Education Flashcards
Please explain the difference between the gross and the net enrollment rate and explain how the
average primary gross enrollment rate can be larger than 100%.
The gross enrollment rate : The number of children enrolled at a particular
level of education, regardless of age , as a percentage of the population in
the age group associated with that level.
The net enrollment rate : The number of children enrolled at a particular
level of education, who are of the age associated with that level of
schooling , as a percentage of the population in the age group associated
with that level.
So the difference is that gros enrollment rate looks at The number of children enrolled at a particular
level of education, regardless of age, where the net enrollment rate only looks at those who are of the age associated with that level of
schooling. Therefore, can the gross enrollment rate exceed 100% if there are students who are older or younger than the typical age group attending the level of education in question. This could happen due to early or late school entry, repeating grades, or older students returning to education.
Please explain (graphically) the Mincerian investment decision in education.
See graph in review
Two possible tracks
- Primary education, gives a lower earnings through your life. There is some cost in the beginning, but it end up with possitive earnings
- Secondary educatioon, gives higher cost in the beginning, but also higher earnings. In this case we loose some of the earnings, we would have got if we went earlier to work. Alternative cost.
We can then look at government expenditures on education. In the example in figure 8-6, we see that it gives some cost, but also a positive externality in the earnings. We would have to calculate if the positive externality exceeds the government cost.
Please give three examples of problems of school funding in developing countries.
- Developing countries underfund education compared to developed countries.
- Developing countries spend relatively too much on tertiary education.
- Resources for primary education have huge leakage problems moving from the central government to the local schools. Corruption.
- Aid donors had too strong preferences for building new schools (investment) rather than funding current expenditures on education. The quality of teachers, needs to be better. We can not just build a school, but we also need to use money on the quality of education.
Please illustrate and describe the 3 main lessons learned from the Preston Curves (life expectency)
Preston Curves pr defintion shows the relation between GDP pr capita (x-axis) and life expectancy (y-axis). It’s a postive relationship, so when GPD pr capita increases so does the life expectancy.
First, people in richer countries can expect to live longer than those in poor countries.
Second, as indicated by the upward shift of the Preston Curve, there have been dramatic improvements in the health of populations over time. These improvements have been greatest among the poorest.
* If all the improvements in health were driven by aggregate income growth, countries would have merely moved along the Preston Curve over time. The upward shift of the curve suggests that factors other than income are important.
Third, in 2004 there are several countries where life expectancy is substantially lower than its predicted level. Have the technological improvements of the last 70 years passed over these countries?
* Disease (HIV/AIDS, malaria, TB) and lack of ”oil benefits”?
Include this view on increasing economic growth (GDP pr capita) on life expectancy for the perfect answer.
d. Capability Expansion through Economic Growth. In this view, economic growth expands
capabilities (and health) directly (income effect).
e. Capability Expansion through Poverty Reduction. This view starts from the premise that the
relationship between individual capabilities and income is steepest at low incomes, and
quite flat beyond some point. Thus, social outcomes can only be improved appreciably if
poverty is reduced; it is not growth in aggregate incomes that matters, but a reduction in
poverty. In this view, the correlation reflects a tendency - although only a tendency - for
growth to lead to lower absolute poverty, and thereby improved health.
f. Capability Expansion through Social Services. By this view, the public provision of essential
goods and services-clean drinking water, sanitation, health care, epidemiological
protection, elementary education, and so on-leads to improved social outcomes. Growth only matters for health if it is used to finance suitable public services. In this view, the
correlation reflects a tendency for economic growth to lead to better provision of social
services and thereby health (improved life expectancy).
Please explain the theoretical relationship between health and education using the envelope theorem.
As the success in enrollment has become visible, we have started to ask why we do not see a
boost in economic growth. One answer is that schooling is not education, and that we need to
move from quantity to quality of schooling – the Envelope Theorem argument (Bleakley, 2010).
The Envelope Theorem suggests that better health in childhood primarily boosts income by enhancing the productivity of an individual’s accumulated skills and knowledge—human capital—rather than by encouraging more education. In essence, being healthier as a child allows one to learn more effectively and develop into a stronger adult. This doesn’t necessarily mean that healthier individuals will spend more time in education. In fact, if health improvements lead to higher wages that exceed the financial returns of additional schooling, individuals might choose to work instead of staying in school longer.
When evaluating the effects of health on lifetime earnings, it’s not enough to just look at the years spent in education. This is because health can lead to higher earnings without necessarily increasing the time spent in school. So, according to the Envelope Theorem, we should really be looking at the immediate, direct benefits of health improvements (like being able to work more or perform better at work) rather than secondary effects (like potentially longer schooling).
Is it difficult to compare education across countries?
YES, it is really difficult, because people use different systems.
exmaple:
US have high amount of students with tertiary education, but it gives a misleading conclusion, because in US tertiary education include different level of education then other places.
Average year of schooling, why? What is the surprising result both on macro and micro level.
More used then the gross and net enrollment.
Average year of schooling is a measure of human capital. School systems differ so some standardization is necessary.
It is used in both macro and micro
In macro ecenomics Baro and Lee (2013) estimates returns to shcooling by educational level. They look at rates of return to human capital for an additional year of schooling by region. They conclude it its different across regions
In micro looks Jacob Mincer (1958) into estimating returns to schooling. He also finds a postive relationship between education and economic growth.
We see a high enrollment rate, but it seems to have a low effect on economic growth, which is surprising thinking of how much money we use on it.
What could be the answer to the paradox of high enrollment but Low effect on economic growth?
One answer is that schooling is not education, and that we need to move from quantity to quality of schooling - The so called envelope theorem argument.
The Mincer regression, linking individual outcomes to individual education will tell us
higher education seems to be “productive”: more education leads to higher income. The aggregate regression would lead to a zero impact from education since (in this thought
experiment) education only “redistributes” income but does not create income. If a bigger
fraction of the population becomes educated (so that average years of schooling rises) the
only effect is that the income of the poor shrinks
Please explain how the return to schooling can be estimated using (a) micro data on individual
earnings and (b) macro data on average schooling and GDP per capita. Describe the differences that
are obtained using micro and macro data, respectively, to estimate the return to schooling and give
possible explanations for the discrepancy.
The return to schooling is traditionally estimated using the so-called Mincer regression in which wages (in logs) are regressed on years of schooling and experience:
… math see review
Here the left hand side variable is the wage rate for an individual, while the explanatory variables are years
of schooling (S) and experience (years working) . Here the return to an additional year of schooling is given
by b1. An alternative formulation that may be used is
… math
where the return to an additional year of primary, secondary and tertiary education is estimated by r_P, r_S and r_T, respectively.
When return to schooling is estimated using macro data (as in Barro and Lee), GDP per worker is regressed on capital per worker and average years of education in the population:
… math
Where the index is now over countries. Here, si is the average year of schooling in country i, say in India,
and t, measures the return to an additional year of education (in terms of increased GDP per worker),
analogous to the Mincer regression.
Comparing results for the two types of estimates of returns to schooling across countries there are
noticeable differences. For the “world” the estimates differ slightly, as Barro and Lee find returns about
12% for an additional year of schooling (using macro data) while the micro data yields an overall average
return about 10%. However, the differences are substantial when comparing regions and ricer vs. poorer
countries. Specifically Sub-Saharan Africa and Latin America are estimated to have the largest returns
(about 12%) using micro data, while the macro results shows the lowest returns in these regions (just
below 7%).
One possible explanation of the discrepancy is that education in some developing countries does not lead
to increased productivity; while at the same time the education induced wage differences do not reflect
productivity differences. Specifically, one could assume that tertiary education leads to employment in the
public sector, with a high wage, while such work does not increase overall productivity in the country.
Human capital accumulates through education. The return to schooling is often measured using a
“mincerian wage regression” on a cross section of individuals (i) like the one given in equation (3).
β_1 is a measure of the semi-elasticity of wages (E_i) with respect to years schooling (S_i). exp_iis
a measure of experience. Please discuss potential drawbacks to using this method and explain how
“Second generation estimates” can be seen as an attempt to address these issues.
𝑙𝑛𝐸𝑖 = 𝛼 + 𝛽1𝑆𝑖 + 𝛽2 exp𝑖 + 𝛽3 exp𝑖^2 (3)
This question draws on PRLB ch. 8.
Endogeneity problems: The main problem with using (3) to estimate the benefits of schooling is that people might choose to go to school based on their own abilities. If more skilled people are more likely to attend school (because they get more benefit from it), and these skilled people also tend to earn higher wages on average, regardless of their education level, we mistakenly attribute part of the advantage from skills to schooling. Since we can’t directly see an individual’s skill level, we can’t adjust for it by adding another variable to (3).
Second generation estimates attempt to address this issue of endogeneity by exploiting random
variation in who gets schooling. One way of doing this is to exploit natural experiments. An example of
this could be the school construction programme in Indonesia used by Duflo (2001)
The Instruksi Presiden Sekolah Dasar program (Inpres SD) became one of the largest schooling expansions ever, roughly doubling the country’s stock of primary schools in the first six years alone (Duflo 2001)
In comparing levels of development across countries: What is the difference between using the
HDI index (Human Development Index) and GDP per capita?
The difference is that health and schooling enters into the HDI index in addition to GDP per capita. If
the student notes that the HDI index is (loosely) associated with the “capability approach” to
development, it is a plus.
Another part of human capital is health. As figure 1, which is taken from Weil (2013) , illustrates,
there is a clear correlation between life expectancy and GDP per capita. Please discuss:
a. Is life expectancy a satisfactory indicator of health?
b. Is it reasonable to infer that the causal mechanism behind the correlation in figure 1 is that
high levels of income causes high levels of life expectancy?
This question draws on PRLB ch. 9 and Weil ch. 6.1.
@a: Life expectancy is a summary statistic that gives a snapshot of the health of a country in a single
number. However, it does not give the full picture. First, life expectancy does not fully capture morbidity,
i.e. rates of disease and illness. Second, health is not only the absence of disease and death, but rather a
more comprehensive concept that can include mental, physical and social well-being (cf. the WHO
definition PRLB references on p. 302). To conclude, life expectancy is not a sufficient indicator of health.
@b: Income can affect health positively, e.g., through better access to health inputs. However, health
can also affect income positively. For instance, better nutrition and a higher caloric intake increases work
capacity. In general, healthy workers are more productive. Therefore, there may also be some degree of
reverse causality, i.e., that higher levels of health (measured by life expectancy) cause higher levels of
income.
