There is ample empirical evidence on credit constraints. The literature distinguishes between short-run and long-run constraints. The former refers to a lack of means to di- rectly finance the costs of higher education. The latter refers to a lack of means to make investments earlier in life that improve the learning process of the child and therefore improve his or her future access to and return from participation in higher education. Research finds strong and increasing correlations between family income and college attendance in the United States. Carneiro and Heckman (2002) show that the difference in college attendance between the top half and the bottom quantile of parental family in- come is around 20% in the 1980s and 30% in the 1990s. But this does not tell us which type of constraint is mainly responsible for the observed attendance gap. The available research generally indicates that long-run constraints are dominating. Cameron and Heckman (2001) show that the effect of income on college attendance is low once we correct for maternal ability. If maternal ability is a good proxy for long-run family fac- tors like genetics, early family environment and previous school quality, then long-run constraints are crucial. Carneiro and Heckman (2002) also compare such conditional enrolment rates and conclude that at most 8% of the population faces short-run credit constraints with respect to college enrolment. Duncan and Brooks-Gunn (1997) confirm that long-run constraints are far more important than short-run credit constraints for col- lege attendance. The children of wealthy and highly educated parents perform better (by nature and nurture) at compulsory school, which is a crucial proviso for college access and success. 13 Keane and Wolpin (2001), in contrast, use a structural model and show that short-term credit constraints do exist and are tight (in the sense that students can- not finance college costs using uncollateralized loans). Yet, in line with the previous evidence these constraints turn out to have little effect on college attendance. Their simulations show that relaxing the constraints mainly affects other choice margins, e.g., students work less while in college and consume more.
In recent years demand-side financing – as an alternative to supply-side financing – has become more and more popular among governments in many countries. Basically the difference between demand-side financing and supply-side financing is whether the public budget (to finance certain publicly provided goods, like health-care, day- care, and education) goes to the consumers or to the suppliers. If the consumers buy these goods themselves (with money from the government) we speak of demand-side financing. If the government buys them, we speak of supply-side financing. However, the borderline between demand-side and supply-side financing is not sharp: it is often only an accounting question. For instance, the economic effects are the same whether every student receives a voucher of €5000 to be spent on higher education, or that the government pays the universities €5000 per student. For the purpose of this report, we will speak of demand-side financing when the consumer decides which institution will receive the public money. Thus, both examples described above will classify as demand-side financing.
During the past few decades, two consistent trends in college financing have arisen. The first trend is that student loans now make up a much larger portion of the average student’s college financing portfolio: from 1993 to 2014 there was a 40% increase in the number of college graduates who had borrowed student loans to fund their education. During the same time period, many states rolled out large scale merit-based scholarships. The interaction of these concurrent changes is the subject of this paper: I examine the effect of lottery scholarship eligibility on a broad measure of debt within a family, which includes student loans and credit card bills as well as other debt, using a simple difference-in- difference analysis.
outcome much incentive to support this or that kind of physics. Few believe that the outcome can be influenced in this way: at best discoveries can be hastened or delayed. But with, say, minimum wages, the case is quite different: every organised interest with a stake in the matter is free to commission their own research, in-house, or bought in from private and public sector research organisations, in the same way as they might employ legal staff to defend their interests at law. The purpose here is not the discovery of truth, but the generation of effective rhetoric. Those hoping for cold fusion would generally accept that there is a truth, and they’re probably better off knowing it sooner rather than later. Those whose interests will be promoted or damaged by this or that policy on minimum wages, or some change to the tax system, are principally interested in ensuring that their interest is represented as the interest of society, and hence acquiring social acceptance for their desired outcome. As long as interests within society are various, therefore, we can expect economics itself to be various.
This paper exploits exogenous variation in the federal funds received by municipal gov- ernments in Brazil to examine the impact of transfers on local government revenues and expenditures, and in turn, on education provision. Consistent with a strong Flypaper Effect, we find that increased transfers lead to an immediate rise in current and capital spending. These increases are focused on education and welfare expenditure in poorer municipalities, while richer municipalities expand capital spending in the transport and housing sectors. Furthermore, particularly in wealthier municipalities, increases in transfers cause a short- term increase in local tax revenues. Evidence from municipal education resources suggests that these increases in spending are not being wasted. Positive transfer shocks are associated with increases in the number of teachers and, to a lesser extent, the number of classrooms. Transfers are also associated with substantial re-allocation of resources across schools offer- ing classes at different levels, with secondary schools and schools teaching senior primary grades expanding at the expense of junior primary schools.
For those going on to Tertiary Type B education (such as HND‟s) there is a jump in earnings well above earnings for upper secondary leavers, with females gaining greater benefit than males (in the case of the UK, although this trend holds true for a number of other OECD nations).
high school students’ drinking on subsequent wages, as mediated through human capital accumulation. He found that moderate high school drinking had a positive effect on returns to education and therefore on human capital accumulation. Heavier drinking reduced this gain slightly, but net effects were still positive. The other four studies approached the question directly by focusing on the asso- ciation between drinking and GPA. Three of the GPA studies used data from the Harvard College Alcohol Study. Analyz- ing data from the study’s 1993 wave, both Wolaver (2002) and Williams et al. (2003) estimated the impact of col- lege drinking on the quality of human capital acquisition as captured by study hours and GPA. Both studies found that drinking had a direct negative effect on GPA and an indi- rect negative effect through reduced study hours. Wolaver (2007) used data from the 1993 and 1997 waves and found that both high school and college binge drinking were asso- ciated with lower college GPA for males and females. For females, however, study time in college was negatively cor- related with high school drinking but positively associated with college drinking.
to the right of the cut-off. Not all eligible students might be interested in using the additional excused hours of absences that they were provided with. Intuitively, students who are at the top percentiles of the ability distribution might decide to spend more hours preparing for the national exams. The education literature suggests that there is the so called self-regulated learning that is more pronounced for the high achieving students. (Barry J. and Manuel 1990,Nicola and Debra 2006). The intuition is that high achieving students might be/or believe that they are more constructive when studying at home rather than staying in the classroom, especially if it is noisy. The research on self regulated-learning suggests that these students might set goals for their learning and monitor, regulate, and control their cognition, motivation, and behaviour better than students of lower academic ability. So the marginal utility of spending an additional hour in the class might be different for a student who had an eleventh grade gpa equal to 16 and another student who had a gpa equal to 19 although there are both eligible to use the reform. This makes us think that there might not be an effect around the performance cut-off (15/20). As shown in Figure 4.2, some covariates exhibit a jump around the thresh- old that violate the assumptions of identification of the treatment effects using an regression discontinuity approach. The RD approach may be inappropriate for iden- tification if individuals to the left of the cut-off differ in more than one ways from individuals to the right of the cut-off. To control for individual specific drivers of the observed behaviour we take first differences of observed variables between twelfth and eleventh grade. The change in these differences around the cut-off is shown in Figure 4.3.
In addition to the low number degrees conferred, STEM fields also suffer from a low participation of women and minorities (Hernandez et al., 2018; US Department of Health and Human Services, 2015; Olson and Riordan, 2012). Despite the fact that women have outnumbered men in college enrollment, there still exists a significant attainment gen- der gap in STEM degrees (Gayles and Ampaw, 2014; National Science Foundation and Statistics, 2017), with little change since the 1980s (DiPrete and Buchmann, 2013; Eng- land et al., 2007; England and Li, 2006; Mann and DiPrete, 2013). According to National Science Foundation and Statistics (2017), women earned about 57 percent of all bache- lor’s degrees awarded since the late 1990s. More specifically, while women earned more bachelor’s degrees in Psychology, biosciences, and social sciences (except for Economics) compared to men, they earned considerably fewer degrees in computer science, engineer- ing and mathematics (National Science Foundation and Statistics, 2017). Participation in STEM also exhibits a racial gap caused by the fact that underrepresented minorities (URM) are less likely than white and Asian students to attain college degrees (Kao and Thomp- son, 2003). While white and Asian American students are consistently well represented in STEM disciplines (Herrera and Hurtado, 2011; Goyette and Xie, 1999), African-American and Hispanic are underrepresented compared to their overall enrollment (National Sci- ence Foundation and Statistics, 2017).
Table 7 presents robustness checks with alternative skill proxies. Again, column (1) shows the baseline results to facilitate comparison. In column (2) to (4), academic achievement has been replaced by the individual PISA reading, math, and science scores, while the third proxy for noncognitive skills, future orientation, has been added in column (5). Columns (2) to (4) inidicate that the importance of reading, math and science scores differ across types of upper secondary education. The point estimate for reading score in column (2) is higher compared to the point estimates in columns (3) and (4), indicating that reading skills are more important for completion than math and science skills. In addition, the point estimate is also larger compared to the estimate for overall academic achievement in column (1). Furthermore, reading skills (and math and science skills) only seems to be important for completion of high school education. All tests of joint significance between the scores and the scores interacted with the indicator for vocational education are larger than 0.10. Note, however, that the p-value for the joint test of the reading score and the reading score interacted with the vocational education indicator is around 0.82, while it is 0.14 and 0.12 in columns (3) and (4), respectively. Hence, a larger sample size might have shown math and science to significantly predict completion of vocational education. A last thing to notice in column (2) to (4) is that the estimates for the noncognitive skill proxies do not change markedly compared to the results in column (1).
Finally, Table 4.8 provides estimates of the three ex ante treatment effects by counterfactual non-chosen major. The treatment on the treated effects are again generally larger than the treatment on the untreated, with a few exceptions: en- gineering and economics majors with science careers, government occupations with economics and public policy majors, and law with humanities and public policy. It is worth noting that these ex ante treatment effects also exhibit a substantial degree of heterogeneity across majors. Notably, expected premia for business (relative to education) careers are higher for economics majors, while returns to science careers are higher for engineering and natural science majors. The fact that these types of complementarities between majors and occupations still hold when focusing on the majors which were not chosen by the individuals points to the accumulation of occupation-specific human capital within majors. 15
Officially academies should not be funded advantageously relative to maintained schools. However, a 2012 National Audit Office survey of converter academy head teachers found that 77 per cent of academies converted to obtain more funding for front- line education (National Audit Office, 2012). Academies and maintained schools receive comparable Dedicated Schools Grant (DSG) funding which covers mainstream education provision and is the primary source of funding for schools. However, there has been a historical disparity between academies and maintained schools in respect of funding for auxiliary functions. LEAs centrally provide some services to maintained schools that academies need to procure independently. Academies formerly received an additional grant to provide these functions. 3 It boosted some academies’ budgets by more than 10 per cent and was widely considered to overcompensate academies. This grant has now been replaced with the Educational Services Grant (ESG), paid on a common per-pupil rate. Since the 2015/16 school year academies and maintained schools are financed on a comparable basis (Department for Education, 2014).
what they are given where students actually graduate from, allows me to determine whether changes in expected benefits are a result of students’ initial choices or graduation patterns. To compare the costs and expected benefits of each alternative, I perform a rough estimate of what the present value is for each student. To do so, I compute the present value of what the individuals’ expected earnings are, minus what they would have been had he remained as a high school graduate. I assume an interest rate of 3.5% and a payment period of 40 years. I take the difference between the present value of the expected benefits and the actual costs. Costs equal the amount spent on tuition plus the number of years that students spend enrolled times the annual earnings for high school graduates. Previous estimates are a rough approximation of what the expected benefits of pursuing different alternatives may be for students. Earnings for students in my sample may be different to the average earnings for students who have graduated from a given degree, earnings may also evolve through the life cycle, and non-graduates may retrieve some benefit from their years enrolled in higher education. Still, previous estimates provide a picture of what we can expect students to earn given their choices and graduation rates, and how these numbers compare to the costs of attending higher education for these students.
In this paper we analyse the mental health inpatient and long-term care using data of the Portuguese NHS, a case-mix based funding system, which is currently discussing a new financing model. More specifically, we investigate if the current financing system is creating barriers in delivering mental health services. The first element involves using readmissions as a proxy for quality of care. If readmissions are increasing over time this can indicate ineffective community services’ responses or early discharges due to lack of beds or shortage staff. On the other hand, if readmissions are decreasing we claim that inpatient services are providing high-quality care and community services are being effective. Second, we analyse how mental healthcare should be organised since this is the first step in the discussion of a payment design. Suppose unit costs and outcomes of mental healthcare are largely independent of the amount of work performed in each setting. Then, a single payment value, applicable to all units, small and large, would be feasible. Moreover, the size of each unit could be left totally to patients’ preferences or patients’ geographic concentration. At the other extreme, in the presence of strong size (scale) effects, healthcare facilities of different size may need different unit payments and location of activities of hospitals providing mental healthcare services must be actively planned.
This dissertation is comprised of three essays, each studying a different aspect related to topics in the economics of education. These pieces each study how education production is impacted by different economic forces. In Chapter 1, I study the effect of racial segregation on academic achievement, college preparation, and postsecondary attainment in a large, urban school district. To achieve racial balance in its oversubscribed magnet schools, this district conducted separate admissions lotteries for black and non-black students. Because the student body was predominantly black, administrators set aside disproportionately more seats for the non-black lottery. In 2003, the federal Office of Civil Rights forced this district to instead use a race-blind lottery procedure that dramatically increased racial segregation for incoming magnet school cohorts. In an instrumental variables framework that exploits both randomized lottery offers and this unanticipated shock to racial makeup, I test whether student racial composition is a meaningful input in the education production function. As a baseline, I use admissions lotteries to estimate the effect of enrolling in a magnet middle school on student outcomes. In general, enrollment returns are comparable between magnet and traditional schools, but I estimate heterogeneous magnet school effects across student subgroups. Education production is sensitive to school racial composition in that segregation has a deleterious impact on student outcomes. I find that increasing the share of black peers in a cohort decreases student achievement in math, science, and writing for black students with losses primarily driven by high-aptitude black students. Further, racial segregation erodes high school graduation rates and also decreases college attendance by reducing enrollment at 2-year institutions among female black students. These findings suggest that policies aimed at achieving racial balance in schools will likely increase aggregate educational achievement.
Whether driven by diversity or other reasons, examples can be found in which school quality directly enters into admissions. For instance, section 51.805 of the Texas Education Code lists 18 factors that determine admissions for students who do not otherwise gain automatic admission. These include “the financial status of the applicant’s school district” and “the performance level of the applicant’s school.” Similarly, colleges in the University of California system follow a set of 14 admissions criteria designed to evaluate students relative to their life circumstances. Coming from a “disadvantaged social or educational environment” is listed among various personal background factors taken into account. To help achieve this goal, at least two UC campuses used (and displayed on their websites) admissions rubrics giving explicit bonuses to students from lower-achieving schools.
Higher Education Enrollment Rate : The higher education enrollment rate in 1960 is the only SDM (2004) variable related to human capital that I nd robustly signi cant for growth. The sampled countries have an average higher education enrollment rate of 0.027 with a minimum of 0 and a maximum of 0.13. This variable ranks tenth overall with a posterior inclusion probability of 0.142. The posterior conditional mean is negative, which is somewhat unexpected. There are several possible explanations for the negative relationship shown between enrollment levels in higher education and growth. First, many students enrolled in higher education will not complete their education, so high enrollment rates do not necessarily translate into a more educated and productive labor force. Second, as suggested by Mamuneas et al. (2006), structural obstacles exist (particularly in developing countries) that can prevent the educated labor force from being e ciently employed. Much of the human capital gained from higher education requires complementary technologies that are scarce in many countries. This can lead to an unproductive use of educated labor. The third explanation expands on this idea. Kalaitzidakis et al. (2001) discuss evidence that higher levels of education in some countries, particularly those with lower incomes, may be used for rent-seeking activities or in the illegal economy. These explanations are especially relevant considering the large number of developing countries examined in the analysis.
Academic literature on the effect of class-size on student’s academic performance contains an extensive list of studies. Studies that use cross-sectional, non-experimental data find no effect of smaller classes on student achievement (Hanushek (1995, 2003)). This finding of zero effect has been attributed to the bias generated by the endogenous sorting of students into schools and classes (Krueger (2003)). However, experimental studies (Tennessee STAR experiment) and studies that use a quasi-experimental regression-discontinuity (RD) design (Angrist and Lavy (1999)) use exogenous variation in class-size to purge the results of selection bias and find positive and significant effect of smaller class size on student achievement. More recently, Chetty et al. (2011) find that students in small classes in the Tennessee STAR experiment also experience better long- term outcomes. A large and positive effect of smaller class size is also found in Fredriksson et al. (forthcoming), who exploit a maximum class-size rule in Sweden and find that children exposed to small classes at ages 10-13 have higher completed education and higher wages at ages 27-42.