A Regional Analysis of Capabilities and Education in Turkey · education in Turkey as well as of recent developments in the expansion and distribution of educational attainment in

Akkoyunlu-Wigley & Wigley

1

A Regional Analysis of Capabilities and Education in Turkey

Arzu Akkoyunlu-Wigley Department of Economics

Hacettepe University arzus [at] hacettepe.edu.tr

Simon Wigley Department of Philosophy

Bilkent University wigley [at] bilkent.edu.tr

August 31, 2009

Abstract

The purpose of this paper is to assess education in Turkey based on capabilities approach pioneered by Amartya Sen (1999) and, more recently, Martha Nussbaum (2000). In keeping with capabilities approach, we argue that recognizes that educational attainment enables the acquisition of a set of basic cognitive functionings (e.g. problem solving, numeracy, literacy, etc) that are vital to realization of other human functionings (e.g. avoiding preventable illness and premature death). We contend that the value of education should be measured in terms of health outcomes because that measure directly or indirectly captures a number of other fundamental human functionings. With this purpose, we presented an overview of education in Turkey as well as of recent developments in the expansion and distribution of educational attainment in Turkey. We test claim that education has an income-independent effect on health by using a panel data set composed of economic, educational and mortality data for 64 geographic regions in Turkey for the years 1980, 1985, 1990 and 2000. We find that education (as indicated by secondary enrollment ratio) has a significant effect on health functionings (indicated by infant mortality). What we have found in the Turkish case is that the ability to avoid preventable illness and premature mortality constitutes an important basis for evaluating the basic cognitive functionings that are enabled by the education system.

Key words: capabilities approach, education in Turkey, regional analysis of education, effect of education on health

Dr. Arzu Akkoyunlu Wigley graduated from Middle East technical University (B.A, Economics). She

completed her M.A (Economics) at Middle East Technical University and Ph.D. (Economics) at Middle East

Technical University and her Ph.D. (Economics) at Hacettepe University. She was the recipient of a Jean

Monet Scholarship to carry out doctoral research at the London School of Economics. She is currently

working as an Associate Professor at the department of Economics, Hacettepe University. Her interest lies in

the following areas; regional economic integration, economics of European Union and education economics.

Her recent publications are; “Human Capabilities Versus Human Capital: Gauging the Value of Education in

Developing Countries”, Social Indicators Research, September 2006 (with Simon Wigley), “Effects of the

Customs Union with the European Union on the Market Structure and Pricing Behavior of the Turkish


2

Manufacturing Industry”, Applied Economics, 2006, (with Sevinç Mıhçı), “Basic Education and Capability

Development in Turkey” in Education in Turkey (New York/ Münster: Waxmann Publishing) (co-edited with

Arnd-Michael Nohl & Simon Wigley “The Impact of the Customas Union with the European Union on Turkey’s Economic Growth” (Argumenta Oeconomica, 1(22), 2009.

Dr. Simon Wigley graduated from the Department of Philosophy and Politics at the University of Otoga

(New Zealand). He completed his masters and doctoral degrees in political philosophy at the London School

of Economics and Political Science. Dr. Wigley is currently working as an assistant professor in the

Department of Philosophy at Bilkent University. His interest lies in the following areas: democratic theory,

distributive justice and well-being. Among his recent publications are Parliamentary Immunity in

Democratizing Countries: The Case of Turkey (Human Rights Quarterly(forthcoming), Automaticity,

Consciousness and Moral Responsibility (Political Philosophy, 2007) and Voluntary Losses and Wage Compensation (Politics, Philosophy and Economics, 2006)

Introduction

In this paper we assess education in Turkey based on the capabilities approach pioneered by

Amartya Sen (1999) and, more recently, Martha Nussbaum (2000). That approach explicitly

recognizes that education has a positive effect on human well-being independently of its effect on

income growth or poverty reduction. Educational attainment enables the acquisition of a set of basic

cognitive functionings (e.g. problem solving, numeracy, literacy, etc) that are vital to realization of

other human functionings (e.g. avoiding preventable illness and premature death). We contend that

the value of education should be measured in terms of health outcomes because that measure

directly or indirectly captures a number of other fundamental human functionings (Sen, 1998;

Wigley and Akkoyunlu-Wigley, 2006). Moreover, we contend that education has a positive effect on

health both because of its effect on resource accumulation and independently of its effect on

resource accumulation (e.g. being adequately nourished requires both food and an understanding of

one’s nutritional requirements). Hence, a resourcist metric of the value of education (e.g. growth in

GDP per capita, reduction in income poverty, income returns to education) will only partially

capture the beneficial effect of education on life quality (as measured by population health). In

Section 5 we present statistical evidence in support of that claim. We begin by presenting an


3

overview of education in Turkey present statistical evidence in support of that claim. We begin,

however, by presenting an overview of recent development in the expansion and distribution of

educational attainment in Turkey (Sections 1-2). We then turn to consider whether the expansion of

educational attainment has been accompanied by an improvement in learning outcomes as measure

by Programme for International Student Assessment (PISA) (Section 3). In subsequent section we

examine regional disparities in education, infant mortality and average income (Section 4). Finally,

we test claim that education has an income-independent effect on health by using a panel data set

composed of economic, educational and mortality data for 64 geographic regions in Turkey for the

years 1980, 1985, 1990 and 2000 (Section 5).

1. Cross-country Overview

A striking feature of development in Turkey is that it compares well amongst low and middle

income countries in terms of income poverty and income distribution, but not in terms of education

and health outcomes (Akkoyunlu-Wigley and Wigley, 2008, pp. 276-281).1 The divergence between

development as measured in terms of resource growth and development as measured in terms of the

expansion of educational attainment is apparent from Figures 1 and 2. In Figure 1 we take average

calorie intake as our indicator of resource poverty. Amongst developing countries increases in

average calorie consumption are more likely to benefit the poor because of the biological limit on

how much a person can consume and the difficulty of hoarding food.2

1 There is a similar discrepancy with respect to economic and human development in Turkey. Although, Turkey is classified as the seventeenth most industrialized country in the world, it ranked only seventy-sixth out of 179 countries on the UNDP Human Development Index in 2008 (a measure that combines life expectancy, literacy, educational attainment and average income) (UNDP, 2008). 2 See Blaydes and Kayser (2007) and Wigley and Akkoyunlu-Wigley (2009) for empirical evidence in support of that claim.


4

*Data source: Food and Agriculture Organization, ‘Food Balance Sheets,’ data extracted from http://faostat.fao.org/.

For Figure 2 we take the proportion of the female population aged 15 and over with no schooling as

our measure of the shortfall in educational attainment. For both graphs we included representative

low and middle-income countries with a similar proportion of the population aged 14 and below

between the years 1960 and 2000.


5

* Data source: Barro and Lee (2000), 'International Data on Educational Attainment: Updates and Implications,' (CID Working Paper No. 42, April 2000), Retrieved from: http://www.cid.harvard.edu/ciddata/ciddata.html. Note: data not available for China during the years 1960-1975.

What we observe is that even though Turkey has a significantly higher level of calorie consumption

throughout the last four decades of the twentieth century (Figure 1), only China had a (slightly)

higher proportion of non-schooled females in 2000 (Figure 2). The reduction in the share of the

female population without schooling in Turkey between 1960 and 2000 was significant. However,

many of the other representative countries were able to achieve similar results even though they are

poorer with respect to per capita calorie intake. Thus, according to a resource-based metric of

deprivation (calorie intake) Turkey compares favorably. However, according a capabilities-based

metric of deprivation (female schooling) the opposite is the case. Thus, a development strategy that

emphasizes the level of resource deprivation may conclude that there is not an urgent need to rectify

the shortfall in female education in Turkey. What we argue is that public policy should be

determined by assessing the impact of goods such as food holdings and educational attainment on a

generic measurement of human well-being, namely, ability to avoid preventable illness and

premature death.


6

2. Recent Changes in the Distribution of Educational Attainment in Turkey

Since the turn of the century there has been a significant improvement in the level and distribution

of educational attainment in Turkey. In 1997 compulsory education was extended from grade 5 to

grade 8 (ages 11 to 13). Five year primary schooling was combined with three year lower secondary

schooling to create an eight-year basic education cycle.

* Data source: Filmer (2009) 'Educational Attainment and Enrollment around the World,' (Washington, DC: World Bank). Data retrieved from http://iresearch.worldbank.org/edattain/

In order to examine the impact of that reform on attainment levels we refer to the Demographic

and Health Surveys for the years 1993, 1998 and 2003. Those surveys allow us to pinpoint the

proportion of 13 year olds currently enrolled in primary education and, therefore, to estimate the

proportion of children actually completing 8 years of schooling. Figure 3 shows the net enrolment

ratio for all 13 year olds, those from the richest 20% of the population, those from the poorest 40%

of the population, those who are male or female, as well as the ratio of female enrolment to male

enrolment. Across all six categories there was only a marginal improvement between the 1993 and

1998 surveys, but a dramatic improvement after the expansion of compulsory schooling.


7

Nevertheless, it remains the case that a significant proportion of those aged 13 were not completing

eight years of compulsory education in 2003 (approximately 9% of all 13 years olds, 18% of the

poorest 13 year olds and 15% of female 13 year olds).

3. The Distribution of Learning Outcomes in Turkey

We now turn to consider the quality of education that students receive if they do complete the eight

years of compulsory education. To evaluate learning outcomes in Turkey we refer to the Programme

for International Student Assessment (PISA) studies of 15-year-olds enrolled in formal education in

2003 and 2006 (OECD, 2004a; OECD, 2007b). The PISA studies show that socioeconomic

background has a significant impact on learning outcomes in Turkey. In 2003 Turkey recorded the

highest variance amongst the 40 participating countries in terms of between school variance in

mathematics proficiency. Moreover, most of that variation was explained by the socioeconomic

profile of schools (OECD, 2004a, pp. 162, 187-190). Similar results were recorded for the curricula

domain of science in 2006 (OECD, 2007b, pp.171, 187-189). In addition, a significant proportion of

15-year-olds in Turkey could not achieve more than the lowest proficiency level in the domains of

problem solving, mathematics, science and reading (OECD, 2004b, table 2.1; OECD, 2007b, tables

2.1a, 6.1a, 6.2a). In the domain of science, for example, 47 per cent of 15 year olds in 2006 could not

achieve above 1 on the 6 level proficiency scale. Moreover, 13 per cent could not even achieve level

1. According to the PISA criteria, students at level 1 “…have such a limited scientific knowledge

that it can only be applied to a few, familiar situations” (OECD, 2007b, p. 43).

One shortcoming of the PISA assessments is that they are based on the performance of 15-

year-olds that are enrolled in formal education. Thus, differential drop-out rates between countries

may significantly affect the results. Indeed Turkey has the lowest proportion of enrolled 15-year-olds

amongst those countries that participated in PISA 2003 and PISA 2006 (OECD 2004a, table A10.1;

OECD 2007b, table A3.1). Thus, it is very likely that the PISA assessments overstate the average

proficiency scores of those students in Turkey who have completed primary education and

understate the impact of socioeconomic status in Turkey (OECD, 2004a, p. 184; OECD, 2007b, p.

190).

4. Overview of Regional Disparities

In this and the following section we consider the effect of regional variations in educational

attainment on infant mortality in Turkey. The link between education (especially female education)


8

and the mortality rates of infants and children is well-established in the literature (see e.g. Sen, 1999,

pp. 195-198; Cutler, Deaton, Lleras-Muney, 2006). Elsewhere we have argued that the incidence of

morbidity and mortality represents a more appropriate metric of the value of education than (micro

or macro level) income growth (Wigley and Akkoyunlu-Wigley, 2006). There we presented cross-

country evidence in support of the claim that education (as indicated by average years of schooling)

has a positive and significant impact on health (as indicated by life expectancy) independently of the

impact it has by way of income growth. Thus, income returns to education represents an

impoverished way of measuring the impact of education on human well-being (interpreted as the

ability to avoid preventable illness and premature death).

This study differs in two key respects. Firstly, our statistical analysis is based on a panel of

Turkish regions rather than panel of developing countries. Secondly, we take infant mortality

(number of children who perish during the first year of life, per 1,000 live births), rather than life

expectancy as our indicator of population health. Infant mortality typically afflicts the poorest

segment of the population and it is usually due to causes which are comparatively easier to prevent

than the causes of adult mortality. Thus, we would expect that it is particularly sensitive to changes

in the education levels of the most disadvantaged members of society. By extension we would

expect that the poorest geographic regions are characterized by higher infant mortality rates. Indeed,

as we will now see, that pattern is clearly evident in the Turkish case.

When we consider the regional variation in infant mortality and adult literacy between 1980

and 2000 (Table 1) we can see that both those indicators have improved within each region over

time. Even so there remains considerable disparity between the various regions. Perhaps,

unsurprisingly, those disparities are in keeping with regional differences in average income

(represented here as the ratio of GDP per capita for each region divided by national GDP per capita

for each year).3

3 There is also significant discrepancy between male and female enrolment (at both the primary and secondary levels) within each of the poorer regions, as well as between female enrolment in those regions and female enrolment in the more prosperous western regions and the country as a whole (Akkoyunlu-Wigley and Wigley, 2008, p. 285).


9

Table 1: Regional Disparities in Infant Mortality, Education and Income

REGION YEAR

INFANT MORTALITY RATE

GDPPERCAPITA-RATIO

LITERACY RATE

MEDITERRENIAN 1980 112.00 0.99 67.46 1985 97.50 0.87 78.22 1990 59.67 0.95 81.00 2000 37.71 0.82 87.94 EASTERN ANATOLIA 1980 146.00 0.46 50.60 1985 132.64 0.44 63.28 1990 75.64 0.39 67.33 2000 52.77 0.33 78.36 SOUTH EAST 1980 128.00 0.50 42.08 1985 115.57 0.47 55.39 1990 71.14 0.63 59.80 2000 49.00 0.49 72.75 MARMARA 1980 113.40 1.38 76.91 1985 95.30 1.45 84.01 1990 60.50 1.40 86.22 2000 39.36 1.49 91.40 CENTRAL ANATOLIA 1980 139.40 0.81 68.70 1985 104.70 0.79 78.77 1990 69.10 0.77 82.09 2000 41.77 0.78 88.83 AGEAN 1980 123.50 1.05 71.43 1985 102.88 1.09 80.07 1990 65.50 1.05 82.82 2000 39.63 1.11 89.10 BLACK SEA 1980 135.33 0.72 62.98 1985 116.73 0.67 75.36 1990 69.47 0.67 78.46 2000 42.16 0.76 86.10

Source: State Institute of Statistics (2003)

Has the variation between regions decreased over time? To answer that question we

calculated the coefficient of variation for each indicator for 1980 and 2000 (Coefficient of variation

is calculated by dividing standard deviation with arithmetic mean).


10

Table 2: Coefficient of Variations of Regional Indicators

Coefficient of Variations INFANT MORTALITY 0.0931 0.1193 LITERACY RATE 0.1812 0.074 SECONDARY ENROLLMENT RATIO 0.1755 0.1986 GDP PER CAPITA 0.3593 0.4277

According to the coefficient of variations in Table 2, the largest regional disparity is in GDP

per capita. The change in the coefficient of variations captures the evolution of the magnitude of the

disparities for the two time periods. The change in the coefficient of variations from 1980 to 2000

shows that only the coefficient of variation for literacy rate has declined among the four variables.

In other words regional diversity only declined with respect to literacy rates, whilst increasing with

respect to GDP per capita, infant mortality and the secondary enrollment ratio.

5. Estimation of the Income-Independent Effect of Education on Health in Turkey

In order to examine the relationship between education and infant mortality we use a panel data set

composed of 64 Turkish cities for the years 1980, 1985, 1990 and 2000. The main data set is

obtained from State Institute of Statistics. The estimation equation is specifically designed to clarify

the effects of education on health (as indicated by infant mortality). The dependent variable

INFANT (Infant Mortality) is calculated as the ratio of infant mortality of each city in the national

overall infant mortality for each year. Rather than using the absolute amounts, we prefer to use the

difference of each city from overall average since it shows the proportionate deviation from the

national average. In terms of the explanatory variables we take the secondary school enrolment ratio

(EDU) as our indicator of education levels in each region. As with the infant mortality variable this

variable is also the ratio of secondary school enrolment ratio of each city in the overall average

secondary school enrolment ratio for each year. GDP per capita (GDP) is the other explanatory

variable introduced to analyze the effects income on infant mortality. The GDP variable is obtained


11

by dividing the GDP per capita of every city by national GDP per capita for every year. The last

explanatory variable introduced in our model is the unemployment rate (UNEMPLOYMENT) at

the city level. The main motivation for including this variable in our equation is that it is a good

proxy for poverty. This variable is also obtained by dividing each city’s unemployment rate to overall

unemployment rate. Accordingly following equation is estimated;

NTUNEMPLOYMEGDPEDUINFANT 3210 (1)

Table 3 shows the estimation results of the infant mortality equation for Turkey. The fixed-effect

specification of the panel data that we use helps to control for the possibility of unmeasured region-

specific factors. This is necessary given that regional education levels may be due to unmeasured

factors (e.g. proportion of parents that are tradition-bound) that are also correlated with mortality

rates.

Table 3: Estimation Results for Equation (1)

Variable Coefficient Std. Error t-Statistic Prob.

C 1.077269 0.032733 32.91063 0.0000

GDP -0.042127 0.024837 -1.696147 0.0915

EDU -0.087021 0.024121 -3.607698 0.0004

UNEMPLOYMENT 0.057216 0.018752 3.051282 0.0026

R2 0.995972

Adj.R2 0.994566

Durb.Wat. 2.081894

F-stat

708.1248

Note: Standard errors and t-statistics of coefficients are computed using White’s heteroscedasticity consistent variance-covariance estimator.


12

From Table 3, one can see that all of our explanatory variables for infant mortality are

statistically significant at the 1% level. EDU variable has a negative sign in line with the theoretical

expectations and indicates a negative relationship between education and infant mortality. In other

words, an increase in the education level which is measured by the secondary enrollment ratio

decreases the infant mortality in the Turkish case. We can conclude, therefore, that educational

functioning which is represented by the secondary school enrolment ratio (EDU) has a positive

effect on health functionings captured by infant mortality. As we expected our income variable

GDP per capita has a negative sign indicating that relatively high income cities have a low infant

mortality rate. As expected our indicator or income poverty (UNEMPLOYMENT) is negatively

associated with infant mortality. Taken together these results suggest that education has a reductive

effect on infant mortality independently of its effect on average income and poverty.

Conclusion Our analysis of geographic regions in Turkey provides further support for the claim that income

growth represents an impoverished metric of the value of education. The ability to avoid

preventable illness and premature mortality is crucial to human well-being (more precisely it

encapsulates heath functionings that are fundamental to leading a life worth living). Thus, it

constitutes an important basis for evaluating the basic cognitive functionings (problem solving,

numeracy, literacy etc) that are enabled by the education system. What we have found in the

Turkish case is that the impact of education on resource accumulation (i.e. average income and

poverty reduction) does not fully capture the full effect of education on health.

References

Akkoyunlu-Wigley, A. and S.D. Wigley,(2008) A. Basic Education and Capability Development in Turkey, in Education in Turkey, A.-M. Nohl, A. Akkoyunlu-Wigley, and S. Wigley, eds., Waxmann Publishing, New York/Münster

Barro, R.J and Jong-Wha Lee, (2000) 'International Data on Educational Attainment: Updates and

Implications,' (CID Working Paper No. 42, April 2000),

Blaydes, L. and M. Kayser, (2007) ‘Counting Calories: Democracy and Distribution in the

Developing World’, paper delivered to American Political Science Association Annual

Meeting 2007, retrieved from http://www.stanford.edu/~blaydes/Calories.pdf.


13

Cutler, D., A. Deaton and A. Lleras-Muney, (2006) ‘The Determinants of Mortality’, Journal of

Economic Perspectives, 20(3), 97-120

Filmer, D. (2009) Educational Attainment and Enrollment Around the World, Development Research Group (World Bank, Washington DC), Data retrieved from http://econ.worldbank.org/projects/edattain

Food and Agriculture Organization, ‘Food Balance Sheets,’ data extracted from

http://faostat.fao.org/.

Nussbaum, M., 2000, Women and Human Development: The Capabilities Approach (Cambridge University Press, Cambridge)

OECD, 2004a, Learning for Tomorrow’s World – First Results from PISA 2003 (OECD, Paris)

OECD, 2004b, Problem Solving for Tomorrow’s World – First Measures of Cross Curricular Competences from PISA 2003 (OECD: Paris)

OECD, 2007a, Reviews of National Policies for Education: Basic Education in Turkey (OECD, Paris)

OECD, 2007b, PISA 2006 Science Competences for Tomorrow’s World (OECD, Paris)

Sen, A. (1999) Development as Freedom (New York: Anchor Books, 1999)

Sen, A. (1998) ‘Mortality as an Indicator of Economic Success and Failure’, The Economic Journal 108

(446), 1-25

State Institute of Statistics (2003), Provincial Indicators, 1980-2003

UNDP (2008) Human Development Report (UNDP, New York)

Wigley, S. and Akkoyunlu-Wigley, A., (2006), ‘Human Capabilities versus Human Capital: Gauging the Value of Education in Developing Countries,’ Social Indicators Research, 78(2): 287-304.

Wigley, S. and Akkoyunlu-Wigley, A. (2009) ‘The Political Determinants of Health: A Cross-

National Study,’ Paper presented to the Annual Meeting of the American Political Science

Association, Toronto, Canada, September 3-6, 2009

Documents

A Regional Analysis of Capabilities and Education in Turkey · education in Turkey as well as of recent developments in the expansion and distribution of educational attainment in