_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