very similar to those from JOIN in 2013. However, for other countries experiencing changes in employment rates, the methodological changes are not well documented. These measurement issues also motivate using a more specific definition of employment in the full UHCI (discussed in the next section).

# **3.2 Data — Full UHCI**

In addition to the HCI and employment-to-working-age-population (basic utilization) rate, the full UHCI requires data on the share of employment in better jobs (SEBJ) as in Equation 12.<sup>33</sup> The SEBJ is then multiplied by the basic utilization (employment) rate to form the Better Employment Rate (BER, as in Equation 11). For those countries with HCI data, the BER is available for 161 countries (and 12 other countries with missing HCI data).

The primary source of data on employers, non-agricultural employees and total employment is ILO series “Employment by sex, status in employment and economic activity (Thousands)”, using the most recent year available.<sup>34</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>35</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 UHCI.<sup>36</sup> For countries with HCI data, these ILO data are available for 115 countries or 71% of the sample.

The secondary source is the JOIN database microdata. At the time of writing, 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>37</sup> These data are used for 28 developing countries.

Finally, there a number of countries/regions that have ILO data on the total number of employees and employers, but are missing data on the non-agricultural share of employees.<sup>38</sup> In these countries I interpolate the agricultural a <u>e y</u> share of employees, ~~.~~ Hong Kong SAR, China, and Macao SAR, China, are city states, so aammaaf AAffa ff eemm effee ye ee we know almost all employees work outside agriculture, so the total number of employees will be virtually identical AANNEE= AAeeAAaaff eemmeeffeeyye ee to the number of non-agricultural employees. For other countries, I utilize ILO data on �= <u>e y</u> (employment not employees) which has wider coverage.<sup>39</sup> Using a sample of countries AAeeAAaaff a aammaaf AAffa ff eemm effee ymmeemmAA � AANNEE with both variables, I estimate a regression of , and find that for high income countries and AAeeAAaaff eemmeeffeeyymmeemmAA for other countries.<sup>40</sup> These estimates of are not surprising as self-employment and family work are AANNEE= θθAANNEE θθ≈1/2 especially common in agriculture. Given these estimates, I then interpolate the SEBJ as: θθ≈1/4 θθ

> 33 Recall the SEBJ is defined as the number of employers plus non-agricultural employees, as fraction of total employment.

> 34 Downloaded from https://ilostat.ilo.org/data/browse-by-subject/ on 20 February 2020 (with ICSE-93 option).

> 35 Note that I must use ICSE-93 rather than “Aggregate status in employment”, as the latter is missing data on employers.

36 The reason for this is that the SEBJ data are based on total employment, whereas the employment-to-population ratios are based on the 15-64 age group.

> 37 I thank Michael Weber for providing these data (accessed on 25th February 2020). In cases where multiple JOIN surveys are available for the same year, I use the one with the largest total employment.

> 38 The ILO series used here is “Employment distribution by status in employment (by sex) (%) – Annual”, downloaded from <u>https://www.ilo.org/shinyapps/bulkexplorer7/ on 22nd January 2020.</u>

39 ILO data set “Share of agriculture in total employment (%)- Annual” (latest period), accessed 22nd January 2020 from https://www.ilo.org/shinyapps/bulkexplorer7/ 40 More specifically, I trim the top 5% of More specifically, I trim the top 5% of � outliers in each case, and estimate using OLS without a constant. The estimates of 0.53 and 0.30 are rounded to 1⁄2 and 1⁄4 respectively. AANNEE 13

> 40 More specifically, I trim the top 5% of  More specifically, I trim the top 5% of � outliers in each case, and estimate using OLS without a constant. The estimates of 0.53 and 0.30 are rounded to 1⁄2 and 1⁄4 respectively.

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