Figure 2 shows that when the better employment rates are very low, the full UHCI converges to 0.2 ( = H m ), regardless of the value of the HCI. This is because even if people are potentially more productive due to a high mm HH human capital, they are not able to utilize that human capital. The UHCI increases as better employment expands, as people can increasing use their human capital to increase productivity, with a faster rate of increase for those countries with a high HCI.

# **Section 3. Data and Measurement**

The 2020 HCI data are available for a large number of countries (174), and one of the goals of the UHCI is to maintain the wide cross-country coverage of the HCI.<sup>22</sup> To calculate the UHCIs also requires a range of employment data, which are taken from the International Labour Organization (ILO) and the World Bank’s Jobs Indicator Database (JOIN), generally using the most recent year if multiple data sources are available.<sup>23</sup> Both the ILO and JOIN also provide splits by gender.

# **3.1 Data — Basic UHCI**

For the basic UHCI, the main extra data series required is the employment-to-population ratio of 15-64-year-olds. Employment data are available for 185 countries (182 after conditioning on the availability GNI per capita).<sup>24</sup> Combining the employment rate data and HCI data, I can calculate the basic UHCI for 169 countries/regions.

The main source of employment-to-population data is the ILO series “Employment-to-population by sex and age (%) – Annual”, Age (Youth,Adults): 15-64, using the latest period available.<sup>25</sup> The ILO employment-to-population data are the most recent for 150 countries. Some of these countries do not have HCI data, so ILO employment-topopulation data are used for 83% of the basic UHCI sample (140 countries). The median year of the survey used is 2018 (and mostly range from 2010-2019).<sup>26</sup> The ILO data are mostly sourced from the ILO “microdata repository”.<sup>27</sup> It should be noted that the ILO data usually applies to the whole population, but occasionally the underlying surveys focus on certain geographies (like the urban population), exclude borderline ages (like 15 year olds), or exclude certain subgroups (like those employed in the armed forces).<sup>28</sup> Note that employment rates in other publications (and in policy discussions) are sometimes quoted for the 15+ age group rather than the 15-64 age group (possibly using from other underlying surveys), and so can differ from the ones used in this paper.

22 HCI data can be downloaded from the World Development Indicators website: http://wdi.worldbank.org 23 In practice, the algorithm is slightly more complicated because I make sure that both basic and full utilization rates come from the same data set and give preference to actual data over interpolated data. The data requirements of the basic utilization rate are a subset of those of the full utilization rate. First, if only one data set has the full data available, I choose that one. Second, I choose JOIN data if the ILO data are interpolated. Third, I choose the most recent data set if both are available, with a preference for ILO data if both data sets are from the same year.

24 The three large countries/regions without GNIPC data are Somalia, the Syrian Arab Republic, and Taiwan, China. They also do not have HCI data, and so I cannot calculate either UHCI or the full utilization rate.

> 25 Downloaded from https://www.ilo.org/shinyapps/bulkexplorer7/ on 13 December 2019. In cases where there are multiple sources for a country for the latest period (e.g. Thailand, Costa Rica and Armenia), I use the labor force survey (LFS).

26 The reported “year” of the ILO data reflects the average of the years of different data sources used in the construction of the full utilization measure. Generally, this is the average of the year of the employment rate data (basic utilization) and the data used to construct the SEBJ. In countries with interpolated ILO data, the year of the SEBJ data is itself an average of the year of the agricultural employment data and the all-sector employee/employer data. This averaging process explains why the reported year is not always an integer. For countries missing the full utilization rate, the year is only that of the employmentto-population data. The only ILO data from before 2010 are for St. Kitts and Nevis, which come from the 1990s.

27 That is, they are calculated by ILO staff based on microdata from labor force surveys or living standards surveys – though there are some from other sources. They are similar to the ILO’s “National Estimates” reported in the World Development Indicators. The ILO also produces “Modelled Estimates” that are highly interpolated and are not used in this paper. 28 For example, the Labor Force Survey underlying the Peruvian data covers the “main city of metropolitan area.” 11