of education: 1 =years less than 12; 0 otherwise. EDUC< 12 EDUC= 12 1= years equal to 12; 0 otherwise. EDUC> 12 1= years greater than 12; 0 otherwise. HOURS MAR RACE SMSA WAGE OtherY

Average hours worked per week. 1 = Currently married; 0 otherwise. 1 = Nonwhite; 0 otherwise. 1 = Resides in a Standard Metropolitan Statistical Area; 0 otherwise. Hourly wage payments. ‘Other’ monthly income = total income - labor income - public assistance. In dollars per month.

5For a complete description of this data source, see U.S. Department of Commerce, Bureau of the Census, Technical Documentation: Current Population Suroey, March 1979 (1980). 6Note that other income includes alimony payments. There are very few households in this survey for whom child support payments exceed $5,000 and most are much smaller. In fact, this sort criterion eliminated very few female-headed households at all. Those that were thrown away tended to be older women with older children.

R.M. Blank, Household welfare and labor force behavior

29

Table 2 Data means. Welfare (P’l) Total sample AGE

participants

Nonworkers (H=O)

Welfare nonparticipants (P=O)

Workers (H>O)

Nonworkers (H=O)

Workers (H>O)

37.43 (12.3)

36.07 (9.3)

0.

0.

34.65 (9.6)

32.46 (9.3)

32.87 (9.5)

102.55 (148.8)

286.32 (128.6)

169.47 (121.6)

CHt6

0.50 (0.7)

0.83 (0.9)

0.60 (0.8)

0.54 (0.7)

0.30 (0.5)

#DEP

2.00 (1.2)

2.43 (1.3)

2.30 (1.3)

1.96 (1.1)

1.69 (1.0)

EDVC (in years)

11.2 (2.6)

(Z:;)

11.0 (2.1)

10.6 (2.7)

11.9 (2.4)

BEN (%/Month)

0.38

0.64

0.43

0.48

0.24

EDVC=

12

0.42

0.28

0.42

0.38

0.48

EDVC>

12

0.20

0.08

0.15

0.14

0.28

EDUC<12

HOURS (weekly)

24.38

0.

22.78

0.

(3.4)

(8.4)

38.26 (1.5)

MAR

0.04

0.03

0.01

0.18

0.04

RACE (percent

0.34

0.42

0.43

0.26

0.28 0.63

nonwhite) 0.76

0.62

0.66

WAGE ($/hour)

2.91 (2.6)

0.

3.12

0.

4.48 (2.1)

Other Y ($/month)

46.05

18.80

69.45

63.27

0.66

SMSA

Number of observations Percent

2459

of sample

‘Standard

deviations

(1.8) 28.18

624

434

107

1294

25%

18%

4%

53%

in parentheses

of expected results. In a year when the minimum wage was $2.90, the AFDC recipients who worked received a mean wage of only $3.12 and worked 23 hours per week on average. Non-welfare recipients who worked had a somewhat higher mean wage of $4.48 and worked 38 hours per week on average, not surprising since this is a group for whom work opportunities should appear more attractive. Levels of other income (which excludes welfare payments) are small for all individuals, though non-welfare recipients have close to three times as much other income available as welfare

J.P.E.-

B

R.M. Blank, Household welfare and labor force behavior

30

recipients. Welfare recipients tend to be younger, non-white, have less than a high school education, live in the central city, have more children, and more children under age 6. Patterns between workers and non-workers vary, depending on welfare status. Though all individuals in the sample are identified as household heads, a small percent list themselves as married. This can occur only if the husband is permanently absent from the household but the wife does not consider herself separated or divorced. A dummy variable separating this group from the others is included in several of the estimations, but rarely has any additional explanatory power. One possible source of error in this data is unreported income. Underground economic activity, not reported on tax forms, to case workers, or to census data collectors, is commonly believed to be extensive among lowincome groups. Unfortunately, the extent to which this occurs can only be guessed at, and no existing data set eliminates this concern. Given the incentives among this group to cheat (i.e. high marginal tax rates and high marginal utility of income) we can probably assume that both wages (w) and other income (OtherY) will be consistently undermeasured, creating an errors in variables problem. In general, we would expect this to bias any estimate of the effect of these variables downward. To the extent that we find wages and other income have significant effects even with this data difficulty, we might want to believe that our estimates are a minimum measure of the true effects.

3. Estimating 3.1. Estimating

expected wages, welfare, work and total income across states expected

wages

Expected wages must be estimated for two reasons. First, wages must be imputed for those people not currently working in order to explore their work/no work decision. Second, to the extent we are interested in interstate differences, the expected wage a worker would face if she were located elsewhere must be calculated. In order to reach these estimates it is assumed wages are associated with a variety of predetermined characteristics of the worker. This allows us to first estimate expected wage rates, and then use these wage rates as exogenous inputs into labor supply decisions. To the extent that wages depend on hours worked our wage estimates will be inconsistent. Wages are assumed to reflect a variety of personal characteristics which add or detract from job performance. For any individual i, this is typically specified by the equation: In (wi) = Xib + ei, where wi is the wage rate for individual vector of individual i’s characteristics,

(1) i, ei is a random variable, Xi is a usually including education, ex-

R.M. Blank, Household welfare and labor force behavior

31

perience, race, sex and perhaps other variables, and b is a vector of corresponding coefficients. However, straightforward OLS estimation of this equation is prevented by two problems. First, we have a strong concern with geographical differences in income expectations. Long-term wage variation between regions has been clearly noted in previous empirical work - many researchers include at least one or two regional dummies in their wage equations. Since we are interested in state-level differences, we include 48 dummies for the various states’ (and exclude a constant). This creates a state-specific constant for individuals in each state in the wage regression. Thus, eq. (1) becomes: In ( wi)= Csia + X,b + q,

(2)

where Csi is a vector of state dummies, equal to 1 in the state where individual i lives and 0 elsewhere, while a is the coefficient vector for these dummy variables. The statistical difference between eqs. (1) and (2) will be tested below. Eq. (2) can still be readily estimated by OLS as long as there are enough individuals in each state to include the 48 dummies along with the demographic variables. Second, there is a problem of censored data. Wage rates are observed only for workers, implying the error term of eq. (2) is not unbiased. Instead, it has an expected value conditional upon hours being greater than zero: E(eIH>O). In order to estimate wages properly, this conditioning must be accounted for. Unfortunately, this produces a simultaneity problem with the hours of work decision. We want wages that are exogenously determined prior to making labor supply decisions. Yet, in order to consistently estimate wages, we need a model that determines whether or not hours are greater than zero. There are two ways to handle this. One alternative is simply to use the OLS estimates from eq. (2) on the assumption that they are close to accurate and ignore the censored data bias. The second alternative is to construct a joint model of labor force participation and wage rates which corrects for the censoring problem but which is not necessarily consistent with the fuller model of labor supply discussed below.* To implement the second alternative assume that individual i determines whether or not to participate in the labor force on the basis of some underlying utility measure which can be described by a linear function of individual characteristics. Then LF; = Zig + ui, ‘All observations for Alaska or Hawaii were omitted. ‘There is a third alternative, which is to estimate a completely simultaneous and consistent model of all economic variables, including wages, labor supply and welfare participation. Unfortunately, the econometric complexity involved in this larger model is too much to handle.

32

R.M. Blank, Household welfare and labor force behavior

where Zi is a vector of characteristics, g is a parameter vector, and ui is a random variable. LFf is a latent measure which determines the utility difference between being in or out of the labor force. We cannot observe LFF, but we do observe a discrete variable LF,, where

LF,= 1, if LF: > 0 (in labor force), and

LF,= 0, if LFT 5 0 (out of labor force). If u is assumed to have a standard normal distribution, then eq. (3) becomes a typical probit model of labor force participation. Wage eq. (2) is estimable only if LFi= 1, thus individual i can be in two possible situations. Either she works and has a known wage rate, or she chooses not to work. These two states can be described as

=Pr(Ui> -Zig, e,=ln K-CSia-Xib)

(4)

and Pr (LF,=O)=Pr (LFFSO) = Pr (ui _I - Z,g).

(5)

If we assume e and u have a bivariate normal distribution, likelihood that individual i is observed in either situation is Li=(LFi)*Pr(ui>

-Zig,ei=ln

then the

y;--C,~U-Xib)

+ (1 - LFi)* Pr (pi 6 -Zig).

(6)

The log of this likelihood function, summed across all individuals, can be maximized with respect to g, a, b, the variance of e, ce, and the correlation coefficient between e and u, r_.’ As mentioned above, this model is not consistent with the complete model of labor supply developed later on. In addition, the vector Z should include all the variables that influence wages (providing a reduced form specification of wages in the utility comparison), along with the other exogenous variables which influence labor force participation. This results in a computational problem, for it means including 48 state dummies in both the wage and labor ‘For more information and Hausman (1978).

on this type of censored

data model,

see Lee (1979), or Griliches,

Hall

R.M. Blank, Household

welfare and labor force behavior

33

force participation equations as well as the other parameters of the model, giving us well over 100 coefficients to estimate. This is computationally infeasible so the state dummies have been omitted in the labor force participation equation. In response to the ambiguity regarding the appropriateness of the censored data model’s specification, and the difficulties in estimating all of its parameters, the wage equations have been estimated both by simple OLS techniques and by maximum likelihood using the censored data model. The results are presented in table 3. Column 1 contains the OLS estimates of Table 3 Wage estimation.

Dependent

variable: In (WAGE). Censored

OLS coefficients EDUC<

12

EDUC=

12”

EDUC>

12

- 0.2232* (0.0309)

data model

Wage coefficients -0.0124 (0.0356) -

Participation coefficients -0.5952* (0.0633) -

0.1942* (0.0305)

0.1452 (0.0399)

0.2358* (0.0878)

0.0552* (0.0091)

0.0402* (0.0095)

0.0348 (0.0200)

AGE SQUARED

-0.0006* (0.0001)

-0.0005* (0.0001)

MAR

-0.0092 (0.0683)

0.0780 (0.0834)

- 0.2267* (0.1340)

RACE

-o.O4s1* (0.0294)

-0.0103 (0.0339)

- 0.0325 (0.059 1)

AGE

-

CH<6

-0.2994* (0.0399)

# DEP

-0.1217* (0.0223)

CONSTANT

no

no

48 state dummies

yes

yes

1728 R2=0.215

2459 oe =0.6048* (0.0073)

0,=0.5214

r,, = - 0.8400; (0.0249)

Number of observations

-0.0004 (O.OcO3)

“Standard errors in parentheses. bOmitted for identification. *Significant at the 1 percent level.

0.4659 (0.3690) no

34

R.M. Blank, Household welfare and labor force behavior

eq. (2), while column 2 contains the maximum likelihood estimates from eq. (6). The coefficients are all of the expected sign in both regressions. The magnitudes are uniformly lower in column 2 where the censoring problem is accounted for (except for the marriage dummy, which is insignificant as discussed above.) This indicates that the same demographic variables which determine wages are important in determining labor force participation. Thus the OLS coefficients contain more than just wage effects - they also partly reflect the conditional event that workers have already chosen to participate. The separate constant terms across states demonstrate clear geographic differences. For instance, in the OLS regression, the dummy coefficients vary from 0.401 (in Illinois) to -1.503 (in New Hampshire). Testing these results against the hypothesis of a single constant shows that a single constant can be rejected at the 1 percent level. This reinforces our belief that significant wage disparities exist between areas and should affect behavior. Note there are not significant differences between all state constants. For instance, Illinois is part of a group of high-wage states whose state-specific constant terms are statistically indistinguishable from each other. The variance on the wage estimates (crJ is similar for both estimation techniques. The negative correlation coefficient (r,,) in the censored data model indicates that the unexplained errors in this model occur for individuals with a high estimated probability of labor force participation but a low estimated wage rate (or vice versa.) For the rest of this paper the OLS coefficients are used to derive wage estimates. This decision is based first on the uncertainties about the specification of the labor force participation equation in the light of the model of labor force behavior presented below and second on the computational inability to estimate all the proper parameters of the full censored data model. Third, the work has been duplicated using the alternative wage estimates and no significant differences emerge in the labor force/welfare participation results described in section 3.3 below. From this, it seems safe to conclude that the OLS wage estimates do not contain significant bias and can be used as a measure of expected wages in each state. 3.2. Calculating

AFDC

benefit levels and tax rates

Up to this point we have referred generally to welfare benefits without being explicit about how they are determined and how they interact with other earned income. This section will briefly review the AFDC program as it is currently administered by the states and then discuss the calculation of benefit levels. Welfare in this paper means AFDC, the Aid to Families with Dependent Children program. In 1979 there were 10.2 million recipients of AFDC

R.M. Blank, Household welfare and labor force behavior

35

grouped into 3.5 million families, for a total program cost of $12 billion dol1ars.i’ This makes AFDC the largest social welfare program available to low income groups.” 9.5 percent of all children in the country were in families receiving some AFDC funding. According to a process established by the Federal government, each state establishes a need standard which is defined as the amount of money necessary for a family with a given number of dependents to live ‘adequately’ in that state. However, states’ definitions of ‘adequate’ vary widely, and as a result so do their need standards. Column 1 of table 4 lists the 1979 state need standards for a family of 1 adult and 3 children. Texas is at the bottom with a standard of $187, while Vermont tops the list with a standard of $656, 34 times as high. Little of this difference can be explained by cost-of-living differentials. There was only an 18 percent cost differential between the states with the highest and the lowest cost-of-living in 1979.i2 Actual benefit payments vary even more widely. States can claim inability to meet their need standards and pay whatever reduced amount they choose. Some states do this by setting maximum payment levels. Others simply pay a percentage of the need standard and others have more complex formulas. The second column of table 4 lists actual maximum benefit payments available for a family of 4. Here the difference is between Mississippi at $120 and Vermont at $524 - a ratio of 4.36 to 1. In 1979 only 24 states paid the full need standard, 3 paid the full standard for smaller families but set maximums on benefits available to larger families, while the remaining states all limited payments in a variety of other ways. If no other income is available to a family, the actual benefit payment is just the maximum payment allowed in the state. Once other income is also available, the Federal government has mandated a uniform formula to determine payments. Federal legislation requires that the first $30 of monthly income be exempted from consideration and that one-third of all income “‘U.S. Department of Health and Human Services (1982). “The only other social program close to the magnitude of AFDC aimed at low income families is the food stamp program. In the mid-70s food stamps were the larger program, but cutbacks have reduced its size relative to AFDC. Food stamps do partially offset the inequities in AFDC since their availability levels are Federally standardized. Food stamps have been imputed into the income estimates of this study and made little difference in the simulation results presented in section 4. They somewhat changed the magnitude of expected income, but not the relative comparisons across states or individuals. They are not imputed into these expected income calculations, “State cost-of-living differentials are calculated from the annual data on low income budgets for a family of 4 available from the Bureau of Labor Statistics (1980). Regional metropolitan and non-metropolitan averages are weighted by the state population in each category. For those 24 states in which particular city information is also collected and published, this information is included in the calculations, weighted by the percent of population in that city. At the state level, cost-of-living differentials are not large only 18 percent in this year. The largest differences in cost-of-living occur between city and rural areas within a state, for which we have no data.

36

R.M. Blank, Household weljare and labor force behavior

Table 4 State

need standards

Alabama Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana

and maximum benefit payments, as of September needy adult and 3 dependent children. Need standard

Maximum payment

$240.00 282.00 273.00 511.00 327.00 446.00 287.00 230.00 227.00 421.00 315.00 363.00 419.00 350.00 235.00 446.00 349.00 314.00 419.00 480.00 454.00 252.00 365.00 331.00

S148.00 240.00 188.00 487.00 327.00 446.00 287.00 230.00 170.00 366.00 315.00 275.00 419.00 350.00 235.00 187.00 332.00 294.00 419.00 480.00 454.00 120.00 270.00 331.00

Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pensylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

Source: Characteristics of State Plans for AFDC, Research Admin. (1980).

Tables,

1979 for a family

of 1

Need standard

Maximum payment

$370.00 341.00 382.00 386.00 242.00 476.00 210.00 389.00 431.00 349.00 537.00 373.00 389.00 229.00 361.00 217.00 187.00 519.00 656.00 293.00 483.00 332.00 579.00 340.00

$370.00 297.00 382.00 386.00 242.00 476.00 210.00 389.00 327.00 349.00 456.00 373.00 389.00 140.00 361.00 148.00 140.00 389.00 524.00 263.00 483.00 249.00 510.00 340.00

U.S. Dept of HHS, Sot. Sec.

above $30 be disregarded. l3 Thus, the benefit is decreased by $0.67 for each dollar of earned income over $30. In addition there are a variety of other income deductions allowed, primarily for job-related costs and child care. The rules on these vary from state to state. Some of these deductions are constant and others vary as hours of work change. However, for a given level of hours a certain deduction level, D, is determined and is subtracted from income before it is taxed. If B denotes actual benefits paid, G the maximum amount of benefits available, and D the additional claimed income deductions, then B=G-(2/3)(w*H+OtherY-30-D).‘4

(7)

i3These parameters were changed in the Omnibus Budget Reconciliation Act of 1981. Since that time, the $30 disregard has been eliminated, and the tax rate (after deductions) is 100 percent. Only for participants in their first 4 months on AFDC is the 3% disregard still in effect. i4This basic equation ignores some of the complexities of state formulas in those states that pay less than the full need standard. However, it is close to accurate in most cases. The more complex calculations are done whenever benefits need to be estimated.

R.M. Blank, Household welfare and labor force behavior

Fig. 1. Budget

line

facing potential AFDC recipient (for graphical convenience, income deductions are set to zero in this figure).

3-i

additional

The result of these rules is a rather oddly shaped budget line for those eligible for welfare, as fig. 1 indicates. After $30 and until the breakeven point BE when benefits cut off, participants face a 67 percent tax rate. Beyond the breakeven point families return to the much lower rate of the progressive income tax schedule. Clearly the amount of benefits a worker receives and the hours she works will be jointly determined. While we know the exact value of G for each state, the value of D available to and claimed by any individual is almost impossible to calculate. The net effect of these deductions is to lower the effective marginal tax rate for welfare recipients below two-thirds by exempting some income from taxation. Thus we calculate an average marginal tax rate for each state based on the tax rate paid on full income once the state average amount of ‘other income deductions’ per household are taken into account. The specifics of this calculation are presented in the appendix. This provides a tax rate for welfare recipients, t,, which varies from 0.344 in Tennessee to 0.558 in Connecticut.15 Then the benefit calculation for any individual becomes B = G - t,(w*H + UtherY - 30),

(7’)

where both G and t, vary by state. “For a good discussion of effective marginal tax rates for AFDC participants, see Hutchins (1978). Both Hutchins and Mofitt (1983) estimate marginal tax rates via OLS equations. Instead, we compute actual rates, taking the institutional rules in each state into account. Given accurate institutional data, this should be a more accurate procedure. Though the resulting estimates are difficult to compare, the results in this study seem to be generally somewhat higher than those estimated by Hutchins or Moffitt.

38

R.M. Blank, Household welfare and labor force behavior

3.3. A joint model of weEfare recipiency and labor force participation

Estimation of an individual’s welfare participation decision is a muchresearched issue. As noted above, early studies attempted to explain aggregate state participation characteristics. The negative income tax experiments of the mid-70s provided individual-level data and most recent work has focused on individual decision-making, typically doing a variety of multinomial logit or prohibit estimations to determine participation probabilities.“j Some of the most sophisticated work to date has been done by Hausman (1980), estimating an utility maximizing model that searches across all budget set segments, jointly codetermining participation and hours. More recently, Moff’tt (1983) has developed a variant of the Hausman model which allows for the impact of nonpecuniary factors on participation, which he describes as stigma effects. The model described below which this paper utilizes follows directly from the Hausman and Moffrtt procedures. The decision to participate in AFDC depends upon a relative comparison between the utility level achievable while receiving welfare versus utility without welfare. We can make this comparison using the indirect utility function, V(W,, Y,), where W, is the net wage and K is total nonlabor income. V(W,, Y,) is the solution to the constrained utility problem along a linear budget line, V( W,, x) = max,[ U(X): X 5 x + W,*H], where X is a composite good whose price is 1. The indirect utility available from participating

in welfare is

V(w( 1 - t,), OtherY + qB) + M,”

where the coefficient q allows for the possibility of different utility weights on welfare income (I?) and other nonlabor income (OtherY). The M term captures nonpecuniary effects of welfare participation similar to those estimated by Moflitt. We shall assume: M=Sd+u,

where S is a vector of personal characteristics, d is a coefficient vector, and u is a random error term. This allows individual utility associated with welfare to be affected by variables outside of the economic impact of net wages and nonlabor income. These effects will vary with the individual characteristics S. Among the possible components of S are such items as education, race and ‘%ee Hosek (1980), Barr and Hall (1981) or Ashenfelter (1983). l’We assume there is one linear budget line for welfare participants, ignoring the untaxed budget segment between G and G + 30, due to the $30 exemption. This exemption small enough to be ignored without serious problems.

brief

seems

R.M. Blank, Household weljare and labor force behavior

39

family size. In addition, the inclusion of the random term assumes that there is some stochastic element to these nonpecuniary effects. The indirect utility available to an individual who does not participate in welfare is V( w( 1 - trip), Other Y).

This indirect utility is the result of maximization along a linear budget line with a given tax rate. In reality there are nonlinearities in the progressive income tax schedule. Tax rates for nonrecipients depend upon hours in a nonsimple manner, for as an individual increases work hours she moves into new tax brackets at irregular intervals. Rather than dealing with this complexity, a single expected marginal tax rate has been constructed for each size of household in our particular sample population in each state. (See the appendix for a description of how this is done.) This allows a single specification of indirect utility for those who do not participate in welfare, by giving them one linear budget line along which utility is maximized. Such an assumption has some validity. First, the major nonlinearity facing any household is the jump between net wages of participants and nonparticipants, occurring at the breakeven point (BE in fig. 1). This nonlinearity is eliminated by separating participants and nonparticipants in the estimation process. Second, because the sample is grouped at one end of the income scale, the number of possible tax brackets they face is not large. For instance, a family of 1 adult and 3 dependents earning $11500 or less faces only four possible tax brackets. Third, tax rates are not the major focus of this work and the cost of dealing with tax nonlinearities is high in terms of estimation cost and complexity. This paper is mainly concerned with the variance in tax rates across states, and using average state marginal rates for each household size takes this into account. The participation decision requires evaluation of the indirect utility function at two different points: P*=Q+M-V&,,

(8)

where V, = V( w( 1 - t,), Other Y + qB) and V,, = V( w( 1 - td, Other Y). (Recall that there is a stochastic term imbedded in M. The other terms are assumed to be nonstochastic.) If P* is greater than zero an individual will choose to receive welfare. Our problem is to find a specification for these utility comparisons. Following Hausman (1980), we know there is a correspondence between indirect utility and demand equations defined by Roy’s identity

40

R.M. Blank, Household welfare and labor force behavior

where H* is desired hours of work. Choosing a linear specification for hours we have

(10)

H*=aW,+bY,+Zg+e,

where Z is a vector of individual characteristics and g is its coefficient vector. a and b are the coefficients on net wages and nonlabor income and e is a random error term that allows for stochastic variation in hours. Substituting (10) into (9) provides an ordinary differential equation which can be readily solved. Letting the constant of integration, c, be our cardinal utility index, the solution is V( W,, Y,) = c = ebw(bzY, + baw, + bZg - u).~’

(11)

Since W, and Y, are different for participants and nonparticipants, the indirect utility functions VP and V,, contain different variables. Thus we can rewrite (8) as

=ebw(1-‘P)(b20therY + b2qB+ baw(1 -rJ+

bZg-U)

-ebwcl -‘“p)(b20therY + buw(1 - t,J + bZg-U) + Sd+ U.

(12)

Recall that S is the vector of personal characteristics which describes the nonpecuniary effects in welfare participation decisions. Eq. (12) provides a welfare participation decision equation that is fully consistent with the linear labor force supply decision, H*=aw,+bY,+Zg+e, if

P*>O

W,=w(l-ttP)

and

Y,=OtherY +qB, if

P*IO

W,=w(l-&)

and

Y,=OtherY.

“This function is unique up to a monotonic transformation. See Hausman discussion of other mathematical properties of this indirect utility function.

(13)

(1980) for a brief

R.M. Blank, Household welfare and labor force behavior

41

Though the independent variables which influence desired hours vary between welfare participants and nonparticipants, the hours coefficients for both groups are the same. This simply implies that given the same marginal conditions, participants and nonparticipants will respond similarly. In reality, marginal conditions are not the same. w(1 -t,,) is less than w( 1 -trip), and (OtherY + @) is greater than OtherY Thus this model claims that the difference in hours worked between welfare participants and nonparticipants is not due to different underlying behavioral patterns but to differences in the marginal incentives these two groups face. Eqs. (12) and (13) both involve unobservable variables. P* is a latent underlying measure of welfare utility comparison. We can observe only whether the individual is on AFDC or not on AFDC. Denote this by an indicator variable P where P=l

if

P*>O

P=O

if

P*sO.

and

Similarly, H* is a measure of desired hours, which we do not know. We observe only actual hours worked, H, which is truncated at zero. Thus we observe H=H*

if H*>O

and H=O

H*sO.

Our final model specification is a joint determination and hours of work: P* = ebw(’-‘p)(b20therY

of welfare recipiency

+ b2qB + baw( 1 - t,) + bZg - a)

- ebw(’-‘“p)(b20ther Y + baw( 1 - t,P) + bZg - a) + Sd + U,

(14)

where we observe P= 1 if P* >O, and P= 0 otherwise. For simplicity we write this as the probit model: P*=R+u.

For participants labor supply is determined by H*=aw(l-t,)+bOtherY+bqB+Zg+e,

(15)

R.M. Blank, Household weljare and labor force behavior

42

where we observe H = H* if H* > 0, and H = 0 otherwise. For simplicity we write this as H*=Q,+e.

Similarly, for nonparticipants,

labor supply is determined by

H* = aw( 1 - t,,) + bOtherY + Zg + e,

(16)

where we observe H = H* if H* > 0, and H =0 otherwise. For simplicity we write this as H*=Q,+e.

In eqs. (14)-(16), participation in welfare is determined by a probit model which is estimated simultaneously with a tobit hours supply model, providing consistent estimates of the parameters and taking advantage of the crossequation constraints between labor supply and welfare participation. Assuming bivariate normality between e and u, the joint participation/ hours supply decision has four possible outcomes: Pr(P=l,H=O)=Pr(P*>O,H*50) =Pr(u>

-R,el

-QJ,

Pr(P=l,H>O)=Pr(P*>O,H*=Q,+e) =Pr(u>

-R,e=H-Q,),

Pr(P=O,H=O)=Pr(P*10,H*50) =Pr(us

-R,es

-Q,),

Pr(P=O,H>O)=Pr(P*gO,H*=Q,+e) =Pr(us The contribution

-R,e=H-Q,).

to the likelihood function by a worker is

Li=PTPr(ui>-Ri,ei=Hi-Q,i)+(1-Pi)*Pr(ui~-Ri,ei=Hi-QQ,i). (17)

R.M. Blank, Household welfare and labor force behavior

For nonworkers the contribution Li=PTPr(ui>

-Ri,eis

43

is -Qpi)+(l-Pi)*Pr(uis

-Ri,eis

-Qni).

(18)

The log of the sum of this likelihood function for all individuals can be maximized with respect to a, b, g, q, the variance of e, ge, the variance of U, gU,and the correlation coefficient between e and u, reu. Note that the variance of u, normally unidentifiable in probit models and standardized to 1, is estimable in this model as the inverse square of a coefficient of R. The results of maximizing this likelihood function are presented in table 5. In column 1 are the coefficients of the hours equation, calculated jointly from Table 5 Labor

force-welfare

Hours” coefficients CONSTANT

38.6880b* (0.9339)

Other Y

- 0.0542* (0.0025)

BEN’

-0.1102* (0.0038)

K,” EDUC<

participation

estimation.

Nonpecuniary participation coefficients - 3.0398* (0.3698)

1.0187* (0.0371) 12

-4.8953* (0.7148)

0.9333’ (0.1734)

- 1.0241 (0.8782)

-0.3662* (0.1591)

RACE

- 1.4578* (0.6743)

0.8379* (0.1458)

(T”= 1.7432* (0.2495)

CH<6

-4.0532* (0.4368)

0.6252* (0.1133)

r= -0.5236* (0.0524)

0.8035* (0.2576)

-0.0191 (0.0424)

EDUC=

12’

EDUC>

I2

# DEP SMSA Number = 2459

o, = 17.6977* (0.4108)

-0.3792* (0.1091) of

observations

“Hours measured as hours/week. %tandard errors in parentheses. ‘The coefficient on benefits is the multiple bq in eq. (15). dFor welfare participants, w, = w( 1 - t,); for nonparticipants, “Omitted for identification. *Significant at the 1 percent level.

w, = w( 1 - t,,).

44

R.M. Blank, Household welfare and labor force behavior

the labor force supply and welfare participation equations. In column 2 are the additional coefficients for what we have termed nonpecuniary effects in the welfare participation equation. The results mesh nicely with expectations. Higher wages have a positive effect on hours,lg while other income and welfare benefits have a negative effect. The coefficient on benefits is significantly different from the coefficient on other nonlabor income, implying that welfare income is treated differently by individuals and has a stronger negative impact on labor supply decisions than other forms of nonlabor income. 2o Low education decreases work hours as does minority status and young children. However, having more total dependents increases work, indicating that the income needs of larger families dominate the higher value of time-at-home for a mother with more children. The only counterintuitive result is the negative coefficient on hdgher levels of education. However, this coefficient is small and insignificant. The nonpecuniary effects associated with welfare participation come through strongly, indicating there are differentials in the propensity to be on welfare which exist outside of the simple wage and nonlabor income calculations. (The hypothesis that the coefficients in column 2 are zero can be rejected at the one percent level.) In particular, less educated minority women with more young children are more likely to participate. The negative sign on total dependents is puzzling, but insignificant. The SMSA variable was expected to have a positive sign, indicating welfare is more readily available to city dwellers. The negative coefficient on SMSA status may indicate the presence of more ‘underground’ economic opportunities in the city as alternatives to welfare. Or it may indicate that the ‘hassles’ to apply for and receive welfare from larger bureaucracies are grea.ter. The standard error of the hours equation is relatively high, primarily indicating that many individuals fall below the zero truncation point for hours. The negative correlation coefficient implies that those people who are more likely to be on welfare are likely to work fewer hours. In summary we have a set of coefficients which calculate the probability of welfare participation and the expected hours of work for labor force participants. These are significantly affected by economic opportunities and household characteristics. “The wage rate for each individual is estimated using the techniques described in section 3.1 above. I do not account for the fact that these ‘expected wage rates’ are measured with uncertainty. As a result, the standard errors reported in table 5 are somewhat lower than the true standard errors. [This criticism is made in Heckman and McCurdy (1981).] However, given the high level of significance on the estimates in table 5, I would not expect adjustment for this effect to change the results. ‘“Since the coefficient on BEN is almost double the coefftcient on Other Y, this implies the value of 4 in eq. (15) is around 2. Moffttt (1983) estimates a similar coefficient which he calls y at 1.42. Moffttt’s hypothesis is that the coefftcient should be between 0 and 1, but these results seem to imply that welfare has a larger negative effect than other nonlabor income on labor supply, not a smaller one.

R.M. Blank, Household welfare and labor force behavior

3.4. Dejining expected

45

income

With the estimates of wages, welfare and work participation calculated above, we can estimate total expected income in any state for any household with a given set of characteristics. ” In particular, four possible income situations can occur: a household can participate or not participate in welfare and can simultaneously choose to work or not to work. Specifically, the expected income in each of these situations is: E( Y(H=O, P= 1) = E(B) + OtherY*(l

-t,),

E(YIH>O,P=l)=E(E)+(OtherY+E(w)*E(H(H>O,P:=l))*(l-t,), E(YIH=O,P=O)=OtherY*(l-t,,).

and E(Y~H>O,P=O)=(E(w)*E(H~H>O,P=O)+OtherY)*(l-t,,),

where E(B) is expected welfare payments at a given number of hours; OtherY is nonlabor, nonpublic assistance income (we shall refer to this as ‘other’ income); t, is the tax rate of welfare participants at given hours; t,, is the tax rate of nonparticipants at given hours; E(w) is the expected wage. Total expected income is then E(Y)=Pr[H=O,P=l]*E(YIH=O,P=l) +Pr[H>O,P=l]*E(YIH>O,P=l) +Pr [N=O, P=O]*E(YIH=O, +Pr[H>O,P=O]*E(YIH>O,P=O),

P=O)

(19)

where P equals 1 if welfare is received, 0 otherwise. The four probabilities in eq. (19) can be simply derived from the bivariate normal distribution function, using the parameters of P* and H* presented *rTechnically, the term ‘expected income’ can be misleading, since 1 am not calculating the mean of a single random variable. However, it is not clear that another term would provide a better description. Correlations between variables are being ignored in these calculations. Total expected income is defined as the products and sums of the means of a variety of expectations over wages, welfare, and labor supply.

46

R.M. Blank, Household

welfare and labor force behavior

in table 5. Individual wages for any location are estimated with the coefficients from column 1 in table 3 as discussed above. Expected hours for welfare participants if they work comes from substituting the expected estimated hours coefficients in table 5 into eq. (15). Also, a term which accounts for the nonzero expectation of e due to the double truncation (H* >O and P* >O) must be added. 22 Once an individual’s wages and hours of work while participating in welfare are estimated, expected AFDC benefits can be calculated, taking into account all other expected income. (Expected benefits at zero hours are just the state maximum payments, adjusted for other nonlabor income, Other Y) Similarly, for workers who do not participate in welfare estimated hours coefficients are substituted into eq. (16), along with a truncation adjustment term (H* > 0 and P* 5 0). Comparing expected income terms across states requires strong assumptions about an individual’s information regarding any particular area. One must assume people have good knowledge about tax rates, welfare and wage levels in all 48 states. While this is probably unrealistic, as long as people at least have good comparative information on taxes, wage and welfare levels between states (i.e. they have information on rankings if not on actual values), our estimates will reflect these relative comparisons. Despite such problems, the expected income numbers which can be calculated contain much information. They provide estimates of the extent to which low income individuals face differing economic opportunities across regions and tell us how those differences vary with wage and welfare levels, tax rates and personal characteristics. 4. Simulating

economic expectations

Our goal is to estimate the behavior and income differences due to economic variations between states and demographic differences among households. The previous chapter estimated all the components necessary to calculate these differences. The interstate economic variations will come from three sources: differences in benefit payments to AFDC recipients, tax rates and wage rates. This section provides simulation results which indicate (1) how important households’ characteristics are in inducing different behavior within a given state, (2) the extent to which similar households behave 22For welfare participants who work, we want to estimate they participate and that hours are positive. This is

their

expected

hours

given

that

E(HIH*>O,P*>O)=E(Q,)+E(e\H*>O,P*>O) =Q,+E(ele>

-Q,,u>

-R).

The expectation of this error term is nonzero and its value must be calculated estimate of Qp Similarly, for nonwelfare recipients, estimating

and added

E(H\H*>O,P*sO) also requires

adding

in a term accounting

for the nonzero

expectation

of the error.

to the

R.M. Blank, Household welfare and labor force behavior

47

differently due to differing incentives between states, and (3) the extent to which these differences can be reduced by equalizing welfare parameters across states. 4.1. Household characteristics and economic expectations Table 6 shows the effect of personal characteristics on income expectations. It takes a base individual in the state of Massachusetts. This is a black woman, living in the Boston SMSA region, with less than 12 years of school, of median age (35) and with the median level of nonlabor, nonwelfare income ($542/year). She has two children, one below the age of 6. Column 1 indicates some of the major components of her expectations. Her expected wage is $2.89. The probability she will participate in welfare is 65.6 percent, Table 6 The effect of personal

characteristics

on behavior and income Massachusetts. Change

Base” individual E( WAGE) t, tn!J Pr(P=l)

2.89 0.45 0.24 65.6 (5.5)b

for a set of sample

individuals

1 characteristic

EDUC= 12 3.61 0.45 0.24 43.3

RACE = white

CH<6=2 #DEP=3

3.03 0.45 0.24

2.89 0.45 0.23

AGE= 25 2.41 0.45 0.24

Other?‘= $1200 2.89 0.45 0.24

82.6

66.3

62.8

(1.6) 330

46.6 (8.4) 20.9 (1.9) 330

(5.2) 66.2 (2.5) 389

(5.4) 37.4 (2.3) 330

(5.6) 34.1 (2.2) 306

28.3 (1.6) 15.4 223

25.7 (1.4) 13.6 251

16.4 (2.0) 9.5 336

28.9 (1.7) 12.3 273

28.1

(1.6) 12.4 261

56.1 (9.5) 2.1

53.4

Pr(P=O,H=O)

34.4 (5.5) 3.5

Pr(P=O,H>O)

(0.4) 30.9

(0.3) 54.6

(0.4) 50.0

(2.5) 44.3

(2.7) 43.5

(2.6) 41.1

17.4 (5.2) 3.5 (0.5) 13.9 (2.3) 45.0

33.1 (5.4) 3.6 (0.4) 30.1 (2.5) 44.3

Pr(P=

l,H=O)

E(B(H=O) Pr(P=l,H>O) E(H[P=l,H>O) &B/H) Pr(P = 0)

E(H(P=O,H>O) E(Y P=l) E(Y P=O) Total E(Y)

36.4 (2.2) 330 29.2

383 414 393

in

(9.5) 15.0

392 532 472

“Base individual: EDUC< 12, Black, 2 children, ‘other’ income ($542/yr). ‘Standard errors in parentheses.

(8.4) 3.4

386 420 404 1 under

437 381 427

381 348 370

(1.6) 12.4 237 37.2 (5.6) $) 32.3 (2.5) 42.0 389 425 402

age 6, in SMSA, mean age (35), mean

48

R.M. Blank, Household welfare and labor force behavior

with a 36.4 percent chance of being on welfare and not working. Her maximum welfare benefits would be $330 per month. If she works while on welfare, it will be for 12.4 hours. Her expected income as a welfare participant is $383. If she does not participate, she will work 44.3 hours and earn $414 dollars per month, about $40 more than if she were on welfare. Her total expected income is $393. Columns 2 through 6 present the effects of changing characteristics on these income estimates. Several results stand out. First the effect of additional education (column 2) - here a high school degree - is very strong. The expected wage increases by 25 percent from 2.89 to 3.61. The probability of welfare participation drops from 65.6 percent to 43.3 percent. Because work incentives rise with a higher wage, the probability of being on welfare and not working drops by an even greater relative amount, from 36.4 to 15.0 percent. Total expected income goes up 20 percent and there is now a $140 income advantage to working and not participating in welfare. Second, the effect of additional children (column 4) is also very strong. With two children below age 6, the probability of participation jumps 17 percentage points. Expected income from AFDC increases to such an extent that welfare participation here has a clear income advantage. Other changes in characteristics have significant but less dramatic results. Third, it is interesting to note that for the sample individuals in this table the income of welfare participants and nonparticipants is not far apart. (The exception is column 2. Only if wages increase significantly with more education does work appear greatly preferable.) Since welfare has other nonpecuniary advantages, primarily more time at home, we might expect the individuals depicted in these columns to consistently choose to receive welfare, just as we would expect the individual in column 2 to consistently choose work. Yet, there is a significant spread of probability between welfare and nonwelfare choices. Clearly, income incentives alone do not determine welfare participation. Evidently a great deal of variance in the ‘taste’ for welfare occurs, even among the low skilled, low wage female population. Table 6 demonstrates that individual characteristics are highly important in determining income opportunities. Changes in single characteristics can lead to quite large changes in wages, hours and income expectations. However, table 6 holds location constant. The next question is how the behavioral differences due to differing individual characteristics interact with differing economic incentives between states. 4.2. Locational differences in economic expectations Table 7 presents expectations for two individuals in four different states. The bottom row adjusts these estimates for cost of living differences (see footnote 12). These states are chosen in part because of their contrasts.

R.M. Blank, Household

weljare and Iabor force behavior

49

Table 7 The effect of location

on behavior

Individual

and income:

observing

la

2 individuals

Individual

in 4 states.

2b

ILL E( WA GE) rr

t“P Pr(P=l) Pr(P=l,H=O) E(BIH=O) Pr(P=l,H>O) E(H\P=l,H>O) E(BIH) Pr(P=O) Pr(P=O,H=O) Pr(P=O,H>O) E(HjP=O,H>O) E(Y P=l) E(Y P=O) Total E(Y) Adjusted for cost-of-living

3.69 0.52 0.22 56.8C

MISS 2.43 0.51 0.22

WISC

4.84 0.52 0.18

3.19 0.51 0.17

22.9 (10.2)

9.4 (5.7)

4.5 (0.8) 325

0.1 (0.02)

71.3 (5.0) 50.6

(0.3) 96

(2.5) 386

71.2 (5.0) 49.4 (2.4) 379

37.0 (1.5) 15.5

35.5 (2.8) 29.0

20.7 (1.9) 10.5

21.8 (1.9) 10.7

43.2 (5.8) 3.5 (0.4) 39.7 (2.6) 42.6

MISS

2.75 0.42 0.24

(5.6) 1.5

43

-ILL

3.34 0.50 0.23

(5.8) 19.8 (1.7) 266

139

37.0

NY

311

63.0 (5.6)

28.7 (5.0)

(Z)

,i:Q

58.6 (3.0) 38.1

25.6 (2.5) 45.0

325 28.8 (5.0) 3.3 (0.4) 25.5 (2.5) 45.0

18.4 (1.5) 18.8

120

NY 4.38 0.50 0.19 38.8 (12.9) 24.9 (2.4) 468

(;::)

13.9

39.7

(1.4) 11.0

WISC 3.61 0.42 0.20 38.3 (12.9) 22.8 (2.3) 452 15.5 (1.4) 11.5

123

30d

365

376

77.1 (10.2)

90.6 (5.7)

61.2 (12.9)

61.7 (12.9)

1.3 (0.2) 75.8 (2.0) 43.2

1.7

1.4

(0.2) 88.9

(0.2) 59.8

1.5 (0.2) 60.2

(1.5) 39.9

(2.8) 45.0

(2.8) 44.6

303 521 397

228 326 290

429 477 443

431 393 420

357 758 666

337 479 466

511 707 631

509 576 550

394

291

433

422

661

461

617

553

“Individual 1: EDUC<12, Black, 2 children, 1 under age ‘other’ income ($542/yr). blndividual 2: EDUC= 12, White, 3 children, 1 under age ‘other’ income (%542/yr). Standard errors in parentheses. dAt this level of hours, benefits are calculated at 0. Since I account of all deductions, I allocate a minimal level of AFDC

6, in SMSA,

mean

age (35), mean

6, in SMSA, mean

age (35), mean

do not have the ability to take full benefits ($30) for this individual.

Mississippi has very low wages and low welfare. Wisconsin has low wages and high welfare. Illinois has high wages and average welfare payments. New York has moderate wages and high welfare. The first four columns show the same base individual we observed in Massachusetts in table 6. Her likelihood of being on welfare varies from 37.0 percent in Mississippi to 71.3 percent in New York, a difference of almost 2 to 1, due in large part to the 4 to 1 difference in maximum welfare benefits between those states ($386 versus $961.

50

R.M. Blank, Household welfare and labor force behavior

Quite different work incentives are induced across these four states for an identical individual. There is only a 1.5 percent probability that the Mississippian will choose not to work and collect welfare, while there is a 51 percent chance the New Yorker will do so. The state differences in wages, welfare and taxes are such that this same individual in Illinois, Mississippi and New York has an income advantage in forgoing welfare, while in Wisconsin she expects higher income if she participates. Her total income expectations vary from $433 in New York to $291 in Mississippi. Individual 2 in table 7 is better educated, white, has an extra child and is younger. The change in education and race induces higher wages and greater work incentives, which are somewhat offset by the extra child. In Mississippi even if this person is on welfare she works full-time and receives only minimal welfare benefits. Her expected income in all states is uniformly higher than individual l’s, especially in Illinois and New York, where the wages are high. Because of the extra child her AFDC expectations are also larger. However, larger labor income via higher wages and more hours dominates the higher AFDC benefits - in every location this individual will earn more if she leaves welfare and works full time. The first thing to note in table 7 is that these results look reasonable. The model is producing economically sensible numbers for probability weights, wages, hours and overall income estimates. No extreme values appear. This is a reassuring confirmation of the underlying theoretical structure of the model. Second, this table shows very significant variations in regional behavior for any given household. The probability of participating in welfare varies by more than 100 percent for the same household between these states. Similarly, the probability of work and the expected amount of work among welfare participants in each state varies greatly. Clearly, the same individual faces very different opportunity structures in these states and her behavior differs accordingly. The effect of these differences in economic incentives and behavior results in signiticant expected income differentials of 40 to 50 percent, even after cost of living differences are taken into account. To the extent that one is concerned about horizontal equity for participants of welfare programs, the differences in economic opportunities between states is clearly large. Third, the difference between states depends upon both wage rates and benefit levels. Higher wage rates push up income for both participants and nonparticipants, while higher benefit levels add only to participant income. Thus Illinois, with the highest wage rate, has a higher overall expected income than the other states for individual 2, although its benefit levels are lower. Similar welfare benefits in Wisconsin and New York make the participant/nonparticipant choices look similar, but the Wisconsin households earn much less if they are nonparticipants because of their lower wage

R.M. Blank, Household welfare and labor force behavior

51

rate. Mississippi and New York, with wage and welfare levels which are both low or both high, show the largest gap in probabilities of welfare participation and expected hours of work. Finally, it is clear from table 7 that as household characteristics change the importance of welfare changes. The differences in behavior between individuals 1 and 2 underscores further the fact that certain individuals are far more affected by the parameters of the welfare program. For individual 2, with a higher probability of nonparticipation, wage levels are a more important determinant of income than are welfare benefit levels. Though there is still quite a bit of variance between states in behavior and income for individual 2, this variance is more related to state wage differences than welfare program differences. In summary, the results in table 7 indicate that there are large differences in the economic incentives faced by the same individuals in different states. These result in a very different set of labor market/welfare choices across states. These differences are due to variation in both the welfare program parameters, but also to wage differences. While welfare differences are clearly significant, as labor market participation becomes more attractive, households are increasingly affected by state wage differences.

4.3. Simulating the national equalization of werfare benefits While wage and tax differences among states do create concern, it is differences in state welfare programs which are more frequently discussed for this population. In the last simulation we will observe the effect of expanding the Illinois benefit system nationwide. (Illinois’ benefit levels were just a few dollars below the national mean in 1979.) The result of this can be seen in table 8, which keeps wage and state tax rates at their 1979 levels in all states, but imputes Illinois welfare benefit levels and state welfare tax rates to all states. As before, we observe the effect of this change on our base individual (individual 1 in previous simulations) in Illinois (no change), Mississippi, New York and Wisconsin. For ease of comparison, the expected behavior and income before and after this welfare change are shown for each state. Use of the Illinois welfare scheme nationwide provides $266 in welfare benefits to this household if the head does not work. This is in contrast to the existing 1979 system which would pay $386 in New York, $379 in Wisconsin and $96 in Mississippi. This welfare equalization has several effects. First, the expected behavior of this household becomes much more uniform across states. After equalization, in all states the probability of being on welfare is near 57 percent, the probability of not working while on welfare is near 20 percent, and the probability of working and receiving welfare is near 37 percent. Welfare participants who work will put in about 15 hours and nonparticipants will

R.M. Blank, Household welfare and labor force behavior

52

Table 8 Equalizing

welfare benefits

Individual

E( WAGE) t, t ;P=

1)

E(B(H=O) Pr(P=l,H>O)

ILL

MISS

(Unchanged)

Before

Pr(P=O,H>O) H>O)

Before

After

Before

After

2.43 0.52

3.34 0.50

3.34 0.52

2.75 0.42

2.75 0.52

0.22

0.23

0.22

0.23

0.23

0.24

0.24

56.8b

37.0 (5.6) 1.5 (0.3) 96

(1.5) 15.5

43.2

Pr(P=O,H=O)

After

WISC

2.43 0.51

139

WIH) Pr(P=O)

NY

3.69 0.52

37.0

E(H(P=l,H>O)

E(H]P=O,

1”

(5.8) 19.8 (1.7) 266

Pr(P=l,H=O)

and tax rates at Illinois levels.

(5.8) 3.5 (0.4) 39.7 (2.6) 42.6

35.5 (2.8) 29.0 43 63.0 (5.6) 4.4 (0.5) 58.6 (3.0) 38.1

58.5

57.3

(5.8) 21.6

71.3 (5.0) 50.6

(5.8) 20.4

(1.8) 266

(2.5) 386

36.9 (1.5) 15.2 184 41.5 (5.8) 3.9 (0.5) 37.6 ( 2.6) 42.4

(1.7) 266

71.2 (5.0) 49.4 (2.4) 379

(1.8) 266

20.7

36.9

21.8

37.0

(1.9) 10.5

(1.5) 15.4

(1.9) 10.7

(1.5) 15.3

311 28.7 (5.0) 3.1 (0.4) 25.6 (2.5) 45.0

152

325

42.7

28.8

(5.8) 3.6 (0.4) 39.1

(5.0) 3.3 (0.4) 25.5 (2.5) 45.0

(2.6) 42.5

58.2 (5.8) 21.2

173 41.8 (5.8) 3.8 (0.5) 38.0 (2.6) 42.4

E(YIP=l)

303

228

305

429

304

431

305

E(Y[P=O)

521

326

351

477

464

393

381

Total E(Y) Adjusted for cost-of-living

397

290

324

443

372

420

336

394

291

325

433

364

422

338

“Individual 1: EDUC< 12, Black, ‘other’ income (%542/yr). %tandard errors in parentheses.

2 children,

1 under

age 6, in SMSA,

mean

age (35), mean

work about 42 hours. Given wage differences between states, one might initially expect greater variations in hours of work, but what these numbers show is that welfare looks about equally attractive, even with wage variations taken into account. For welfare participants who do not work, the economic incentives across states are now equal, of course. For working participants at this level of benefits, the level of the grant almost exactly offsets the wage differences between states, so that in all four states expected income of welfare participants is almost identical after equalization. The major continuing differential after equalization is in income earned by labor market participants who do not receive welfare. Although all individuals here expect to work about 42 hours per week if not on. welfare, their

R.M. Blank, Household welfare and labor force behavior

53

earnings vary from %351 in Mississippi to $521 in Illinois, primarily because of differences in their state-specific wage rates. The fact that this variation does not greatly affect welfare participation decisions indica.tes again the extent to which nonincome factors influence welfare decisions. Though all households could earn more off welfare than on, the size of the differential does not seem terribly important in determining behavior. (Note that these results will differ for other types of households. In particular, as noted in table 6, higher skill levels or lack of young children, would change the way in which the household reacts to wage and welfare levels.) The third major effect to note from this table is that the impact of equalization is particularly dramatic for those states shown here whose current welfare benefit levels are far from the mean. After equalization, if many households in Mississippi are similar to this one (and they are), the state would see a dramatic increase in caseloads and particularly in payments to households who are more likely to leave the labor force entirely. The households receiving welfare would experience a significant income increase. In New York, on the other hand, households like this one would see a large income drop under this scheme if they were on welfare and the rolls would surely decrease. These results show the extent to which labor force and welfare behavior is sensitive to benetit levels. Should some sort of national standardization of benefit levels occur, the level of benefits chosen is crucial in determining the resulting effects on participant behavior and income, and also in determining the costs and benefits to the various states of such changes. Note, however, that these results imply nothing about why this sensitivity to welfare levels occurs. Given the very low income levels these households are receiving in these simulations, it is difficult to interpret this as evidence for the attraction of ‘living off welfare’. Rather, it seems primarily due to the combination of low wage opportunities, combined with high demands on these women’s time in the home, as the only parent and adult present in the household. We conclude the standardizing state welfare systems at some mean level would significantly affect individual behavior across states. Much greater uniformity in incentives would occur, as reflected in more uniform welfare and labor force choices. (Simulation results for other types of households show similar effects but are less dramatic as most other households are less likely to have initially been on welfare.) However, this change would still leave significant inequities in income between individuals due to differences in wage and state tax rates. The cost of such equalization would be to reduce income significantly among households in current high-benefit states (if the state did not supplement this reduced level). Conversely, households in current low-benefit states would see an increase in their income and would find welfare a far more attractive option.

54

R.M. Blank, Household welfare and labor force behavior

5. Conclusions

The following conclusions summarize the results of this paper. (1) Existing differences in welfare payments, wages and taxes across states create significant differences in labor force and welfare participation among low income households. These in turn create significant differences in interstate income expectations for female-headed households even after adjusting for cost-of-living differentials. (2) A full accounting of all interstate differences - not just welfare benefits - is important in understanding the behavior of the welfare-eligible population. The micro-data approach of this study allows us to investigate the economic parameters facing specific households. The joint model of welfare and labor force participation indicates that net wage rates, public assistance income and other income all affect the behavior of this population, but the size of their impact varies. (3) Both wage and welfare levels have important effects on labor force participation, welfare participation and expected income. In states with very low welfare levels, household heads will almost surely also be in the labor force. Yet, greater labor force participation also occurs among individuals with high skills or in states with higher wage rates. While equalizing welfare eliminates much of the behavioral differences in interstate choice between work and welfare, interstate income will continue to vary significantly due primarily to wage differences. There are clear policy problems in welfare equalization, since raising welfare levels in low benefit states will induce large increases in the probability of welfare participation and decrease work, at the same time as it raises overall income levels. Lowering welfare in existing high benefit states can cause significant income declines, while it also decreases welfare participation and increases the probability of working. (4) There is a wide variance in the ‘taste’ for welfare. Even among individuals for whom welfare (nonwelfare) provides a higher income a significant percentage will choose not to participate (to participate). (5) Demographic characteristics are important in determin:ing labor force and welfare participation and total income expectations. This is not only because household characteristics affect wages, benefits and tax rates, but also because they have additional nonpecuniary effects on welfare choices. In particular, families with more children and younger children are very likely to receive welfare. For this group the incentives to work may be less attractive, but income needs are larger. Thus, number of children is positively related to labor force participation and also positively related to welfare participation. More education strongly influences people to leave welfare and enter the labor force, both through wage and nonpecuniary effects. The exact elasticity between any household characteristic and the economic behavior discussed here will vary for each type of household.

R.M. Blank, Household werfare and labor force behavior

55

This study has only been able to focus on one major cash assistance program. Much of the work has been duplicated to include an imputation for food stamps which produces changes in the actual total income numbers but makes almost no difference in the relative state compariscms, rankings or conclusions. However, in any given area, there may be a whole variety of other low-income assistance programs available, from housing to fuel. These may provide important offsets to the cash income differences described here. However, states with higher benefit levels often tend to be the states that are more generous in other assistance programs, which would mean that these other programs will merely reinforce the differentials discussed here. This study has demonstrated that there are significant economic inequities in how individual households may expect to fare in different areas of the country, inequities that are due to a variety of interlocking demographic and economic factors. This paper has attempted to increase our understanding of the importance of these inequities by measuring their effects on labor force behavior, welfare participation and income expectations.

Appendix: Estimating

state average marginal tax rates

A.1. Tax rates for welfare participants

Benefits available to households (7) in the text: B = G -(2/3)(X

receiving AFDC are calculated from eq.

- 30 -D),

where X is all nonwelfare income, labor and nonlabor, G is the maximum state grant available and D is additional allowable income deductions over the mandatory $30. Unfortunately D is almost impossible to determine a priori for any individual. It varies by state and by hours worked. If the marginal tax rate for the welfare population is assumed to be the statutory two-thirds and D is not accounted for, we will be seriously overestimating the effective marginal tax rate since D decreases the amount of X subject to tax. Thus, since we cannot compute D for any individual, we calculate the effective marginal tax rate in each state taking the average state levels of D into account. In other words, unable to compute (7), we compute B=G-tP(X-30),

(7’)

where t, varies by state and is adjusted for the level of additional income deductions available on average in that state. This adjustment is done using the 1979 Recipient Characteristics Study [U.S. Dept of HHS (1982)], which gives the total disregarded income by

56

R.M. Blank, Household

welfare and labor force behavior

state and its two components, the amount due to the 30 and 3 income disregard and the amount due to other income deductions. Let the ratio Total ‘additional income deductions’(s) =I’ Total disregarded income(s) for each state s. Let

the fraction of average mandatory income exemptions to average additional allowed income deductions. Then we can write D in terms of total income X, D = f :(X/3 - lo), since (X- 30)/3 is the total available. Thus we can rewrite (7’) as

amount

of mandatory

income

exemptions

B=G-(2/3)(X-30-L(X/3-10)) =G-(2/3)((1

-A/3)X-(30-

1Ofs)).

(7”)

Ignoring the effect of the $30 disregard (30- lOf,), the effective marginal tax rate on income X is (2/3) (1 - f,/3). Thus we set r,=(2/3)

(1 -fJ3)

for each state s. Since r, tends to be near 0.5, f, is usually close to 0.67, and the general effect of this adjustment is to decrease the marginal tax by about one-third to around 45 to 50 percent in most states. A.2. Tax rates for nonwelfare participants The Internal Revenue Service establishes a set of income tax brackets that differ by type of household head. This paper deals only with the brackets applicable to single-headed families. Federal marginal tax rates within each bracket can be calculated by making certain assumptions about family tax behavior. Since we are dealing with a low-income group, assume all families take the standard deduction. (In 1979, this was $2300.) Assume also that the number of personal exemptions equals the number of household members. (The personal exemption is $lOOO/person.) Finally, assume that all indivi-

R.M. Blank, Household welfare and labor force behavior

57

duals who are eligible for the earned income credit (EIC) take advantage of it. The EIC is a tax preference for low income households. It exempts the first $500 of taxes (essentially all taxes on income below $5000). Households pay the standard rate from $5000 to $6000, then as income increases from $6000 to $10000, this $500 exemption disappears so that people are paying the full amount of taxes when their income reaches $10000. The effect of this is to decrease the marginal tax rate by 10 percent on income below $5000 and to increase it by 12.5 percent on incomes between $6000 and $10000, as every dollar earned is not only taxed at normal rates, but also induces a partial loss of the previous tax exemption. In addition, FICA taxes are added into the federal income tax rates (applicable up to $22 500 worth of income.) Thus, even households which were exempted from any federal taxes faced a tax rate of 8.1 percent in 1979, due to FICA payments. This leaves us with a two-way matrix of federal marginal tax rates by income bracket and by number of dependents. Turning to state tax codes the state marginal tax rates are defined in a similar manner across state brackets by number of dependents. We can then match these state brackets against the federal ones and calculate the average marginal state tax rate that would apply within each federal income bracket for each size household. The average state marginal rates within the federal brackets are then added onto the federal marginal rates within those brackets, for a three-way matrix of marginal tax rates by state, by federal income bracket and by number of dependents. Using this matrix, the approximate marginal tax rate paid by any individual can be estimated, given knowledge about his or her total income and certain household characteristics. As the last step, these marginal tax rates are collapsed by income brackets into an average state marginal tax rate for each size household. We have a sample of female-headed households. To calculate their expected average marginal tax rate in any state, the distribution of income within this sample is applied to the state marginal tax rates of each sized household and a weighted sum of marginal rates paid by this population is calculated. This weighted sum is considered the state average marginal rate for a given sized household within this population. For a family of 1 adult and 2 dependents the resulting rates vary from a high of 25.85 percent in Minnesota to 20.68 percent in the nine states which do not collect a state income tax. The matrix of average marginal tax rates in each state for each size household is used to estimate effective state marginal tax rates for nonwelfare participants in this study.

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social programs,

Journal

of

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R.M. Blank, Household

welfare and labor force behavior

Barr, N.A. and R.E. Hall, 1981, The probability of dependence on public assistance, Economica 48, 109-123. Brehm, CT. and T.R. Saving, 1964, The demand for general assistance programs, American Economic Review 54, 1002-1018. Burtless, Gary and Jerry A. Hausman, 1978, The effect of taxation on labor supply, Journal of Political Economy 86, 1103-1130. Gartinkel, Irwin and Larry L. Orr, 1974, Welfare policy and the employment rate of AFDC Mothers, National Tax Journal 27, 275-284. Griliches, Z., R.E. Hall and J.A. Hausman, 1978, Missing data sample selection in large panels, Annales de L&see, 137-176. Hausman, Jerry A., 1980, The effect of wages, taxes and fixed costs on women’s labor participation, Journal of Public Economics 14, 161-194. Hausman, Jerry A., 1981, Labor supply, in: H.J. Aaron and J.A. Pechman, eds., How taxes affect economic behaviour (Brookings Institute, Washington, DC) 27-83. Heckman, James J. and Thomas E. McCurdy, 1981, New methods for estimating labor supply functions: A survey in: Ronald G. Ehrenberg, ed., Research in labor economics, vol. 4 (JAI Press, Greenwich), 65-102. Hosek, James R., 1980, Determinants of family participation in the AFDC-UP program, Review of Economics and Statistics 62, 466470. Hutchens, Robert M., 1978, Changes in AFDC tax rates, 1967-1971, Journal of Human Resources 13, 6C-74. Levy, Frank, 1979, The labor supply of female household heads, Journal of Human Resources 14, 7697. Moffitt, Robert, 1983, An economic mode1 of welfare stigma, American Economic Review 73, 1023-1035. U.S. Department of health and human services, Social security administration, Office of policy, 1982, 1979 recipient characteristics study (G.P.O., Washington, DC).