Team 1

Economic Inequality and Differing Educational Opportunities

Impacts of Educational Structures

Anisha Anil DamaniIan Fang Shengyu

James BazakosKim Dong-hunPaul GouvardShin Jung-min

08 May 2015

This final report is the product of our learning experience within the Global Integrative Module (GIM) project, which is formed by students from business schools around the world. The aim is to engage students with current issues and explore as well as develop potential solutions. This year the question centered on "Why and how can companies contribute to the reduction of economic inequality in the world?" Our team has approached this issue from a perspective of educational structures by grouping countries’ education system according to its similarity to the Finnish and

South Korean model. This is unlike studies done in the past, which mainly emphasizes family background as a key factor in differing educational opportunities. We hope that our collaboration

will hopefully assist in solving some of the challenges faced by these societies.

Contents1) Definitions and Preliminary Studies...............................................................................................2

1.1) Economic inequality: our definition............................................................................................3

2) Research Question............................................................................................................................5

3) Methodology.....................................................................................................................................7

4) Results.............................................................................................................................................11

Table 4.1..........................................................................................................................................11

Table 4.2..........................................................................................................................................12

Table 4.3..........................................................................................................................................13

Table 4.4..........................................................................................................................................14

Table 4.5..........................................................................................................................................15

5) Your Recommendations to the Company/ies Selected...................................................................16

5.1) What can for-profit companies do?..........................................................................................18

5.2) What can Government do?......................................................................................................21

5.3) What can Schools do?..............................................................................................................24

6) What Should Business Schools’ Contribution Be?...........................................................................27

References...........................................................................................................................................29

Bibiliography........................................................................................................................................30

Appendix A..........................................................................................................................................32

Appendix B..........................................................................................................................................33

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1) Definitions and Preliminary Studies

A quick benchmarking of how “economic inequality” is usually defined on the web

gives us the following results:

“Economic inequality is the unequal distribution of household or individual income

across the various participants in an economy.” – Investopedia

http://www.investopedia.com/terms/i/income-inequality.asp

“Income inequality refers to how evenly or unevenly income is distributed in a

society.” – John D. Sutter, CNN

http://www.cnn.com/2013/10/29/opinion/sutter-explainer-income-inequality/

“Income inequality refers to the extent to which income is distributed in an uneven

manner among a population.” – Inequality.org

http://inequality.org/income-inequality/

It appears, not that surprisingly, that for popular scientific literature or, more broadly,

for the general public, “economic inequality” is first a matter of income inequality, that is to

say a matter of an “inappropriate” distribution of income between the actors of the economy.

But to define “economic inequality” by “income inequality” is in fact substituting a

measure – “income” – for a concept – “economic”. In fact there are many other ways than

income to measure what could be appropriately called “economic inequality”: the amount of

capital owned, the time spent in unemployment for a given category of population, the

number of people working as factory workers or office workers vs the number of top-

managers, etc.

In academic research we can distinguish at least two main differing bodies of research

that address the umbrella term “economic inequality”, namely the aforementioned ‘income

inequality’, and the slightly less popular ‘wealth inequality’. To most, the difference between

income and wealth inequality is not readily discernable by the titles afforded to them,

however to economists and people seasoned on the topic, the difference between these two

terms can lead to a very different discussion.

The main difference between income and wealth inequality is that income inequality

measures the share of total income going to a particular percentile whereas wealth inequality

shows the share of total wealth going to a particular percentile. Both of these concepts tend to

end up discussing the disproportionate share of either income or wealth accruing amongst the

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top 10%, 5%, 1% and .01% of the population. Regardless, the concept that one chooses to

discuss can greatly dictate how nuanced this argument can become. For example, wealth

inequality is in fact a much more disconcerting picture than income inequality. That’s not to

say that income inequality is an irrelevant discussion, but it would seem that the

‘blasphemous’ discrepancies people have been sounding in more recent times are

significantly more prominent amongst the distribution of wealth. The different degrees to

which income and wealth inequality have been increasing is illustrated by the following

graph:

1.1) Economic inequality: our definition

Now that we have clearly highlighted the fact that defining “economic inequality” as

“income inequality” is not neutral and that it strongly dictates the type of results obtained, we

can try to provide a more complete definition of “economic inequality” which will also be the

starting point of explaining how we got to define the problem we are tackling.

Our group is in favor of Alvaredo et al. (2013) and their study on economic

inequality, which talks about discrepancies in the repartition of the total income generated by

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the market. However, we are also supportive of the view of inequalities in the repartition of

capital between households (NEF, 2013). The interest of this broader definition is that it takes

into account both the existing inequalities before the repartition of the income – namely

inequalities in the ownership of capital – and the way through which inequalities are

perpetuated through the actual repartition of the income. Thus economic inequality is a both a

state of the economy (discrepancies in the repartition of capital) and an economical process

(capitation of the bigger part of incomes by a certain proportion of the population).

Starting from this definition of economic inequality, we have tried to find how

companies could contribute to reduce it. To this purpose we have tried to identify on which

factors of economic inequality companies could concretely act. Two main observations led us

to define our problem.

First, the rise of economic inequality is linked to the demand of high-skilled worker in

the economy (Alvaredo et al., 2013). It has been observed that cutting taxes could entail

higher incomes for the top 1% of the population. One of the possible factors for this is the

increase of marginal gain per supplementary income : if higher incomes are less taxed, people

in the top 1% of the population have more incentive to bargain for their salary ; meanwhile,

the demand of high-skilled workers being high in developed economies where technology is

well-developed and high-skills provide important gains in productivity, the bargaining power

of companies shrinks if top-skilled workers can expect a significant gain in their income by

bargaining for their salary as taxes are lower ; thus the level of higher incomes rises.

The research we read focused on tax-cuttings, but we decided to investigate further

the link that the paper identified between the demand for high-skill workers and the increase

in economic inequalities. In fact we were wondering if this dimension of the problem still had

an impact independent of the presence of tax cuts in the economy. If yes, it meant that the

increase in demand for high-skilled workers was one of the factors of economic inequality as

it gave more bargaining power to the high-skilled workers to negotiate their salary.

To assess the relevancy of our analysis we looked at a complementary study held in

France for the period 2002-2007; it showed that when the average salary rose of 0.5% on this

period, the salary of high-skilled workers rose by nearly 6% (Amaar, 2010). No significant

tax cuts had taken place, unemployment was around 9%, and growth around 2%. This tended,

if not to directly support our view, to show that something was going on here.

In developed economies, the demand for high-skilled workers is the result of the

existence of high productivity gains due to technology. The presence of high-skilled workers

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in the economy as well as the development of technology is due to the existence of high

quality educational institutions that produce knowledge, top of the art technology and the

elite workers capable of using (and eventually understanding) them.

Thus, if the demand for high-skilled workers really entails income inequality,

education quality, as one of the condition of production of both productivity gain and elite

workers, can have a negative impact on economic inequalities.

And here we reached a paradox. Generally, education inequality is considered as part

of the inequality problem, along with economic inequality: the Inequality-adjusted Human

Development Index measures the losses in human development due to health, education an

income inequality (UNDP, 2010). The general public tend to consider that the higher the

level of education, the lower the economic inequality; as if the existence of educated people

in the economy entails that it would naturally become just.

But if education quality has an impact on the development of the economy, it is not

necessarily true, given what we had found, that it helped reducing economic inequality.

In the end we had identified a tension between two opposite conceptions : on the one

end we had the feeling that if the quality of education was high, the more developed was the

economy and thus the demand for high-skill workers was higher and ultimately the

bargaining power of elite workers insured them the ability to negotiate higher salaries that

would, in the long run, increase their capital, allow them to afford high quality studies for

their children, and durably perpetuate economic inequality; on the other end we had the

generally admitted idea that education is both a factor of economic development and a way to

reduce economic inequalities by introducing opportunities for individuals to rise in the

society.

Our idea was to determine the real contribution of education quality to the increase or

the decrease of economic inequalities, and then to evaluate the opportunities the answer could

represent for companies.

Hence our problem: do varying degrees of educational quality have an impact on

economic inequality, and if so, what are the implications of this?

2) Research Question

Our group has decided to research on economic inequality due to differing

educational opportunities as we believe that education is the fundamental building block of

society, without which, order and democracy would not have existed. Moreover, much debate

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and research has been made of it throughout the times, with studies being conducted to posit

both the positive and negative effects of differing educational opportunities on economic

inequality. Contrary to what our group is going to try and establish; Hendel, Shapiro and

Willen have written a discussion paper that established a positive correlation between

affordable education and the widening inequality in income (2004). By approaching the topic

based on job market signalling, they have concluded that lowering the cost of borrowing for

education has resulted in a steady increase in the number of the poor who become educated,

which has in turn resulted in poor people but of high ability leaving the uneducated group.

Consequently, this simultaneously increases the disparity between skilled workers and

unskilled, low productivity workers; and the skill premium required to move into skilled

labour jobs, thus, increasing income inequality.

This effect is further compounded by the rapid evolution of technology of the 21 st

century. In their book ‘The Race between Education and Technology’, Goldin and Katz

(2008) posited that technological advances have resulted in companies demanding for more

skilled labour, causing the wage gap between skilled and unskilled labour to drift further

apart. This wage gap is prevented from widening further by increasing the supply of skilled

labour, which essentially turns this into an economics play of supply and demand.

However, it is important to note that Hendel, Shapiro and Willen were focused solely

on the educational policies of the United States, and that differing regions might see differing

results depending on the macro-environmental factors of that country involved. For example,

countries heavily reliant on manufacturing or textile industries for revenues might require a

higher unskilled to skilled labour ratio compared to a country that is focused on

pharmaceuticals and bio-chem.

Okun (1975) has summed it up pretty simply in what he describes as the “big

tradeoff”. In his book, both efficiency and equality are opposing polarities and that there is

the opportunity cost of one when seeking another. Okun also talked about transferring income

from the rich to the poor through the government’s system of taxes as a solution to the

inequality problem. However, this is highly inefficient due to the difference in utility of every

dollar between the rich and the poor, assuming that the rich are people of high productivity.

Returning to this scenario, we too, are faced with the question if it is more efficient to adopt

an education-for-all mentality and provide equal education to the majority of the people in the

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name of equality rather than focus on the highly talented minority and nurture them for future

prosperity and growth of a country.

Taking all of this into account, it seems that several studies have linked differing

educational opportunities with the family environment that the child is brought up in. While

this is not incorrect, it might also prove that the family environment factor is in itself, an

effect of the economic inequality from previous generations rather than a cause. In fact, a

study conducted by Sacerdote (2007), has shown that the outcomes of educational attainment

and income are often least affected by the differences in family environment. In his table 5,

Sacerdote estimated that 56% of the variance in family income is derived from external

environmental factors unrelated to family. Out of the remaining 44%, 33% was attributed to

genetic heritability. Only 11% of the outcome was attributed to the family environment,

which in a larger sense, does not actually make for a convincing argument that educational

opportunities between different income groups are vastly different.

To that, our group has decided to approach things from another perspective; that

differing educational opportunities resulting in income inequality is not something that is

caused due to differing family background or lack of access to education but rather, because

of something external and more culture oriented. Intuitively, this would make perfect sense,

given that the family environment is only being assigned a minor role in changing outcomes

as per according to Sacerdote’s study; it would require an intervention from the environment

that also makes up for a huge part of a child’s development for extended periods of time, in

order for beliefs and values to take root. Our group did not consider the other 33% that is

derived from genetic heritability as a possible cause due to its fixed nature from birth. Thus,

our group has decided to explore differing educational opportunities from the perspective of

educational structures, due to schools forming a huge part of a child’s life during pre-school

and elementary school. Through personal experience and asking friends about it, there was a

consensus that the way others perceived a person in primary school had an effect on that

particular person’s behaviour throughout secondary school, which would then snowball into

college and so on and so forth.

3) Methodology

To delve into educational structures, its effects on differing educational opportunities

and economic inequality. We started off by first establishing that there is indeed a positive

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correlation between literacy rates and the wealth of a country; and that differing educational

opportunities have an impact on economic inequality amongst countries. This is done by

simply accessing UNESCO Institute of Statistics to obtain the latest available GDP per capita

and literacy rates.

After establishing a relationship, we then decided to look at two country’s highly

different, yet highly successful education systems in the world: Finland and South Korea. On

average, both are highly ranked in the annual PISA tests and both countries usually take turns

to secure first and second place. Finland’s education system only has one exam, which is

taken when students are 16 years old. Another distinct difference is that there are no inter-

school rankings and no comparisons or competition made between schools. As stated in the

Smithsonian (2011) magazine, every school has the same goal and draws from the same pool

of educators, who are in turn placed in a very selective process that only sees the top 10% of

graduates with a Master’s degree in education being appointed as teachers. In sum, every

child has the same opportunity as the next in getting a good education regardless of where he

or she lives. More importantly, they receive extra help if they are weaker in certain areas of

academia.

Then we have the South Korean education system, which emphasizes 16 to 20 hour

study days until the day of the university entrance exam or ‘examination hell’ as it is known

(BBC, 2013). Exams are in a multiple-choice format which leaves no room for error.

Students that score well are able to enter one of the top 3 universities in Korea, which would

subsequently guarantee them a lucrative, lifelong job at a Korean conglomerate or high

ranking government position. On the other hand, students that tick a few boxes incorrectly are

destined to attend a lesser university and be locked out of the upper tiers of Korean society

(The Economist, 2011).

To determine if a positive relationship exists between the difference in two education

systems and economic inequality, we had to first define and identify other countries

following or having similar elements to each education system. While South Korea’s

education system is markedly easier to identify with other countries (e.g. Singapore, Hong

Kong, USA), Finland’s education system posed a much larger problem as it was undeniably

unique. We considered using geographical proximity to Finland and the ripple effect to

include other countries that would have adopted Finland’s education system but there was no

solid evidence present to say with certainty that those countries (e.g. Belarus, Estonia,

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Norway) adopted elements of Finnish education. As a result, we placed Finland in a group of

its own.

To conduct sufficient testing however, we have decided to include countries whose

education system contradicts the South Korean education system (i.e. encourages equality

and participation of the majority). However, this does not imply that they are similar to the

Finnish education system as most have exams. For example, we have selected Sweden as one

of the countries due to its ongoing attempt at widening participation in higher education by

abolishing fees, having simple rules of admission and acceptance of ethnic minorities and

low-income groups. Another example would be Denmark, as it has statistics similar to

Finland; well-paid teachers, high GDP allocation, high participation rates in education and

little to no private entities being operated within the state (OECD 2015). More importantly, it

is also an advocate of equality among students regardless of social status and gender.

The second component of this would be economic inequality and for this measure, we

used the GINI index to compare and contrast inequality in the selected countries. One

challenge that stood in the way was the figures itself that were provided in databases. Several

countries’ GINI index for certain years was missing and others had entire periods blank. To

overcome this obstacle, our team has decided to take the average GINI index from year 2000

to 2014, and utilize both the CIA database and the World Bank database for accuracy

purposes. One concern we had at first was related to the objectivity of the average figure,

given that having huge time skips might result in larger than imagined deviations in figures.

However, after looking through the selected countries GINI index, we have concluded that

most deviations were negligible.

Should a positive relationship be found, we then looked into the school rankings of

countries with high GINI to see if there are any variances in elementary and secondary school

rankings in the past few years. Intuitively, a low variance in rankings would indicate that

there might be existing differing educational opportunities (or career opportunities).

On the other hand, a high variance in rankings might indicate that schools are ranked

differently and that other aspects of school life have been taken into account; or there might

not even be a ranking at all and that all schools before university are deemed to be equal

amongst their peers, such as in the case of Finland. For this part, most of the research is based

on information from the local government’s website, as well as personal experiences; given

that our group has members from Singapore and South Korea (grades-oriented culture). Thus,

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research in this aspect, while objectively based on information from a government source, is

still debatable as there is the human aspect of it. However, most of the information derived

from personal experience can also be found and sourced from interviews provided online,

thus, it is safe to say that our local perspectives of academia in our respective countries are

well within general expectations.

Moving on, we also started to look at the number of notable alumni each of these

schools had. In this case, we decided to focus and elaborate on Singapore as the data provided

was comprehensive enough to draw conclusions from. With regards to this, the term

“notable” seems to incorporate people that are well-known; locally or globally, regardless of

their impact in society. For that matter, we only counted people who presently hold, or held

governmental positions in the past; or are integral to Singapore's development or well-known

globally. We excluded winners of beauty contests, singing contests, scholarships and local

actors and actresses. Scholarship winners, while debatable, had no positive contribution in

society until they graduated and came from various and very diverse aspects (sports, music,

drama, military, academics etc.) and thus, we refrained from including them. However, even

if included, it would not vary statistically as each school had around 4-6 of these alumni on

average.

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4) Results

To begin, let us start by looking at the global perspective and relationship between a

country’s wealth and literacy rates. As stated before, this is not meant to be conclusive in the

sense that higher literacy rates would significantly improve a country’s GDP. Rather, it is just

presented as a starting point for our group; to check if there is indeed, a positive relationship

between GDP per capita and literacy rates amongst countries.

As seen in Table 4.1, which was provided in the International Literacy Data report by

the UNESCO Institute for Statistics (2013), there is an existing positive correlation between

adult literacy rates and GDP per capita:

Table 4.1

While this might seem too broad a conclusion due to the number of components found

within GDP, this acts as a fundamental step forward for us; showing that there is indeed,

economic inequality amongst countries on average and that literacy rates have an impact on

it, whether significant or not. Moving forward, we then plotted a chart based off the economic

inequality in each country using the GINI index and countries with similar education systems

to South Korea and Finland. This is shown in Table 4.2:

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Table 4.2

Singap

oreChina

India

United St

ates

Korea, R

ep.

Hong Kong S

AR0.0

10.0

20.0

30.0

40.0

50.0

60.0

Countries with similar education systems to Korea

Countries with similar educa-tion systems to Korea

From Table 4.2, it seems that there is a positive correlation between education

systems and economic inequality. On the left, countries that displayed similar elements of

education to South Korea can be seen to have a higher GINI figure than compared with those

on the right. One interesting point to note would be that South Korea’s GINI figure is actually

comparatively lower than other countries with a similar education system. Our group has

theorized that it could possibly be due to the ‘globalization’ factor involved. Logically

speaking, countries that are more connected with other countries; possess strategic locations

and capabilities; have a rapidly growing economy should result in a higher GINI index in

relation to other countries as they become the focal point of investment from overseas

companies wanting to invest within the region. Demand for skilled local labour would then

cause income inequality to rise as talented, ‘global’ employees (people who know multiple

languages, widely travelled etc.) become highly sought after. As such, as a country becomes

more globalized, academic curriculums are also modified to adapt to changing circumstances,

which often results in harder tests and higher standards (as seen in countries with South

Korea’s education system) to promote investment inflows into the country for its highly

skilled workforce. In sum, education is key for these countries, where the nurturing of the

brightest talents is crucial for the country’s growth.

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Swed

en

Finlan

d

Denmark

0.0

10.0

20.0

30.0

40.0

50.0

60.0

Countries with similar education systems to Finland

Countries with similar educa-tion systems to Finland

Now that we have established that economic inequality is related to the education

structure, we also probed deeper and investigated if there were any repercussions in following

either structure. As stated previously, we decided to look into Singapore due to its heavy

emphasis on meritocracy. Do note however, that the data obtained was from year 1999 to

2002, as specific rankings for schools were then abolished in 2004 and replaced by banding;

the grouping of schools with certain grade requirements together.

Table 4.3Secondary Schools Ranking Average GCE O Level Grades

1999 2000 2001 2002 1999 2000 2001 2002Top 20 in 1999Raffles Institution 1 1 1 1 7.9 7.9 7.8 7.8Raffles Girls Secondary 2 2 2 2 8.2 8.3 8.6 8.3The Chinese High 3 4 3 4 8.9 9 8.6 8.5Nanyang Girls High 4 3 4 3 9 8.6 9.1 8.4Dunman High 5 5 5 5 9.2 9.6 9.4 10.1CHIJ St. Nicholas High 6 7 8 7 10.4 10.1 10.9 10.9River Valley High 7 6 6 6 10.6 10 10.6 10.2Singapore Chinese Girls 8 8 10 8 10.9 10.9 11.4 11.4Anderson Secondary 9 11 13 13 11.5 12 11.6 12.4Anglo-Chinese (Independent) 10 10 9 9 11.5 11.5 11 11.5Methodist Girls' Secondary 11 8 7 11 11.7 10.9 10.7 11.8Zhonghua Sec 12 19 15 23 11.8 13.2 11.9 14.4Victoria 13 12 19 17 12 12.2 13 13.2Anglican High 14 21 20 18 12.4 13.3 13.1 13.4St. Joseph's Institution 15 16 16 14 12.7 12.8 12.3 12.6Chung Cheng High (Main) 16 23 21 21 12.8 13.8 13.2 13.8Temasek Sec 17 24 23 30 12.8 13.9 13.5 15.6Cedar Girls' Sec 18 12 12 10 12.9 12.2 11.5 11.7Tanjong Katong Girls' 19 14 10 12 13 12.4 11.4 12.2Crescent Girls' 20 17 18 16 13.1 13.1 12.8 13.1

As seen in Table 4.3 above, there has been little to no change among the top 8 schools

in Singapore from year 1999 to 2002. Based on the following figures, it seems safe to assume

that there is some existing form of differing educational opportunities, given that rankings

have differed much more when we look at the other 12 schools while students from the other

top 8 schools have consistently produced excellent grades. Moreover, as seen in Appendix 3,

grades, there has been little to no fluctuation in terms of admission grades amongst the

schools that have seen little variation in the rankings. Not only does this highlight the

importance and significance of the rankings for parents and students, it also implies differing

educational opportunities as students with excellent grades are always aiming for the same

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schools. However, Singapore is not the only exception in this respect; as seen in Table 4.4

below:

Table 4.4Seoul National University

Years To be admitted Number of applicants

2015 2,531 19,0462014 2,816 19,9902013 1,744 17,7382012 2,496 20,1372011 1,884 12,468

Korea University

Years To be admitted Number of applicants

2015 2,986 68,7832014 2,961 63,2802013 2,881 71,7432012 2,666 83,9062011 2,646 78,645

Yonsei University

Years To be admitted Number of applicants

2015 2,585 45,1562014 2,637 45,6852013 1,154 30,6692012 1,154 32,3922011 1,150 31,863

The table above displays the admission results for “SKY”, an abbreviation for the top

3 Korean universities, made up of the first letter of each university. As seen from the table,

acceptance rates are very low; and admission numbers vary depending on the demand for

each university. On the whole, however, SKY universities have experienced an increase in

number of applicants over the past few years, which also resulted in an increase in admission

numbers. However, the average grade for admission has largely remained unchanged;

students entering SKY have an average grade of 1.x, the equivalent of being within the top

10% in the National University Entrance Examination. Moreover, an interesting point to note

is that SKY universities have increased their admissions rate rather than reducing it further as

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demand increases. One plausible explanation for this might be that students within the top

10% category have scored very similar grades to one another, which forces the universities to

admit all of them due to the lack of other aspects of differentiation.

On the other hand, South Koreans who do manage to get in are guaranteed a bright

future; 38% of CEO's of top 100 Korean companies, 93% of judges in Supreme Court, 60%

of government ministers and about half of the members in National Assembly were Seoul

National University alumni in 2012 (Times Higher Education, 2013). This would adequately

explain the demand seen by these universities.

With that in mind, we conducted a search into the alumni list of Singapore’s top

schools and took note of the number of notable alumni each school had as shown in Table 4.4

below:

Table 4.5

SchoolRanking in 2002

Notable Alumni

Raffles Institution 1 40+Raffles Girls Secondary 2 11Nanyang Girls High 3 10The Chinese High 4 30+Dunman High 5 6River Valley High 6 5CHIJ St. Nicholas High 7 2Singapore Chinese Girls 8 7Anglo-Chinese (Independent) 9 30+Cedar Girls' Sec 10 1Methodist Girls' Secondary 11 5Tanjong Katong Girls' 12 3Anderson Secondary 13 1St. Joseph's Institution 14 20+Crescent Girls' 16 1Victoria 17 20+Anglican High 18 0Chung Cheng High (Main) 21 1Zhonghua Sec 23 0Temasek Sec 30 0

As seen from the table, one might say that with the exception of a couple of schools,

many notable alumni; including government officials, founders and CEOs of internationally

known corporations and government organizations are seen to be heavily weighted within the

top 10 schools of Singapore. This all the more lends weight to the argument that differing

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educational opportunities exist, given that parents are attracted by the “star-power” of such

individuals and are more than willing to send their kids to these schools. One good example

would be Anglo-Chinese (Independent), which was ranked ninth in year 2002. Due to its

huge number of notable alumni; the majority of which being in the active political scene of

Singapore, average admission grades have been steadily rising from year 2000 to 2003,

signifying the influence notable alumni have on parents’ and students’ decisions

Additionally, while South Korea has only gated education in the universal National

University Entrance Examination, Singapore has 3 such exams; the Primary Six Leaving

Examination, the GCE O-Levels, and the GCE A-Levels, with the PSLE usually defining a

child’s future as an exceptional grade would send the child on a through-train to university in

one of the best schools. This is not to say that students from other schools are unable to get

admitted at any of the top schools if they are not affiliated, however, it is extremely difficult,

given the different environment the student is in and the elitism culture within the top schools

that causes affiliated students to look down on students from non-affiliated ones.

In sum, it seems that countries having similar education systems to that of South

Korea have a tendency to reserve the best educational opportunities for the social and

meritocratic elite, while barely maintaining a minimum standard of competency for the

majority that satisfies global standards. On the other hand, Finland’s education system is

meant to target and support weaker and underprivileged students, providing them with the

assistance needed to interact with their peers.

While this marks the conclusion of our research, we would like to add that differing

educational opportunities come from other sources as well. One of which would was

mentioned here, but was not the focal point of our research, would be the globalization factor.

5) Your Recommendations to the Company/ies Selected

Thus far, we’ve concluded that advances in technology increase the need and

therefore demand for high-skilled workers. We’ve also recognized that the “creation” of

high-skilled workers and advances in technology are both fueled by educational institutions.

Having identified educational institutions as a stimulus to both of these factors, and

having observed that economic inequality is in part a play of the supply and demand of high-

skill workers, we realized that by increasing the supply of skilled workers, the bargaining

power of said workers to negotiate higher wages could be in part mitigated.

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In our efforts to identify how educational institutions affect the supply of high-skilled

workers, we turned the focus of this paper on educational systems and their emphasis on

either equality, or efficiency (meritocracy). What we found is that countries with schooling

focused on a highly productive yet smaller group of individuals experienced higher average

GINI coefficients; meanwhile, countries with schooling focused on equal educational

opportunities among students of differing educational capabilities experienced lower average

GINI coefficients. As a result, it’s safe to say that countries with schooling focused on

providing more students with a higher level of education versus countries focusing a

disproportionate amount of resources on educating a lesser, more “capable” group of

students, experience lower economic inequality.

As is such, we now turn to ask ourselves what, if anything, can organizations do to

reduce economic inequality?

Although this paper focuses on educational institutions, we believe we can broaden

the definition of “organizations” for the purpose of this paper to not just schools, but also

government, as well as non-governmental organizations including for-profits companies. In

other words, schools are not the only actor that can play a role in enhancing external

environmental factors, which as Sacerdote identified; contribute to 56% of the variance in

family incomes.

Before delving into the myriad of things organizations as defined above can do, there

is a very important, eye opening characteristic of economic inequality that must first be

addressed. Joseph Stiglitz argues in The Price of Inequality that, “‘market democracy is

incompatible with extreme inequality’. He contends that, ‘the market is at risk because

extreme differences of power make a mockery of the voluntary nature of market

transactions’.” (Allen Lane, 2012) Likewise, the International Monetary Fund (IMF) found

that inequality reduces overall economy growth as well as challenges basic democratic

principles and fairness. (David, Berg, Tsangarides, 2014) While the preceding observation

concerns a slightly more daunting outcome, both point to the same thing; economic inequality

when drastic enough results in mockeries of the voluntary nature of labor transactions until

one day the disadvantaged refuse to transact. Put more simply, economic inequality if not

curbed could be the impetus for a revolution, and at the least extreme market inefficiencies

bad for both laborers and companies. Fortunately, the market has not – yet – reached such a

mockery, which could look something like the overthrowing of wealthy capitalists that Karl

17

Marx theorized in his “March of History”. (A Failed Vision of History, 2003) In order to

maintain the voluntary nature of market transactions, we’ve compiled a list of

recommendations for different organizations, or “Actors”, that we believe can help to

mitigate economic inequality as a result of Skill-Biased Technological Change (SBTC).

(Violante, NYU)

5.1) What can for-profit companies do?

Why do for-profits have a role in reducing economic inequality? Well, for-profits tend

to comprise the largest organizations in the world. These would be the Coca-Cola’s of the

world, the McDonald’s, Johnson & Johnson, Virgin, etc. The rising size of firms has been

identified as a causal factor of economic inequality. Studies show that, “In America, for

instance, the number of workers employed by the country’s 100 biggest firms rose by 53%

between 1986 and 2010; in Britain the equivalent figure is 43.5%” (The Bigger the Less Fair,

2015), therefore this section will focus on what large for-profits, that are typically also

Multinational Corporations (MNCs), can do.

We believe that for the majority of these global firms, recommending they implement

an operational change could be far-fetched. These companies have achieved the size and

economies of scale they possess thus far without drastic changes to their operational model

from third-party sources such as ourselves; therefore we propose something more familiar to

global companies, namely Corporate Social Responsibility (CSR) initiatives. CSR is defined

as a, “Corporate initiative to assess and take responsibility for the company's effects on the

environment and impact on social welfare. The term generally applies to company efforts that

go beyond what may be required by regulators or environmental protection groups.”

(Investopedia, 2007) This approach to socially conscionable actions on behalf of companies

has actually proven to increase shareholder value, more specifically the value of stocks,

especially in the three days following the publishing of a CSR initiative as seen in the

following table:

18

Considering that companies can increase their stock value on average by 1.46% and

thus shareholder value with a CSR initiative, we believe this shared-value proposition makes

for a feasible attempt at getting a program going despite the typical bureaucratic nature of

these companies.

In light of wage-premiums for the skilled labor force as a result of favorable

educational opportunities during youth, we propose a company such as Apple or Microsoft

adopts a school or multiple schools. This could work as follows:

1) Apple/Microsoft identifies low-income underprivileged schools with a lack of

adequate computer technology.

2) Donate (for example sake) 20 computers to these schools. For the sake of this

example we’ll assume 10 schools. (200 computers in total)

For the students at these beneficiary schools, these computers will help to catalyze

their skill-based technical adequacy, making them more competitive in the marketplace and

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over-time increasing the supply of high-skilled workers, in-turn reducing the wage premium

high-skilled workers can negotiate. Not only does this reduce economic inequality as defined

earlier, but it also adds value to the firm both immediately (through stock-price increase) and

in the future (through an increase in profit). The example continues as follows…

Let’s assume that at each of the 10 high schools it donates a lab to, the company

estimates there is a 50% probability of generating additional sales of 100 computers (at the

average price) and a 50% chance of generating no additional sales from the school. For this

example, we will use the following financial assumptions of the company:

Given the above data and the probabilities aforementioned, we can calculate the effect

of this proposal on Apple/Microsoft’s pre-tax profits with 3 steps.

1) We first calculate the relevant cost.

200 xUnit VariableCost=200 x (50+50+36 )=$ 27,200

; Where 50 is Direct Materials, 50 is Direct Labor, 36 is Variable Mfg. Overhead

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2) We then calculate the expected increase in revenue.

50 % x 220 x 100 x 10=$ 110,000

; Where 220 is Price Per Unit, 100 is Donated Computers, 10 is Number of Schools

3) We then calculate the cost of producing the computers.

50 % x 100 x 10x 136=$ 68,000

; Where 100 is Donated Computers, 10 is Number of Schools, 136 is Total Variable Cost

4) We then observe the change in Net Income (profit).

$ 110,000−$ 27,200−$ 68,000=$14,800

In conclusion, we see that donating computers to schools lacking in such technology

can not only serve to bridge the wage-premium gap of high-skilled workers to unskilled

workers, but it can also increase the firm’s pre-tax profit by $14,800 (assuming such

numbers) in the long-run and generate an increase in share price in the short-run. This

example was taken from NYU Professor Hao Xue’s Managerial Accounting course and

adapted for the following recommendation.

5.2) What can Government do?

Governments play a large role in the creation, facilitation and implementation of laws

and programs alike. As is such, we’ve identified some proposed legislations and ideas

governments can put forth to reduce the educational opportunity gaps, specifically related to

disparities amongst students’ access to technology that enable them to gain technological

competency.

In the context of economic inequality, the Occupy movement swept the U.S. media

shedding light on the 99% and 1% disparities. One of their proposed solutions to economic

inequality is to increase the minimum wage. Contrary to popular belief, increasing the

minimum wage makes a mockery of the voluntary nature of market transactions because at a

point, companies will stop employing young people that normally take low-paying jobs

because to the company, these employees are no longer worth the new minimum wage cost of

labor. As U.S. News describes it, “The harm done by minimum wage increases gets

compounded for young workers because it prevents them from gaining experience, thus

21

increasing their chances of being unemployed in the future.” The chart below shows how an

increase in minimum wage policy causes a reduction in youth employment.

As is so, we recommend that governments do not impose a more stringent minimum

wage policy as this can lead to a decrease in the amount of youth employed, resulting in less

skills-training and a larger group of unskilled labor leaving the demand for high-skilled labor

high. (Slavov & Aspen, U.S. News) Looking forward we’ve identified two possible actions

the government can take to increase the amount of skilled labor in the workforce.

One such proposal is for the U.S. government to utilize the “National Report Card” as

a benchmark with which it can measure its level of spending on a per-student basis by state.

“The National Report Card (NRC) examines each state's level of commitment to equal

educational opportunity, regardless of a student's background, family income, or where she or

he attends school. Providing fair school funding -- at a sufficient level with additional funds

to meet needs generated by poverty -- is crucial if all students are to be afforded the

opportunity to learn and be successful. The NRC evaluates all 50 states and the District of

Columbia on four separate, but interrelated, funding "fairness indicators" -- funding level,

distribution, state fiscal effort, and coverage.” From here the government can work to

equalize its spending per-student across all 50 states and the District of Columbia to ensure

that each student is receiving similar educational opportunities, in-turn reducing the GINI

22

coefficient of the U.S. by acting more equitably. An example of a nation with a low GINI

coefficient as a result of such an equitable system would be the aforementioned Finland.

Another such proposal is to impose, and in some cases increase inheritance tax.

Inheritance tax is defined as, “a percentage of the value of a decedent's estate transferred to

beneficiaries by will, heirs by intestacy and transferees by operation of law. The tax rate

varies depending on the relationship of the heir to the decedent.” (PA.gov) By imposing an

inheritance tax, or increasing it in societies where one is already in place, wealth captured in

the top-percentiles can be de-cumulated and redistributed to educational institutions, in

particular to early education where most economic inequality can be mitigated early on.

(Powell, Haas Institute)

23

As seen from the above info-graphic, the success of investments in early childhood

education can be measured be observing reductions in dropouts, and higher mean test scores.

We believe that there are other metrics with which we can measure the effectiveness

of investments in early childhood education, however these are most appropriately looked at

in the context of what schools can do to help mitigate economic inequality. First though, it’s

important to identify how earnings from inheritance tax should be used. The Haas Institute

has identified two successful organizations that invest in early childhood education, namely

Head Start and Universal Pre-K. In lieu of the credibility of the Haas Institute, we too

recommend that proceeds from the increased/imposed inheritance tax be funneled into these

two organizations working to support youth educational opportunities.

5.3) What can Schools do?

It should come as no surprise that the very schools, in which economic inequality is

exacerbated due to a lack of proper resources, could play some form of an active role in

reducing this trend. Thus, we only saw it appropriate to advise schools on some methods

through which they could play an active role; methods that do not need additional resources;

and methods that are cognoscente of the fact that educators in low-income/underprivileged

schools are not necessarily top-notch instructors with great expertise. These

recommendations will be provided with data metrics with which they can be measured as

well as used to measure the effectiveness of the distribution of inheritance tax proceeds and

its effect on subject schools.

Johannes Haushofer describes his findings of the existing relationship between

poverty, time-discounting and risk-taking in the article “On the psychology of poverty”. What

he finds is that “Poorer households were more likely to choose smaller and earlier monetary

rewards over larger, delayed ones.” (Hausofer, E. 2014) To sum up Haushofer’s findings, the

state of poverty is enough to reduce the body’s production of the hormone cortisol, which is

essential to maintaining a state of mental homeostasis or in other words managing stress-

levels. Because the state of poverty is enough to cause a mental predisposition to stress, this

in turn leads to time-discounting and reduced risk-taking actions inherent to entrepreneurial

activity.

We can see the numerous ways in which this permeates the trials of the impoverished

teenager working to finish high school. The National Center for Education Statistics found in

a recent study that, “Low-income students fail to graduate at five times the rate of middle-

24

income families and six times that of higher-income youth.” (U.S. Department of Education)

Similarly, 12% of students in a recent report by Harris Interactive and Everest College

indicated students dropped out of school to work and support their families. (Sheehy, U.S.

News) You may be wondering, ‘well how does this contribute to economic inequality?’

According to a recent earnings report by the Bureau of Labor Statistics, “Students without a

high school diploma also earn about 30 percent less than their peers who stayed in school.”

(U.S. BLS, 2015) From these eye opening statistics, it’s evident that the stress of being

impoverished causes risk-averse actions such as dropping out to support immediate family

needs. This makes perfect sense. The risk of defaulting on the mortgage and thus losing their

home and ending up in a homeless shelter or even worse on the street is more imminent a

threat than that of not having the wherewithal to sustain themselves in the future (i.e.

obtaining a college degree). This is the discouraging, perpetual effect of time-discounting

actions.

To mitigate these disheartening factors, we propose that schools introduce a number

of life-skill programs to provide students with the wherewithal to combat becoming a dismal

statistic. The life-skills we propose teaching are as follows:

1) Teach interview skills.

By teaching students interview skills, this can help them secure part-time jobs around

school that enable them to produce income for family needs without dropping out of

school to do so.

2) Teach time management skills.

Because working a part-time job around school is time consuming, it’s pivotal that

teachers arm their students with the proper knowledge of how to prioritize their

school work and outside work to make the most of their situations. A renowned time-

management tool is Stephen Covey’s Time-Management Matrix as seen below:

25

(U.S. Geological Survey)

3) Teach personal finance skills.

Working a part-time job is great if the necessary money for family needs is actually

making it to the family. Because students will be in part supporting themselves and

the unmet financial needs of their households, it’s crucial they have a basic

understanding of personal-finances so they can make a disciplinary budget. A

renowned program for personal-finance is Dave Ramsey’s Foundations in Personal

Finance: High School Edition. (Daveramsey.com) This curriculum in personal-

finance needs no administrator, rather it is a series of educational yet entertaining

videos that can be watched on a computer, played in an auditorium or physically

distributed.

Through the culmination of these efforts, there should be an observed impact on the

reduction of the status-quo time-discounting and risk-averse tendencies plaguing low-income

schools. This impact should be measure and we anticipate it can be measured through the use

of the following metrics:

1) Reduced drop out rate.

2) Higher class attendance.

3) Higher levels of cortisol (if test subjects agree to testing for some monetary gain).

4) Higher average test scores.

26

These tactics can be used in a pilot school and if there is an observed impact as we

anticipate there should be, it can be implemented in low-income schools anywhere in the

world, including Singapore.

6) What Should Business Schools’ Contribution Be?

Business students at top universities will go on to fill pivotal roles in society such as

leading some of the largest, most influential companies in the world and directing

government policy that affects whole nations. The reach top-business students can and tend to

have is tremendous and as such they should be educated with the capability to make informed

decisions that can either reduce or increase economic inequality in our increasingly

interconnected global community. Together, we’ve indentified some recommendations we

believe business school administrators would be wise to consider. Namely, they are to

provide:

1) Good grounding in ethics.

“The proper role of ethical reasoning is to highlight acts of two kinds: those that

enhance the well being of others—that warrant our praise—and those that harm or diminish

the well-being of others—and thus warrant our criticism.” (Elder & Paul, 2011) Having such

a grounding provides business students with a formal lense through which they can evaluate

the social implications of the big decisions they will often go on to make for years to come.

2) Socially grounded missions.

We believe it is the responsibility of business schools to appropriate some of the

monetary resources students provide the school through large tuition payments to socially

grounded missions. This can include but is not limited to charitable events, volunteering time

at underpriveleged schools to either teach or provide necessary physical labor, or even

volunteering with social entrepreneurs.

3) Analytical skills on how to measure social impact using data metrics.

As business students are dispersed amongst companies heavily focused on their

bottom lines (i.e. profit), it’s important that just as students learn analytical skills in

operations, finance, personnel management and the likes, they also learn how to analyze and

measure the social implications of their executive decisions. This provides a data-driven

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framework with which to develop concrete evidence of social impact to complement the

ethical framework through which to perceive the implications of decisions.

4) Policy Classes.

Not every business student ends up working at a financial firm, in fact a considerable

number of students study economics and end up on the policy side of things. Taking this into

consideration, we feel its appropriate for business schools to provide some sort of

understanding of the process of policy making. This process can often be slow and

bureaucratic, therefore it would be especially useful for students to understand the lense of

policy-makers when proposing legislation or reform in order to help expedite the process of

benefical social policies.

5) Balancing Equity and Efficiency.

The observation of equitable wage distribution and the efficiency of workers in the

workplace have often been viewed as polarities. We understand the implications of this, and

believe that it is thus necessary to observe companies and/or case studies thereof highlighting

how striking a balance between equitable wages and efficient production of labor can be

more rewarding than either end of the spectrum.

Ultimately, we believe that by following the aforementioned recommendations for

governments, companies, schools and business schools, there is a fighting chance at reducing

some of the factors that lead to the wage premium for high-skilled workers in the labor

market. In doing so, we can see the slow return of capital to the demand side of the economy,

and the equitable dispersion of wages that should in turn reduce the perpetual effects of

income discrepencies compouding wealth accumulation in the top percentiles of the working

population. We hope that this paper has helped you grasp a clear, if not enhanced

understanding of economic inequality, the dynamic nature of the term, contributing factors,

and how they can be in-part mitigated through a careful, multi-faceted approach involving

multiple “actors” and their duties to societal equity.

28

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Appendix A

Data SourceWORLD BANK GINI INDEX (%)

Country Name 2000 2002 2004 2005 2007 2008 2009 2010 2011

Countries with similar education systems to KoreaSingaporeChina 42.6 42.48 42.63 42.06 37.0India 33.38 33.9 33.6United States 40.2 40.57 41.64 41.12Korea, Rep.Hong Kong SAR

Countries with similar education systems to FinlandNorway 27.6 30.17 26.48 26.83Sweden 27.5 26.08Finland 27.2 28.27 28.2 27.79Denmark 24.1 24.6 25.98 26.88

Data Source CIA GINI AVERAGE GINI

Country Name % Year %

Countries with similar education systems to KoreaSingapore 46.3 2013 46.3China 47.3 2013 42.4India 36.8 2004 34.4United States 45 2007 41.7Korea, Rep. 31.3 2011 31.3Hong Kong SAR 53.7 2011 53.7

Countries with similar education systems to FinlandNorway 25 2008 27.2Sweden 23 2005 25.5Finland 26.8 2008 27.7Denmark 24.8 2011 24.9

32

Appendix B

Secondary SchoolsAverage Entrance Grades* ( From

Elementary School)2000 2001 2002 2003

Top 20 in 1999Raffles Institution 261-282 261-286 260-281 262-285Raffles Girls Secondary 263-285 266-288 251-281 264-282The Chinese High 257-283 261-287 258-282 259-283Nanyang Girls High 259-277 262-286 260-286 260-281Dunman High 255-275 259-288 257-281 258-284CHIJ St. Nicholas High 250-276 258-286 256-276 255-276River Valley High 252-276 255-281 253-278 251-275Singapore Chinese Girls 252-273 256-277 249-275 254-274Anderson Secondary 247-271 253-275 249-269 250-270Anglo-Chinese (Independent) 245-267 247-270 248-275 250-279Methodist Girls' Secondary 249-268 256-274 255-269 257-280Zhonghua Sec 234-261 241-256 236-259 238-263Victoria 244-264 243-280 241-263 242-266Anglican High 250-272 251-277 243-252 244-252St. Joseph's Institution 236-264 239-269 239-269 241-264Chung Cheng High (Main) 250-262 241-252 235-252 236-252Temasek Sec 237-264 241-276 237-267 238-263Cedar Girls' Sec 239-270 241-273 236-261 241-264Tanjong Katong Girls' 237-271 231-270 231-267 236-268Crescent Girls' 232-267 236-271 234-274 236-261

33