III. Wage Determination and Labour Market Discrimination. 7. Investment in Education. 1. Motivation and Basic Trends

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1 III. Wage Determination and Labour Market Discrimination 7. Investment in Education 1. Motivation and Basic Trends 2. Human Capital Theory a. Theoretical foundations of the Mincer model b. Extensions to the Mincer Model 3. Challenges to the Mincer Wage Equation 4. Challenges to human capital theory as an explanation for education decisions

2 7.1 Motivation and Basic Trends Labour supply deals with quantities of labour supplied, but there is also a quality aspect to labour, which can be enhanced by investment in education o Literacy and numeracy are the first goals of education Investment in education have long been seen to be key to economic well-being and growth o Education policies are important instruments towards economic growth o Basic education is indeed subsidized in most industrialized countries o Moreover, schooling is compulsory until years of age in many countries o More recently, policies have also focused on the early childhood learning More highly educated workers are paid more than less educated workers o Why? o Why isn t everyone getting a Ph.D.? The benefits of education are seen not only in term of a more productive workforce, but also in term a healthier population, a more political engaged one, and a happier one! o There are social as well as private returns to education

3 In summary, we can motivate our study of human capital policy with three major worries of our times: economic growth, inequality and poverty. Human capital is not a magic bullet that will solve all social problems, but is a promising avenue for substantial social improvement. A FEW MOTIVATING FACTS There is a lot of empirical evidence that schooling, literacy/numeracy skills, and many other indicators of human capital correlate positively with income and other outcomes Positive correlation holds at the individual, group, or country level Large increase in educational achievement through most of the 20 th century, but much less changes (especially in the United States) in more recent decades, despite increasing returns to education.

4 GDP and PISA Male Math Scores: All Countries GDP and PISA Female Math Scores: All Countries log GPD per capita QAT LIE LIE NOR LUX LUX LUX LIE NOR NOR LIE CHE MAC QAT LUX AUS CHE SWE LUX IRL NOR ISL DNK IRL DNKCHE USA USA SWE USA CHE USA SWE AUT AUT CAN IRL FIN BEL USA NOR IRLDNK CHE JPN DEU AUT SWE AUT AUS BEL NLD BEL AUS NLD FIN JPN SGP ISL CAN DEU FIN ITA ESP GBR ISLDEU NLD ITA GBR NZL MAC JPN SGP ITA GBR ISL JPN DNK DEU IRL SWEBEL NLD HKG GRC FIN ITAESP MAC GBR CANHKG HKG ISR ESP NZL AUTBELAUS AUS FIN CAN HKG NLD ISR ISRGRC ITA ESP PRT NZL HKG KOR KOR GRCPRT PRT CZE SVN SVN PRT MAC KOR TTO CHL SVK CZE URY ESP SVK CZE EST HRV HRV NZLKOR GRCPRT SVK EST BRA LTU RUS HRV HUN HUN HUN EST KOR BRAMEX CHL CHL LTU LVA LVA POL POL CRI MEX TUR LTU LVA CZE HUN POL TUR SVK MEX RUS BRAMEX BGR CHL ROMURY COL CRI MEX PAN COL ROM URYTUR ROM RUS BGR POL CZE JOR THA BGRTUR POLVA TUN HUN BRA COL JOR TUN URY BRA TUN THA LVA TUNJOR THA RUS THA THA ROM BGR RUS CHN log GPD per capita QAT BRA LIE LUX LIE LUX LUX NOR LIE LIE LUX NOR NOR CHEMAC QAT AUS CHE LUX IRL DNK NOR IRLISL DNKCHE USA USA USA SWE CAN AUT IRL CHE USA AUTBEL USA AUT NOR IRL SWE DNK AUS CHE DNK DEU AUT IRL ISL ISL JPN DEU AUT SWE AUS BEL NLDFIN CAN DEU FIN ITA ESP GBR ISL DEU FIN JPN SGP GBR ISL NLD NZL MAC JPN SGP ITA GBR JPN JPN SWEBEL NLD HKG FIN ITA GRC MAC BEL AUS GBR CAN HKG HKG ISR ESP NZL AUS CAN FIN NLD ISR ISR GRC GRC ITA ESP PRT SVN NZL KOR HKG KOR GRC PRT PRT SVN SVN PRT MAC KOR CHL SVK CZE URY ESP SVK CZE EST TTO HRV HRV KOR NZL GRC PRT SVK EST BRA HRV HUN LTU RUS HUN HUN EST KOR BRA CHL CHL LTU LVA LVA POL POL CRI MEX TUR MEX URY LVA LTU CZE HUN SVK POL MEX PAN COL TUR ROM TUR ROM RUS MEX RUS BRA MEXCRI CHL ROM URY BGR BGR COL POL CZE JOR THA TUR BGR POLLVA TUN TUN HUN COL JOR URY BRA TUN THA LVA TUN JOR THA RUS THA THA ROM BGR RUS CHN Male Mathematics Mean Fitted values Female Mathematics Mean Fitted values Source: Algan and Fortin (2015)

5 10 - ACEMOGLU & ANGRIST Figure 1 LOG OUTPUT PER WORKER AND YEARS OF SCHOOLING ACROSS COUNTRIES r FR USA D 1 ITA FRA S a) SPA ALPW?LR UKG1I DEN NZE C- SGP HK E.6#,Wl JP C) 0 SYAiE VEN P O R ALGIR BRA COLM A U U.. clu CUG PAN HU EGY U E~ SRL - PMN P -2 IDN GUY PHL ROM t.j IND ZBW LES PA CHN SNGIB A ZAM o MOZRWYOG 0) MAN M)BRGR jlw )GfGBI c r OR HUN URS CZE years of schooling The line shows the fitted OLS relationship. The slope coefficient is 0.29, and the standard error is 0.02.

6 Fortin/Lemieux Econ 561 Lecture 3B Source: Frenette, 2014

7 Fortin/Lemieux Econ 561 Lecture 3B Source: Frenette, 2014

8 Fortin/Lemieux Econ 561 Lecture 3B Source: Frenette, 2014

9 Fortin/Lemieux Econ 561 Lecture 3B Source: Frenette, 2014

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11 Source: Heckman and Lafontaine (2007) Figure XIII. Educational Attainment Decompositions, Males and Females Birth Cohorts Percentage 90% 85% 80% 75% 70% 65% 60% 55% 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Graduate HS Attend College Attend Given HS Graduate College Graduate Given Attend Year of Birth Notes: 3-year moving averages based on CPS October, Census, CPS March and NCES data. HS graduates are those who obtained a regular public or private HS diploma (excluding GEDs) from the NCES. "Graduate HS" is the fraction of 8th grade enrollments for a given cohort who report a regular HS diploma. "Attend Given HS" is the fraction of recent HS graduates who report being enrolled the fall of the year following graduation. "Attend College" is college enrollments of recent HS graduates as a fraction of 18 year old cohort size. College graduates are those who report a BA or higher by age 25. "Graduate Given Attend" is those who obtained a four year degree as a fraction of the college enrollment total for that cohort. Two-year degrees are not included. "Graduate College" is the number of college graduates as a fraction of the 18 year old cohort size. Population estimates are from the Census P-20 reports. HS diplomas issued by sex are estimated from CPS October data after 1982.

12 Source: Heckman and Lafontaine (2007) Percentage 90% 85% 80% 75% 70% 65% 60% 55% 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Figure XIV. Educational Attainment Decompositions, Males Birth Cohorts Graduate HS Attend College Attend Given HS Graduate College Graduate Given Attend Year of Birth Notes: 3-year moving averages based on CPS October, Census, CPS March and NCES data. HS graduates are those who obtained a regular public or private HS diploma (excluding GEDs) from the NCES. "Graduate HS" is the fraction of 8th grade enrollments for a given cohort who report a regular HS diploma. "Attend Given HS" is the fraction of recent HS graduates who report being enrolled the fall of the year following graduation. "Attend College" is college enrollments of recent HS graduates as a fraction of 18 year old cohort size. College graduates are those who report a BA or higher by age 25. "Graduate Given Attend" is those who obtained a four year degree as a fraction of the college enrollment total for that cohort. Two-year degrees are not included. "Graduate College" is the number of college graduates as a fraction of the 18 year old cohort size. Population estimates are from the Census P-20 reports. HS diplomas issued by sex are estimated from CPS October data after 1982.

13 Source: Heckman and Lafontaine (2007) Figure XV. Educational Attainment Decompositions, Females Birth Cohorts Percentage 90% 85% 80% 75% 70% 65% 60% 55% 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Graduate HS Attend College Attend Given HS Graduate College Graduate Given Attend Year of Birth Notes: 3-year moving averages based on CPS October, Census, CPS March and NCES data. HS graduates are those who obtained a regular public or private HS diploma (excluding GEDs) from the NCES. "Graduate HS" is the fraction of 8th grade enrollments for a given cohort who report a regular HS diploma. "Attend Given HS" is the fraction of recent HS graduates who report being enrolled the fall of the year following graduation. "Attend College" is college enrollments of recent HS graduates as a fraction of 18 year old cohort size. College graduates are those who report a BA or higher by age 25. "Graduate Given Attend" is those who obtained a four year degree as a fraction of the college enrollment total for that cohort. Two-year degrees are not included. "Graduate College" is the number of college graduates as a fraction of the 18 year old cohort size. Population estimates are from the Census P-20 reports. HS diplomas issued by sex are estimated from CPS October data after 1982.

14 Source: Card and Lemieux (2001) VOL. 91 NO. 2 YOUTHS AND RI ~ ~ ~ ~ S 01.5 X -0.5E X _ co ~~~~~ I ~ n1.1 - Male/Female College Ratio (left axis) Male Veteran Rate (right axis) Year of Birth FIGURE 1. MALE-FEMALE COLLEGE GRADUATION RATE AND MALE VETERAN RATE, BY BIRTH COHORT

15 Source: Card and Lemieux (2001) Figure 3: Age-Group Specific Relative Supplies of College-Educated Labor A Year Old Men -0.1 Log Relative Supply Index United States United Kingdom Canada B Year Old Men -0.1 Log Relative Supply Index United States Canada United Kingdom

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17 7.2. Human Capital Theory Of the five principal circumstances for which Adam Smith argued there should be compensating wage differentials, the second one was the easiness and cheapness, or the difficulty and expence of learning a business. But it is when he compared an educated human to an expensive machine that laid out the basics of human capital theory When any expensive machine is erected, the extraordinary work to be performed by it before it is workn out, it must be expected, will replace the capital laid out upon it, with at least the ordinary profit. A man educated at the expence of much labour and time to any of those employments which require extraordinary dexterity and skill, may be compared to one of those expensive machines.... It must do this too in a reasonable time, regard being had to the very uncertain duration of human life, in the same manner as to the more certain duration of the machine. The difference between the wages of skilled labour and those of common labour, is funded on this principles. With human capital theory, investments in human resources are considered similarly to other types of investments. Costs are incurred in expectation of future benefits and the investment in the last unit of human capital is made only if the benefits are expected to exceed the costs.

18 On the supply side: additional schooling entails opportunity costs in the form of foregone earnings plus direct expenses such as tuition. To induce a worker to undertake additional schooling, he must be compensated by sufficiently higher lifetime earnings. On the demand side: to command higher earnings, more schooled workers must be sufficiently more productive than their less schooled fellow workers. In the perfect competition framework, the wage guides the allocation of workers across firms as to achieve an efficient allocation of resources by equating the wage to the value of the marginal product. In the long run equilibrium, the relationship between lifetime earnings and the schooling must be such that i) the supply and demand for workers of each schooling level are equated and ii) no worker wishes to alter their schooling level. The study of the effects of investment in schooling and on-the-job training on the level, pattern and interpersonal distribution of life-cycle earnings have been pioneered by Becker (1964, 1975) and Mincer (1958, 1962, 1979). This lead to one of the more successful empirical equation called the Mincer earnings function 2 ln( w ) = β 0 + rs + β1e + β 2E + ε, where w is earnings (or the hourly wage when available), S is years of schooling, E is years of labor market experience, and ε is an error term.

19 Years of schooling are a fairly direct measure of education human capital, while years of labour market experience is viewed as a proxy for on-the-job training. Mincer uses the transformation Experience equals Age minus Schooling minus 6, E = A S 6, to capture the interaction between schooling and experience, but assumes multiplicative separability. The Mincer equation thus captures the important empirical regularities: 1) increase in earnings with schooling, 2) concavity of log earnings in experience, 3) log earnings experiences profiles are parallel across different education groups (ratio of earnings for persons with education levels differing by a fixed number of years is roughly constant across schooling levels) no interaction between schooling and experience 4) log earnings-age profiles diverge across different education groups. Almost all empirical studies find that schooling has a positive and significant effect on earnings ( r > 0 ) that earnings are a concave function of labour market experience ( β 1 > 0 and β 2 < 0). A simple regression model with a linear schooling term and a low-order polynomial in potential experience explains 20-35% of the variation in observed earnings data, with predictable and precisely-estimated coefficients in almost all applications.

20 Source: Lemieux (2001) Figure 1 Source: Jacob Mincer, Schooling, Experience, and Earnings

21 1104 Journal of Economic Literature, Vol. XXXIX (December 2001) A. United States B. Sweden Source: Krueger and Lindahl 10 - ~ ~ ~ ~ 05-, _,_, _, _, _,_,_,_,_,_,, 3.5-, _._._._._._._._._ Years of Schooling Years of Schooling C. West Germany D. East Germany ~~~~~~~~~~~ l 7,,,,,6 A _ Years of Schooling Years of Schooling Figure 1. Unrestricted Schooling-Log Wage Relationship and Mincer Earnings Specification

22 b. Theoretical foundations of the Mincer model The theoretical foundations of Mincer specification can arise from two theoretical framework, i. compensating wage differentials (Mincer, 1958) ii. accounting identity framework (Mincer, 1974). Let w (s) represent the annual earnings of a representative individual with s years of education, and let r be the discount rate, both assumed to be constant over his lifetime, the present value of the stream of earnings (for infinitely lived individuals) is ( ) =0+ + ( ) ( ) + + ( ) ( ) ( ) ( ) + + ( ) ( ) = ( ) = ( ) [ ( ) + ( ) ( ) + ( ) ( ) ( ) ( ) + ( ) + + ( ) ] = ( ) ( ) ( ) Assuming no direct costs of schooling and comparing an individual with 1 year of schooling, who has to wait that one year before earning a salary, to one with 0 year of schooling, and equalizing present values, we get (1) 1+ 1 (1) = = (0) 1+ 1+

23 (1) (0) =1+ Taking the logarithm on both side, ln (1) ln ( (0)) =ln (1+ ) for <0.2 the wage increment for one more year of schooling must approximately be equal to the rate. Or in continuous time, let w (s) represent the annual earnings of a representative individual with s years of education, where T is the length of working time. T V ( s) = e s rt w( s) = r w( s) dt rs rt ( e e ) Letting the compensating differentials take their time to equalize lifetime earnings: V ( s) = V (0) ln( V ( s)) = ln( V (0))

24 The earnings difference between an individual with s years of schooling and 0 years will be given by rt r( T s) ln w( s) = ln w(0) + rs + ln (1 e ) /(1 e ) since the last term goes to zero as T gets large. = ln w(0) + rs, ( ) This yields the nice interpretation of r as an internal rate of return to schooling, that is, the discount rate equates the lifetime earnings streams from different educational choices. But this framework ignores the direct costs of schooling, assumes that earnings do not vary over the life-cycle, and that the age of retirement does not depend on years of schooling. An alternative framework used by Mincer (1974) builds on an accounting identity initially studied by Becker (1974) and Becker-Chiswick (1966). It allows earnings to vary over the life-cycle

25 Let E t be potential earnings at time t. Investments in training are expressed as a fraction of potential earnings invested, C t = ktet, where kt is the fraction invested at time t and let ρ t be the return to training investments made at time t. Then E = E + C ρ = E + k ρ Repeated substitution yields t ( j j ) 0 E t 1 j = Π 1 + ρ k E = 0. ( ) t+ 1 t t t t 1 t t. Formal schooling is defined as years spent in full-time investments ( k t = 1). Assume that the rate of return on formal schooling is constant ( ρ = ρ ) and that formal schooling takes place at the beginning of life. t s Also assume that the rate of return to post-school investment, ρ t, is constant over time and equals ρ 0. Then, we can write (in logs) ln E t = ln E + S ln(1 + ρ ) + 0 s j= s t 1 ln(1 + ρ k 0 j ), Which yields the approximate relationship (for small ρ s and ρ 0 ) using the fact that ln( 1 + ρ ) ρ, if ρ < 0. 2,

26 . ln ln = + + t s j t k j S E E ρ ρ To establish a relationship between potential earnings and years of labour market experience, Mincer (1974) further assumes a linearly declining rate of post-school investment: ) (1 T x k x s = + κ, where 0 = s t x is the amount of work experience as of age t and T is the length of working life. Under these assumptions, the relationship between potential earnings, schooling and experience is given by: [ ]. 2 2 ln ln x T x T S E E s s x κ ρ κ ρ κ ρ ρ κρ Observed earnings equal potential earnings less investment costs, producing the following relationship for observed earnings: + T x E x s w s x 1 ln ), ( ln κ [ ] ln ), ( ln x T x T T s E x s w s κ ρ κ κ ρ κ ρ ρ κ κρ

27 α ρ ss + β0x β1x. When E s andκ are uncorrelated, the variance of log earnings will be minimized at / 0. 1 ρ. (See Heckman et al.) b. Extensions to the basic Mincer model In most applications of the Mincer model, it is assumed that the intercept and slope coefficient are the same across persons and do not depend on the schooling level, however allowing κ and ρ s to differ across persons, as in Mincer (1974) produces a random coefficient model 2 2 ln wi ( s, x) = β 0 + ρss + β1x + β 2x + [( β 0i β 0 ) + ( ρsi ρs ) s + ( β1 i β1) x + ( β 2i β 2 ) x ], where the terms in bracket are part to the error term, expressed in deviations from the mean coefficients. It turns out that this is a crucial extension to the basic model. Since if individuals were identical, why should they choose different amounts of education? However, although heterogeneity explains why we observe different individuals having different schooling, an additional set of problems is introduced: Schooling is endogeneous and, if this is not allowed for, estimated return may be biased. Returns as well as schooling may vary across individuals.

28 This will have important implications (see, Card (1999)) for the econometric estimation of the Mincer equation, which we will talk about in the estimation section. Other extensions to the Mincer model: Heckman, Lochner and Todd (2003, 2008) examine the importance of relaxing functional form assumptions in estimating internal rates of return to schooling and in accounting for taxes(τ ), tuition ( v ), independence of the length of work-life from schooling ( T ( s) = 1), nonlinearity in schooling, and non-separability between schooling and work experience( w( s, x) u( s) ϕ( x) ): V ( s) = T ( s) s e (1 τ ) r ( x+ s) w( s, x)(1 τ ) dx 0 0 Inferences about trends in rates of return to high school and college obtained from the more general model differ substantially from inferences drawn from estimates based on the basic Mincer earnings regression. Heckman et al. also examine the implications of accounting for uncertainty and agent expectation formation. Even when the static framework of Mincer is maintained, accounting for uncertainty substantially affects rate of return estimates. s ve (1 τ ) rz dz,

29 They propose a substantial break from Mincer's approach by allowing for the sequential resolution of uncertainty. That is, with each additional year of schooling, information about the value of different schooling choices and opportunities becomes available generating an option value of schooling. The return in excess of the direct return (the lifetime income received at a given schooling level) is the option value. For example, finishing high school provides access to college, and attending college is a necessary first step for obtaining a college degree. Each additional year of schooling reveals new information about uncertain college costs or future earnings. Individuals with s years of schooling must decide whether to quit school and receive lifetime earnings of Y (s), or continue on in school for an additional year and receive an expected lifetime earnings of E s ( V ( s + 1)). When the schooling decision is made at the beginning of life and age-earnings streams across schooling levels are known and cross only once, then the internal rate of return (IRR) can be compared with the interest rate as a valid rule for making education decisions (Hirschleifer, 1970). But the sequential resolution of uncertainty and non-linearity in returns to schooling contribute to sizeable option values. Accounting for option values challenges the validity the internal rate of return. They conclude that in the presence of sequential resolution of uncertainty and option values, the internal rate of return-a cornerstone of classical human capital theory-is not a useful guide to policy analysis.

30 3. Challenges to the Mincer Wage Equation The basic Mincer human capital earnings function does not appear to fit the data nearly as well in the 1980s and 1990s as it did in the 1960s and 1970s: 1) Log wages are an increasingly convex function of years of schooling: that is, the log-linearity in schooling no longer seems to hold. 2) The quadratic in experience tends to understate earnings growth at the beginning of the lifecycle and overstate the decline in earnings towards to the end of the lifecyle. Murphy and Welch (1990) find that a quartic fits the data better.

31 Fortin/Lemieux Econ 561 Lecture 3B Source: Lemieux (2001)

32 Fortin/Lemieux Econ 561 Lecture 3B Source: Lemieux (2001)

33 Source: Lemieux (2001)

34 3) Experience-wage profiles are no longer parallel for different education groups: that is, the multiplicative separability between experience and schooling no longer holds. For example, the college-high school wage gap is now much larger for less experienced than for more experienced workers. The Mincer model is for individuals tracked over school and working ages, and should be estimated by tracking single cohorts through education and the labour market. But it is usually estimated from a cross-section of individuals, which is actually only legitimate if the economy is in a long run steady state. Rapid technical change or secular improvement in the quality of education would violate this assumption and invalidate the use of cross-sectional data for estimation: cross-section and cohort based estimates of returns to education would not be expected to be the same. Mincer was well aware that this may be a problem in his early work (Mincer, 1958), and conjectured that the crosssectional age-earnings profiles were probably understating life-cycle earnings growth since, in those days, there was substantial secular growth in average earnings.

35 Now many researchers believe that the cross-sectional age-earnings profile overstates life-cycle earnings growth. Beaudry and Green (2000) conclude that the slope of the cross-sectional age-earnings profile has increased because new cohorts of men are entering the labour market at increasingly low earnings level in Canada. This highlights the well-known problem of distinguishing between age, cohort, and year effects in earnings growth. Card and Lemieux (2001) investigate this issue in detail and conclude that the dramatic increase in the college-high school wage gap for post-1950 cohorts is primarily a consequence of an equally dramatic break in inter-cohort trends in educational achievement. Until 1950, each successive cohort of men was more educated than the preceding cohort. This secular trend abruptly stopped with men born around After 1975, the entry of the post-1950 cohorts were no more educated, and in some case less educated, than previous cohorts. o The relative supply could no longer offset the rising demand for skilled young workers and, as a result, the college-high school wage gap started expanding dramatically for young workers.

36 920 P. Beaudry and D. Green a}~~~~~~~~~~~~~~~ n 1982 Entry -- 19B6 Entry 350 S * 1990 Entry 990 Cross Section 300 ' ' ' ' FIGURE 3b Age-earnings profiles allowing differing slopes by cohort: males, university education Age

37 Take-Away on the Mincer Equation In summary, recent evidence suggests that the basic Mincer human capital earnings model remains a parsimonious and accurate model in a stable environment where educational achievement grows smoothly across cohorts, as it did in the time period originally studied by Mincer (1974). In a less stable environment, however, major shifts in the relative supply of different age-education groups can induce important changes in the structure of wages that have to be taken into account when estimating a standard Mincer equation. Issue to be discussed later in the course: How to interpret the coefficient on schooling? o Ability bias: Upward bias o Heterogeneity in effects:??? o Measurement Error: Downward bias o Signalling vs. Human Capital

38 Challenges to human capital theory as an explanation for education decisions One of the clearest implications of human theory is that education decisions should be highly responsive to pecuniary factors such as the rate of return to education, tuition fees, etc. However, this is generally not supported by the empirical evidence Rates of returns to college/postgraduate educations almost doubled in the United States since about 1980, but enrollments did not increase much, especially for men Most studies also suggest that enrollment elasticities with respect to tuition are modest Behavioral economics suggest other possible explanations

39 For instance, Bettinger, Long, Oreopoulos, and Sanbonmatsu (QJE 2012) The Role of Application Assistance and Information in College Decisions use a field experiment (in collaboration with H&R Block) to look at the role of factors beyond the pecuniary ones suggested by HC theory. Sample of low income individuals going to H&R Block to get help filling their US tax forms Some of them were randomized into a treatment where the H&R Block agent helps them fill the paperwork for federal college loans (free application for federal student aid, or FAFSA) Another treatment consisted in providing information on how much it would cost to go to college (tuition) compared to the aid available. Large effect found: combined treatment (FAFSA plus information) increased probability of completing two years of college by 8 percentage points (from 28 to 36 percent). But information experiment alone has no impact

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