Public Economics Lectures Business Taxes, Education…scholz/Teaching_742/Public_Dividends_Education... · Public Economics Lectures Business Taxes, Education, and Income Transfers

Public Economics LecturesBusiness Taxes, Education, and Income Transfers

John Karl Scholz

University of Wisconsin —MadisonFall 2010

JK Scholz ()Dividends - Human Capital 1 / 19

I Saw a Set of Interesting Papers Last Week

The 2009 "Cash for Clunkers" program (Atif Mian, Berkeley, andAmir Sufi, Chicago Booth School).

Use cross-community variation in the fraction of "clunkers" driven in aMSA (CBASA). Analyze car sales conditioning on X ′s and the fractionof clunkers. Saw a big, positive correlation, so that the programincreased car sales by 360,000 in July-August, 2009. The effects ofthe program appeared to be completed reversed by March 2010 (7months latter). There was no discernible effect on employment, houseprices, or household default rates, except, perhaps, in communities thatmade automobiles.


Interesting Papers Last Week, #2

"Do Expiring Budgets Lead to Wasteful Year-End Spending?Evidence from Federal Procurement" (Jeff Liebman and NealeMahoney).

Spending in the last week of the fiscal year is 4.9 times higher than therest-of-year weekly average. Quality scores of the IT spending in thelast week are 2.2 to 5.6 times more likely to be below the central value.You can do a study like this! See http://www.data.gov/ and/orcontracts at http://www.usaspending.gov/ and/orhttp://www.itdashboard.gov/Amazing data resources for the entrepreneurial student. Think aboutcollaboration!


Interesting Papers Last Week, #3

"The Price Effects of Cash Versus In-Kind Transfers" (Jesse Cunha,Naval Postgraduate School; Giacomc De Giorgi, Stanford; and SeemaJayachandran, Stanford).

Uses interesting data from a field experiment in Mexico. But it showsan important difference between cash and in-kind transfers of food.Cash transfers increase prices (by increasing demand for products).In-kind will result in lower prices, since they increase supply, whichshifts some surplus from producers to consumers. In their case study,they find these price effects are large (the price effects of the in-kindprogram increase net transfers by 12 percent; the price effects for thecash program dissipates 11 percent of the transfer).


Start with Chetty and Saez, August 2005 QJE

Dividend taxes on the highest MTR households fell from 35 percentto 15 percent.

Proposed on 1/7/03. Signed into law 5/28/03, but made retroactiveto 1/1/2003.

This paper is an event study, examining how dividend payout policy isaffected by the change in the tax treatment of dividends.

The dividend puzzle: why do firms pay dividends when they couldrepurchase shares (and this give shareholders capital gains treatmenton their income)?

Bernheim and Wantz (AER —signalling explanation) — it’s stillsomething of a puzzle.


Theories of Dividend Taxation

"Old" view

Dividend taxes reduce the net return on investment and hence thesupply of saving (assuming SEs outweigh IEs).Dividend tax cuts increase saving, investment, profits, and dividendpayouts.

New (but really not new), tax capitalization (or trapped equity) view.

Marginal investments are entirely financed by retained earnings (ratherthan by new share issues, which may have "lemon’s issues").Dividend taxes are then capitalized into the value of the firm (equity istrapped). Hence, reductions in dividend taxes will not affect corporatedecisions.Rather, they simply lower tax burdens on individuals who receive them.


Data

CRSP: Center for Research on Security Prices

Dividend, stock price, and volume information on all companies on theNY, Amex, and NASDAQ stock exchanges.The looks at 80:Q1 to 04:Q2, dropping utilities and financialcompanies, both of which have odd dividend payout policies.Huge fluctuations in the number of firms in the data, so they alsodevelop a "constant number of firms" sample.Merge in balance sheet information from Compustat, Execucomp, anddata on institutional ownership.



Figure 1 (dollars)

Can’t really tell much.

There’s a post-reform bump, but a time series regression that includesasset and earnings doesn’t reveal a significant change.Why the heck is this a QJE paper???Does entry and exit (sample churning) distort time series patterns?



Figure 2 (initiations)

A substantial, statistically significant number of firms started payingdividends following the 2003 change.

The result is robust (and identical) in a specification that conditions onmany factors thought to influence dividend payouts.

Figure 3 (by month) shows the same thing.

Initiations went from roughly $13 million per quarter (prior to the taxchange) to $205 million per quarter after the reform.The 6 largest quarterly initiation amounts since 1990 took place afterthe tax change.



Figure 5

There was a sharp uptick in the number of firms that increased theirdividends by 20 percent or more.

This is evidence on intensive margin changes

One can’t help but conclude that something happened. Was the taxcut the causal force behind the dividend changes?


Did Tax Cuts Cause the Dividend Changes?

There was a truckload of corporate scandals in 2001-03, largelyinvolving accounting fraud.

Perhaps this lead to shareholders being worried about management,and therefore forcing firms to get money quickly out of the firm (viadividends).

It turns out that only dividends income distributed to individualsthrough non-tax-favored accounts was affected by the reform.Dividends distributed to pensions, for example, didn’t change.

Use Thomson financial data on institutional ownership. Look atcompanies controlled by insurance companies, pensions, non-profits,etc.In Table 3, diff-in-diff estimates show that the significant increase ininitiations only apply to the companies not controlled by "unaffectedentities."


Which firms responded?

An executive who holds a large stake in the company experiences alarge change in personal tax payment from a dividend payout. Anexecutive with no shares doesn’t experience any change.

Figure 7 breaks firms into quintiles of share ownership: sure enough,dividend changes are largest for companies with the largest shareownership.

Executives with large stock options are hurt by dividend payouts,since they will lower the value of the firm and hence make it less likelythat the options will be valuable.



Agents and Principals

A literature argues that there is an association between the presenceof a large individual or institutional shareholder and the degree towhich firm behave in a value-maximizing fashion.

The literature looks at a) institutional ownership and b) the existenceof a large, unaffi liated director.

Figure 8 shows institutional ownership matters. But either a largeinstitutional owner or an independent director is suffi cient.

"Our results show that principal-agent issues play a first-order role indetermining behavioral responses to taxation and should be explicitlyincluded in models of optimal dividend taxation."

There is no obvious substitution of dividends for share repurchases,though the counterfactual (would share repurchases have exploded)cannot be ruled out.



Conclusions

Total dividends by nonfinancial, nonutility companies increased by 20percent within 6 quarters of the tax change.

No conclusive proof for either the old view or the new view, though theresults are probably closer to the old view, in that taxes do seem tomatter (though the response is very fast).They call for a theory of dividend behavior that incorporatesprincipal-agent relationships.Perhaps one could get leverage from comparisons of C- and S-Corps,since S-Corps are unaffected. Moreover, one might be able to look atother margins, like investment. How do the dividend tax changesaffect investment?


A New Test of Borrowing Constraints forEducation

Meta Brown John Karl Scholz Ananth Seshadri

New York Fed (Brown) and the University of Wisconsin - Madison

December 11, 2009

What is this paper about?

Going back at least to Becker (1967), economists haveworried that borrowing constraints may impede efficienthuman capital investment.

But the large literature on the topic does not focus on thefamily’s ”expected family contribution” (EFC) – the differencebetween the cost of attending college and what federalformulas determine is the family’s adjusted available incomefor college.

The EFC, however, is neither legally guaranteed noruniversally offered.

Children whose parents refuse or are unable to make their EFCmay face financial constraints in attending college.

Anecdotal EvidenceFrom the Becker-Posner blog

“Currently if you are under 25 and not in graduate school you areconsidered dependent on your parents’ income and have to includetheir income on your FAFSA which will count against you whenfiguring your expected family contribution. For those of us who didnot receive any financial support from parents other than cosigningloans this is a real kick in the ass. Not only is my family lowermiddle-class and unable to contribute to my education, but thegovernment will tell me that they expected them to contribute andwill punish me by lowering my available loans total.”

Posted by Diana on January 11, 2005 at 11:10am in response to”Government’s Role in Student Loans-BECKER”

Our New ApproachKey assumptions

It is difficult to study the EFC directly. One problem, forexample, is that it is difficult to determine what a parentwould have contributed to a child that does not go to college.

For the EFC to matter, there must be some scope fordisagreement between parents are children. Parents withcollege-age children typically have extensive access to credit.With unitary household preferences, contracts could be madebetween parents and children to repay loans made by parents.

There is considerable evidence against key implications of theunitary model of intergenerational relationships (for example,Altonji, Hayashi, and Kotlikoff, 1992).

If children and parents can write legally binding contracts,where the parent pays for the child’s college and the childrepays the loan with interest, our paper is less interesting.

Our New ApproachThe world we’re living in: children and parents may not always agree

If parents and children are independent decision-makers, weknow college-age children are likely to be constrained sincethey generally cannot borrow against future human capital. Arelevant question in this context is whether parents choose torelieve the constraint?

HRS parents report that 33% of their children who went tocollege did so without their financial backing.

25 percent (16 percent) of children whose parents had$200,000 to $400,000 (over $400,000) of net worth in 2000received no parental financial support for college.

Our New ApproachFinancial aid policy assumes parents make their EFC

U.S. financial aid policy (like the prior academic literature onborrowing constraints for education) assumes that parents who areable to pay for college are willing to pay for college:

Students’ access to federal loans and grants is contingent onparents’ income and assets.

But parents are under no legal obligation to make their”Expected Family Contributions” (EFCs).

The standard for students to be independent is strict: Namely,the child needs to be an orphan, veteran, parent, 24 or older,a grad student, or married.

Do borrowing constraints affect the educational attainment ofstudents whose parents do not meet their EFCs?

Our New ApproachA simple analytic model points to a new way to examine this issue

We model interactions between parents and children as adynamic non-cooperative game, following the intuition ofBruce and Waldman (1991).

Parents care about their own consumption and their children.Children, however, care only about their own consumption(”one-sided” altruistic preferences).

Our New ApproachA simple analytic model points to a new way to examine this issue, part 2

There are two regions to the equilibrium of our model.

In one, parents make post-schooling cash transfers. Childrenachieve the efficient level of education. Financial aid will haveno effect on educational attainment.In the other, parents do not make post-schooling cashtransfers. Children do not (or are less likely) to achieve theefficient level of education. Financial aid will, therefore, affecteducational attainment.

The model tells us how to split the data and where to look forevidence for credit constraints. As I will describe, there isconsiderable empirical evidence that we think is consistentwith the presence of borrowing constraints for children infamilies where parents are unwilling or unable to makepost-college transfers.

ModelObjectives, constraints and timing

Preferences

Uk(ck1 , ck

2 ) = u(ck1 ) + βu(ck

2 )

Up({

c it

}i=p,k; t=1,2

)= u(cp

1 ) + βu(cp2 ) + αUk(ck

1 , ck2 )

Physical capital: ai invested in 1 returns Rai in 2 for i = p, k

Human capital: ep & ek returns h(ep + ek + τ) to the child in pd 2

Choices, constraints & timing:

In 1 Parent moves, choosing cp1 + g1 + ep + ap ≤ xp; g1, e

p ≥ 0.

Child chooses ck1 + ek + ak ≤ g1; ak , ek ≥ 0.

In 2 Parent chooses cp2 + g2 ≤ Rap;

Child consumes ck2 = Rak + h(e) + g2.

Period 2There is a discontinuity in optimal second period transfers

Parent maxg2≥0

{u(Rap − g2) + αu(Rak + h(e) + g2)

}

g2(RaP ,Rak +h(e)) =

g2 s.t. u′(Rap − g2) = αu′(Rak + h(e) + g2)

where u′(Rap) < αu′(Rak + h(e))0 otherwise

(1)

Second period transfers are compensatory, since they decrease withthe child’s income and assets.

Period 1Children may overconsume and credit constraints for college may arise rationally

Child’s Euler equation

u′(ck1) = β max

{R, h′(e)

} (1+

∂g2

∂(Rak + h(e))

)u′(ck

2) (2)

We show ek= ak= 0. There are two cases to consider. Wheng2> 0

u′(cp1) = αu′(ck

1); u′(cp1) = βRu′(cp

2); (3)

h′(e) = R; u′(cp2) = αu′(ck

2)

If g2 ≥ 0 binds, however

u′(cp1) = αu′(ck

1); u′(cp1) = βRu′(cp

2); u′(ck1) = βh′(e)u′(ck

2 );

h′(e) > R; u′(cp2) > αu′(ck

2 )

Description of equilibriumTwo regions based on whether second period transfers are made

Partition the xp × α× h space into 2 regions

Region 1

→ xp and/or α large and/or h′(·) to R quickly

→ g2 > 0, h′(e) = R

→ Strategic concerns; efficient HC investment

Region 2

→ xp and/or α small and/or h′(·) to R slowly

→ g2 = 0, h′(e) > R

→ No strategic concerns; but underinvestment in HC

When does financial aid matter?Our central Proposition

Proposition 2: In any equilibrium in which g2 > 0, ∂(ep+ek )∂τ = −1.

Financial aid does not influence totaleducational attainment.

In any equilibrium in which g2 ≥ 0 binds,∂(ep+ek )

∂τ > −1. Financial aid increases totaleducational attainment.

Empirical strategyOur model calls for data on parent-child pairs, g2, and financial aid

The model implies the response of educational attainment tofinancial aid should differ for g2 > 0 and g2 = 0 families.

No data set we know contains full financial aid information,realized educational attainment, and post-schooling transfers.

However, we can go to the HRS for educational attainmentand post-schooling transfers, if we can find an informativefinancial aid proxy.

A consistent feature of U.S. federal aid formulas generatessubstantial aid variation with family structure:

EFC(I,A) calculated at the parent level

At the student level, Aid = COA -EFC(I,A)

NTherefore a student’s federal aid can vary substantially withthe number of siblings he or she has in college.We use birth spacing as a proxy for financial aid.

Is Birth Spacing a Reasonable Proxy for Financial Aid?Evidence from the NLSY-97 suggests it is

ParameterIndependent variable (Std error)Sibling-years of overlap in first term of college 358.44**

(179.52)Parent's 1997 income, 1000s -23.84***

(6.38)Parent's 1997 income squared, 10000s 7.14***

(2.33)Parent's 1997 net worth, 1000s -2.19**

(0.98)Parent's 1997 net worth squared, 10000s 0.06*

(0.03)AFQT percentile -28.97*

(16.69)AFQT percentile squared 0.63***

(0.15)Constant 3,152.17***

(463.69)Observations 2608R-squared 0.06* significant at 10%; ** significant at 5%; *** significant at 1%

Appendix Table 2: OLS Estimates of Financial Aid, NLSY-97

The Data Used to Examine Our Central PropositionThe Health and Retirement Study

U.S. national panel study initiated in 1992.

Cohorts: HRS (born between 1931-1941); in 1998 additionalcohorts were added, including the AHEAD (born before 1923);CODA (1923-1930); and War babies (1942-1947).

For much of the analysis we start with 13,091 families in the1998 HRS. We restrict the sample by requiring:

Those with complete information on the child’s DOB,education, gender, gift information, and relationship to theHRS respondent.Children who are 24 or older in 2000 with at least one siblingThe main sample is reduced to 9,471 families with 34,593children.The module sample (see next slide) goes from 427 families to334, with 1,262 children.

HRS Measures of Post-College TransfersThe HRS poses two specific, gift questions that are useful to this study

We rely on transfers reported by parents over the period 1998-2004in response to the Waves 4, 5, 6 & 7 questions:

”Including help with education but not shared housing or sharedfood (or any deed to a house), in the last 2 years did [theRespondent or Spouse] give financial help totaling $500 or more toany of their children or grandchildren?”

An even more ideal question was asked of a smaller group ofrespondents in the 1994 Wave 2 Transfers Module:

“Other than contributions toward education expenses, have youever given substantial gifts to your grown children?”

Central Empirical Model

Many observable and unobservable factors influence schoolingdifferences between two arbitrarily chosen individuals, includingparental attitudes and investments in their children, and heritablecomponents of aptitude.

We account for time invariant family-specific factors by estimatingthe following model.

eis = ωi + Xis β + γois + ε is ,

wherei = 1, ...,N familiessi = 1, ...,Si children of family ieis education of child s of family iωi family i fixed effectXis exogenous characteristics of child s of family iois years of overlap in college ages of child s with family i sibs

Central ResultsBirth spacing affects schooling, but only for the no-gift samples

Table 2: Family Fixed Effect Estimates of Years of Schooling, HRS, Gift v. No Gift

Gifts No Gifts Gifts No GiftsParameter Parameter Parameter Parameter

Independent variable (Std error) (Std error) (Std error) (Std error)Child gender, male=1 -0.242*** -0.086 -0.249 -0.314**

(0.087) (0.088) (0.195) (0.134)

Child age -0.014 -0.048*** -0.008 -0.051***(0.013) (0.012) (0.029) (0.018)

Oldest child indicator 0.147 0.296** 0.079 0.290(0.117) (0.121) (0.258) (0.186)

Youngest child indicator 0.119 -0.089 0.187 -0.056(0.124) (0.126) (0.265) (0.194)

Sibling-years of overlap 0.034 0.105*** -0.050 0.094**in college ages (0.031) (0.030) (0.064) (0.046)

Number of Children 16,892 17,701 467 795Number of Families 4890 4581 125 209R-squared 0.5934 0.6521 0.5713 0.5941Adjusted R-squared 0.4276 0.5304 0.4073 0.4454

* indicates significance at the 10 percent, ** at the 5 percent, and *** at the 1 percent level.

1998-2004 Gifts to Children Transfer Module Gifts to Children

Table 2: Family Fixed Effect Estimates of Years of Schooling, HRS, Gift v. No Gift

Gifts No Gifts Gifts No GiftsParameter Parameter Parameter Parameter

Independent variable (Std error) (Std error) (Std error) (Std error)Child gender, male=1 -0.242*** -0.086 -0.249 -0.314**

(0.087) (0.088) (0.195) (0.134)

Child age -0.014 -0.048*** -0.008 -0.051***(0.013) (0.012) (0.029) (0.018)

Oldest child indicator 0.147 0.296** 0.079 0.290(0.117) (0.121) (0.258) (0.186)

Youngest child indicator 0.119 -0.089 0.187 -0.056(0.124) (0.126) (0.265) (0.194)

Sibling-years of overlap 0.034 0.105*** -0.050 0.094**in college ages (0.031) (0.030) (0.064) (0.046)

Number of Children 16,892 17,701 467 795Number of Families 4890 4581 125 209R-squared 0.5934 0.6521 0.5713 0.5941Adjusted R-squared 0.4276 0.5304 0.4073 0.4454

* indicates significance at the 10 percent, ** at the 5 percent, and *** at the 1 percent level.

1998-2004 Gifts to Children Transfer Module Gifts to Children

The Results and Next Steps

A student with a sibling 5 years (or more) younger or older,will get roughly one semester less schooling than an otherwiseidentical twin.

Themes and variation

Overlap won’t matter for low-income families (who get fullfinancial aid) or high income families. Use net worth terciles todistinguish groups.Make use of historical changes in financial aid.The margin should be college: it is.Identification in the fixed effects models come from familieswith three or more children. This, in a sense, throws away alot of information. Are results robust to random effects?Split the sample by altruism.

Cross-Child Financial Aid Differences are Likely Small inLow- and High-Income Families

The EFC for high-income families is likely the full cost ofcollege. Therefore, birth spacing will generate no financial aiddifferences for children in these families, since financial aid willbe $0 regardless of spacing.

The EFC for low-income parents is $0. Therefore, birthspacing will also generate no financial aid differences forchildren in these families, since financial aid will be completeregardless of spacing.

We don’t know parental income or net worth at the time thechild attended college.Instead, we separate the overlap coefficients for each parentalnet worth tercile.We expect overlap to matter most (or only matter) in themiddle tercile of the no-gift sample.

Sensitivity AnalysesThe effects hold in the middle net worth tercile of the no-gift sample

Table 3: Family Fixed Effect Estimates of Years of Schooling, HRS

1998-2004 Gifts to ChildrenGifts No Gifts Gifts No Gifts

Parameter Parameter Parameter ParameterIndependent variable (Std error) (Std error) (Std error) (Std error)Child gender, male=1 -0.241** -0.088 -0.341 -0.379*

(0.0878) (0.089) (0.0204) (0.153)Child age 0.014 -0.048** -0.014 -0.067**

(0.013) (0.012) (0.029) (0.023)Oldest child indicator 0.145 0.297* 0.094 0.331

(0.117) (0.121) (0.273) (0.216)Youngest child indicator 0.119 -0.089 0.547 -0.274

(0.125) (0.127) (0.286) (0.224)Sibling-years of overlap 0.020 0.070 0.048 0.071in college ages*Tercile 1 (0.045) (0.046) (0.103) (0.063)

Sibling-years of overlap 0.048 0.189** 0.060 0.197*in college ages*Tercile 2 (0.051) (0.047) (0.083) (0.082)

Sibling-years of overlap 0.040 0.046 -0.044 -0.047in college ages*Tercile 3 (0.055) (0.054) (0.128) (0.114)

Number of Children 16,824 17,609 364 582Number of Families 4869 4557 92 150R-squared 0.59 0.65 0.59 0.57Adjusted R-squared 0.43 0.53 0.44 0.41* significant at 5%; ** significant at 1%

Transfer Module Gifts to Children

Table 3: Family Fixed Effect Estimates of Years of Schooling, HRS

1998-2004 Gifts to ChildrenGifts No Gifts Gifts No Gifts

Parameter Parameter Parameter ParameterIndependent variable (Std error) (Std error) (Std error) (Std error)Child gender, male=1 -0.241** -0.088 -0.341 -0.379*

(0.0878) (0.089) (0.0204) (0.153)Child age 0.014 -0.048** -0.014 -0.067**


(0.117) (0.121) (0.273) (0.216)Youngest child indicator 0.119 -0.089 0.547 -0.274

(0.125) (0.127) (0.286) (0.224)Sibling-years of overlap 0.020 0.070 0.048 0.071in college ages*Tercile 1 (0.045) (0.046) (0.103) (0.063)

Sibling-years of overlap 0.048 0.189** 0.060 0.197*in college ages*Tercile 2 (0.051) (0.047) (0.083) (0.082)

Sibling-years of overlap 0.040 0.046 -0.044 -0.047in college ages*Tercile 3 (0.055) (0.054) (0.128) (0.114)

Number of Children 16,824 17,609 364 582Number of Families 4869 4557 92 150R-squared 0.59 0.65 0.59 0.57Adjusted R-squared 0.43 0.53 0.44 0.41* significant at 5%; ** significant at 1%

Transfer Module Gifts to Children

Results Should Be Larger When There is More AvailableFinancial Aid

The Middle Income Student Assistance Act of 1978substantially increased financial aid to middle and higherincome families.

We split the sample that includes only students who reachedage 18 by 1978; and those who reached 18 after 1978.We expect overlap in the post-MISAA sample to larger than inthe pre-MISAA sample for the middle tercile of the no-giftsample.

Sensitivity AnalysesFinancial aid got much more generous in 1978 - results are larger then

Table 4: Family Fixed Effect Estimates of Years of Schooling, HRS, Around Reform

College Entry1998-2004 Gifts to Children Gifts No Gifts Gifts No Gifts

Parameter Parameter Parameter ParameterIndependent variable (Std Error) (Std Error) (Std Error) (Std Error)Child gender, male=1 -0.0707 -0.146 -0.443** -0.0104

(0.113) (0.121) (0.142) (0.157)Child age 0.00553 -0.0610** -0.00152 -0.0211


(0.145) (0.159) (0.201) (0.242)Youngest child indicator 0.0184 -0.164 0.258 0.0487

(0.172) (0.187) (0.196) (0.215)Sibling-years of overlap -0.231** 0.239** 0.111 -0.0145in college ages*Tercile 1 (0.0688) (0.0684) (0.0656) (0.0829)

Sibling-years of overlap 0.0710 0.143* 0.0403 0.284**in college ages*Tercile 2 (0.0716) (0.0670) (0.0828) (0.0874)

Sibling-years of overlap -0.0296 0.0692 0.114 0.0533in college ages*Tercile 3 (0.0745) (0.0758) (0.0929) (0.0974)

Number of Children 8180 11,036 8712 6636Number of Families 3133 3579 3416 2623R-squared 0.7339 0.6559 0.6248 0.7327Adjusted R-squared 0.5682 0.4903 0.3821 0.5572* significant at 5%; ** significant at 1%

Post-reformPre-reform

Table 4: Family Fixed Effect Estimates of Years of Schooling, HRS, Around Reform

College Entry1998-2004 Gifts to Children Gifts No Gifts Gifts No Gifts

Parameter Parameter Parameter ParameterIndependent variable (Std Error) (Std Error) (Std Error) (Std Error)Child gender, male=1 -0.0707 -0.146 -0.443** -0.0104

(0.113) (0.121) (0.142) (0.157)Child age 0.00553 -0.0610** -0.00152 -0.0211


(0.145) (0.159) (0.201) (0.242)Youngest child indicator 0.0184 -0.164 0.258 0.0487

(0.172) (0.187) (0.196) (0.215)Sibling-years of overlap -0.231** 0.239** 0.111 -0.0145in college ages*Tercile 1 (0.0688) (0.0684) (0.0656) (0.0829)

Sibling-years of overlap 0.0710 0.143* 0.0403 0.284**in college ages*Tercile 2 (0.0716) (0.0670) (0.0828) (0.0874)

Sibling-years of overlap -0.0296 0.0692 0.114 0.0533in college ages*Tercile 3 (0.0745) (0.0758) (0.0929) (0.0974)

Number of Children 8180 11,036 8712 6636Number of Families 3133 3579 3416 2623R-squared 0.7339 0.6559 0.6248 0.7327Adjusted R-squared 0.5682 0.4903 0.3821 0.5572* significant at 5%; ** significant at 1%

Post-reformPre-reform

Greater Education Should Be College and Not High School

Overlap is insignificant in the g2 > 0 and g2 = 0 sample ifboth are restricted to those children with high school or lesseducation.

Not surprisingly, the overlap coefficients get substantially largerif high school dropouts are excluded.

Fixed or Random Effects?

The fixed effect estimates are identified off spacing differencesbetween children in families with three of more children.

Children in 2-child families will have identical years of overlap,and hence will be captured by the fixed effect.

Random effects allow us estimate the overlap coefficient usingfamilies with two or more children.

Sensitivity AnalysesResults are robust using cross-sectional variation: the RE estimates

Table 5: Family Random Effect Estimates of Years of Schooling, HRS1998-2004 Gifts to Children: Gifts No gifts

Independent variable Parameter (SE)Sibling-years of overlap 0.03 0.07** in college ages (0.03) (0.03)Number of children 0.01 -0.26**

(0.12) (0.11)Number of children squared -0.01 0.01

(0.01) (0.01)Child gender, male=1 -0.25*** -0.13

(0.08) (0.08)Child age 0.02*** -0.01

(0.01) (0.01)Oldest child indicator 0.12 0.12

(0.10) (0.11)Youngest child indicator 0.13 0.06

(0.11) (0.12)Parent's 2000 income in 100,000s 0.04 0.59

(0.15) (0.42)Income squared in billions -0.00 -0.02

(0.00) (0.01)Parent's 2000 net worth in millions 0.37*** 0.39

(0.13) (0.26)Net worth squared in 100 billions -2.24** -0.98

(0.92) (0.76)Black 0.44** 0.63***

(0.20) (0.23)Hispanic 0.33 -0.94***

(0.30) (0.27)Parent's education less than HS -0.75*** -0.75***

(0.18) (0.20)Parent some college 0.70*** 0.91***

(0.17) (0.25)Parent college graduate 1.27*** 0.98**

(0.22) (0.39)Parent post graduate education 1.72*** 1.79***

(0.23) (0.44)Mean family effect 13.02*** 14.74***

(0.43) (0.50)Total number of children 16565 17327Number of families 4820 4488* indicates significance at the 10 percent, ** at the 5 percent, and *** at the 1 percent level.

Parameter (SE)

Table 5: Family Random Effect Estimates of Years of Schooling, HRS1998-2004 Gifts to Children: Gifts No gifts

Independent variable Parameter (SE)Sibling-years of overlap 0.03 0.07** in college ages (0.03) (0.03)Number of children 0.01 -0.26**

(0.12) (0.11)Number of children squared -0.01 0.01

(0.01) (0.01)Child gender, male=1 -0.25*** -0.13

(0.08) (0.08)Child age 0.02*** -0.01

(0.01) (0.01)Oldest child indicator 0.12 0.12

(0.10) (0.11)Youngest child indicator 0.13 0.06

(0.11) (0.12)Parent's 2000 income in 100,000s 0.04 0.59

(0.15) (0.42)Income squared in billions -0.00 -0.02

(0.00) (0.01)Parent's 2000 net worth in millions 0.37*** 0.39

(0.13) (0.26)Net worth squared in 100 billions -2.24** -0.98

(0.92) (0.76)Black 0.44** 0.63***

(0.20) (0.23)Hispanic 0.33 -0.94***

(0.30) (0.27)Parent's education less than HS -0.75*** -0.75***

(0.18) (0.20)Parent some college 0.70*** 0.91***

(0.17) (0.25)Parent college graduate 1.27*** 0.98**

(0.22) (0.39)Parent post graduate education 1.72*** 1.79***

(0.23) (0.44)Mean family effect 13.02*** 14.74***

(0.43) (0.50)Total number of children 16565 17327Number of families 4820 4488* indicates significance at the 10 percent, ** at the 5 percent, and *** at the 1 percent level.

Parameter (SE)

Further Evidence: split the sample on a direct altruismquestion

Would parents give 5 percent of their income to a child with athird, half, or three quarters of the parent’s income?

62 percent of the subsample (914 parents with 3,292 childrenhave useable data) answered ”yes” if their child hadthree-quarters of the parents income. These parents were mostaltruistic.We expect financial aid (and hence overlap) should not matter(or matter less) for the most altruistic families.

We interact the overlap coefficient with three dummies thatseparate the sample by self-reported altruism.

Sensitivity AnalysesAltruism: financial aid matters less for more altruistic families

Table 6: Family Fixed Effect Estimates of Years of Schooling, HRS 2000 Economic Altruism Module

Altruism ModuleParameter

Independent variable (Std error)Child gender, male=1 -0.0877

(0.203)Child age -0.0464

(0.0313)Oldest child indicator 0.581*

(0.276)Youngest child indicator 0.134

(0.286)Sibling-years of overlap 0.0982in college ages*Give 3/4 (0.0894)

Sibling-years of overlap 0.644**in college ages*Give 1/2 (0.109)

Sibling-years of overlap 0.398*in college ages*Never give (0.157)

Number of Children 3292Number of Families 914R-squared 0.3035Adjusted R-squared 0.0332* significant at 5%; ** significant at 1%

Table 6: Family Fixed Effect Estimates of Years of Schooling, HRS 2000 Economic Altruism Module

Altruism ModuleParameter

Independent variable (Std error)Child gender, male=1 -0.0877

(0.203)Child age -0.0464

(0.0313)Oldest child indicator 0.581*

(0.276)Youngest child indicator 0.134

(0.286)Sibling-years of overlap 0.0982in college ages*Give 3/4 (0.0894)

Sibling-years of overlap 0.644**in college ages*Give 1/2 (0.109)

Sibling-years of overlap 0.398*in college ages*Never give (0.157)

Number of Children 3292Number of Families 914R-squared 0.3035Adjusted R-squared 0.0332* significant at 5%; ** significant at 1%

Table 6: Family Fixed Effect Estimates of Years of Schooling, Bequest Measures from HRS 1994 Economic Altruism Module & HRS 2000 Core

Altruism Module HRS 2000 CoreParameter Parameter

Independent variable (Std error) (Std error)Child gender, male=1 -0.0801 -0.196**

(0.247) (0.0755)Child age -0.0738* -0.0127

(0.0367) (0.0111)Oldest child indicator 0.224 0.299**

(0.335) (0.102)Youngest child indicator -0.0123 0.600

(0.354) (0.107)

Sibling-years of overlap 0.0552 --in college ages*bequest (0.165)very important

Sibling-years of overlap 0.186** --in college ages*bequest (0.0868)somewhat or not at allimportant

Sibling-years of overlap -- 0.0466in college ages*100,000 (0.0347)bequest Pr >= 50%

Sibling-years of overlap -- 0.109**in college ages*100,000 (0.0336)bequest Pr < 50%

Number of Children 3292 23,326Number of Families 568 7628R-squared 0.3035 0.5458Adjusted R-squared 0.0332 0.3248* significant at 5%; ** significant at 1%

Independent variable Parameter (SE)Parent's 1997 income, 1000s -0.002* (0.001)Parent's 1997 income squared, 10000s 0.001** (0.000)AFQT percentile 0.002*** (0.000)Mother's education <HS 0.022 (0.038)Mother HS grad -0.010 (0.023)Number of siblings -0.005 (0.007)Female 0.069*** (0.023)Black -0.010 (0.029)Hispanic 0.030 (0.036)Broken home -0.024 (0.022)Urban -0.045 (0.029)South -0.009 (0.023)12 years old in 1997 wave -0.115*** (0.019)13 years old in 1997 wave -0.072*** (0.021)14 years old in 1997 wave -0.032 (0.024)15 years old in 1997 wave -0.012 (0.027)Observations 511Pseudo R-squared 0.176* significant at 10%; ** significant at 5%; *** significant at 1%

Table 7: Probit Estimates (Marginal Effects) of College Completion, NLSY-97

Respondents Whose Parent(s) Don't Pay for College, EFC>0

ParameterIndependent variable (Std error)Parent's 1997 income, 1000s 0.010***

(0.002)Parent's 1997 income squared, 10000s -0.003***

(0.001)Parent's 1997 net worth, 1000s 0.000*

(0.000)AFQT percentile 0.018***

(0.001)Mother's education <HS -0.051

(0.129)Mother HS grad -0.076

(0.080)Number of siblings -0.050**

(0.021)Female 0.283***

(0.066)Black 0.432***

(0.107)Hispanic 0.114

(0.108)Broken home -0.273***

(0.078)Urban -0.038

(0.076)South 0.008

(0.071)12 years old in 1997 wave -1.377***

(0.123)13 years old in 1997 wave -0.984***

(0.126)14 years old in 1997 wave -0.243*

(0.131)15 years old in 1997 wave -0.049

(0.145)Funding gap in first term of college, 100s -0.024***

(0.005)Constant 13.342***

(0.206)Observations 1318R-squared 0.330* significant at 10%; ** significant at 5%; *** significant at 1%

Table 8: OLS Estimates of Highest Grade Completed, NLSY-97

Conclusions

As we mention at the outset, many prior papers discussborrowing constraints for higher education.

In our framework, all else equal, educational attainment willvary inversely with parental resources for g2 = 0 families.

EFC rises with parental resources. Hence, as income increases,children must finance a greater share of education costs ifparents are unwilling or unable to contribute.

The relationship between education and parental income isdifficult to interpret when studies mix together families willingto meet and unwilling to meet their EFCs.

Conclusions, continued

We develop a theory of human capital investment thatrecognizes the distinct roles of parents and children.

The model reveals an identifiable category of students who arelikely to be meaningfully borrowing constrained.

Estimates using HRS and NLSY97 data are consistent with theexistence of such constraints.

Hence, we think the literature suggesting there are noimportant financial barriers to college is mistaken.

Aid policy is incomplete to the extent that it leaves children ofnoncontributors under-funded.

Policy prescriptions based on these results are unclear:Though increasing financial aid is would move the HC ofnoncontributors’ children toward the efficient level, manychildren of contributors would receive inframarginal subsidies.

THE EARNED INCOME TAX CREDIT AND LABOR MARKET PARTICIPATION OF LABOR MARKET PARTICIPATION OF FAMILIES ON WELFARE

V. Joseph Hotz, UCLA & NBER Charles H. Mullin, Bates & WhiteJohn Karl Scholz, Wisconsin & NBER

What is the Federal EITC?What is the Federal EITC?

It is a refundable tax credit directed primarily at p ylow-income working families.

There is a small credit available to childless taxpayers.There are three ranges to the credit: the phase-in (or subsidy); flat; and phase-out (or clawback) ranges.

C $3 2000EITC is a large program: $31.5 billion in FY2000 (larger than the combined federal spending on food stamps and TANF)stamps and TANF).

Coincident Trends: Are They Related?Coincident Trends: Are They Related?

Expansion of the Earned Income Tax CreditBetween 1990 and 1999Between 1990 and 1999

Real EITC spending increased from $9.6 billion to $31.9 billion (in 1999 dollars)billion (in 1999 dollars)

Spending on Cash and Near-Cash Means Tested

$35 000

Spending on Cash and Near-Cash Means Tested Transfers, 1999 Dollars

$25,000

$30,000

$35,000

$10 000

$15,000

$20,000

Mill

ions

$0

$5,000

$10,000

Year

AFDC/TANF EITC SSI Food Stamps

Coincident Trends (cont.)( )Employment Rates of Single Women with ChildrenBetween 3/1990 and 3/2000

Employment rates of single women with children rose from 55.2% to 73.9%.Rates for female-headed households on AFDC/TANF in previous year also went up during 1990s (see graph)previous year also went up during 1990s (see graph)

Percentage of Low-Income Female Heads Employed by Year California Welfare Sample

65

70Year, California Welfare Sample

55

60

50

55

40

45

30

35

30

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Coincident Trends (cont )Coincident Trends (cont.)

Standard labor-leisure model:Expansions of (wage) subsidy like EITC should generate increases in employment of low-wage workers.

Question: Did EITC play in substantial role in increases l f l h h ld ?in employment of single women with children?

Previous Work on Relationship between EITC & EmploymentEmployment

Fairly Large Number of Papers on this Issue

Dickert, Houser, Scholz (1995, TPE); Keane & Moffitt (1998, IER).Eissa & Liebman (1996, QJE); Ellwood (2000, NTJ); Meyer & Rosenbaum (2000 NTJ; 2001 QJE); Grogger (2003, ReStat)H d Ei (2001 ) i EITC l t ff t f 2 t Hoynes and Eissa (2001, wp) examine EITC employment effects for 2-parent families.

All find Positive, Large EITC effects on employment.

Employment elasticities with respect to net income of 0.69 to 1.16.(See Hotz & Scholz, 2003 for full survey of these results.)

All but first two papers use “Diff-in-Diff” approach.

Use episodic “expansions” in EITC and compare changes between groups who were “eligible” and “not eligible” for EITC (e.g., single mothers vs. single were eligible and not eligible for EITC (e.g., single mothers vs. single women).

Potential Concerns about Inferences drawn from Previous EITC – Employment Studiesfrom Previous EITC – Employment Studies

A. Use of national episodic expansions of EITC to explain national trends vulnerable to possibility th t th thi h dthat other things changed.

Secular Changes inWelfare programs (AFDC/TANF Food Stamps Child Welfare programs (AFDC/TANF, Food Stamps, Child Care subsidies)Aggregate labor market conditions

could be driving changes in employment rates.

Spending on Cash and Near-Cash Means Tested Transfers, 1999 Dollars

$30 000

$35,000

$20 000

$25,000

$30,000

s

$

$15,000

$20,000

Mil

lio

n

$5,000

$10,000

$0

Year

AFDC/TANF EITC SSI Food Stamps

Annual Real Earnings per Worker in Service Sector in California, 1992-2000

$60

$50

99

9

$40

10

00

s of

$1

9

$30

$20

1992 1993 1994 1995 1996 1997 1998 1999 2000

Bay Area Counties Central Valley Counties

Central & Southern Farm Counties Los Angeles County

Northern and Mountain Counties Southern Calif. Counties, Other than LA

All Counties

Annual Employment to Population Ratios in California, 1992-2000

0 75

0.80

0.70

0.75

0.65

0.55

0.60

0.50

1992 1993 1994 1995 1996 1997 1998 1999 2000

B A C ti C t l V ll C tiBay Area Counties Central Valley Counties

Central & Southern Farm Counties Los Angeles County

Northern and Mountain Counties Southern Calif. Counties, Other than LA

All Counties

Potential Concerns about Inferences (cont.)

B. Use of “Second Diff” in “Diff-in-Diff” strategy gyrequires composition of “comparison group” doesn’t change over time.

Previous studies use Repeated Cross-Sectional data –typically from CPS – in Diff-in-Diff analyses.Population we analyze – single mothers on welfare in Population we analyze single mothers on welfare in California during 1990s –

Sizeable changes in racial composition, family structure d th h t i tiand other characteristics.

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Potential Concerns about Inferences (cont.)( )

C. If EITC expansion truly caused increases in employment C. If EITC expansion truly caused increases in employment rates of single mothers, should see “similar” systematic changes in rates of EITC take-up, i.e., claiming EITC on tax returns

Analogous to studies of effects of welfare & other social lprograms on employment

Look for changes in program participation to corroborate program effects on employment.

Systematic examination of relationship btwn. EITC expansions & differences in EITC take-up rates btwn. “treatment” & “comparison” groups has not been donecomparison groups has not been done.

Contributions of this Paper

1 We use data from a single state (California) to 1. We use data from a single state (California) to mitigate influence of secular changes in social policies & local labor market conditions.

Over period we examine, low-income populations subject to limited set of policy changes.Better able to control for changes in t t li Better able to control for changes in state policy, some of which vary at county level.Also control for detailed set of county-level measures yof labor market conditions to capture local conditions more accurately.

Contributions of Paper (cont.)p ( )

2 We exploit longitudinal data on households and 2. We exploit longitudinal data on households and focus on temporal “within” household changes to control for potential “composition bias” problem in p p p“Diff-in-Diff” estimation strategy.

Use longitudinal data on households in estimation.

Contributions of Paper (cont.)Contributions of Paper (cont.)3. Use different “Diff-in-Diff” identification strategy

than in most previous work.Compare differential behavior of families with 2+ children vs. 1-child families before & after EITC expansion in 1990s. EITC expansion in 1994 substantially increased EITC expansion in 1994 substantially increased generosity of EITC for 2+ children vs. 1-child households.

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00--66,,880000 00--99,,554400

22,,331122 33,,881166

11,,550044

1155..9988 2211..0066

1122,,446600 –– 2266,,992288 1122,,446600 –– 3300,,558800

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00--44,,553300 ,,334477

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,, ,,55,,667700 –– 1100,,220000

22000000 3344..00 4400..00 77..6655

00--66,,992200 00--99,,772200 00--44,,661100

22,,335533 33,,888888

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11,,553355

1155..9988 2211..0066 77..6655

1122,,669900 –– 2277,,441133 1122,,669900 –– 3311,,115522 55,,777700 –– 1100,,338800

Contributions of Paper (cont.)

3. We use a different “Diff-in-Diff” identification t t ( t )strategy (cont.)

We systematically “assess” the validity of implication of this identification strategyimplication of this identification strategy

EITC policy “treated” all households with 2 or more children the same (i.e., same credit).So, we should expect to see no difference in outcomes of interest (e.g., employment) between 2+ and 3+ child households.households.


4. Focus on effects of EITC on employment for important population.

Female-Headed households on welfare sometime during 1990s.Look at behavior of these households both on & off of welfareof welfare.Estimating effects of EITC for this population is particularly relevant from public policy perspective.particularly relevant from public policy perspective.


5 Most Novel Feature of Paper: Examine differential 5. Most Novel Feature of Paper: Examine differential effects (2+ vs. 1-Kid households) of EITC on incidence of claiming EITC.g

Exploit access to data on federal tax returns for households in sample over 1990s.If our “Diff-in-Diff” identification strategy is isolating EITC effects on employment, should see differential rates of EITC claiming by 2+ vs 1 Kid households rates of EITC claiming by 2+ vs. 1-Kid households before & after expansion.

Our Data Combine Several Ad i i t ti SAdministrative Sources

/Monthly AFDC/TANF case records.Demographic information and benefit receipt.Prior information starting in 1987 on benefits come from Medicaid data.

Q t l d t f UI tQuarterly data from UI system.Measure employment starting in 1986

F d l i f i Federal tax return information. Data from CA Franchise Tax Board beginning in 1990.

Our Data Combine Several Administrative Sources

County-level (local) local labor market dataCounty-level (local) local labor market dataCounty-level policy data (from county welfare & training program implementation)g p g p )Sample exclusions:

Child-only casesCases with more than 2 adults in household

We focus most of analyses on AFDC-FG cases.

SamplingStart random stratified sample of all assistance units on Welfare in California between 1987 & 2000on Welfare in California between 1987 & 2000.

Drawn by Rand for another evaluation. Sample includes ~ 50% of all cases.

Define a “sampling date” – 4th quarter of a household’s spells on welfarehousehold s spells on welfare.

We determine number & ages of children when household on welfare. All cases, on and off welfare, are treated symmetrically. Avoid “overweighting” long-term welfare recipients in our sample.p

Sampling (cont.)

Utilize two samples of households in our analysesCross sectional:

Employment in year following sampling date.This sample mimics repeated cross-section data used in previous studies.

Longitudinal: Employment in periods 3 2 1 and 0 (sampling)Employment in periods -3, -2, -1, and 0 (sampling).Longitudinal data on households allow control for household-specific fixed effects, so focus on within household changes to identify EITC effects.

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((119999xx --11999911--9933 AAvveerraaggee))

11999911 2244..1133 2255..4488 2233..0022 --22..4466 [[2277,,556688]] [[3377,,999999]]

11999922 2222 7766 2233 8800 2211 8855 11 995511999922 2222..7766 2233..8800 2211..8855 --11..9955 [[2244,,443333]] [[3322,,335544]]

11999933 2222..8888 2233..9900 2211..9977 --11..9933 [[2233,,009988]] [[2299,,553322]]

11999944 2277.8844 2288.8877 2266.9911 --11.9966 00.117711999944 2277..8844 2288..8877 2266..9911 11..9966 00..1177 [[2244,,000055]] [[3300,,440022]] ((00..5599))

11999955 3322..9955 3344..1111 3311..8899 --22..2222 --00..0099 [[2255,,550000]] [[3311,,116644]] ((00..6611))

11999966 3355..6600 3355..8833 3355..3344 --00..4499 11..6699****** [[2255,,445577]] [[3300,,225566]] ((00..6633))

11999977 4422..3344 4422..8800 4411..9911 --00..8899 11..2244** [[2244,,779944]] [[2288,,665500]] ((00..6666))

11999988 4477..4466 4466..7722 4488..1188 11..4466 33..6600****** [[2222 447799]] [[2266 117744]] ((00 6699)) [[2222,,447799]] [[2266,,117744]] ((00..6699))

11999999 5511..2288 5500..1188 5522..3355 22..1177 44..3311******

[[1199,,006666]] [[2211,,997733]] ((00..7755))

22000000 5511..9944 4499..8800 5533..9999 44..1199 66..3322****** [[1199 773311]] [[2222 449900]] ((00 7755)) [[1199,,773311]] [[2222,,449900]] ((00..7755))

2000 2000 9 17

Econometric Specifications (Diff-in-Diff)2000 2000 9 17

1992 1994 1 1

55

_ [2 _ ] _ _ict i S t s ict t j ict k itcs s j k

Y Year s Kids Year s Kids j KidsAge k

X W L C

φ α β δ ψ

λ θ

= = = =

= + + + ⋅ + +∑ ∑ ∑ ∑

∑1

_ic ct ct m c ictm

X W L County mκ λ η θ ε=

+ + + + +∑

⎧where Yict

th1, if at least 1 adult in household in county is employed in year 0, otherwise ict

i c tEmp

⎧= ⎨⎩

th1 if household in county files tax return and claims EITCin yeari c t⎧

(1)

1, if household in county files tax return and claims EITC in year 0, otherwise ict

i c tClaimEITC

⎧=⎨⎩

2+Kidsict is 2+ kids indicator variable;ict ;Kids_jict is j children in household indicator variable; KidsAge_kict = # of children in the household age k; Xic time-invariant demographics of household; Wct county-level measures of California’s welfare caseload and policies; ct y pLct time-varying measures of county-level labor market conditions; County_mict indicator variable for residing in county m

Econometric Specifications (Diff-in-Diff) (Cont.)

In some regressions for “testing” we also include an ( l ) 2 hild i di i bl(exactly) 2 children indicator variable:

[ ] 0, if 2 or 32 3 it itKids Kids

Kid Kid< ≥⎧

+ + ⎨[ ]2 31, if 2

it itit it

it

Kids KidsKids

+ − + = ⎨ =⎩

CovariatesCovariatesDemographic characteristics

Number of kids & number of kids by age.

Local labor marketsYear dummies, employment share by sector, avg. income by sector

W lf lWelfare rulesProportion of population in GAIN program

Ti i i t i t (i OLS C S ti l Time-invariant covariates (in OLS Cross-Sectional Models).

Race/ethnicity county dummies gender age timing of entry Race/ethnicity, county dummies, gender, age, timing of entry onto welfare.

Empirical Strategy for Understanding the EITC's Effect

Our strategy for assessing validity of estimated EITC’s effect on employmenton employment:

1. Does employment of families with 2+ children increase relative to 1-Kid families? (They should )to 1-Kid families? (They should.)

2. Does employment of 2+Kid families differ from effects for 3+Kid families? (They should not)D t l tt f EITC l i i i l t 3. Do temporal patterns of EITC claiming mirror employment patterns in #1 and #2? (They should)

4. Do see any differences in employment btwn. 2+Kid and 1-Kid h h ld t fili t t ? (W h ld t!)households not filing tax returns? (We should not!)

Similar strategy for #1 - #3 should apply to Claiming EITC.

TTaabbllee 33:: EEssttiimmaatteess ooff EEIITTCC EEffffeeccttss oonn HHoouusseehhoolldd EEmmppllooyymmeennttEEmmppllooyymmeenntt

OOLLSS,, CCrroossss--

SSeeccttiioonnaall

HHoouusseehhoolldd

FFiixxeedd EEffffeeccttss,,VVaarriiaabbllee SSaammppllee

,,PPaanneell DDaattaa

22++ KKiiddss iinn 11999944 00..00000011 --00..00003355 ((00..00006655)) ((00..00004411)) 22++ KKiiddss iinn 11999955 --00..00006633 00..00007722 ((00..00006655)) ((00..00005522)) 22++ KKiiddss iinn 11999966 00..00004400 00..00112233**** ((00..00006666)) ((00..00006611)) 22++ KKiiddss iinn 11999977 00..00009922 00..00226611****** ((00..00006677)) ((00..00007711)) 22++ KKiiddss iinn 11999988 00..00338822****** 00..00332244****** ((00..00007700)) ((00..00008833)) 22++ KKiiddss iinn 11999999 00..00227788****** 00..00334411******ds 999 0 0 8 0 03 ((00..00007766)) ((00..00110000)) 22++ KKiiddss iinn 22000000 00..00442255****** 00..00229955**** ((00..00007755)) ((00..00112233)) NNoo.. ooff OObbsseerrvvaattiioonnss 552277,,112255 11,,663377,,885555Noo. oof OObbsseervaattioonss 5527,,1255 1,,66337,,885555PP--VVaalluuee ffoorr TTeesstt ooff 22++ KKiiddss iinn 11999944--22000000 == 00 00..00000000

00..00000044

TTaabbllee 44:: EEssttiimmaatteess ooff EEIITTCC EEffffeeccttss oonn WWhheetthheerr HHoouusseehhoollddCCllaaiimmeedd tthhee EEIITTCC oonn TTaaxx RReettuurrnnCCllaaiimmeedd tthhee EEIITTCC oonn TTaaxx RReettuurrnn

OOLLSS,, CCrroossss--

SSeeccttiioonnaall

HHoouusseehhoolldd

FFiixxeedd EEffffeeccttss,,VVaarriiaabbllee SSaammppllee PPaanneell DDaattaa

22++ KKiiddss iinn 11999944 00..00004411 00..00000066 ((00..00005599)) ((00..00003388)) 22++ KKiiddss iinn 11999955 --00..00003322 00..00006655 ((00..00006611)) ((00..00004499))22++ KKiiddss iinn 11999966 00..00110033** 00..00118844****** ((00..00006622)) ((00..00005588)) 22++ KKiiddss iinn 11999977 00..00005500 00..00119900******

( ) ( 8) ((00..00006666)) ((00..00006688))22++ KKiiddss iinn 11999988 00..00224499****** 00..00117700**** ((00..00007700)) ((00..00008811)) 22++ KKiiddss iinn 11999999 00..00228833****** 00..00223333****

((00 00007777)) ((00 00009988)) ((00..00007777)) ((00..00009988))22++ KKiiddss iinn 22000000 00..00444488****** 00..00119944 ((00..00007766)) ((00..00112222)) NNoo.. ooff OObbsseerrvvaattiioonnss 552277,,112255 11,,663377,,885555 PP VV ll ff TT tt ff 22++ KKiiddPP--VVaalluuee ffoorr TTeesstt ooff 22++ KKiiddssiinn 11999944--22000000 == 00 00..00000000 00..00335533

TTaabbllee 55aa:: AAsssseessssiinngg tthhee VVaalliiddiittyy ooff SSttrraatteeggyy ffoorr IIddeennttiiffyyiinngg EEIITTCC EEffffeeccttss oonn HHoouusseehhoolldd EEmmppllooyymmeenntt

[[FFaammiillyy FFiixxeedd EEffffeeccttss EEssttiimmaattiioonn oonn PPaanneell DDaattaa]]

HHoouusseehhoollddss tthhaatt DDiidd NNoott

FFiillee TTaaxxVVaarriiaabbllee AAllll HHoouusseehhoollddss

FFiillee TTaaxxRReettuurrnn

((11)) ((22)) ((33)) 22++ KKiiddss iinn 11999944 --00..00003355 --00..00001166 --00..00002200 ((00..00004411)) ((00..00005511)) ((00..00006655)) 22++ KKiiddss iinn 11999955 00..00007722 00..00004433 00..00002288

((00 00005522)) ((00 00006677)) ((00 00008866)) ((00..00005522)) ((00..00006677)) ((00..00008866))22++ KKiiddss iinn 11999966 00..00112233**** 00..00111144 --00..00001166 ((00..00006611)) ((00..00008822)) ((00..00110077)) 22++ KKiiddss iinn 11999977 00..00226611****** 00..00228899****** 00..00111133 ((00..00007711)) ((00..00009988)) ((00..00113311)) 22++ KKiiddss iinn 11999988 00..00332244****** 00..00333311****** 00..00004488

((00 00008833)) ((00 00111166)) ((00 00116611)) ((00..00008833)) ((00..00111166)) ((00..00116611))22++ KKiiddss iinn 11999999 00..00334411****** 00..00336688****** 00..00003377 ((00..00110000)) ((00..00113377)) ((00..00220000)) 22++ KKiiddss iinn 22000000 00..00229955**** 00..00229911** --00..00000099 ((00..00112233)) ((00..00116688)) ((00..00225577)) [[22++ KKiiddss –– 33++ KKiiddss]] iinn 11999944 --00..00003300 --00..00000099

((00..00005511)) ((00..00006622)) ((00..00005511)) ((00..00006622))[[22++ KKiiddss –– 33++ KKiiddss]] iinn 11999955 00..00005555 --00..00001100 ((00..00006633)) ((00..00008800)) [[22++ KKiiddss –– 33++ KKiiddss]] iinn 11999966 00..00002211 --00..00001144 ((00..00007744)) ((00..00009966)) [[22++ KKiiddss –– 33++ KKiiddss]] iinn 11999977 --00..00004422 --00..00001122

((00..00008866)) ((00..00111155)) ((00..00008866)) ((00..00111155))[[22++ KKiiddss –– 33++ KKiiddss]] iinn 11999988 --00..00000044 00..00000055 ((00..00110000)) ((00..00114411)) [[22++ KKiiddss –– 33++ KKiiddss]] iinn 11999999 --00..00003399 --00..00007711 ((00..00111199)) ((00..00118811)) [[22++ KKiiddss –– 33++ KKiiddss]] iinn 22000000 00..00001155 00..00007777

((00..00114499)) ((00..00224444)) ((00..00114499)) ((00..00224444))NNoo.. ooff OObbsseerrvvaattiioonnss PP--VVaalluuee ffoorr TTeesstt ooff 22++ KKiiddss iinn 11999944--22000000 == 00

00..00000044

00..00111122

00..88442233

PP--VVaalluuee ffoorr TTeesstt ooff [[22++ KKiiddss –– 33++ KKiiddss]] iinn 11999944--22000000 == 00

00..77338899

00..99998899

TTaabbllee 55bb:: AAsssseessssiinngg tthhee VVaalliiddiittyy ooff SSttrraatteeggyy ffoorr IIddeennttiiffyyiinngg EEIITTCC EEffffeeccttss oonn WWhheetthheerr HHoouusseehhoolldd CCllaaiimmeedd tthhee EEIITTCC oonn

TTaaxx RReettuurrnn [[FFaammiillyy FFiixxeedd EEffffeeccttss EEssttiimmaattiioonn oonn PPaanneell DDaattaa]]

VVaarriiaabbllee ((11)) ((22)) 22++ KKiiddss iinn 11999944 00..00000066 00..00005577 ((00..00003388)) ((00..00004488)) 22++ KKiiddss iinn 11999955 00..00006655 00..00113311**** ((00..00004499)) ((00..00006644)) 22++ KKiiddss iinn 11999966 00..00118844****** 00..00223333****** ((00..00005588)) ((00..00007788)) 22++ KKiiddss iinn 11999977 00..00119900****** 00..00227799****** ((00..00006688)) ((00..00009944)) 22++ KKiiddss iinn 11999988 00..00117700**** 00..00117700 ((00..00008811)) ((00..00111122)) 22++ KKiiddss iinn 11999999 00..00223333**** 00..00334444****

((00 00009988)) ((00 00113344))((00..00009988)) ((00..00113344))22++ KKiiddss iinn 22000000 00..00119944 00..00225555 ((00..00112222)) ((00..00116655)) [[22++ KKiiddss –– 33++ KKiiddss]] iinn 11999944 --00..00007788** ((00..00004477)) [[22++ KKiiddss –– 33++ KKiiddss]] iinn 11999955 --00..00110000** ((00..00005599)) [[22++ KKiiddss –– 33++ KKiiddss]] iinn 11999966 --00..00006677 ((00..00007700)) [[22++ KKiiddss –– 33++ KKiiddss]] iinn 11999977 --00..00113311 ((00..00008822)) [[22++ KKiiddss –– 33++ KKiiddss]] iinn 11999988 00..00003333 ((00..00009977)) [[22++ KKiiddss –– 33++ KKiiddss]] iinn 11999999 --00..00115588[[22++ KKiiddss 33++ KKiiddss]] iinn 11999999 00..00115588 ((00..00111199)) [[22++ KKiiddss –– 33++ KKiiddss]] iinn 22000000 --00..00006633 ((00..00114499)) PP--VVaalluuee ffoorr TTeesstt ooff 22++ KKiiddss iinn 11999944--22000000 == 00 00..00335533 00..00117744 PP--VVaalluuee ffoorr TTeesstt ooff [[22++ KKiiddss

33++ KKiiddss]] iinn 11999944 22000000 = 00 00 00666699–– 33++ KKiiddss]] iinn 11999944--22000000 == 00 00..00666699

Sensitivity Analysesy y

Change base year(s) used to measure before- [T bl 5 ]expansion outcomes. [Table 5c]

Change thresholds of UI earnings used to define h th h h ld l d [T bl 5d]whether household employed. [Table 5d]

Control for measure of county expenditures on hild f w lf i i t t ichildcare for welfare recipients to regressions.

No qualitative difference in estimated EITC effects on employment or EITC claiming for above variants of our employment or EITC claiming for above variants of our basic results.

Sensitivity Analyses (cont.)y y ( )

Examine EITC effects on household employment and Examine EITC effects on household employment and EITC Claiming for two-parent households on welfare (AFDC-UP cases). (AFDC UP cases).

Do not find any evidence of positive employment effects for AFDC-UP households.Perhaps employment barriers are larger for the subset of UP households with no workers.

Conclusions

W fi d b ff f h diff i l We find robust effects of the differential expansion of EITC btwn. 2+Kid vs. 1-Kid households on employment rates & rates of EITC claiming.gOur identification strategy for identifying EITC effects is validated several different EITC effects is validated several different ways.

Conclusions (cont.)Conclusions (cont.)EITC increased employment for families with 2+ kids b h 3 4 l K d by as much as 3.4 percentage points relative to 1-Kid families.

Ill strationIllustration:11.8% of 31 percentage point employment increase for families with two or more children between 1991 and 2000. Smaller than Meyer & Rosenbaum (2001) or Grogger (2003)

But, another way to look at this is that 77 percent of the differential gain in employment for families with two or more differential gain in employment for families with two or more kids.

Conclusions (cont.)( )Implied Elasticity of Employment w.r.t. household disposable incomedisposable income:

Average EITC differential for families with two or more children was $439 in 1998.$Average disposable income (including transfers) was around $10,000.

The EITC increased disposable income around 4.4 percent.

Employment rates in 1998 were around 60 percent.The EITC increased the relative employment of families with two or The EITC increased the relative employment of families with two or more children 3.2 percentage points, or 5.6 percent.

The implied employment elasticity with respect to disposable i i 1 3 hi h i t d f ti t i EITCincome is 1.3, which is at upper end of estimates in EITC-Effects-on-Employment Literature.

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