What is that thing which is behind regulations and beyond economics?

A few days ago I received in my inbox an email from Greenpeace about the recent disaster in the Philippines claiming how climate change and the failure to adopt tight enough environmental regulations lead to the higher frequency of natural disasters across the world. So one should ask: what is beyond the acceptance or denial of environmental regulations?

Well, it turns out that most governmental agencies in developed countries (namely the US) use a concept coming from Economics of Health to assess the need of environmental regulations and perform Cost-Benefit Analyses (CBA): the Value of a Statistical Life (VSL).

We need these type of measures to perform CBA because associated with all regulations there are enforcement costs. For instance, setting a cap to any car’s CO2 emissions may lead to the need of buying more expensive motors, which take productions costs up and eventually result in unemployment.  Thus, having an idea of what society is prepared ex ante to pay to avoid the death of an unidentified individual provides a benchmark for decision-making. There are different methods to compute the VSL, but they all try to answer the question of how much each individual is prepared to pay for a given reduction in the risk of premature death.

THE WORST FORM OF MEASURING LIFE

My argument in this blog entry, taking inspiration from Winston Churchill, is that for the purposes of CBA the VSL Method is perhaps the worst form of measuring life, except all the others. I make this argument because this method has many drawbacks: if computed using wage differentials it can hardly be used among population too young or too old to work, it is conditional on workers knowing the risks of their jobs and external factors such as the unemployment rate, it is easily subject to government manipulation and country-dependent.

This is why the VSF of a Filipino is much lower than that of an American and this might help explain why do more developed economies like the US fail to adopt the right environmental regulations that could prevent climate change and hurricanes like the one portrayed in the picture above.

However, this method provides us a simple-enough way for populations to understand decision-making and for governments to perform CBA.

CONCLUSION

The way we value life is intrinsically connected with ability to enforce the regulations that will make our life better, so to a good extent the more we value our life the better will be our life standards. In the end, the value of life is that thing which is behind regulations and beyond economics. So, when considering whether or not to pass regulations we need to take into account not only the numbers coming from VSL analyses, but also values chosen on moral grounds.

 

REFERENCES

STATS Articles 2011 (here)

Mortality risk valuation, OECD 2012 (here)

How much is a life worth? (here)

The Political Valuation of Life (here)

Priceless Book (here)

 

Student

João Rafael Brites

Masters in Economics

CEMS MIM Masters in International Management

602

Signalling vs. Human Capital Model: The Economics of College Majors

Under the human capital model (HCM) different years of schooling leads to different earnings, given other variables constant between individuals. Thus, assuming that studying one major instead of another does not affect human capital value for individuals, then a student graduating in one major should receive the same earnings as another student graduating in a different major. However, this is not true.

Using the American Community Survey data for the period 2009-2011, Carnevale, Cheah and Strohl (2013,2012) showed that there is high disparity in earnings across majors. In particular, they showed that scientific majors are the ones with the highest earnings.

How is this possible?

From a theoretical viewpoint, when the HCM does not directly explain an empirical fact it is proper to check whether its historical enemy, the Signalling model, can explain it. To answer this question, it is necessary to verify that signals sent from the agent (student) to the principal (firm) are costly (i.e. credible).

To measure this cost it is necessary to consider a variable that quantifies this cost and that is specific for each major. A good (proxy) variable is represented by the graduate completion rate. The lower the graduate completion rate; the lower the probability that a student will graduate in that major. Thus the difficulty of graduating in that major is high. As reported by Chang et al.(2010) scientific majors have the lowest completion rate.

Given that studying one major instead of another does not affect the human capital value for individuals, then a person graduating in Mathematics, surviving from the hard natural selection process,  sends a costly and so credible signal. Thus, using this signal, firms give higher wage to him than to other students. Under this logic, the influence of human capital is absent.

This result is supported empirically by two facts. First, when we compare earnings of recent college graduate in different majors, we obtain that scientific majors are the highest. This difference should not exist under the HCM, because, especially in the first years (where abilities do not have time to be transferred in higher wages, because of rigidities), workers with the same years of schooling should earn the same wages.

Second, when we compare earnings of an experienced college graduate with scientific majors with earnings of graduate degree holder with humanities majors, the former is higher than the latter. This directly contradicts the HCM where higher number of years schooling leads to higher wages.

In a world without signalling and where majors are qualitatively equal, students should not choose to study Mathematics due to its cost. In fact, they study it because there is a premium for that cost, which is not based on the quality of knowledge, but on the cost of achieving it. Thus signalling effect prevails.

In conclusion, a principal weakness is the assumption considered (i.e majors are qualitatively equal). However, if this assumption holds, then the logic holds. Despite the results supporting the Signalling model, we need to take into consideration that “years of schooling” is just a proxy for human capital accumulation and it does not reflect its real value. In fact, different majors can potentially lead to different increases in human capital, making the human capital theory relevant.

Riccardo Passeggeri 677 Economics

The data source of the following graphs are Carnevale, Cheah and Strohl (2013,2012).

References:

Mitchell Chang, Sylvia Hurtado, Kevin Eagan and Josephine Gasiewski, “Degrees of Success: Bachelor’s Degrees Completion Rates among Initial STEM Majors”, Higher Education Research Institute at UCLA, January 2010. Available on 13/11/2013 at: http://www.heri.ucla.edu/nih/downloads/2010%20-%20Hurtado,%20Eagan,%20Chang%20-%20Degrees%20of%20Success.pdf

Anthony P. Carnevale, Ban Cheah and Jeff Strohl “Hard Times: College Majors, Unemployment and Earnings. Not All College Degrees Are Created Equal”, Centre on Education and the Workforce, Georgetown University, January 2012. Available on 13/11/2013 at: http://cew.georgetown.edu/unemployment/

Anthony P. Carnevale and Ban Cheah, “Hard Times: College Majors, Unemployment and Earnings”, Centre on Education and the Workforce, Georgetown University, May 2013. Available on 13/11/2013 at: http://cew.georgetown.edu/unemployment2013/

Why do Finnish students exhibit better results than the other European ones?

This question comes up when observing the results of the PISA tests in the last decade[1]. By observing the results, we can see that Finland is always in the top spots in all subjects (math, science) and capacities (reading, speaking). Are Finnish students better than all the others? I don’t believe and nobody thinks that! So which characteristics of their educational system allow them to have such great results comparing with students from the other European countries?

First point of analysis is whether the Finnish Government spends more in education than the other European and OECD countries. The report made by the OECD, “Education at a Glance, 2013”, shows that the Finnish government spends in average 6.5% of their GDP in education, which is about the average of all the OECD countries[2]. In the PISA tests, it was already observed that there was a low or even zero correlation between the test results and the amount spend in each individual student. For example, the US government spends more per student but has very low results when comparing with Finland.

Finnish students rarely have tests or exams (only have the exams for university at the end of secondary school), rarely have homework and spend less time in classes than the average European student, but have the best results. I know it sounds contradictory but it is the reality.  This is a way to attract student to engage in extra-curricular activities, like music or art and physical activity. For the Finnish, this is a way to create interest in other activities and for students not to get tired of going to school.

One of the main, and most important, differences of the Finnish educational system is the professionalism and the high academic level of their teachers. Most of the teachers in Finland have at least a Masters Degree and their income is in the average of the population. Not only that but they are also very respected by the whole society and have a very strong union.  Another huge point of difference is the fact that classes are very small (maximum of 20 students) and teachers have huge autonomy in the way their classes are taught, every teacher can have its specific teaching techniques in class, adapting to each type of students that they have, no main textbook is used for all students so that each teacher can chose its own textbook. Having small classes also allows for teachers to pay more attention to each individual student, even in some cases be their tutors outside classes. Something that, for example, does not happen in Portugal, where you can have secondary classes of 30 to 35 students (as was my case and which is above the legal limit), and teachers don’t have the time nor the ability to access to every student’s needs (not saying that is their fault, the problem is how the system is designed).

Just to conclude, I believe that the problem are not the students but the way the educational system is designed in various European countries, we should look at the Finnish case and try to learn something from it. For those more interested in the subject, I hugely advice to see the documentary: “The Finland Phenomenon, Inside the World’s Most Surprising School System”.

Renato Poirier

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[1] http://www.oecd.org/pisa/46643496.pdf (Results from 2009)

[2] http://www.oecd.org/edu/eag2013%20(eng)–FINAL%2020%20June%202013.pdf

The number of students per teacher in OECD

“Education is the most powerful weapon which you can use to change the world”

Nelson Mandela

 

Today I will write about some interesting data that I found on Pordata, related to the topic of education. I will analyze the average number of students per teacher in lower secondary education (ISCED 2).

First we need to understand education as a cumulative process and, due to that, in principal, the steps that one individual take in their education has impact in their future life.  The point is to understand how correlated is the impact of the institutions quality with the indicator that I presented. As I don´t present an econometric or an empirical study about that, I will use this indicator to see, empirical, what is the relation of that and the kind of the country presented. Notice that, I only present information related to the European countries.

According to the graph, for the countries presented, we verify that, on average, the number of students per teacher, in general, decreases from 2000 to 2011. Why does this happened? One obvious reason is because the birth decreases, so the number of students per teacher decreases because we have less children in European countries. According to this logic we can argue that, if this happens, probably the quality of institutions in education was not the reason for this change. But, why I am talking about the quality of institutions? What I am trying to focus is that, in a very simple model, the decision of how many students each teacher can have in each country is a decision for the government that can represents and determine the quality of education on these countries, which is a different indicator from class size and it is not a good predictor of educational outcomes. Due to the fact that in primary school learning is much more important than signaling, the quality of institution assumes a very important key, in particular, teachers assumes an important role in children. For the secondary education maybe it is less important. The decision of how many students each class will have depends on the number of teachers that one country has in certain areas and, for that, is natural that these values varies between countries. However, according to the figure we can see that European countries are homogenous in this indicator. Afterwards, what we are interested to see, as economists, is that how much will be the return on growth in each country for investing (or not) on education.

Concluding, the main point is that few students per teacher in first years of education assumes an important key in student development. In last decade, we assisted to a decrease on the indicator presented, as a general trend is most of OECD countries, but the reason of that can be associated with a demographic change and not a change on quality of institutions of the education sector. Thus, this indicator is not a good measure of the quality of institutions.

Link to Pordata:

http://www.pordata.pt/en/Europe/Average+number+of+students+per+teacher+in+lower+secondary+education+(ISCED+2)-1666

Pedro Luís Silva

Research master in Economic, #87

The recent economic crisis and the expenditure on education

Countries invest in educational institutions mostly because they expect to foster economic growth, productivity and development, among other reasons. However, they do not all invest the same amount. In OECD countries the preferences of public and private agents regarding the proportion of education expenditure relative to GDP are different. In Portugal, for all levels of education combined the expenditure on educational institutions as a percentage of GDP (from public and private sources) was 5,5 in 2005 and 5,8 in 2010, values that, albeit growing, are inferior to those of the OECD average, 5,8 in 2005 and 6,3 in 2010.

Analyzing the expenditure on educational institutions as a percentage of GDP for all levels of education by source of fund in 2010, we verify that private expenditures in Portugal were 0,4% and in OECD average  they were 0,9%, and that public expenditures both in Portugal and OECD average correspond to 5,4% of GDP. As a result, the difference comes, mostly, from private expenditure. Notice also, the large public weight on education funding in OECD countries

In fact, many of the OECD countries with the greatest growth in private spending have also had the largest increases in public funding, denoting that private spending tends to complement public investment rather than replace it.

The demand for high-quality education has costs that must be balanced against other demands on public expenditure (health care, justice, social support…) and the overall revenues (mostly fiscal) in order to compensate de deficit originated, especially in the current international economic context.

Indicators such as the presented ones can be affected by financial crises. It is not possible yet to analyze the full extent of the impact of the recent crisis, but it is still interesting to look at the already available data, as the following picture, from “Education at a Glance 2013 – OECD”.

We can see that public expenditure on educational institutions increased while GDP decreased in most of these countries between 2008 and 2010. Nevertheless, when the changes between 2008-2009 and 2009-2010 are analyzed separately the picture is not so positive.

In the period 2008 to 2009, GDP decreased in most countries while public expenditure on educational institutions increased (4% – OECD average). Between 2009 and 2010, while GDP rose in most countries, public expenditure on educational institutions fell in one-third of OECD countries.

This reinforces the trend that the cuts in education budgets observed in one-third of countries in 2010 will also begin to appear in more OECD countries over the next years.

Once more than three-quarters of education expenditure in most countries comes from public sources, we could expect that the downturn in GDP growth would affect public spending on education, yet data show us that the education sector has been relatively protected from early budget cuts.

The effect of the financial crisis on education budgets is more evident in the OECD countries that had substantial budget deficits in 2010 and 2011 (such as Greece, Ireland, Portugal or Spain). In 2011-2012, cuts in education budgets of more than 5% were observed, for instance in Portugal.

Therefore, we cannot forget that these are still first-stage results of the recent economic crisis and that in the next years it is going to be possible to have a clearer and more complete image of these consequences.

Sara Simões #643

The Problem of Mismatch Skills

Nowadays, one of the most worrying aspects of the entrance of youngsters in the labour market is the problem of skills matching.

It is common for people to be overqualified for a certain type of job, or, on the contrary, to be under qualified. And the problem that arises is: How to match people with the correct job given their skills?

As it is quite difficult to observe the skills a certain person holds, this problem will, in many cases, be linked to increasing youth unemployment rate. It is said, that this situation in particular may be the principal obstacle for young people in the labour market.[1]

This is shown when the existence of a large number of vacancies in the labour market is still associated with a high level of unemployment. This happens in a lot of countries in Africa, and South Africa has one of the most extreme cases, with 600 000 unemployed graduates and 3 million youngsters not incurring in any training or job related activity, and still, in the private sector, experienced 800 000 vacancies (data of 2010).

So how is, in fact, possible to address this problem?

One approach to this situation is the increase in education levels, in order to oblige young people to acquire higher levels of education.

However, it is not true that this will lead to a reduction in the problem described above. The quality of the education must be taken into account, and must be improved. On the other hand, it should be taken into account the job one may wish to perform in the future, and orientate youngsters to a field of education more in line with the task to execute (such as specialized and more practical courses).

Regarding the side of the firm, a mismatch in skills in the workers may lead to unskilled employees performing tasks they are not capable of performing, reducing the firm’s productivity, and still the worker is receiving a higher wage than it should. On the other hand, an overqualified worker performing a task that is below his skills might lead to alienation of the worker since he feels he should be receiving a higher wage and performing tasks more in line with the skills and   qualifications he possesses. So, in order to address this problem it is necessary to provide a screening and recruitment program. Though it is still rather problematic to overcome it, due to the problems of asymmetric information (adverse selection and moral hazard).

The firms should be thorough concerning the employees and the skills they bear; it may be in the firm’s interest to invest in training and formation, and so it may choose not a very high skilled worker but one that it has confidence that could be a valuable asset in the future.

To sum up, mismatch in the skills possessed by the employees is a difficult situation to address and can cause serious problems not only to the worker but to the firm as well.  It is required to invest in screening programs to help identify the workers and, on the side of the employers, an early investment in education an on its quality.

References:

Skill Mismatch: The Role of the Enterprise, research project nr 21, Publications office of the European Union 2012

Maria Almeida – 637

The “higher education bubble” in the USA: could going to university become a bad investment?

Tertiary education is widely believed to be a very good private investment, even in countries like the USA, where the tuition fees are very high.

However, some authors suggested that higher education could behave like a bubble in the USA: tuition fees are constantly rising and so is the amount of student loans, and the return on higher education could become very bad if college students don’t find a well-paid job after their studies.

From an economic point of view, to know if there is an education bubble in the USA, we need to compare the return on higher education (which can be expressed as a net present value) and the cost of it for the individual, i.e. the tuition fees.

The fact that tuition fees have constantly increased in the USA in the last 20 years can be partly explained by political changes: the education system has been liberalized and a share of the cost of higher education has been transferred from government to the students.

Still, the private returns on education also kept increasing, but with a slower pace. Economic studies show that on average, the expected return is still largely positive, even if the high level of student loans are postponing the moment when the return becomes positive. Indeed, a study from 2005 showed that the average time to cover the cost of tuition fees was 10 years, but the final net present value was 300 000 dollars in average (Lammela 2007).

In addition, if we take into account the total individual cost of tertiary education – including social contribution, income tax effect and foregone earnings – the OECD data(OECD 2012) suggests us that the USA has an excellent net present value of private education.

Indeed, it is the country with the highest private cost but also the highest private return and the net return (above 300 000dollars) is the second highest among the OECD countries. As compared to countries like Denmark or Sweden, which are more equalitarian and have redistributive tax systems, higher education is a much better private investment.

Therefore, the empirical data suggests that the concept of an “education bubble” is poor.

However, the increasing level of student loans’ defaults(Diverse education 2013) also suggests that this average hide very different situations, according to the level of the university and the major chosen. For some majors, for example human sciences like psychology and sociology, the net present value, when calculated, is actually very low (Lammela 2007).

The bubble effect might also be explained by the growing inequalities between universities in terms of level and return on education. Unlike many countries, not only the best universities are expensive in the USA, and some authors suggest a strong asymmetry of information between the universities and their students on the correlation between tuition fees and the level and return on investment provided by the university.

We could also add that in a society where a large part of the population has a college degree, the signaling role of tertiary education might decrease and the effect of tertiary education on productivity becomes crucial.

To conclude, this analysis suggests that quality of tertiary education plays a very large part in explaining the return on investment of going to college and paying tuition fees.

Elise Saunier

#1493

References:

–          Lammela Jason, 2007, Net present value of a higher education: a study of majors across different universities, Duquesne University, Pennsylvania

–          Abdul-Alim Jamaal, 2013, Experts find increased default rate on student loans troubling, Diverseeducation.com

–          OECD 2012, Education indicators in focus: What are the returns on higher education for individuals and countries?

How does poverty affect children’s education in United States?

Poverty and children’s education are commonly related with each other and it is an issue that kids face more and more. As we know, there are many children being at risk to fail in school because of their life’s social circumstances. Even there are some factors that contribute for children fail in school, such as very young parents, unemployment, low educational level of parents, poverty is considered a major at risk factor.

Identify kids in those situations it is fundamental to support their growth and development. Thus, it is important to established caring relationships between students and teachers in order to prevent bad performances in school.  Academic and behavioral problems can be indicators of impending failure. Teachers may find children who do not study for tests, miss classes because of poverty related conditions in the home environment. These issues not only have an impact on the learning of the child of poverty but can also impact the learning of other children.

The increase on the number of children in poverty has contributed for classes more diverse and turn teaching and learning more challenging. Since many children are from different cultures and have different values, the social contexts have a significant impact on the development of children.

Those children usually live in bad conditions and their day-to-day experience can have a substantial effect on their education and achievement. Additionally, they are always moving from another place and therefore changing to another school so it is difficult for them to make friends and assimilate all that they have learned. It is challenging for schools to place these children in classrooms and get them additional services they may need. Even if placement is successful, these children will likely move again within the school year. It is also challenging to help these students to learn at least something of value.

By providing emotional support teachers may help students to use their experiences and knowledge to develop and learn. Thus, they can deal better with their own issues solving them and become people more active in the social life.

The differences in performance among students of different classes of groups it is called as the achievement gap. As we studied, children from families with lower income are more likely to fail in school and the most significant factor that contributes for these outcomes is related with the home environment. It happens because low-income families often have limited education, reducing their ability to provide a responsive stimulating environment for their children.

So it is important to develop mechanisms that could help these children to having a better performance at school. Many programs were created to fight against this social issue and an example of that is by improving the school readiness. The Perry Preschool Program  and the Abecedarian  trials are randomized controlled trials of early educational programs that targeted low-income children and showed benefits that extended beyond formal school years into adulthood. Children in the intervention were more likely to graduate from high school, attend college, have fulltime employment, and be enrolled in health insurance, and less likely to have felony arrests or depressive behaviors. These programs also contribute to their social and intellectual development and school success.  Another way to prevent this problem is by improving the family’s capacity to support Children’s development and academic achievement. These programs are aimed to decrease the negative effects of poverty on those families with the goal of improving the children well-being. They have a variety of delivery mechanisms, including a health center or system, home visiting by a trained worker, combining counseling with growth monitoring and providing group sessions for parents.

To conclude, we know that poverty is clearly a risk factor for children’s poor development and limited educational outcomes. So it is essential to find ways of reducing poverty for children’s healthy development such as adoption of strategies by governments, communities, and families that alter the deleterious processes whereby poverty limits and disturbs typical development. 

 

Rita Cordeiro #672

References

http://www.teach-nology.com/tutorials/teaching/poverty/

http://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=1002&context=psycd_fac

Patrice L. Engle and Maureen M. Black, “The Effect of Poverty on Child Development and Educational Outcomes”, California Polytechnic State University, San Luis Obispo, California, USA and University of Maryland Baltimore, Baltimore, Maryland, USA

Trends and differences in tertiary education gross enrollment of similar countries. How to compare Italy, Spain, Portugal and Greece?

Italy, Spain, Portugal and Greece are all both OECD countries and members of European Union. These countries have similar culture, policy-making style and have been seriously affected by the World Crisis. For those reasons it is possible to compare their gross enrolment rates at tertiary level.

A common trend is that enrolment is increasing on average, being the rates of 2010-2011 higher than those of 2004.

Tertiary Gross Enrolment Rate in Italy, Spain, Portugal and Greece (2004-2012)

	2004	2005	2006	2007	2008	2009	2010	2011
World	23,4%	24,1%	24,9%	25,9%	27,1%	28,2%	29,6%	30,1%
Italy	61,5%	64%	65,8%	66,4%	66%	66%	65,5%	n.a.
Spain	65,8%	66,6%	68%	69%	70,7%	73,2%	78,1%	82,6%
Portugal	55,5%	55,4%	55,4%	57,4%	61,1%	62,2%	65,5%	n.a.
Greece	79%	88,6%	93,1%	89,4%	n.a.	n.a.	n.a.	n.a.

Source: World Bank Database

This trend can be associated with the exponential growth of total youth unemployment in recent years (reaching in Spain values close to 50% of youth workforce).

 Total Youth Unemployment (as % of total labour aging 15-24) from 2004 to 2011

	2004	2005	2006	2007	2008	2009	2010	2011
Italy	24,6%	24%	21,6%	20,3%	21,3%	25,4%	27,8%	29,1%
Spain	22%	19,7%	17,9%	18,2%	24,6%	37,9%	41,6%	46,1%
Portugal	19,3%	16,1%	16,2%	16,6%	16,5%	20%	22,3%	30,1%
Greece	26,9%	26%	25,2%	22,9%	22,1%	25,8%	27,8%	29,1%

Source: World Bank Database

Many economists agree that there is a positive correlation between growth of unemployment and gross enrolment rates, and it is what we can see even in this case looking at the data. However, recent trends show differences, as we observe a slow decrease in enrolment rates in Portugal and Italy and a sustained growth in Spain. We can state that higher level of enrolment during recessions can be explained by lower opportunity costs of schooling (assuming that people want acquire more education till the expected income is higher than the average).  By contrast, I would argue that costs are not lower because of imperfect financial markets, reduced saving propensity rate and decreasing level of GDP per capita.

GDP per Capita (Current US$) in Italy, Spain, Portugal and Greece (2004-2012)

	2004	2005	2006	2007	2008	2009	2010	2011	2012
Italy	29.832$	30.478$	31.777$	35.826$	38.563$	35.073$	33.761$	36.103$	33.048$
Spain	24.468$	26.056$	28.024$	32.118$	34.977$	31.714$	29.956$	31.984$	29.195$
Portugal	17.653$	18.185$	19.065$	21.845$	23.716$	22.019$	21.381$	22.503$	20.182$
Greece	20.607$	21.620$	23.475$	27.288$	30.398$	28.451$	25.850$	25.630$	22.082$

Source: World Bank Database

GDP per Capita in Italy (Constant price in US$) in Italy, Spain, Portugal and Greece (2004-2012)

	2004	2005	2006	2007	2008	2009	2010	2011	2012
Italy	28.227$	28.280$	28.728$	29.008$	28.454$	26.729$	27.059$	27.081$	26.316$
Spain	26.882$	27.352$	28.075$	28.530$	28.330$	27.045$	26.907$	26.890$	26.427$
Portugal	21.300$	21.369$	21.607$	22.068$	22.037$	21.376$	21.780$	21.539$	20.929$
Greece	23.896$	24.348$	24.587$	26.387$	26.226$	25.301$	23.997$	22.307$	20.904$

Source: OECD Statistics

For these reasons I suggest that a growing enrolment is to be, at least partly, attributed to the signalling effect, as we observe a growing individual demand for high ranked schools.

Anyway, even if the enrolment rates are markedly higher than world level (always more than the double) we observe significant differences across the countries. Observing the data we have to exclude that GDP per capita plays any influence in explaining these differences (a Gini index analysis may make more efforts). In that, in fact, we should observe a higher enrolment rate in Italy, where it is instead lower than Greece, Spain and more or less equal to Portugal level.

It is to notice that even public expenditure per student (as % GDP per Capita) is not very relevant, being the expenditure rates, for instance, higher in Portugal than in Spain and Italy in the last years where enrolment rates are higher. This is only one method to analyse the resources per student. An alternative indicator is the real expenditure per student. In this case a comparison is more difficult because we use absolute values, but results are similar to the previous indicator.

Expenditure per student, tertiary education (as % GDP per Capita) in Italy, Spain, Portugal and Greece (2004-2010)

	2004	2005	2006	2007	2008	2009	2010
Italy	22,6%	22,1%	23,2%	22%	24,8%	25,7%	25,8%
Spain	22,6%	22,7%	23,4%	25,1%	27,3%	29,2%	28,5%
Portugal	22,5%	26,2%	27,8%	33,5%	26,7%	30,6%	n.a.
Greece	24,8%	25,4%	n.a.	n.a.	n.a.	n.a.	n.a.

Source: World Bank Database

In Greece (data available only till 2007) we observe very high enrolment rates at tertiary level. One possible explanation is that it is the only of the considered countries with no fees for university, according to the very worth democratic principle that education must be universally guaranteed.

It is difficult to compare these countries and identify which is the best in tertiary education attainment and with the most efficient outcome. Entry rates and graduation rates should have similar values in the same year, however a significant difference between the two measures in the countries tells us that the number of graduated is always much lower than the enrolled.

Tertiary Education Entry rates* in Italy, Spain, Portugal and Greece (2000-2007)

	2000	2001	2002	2003	2004	2005	2006	2007
Italy	39%	44%	50%	54%	55%	56%	55%	53%
Spain	47%	47%	49%	46%	44%	43%	43%	41%
Portugal	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	53%	64%
Greece	30%	30%	33%	35%	35%	43%	49%	43%

Source: OECD Statistics

*First time entrants as a percentage of population in corresponding age group

Tertiary education graduation rates

	2000	2001	2002	2003	2004	2005	2006	2007
Italy	19%	21,5%	25,2%	n.a.	36,1%	41%	39,4%	35%
Spain	30,4%	31,5%	32,3%	32,3%	32,9%	32,7%	32,9%	32,4%
Portugal	23,2%	27,6%	30,1%	32,6%	32,1%	32,3%	32,9%	42,6%
Greece	14,5%	15,7%	18,4%	20,3%	24,4%	24,9%	20,4%	17,7%

Source: OECD Statistics

To conclude, to explain differences in enrolment at tertiary level across similar countries could be useful to consider all the limits of economic indicators (even if only few of them have been utilised in this analysis), that in my opinion tend to attribute relevance only to what is measurable. Further explanations may be found analysing the non-economic environment, such as the cultural propensity of studying and the non-monetary benefits of schooling.

 Francesco Morandotti

The educational expansion in Finland

Nordic countries have a tradition of state control of higher education, and participation has been free of charge due to the view of higher education as a social good. Finland has performed very well in school achievements: in 2000, 2003 and 2006 the country was ranked first in the PISA assessments in reading literacy, mathematics and science, respectively. This set-up motivates the analysis of the Finnish education system.

Although basic education had become compulsory for everyone in the 1920s, the exceptions allowed for the municipalities in providing education services and the economic harshness during the 1930s had made educational levels still low in Finland in the inter-war period. Since the 1960s, the supply of education increased rapidly on all levels. In the 1970s nine years of compulsory primary education were introduced, which is usually also seen as the beginning of a truly egalitarian schooling system and the laying of the basis for a knowledge-society. After that, youngsters could go on to the secondary level, either to an institute of vocational training or continue in secondary school, after which they could continue at the tertiary level. This has also been the aim of educational policies since then, supported by student grants and student loans.

In the 1960s around 90 per cent of the population still had no further education, but only some sort of primary education. By 1970, already 20 per cent of the population had completed some vocational training, and among the younger age groups the level of education started to rise very rapidly. Intergenerational comparisons show this rapid growth: in 1980, already 54 per cent of the youngster cohort (25-29) had completed some further vocational or higher education, but only 13 per cent of those exceeding 65.

This late, but extremely rapid expansion was a result of growing belief among politicians and authorities of the importance of human capital investment as a source of growth. Such ideas were also influenced by the policies during the Golden Age, when human capital and growth theories evolved. Some authors argue that the driving forces behind Finnish universal mass higher education were, on the one hand, changes in the structure of society, and on the other hand, individual demand for education and increased need for skills in production processes. Nevertheless, the actual location of universities in the era of expansion was a function of local political actors who were able to have an influence on ruling political parties, thus, regionalism played a crucial role. The idea of equal opportunities in education also became more pronounced, as a consequence of policies to reduce inequality. This has meant that education on all levels has been publicly funded, and free of charge, becoming an important route for social mobility of individuals.

The publicly funded educational investments were of great significance with respect to the development of the business sector: technical education on all levels, in vocational schools, in technical institutes and technical universities, was regarded as particularly important. The proportion of degrees in technical and natural science in relation to all degrees has for a long time been higher in Finland than in other OECD countries (in 2010, 24 percent of the students were graduated in engineering, the highest value among the EU-27). Since the 1960s, business schools and business colleges expanded at a rapid pace.

In sum, the great belief in education as a source of growth has also been an argument for making education of all types, including higher education primarily intended for business life, publicly funded.

References:            

Fellman, S., Jes Iversen, M., Sjögren, H., Thue, L. Creating Nordic Capitalism: the Business History of a Competitive Periphery

Fellman and Lindholm (1996) Statistical Yearbook

International Institute for Educational Planning: http://www.unesco.org/iiep

Saarivirta, T. Finnish Higher Education Expansion and Regional Policy, Article first published online: 9 APR 2010

Nuno Lourenço, 85

The importance of teacher quality

The educational achievement of a country’s individuals is a key determinant of economic growth when measured not just in terms of quantity but in a qualitative matter. Therefore, improving educational attainment should be a priority in most countries. Research suggests that, among school-related factors, teachers matter most, and so the answer in improving education, it appears, is linked with teaching quality. Do better teachers positively influence students’ knowledge and future educational decisions?

First of all, assessing teachers’ quality is not straightforward and it is, in fact, very difficult to measure. Despite common perceptions, effective teachers cannot accurately be identified based on where they went to school, whether they have graduated, neither for how long they have been teaching. The best way to weigh teachers’ effectiveness is to look at their on-the-job performance, including what they do in the classroom and by measuring the progress their students make. Furthermore, student’s grades or test scores may reflect a vast host of influences that do not derive from schooling, meaning that teacher quality is not the only factor that affects student achievement neither is just attending school. The student’s own motivations, inherent ability, support from family and peers play crucial roles as well.

It has been, however, a strong effort to isolate the impact of teachers from all other effects on student’s performance. What research constantly shows is that a more effective teacher, while comparing to the average one, “produces students whose level of achievement is at least 0.2 standard deviations higher by the end of the school year”.

In two new working papers, Raj Chetty, John Friedman and Jonah Rockoff deploy statistical expertise to evaluate the value of teaching with an interesting feature on student’s future earnings, using the “value added” method.

They use a large data-set from an urban American school district that covers 20 years of results including teacher assignments and students’ test scores from the 3^rd to the 8^th grade. The authors calculate the effect each teacher has on students’ performance after adjusting for demography and previous test scores. They argue that previous test scores are a good proxy for the variety of external influences inherent to each student. Moreover, recent test score do a better job in reflecting teacher’s effectiveness and so the authors also control for that. They also question the possibility that good teacher scores reflect the lucky circumstances of their student’s rather than their abilities but the results do not seem to back up this hypothesis.

In their second paper, the authors compare their measure of teacher quality (“value-added”) against students’ earnings as adults, after once again controlling for previous test scores and demography. Expectedly, exposure to better teachers is associated with an increased probability of attending university and, among pupils who go on to university, as well as with higher earnings. The authors estimate that substituting a teacher at the bottom of the value-added spectrum with one of average quality raises the collective lifetime income of each class they teach by $1.4m.

Concluding, improving the quality of the education system through improving teaching brings a higher chance of students’ going to university as well higher expected earnings.  Teacher quality can be improved by replacing teachers with better ones, but this process may be slow, and of limited impact. This suggests that future economic prosperity requires improving the quality of the teachers already working in schools, because “good teachers are worth their weight in gold”.

Rita Azevedo

625

Sources:

“Valuing Teachers: How Much is a Good Teacher Worth?”, Eric A. Hanushek (2011), Education Next

“Measuring the impacts of teachers I: evaluating bias in teacher value-added estimates”, Raj Chetty, John Friedman, and Jonah Rockoff, NBER working paper 19423, September 2013

“Measuring the impacts of teachers II: teacher value-added and student outcomes in adulthood”, Raj Cheety, John Friedman, and Jonah Rockoff, NBER working paper 19424, September 2013

Italian Education paradox: scarcity and low return

Economic theory predicts that investment on scarce goods yields higher returns, since rarity represents a source of competitive advantage.

In Italy, the system of education displays an apparent paradox: despite the scarcity of the “good” education, investment on education yields low private returns, as proxied by earnings difference between a university and a secondary school graduate. In 2008 and 2011:

–          population share (25-64 years) with completed tertiary education was 14 in both years, a much lower value than the average for OECD countries, 43 and 31;

–          earnings from employment for tertiary education were 150 and 148, against higher average values of 152 and 157 for OECD countries.

An analysis of the interaction between demand and supply of human capital offers possible explanations for the observed phenomenon. First of all, the firms’ difficulties to find adequate competences and skills compatible with the use of new technology reduce the return of education. The resulting decrease in the supply of high qualification restrains, even more, the demand of competences, triggering a vicious cycle. The roots of this circle can be found in the existence of informative problems and in the peculiar structure of labor market.

Informative problems in the recruitment stage are reflected by the difficulties of firms to recognize superior competences of high qualified individuals. In fact, the training offer provided by the scholastic system is not labor-market oriented; hence, the educational offer doesn’t provide students with distinctive labor skills compared with those of individuals with lower educational attainments.

The problem is sharpened by the labor market conditions. One concern is the limited labor mobility that, combined with the prevailing structure of small and medium businesses, has hampered the flexibility of labor market at the expense of a low human capital.

A higher investment in R&D might be beneficial through two channels: directly, by requiring more educated individuals for research activities; indirectly, because investment in new technology allows to increase labor market flexibility and, therefore, is likely to result in the request of a higher human capital. Besides, the institute of internships (already included in some universities’ programs), providing labor skills, can reduce the size of the mentioned informative problems.   

 

Moreover, the analysis of wage premia cannot exclude the concern for horizontal equity, namely, for the wage differentials of individuals with the same observable characteristics, including education. As underlined by Franzini and Reitano, since wage differentials within graduates are even deeper than the ones across individuals with different educational attainments, the analysis should focus also on the differences in unobserved characteristics, such as individual abilities, as well as on the differences in the quality of education provided by different institutions.

All in all, the research of a solution to the Italian paradox is an issue of primary order. Investing in knowledge and increasing the value of education on labor market are necessary requirements for the country’ s development.       

 

                                                                                                                                             Silvia Sarpietro – 676

 

 Sources:

http://www.oecd.org/edu/eag2013%20(eng)–FINAL%2020%20June%202013.pdf

http://www.bancaditalia.it/pubblicazioni/econo/quest_ecofin_2/qef180/QEF_180_ITA.pdf

http://www.siecon.org/online/wp-content/uploads/2012/08/Franzini-Raitano1.pdf

 

The best Portuguese schools: should we trust the rankings?

Every year, the rankings of the best Portuguese schools are published and disseminated throughout the media. The indicator used to organize such ranking is the average national exams score results.

The analysis of schools according to such rankings has received, over the past few years, a large criticism. This year was no different: after the announcement, the controversy came up again.

A thorough analysis of the results would be interesting, but for lack of space, there are two consequent conclusions I would like to emphasize. First, private schools continue to have on average much better results than public schools. To illustrate this idea just notice that there are only 9 public high schools in the top 50, while in the similar ranking for the 9th year there are only 7 public schools. Regarding the 9^th grade, this difference is quite visible in the following figure.

Secondly, analyzing only the results of schools where there were over 50 tests, the results show a large difference at the territorial level, being the first places dominated by schools in the North Coast[1].

The results and consequently the corresponding rankings are undoubtedly objectives; but their interpretation is far from consensual. The debate is on whether we should use this indicator as a measure of school performance.

In my point of view, these results should not be used, in a decontextualized way, to assess school performance. In reality, this indicator does not take into account that schools have different characteristics, both in terms of social environment and in terms of human and material resources.

Because of this, there is a huge literature that points out to the weakness of this indicator. In particular, it is interesting to consider the Rita Azevedo’s WP[2] (2011) regarding Portuguese 9^th grade exam results in which the author found evidence for a significant causal effect between socioeconomic aspects and school achievement. The conclusion reinforces the weakness of the indicator for the Portuguese case.

The data released by the Ministry of Education shows also socio-economic indicators of public schools (not revealing any information on private schools). Based on this information, the Público’s study divided schools according to economic contexts and calculated an expected value of the average results for each context.

Based on this, I think it would be more appropriate to use an indicator which compares the expected average test scores results with the obtained values  (in this line, Azevedo also proposed that an alternative solution would be to consider the ratio between the expected value and the current average school scores). Although it has some limitations, it would help to draw more truthful conclusions regarding school performances.

However, I cannot deny the importance of the divulgation of the results obtained by the students, because they are a key tool to make comparisons, within the same school (comparing with the previous years) or even between schools at the same context. However, we have to take into account that a comparison over time requires that the tests are comparable between different years which may not be the case of Portugal. Moreover, they are important for the government to be able to identify and combat the causes of the poor performance of some schools and regions.

To conclude, the results should be presented to the public opinion in a careful way; the media should not label the rankings as “the best Portuguese schools”. It is important that the public opinion realize that a school cannot be considered good or bad based only on the average test scores. This conclusion is not only relevant for Portugal since there are other empirical studies that find the same evidence for different countries[3].

Filipe Silvério, #617

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[1]This situation is perfectly depicted in the following images, in which is possible to see the average test scores (of Mathematics and Portuguese) for high school and for the 9^th year of Mathematics and Portuguese by municipality: http://imagens5.publico.pt/imagens.aspx/809225?tp=UH&db=IMAGENS http://imagens6.publico.pt/imagens.aspx/809226?tp=UH&db=IMAGENS

[2] Azevedo, Rita, “Critical Analysis: Portuguese 9TH Grade Exam Results and Socioeconomic Factors” 2011, Work Project for the Master in Economics, Nova SBE

[3] For instance, a similar study for Chile: Mizala, Alejandra, Pilar Romaguera, and Miguel Urquiola, 2007, “Socioeconomic status or noise? Tradeoffs in the generation of school quality information”, Journal of Development Economics, 84, pages 61-75

Education externalities, the effect on crimes

In class we have discussed the reasons that may push the demand for education, in particular among the others factors, the externalities that education can produce. We focused our attention on the “functioning effect”, or better, the effect of education (at a primary level in this case) to facilitate social and economic interaction necessary to conduct a normal life (let’s say basic calculus for example). This topic generated  a lot of interest in class and gave me thoughtful insights to explore it better. In my opinion one of the most interesting externality generated by education is the effect on crime: a criminal behavior in the related literature has been connected to the “inner structure” as a deviation from a psychological and mental health or connected to impact of social circumstances, it’s clear that the effect of education can be easily included in both channels. The first question that naturally arises in me regards the direction in which this relation operates. The easiest way to think a manner by which education affects crime is an income effect that is due to better education: it directs an individual to more social acceptable goals and to respect of law, reducing her propensity to crime, it increases the income from legitimate work and raise the opportunity cost of criminal activity. Moreover increasing the wage it makes more costly the time spent in prison and outside the labor market. Time spent in school reduces the time available to spent in criminal activities (Tauchen et al.). Other effects involve the psychic ambit, in fact some evidence (Becker and Mulligan) show impact on patience and risk aversion (more patience means low discount rate and so a higher valuation of future earnings, people that drop out from school instead, reveal, according to Oreopulos, a high focus on immediate cost of schooling). If we think in that way we expect the relation between the two variables to be negative, but going deeper in literature it comes out that there is a possible channel that can drive the relation in a positive way, in fact more education may increase the self-protection against punishment by law (we can think that the best way to evade the law is to know the law) also the skills learnt in school could be used in criminal activities and so increase the income from those. I decided to construct my own dataset to try to figure out if a correlation between education and crime was relevant and in which direction goes. Data on crime are taken from UN’s dataset in crimes and the proxy I used is “Intentional homicide” defined here as unlawful death purposefully inflicted on a person by another person in 2012. Instead data for education are provided by OECD and the proxy I used is the percentage of people that attained only primary school with an age in the range 25-64 years as a measure of low level of education. Then I matched the two proxies for each country available and what I obtained is a sample composed by 36 observations. It’s shown below a subsample:

	Attained only primary school 25-64 years	Crime rate
Argentina	43,50	5,54
Australia	6,40	1,08
Belgium	12,40	1,84
Canada	3,20	1,54
Chile	14,30	3,68
Czech republic	0,00	0,79
Finland	6,40	2,15
France	10,60	1,18
Denmark	5,00	0,79
Estonia	0,80	4,85

 

As we can see from the graph there seems to be a positive correlation between an high level of people who attained only the primary school in a country and the rate of homicide in the same country. A simple calculation of the Pearson correlation coefficient shows a value of 0,313 to confirm my expectation. But as we can imagine this is only a correlation and not  a causation (the STATA regression gives a coefficient on crimeRATE of 0,13 but is not consistent as there are a lot of variables that can cause the relation that are missing, for example income), in fact I used a broad measure of both the measures, for a more accurate analysis is better to work with more microeconomic data.

 Angelo Saponara

References:

On the relation between education and crime – Isaac Ehrlich

The Crime Reducing Effect of Education –  Stephen Machin, Olivier Marie, Sunčica Vujić

Education as a Deterrent to Crime – Dan Usher

Work and Crime: An Exploration Using Panel Data – Ann Dryden Witte, Helen Tauchen

The endogenous determination of time preference – Gary S. Becker and Casey B. Mulligan

The MOOCs: the future of higher education?

The information and communication technologies revolution has also affected education. The emergence of the MOOCs (= Massive Open Online Courses) is a hot topic since 2011 even if e learning has existed for many years already. The concept is simple: it began with the most prestigious universities in the world, among them Harvard, Stanford or MIT. These universities set up online platforms so that every student in the world can attend a wide range of courses for free (edX, Udacity or Coursera to name the main ones). At first sight, this “revolution” looks exciting as it breaks the financial constraints students can face and then make at the same time the outcome of higher education efficient.

To be sure that the outcome is really efficient, two issues are still unsettled: the measurement of the benefit of such online courses and the remaining question of the degree that acts as a signal for students to find a job.

To my knowledge, there is no research paper that brings us some evidence that the MOOCs lead to a global positive return for participants. It is still too soon to draw conclusions. A measurement of courses’ completion rates would be much more relevant than a measurement of enrolment rates. In her paper, Crawford took the example of the edX MOOC ‘Circuits and Electronics’. “155 000 students registered for this course in February 2012, only 23 000 got points for the first problem set, 9 300 passed the mid-term, 8 200 sat the final, and 7 000 earned a final passing grade”, the completion rate is then about 4,5% for this MOOC, which is much lower than what we can observe for traditional higher education systems.

Moreover, the massive access to knowledge the MOOCs provide can pose the problem of the quality of schooling. Indeed, if too many students virtually attend a course, individual feedbacks are not available any more and the speaking time of everybody is reduced to none. The issue of quantity decreases the quality of courses even if Harvard professors teach them.

The second issue is about degree. Currently, students who follow a course on a MOOC can get a certificate of completion after passing a test they have to pay for. The first problem is that if the students want a proof that can act as a signal, it is not free of charge anymore. Indeed, if participants do not consider schooling as a consumption good they want to be able to provide a proof of their potential productivity to firms they apply to. If they are able to do so, how firms are going to react in front of a “virtually” certificate, does their screening device can be affected by this new parameter? Is it credible in our mind so far?

Are the MOOCs really going to revolutionize the world of higher education? As a very new concept, this opens a large debate and rules still have to be shaped. One thing is certain, the development of the MOOCs encourages the globalization of competition in the educational sector and higher education will be affected.

Elise Jaillant

 

References:

• Behrooz Parhami, “Too early for verdicts on MOOCs”, Communications of the ACM, Jul2013, Vol. 56 Issue 7, p8
• Vardi Moshe Y., “Will MOOCs Destroy Academia?”, Communications of the ACM, Nov2012, Vol. 55 Issue 11, p5-5. 1p.
• Cooper S. & Sahami M., “Reflections on Stanford’s MOOCs”, Communications of the ACM, Feb2013, Vol. 56 Issue 2, p28-30. 3p.
• Crawford F., “Shaping the future of higher education”, Charter, Jul2013, Vol. 84 Issue 6, p14-18. 5p.