Human Capital Index seeks to measure. However, the HCI is expressed relative to a theoretical maximum of complete education and health (which are assigned a score of one), rather than relative to the productivity of raw labor, as is needed here. Hence, we need to calculate h= H M where H M = 0.2 is the theoretical minimum HCI (for someone with no education and poor health).<sup>20</sup> I also express the employment variables in in MM MM II/H II II terms of BERs and OERs. So, the weight becomes (equivalent to Equation 6): ωω ωω h×(B +B <u>)</u> (9) where h= H M h×(B +B RRMM RRFF ωω= RRMM RRFF)+OOBBRRMM+OOBBRRFF II/H II MM

# **3.3 Data – Full GEGI**

The primary source of data on employers, non-agricultural employees and total employment for the numerator of the SEBJ is ILO series “Employment by sex, status in employment and economic activity (Thousands)”, using the most recent year available.<sup>22</sup> The “Status in Employment” used is defined by the International Classification by Status in Employment, 1993 (ICSE-93), which breaks total employment into Employees, Employers, Own-account workers, Members of producers’ cooperatives, Contributing family workers, and Workers not classifiable by status, where I use data on the first two categories as parts of better employment.<sup>23</sup> In terms of “economic activity”, I use the broad sectors “Total” (for employers and total employment) and “non-agriculture” (for employees). The SEBJ calculated using ILO data is multiplied by the employment-to-population ratio from the ILO used in the basic GEGI.<sup>24</sup> The secondary source is the JOIN database microdata. At the time of data collection, the public JOIN data set provides data by Status in Employment or Economic Activity, but not both. As such splitting by both (with the latest surveys) needs to be constructed manually from the underlying microdata.<sup>25</sup> 20 For the backward-looking application, one could use the human capital measure from the Penn World Tables directly. This is expressed in productivity units relative to unskilled labor—like h —based on the years of schooling of the workforce today. 21 Recall the SEBJ is defined as the number of employers plus non-agricultural employees, as fraction of total employment. 22 Downloaded from https://ilostat.ilo.org/data/browse-by-subject/ on 20 February 2020 (with ICSE-93 option).

23 Note that I must use ICSE-93 rather than “Aggregate status in employment”, as the latter is missing data on employers. 24 In the initial sample based on data from Pennings (2020), Papua New Guinea, Kiribati and Tuvalu are missing data on Better Employment, and so were missing from the full GEGI. This was due to missing data on the number of employers, or missing data using the ICSE-93 classification. To ensure a more complete measure of employment gender gaps, I downloaded additional data on “Employment by sex, status in employment and economic activity (Thousands) -Annual” from <u>https://www.ilo.org/shinyapps/bulkexplorer7/ (accessed 30 September 2021), using non-ICSE-93 categories and also assuming</u> that the number of employers was zero (if it was missing). This allowed me to calculate the SEBJ for these three countries, and hence the Better Employment Gap and the full GEGI. SEBJ data for Tonga was also updated to 2018 (from ILO). 25 I thank Michael Weber for providing these data (accessed on 25 February 2020). In cases where multiple JOIN surveys are available for the same year, I use the one with the largest total employment.

9