metadata: authors: 0: author: Demirguc-Kunt,Asli
1: author: Torre,Ivan
count: Europe and Central Asia
docna: 0: docna: Measuring Human Capital in Europe and
            Central Asia
docty: Policy Research Working Paper
subtopic: Educational Sciences,Health Care Services Industry,Tertiary Education,Primary Education,Disease Control & Prevention
teratopic: Industry,Health, Nutrition and Population,Education
abstracts: cdata!: This paper outlines an extension of the
            Human Capital Index that addresses the specific challenges
            in education and health faced by countries in Europe and
            Central Asia. Good basic education will not be enough, as
            job markets today demand higher levels of human capital than
            in the past. As the region’s population becomes older, it is
            important that adults remain healthy to ensure productive
            aging. The Europe and Central Asia  Human Capital Index
            (ECA-HCI) extends the Human Capital Index by adding a
            measure of quality-adjusted years of higher education to the
            original education component, and it includes the prevalence
            of three adult health risk factors -- obesity, smoking, and
            heavy drinking -- as an additional proxy for latent health
            status. This extension of the Human Capital Index could also
            be useful for assessing the state of human capital in
            middle-income countries in general.
repnme: repnme: Measuring Human Capital in Europe and
            Central Asia
display_title: Measuring Human Capital in Europe
            and Central Asia
listing_relative_url: /research/2020/10/32519158/measuring-human-capital-europe-central-asia
topicv3: Primary Education m1326139 480,Obesity m1326327 1435,Tertiary Education m1326349 482,Education m1327048 470,Health m1504661 3480
publishtoextweb_dt: 2020-10-27T00:00:00Z
historic_topic: Industry,Health, Nutrition and Population,Education
origu: Office of the Chief Economist (ECACE)
no_of_pages: 47
author_keys: 0: author_key: 94122
1: author_key: 1521219
model_extractions: Policy Research Working Paper 9458 Measuring Human Capital in Europe and Central Asia _Asli Demirgüç-Kunt Iván Torre_ Europe and Central Asia Region Office of the Chief Economist October 2020
Policy Research Working Paper 9458

# **Abstract**

This paper outlines an extension of the Human Capital Index that addresses the specific challenges in education and health faced by countries in Europe and Central Asia. Good basic education will not be enough, as job markets today demand higher levels of human capital than in the past. As the region’s population becomes older, it is important that adults remain healthy to ensure productive aging. The Europe and Central Asia Human Capital Index (ECA-HCI) extends the Human Capital Index by adding a measure of quality-adjusted years of higher education to the original education component, and it includes the prevalence of three adult health risk factors—obesity, smoking, and heavy drinking—as an additional proxy for latent health status. This extension of the Human Capital Index could also be useful for assessing the state of human capital in middle-income countries in general.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ademirguckunt@worldbank.org and itorre@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Measuring Human Capital in Europe and Central Asia

Asli Demirgüç-Kunt and Iván Torre

**Keywords:** human capital, education, health, Europe and Central Asia.

**JEL:** I1, I2, O1, O4

> *The authors are at the World Bank. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. We thank Aart Kraay for his guidance and advice, and Tania Dmytraczenko, Roberta Gatti, Harry Patrinos, Fadia Saadah, Gil Shapira and Christel Vermeersch for useful comments. Sharanya Venu Pillai provided excellent assistance.
# **_1. Introduction_**

In 2018, the World Bank launched the Human Capital Project (HCP), an initiative aimed at raising awareness among policy makers about the importance of investing in human capital. The HCP includes an advocacy component which features the Human Capital Index (HCI), a measure of the human capital that a child born today can expect to attain by age 18, given the risks of poor health and poor education that prevail in the country where she lives (Kraay, 2019).

The HCI quantifies the trajectory from birth to adulthood in terms of the consequences for productivity by means of three components: (1) a measure of whether children survive from birth to school age (age 5); (2) a measure of expected years of basic education (primary and secondary), adjusted for quality; and (3) two broad measures of health: child stunting rates and adult survival from age 15 to age 60. The index is constructed so that a value of 1 represents the productivity in adulthood of a child born today if he or she enjoyed complete education and full health until age 18. Countries are measured with respect to this benchmark; the value of the index can thus be interpreted as a percentage of that productivity level.

While useful in a global context, the original version of the HCI may not adequately reflect the education and health challenges that are relevant for specific regions of the world. Countries in Europe and Central Asia provide their citizens relatively good basic education and health services; the region’s citizens begin their productive life in a much better position than their peers in other regions of the world. But job markets today demand higher levels of human capital than in the past. Good basic education will not be enough; higher education institutions must prepare students for the challenges the future of work may hold. Health care systems will need to ensure that citizens remain healthy throughout their adult life, as, more and more, learning and skill acquisition will take place along an individual’s life cycle, not just in the initial years of life. This is also more important as the region’s workforce is becoming, on average, older, and therefore improving adult health will be needed to ensure a productive aging for the region’s population.

This paper outlines an extension of the HCI which addresses the relevant education and health challenges of Europe and Central Asia, namely by including higher education in the education component of the index and by looking at three crucial adult health risk factors -obesity, heavy alcohol consumption and tobacco smoking- in the health component. This extension could also be useful for assessing the state of human capital in middle income countries in general, particularly for those where basic education attainment and child health are less of a concern but where significant challenges remain as young people transition into the labor market.

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This paper is organized as follows. Section 2 presents the main analytical framework of the ECA-HCI, the proposed extension of the original HCI. Section 3 discusses the education component and section 4 discusses the health component. Section 5 presents the overall results and section 6 concludes.

# **_2. Main Framework_**

The basic structure of the Human Capital Index (HCI) is made up of three components: <u>p</u> H (1)<sup>−SS∗)−ZZ∗)</sup> HH= pp<sup>∗</sup> × ee<sup>φφ(SSN</sup> × ee<sup>γγ(ZZN</sup> The first term captures forgone productivity caused by child mortality. The second term captures forgone productivity as a result lack of full education, where _SNG_ refers to the schooling level of the generation of children born today and _S_<sup>_*_</sup> refers to the full education benchmark. The productivity return to education is measured by parameter φ. The third terms captures forgone productivity as a result of lack of proper health, where _ZNG_ refers to the expected adult health status of the generation of children born today and _Z_<sup>_*_</sup> refers to the full health benchmark. The productivity return to good health is measured by parameter γ.

The HCI’s measure of child mortality is the probability of survival to age five. The education component of the HCI uses learning-adjusted years of schooling, a quality-adjusted measure of years of basic education. The benchmark is set at 14 years of schooling, equivalent to the whole cycle of primary and secondary education plus two years of preprimary education. The parameter φ is set at 0.08, based on estimations of the average return of one year of basic education.

The health component of the HCI uses child stunting (when available) and the adult survival rate (the probability that a child age 15 reaches age 60) as health status indicators. The benchmark is zero stunting and 100 percent adult survival rate. To establish a quantifiable productivity return to good health, both variables are transformed into implied adult height in centimeters, which has a productivity return of 0.034 per centimeter. Adult height is implied to be the most relevant proxy variable for latent health status (captured by _Z_ in the equation above). The value of γ is 0.35 for child stunting and 0.65 for the adult survival rate.

The HCI is calculated using the following formula: 1−U U m m H (2) 1 UU 5 m UUm UUm UU<sup>LLSS−14∗)SSmmSSUUmmURRmUU−1)+0.65(LLSSRR−1))/2</sup> HH= × ee<sup>0.08(L</sup> × ee<sup>(0.35(NNm</sup> This paper outlines an alternative specification that may be particularly relevant for the education and health challenges faced in Europe and Central Asia. For the education component, we add higher education in addition to basic education. For the health component, we use a proxy of latent adult health status (based on the incidence of obesity, smoking, and alcoholism), along with the outcome proxy based on child 3
stunting and adult survival rate used in the original HCI. The basic formulation of the Europe and Central Asia HCI (ECA-HCI) is as follows: where _B_ refers to the quality-adjusted basic education schooling level of the generation of children born today, with an associated productivity return captured by parameter _η_ and full basic education benchmark _B_<sup>_*_</sup> ; _C_ refers to the quality-adjusted higher education schooling level, with an associated productivity return captured by parameter _ω_ and full higher education benchmark _C_<sup>_*_</sup> ; _RF_ refers to the prevalence of adult health risk factors (namely the share of non-obese individuals in the adult population, the share of adult nonsmokers, and the share of adults who report no heavy drinking), with an associated productivity return captured by parameter _γRF_ . The benchmarks for these shares are set to 100 percent non-obese, nonsmokers, and non–heavy drinkers. _O_ refers to the value of the relevant health outcomes (adult survival rate and child stunting); γ _o_ refers to their productivity effects, estimated via their relationship with adult height, as in the original HCI.

# **_3. Education Component_**

The 2019 _World Development Report_ highlights the changing nature of work across the globe. In highincome countries, which include most of the countries in Europe and Central Asia, having a good basic education will not be enough for individuals to be productively included in the labor market in the next decades; higher education of good quality will be necessary for the next generations to be productive workers. The education component of the ECA-HCI therefore extends the original education component by adding a measure of quality-adjusted years of higher education (QAYH) to the measure of learningadjusted years of basic education. Like learning-adjusted years of basic education (LAYS), QAYH measures both quantity and quality.

The basic formulation of the education component of the ECA-HCI is the following: U m<sup>LLSS−LLLSS∗)+ωω(QQLQQ−QQLQQ∗)</sup> SSeem UU Where and _ω_ EEHHEE−H HH = ee<sup>ηη(L</sup> Where _η_ and _ω_ are the productivity returns of one additional year of quality basic and higher education respectively, and _LAYS*_ and _QAYH*_ are the benchmark number of years equivalent to full basic and higher education respectively.

As shown in equation 4, the education component of the ECA-HCI includes two subcomponents. The first measures the basic education schooling level expected for the generation of children born today. This 4
component is the same as the overall education component in the standard version of the HCI. The main variable is learning-adjusted years of education, a quality-adjusted measure of schooling years in basic education. The benchmark ( _LAYS)_ is set at 14 years of basic education. The associated return in productivity terms ( _η_ ) is set at 0.08.

The second component focuses on higher education. A quality-adjusted measure of years of higher education requires two inputs: a measure of expected years of higher education and a measure of the quality of higher education. The basic structure of the main outcome variable—quality-adjusted years of higher education (QAYH)—is the following: where _EYHc_ represents the expected years of higher education of country _c_ , and _QAc_ represents the average quality of higher education in country _c_ , which has a maximum of 1 and a minimum of _m_ . The minimum is greater than 0 on the assumption even very low-quality higher education has some intrinsic value, even if minimal. _QAYH_ is expressed in years of higher education of maximum quality.

# _3.1 Expected years of higher education_

The standard approach for estimating expected years of basic education uses the age-specific enrollment rates over all ages in the 4–18 age range as the main input. The nature of higher education requires a different treatment, for several reasons.

First, there is no theoretical age at which higher education is expected to happen. Second, higher education is not always carried out full time; many students combine their studies with part-time employment. Third, the number of years required to obtain a higher education degree varies across disciplines and across countries (the norm in EU countries, after implementation of the Bologna Process, is for initial degrees to take three years; in the Russian Federation, a bachelor’s degrees take four years).

The approach adopted in this paper uses the percentage of individuals with a higher education degree at age 30–34 as the measure of educational attainment. To express it in years of education, we assume that a university degree is equivalent to 3.5 years of higher education, to account for differences across disciplines and educational systems. The calculation of expected years of higher education ( _EYH_ ) is straightforward: T T T T × 3.5 (6) mmaaUU 30−34 ee ee where _Tertiary attainment_ EEQQHH corresponds to the share of individuals 30–34 in country = TTeeT TTa eeaaTT _c_ who hold a tertiary degree.

5
# _3.2 Quality adjustment of higher education attainment_

Quality adjustment of higher education should be done primarily by measuring the quality of outputs, such as the skill proficiency of university graduates (just as harmonized test score results are used to measure the quality of learning among primary and high school students). However, measures of adult skill proficiency (from the <mark>Programme for the International Assessment of Adult Competencies [P</mark> IAAC] or <mark>Skills Towards Employability and Productivity [STEP]</mark> surveys, for example) are available only for a limited set of countries.<sup>1</sup> The ECA-HCI therefore measures the quality of inputs—such as the quality of universities—which are more widely available. However, measures of the quality of universities and adult skill proficiency correlate very well for countries for which both measures are available (see appendix A for a comparison of the input-based quality adjustment presented here and an alternative skill-based quality adjustment).

The quality of higher education is calculated under the assumption that a high-quality degree is a degree that makes its holders more productive in the labor market—the working assumption of the broad literature on the effects of college quality on earnings in the United States. Standard ordinary least squares (OLS) estimates of the impact of college quality (usually measured by the average SAT score of admitted students) on earnings show that there is a positive and significant association between them. Given the existence of a selection process into college—high school students decide which colleges to apply to—these estimates may suffer from a substantial selection bias.

To address this issue, the literature has followed two approaches. The first is a “selection-on-observables” approach, in which the decision to apply to a given type of college is modeled based on observable variables such as net college costs or high school grade point average (Brewer, Eide, and Ehrenberg 1999; Andrews, Li, and Lovenheim 2016). This approach has confirmed the existence of a positive and significant return of the quality of college education on earnings.

The second is a “selection-on-unobservables” approach, in which, rather than modeling college choice, the researcher compares the outcomes of students who were admitted to the same set of colleges but chose to go to different ones (Dale and Krueger 2002, 2014). This approach is a “self-revelation” method, because it assumes that the set of students admitted to a given college share the same “unobservable” characteristics. This method shows that, for the average student, there is no significant effect of college quality on earnings. The effect is significant for minority students and those from poor backgrounds, however.

> 1 For a comparison of output quality in tertiary education, see Loyalka and others (2019), who compare the computer science skills of computer science undergraduates in their last year in China, India, the Russian Federation, and the United States.

6
The quality-adjustment factor in our study is calculated in the following way: where _m_ corresponds to the productivity of a tertiary degree coming from a “zero-quality” institution; _Q_ corresponds to the average quality score of universities in country _c_ , ranging from 0 to 100; _β_ is a productivity-adjustment factor that transforms the quality score into productivity units; and _m_ is scaled in a way that quality adjustment ( _QAc_ ) equals 1 if _Qc_ equals 100.

The measure of quality corresponds to the information collected by global university rankings. These rankings, published by private, for-profit companies, have grown in number over the years. They are usually based on an underlying score that is usually a weighted average of scores on different aspects of higher education (the volume and quality of research, research influence, the quality of teaching, international outlook, links to industry). These rankings do not include all higher education institutions (universities need to send their information, usually at a cost, to the publishers), and they use different methodologies. Our analysis relies on a combination of several of these ranking, including the scores from the Times Higher Education (THE) ranking; the Quacquarelly Symonds (QS) ranking; the Academic Ranking of World Universities (ARWU, also known as the “Shanghai” ranking); the Center for World University Rankings (CWUR); the U.S. News Global Universities Ranking; and the U-Multirank ranking (a nonnumeric, userdefined ranking). These rankings contain information on 400–1,000 universities in 43 countries in Europe and Central Asia. We generate a country-level average by averaging the scores for all the universities in a given country included in each ranking, yielding six values for each country (one for each ranking source). As detailed later, we normalize each of them, and then take the average of them as the aggregate quality score.

# _University rankings_

Table 1 describes the six university rankings used in this analysis. The CWUR includes the largest number of universities (2,000); the ARWU/Shanghai includes the smallest number (1,000). The rankings include 385–1,040 higher education institutions in Europe and Central Asia. The total number of countries covered ranges from 63 to 98; the number of countries in Europe and Central Asia ranges from 32 to 43. Five of the six rankings (THE, QS, ARWU, CWUR, and U.S. News rankings) have scores that (theoretically) range from 0 to 100, although no institution included in any of the rankings has a score of 0. The U-Multirank is a nonnumeric, multidimensional, user-defined ranking. To use it, we imputed numeric values (ranging from 0 to 100) to the letter-based scores assigned. The CWUR has the highest minimum score (65.8) and the lowest dispersion (5.07). The ARWU/Shanghai overall score is reported only for the world’s top 100 universities.

7
Given that the six rankings include subcomponents on the quality of research, faculty performance, and reputation, an alternative score can be estimated as the simple average of the scores of those subcomponents—the research, teaching, and citations (RTC) quality score. This score captures the quality of the subcomponents that are common to all the rankings. This calculation is not possible for the CWUR and U.S. News rankings, which do not publish the scores on the subcomponents.

**Table 1 Descriptions of six systems of university ranking**

|_Item_|_Times_<br>_Higher_<br>_Education_<br>_(THE)_|_Quacquarelly_<br>_Symmonds_<br>_(QS)_|_Academic_<br>_Ranking of_<br>_World_<br>_Universities_<br>_(ARWU)_<sup>_a_</sup>|_Center for_<br>_World_<br>_University_<br>_Rankings_<br>_(CWUR)_<sup>_b_</sup>|_U.S. News_<br>_Global_<br>_Universitie_<br>_s Ranking_|_U-Multirank_<br>_(UMR)_<sup>_c_</sup>|
|---|---|---|---|---|---|---|
|Number of|1,397|1,021|1,000|2,000|1,500|1,666|
|universities included|||||||
|Of which in ECA|540|418|385|708|556|1,041|
|Number of countries|91|85|63|98|81|92|
|Of which in ECA|37|35|32|36|36|43|
|**Ranking**<br>**components**<br>**covered**|||||||
|Research/|||||||
|innovation on|||||||
|outputs|||||||
|<br>Faculty performance|||||||
|<br>Internationalization|||||||
|Reputation|||||||
|<br>STEM focus|||||||
|**Overall score**|||||||
|Global mean|34.57|29.90|37.00|71.64|42.45|59.27|
|Dispersion|17.07|19.75|12.71|5.07|16.28|14.41|
|Range|16.4–95.4|10.7–100|26–100|65.8–100|15.5–100|16.7–100|
|**Research,**|||||||
|**Teaching, and**|||||||
|<br>**Citations score**<sup>**d**</sup>|||||||
|Global mean|33.43|30.83|20.96|n.a.|n.a.|63.56|
|Dispersion|17.45|20.00|9.82|n.a.|n.a.|16.54|
|Range|9.3–96.4|10.7–99.9|8.2–92.7|n.a.|n.a.|20–100|

_Note:_ ECA = Europe and Central Asia; STEM = science, technology, engineering, and mathematics.

a. The overall score for the ARWU ranking is published only for the top 100 universities. For the remaining institutions, only the individual subcomponents are published.

b. The CWUR publishes only the overall score, not the subcomponent scores.

c. The UMR provides a letter-based, not a numeric, score. To estimate a numeric equivalent, the following scale was used: A = 100; B = 75; C = 50, D =2 5, E = 0. The overall score represents the average of the numeric score of all the UMR categories (teaching and learning, research, knowledge transfer, international orientation, and regional engagement).

d. The Research, Teaching, and Citations score is composed of the simple average of the components of research, faculty performance, and reputation.

The correlation between these rankings is very high. Partial correlations across the rankings for a subset of 98 U.S. universities included in the six rankings range from 0.64 to 0.97 (Table 2). Partial correlations 8
across the country averages for the 54 countries that have at least one university present in all six rankings are also high, ranging from 0.61 to 0.91 (Table 3).

**Table 2 – Partial correlation across US universities (n=98)**

||Ov.<br>THE|Ov.<br>QS|RTC<br>ARW<br>U|Ov.<br>CWUR|Ov.<br>US<br>News|Ov.<br>UMR||RTC<br>THE|RTC<br>QS|RTC<br>ARW<br>U|Ov.<br>CWU<br>R|Ov.<br>US<br>News|RTC<br>UMR|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Overall<br>THE|1||||||RTC<br>THE|1||||||
|Overall<br>QS|0.9728|1|||||RTC<br>QS|0.9544|1|||||
|RTC<br>ARWU|0.8762|0.8895|1||||RTC<br>ARWU|0.8771|0.8735|1||||
|Overall<br>CWUR|0.9375|0.9492|0.9396|1|||Overall<br>CWUR|0.9384|0.9437|0.9396|1|||
|Overall<br>US<br>News|0.9395|0.9350|0.9381|0.9620|1||Overall<br>US<br>News|0.9386|0.9246|0.9381|0.9620|1||
|Overall<br>UMR|0.6886|0.7374|0.6486|0.7187|0.7274|1|RTC<br>UMR|0.7230|0.7666|0.6412|0.7246|0.7476|1|

**Table 3 – Partial correlation across country averages (n=54)**

||Ov.<br>THE|Ov.<br>QS|RTC<br>ARW<br>U|Ov.<br>CWUR|Ov.<br>US<br>News|Ov.<br>UMR||RTC<br>THE|RTC<br>QS|RTC<br>ARW<br>U|Ov.<br>CWU<br>R|Ov.<br>US<br>News|RTC<br>UMR|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Overall<br>THE|1||||||RTC<br>THE|1||||||
|Overall<br>QS|0.9044|1|||||RTC<br>QS|0.8833|1|||||
|RTC<br>ARWU|0.8514|0.8587|1||||RTC<br>ARWU|0.8436|0.8533|1||||
|Overall<br>CWUR|0.8741|0.8443|0.8461|1|||Overall<br>CWUR|0.8819|0.8427|0.8461|1|||
|Overall<br>US<br>News|0.9272|0.8220|0.7975|0.8961|1||Overall<br>US<br>News|0.9144|0.7962|0.7975|0.8961|1||
|Overall<br>UMR|0.7453|0.7711|0.6977|0.7122|0.6387|1|RTC<br>UMR|0.7354|0.7231|0.7210|0.7125|0.6138|1|

A positive correlation also exists between the quality scores and the income level of countries (see Figure 1). Singapore is ranked as the country with the highest quality score in the THE, QS and ARWU rankings, while for the CWUR ranking the highest ranked country is the Netherlands.

9
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attendance costs, instructional expenditure, and percentage of students in each type of major. We match this data set with the six university rankings. Among U.S. higher education institutions, 294 are present in at least one of the rankings, and 108 are present in all four.

The simple OLS regression estimated is the following: 2014 ) ) p (8) bb,aa,ee aa aa ee 1,aa bb,aa,ee 2,aa bb 3,aa ee bb,aa,ee where the dependent variable is the annual average log earnings in 2014 of the cohort born in year log (eeT aaTTaae = αα + ββ QQ + γγ log (ppeeT aaTTaae + γγ TTe + γγ TT_SSTTEESS + εε _b_ of gender _g_ that went to college _c_ . The main regressor of interest is _Q_ , the quality measure based on the six rankings for college _c_ . Coefficient _β_ is the productivity effect of quality; it is used as the quality-adjustment factor in equation (7) which feeds into the ECA-HCI. Other regressors are the log parental earnings of the cohort born in year _b_ of gender _g_ that went to college _c_ when the individuals were 15–19; the age of cohort _b_ in 2014; and percentage of STEM majors in college _c_ in year 2000 (included to control for the STEM wage premium). Standard errors are clustered at the college level.

Table 4 provides the results for the aggregate quality score derived from the combination of the six rankings, shown for the sample of universities that are present in at least one of the rankings (323 universities in total) and for the common sample of 98 universities that are present in all the rankings. Table 5 summarizes the values of _β_ and _m_ (the implied productivity of a “zero-quality” institution) that arise from the results of the OLS estimations of equation (8), focusing only on values that refer to both genders. Full results are available in Table A.1 in the appendix.

**Table 4. Ordinary least squares estimates of aggregate quality scores of universities**

||<br>|Log<br>Full sample|annual earnin|gs in 2014<br>Com<br>|mon sample||
|---|---|---|---|---|---|---|
||Both genders|Men|Women|Both genders|Men|Women|
|Aggregate quality score|0.0024<sup>***</sup>|0.0031<sup>***</sup>|0.0016<sup>***</sup>|0.0044<sup>***</sup>|0.0052<sup>***</sup>|0.0036<sup>***</sup>|
||(0.004)|(0.0004)|(0.0004)|(0.0009)|(0.0010)|(0.0008)|
|Log parental earnings|0.2986<sup>***</sup>|0.3142<sup>***</sup>|0.2646<sup>***</sup>|0.3202<sup>***</sup>|0.3597<sup>***</sup>|0.2543<sup>***</sup>|
||(0.0136)|(0.0150)|(0.0134)|(0.0248)|(0.0283)|(0.0225)|
|Age<br>STEM majors in|0.1074<sup>***</sup><br>(0.0013)<br>0.0058<sup>***</sup>|0.1237<sup>***</sup><br>(0.0014)<br>0.0053<sup>***</sup>|0.0894<sup>***</sup><br>(0.0013)<br>0.0049<sup>***</sup>|0.1157<sup>***</sup><br>(0.0023)<br>0.0046<sup>***</sup>|0.1295<sup>***</sup><br>(0.0024)<br>0.0043<sup>***</sup>|0.0979<sup>***</sup><br>(0.0022)<br>0.0032<sup>***</sup>|
|college (0–100)|(0.0005)|(0.0005)|(0.0006)|(0.0008)|(0.0008)|(0.0010)|
|(percent)|||||||
|Constant|3.8250<sup>***</sup>|3.2798<sup>***</sup>|4.6636<sup>***</sup>|3.2758<sup>***</sup>|2.4979<sup>***</sup>|4.5139<sup>***</sup>|
||(0.1679)|(0.1881)|(0.1606)|(0.3061)|(0.3469)|(0.2707)|
|Observations|3,784|3,689|3,738|1,159|1,159|1,156|
|Number of colleges|323|315|321|98|98|98|

_Note:_ The common sample is composed of universities that are present in all six rankings. Clustered standard errors at the college level are in parentheses. STEM = science, technology, engineering, and mathematics. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01.

12
**Table 5. Parameters of the quality-adjustment factor used to assess universities**

||TH<br>Overall|E<br>RTC|Q<br>Overall|S<br>RTC|ARWU<br>RTC|CWUR<br>Overall|U.S.<br>News<br>Overall|U-Mul<br>Overall|tirank<br>RTC|Aggreg<br>score<br>All<br>sample|ate quality<br>(overall)<br>Common|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|_β_|0.0032|0.0031|0.0027|0.0024|0.0045|0.0073|0.0019|0.0040|0.0032|**0.0024**|0.0044|
|_m_|0.726|0.733|0.763|0.787|0.638|0.747|0.826|0.668|0.728|**0.787**|0.647|

_Note:_ ARWU = Academic Ranking of World Universities; CWUR = Center for World University Rankings; QS = Quacquarelly Symonds; RTC = research, teaching, and citations; THE = Times Higher Education.

To estimate the ECA-HCI, we use the values estimated from the use of the aggregate quality score in the extended sample (Table 5, column 10). These values can be understood as a conservative estimate of the productivity effects of quality, as the estimates from the sample of universities present in the six rankings (Table 5, column 11) imply a larger effect. The parameters are derived from the implied differences in the wages of graduates of a low-quality university compared with those of a high-quality university in the United States. This implied wage differential may be even higher when comparing a low-quality university in a given country with a high-quality university in another country. Interpretation of the results emerging from the use of this quality-adjustment factor needs to take these limitations into account.

# _3.3 Quality-adjusted years of higher education_

Based on the estimates of the previous paragraphs, the detailed calculation formula for the quality-adjusted years of higher education ( _QAYH_ ) is as follows: where _Q_ is the aggregate quality score for higher education for country _c_ . There is also positive association between quality-adjusted years of higher education and income level (see Figure 3).

13
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|**Eastern Europe**|**9.9**|**0.525**|**16.3**|**1.50**|**0.534**|**0.723**|
|---|---|---|---|---|---|---|
|Belarus|10.8|0.421|22.1|1.22|0.547|0.773|
|Moldova|8.3|0.351|5.8|0.98|0.432|0.633|
|Ukraine|9.9|0.560|15.9|1.60|0.539|0.719|
|**Northern Europe**|**11.4**|**0.512**|**37.3**|**1.54**|**0.605**|**0.814**|
|<br>Denmark|11.1|0.579|42.8|1.77|0.609|0.793|
|Finland|11.7|0.428|35.1|1.28|0.596|0.835|
|Iceland|10.7|0.537|30.4|1.59|0.575|0.769|
|Norway|11.2|0.502|32.8|1.50|0.591|0.801|
|Sweden|11.6|0.524|38.1|1.58|0.616|0.824|
|**Russian Federation**|**10.9**|**0.610**|**25.9**|**1.79**|**0.601**|**0.780**|
|**South Caucasus**|**8.2**|**0.299**|**8.5**|**0.84**|**0.421**|**0.630**|
|Armenia|8.0|0.303|9.4|0.85|0.414|0.619|
|Azerbaijan|8.3|0.254<sup>a</sup>|6.4|0.71|0.414|0.633|
|Georgia|8.3|0.417|13.1|1.19|0.445|0.632|
|**Southern Europe**|**10.5**|**0.341**|**29.8**|**1.01**|**0.518**|**0.756**|
|<br>Cyprus|10.9|0.558|27.3|1.64|0.589|0.781|
|<br>Greece|10.0|0.446|25.5|1.30|0.519|0.724|
|Italy|10.5|0.271|31.6|0.80|0.500|0.753|
|Malta|10.2|0.341|16.0|0.98|0.502|0.737|
|Portugal|11.3|0.327|28.5|0.96|0.548|0.806|
|Spain|10.5|0.406|28.7|1.20|0.533|0.757|
|**Turkey**|**9.2**|**0.275**|**22.2**|**0.80**|**0.453**|**0.683**|
|**Western Balkans**|**8.8**|**0.285**|**13.3**|**0.81**|**0.442**|**0.664**|
|Albania|9.0|0.235|6.9|0.66|0.434|0.668|
|Bosnia and Herzegovina|7.8|0.210|5.5|0.59|0.391|0.609|
|Kosovo|7.9|||||0.612|
|Montenegro|8.9|0.340|1.5|0.94|0.451|0.665|
|<br>North Macedonia|7.3|0.299|7.3|0.84|0.390|0.585|
|Serbia|9.8|0.333|22.6|0.97|0.485|0.712|
|**Western Europe**|**11.3**|**0.459**|**36.2**|**1.38**|**0.583**|**0.803**|
|<br>Austria|10.9|0.472|32.1|1.40|0.568|0.781|
|Belgium|11.2|0.489|42.3|1.49|0.588|0.798|
|France|11.3|0.470|33.1|1.40|0.584|0.804|
|Germany|11.0|0.340|35.5|1.02|0.541|0.789|
|<br>Ireland|11.6|0.597|33.2|1.78|0.635|0.825|
|Luxembourg|9.8|0.497|28.1|1.46|0.524|0.714|
|<br>Netherlands|11.5|0.550|46.8|1.70|0.624|0.821|
|Switzerland|10.9|0.512<sup>b</sup>|45.9|1.58|0.584|0.782|
|United Kingdom|11.5|0.550|35.9|1.65|0.620|0.821|
|<br>**ECA (country average)**|**10.1**|**0.403**|**23.0**|**1.18**|**0.520**|**0.736**|
|**ECA (population-weighted**<br>**average)**|**10.4**|**0.424**|**26.2**|**1.25**|**0.539**|**0.755**|

16
_Sources:_ Attainment data were calculated from the _European_ <mark>Union Statistics on Income and Living Conditions</mark> and household surveys. Learning-adjusted years of basic education (LAYS) were obtained from the HCI database. _Note:_ For the average standardized quality score for higher education, the quality scores from each of the six university rankings (the Times Higher Education, the Quacquarelly Symonds, Academic Ranking of World Universities, the Center for World University Rankings, the U.S. News Global Universities Ranking, and U-Multirank) are first standardized to a global mean of 0 and a standard deviation of 1 and then averaged for every country. For presentational purposes, this value is then rescaled to range from 0 to 100. A value of 0 for the quality measure implies that no university in that country appears in any of the six university rankings. The education component of the original HCI was updated with PISA 2018 results or the latest available data. HCI = Human Capital Index; ECA-HCI = Europe and Central Asia extension of the HCI.

— Not available.

a. Based on population age 25 and older.

b. Based on population 25–34.

# **_4. Health Component_**

The health component of the HCI seeks to measure the productivity losses associated with poor health that a child born today will face later in life as an adult. The original HCI calculates this component based on two variables: the child stunting rate and the adult survival rate (the chance that a 15-year-old survives to age 60). These variables are understood to be good proxies for unobserved latent health status in a global context. Their effects on productivity are measured by the returns to adult height.

The ECA-HCI takes a different approach. It starts by assuming that good health means the absence of disease and bad health means the presence of disease. To measure latent health status, the ECA-HCI focuses on the factors that may cause disease. A low prevalence of these risk factors implies a lower disease burden; a high prevalence could imply a higher disease burden. The risk factors that are relevant as indirect measures of latent health status depend on the types of disease prevalent in each context. Smith and Nguyen (2013) show that in Europe and Central Asia, cardiovascular disease, followed by external causes (mainly alcoholrelated road traffic injuries), explains most of the differences in adult life expectancy. Data from the COVID-19 pandemic also show that people with underlying cardiovascular conditions have a higher mortality rate than people without them (Wu and McGoogan 2020; Zhou and others 2020). In view of these findings, the ECA-HCI uses the prevalence of three health risk factors associated with cardiovascular disease: obesity, tobacco smoking, and heavy alcohol consumption. The higher the prevalence of these risk factors, the higher the probability of disease and the worse the health status. The prevalence of these risk factors increases the probability of suffering from noncommunicable diseases and increases the mortality and morbidity consequences of some infectious diseases like COVID-19. The health benchmark in the ECA-HCI with which countries are compared is zero prevalence of obesity, smoking, and heavy drinking.

The impact on productivity of specific health conditions is difficult to estimate. There is more evidence on the productivity effects associated with the risk factors behind such health conditions. The literature has quantified the effects on productivity of obesity, tobacco smoking, and heavy drinking, making it possible 17
to incorporate their prevalence directly into the ECA-HCI without the intermediating factor of adult height, as in the original version of the index.

Focusing only on risk factors has its limitations, however. Between risk factors and morbidity lies a mediating institutional factor: health care systems. The capacity of health care systems to manage the consequences of increased risk factors—and the diseases associated with them—ultimately determines whether that increased risk ends in increased morbidity and, eventually, mortality. Good health care systems strongly alleviate the morbidity and mortality consequences of the increased prevalence of risk factors.

To account for the effects of health care systems, the model uses a health outcome measure as a proxy for latent health status—the child stunting and adult survival rates used in the original HCI. The health component of the ECA-HCI uses the average of a risk factor–based proxy of health status and an outcomebased proxy. The productivity effects of child stunting and adult survival rates are retained, as in the original HCI. The health component of the ECA-HCI has the following basic formulation: where γRF is the productivity effect associated to the prevalence of risk factors _RF_ ; _RF_<sup>_*_</sup> is the benchmark rate of zero prevalence of risk factors; and γO is the productivity effect of health outcomes _O_ , with the benchmark of “full” health outcomes being _O_<sup>_*_</sup> . For risk factors, the ECA-HCI uses the share of non-obese adults ( _NOB_ ), the share of nonsmokers among adults ( _NSM_ ), and the share of adults not reporting heavy drinking ( _NAL_ ). The productivity effects of these risk factors (γOB, γSM, γAL) are assumed to be additive.<sup>3</sup> For health outcomes, the ECA-HCI uses the adult survival rate ( _ASR_ ) and the share of children not stunted ( _NSTNT_ ). As in the original HCI, these rates are intended to proxy the same variable: latent health status. Their productivity effects (γASR, γSTNT) are therefore averaged. The equation for the health component is the following: The values of γSTNT and γASR, the productivity effects associated with child stunting the adult survival rate, are kept as in the original HCI. They are derived from the correlation of these rates with adult height, for which the literature provides reliable microeconometric estimations of productivity. These values are assumed to be 0.35 for γSTNT and 0.65 for γASR. Adult survival rates are widely available; child stunting rates

> 3 Perfectly additive productivity effects imply that the productivity effect of smoking and obesity (combined) is simply the summation of the productivity effect of smoking and the productivity effect of obesity. This figure can be understood as an upper-bound estimation of the combined productivity effects of risk factors.

18
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|Poland|16.7|17.4|26.1|0.894|–|0.930|0.934|
|---|---|---|---|---|---|---|---|
|Romania|9.1|34.9|25.7|0.878|–|0.913|0.924|
|Slovak Republic|15.9|12.8|29.5|0.898|–|0.934|0.936|
|Slovenia|18.6|19.0|24.2|0.935|–|0.941|0.959|
|**Eastern Europe**|**25.8**|**22.0**|**26.9**|**0.822**|–|**0.901**|**0.893**|
|Belarus|26.6|28.2|26.2|0.853|93.6|0.903|0.909|
|Moldova|20.1|28.6|24.2|0.836|–|0.921|0.937|
|Ukraine|26.1|20.2|27.3|0.815|–|0.899|0.886|
|**Northern Europe**|**14.4**|**31.5**|**18.8**|**0.941**|–|**0.936**|**0.962**|
|Denmark|14.4|37.4|20.9|0.932|–|0.926|0.957|
|Finland|17.8|33.9|19.2|0.930|–|0.928|0.956|
|Iceland|19.0|25.7|18.8|0.955|–|0.943|0.971|
|Norway|12.6|44.0|20.1|0.945|–|0.925|0.965|
|Sweden|13.4|20.4|16.7|0.950|–|0.950|0.968|
|**Russian Federation**|**25.0**|**38.8**|**30.3**|**0.804**|–|**0.879**|**0.880**|
|**South Caucasus**|**20.8**|**11.1**|**23.1**|**0.876**|–|**0.934**|**0.930**|
|Armenia|20.9|11.5|24.5|0.886|90.6|0.941|0.948|
|Azerbaijan|19.9|8.2|20.8|0.882|82.2|0.939|0.933|
|Georgia|23.3|18.5|28.0|0.853|–|0.913|0.909|
|**Southern Europe**|**13.6**|**8.2**|**24.3**|**0.947**|–|**0.957**|**0.966**|
|Cyprus|13.1|5.2|29.1|0.952|–|0.960|0.969|
|Greece|16.9|10.3|32.6|0.933|–|0.945|0.957|
|Italy|10.5|6.6|22.7|0.953|–|0.963|0.970|
|Malta|25.2|19.2|24.1|0.951|–|0.943|0.969|
|Portugal|16.1|10.2|20.0|0.933|–|0.952|0.957|
|Spain|16.2|9.3|25.3|0.946|–|0.954|0.966|
|**Turkey**|**19.8**|**4.3**|**32.5**|**0.911**|**94.0**|**0.952**|**0.961**|
|**Western Balkans**|**22.5**|**27.9**|**35.0**|**0.906**|**92.4**|**0.925**|**0.957**|
|Albania|22.3|22.9|28.9|0.929|88.7|0.933|0.958|
|Bosnia and Herzegovina|19.4|22.7|38.1|0.914|91.1|0.930|0.957|
|Kosovo|–|–|–|0.906|–|–|0.941|
|Montenegro|24.9|26.9|35.4|0.906|90.6|0.923|0.954|
|North Macedonia|23.9|26.5|35.0|0.909|95.1|0.928|0.962|
|Serbia|23.5|32.9|36.0|0.893|94.0|0.919|0.956|
|**Western Europe**|**16.5**|**29.7**|**23.0**|**0.933**|–|**0.932**|**0.957**|
|<br>Austria|14.3|18.7|30.0|0.937|–|0.941|0.960|
|Belgium|13.7|27.5|23.0|0.931|–|0.935|0.956|
|Germany|14.7|36.0|28.3|0.926|–|0.922|0.953|
|<br>France|16.4|33.0|21.7|0.931|–|0.929|0.956|
|Ireland|28.1|32.3|22.0|0.944|–|0.928|0.964|
|Luxembourg|15.1|34.5|20.5|0.942|–|0.932|0.963|
|Netherlands|12.9|31.6|25.2|0.946|–|0.935|0.966|
|Switzerland|11.3|15.9|27.1|0.954|–|0.952|0.970|

21
|United Kingdom|20.1|22.1|17.3|0.933|–|0.940|0.958|
|---|---|---|---|---|---|---|---|
|**ECA (country average)**|**18.0**|**21.1**|**25.9**|**0.904**|**90.3**|**0.932**|**0.945**|
|**ECA (population-weighted average)**|**18.4**|**22.5**|**25.6**|**0.894**|**91.4**|**0.927**|**0.938**|

_Source:_ Data on obesity, smoking, and alcohol consumption are from the European Health Interview Survey, <mark>Health Equity and Financial Protection Indicators,</mark> and the World Health Organization. The ECA average for the share of children not stunted is calculated based on countries for which data are available only.

_Note:_ HCI = Human Capital Index; ECA-HCI = Europe and Central Asia extension of the HCI.

- Not available.

- a. Includes consumption of smokeless tobacco.

# **_5. Estimation of the ECA-HCI_**

The ECA-HCI is the product of three components: The three components are defined as follows: The estimates of the ECA-HCI in table 8 show that countries in the region can achieve large increases in their long-run productivity if they reduce the distance between the expected educational attainment and adult health status of children born today and the benchmarks of complete education and full health. The average country for which the ECA-HCI is calculated has a value of 0.481, meaning that children born today in the average country in the region will be almost half as productive as they would have had they reached the benchmark of complete education and full health (14 years of basic education; 3.5 years of higher education; no obesity, tobacco smoking, or heavy drinking; no statistically significant child stunting; and 100 percent adult survival rate to age 60). The correlation between income levels and the ECA-HCI is positive, as it is for the original HCI (figure 7).

## **Figure 7. Estimates of ECA-HCI and country income levels**

22
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|Slovenia|0.998|0.572|0.941|0.537|0.775|
|---|---|---|---|---|---|
|**Eastern Europe**|**0.992**|**0.534**|**0.901**|**0.477**|**0.640**|
|Belarus|0.997|0.547|0.903|0.492|0.700|
|Moldova|0.984|0.432|0.921|0.391|0.584|
|Ukraine|0.991|0.539|0.899|0.480|0.631|
|**Northern Europe**|**0.997**|**0.605**|**0.936**|**0.564**|**0.781**|
|Denmark|0.996|0.609|0.926|0.562|0.755|
|Finland|0.998|0.596|0.928|0.552|0.796|
|Iceland|0.998|0.575|0.943|0.541|0.745|
|Norway|0.997|0.591|0.925|0.545|0.771|
|Sweden|0.997|0.616|0.950|0.583|0.795|
|**Russian Federation**|**0.993**|**0.601**|**0.879**|**0.525**|**0.681**|
|**South Caucasus**|**0.983**|**0.421**|**0.934**|**0.386**|**0.576**|
|Armenia|0.988|0.414|0.941|0.385|0.579|
|Azerbaijan|0.978|0.414|0.939|0.381|0.578|
|Georgia|0.990|0.445|0.913|0.402|0.569|
|**Southern Europe**|**0.997**|**0.518**|**0.957**|**0.494**|**0.728**|
|Cyprus|0.998|0.589|0.960|0.564|0.756|
|Greece|0.996|0.519|0.945|0.488|0.690|
|Italy|0.997|0.500|0.963|0.480|0.728|
|Malta|0.993|0.502|0.943|0.470|0.709|
|Portugal|0.996|0.548|0.952|0.520|0.769|
|Spain|0.997|0.533|0.954|0.507|0.728|
|**Turkey**|**0.989**|**0.453**|**0.952**|**0.426**|**0.649**|
|**Western Balkans**|**0.993**|**0.442**|**0.925**|**0.406**|**0.631**|
|Albania|0.991|0.434|0.933|0.401|0.634|
|Bosnia and Herzegovina|0.994|0.391|0.930|0.362|0.580|
|Kosovo|0.985||||0.567|
|Montenegro|0.997|0.451|0.923|0.415|0.633|
|North Macedonia|0.990|0.390|0.928|0.359|0.557|
|Serbia|0.994|0.485|0.919|0.443|0.677|
|**Western Europe**|**0.996**|**0.583**|**0.932**|**0.541**|**0.765**|
|Austria|0.996|0.568|0.941|0.533|0.747|
|Belgium|0.996|0.588|0.935|0.548|0.760|
|France|0.996|0.584|0.922|0.537|0.763|
|Germany|0.996|0.541|0.929|0.501|0.751|
|Ireland|0.996|0.635|0.928|0.587|0.793|
|Luxembourg|0.998|0.524|0.932|0.487|0.686|
|Netherlands|0.996|0.624|0.935|0.581|0.790|
|Switzerland|0.996|0.584|0.952|0.553|0.756|
|United Kingdom|0.996|0.620|0.940|0.580|0.783|
|**Simple average**|**0.993**|**0.520**|**0.932**|**0.481**|**0.691**|
|**Population-weighted average**|**0.993**|**0.539**|**0.927**|**0.496**|**0.704**|

25
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_Note:_ Figures are based on the Europe and Central Asia extension of the Human Capital Index extension of (ECAHCI).

**Table 9 Gender-disaggregated estimates of the Europe and Central Asia extension of the Human Capital Index (ECA-HCI)**

||_Probab_<br>_survival t_|_ility of_<br>_o age 5_|_Educa_<br>_compo_|_tion_<br>_nent_|_Health co_|_mponent_|_EC_|_A-HCI_|
|---|---|---|---|---|---|---|---|---|
|_Subregion/country_|_Men_|_Women_|_Men_|_Women_|_Men_|_Women_|_Men_|_Women_|
|**Central Asia**|**0.978**|**0.983**|**0.433**|**0.434**|**0.920**|**0.961**|**0.390**|**0.411**|
|Kazakhstan|0.989|0.991|0.449|0.474|0.900|0.956|0.399|0.449|
|Kyrgyz Republic|0.979|0.983|0.424|0.442|0.909|0.962|0.378|0.418|
|Tajikistan|0.961|0.969|–|–|–|–|–|–|
|Uzbekistan|0.976|0.982|0.426|0.411|0.933|0.964|0.388|0.389|
|**Central Europe and Baltic**<br>**countries**|**0.995**|**0.995**|**0.503**|**0.549**|**0.909**|**0.957**|**0.456**|**0.524**|
|Bulgaria|0.992|0.994|0.431|0.457|0.914|0.953|0.391|0.432|
|Croatia|0.995|0.996|0.479|0.525|0.921|0.961|0.439|0.502|
|Czech Republic|0.996|0.997|0.523|0.570|0.921|0.957|0.480|0.544|
|Estonia|0.997|0.998|0.579|0.641|0.894|0.954|0.516|0.610|
|Hungary|0.995|0.996|0.487|0.509|0.910|0.953|0.441|0.483|
|Latvia|0.996|0.996|0.521|0.600|0.873|0.945|0.453|0.565|
|Lithuania|0.996|0.996|0.562|0.625|0.873|0.952|0.488|0.592|
|Poland|0.995|0.996|0.557|0.622|0.923|0.964|0.512|0.597|
|Romania|0.992|0.993|0.418|0.436|0.877|0.949|0.364|0.411|
|Slovak Republic|0.994|0.995|0.470|0.518|0.911|0.958|0.426|0.494|
|Slovenia|0.998|0.998|0.533|0.614|0.923|0.959|0.491|0.588|
|**Eastern Europe**|**0.991**|**0.993**|**0.517**|**0.551**|**0.860**|**0.942**|**0.441**|**0.515**|
|<br>Belarus|0.996|0.997|0.534|0.560|0.863|0.943|0.459|0.526|
|Moldova|0.982|0.986|0.419|0.443|0.888|0.953|0.366|0.416|
|Ukraine|0.990|0.992|0.521|0.558|0.857|0.940|0.443|0.520|
|**Northern Europe**|**0.997**|**0.997**|**0.570**|**0.643**|**0.921**|**0.950**|**0.523**|**0.610**|
|Denmark|0.995|0.996|0.575|0.647|0.912|0.940|0.522|0.606|
|Finland|0.998|0.998|0.553|0.644|0.907|0.949|0.500|0.611|
|Iceland|0.998|0.998|0.536|0.621|0.934|0.953|0.500|0.591|
|Norway|0.997|0.998|0.557|0.630|0.911|0.939|0.506|0.590|
|Sweden|0.997|0.998|0.584|0.648|0.939|0.962|0.546|0.622|
|**Russian Federation**|**0.992**|**0.994**|**0.582**|**0.623**|**0.840**|**0.917**|**0.485**|**0.568**|
|**South Caucasus**|**0.981**|**0.985**|**0.417**|**0.426**|**0.908**|**0.958**|**0.371**|**0.402**|
|Armenia|0.986|0.989|0.401|0.427|0.912|0.967|0.361|0.409|
|Azerbaijan|0.976|0.981|0.416|0.412|0.921|0.957|0.374|0.387|
|<br>Georgia|0.989|0.991|0.431|0.461|0.871|0.954|0.371|0.436|
|**Southern Europe**|**0.997**|**0.997**|**0.503**|**0.534**|**0.945**|**0.968**|**0.473**|**0.515**|
|Cyprus|0.997|0.998|0.574|0.604|0.944|0.974|0.541|0.586|
|Greece|0.995|0.996|0.499|0.540|0.929|0.961|0.462|0.517|

27
|Italy|0.997|0.997|0.481|0.519|0.953|0.969|0.457|0.502|
|---|---|---|---|---|---|---|---|---|
|Malta|0.992|0.994|0.478|0.530|0.930|0.956|0.441|0.504|
|Portugal|0.996|0.997|0.529|0.568|0.933|0.969|0.492|0.549|
|Spain|0.997|0.997|0.524|0.542|0.942|0.967|0.492|0.523|
|**Turkey**|**0.989**|**0.990**|**0.453**|**0.452**|**0.939**|**0.965**|**0.421**|**0.432**|
|**Western Balkans**|**0.993**|**0.994**|**0.429**|**0.460**|**0.904**|**0.947**|**0.385**|**0.432**|
|Albania|0.991|0.992|–|–|0.910|0.957|–|–|
|Bosnia and Herzegovina|0.994|0.995|0.378|0.405|0.910|0.951|0.341|0.384|
|Kosovo|0.983|0.988|–|–|–|–|–|–|
|Montenegro|0.997|0.998|0.443|0.458|0.904|0.943|0.400|0.431|
|North Macedonia|0.989|0.991|0.375|0.407|0.909|0.949|0.337|0.383|
|Serbia|0.994|0.995|0.469|0.502|0.897|0.941|0.418|0.470|
|**Western Europe**|**0.996**|**0.996**|**0.571**|**0.595**|**0.914**|**0.949**|**0.520**|**0.563**|
|Austria|0.996|0.997|0.564|0.572|0.927|0.956|0.520|0.545|
|Belgium|0.996|0.997|0.567|0.611|0.919|0.950|0.519|0.579|
|France|0.996|0.996|0.558|0.610|0.897|0.948|0.499|0.576|
|Germany|0.996|0.997|0.537|0.545|0.913|0.945|0.488|0.514|
|Ireland|0.996|0.997|0.613|0.655|0.917|0.951|0.560|0.621|
|Luxembourg|0.997|0.998|0.501|0.547|0.916|0.948|0.458|0.517|
|Netherlands|0.996|0.997|0.601|0.649|0.916|0.955|0.548|0.618|
|Switzerland|0.996|0.996|0.575|0.593|0.942|0.962|0.539|0.568|
|United Kingdom|0.995|0.996|0.615|0.625|0.927|0.952|0.568|0.592|
|**Simple average**|**0.993**|**0.994**|**0.507**|**0.545**|**0.910**|**0.954**|**0.459**|**0.517**|
|**Population-weighted average**|**0.993**|**0.994**|**0.527**|**0.556**|**0.905**|**0.950**|**0.473**|**0.524**|

_Source:_ Authors’ calculations. _Note_ : – Not available.

# _Uncertainty intervals of ECA-HCI_

The components of the ECA-HCI are measured with some error; just as in the original HCI, an uncertainty interval can be calculated to provide a measure of the precision of the estimates. This uncertainty interval is not a statistical estimation but rather a calculation of the ECA-HCI under worst- or best-case scenarios. The worst-case scenario indicates that all the components take the lower-bound values; the best-case scenario indicates that all the components take the upper-bound values. As Kraay (2019) points out, this approach is conservative, equivalent to assuming that the measurement error is highly correlated across components. The variables for which lower- and upper-bound values are available are the probability of survival to age five; quality-adjustment factors for basic education (harmonized learning outcomes) and higher education (aggregate quality score); the prevalence of adult health risk factors (obesity, smoking, and heavy drinking); the adult survival rate; and the share of stunted children.

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# **Table 10 Uncertainty intervals for the Europe and Central Asia extension of the Human Capital Index (ECA-HCI)**

|_Subregion/country_|_ECA-HCI_|_Lower_<br>_bound_|_Upper_<br>_bound_|_Countries within the uncertainty interval_|
|---|---|---|---|---|
|**Central Asia**|**0.391**|**0.380**|**0.403**||
|Kazakhstan|0.424|0.416|0.438|TUR|
|Kyrgyz Republic|0.398|0.390|0.405|ALB, GEO, MDA|
|Tajikistan|0.330|0.314|0.343||
|Uzbekistan|0.389|0.377|0.401|ARM, AZE, KGZ, MDA, ROU|
|**Central Europe and Baltic countries**|**0.486**|**0.477**|**0.499**||
|Bulgaria|0.411|0.402|0.422|GEO, MNE|
|Croatia|0.469|0.461|0.478|HUN, MLT|
|Czech Republic|0.511|0.502|0.523|ESP, LVA, PRT|
|Estonia|0.559|0.550|0.574|CHE, CYP, DNK, FIN|
|Hungary|0.461|0.454|0.471|HRV, MLT, SVK|
|Latvia|0.506|0.495|0.525|CZE, DEU, ESP, PRT, RUS<br>|
|Lithuania|0.538|0.529|0.557|AUT, BEL, CHE, FIN, FRA, ISL, NOR,<br>POL, SVN|
|Poland|0.546|0.537|0.560|BEL, CHE, EST, FIN, ISL, LTU, NOR|
|Romania|0.387|0.377|0.400|ARM, AZE, KGZ, MDA, UZB|
|Slovak Republic|0.458|0.451|0.468|HUN|
|Slovenia|0.537|0.529|0.548|AUT, BEL, FRA, ISL, LTU, NOR, POL|
|**Eastern Europe**|**0.477**|**0.461**|**0.498**||
|Belarus|0.492|0.477|0.512|CZE, DEU, ESP, GRC, ITA, LUX, LVA,<br>UKR|
|Moldova|0.391|0.381|0.408|ALB, ARM, GEO, KGZ, ROU, UZB|
|Ukraine|0.480|0.464|0.503|BLR, DEU, GRC, HRV, ITA, LUX, MLT|
|**Northern Europe**|**0.564**|**0.553**|**0.579**||
|Denmark|0.562|0.551|0.577|CHE, CYP, EST, FIN|
|Finland|0.552|0.542|0.566|BEL, CHE, CYP, DNK, EST, NOR, POL|
|Iceland|0.541|0.527|0.562|AUT, BEL, CHE, DNK, EST, FIN, FRA,<br>LTU, NOR, POL, SVN|
|Norway|0.545|0.535|0.561|BEL, CHE, EST, FIN, FRA, ISL, LTU, POL,<br>SVN|
|Sweden|0.583|0.571|0.597|GBR, IRL, NLD|
|**Russian Federation**|**0.525**|**0.506**|**0.554**|AUT, BEL, CHE, CZE, ESP, FIN, FRA, ISL,<br>LTU, LVA, NOR, POL, PRT, SVN|
|**South Caucasus**|**0.386**|**0.375**|**0.401**||
|Armenia|0.385|0.376|0.398|AZE, MDA, ROU, UZB|
|Azerbaijan|0.381|0.368|0.396|ARM, MDA, ROU, UZB|
|Georgia|0.402|0.392|0.417|ALB, BGR, KGZ, MNE|
|**Southern Europe**|**0.494**|**0.487**|**0.504**||
|Cyprus|0.564|0.548|0.581|CHE, DNK, EST, FIN, GBR, NLD|
|<br>Greece|0.488|0.479|0.501|BLR, DEU, ITA, LUX, UKR|
|Italy|0.480|0.473|0.489|GRC, LUX, UKR|
|<br>Malta|0.470|0.462|0.482|HRV, ITA, UKR|

30
|Portugal|0.520|0.512|0.530|RUS|
|---|---|---|---|---|
|Spain|0.507|0.501|0.517|CZE, DEU, LVA|
|**Turkey**|**0.426**|**0.420**|**0.435**|KAZ|
|**Western Balkans**|**0.406**|**0.395**|**0.419**||
|Albania|0.401|0.393|0.414|BGR, GEO, KGZ|
|Bosnia and Herzegovina|0.362|0.352|0.373|MKD|
|Kosovo|||||
|Montenegro|0.415|0.396|0.428|ALB, BGR, GEO, KAZ, KGZ, TUR|
|North Macedonia|0.359|0.353|0.368|BIH|
|Serbia|0.443|0.429|0.457||
|**Western Europe**|**0.541**|**0.530**|**0.556**||
|Austria|0.533|0.522|0.549|BEL, FRA, ISL, LTU, NOR, POL, RUS,<br>SVN|
|Belgium|0.548|0.536|0.566|CHE, CYP, DNK, EST, FIN, FRA, ISL,<br>LTU, NOR, POL, SVN|
|France|0.537|0.526|0.555|AUT, BEL, CHE, FIN, ISL, LTU, NOR,<br>POL, SVN|
|Germany|0.501|0.491|0.512|BLR, CZE, ESP, LVA|
|Ireland|0.587|0.575|0.607|GBR, NLD, SWE|
|Luxembourg|0.487|0.475|0.503|BLR, DEU, GRC, ITA, UKR|
|Netherlands|0.581|0.568|0.597|GBR, IRL, SWE|
|Switzerland|0.553|0.542|0.570|BEL, CYP, DNK, EST, FIN, NOR, POL|
|United Kingdom|0.580|0.570|0.596|IRL, NLD, SWE|
|**Simple average**|**0.481**|**0.471**|**0.496**||
|**Population-weighted average**|**0.496**|**0.484**|**0.511**||

_Source_ : Authors’ calculations.

# **_6. Concluding Remarks_**

This paper provides an extension of the Human Capital Index that makes it more relevant for the education and health challenges faced by countries in Europe and Central Asia. Specifically, the extension incorporates two elements that are particularly important for the region. First, there is an additional focus on quality adjusted years of tertiary education, in addition to basic education. Second, health status is captured by including risk factors such as obesity, smoking and heavy alcohol consumption, all of which are prevalent in the region. This exercise highlights the importance of investing in tertiary education for many countries in the region, as well as the importance of preventing risk factors for noncommunicable and infectious diseases in the aging societies of the region.

As in any cross-country benchmarking exercise, there are limitations. When analyzing the contribution to productivity from higher education, the ECA-HCI does not distinguish between types of disciplines and the 31
measure of quality can be imprecise. Moreover, data on tertiary attainment are missing for some countries. In terms of the health component, the contribution of adult health risk factors to productivity is based on estimates from the literature which can be imprecise. In any case, the ECA-HCI is not to be interpreted as a measure of welfare but as a reference for policy makers on the productivity gains that can be expected from investing in the different aspects of human capital in Europe and Central Asia. Despite these caveats, the extension of the Human Capital Index presented in this paper could be useful for all middle-income countries where investments in improving tertiary education and limiting health risk factors are likely to be priorities.

32