Title : An empirical analysis of joint decision on labor supply and welfare participation

Author : Kang, Gi Choon *

* Associate Professor, Department of Economics, Cheju National University

E-mail : kanggc@cheju.ac.kr

The purpose of this paper is to examine the effect of welfare reform enacted in 1996 to the labor supply and welfare participation decisions of low-income families. The core of the transfer system for the low-income population of the non-elderly and non-disabled includes AFDC (now TANF), Food Stamps, Medicaid, and public housing. Among them the Temporary Assistance to Needy families (TANF) is the most widely known cash transfer program for the poor in the United States. This study uses data from the survey of SIPP to analyze labor market and TANF participation decisions among all low-income families in the Midwest region. A static model of family behavior is developed where work and program participation are chosen to maximize family utility function given resource constraint. The model is used to explain the joint decisions to participate in TANF and the labor market for the population of families eligible for TANF. The paper estimates from a bivariate probit model of participation in the labor force and TANF program since it can incorporate simultaneous decisions on labor market and TANF participation.

The results of bivariate probit model are consistent with what theory predicts and most previous results. Predicted wage has a negative effect on TANF participation and positive effect on labor supply. TANF benefit increases the probability of TANF participation and decreases the probability of labor force participation. The negative cross-equation correlation is statistically significant, which implies that labor supply and TANF participation decisions are jointly determined.

This study could be extended by using the nested logit model or trivariate probit model with other welfare program.

Key words :  joint decision, welfare reform, labor supply, welfare participation, bivariate probit model

Ⅰ. Introduction

Between 1965 and 1985 the U.S. experienced a huge increase of the caseload in the welfare programs. There can be many explanations on the high level of caseloads. Two major explanations of them are long-term welfare dependency and work disincentives. It has been a major goal of state and federal welfare reforms to increase work participation among welfare recipients. The Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) enacted in 1996 has brought major changes in the scope, structure and impact of major programs targeted to the low-income population, including Aid to Families with Dependent Children (AFDC), Food Stamps, Medicaid, Supplemental Security Income (SSI), child welfare and child support. After the introduction of PRWORA it has been observed that welfare recipients has declined across nation. Two possible sources are responsible for declining welfare rolls. The aim of PRWORA to reduce low-income individuals' and families' dependence on government assistance by promoting participation of labor market is partially fulfilled. America's growing economy also has been providing a lot of employment opportunity to those who were previously dependent on welfare program.

The changes in the linkages among social assistance programs under the welfare reform have significant effects on the behavior of the low-income individuals and families. The overall purpose of the research on welfare program is to examine the economic and welfare program factors affecting the well-being of the low-income families and the effects of the welfare reform on the labor supply decisions. This study examines the effects of cash transfers on labor supply and welfare participation decisions and attempts to get general understanding of welfare and labor market activity of the poor.

Moffitt (1992) reviews the results of research on the effects of welfare system on work incentives, welfare dependency, family structure, migration, and intergenerational transmission of dependency. Keane and Moffitt (1998) use a structural model to examine work and multiple-welfare program participation decisions among single-adult female families. Results of recent research show that eligibility and benefit structure have significant effects on labor and welfare participation. Hagstrom (1996) examines the effect of the Food Stamp Program on intra-family labor supply and program participation decisions. Married couples simultaneously choose the labor supply of the husband and wife and whether to participate in the Food Stamp Program. The nested multinomial logit model was estimated using data from the Survey of Income and Program Participation (SIPP). He found smaller labor supply effects of married couples to changes in the food stamp benefit than those of single parents. Program participation by married couples is also found to be responsive to changes in food stamp benefits.

Many researchers have analyzed the effects of government transfer programs on labor supply decision among the low-income population. Most of the empirical literatures have provided some insights on how welfare-transfer affects labor supply decisions of low-income families and focused on solely on females or married couples.

The purpose of this paper is to examine the effect of welfare reform enacted in 1996 to the labor supply and welfare participation decisions of low-income families. There are three types of family: two parent families, male-headed families, and female-headed families. The core of the transfer system for the low-income population of the non-elderly and non-disabled includes AFDC (now TANF), Food Stamps, Medicaid, and public housing. Among them the Temporary Assistance to Needy families (TANF) is the most widely known cash transfer program for the poor in the United States. This study uses data from the survey of SIPP to analyze labor market and TANF participation decisions among all low-income families in the Midwest region. A static model of family behavior is developed where work and program participation are chosen to maximize family utility function given resource constraint. The model is used to explain the joint decisions to participate in TANF and the labor market for the population of families eligible for TANF. The paper estimates from a bivariate probit model of participation in the labor force and TANF program since it can incorporate simultaneous decisions on labor market and TANF participation.

Ⅱ. TANF program

The PRWORA gives the states a fundamental role in assisting poor families. Under TANF, the eligibility rules and benefits differ across states. To be eligible for TANF an applicant family must pass both non-financial tests based on the demographic characteristics of the family and its member, and financial tests based on the income and asset holdings. At the most basic level of non-financial tests, the family must include a child or, in some states, a pregnant woman. If the head of family is a teenager, she may or may not be eligible to receive a benefit on her own. In most states, she is eligible only if she is living with her parents.

The financial tests require that an applicant family must have sufficiently low income and asset levels. The asset limits that states have adopted under TANF differ greatly by state. Thirty-nine states have increased the asset limit for recipients above the $1,000 limit allowed under AFDC. Twenty-two states allow recipients to accumulate additional savings in a restricted savings set aside for a specific purpose allowed by the state. If the family's total assets exceed the amounts determined by the state, the family is ineligible for TANF.

Once the family has passed the states asset tests, its available income is computed for eligibility purposes. States use the total gross income calculated from the units earned and unearned income as a starting point for income eligibility tests. Many states now impose just one income test on applicants; however, others use a combination of a gross income test, gross earnings test, and/or net income test. States may also impose net income tests that require that net family income should not exceed a maximum benefit level that varies by family size and state of residence. Net income is calculated by subtracting the each states earned income disregards from the units gross earned income and then adding to this amount the units unearned income. The net income is then compared to an income standard determined by the state. If the net income is less than the standard, then a benefit is calculated.

Although states use many different formulas to determine, there are general rules that most states apply. All but two states allow recipients to disregard a portion of their earned income before benefit computation and vary the units benefits by income. In more straightforward calculations, net income is subtracted from a state-determined standard, so called the payment standard, which varies by family size.  The benefit paid is the difference.

Ⅲ. Theoretical and Empirical Model

1. Theoretical model

The Family head is assumed to choose its labor supply and welfare (here, TANF) participation simultaneously to maximize its utility subject to its budget constraint. Assume that family's utility is a function of leisure time and disposable income and is represented by

(1)

where H is monthly hours of work supplied by family, Y is monthly disposable income, and  represents tastes for receiving TANF benefit. Monthly disposable income may be written as the following budget constraint:

(2)

where w is the hourly wage rate, N is non-labor income,  is defined to be an indicator equal to 1 if family head participates in the TANF program and 0 otherwise,  is the TANF benefit given the family's labor supply, and C is the monetary cost participating in the TANF program. The family head is assumed to simultaneously choose H and  that maximizes its utility given in Equation (1) subject to the budget constraint given in Equation (2). That is, the family head chooses the  combination that gives the greatest indirect utility.

Participation in welfare program is not costless. There are costs associated with the application process and with application itself. The costs include the transportation and time costs of applying for the program and some compliance costs while participating. These costs vary by individual families and by location. However, there are benefits with TANF participation. A family with no income is eligible to receive the maximum TANF grant. For a family with income, the TANF benefits are calculated as the difference between the maximum benefit and net-family income. The TANF benefits are calculated according to the formula given in Equation (3).

if     (3)

where  is the monthly TANF benefit, P is the maximum permitted payment in the state,  is the maximum amount paid,  is the earnings disregard, BRR is the benefit reduction rate, CC is child care deductions, and L is living costs. P, , L, E, and BRR vary by state and family size.

As Figure 1 shows, the labor supply decisions depend on the TANF benefit through its effect on the budget constraint and TANF participation depends on labor supply through its effect on the TANF benefit. Therefore the TANF participation and labor supply decisions are both endogenous and interdependent.

Figure 1: Simultaneity of labor supply and TANF participation decisions

2. Empirical Model

The choice set is simplified by assuming that each family chooses to work or not to work under the labor supply decision and to participate or not to participate under TANF participation decision. The resulting choice set has four alternatives, each of which is a combination of the labor supply and TANF status. Each alternative provides indirect utility . The subscripts  and combined denote an alternative, which is a combination of labor supply decision and TANF participation decision. The family choose the alternative  such that  for all .

The indirect utility  is assumed to be a function of known measured variables and an unobserved stochastic component. Two types of measured variables exist in the context of TANF participation and labor supply decision problem. The first type varies across alternatives and is called choice-specific variable or attributes of the choices. The choice-specific variables include the TANF benefit for different combinations of labor supply and wages across hours of work choices. The second type varies by family rather than alternative and is called individual-specific variable or characteristics of the individual. The individual-specific variables include information on family heads age, education, marital status, number of children, etc.

The stochastic error component captures the effect of unmeasured variables and unobserved differences in preferences across families. Given the form of utility function and the probability distribution of the stochastic term, the probability that the family choose alternative  can be written  for all .

Several models are available for estimating the above random utility model based on different assumptions about the stochastic component of indirect utility function, . Maddala (1983) presents an extensive discussion of limited-dependent and qualitative-variable models in econometrics. One possible model to incorporate simultaneous decisions on labor market and TANF participation is a bivariate probit model. Define  and  as participation in the labor market and TANF respectively. All of families are then classified into four mutually exclusive regimes based on the discrete choice outcome on  and :

: (Those who participating in labor market and TANF)

:  and (Those who participating in labor market but not in TANF)

:  and (Those who participating in TANF but not in labor market)

: (Those who are not participating in labor market and TANF)

All observations have a non-zero probability of being assigned to one of four regimes. This probability can be evaluated with the following bivariate probability statements:

(4)

(5)

(6)

(7)

Although  and  are unobservable variables, we can observe the dummy variables  and  such that  if  and  otherwise, and  if  and  otherwise. Define  and  as vectors of exogenous variables,  and  as parameter vectors, and  and  as disturbance terms. Maximum-likelihood estimation of bivariate probit regressions are used to estimate  and . These estimates are used to calculate the probabilities of (4)-(7).

The empirical specification of the human-capital based wage equation is:

(8)

where X is a vector of exogenous variables including education, marital status, gender, race, and metro/non-metro location of family head. Y is a vector of another exogenous variables including local unemployment rate, experience, and some interaction term between experience and education, and  is a normal random error. The wage equation also needs to be corrected for potential selection bias.

Ⅳ. Data and Estimation results

The effectiveness of welfare programs has been debated for a long time. The difficulty of evaluating these programs comes from the lack of appropriate data. The advent of the SIPP data shed some lights on evaluating the efficacy of welfare programs. One of the major goals of SIPP is to examine some interactions among transfer programs, labor force participation, and living arrangements because it contains detailed information about the characteristics of, and actual choices made by, both participants and non-participants whereas the administrative record data contains information only on participants. The Midwest state subset of 1996 SIPP (wave 3) was used in this study.

Only non-elderly (age less than 65), non-disable family heads are included in the sample because both elderly and disabled people are eligible for other transfer programs. Family heads are also excluded if they are categorically ineligible for TANF program, i.e., if they do not have any children under age 18 in the family. In this study we assume that family assets are exogenous.  Therefore family with assets that exceed the asset limit, discussed in Table 1, are excluded from the sample. We do not use income eligibility in selecting sample because hours of work is an endogenous variable, i.e., the family's head decision to earn an amount that causes the family income to exceed the breakeven level is a matter of choice. We select whoever gets a higher wage as the family head in the case of married couple families since it is more likely that family member who gets a higher wage works first. The resulting sample includes 1,512 family heads with low-wealth that live in the Midwest region, 60% of which are married couple families, 6% are male-headed families, and 34% are female-headed families.

Table 1. Asset limit and income eligibility rules for TANF

State	Asset limit($)	Income eligibility rules	Implementation
Illinois	$3,000	GI must be less than FPL4 NI must be less than PS5	After 8/96
Indiana	$1,500	NI must be less than FPL NI must be less than PS	1/92-8/96
Iowa	$5,000	GI must be less than 185% of NS6 NI must be less than PS	1/92-8/96
Kansas	$2,000	GI must be less than 185% of NS NI must be less than PS	After 8/96
Michigan	$3,000	NI must be less than PS	After 8/96
Minnesota	$5,0001	NI must be less than PS	After 9/96
Missouri	$5,0002	GI must be less than 185% of NS NI must be less than PS	1/92-8/96
Nebraska	$4,000/$60003	NI must be less than PS	After 8/96
Ohio	$1,000	GI must be less than 185% of NS NI must be less than PS	Before 1/92
Wisconsin	$2,500	GI must be less than 115% of FPL	1/92-8/96

1 The asset limit is effective from 1/1/98. Prior to 1/1/98 the asset limit is $1,000.

2 This is the asset limit for families with self-sufficiency agreements.

3 The asset limit is $4,000 for a single individual, and $6,000 for more than two.

4 GI and FPL represent the Gross Income and Federal Poverty Level respectively.

5 NI and PS represent the Net Income and Payment Standard.

6 NS represents the Need Standard.

All the dependent variables are defined for the month of November 1996. A family is recorded as a TANF participant if a member reports receiving TANF support in the month. For the labor supply, a family head is classified as not working if he or she reports working zero hours during the month, and as working if he or she reports working at least one hour per week during the month. Variables used in our analysis include a set of demographic variables and a set of structural variables designed to capture differences in labor market conditions and transfer programs. The demographic variables for the family head include gender, education level, race, marital status, and experience that is defined by age minus education minus 6. The set of individual characteristics include metro variable that indicates that the family lives in a metro area versus non-metro area, the states monthly unemployment rate. Also, relevant are the observations of actual unearned income, number of children, program participation choices. The wage rate, earned income, and benefit level are all predicted and then used in the following analysis.

Table 2 displays the means and standard deviations of variables. The payment standard is a maximum TANF grant per month a participant can get if he or she is eligible for TANF. The actual TANF benefit a person can get depends on his or her participation of labor force and income eligibility. If he or she is eligible for TANF based on the income eligibility rules in Table 1, then we can calculate TANF benefit according to the formula given in Equation (3). If a person is ineligible for TANF his or her TANF benefit should be zero. The average of calculated TANF benefit is $53.25 per month if he or she works and it is $378.68 per month if he or she does not work.

Table 3 shows the distribution of the dependent variables-labor market and welfare program participation.  About 10 percent of the asset-eligible families receive TANF grant, and 86 percent of them participate in the labor market.  The workers are concentrated in the TANF non-participation cell-82 percent of the sample; 8 percent of the sample do not work and do not participate in TANF; 6 percent of the sample do not work and participate in TANF; and 4 percent of the sample work and participate in TANF.

Table 2. Definitions, means and standard deviations of variables (N=1512)

Variable	Mean (Std. deviation)	Definition
Age	35.41(8.19)	Age of family head
Education	12.63(2.26)	Years of schooling of family head
Male	0.47(0.5)	Dichotomous variable equal to 1 if family head is male
Married	0.59(0.49)	Dichotomous variable equal to 1 if family head is married
White	0.8(0.4)	Dichotomous variable equal to 1 if family head is white
Metro	0.76(0.42)	Dichotomous variable equal to 1 if family head lives in metro
Children	2.02(1.09)	Number of children under 18
Experience	17.39(8.59)	Age-Education-6
Unemployment rate	4.35(0.69)	Local unemployment rate (%)
Non-labor income	133.87(335.85)	Family non-labor income per month ($)
Wage	10.03(2.4)	Predicted hourly wage ($)
Earned income	2,474.98(1,392.96)	Predicted earned income per month ($)
Payment standard	446.51(136.66)	Maximum TANF grant per month given participation ($)
Expected benefit 1	53.25(149.1)	Calculated TANF benefit given work and income eligibility ($)
Expected benefit 2	378.68(190.57)	Calculated TANF benefit given not working and income eligibility ($)
Labor force participation	0.85(0.34)	Dichotomous variable equal to 1 if family works
TANF participation	0.09(0.29)	Dichotomous variable equal to 1 if family head participates in TANF

Table 3. Distribution of the sample by labor supply and welfare participation

	Participation		No participation		All
Work	64	4%	1,234	82%	1,298	86%
Not work	85	6%	129	8%	214	14%
All	149	10%	1,363	90%	1,512	100%

Before the estimation of the bivariate probit model of labor force and welfare participation we need to estimate a wage equation for the family heads and then use predicted wage as an instrument of the actual wage. We also need to calculate the expected TANF benefit. The estimates of wage equation are reported in Table 4. Added experience increases his or her wage through increased labor productivity, holding other things being equal. The wage equation is concave in family head's experience. One additional year of experience has the direct effect of increasing wage by 3.1 percent. The findings on other variables are consistent with other studies. Being male increases the family head's wage. Individuals living in metro area received higher wage rates than those living in non-metro area.

Table 4.  Estimates of the log wage equation

Variable	Estimate
Intercept	0.969(0.127)*
Education	0.062(0.006)*
Male	0.307(0.035)*
Metro	0.071(0.022)*
Experience	0.031(0.004)*
Experience squared	-0.0005(0.0001)*
Lambda	-0.12(0.109)
R-square	0.28
F statistics	92.78
Number of observation	1,443

* Statistically significant at 5% level.

Standard errors are in parentheses.

Table 5 presents the full information maximum likelihood (FIML) estimates of the bivariate probit model of labor force and welfare participation. Variables, which enter directly into the family budget constraint, include the non-labor income and TANF benefits. Non-labor income includes all non-wage family income excluding income from any transfer programs. Non-labor income has a statistically significant negative effect on TANF participation. Predicted wage has a statistically significant negative effect on TANF participation and positive effect on labor force participation. As expected, higher unemployment rate makes it difficult to find jobs and thus a statistically significant negative effect on labor market participation. Having more children increases the probability of TANF participation and decreases the probability of labor market participation. TANF benefit is supposed to increase the probability of TANF participation and decrease the probability of labor force participation. The result is consistent with this and the effect of TANF benefit on the TANF participation is statistically significant while the effect of TANF benefit on the labor force participation is statistically insignificant.

Several variables are also included to capture the differences in taste and opportunities across families. The signs of these coefficients suggest that married or white family heads are less likely to participate in TANF. White or male family heads are more likely to participate in labor market while married families are less likely to work. Having children increases the probability of TANF participation and decrease the probability of labor market participation.

The cross-equation correlation between labor supply and TANF participation decisions is 0.63 and statistically significant. This is an indirect indication that two decisions are related through the error term.

Table 6 presents the estimates of marginal effects in the bivariate probit model. In labor supply equation married family has the strongest negative effect and male family has the strongest positive effect on the probability of labor market participation given TANF participation. The experience has the smallest effect on the probability. An increase of 1 percent in experience only raises the probability of labor market participation by 0.0028 percent. The non-labor income and wages have significant negative and positive effect on the probability respectively. In TANF participation equation most variables seem to have smaller effect on the probability of TANF participation given labor market participation. This implies that it really doesn't make any difference whether to participate TANF or not if family heads work.

Table 5. FIML estimates of the bivariate probit model

Variable	Labor participation	TANF participation
Intercept	-0.198(0.434)	0.262(0.539)
Married	-0.364(0.111)*	-0.957(0.173)*
Male	0.627(0.145)*	-0.065(0.21)
White	0.42(0.103)*	-0.57(0.124)*
Experience	0.006(0.012)	-
Experience squared	-0.036(0.026)	-
Children	-0.138(0.043)*	0.123(0.062)*
Non-labor income	-0.02(0.013)	-0.058(0.035)+
Predicted wage	0.204(0.032)*	-0.212(0.038)*
Unemployment rate	-0.122(0.066)+	0.138(0.094)
Expected TANF benefit	-0.009(0.032)	0.092(0.048)*
Rho(correlation coefficient)	-0.631(0.057)*

* Statistically significant at 5% level.

+ Statistically significant at 10% level.

Standard errors are in parentheses.

Table 6. Marginal effects in the bivariate probit model

Variable	Direct	Indirect	Total	Std. error
Labor supply: Married	-0.1801	-0.2625	-0.4426	0.0684*
Male	0.3107	-0.018	0.2926	0.0793*
White	0.208	-0.1564	0.0516	0.0565
Unemployment	-0.606	0.0379	-0.0227	0.0367
Experience	0.0028	0	0.0028	0.0061
Experience squared	-0.0179	0	-0.0179	0.0131
Children	-0.0685	0.0337	-0.0347	0.0241
Non-labor income	-0.0099	-0.016	-0.0259	0.0104*
Wage	0.1013	-0.0582	0.0431	0.0171*
Benefit	-0.0049	0.0253	0.0203	0.0191
TANF participation: Married	-0.0505	-0.0058	-0.0563	0.0118*
Male	-0.0035	0.0101	0.0066	0.0105
White	-0.0301	0.0067	-0.0233	0.0072*
Unemployment	0.0073	-0.002	0.0053	0.0045
Experience	0	0.0001	0.0001	0.0002
Experience squared	0	-0.0006	-0.0006	0.0004
Children	0.0065	-0.0022	0.0042	0.0034
Non-labor income	-0.0031	-0.0003	-0.0034	0.0019+
Wage	-0.0112	0.0033	-0.0079	0.0019*
Benefit	0.0049	-0.0002	0.0047	0.0025+

* Statistically significant at 5% level.

+ Statistically significant at 10% level.

Ⅴ. Summary and Conclusions

This study examined the joint decision on labor supply and TANF participation using the SIPP data of wave 3. All variables used in this study are defined for the month of November 1996. Thus we can examine the effect of welfare reform enacted in 1996 to the labor supply and TANF participation decisions of low-income families. As an empirical model the bivariate probit and nested logit model is used and regressors in the model are selected based on the budget constraints in the theoretical model and some demographic variables are also included in the analysis. The results of bivariate probit model are consistent with what theory predicts and most previous results. Predicted wage has a negative effect on TANF participation and positive effect on labor supply. TANF benefit increases the probability of TANF participation and decreases the probability of labor force participation. The negative cross-equation correlation is statistically significant, which implies that labor supply and TANF participation decisions are jointly determined.

This study could be extended by using the nested logit model or trivariate probit model with other welfare program such as Food Stamp Program. This study deals with welfare programs of the United States but we can extract some implications from this study for the development of Korean welfare programs.

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