Table 1.

a: Studies of the Role of Income on Child Outcomes

	Dataset	Outcome Studied: Test Scores (T), Schooling (S)	Timing of Income (Developmental Stage of the Child at Which Income Effects are Studied)	Separate the Effect of Income from Changes in Labor Supply or Family Environment	Distinguishes the Effects of Contempora-neous vs Permanent Income	Sources of Income Whose Effects are Studied	Instrument Used	Effect of Income on Human Capital Investments
Carneiro and Heckman (2002)	NLSY79^*	S	E and L College Enrollment	X	✓	Total family income	None	Conditioning on ability and family background factors, the role of income in determining schooling decisions is minimal. The strongest evidence is in the low ability group. The test is not robust to accounting for parental preferences and paternalism. Observed differences in attendance might be due to a consumption value of child’s schooling for parents. Percentage of people constrained = weighted gap in educational outcome to highest income group: 5.1% are constrained in college enrollment (1.2% among low income, low ability, 0.2% low income high ability), 9% in completion of 2- year college (5.3% among low income, low ability, 0.3% low income high ability). There is no evidence of a independent effect on college enrollment of early or late income once permanent income is accounted for. The claim that higher IV than OLS estimates of the Mincer coefficient implies credit constraints are incorrect: instruments used are invalid, the quality margin is ignored and self selection and comparative advantage can produce the result also in absence of financial constraints.
Belley and Lochner (2007)	NLSY79^, NLSY97^	S	L High school completion and College Enrollment	X	X	Total family income	None	High school completion: +8.4% for highest income quartile compared to lowest in 79, +6.7 in 97 cannot reject equal effect of income; college enrollment: +9.3% for highest income quartile compared to lowest in 79, +16 in 97, cannot reject equal effect of income.
Dahl and Lochner (2012)	NLSY79^, C-NLSY79^	T	E Preadolescence (ages 8 to 14)	X^a	X^b	Total family income	Policy variation in EITC eligibility	$1,000 extra per year for 2 years: +6% of a standard deviation in math and reading combined PIAT score.
Duncan et al. (1998)	PSID^*	S^d	E and L Childhood and Preadolescence (ages 0 to 15)	X	✓	Total family income	None	$10,000 increase in average (age 0-15) family income: +1.3 years of schooling in low income (<$20,000) families, +0.13 in high income ones. Relevance of income is stronger in the early years (age 0-5): $10,000 increase in average (age 0-5) family income leads to extra 0.8 years of schooling in low income families, 0.1 in high income ones. Income at age 6-10 and 11-15: no significant effect. Similar results in a sibling differences model.
Duncan et al. (2011)	Randomized Interventions on Welfare Support	T	E Early Childhood (ages 2 to 5)	X	X	Total family income	Random assignment to programs offering welfare transfers conditional on employment or education related activities, or full time work	$1,000 extra per year for 2 to 5 years: +6% of a standard deviation in child’s achievement score.
Loken (2010)	Norwegian Administrative Data	S	E Childhood (ages 1 to 11)	✓^c	X	Total family income	Oil discovery (inducing regional increase in wages)	OLS: positive relationship of average (age 1-13) family income on children’s education, IV: no causal effect. Results are robust to different specification and splitting the sample by parental education.
Loken et al. (2012)	Norwegian Administrative Data	S	E Childhood (ages 1 to 11)	X	X	Total family income	Oil discovery (inducing regional increase in wages)	Non-linear IV (quadratic model): increase of $17,414, +0.74 years of education for children in poor families, +0.05 for children in rich families.^†
Milligan and Stabile (2011)	CCTB^, NCBS^*	T	E Childhood (ages 0 to 10)	X	X	Child related tax benefits and income transfers	Variation in benefits eligibility	Low education mothers: positive effects of child benefits on cognitive outcomes for boys, on emotional outcomes for girls, weak on health. Results are non robust to the exclusion of Quebec.
Carneiro et al. (2013)	Norwegian Registry	S^e	E and L Childhood to Adolescence (ages 0 to 17)	X	✓	Total family income	None	All outcomes: monotone and concave relationship with permanent income. £100,000 increase in permanent father’s earnings: +0.5 years of schooling. Timing of income: a balanced profile between early (age 0-5) and late childhood (age 6-11) is associated with the best outcomes; shifting income to adolescence is associated with better outcomes in dropping out of school, college attendance, earnings, IQ and teen pregnancy. Early and late childhood income are complements in determining schooling attainment, early and adolescent income are substitutes.
b: Studies of Credit Constraints

	Dataset	Outcome Studied: Test Scores (T), Schooling (S)	Timing of Income (Developmental Stage of the Child at Which Constraints are Studied)	Explicit Dynamic Model	Who is Affected by Constraints: Parent of the Agent (P), Agent / Child (C)	Method to Test for Credit Constraints	Find Presence of Credit Constraints	Effect of Income or Constraints on Human Capital Investments

Keane and Wolpin (2001)	NLSY79^*	S	L College Enrollment	✓	C	Structural estimation of the lower bound on asset level	YES But irrelevant for schooling decisions	Increase borrowing limit to $3,000 (3× max estimated): no change in mean highest grade completed; +0.2% in college enrollment; -0.2$ on mean hourly wage rate; increase in consumption and reduction in market hours; moderate reduction in parental transfers especially for the least educated parents.
Cameron and Taber (2004)	NLSY79^*	S	L Adolescence and College Enrollment	X	C	IV estimation of “returns” to schooling using costs of schooling or foregone earnings as instruments	NO	Theoretical prediction: if borrowing constraints, IV estimates using direct costs of schooling higher than using opportunity costs. Data: IV estimates using the presence of a local college are smaller than the ones using foregone earnings. Regressions which interact college costs and characteristics potentially related to credit availability: no evidence of excess sensitivity to costs for potentially constrained sample. Structural model: almost 0%of the population is found to borrow at a rate higher than the market one.
Caucutt and Lochner (2012)	NLSY79^, C-NLSY79^	T^a	E and L Childhood and Adolescence	✓	P	Structural estimation of the lower bound on asset level	YES Stronger effect on high skilled parents	50% of young parents are constrained: high school dropouts (50%), high school graduates (38%), college dropouts (60%), college graduates (68%); and 12% of old parents are constrained. Families with college graduate parents benefit the most from a reduction in credit constraints.

E refers to “early” years (childhood), L refers to “late” years (adolescence).

Control for labor supply, but endogeneity is not considered,

Only analysis of past versus contemporaneous income,

Labor supply is not modeled, but the effects of the instrument on it are studied and found insignificant,

Hazard of non marital birth is also studied,

Other outcomes (health, teenage pregnancy, IQ) are studied as well.

NLSY79: National Longitudinal Survey of Youth 1979, NLSY97: National Longitudinal Survey of Youth 1997, C-NLSY79: Children on the National Longitudinal Survey of Youth 1979, PSID: Panel Study of Income Dynamics;

^**

Canada Child Tax Benefit;

^***

National Child Benefit Supplement.

^†

Unfortunately, this study is flawed by using an IV procedure for ordered choice models of schooling that counts outcomes for certain subsets of the population multiple times and is difficult to interpret economically (see Heckman et al. (2006)) for a critical discussion of the method used.

E refers to “early” years (childhood), L refers to “late” years (adolescence).

The consequences on child’s schooling outcomes are studied as well.

NLSY79: National Longitudinal Survey of Youth 1979, NLSY97: National Longitudinal Survey of Youth 1997, C-NLSY79: Children on the National Longitudinal Survey of Youth 1979.