metadata: authors: 0: author: Pennings,Steven Michael
count: World
docna: 0: docna: The Utilization-adjusted Human Capital Index (UHCI)
docty: Policy Research Working Paper
subtopic: Employment and Unemployment,Labor Markets,Educational Sciences,Labor & Employment Law,Health Care Services Industry
teratopic: Social Protections and Labor,Education,Law and Development,Industry
abstracts: cdata!: The World Bank Human Capital Index (HCI)
            is based on the productivity gains of future workers from
            human capital accumulation. But in many developing
            countries, a sizeable fraction of people are not employed,
            or are in jobs in which they cannot fully use their skills
            and cognitive abilities to increase their productivity. The
            Utilization-adjusted Human Capital Indices (UHCIs) adjust
            the HCI for labor-market underutilization of human capital,
            based on fraction of the working age population that are
            employed, or are in the types of jobs where they might be
            better able to use their skills and abilities to increase
            their productivity (“better employment”). The UHCIs
            generalize the growth-based interpretation of the HCI: the
            inverse of a country's UHCI score represents long-run
            GDP per capita with complete human capital and complete
            utilization, relative to that under the status quo. The
            UHCIs are designed to complement the HCI, and not to replace
            it: they have different purposes, and the challenges of
            measuring utilization mean that the UHCIs should be
            interpreted with caution for policy analysis. Both
            utilization measures are available for more than 160
            countries, and are roughly U-shaped in per capita income,
            suggesting human capital is particularly underutilized in
            middle-income countries. Human capital is also underutilized
            for women: while the HCI is roughly equal across boys and
            girls, female UHCIs are typically lower than those for
            males, driven by lower employment rates.
repnme: repnme: The Utilization-adjusted Human Capital
            Index (UHCI)
display_title: The Utilization-adjusted Human
            Capital Index (UHCI)
listing_relative_url: /research/2020/09/32407464/utilization-adjusted-human-capital-index-uhci
topicv3: Education m1327048 470,Jobs m1503931 2670,Working Age Population m1504053 2749,Employment Rate m1504079 2895,Health m1504661 3480
publishtoextweb_dt: 2020-09-16T00:00:00Z
historic_topic: Social Protections and Labor,Education,Law and Development,Industry
origu: DECRG: Macroeconomics & Growth (DECMG)
no_of_pages: 34
author_keys: 0: author_key: 1324256
model_extractions: Policy Research Working Paper 9375 The Human Capital Index 2020 Update _Background Paper_ The Utilization-adjusted Human Capital Index (UHCI) _Steven Pennings_ Development Economics Development Research Group September 2020
Policy Research Working Paper 9375

# **Abstract**

The World Bank Human Capital Index (HCI) is based on the productivity gains of future workers from human capital accumulation. But in many developing countries, a sizeable fraction of people are not employed, or are in jobs in which they cannot fully use their skills and cognitive abilities to increase their productivity. The Utilization-adjusted Human Capital Indices (UHCIs) adjust the HCI for labor-market underutilization of human capital, based on fraction of the working age population that are employed, or are in the types of jobs where they might be better able to use their skills and abilities to increase their productivity (“better employment”). The UHCIs generalize the growthbased interpretation of the HCI: the inverse of a country’s UHCI score represents long-run GDP per capita with complete human capital and complete utilization, relative to that under the status quo. The UHCIs are designed to complement the HCI, and not to replace it: they have different purposes, and the challenges of measuring utilization mean that the UHCIs should be interpreted with caution for policy analysis. Both utilization measures are available for more than 160 countries, and are roughly U-shaped in per capita income, suggesting human capital is particularly underutilized in middle-income countries. Human capital is also underutilized for women: while the HCI is roughly equal across boys and girls, female UHCIs are typically lower than those for males, driven by lower employment rates.

This paper is a product of the Development Research Group, Development Economics. 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 author may be contacted at spennings@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
# **The Utilization-adjusted Human Capital Index (UHCI)**

Steven Pennings (spennings@worldbank.org)<sup>1</sup>

**JEL Codes:** J24, I1, I2, O4

**Keywords** :  human capital, labor market utilization, employment, health, education, development accounting

1 Any views expressed here are the author’s and do not necessarily represent those of the World Bank, its Executive Directors, or the countries they represent. This work is a background paper for the 2020 HCI update. UHCI and utilization data can be downloaded from <u>http://worldbank.org/humancapital I thank Aart Kraay, Roberta Gatti, Michael Weber, Kathleen Beegle,</u> David Weil and a number of other reviewers for helpful comments. I am also indebted to Michael Weber for providing updated data from the JOIN database which splits employment by both status and economic activity.
# **1. Introduction and Overview**

The World Bank's Human Capital Index (HCI) captures the size of the income gains when today's better-educated and healthier children become tomorrow's more productive workers. Specifically, a child born today can expect to be HCI ×100% as productive _as a future worker_ as she would be if she enjoyed complete education and full health. But this implicitly assumes that when today's child becomes a future worker, she will be able to find a job—which may not be the case in countries with low employment rates. Moreover, even if today's child is able to find employment in the future, she may not be in a job where she can fully use her skills and cognitive abilities to increase her productivity. In these cases, human capital can be considered _underutilized,_ because it is not being used to increase productivity to the extent it could be. For example, unemployed future workers may be underutilizing their human capital, as are those out of the labor force. Likewise, engineers driving taxis are underutilizing their human capital because, even though they are employed, they do not hold jobs in which their education increases their productivity. Moreover, there is a stark gender gap in terms labor market outcomes: in many countries, women face inferior job and income opportunities compared to men, even with the same human capital. As such, simply considering the HCI by sex may give a distorted view in terms of realizing the potential of human capital investments.

This paper introduces two Utilization-Adjusted Human Capital Indices (UHCIs) that, as their name suggests, adjust the HCI for labor-market underutilization of human capital in a consistent way across a large number of countries. The UHCIs are designed to complement the main HCI, and _not_ to replace it. In part this is because they have different purposes: the HCI is an index of supply of a factor of production (in the future), whereas the UHCIs are a hybrid between an index of factor supply (capturing investment in human capital), and a productivity index (capturing how efficiently that human capital is used in production). Second, there are numerous challenges in defining and measuring utilization in a consistent way across diverse country contexts. As such, the UHCIs should be seen as a first step towards a simple and consistent cross-country measure of human capital utilization and should be interpreted with caution for policy analysis.

Like the HCI, the UHCIs focus only on the private gains to worker productivity from human capital, which does not include broader gains from human capital to the production of children’s human capital, women’s empowerment, technological advancement, political economy and other areas. Moreover, incomplete utilization should not be interpreted as there being no gains from human capital investments, but rather that private labor market gains are just smaller than they could be.

Also like the HCI, the UHCI is derived to measure the effect of human capital on future GDP per capita. Specifically, Kraay (2018) writes that "Projected future per capita GDP will be approximately 1/ H times higher in a 'complete education and full health' scenario than in a ‘status quo’ scenario." Both UHCI measures are derived in a similar HH way. However, for the UHCIs, utilization rates are now different in the status quo and full human capital scenarios. Specifically, both UHCIs are derived as future GDP per capita under the _status quo_ relative to future GDP per capita with complete health, education, _and full utilization_ .<sup>2</sup> This means that, in the long run, GDP per capita will be 1/ _UHCI_ higher in a world of full utilization, full health, and complete education than in the _status quo_ .

While the two UHCIs take different approaches to measuring utilization, they have the following simple form, as the utilization rate multiplied by the HCI: (1) b f f ) = U b f f ) × HCI 2 In other words, a child born today can expect to be only UHCI×100% as productive as she would be, on average, if she enjoyed UUH HH(b bb o bbffbbUUbbUUbbooUU RRbbUURR (b bb o 2 In other words, a child born today can expect to be only UHCI×100% as productive as she would be, on average, if she enjoyed complete education and full health, and her future labor was fully utilized in better employment.

2
The first measure is the **basic UHCI** **_,_** which captures the income gains from employing all potential workers. The basic utilization measure is the employment-to-working-age-population ratio, drawn from the ILO and the World Bank’s JOIN database. The basic UHCI has the advantage of simplicity, ease of construction and measurement. It is also available for a wider variety of countries and helps to fix concepts.

# (2) Utilization (basic measure) = Employment/population (working age)

Second, I produce the **full UHCI** which, in addition to the gains from higher employment rates, also takes account of the income gains from moving workers to jobs where they can better use their human capital to increase productivity (“better employment”). The full utilization measure also has the desirable property that it is measured relative to potential: countries with high levels of human capital are penalized more heavily if people are not in the types of jobs where their human capital can be used to increase productivity. I define “better employment” as nonagricultural employees plus employers, which excludes the lowest productivity jobs in developing countries where human capital could have less effect on productivity: subsistence own-account/family agriculture, non-farm selfemployment (which is often working in household microenterprises, as own-account workers or contributing family workers), and landless agricultural laborers.<sup>3</sup> This definition is not intended as a value judgment, but rather is based on the type of jobs that are relatively rare in low-income countries, but are common in high-income countries— suggesting they are associated with higher productivity (Merotto et al. 2018). The better employment rate (BER) is defined as the fraction of the working age population in better employment: (3) Better employment rate (BER) = (Non-agricultural wage employees+ employers)/Population (working age) The full UHCI is a weighted average of the HCI score for those in the better employment (who are as productive as their human capital allows), and the theoretical minimum HCI for the rest of the working age population. The theoretical minimum HCI ≈0.2 represents the relative productivity of “raw labor”, before it is boosted by human capital (see Box 1): (4) UHCI (full measure) = BER × H +(1-BER) × (minimum HCI) HH The full utilization rate is given by Equation 5 and is the full UHCI divided by the HCI. This means that the utilization rate is a weighted average of 1 (complete utilization for those in better employment), and reduced utilization rates for the rest of the working age population. The utilization rate for the rest of the working age population (1-BER) is the productivity of raw labor (HCI min) relative to potential productivity of those workers (HCI). While the full UHCI is my preferred measure because it is more comprehensive and realistic, it has the disadvantages of greater complexity, slightly reduced country coverage and taking a stand on the definition of better employment.

# (5) Utilization (full measure) = BER × 1 +(1-BER) × (minimum HCI)/HCI

Despite different methodologies, the basic and full measures produce broadly similar utilization rates and UHCI scores. They are both available for around 165 countries, and the average UHCI score is about 2/3 the size of the HCI (mechanically it will be lower). Utilization rates are roughly U-shaped in per capita income across countries, slightly declining with per capita income across low and middle income levels and the rising rapidly from middle to higher income levels. This feature of utilization rates implies that UHCIs are low in the poorest countries, where

> 3 This definition of “better employment” came out of discussions with Michael Weber and Kathleen Beegle, and I thank them for both their constructive criticisms of the basic measure and helpful suggestions for refinements.

3
the HCI is also low on average, but remain low over a wider range of countries where rising HCI with income is offset by declining utilization.

Despite the similar average utilization rates for different income groups, the reason for those different utilization rates varies across methodologies at low-middle income levels, as do the scores for many individual countries. For the basic measure, higher employment rates among low income countries are driven, in part, by necessity to sustain livelihoods. But for the full measure, only a very small fraction of the working age population is in better employment, and so higher utilization rates are driven by low HCI scores that reduce the potential for underutilization. At lower income levels, there is substantial variation in basic utilization (employment) rates, due in part to an inconsistent classification of employment of subsistence agriculture at the country level. This also leads to differences between the full and basic utilization rates for individual countries. However, almost all high income countries have both high rates of employment and high rates of better employment, leading both utilization scores to be bunched in a narrow range.

Both UHCIs reveal a starkly different result from the HCI in terms of gender gaps. While the HCI is roughly equal across boys and girls, with a slight advantage for girls on average, the female UHCIs are typically lower than those for males. This is driven by lower overall utilization rates for females, especially in the Middle East and North Africa (MENA) and South Asia (SA). Gender gaps are smaller for the full utilization measure than the basic measure. In general, the UHCI ranges from around 1⁄4 in Sub-Sharan Africa (SSA) and SA, around 1/3 in Latin America (LAC) and MENA, 0.4 and East Asia and the Pacific (EAP) to almost 1⁄2 in Europe and Central Asia (ECA).

The rest of this paper is as follows. Section 2 outlines the conceptual framework behind the measurement of the UHCI (basic and full measures). Section 3 discusses the data used, and various measurement issues. Section 4 presents the main results for the aggregate UHCI and utilization rates (basic and full measures), and explores how they vary by income level and HCI score. Section 5 disaggregates the UHCI measures by gender and region. Section 6 concludes. The Appendix reports several extensions and robustness tests. Utilization rates, UHCIs and their components are available for download from http://www.worldbank.org/humancapital

# **Section 2. Conceptual Framework**

As mentioned above, my goal is to produce an index that adjusts the HCI for human capital utilization, while maintaining the simplicity, broad cross-country coverage and productivity-based approach of the HCI. The UHCI meets these goals by introducing a new variable to scale the HCI, the utilization rate, as in Equation 1. **2.1 The basic UHCI** Labor market utilization in the basic UHCI is a ratio of current employment relative to a measure of potential employment under “full utilization” (Equation 6). In keeping with the spirit of the HCI, I define the target as the LL maximum theoretical employment (“complete employment”). The standard definition of the potential labor force is the working age population 15-64 years old, which I also adopt for LL<sup>⋆</sup> .<sup>4</sup> Employment is defined as the number of people 15-64 years old who are employed, to be consistent with the definition of the potential labor force. The LL<sup>⋆</sup> LL definition of employment includes informal workers and most of the self-employed.<sup>5</sup> Note that for simplicity and 4 For example, the working age population 15-64 is the denominator of the labor force participation rate. Naturally, no countries will have employment rates equal to 1, but this is consistent with approach in the HCI, where no country achieves full schooling or health either.

5 Own-account agriculture workers (subsistence agriculture) are often excluded from the definition. By having the working age population in the denominator, I abstract from changes in dependence ratio, and changes in the retirement age.

4
due to data constraints, the definition of employment in the numerator does not adjust for the intensity of work (part time or full time), and the denominator does not adjust for people who are not available to work, or those who do not want a job.<sup>6</sup> Just like for the HCI, the basic UHCI is derived based on long-run income gains. Whereas the HCI measures the increase in long run per capita income a country could gain from moving to complete schooling and health alone, the UHCI measures the increase in long run per capita income from complete schooling, health _and complete utilization of the potential labor force._ Just like for the HCI, this involves comparing two different worlds: a status quo world where today’s policies continue, and an alternative world with complete health, education, and utilization. An important, if technical, advantage of this setup that in the alternative world, everyone works and so one can rigorously apply the Mincer returns to human capital that underlie the HCI.<sup>7</sup> To derive the basic UHCI, I start with a standard Cobb-Douglas production function as in Equation 7, identical to that in Kraay (2018) (but with slightly different notation). Here is GDP, h is the stock of human capital per worker, is the number of workers, is the stock of physical capital, is Total Factor Productivity (TFP) and is a parameter for the labor share of income. As in Kraay (2018), each of these YY , , , h and are for the next generation (NG), LL KK AA ββ because it takes at least a generation for the education and health policies affecting today’s children (on which the YY AA KK LL HCI is based) to affect the productivity of the workforce that affects GDP (I omit the subscript for brevity).<sup>8</sup> Note that _h_ is not the HCI, but is worker productivity relative to that of raw labor (normalized to _h_ =1). UU= N In the status quo world, _h_ represents human capital under current policies and _L_ represents the number workers under constant/status-quo employment rates.<sup>9</sup> In the alternative world, there is complete human capital per worker, denoted h<sup>⋆</sup> , and complete employment of potential workers, denoted . Long run GDP in this complete HC-complete employment world is denoted : LL<sup>⋆</sup> YY<sup>⋆</sup> (8) (h<sup>⋆</sup> )<sup>ββ</sup> YY<sup>⋆</sup> = AAKK<sup>1−ββ</sup> LL<sup>⋆</sup> In order to derive implications for per capita GDP, (lower case), we need to divide both sides of Equation 7 and 8 by the population in the next generation N, which I assume follows the same trend under status quo and complete yy HC-utilization scenarios. We also need to assume, as in Kraay (2018), that the physical capital-to-output ratio is 6 These simplifications are partially offsetting and relaxing them is an interesting area for future research. The simplifications also apply to the full utilization measure.

> 7 Mincer regression weights determine how changes in the years of schooling and health components map into the HCI. But Mincer returns are _only_ estimated on a sample of wage workers, and so are not informative about the return to education or health for those currently not employed (or self-employed).

8 Some of the gains from human capital or utilization are indirect (through physical capital accumulation), which only occurs in the long run. For more on the short-medium run dynamics of how human capital affects growth, see Collin and Weil (2018) and the Long Term Growth Model – Human Capital Extension (LTGM-HC) (www.worldbank.org/LTGM).

9 As in Kraay (2018), only the health and education components of the HCI are used in discussions of economic growth. While the under-5 survival rate is naturally important in general, it has little direct effect on the human capital of workers in the long run, and so it does not appear in calculations here (or in Kraay 2018). Given the under-5 survival rates are usually very high, this exclusion has little quantitative impact, and so I use HCI and the version excluding the under-5 survival rate interchangeably.

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constant in the long run ⁄ (one of the Kaldor’s stylized facts). This keeps the marginal product of physical capital and the return to investment constant in the long run, and is how extra human capital spurs future investment in KK<sup>�</sup> YY<sup>�</sup> physical capital.<sup>10</sup> In per capita terms, Equations 6 and 7 become Equation 8 and 9, for status quo and complete HCUtilization worlds respectively.

One can use Equation 10 to interpret the basic UHCI. Long run GDP per capita in a complete-HC-complete utilization world will be 1/UHCI times larger than that under the status quo. For example, for countries at the mean UHCI of around 1/3, a world with complete human capital and full utilization will triple long-run per capita incomes. As the UHCI is lower than the HCI, the potential income gains are larger. For example, the mean HCI is around 0.5, suggesting a doubling rather than tripling of long run incomes from complete human capital alone. The difference – doubling rather than tripling – is due to the effect of increasing utilization.<sup>13</sup>

# **2.2 The full UHCI**

One conceptual issue with the basic utilization measure (employment rate) is that it assumes that all jobs are the same in terms of their ability to utilize human capital. But in practice, a large share of employment in developing countries is in jobs where workers cannot fully use their human capital. For example, in the poorest countries, around half of all workers work are on family farms or as agricultural laborers, where productivity is low (Merotto et al. 2018). For the rest, around two-thirds of non-agricultural workers are self-employed or working unpaid in family businesses. These include many small-scale traders selling household goods or food, where the majority of time is spent waiting for customers.

While there is some scope for human capital to increase productivity in these jobs, it is more limited. Filmer and Fox (2014) compare the income of household-enterprise owners of different education levels in four African 10 The marginal product of [physical] capital is M , and the return to physical investment is M (where is the constant depreciation rate). As such, a constant ⁄ , implies a constant return on investment. Higher human capital increases Y, which increases in the MPK in the short run. Higher investment is then required to reduce the MPK back to its KK= (1 −ββ)YY/KK KK−δδ δδ equilibrium level, further boosting the growth from human capital accumulation. KK<sup>�</sup> YY<sup>�</sup> 11 In Equation 10, population N, the capital-to-output ratio ⁄ ) and TFP A are identical in both worlds and so cancel out. 12 Equation 10 means that the long-run income gains from investing in human capital do not depend on the utilization rate. That is, in the framework of the basic UHCI, an increase in human capital alone has the (KK�YY� _exact_ same effect on long run economic growth as in the HCI, but just that countries can do better by also increasing utilization (it is not one or the other). 13 This also captures some extra output from a higher supply of raw labor, which I adjust for in the full UHCI measure.

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countries. On average, the increase in income due to education, while positive, is much less than would be predicted given the number of years of schooling.<sup>14</sup> Most developing countries suffer from high rates of mismatch between the level of education required for a job, and the education of the people actually doing it: the well-known anecdote of unemployed engineers driving taxis.<sup>15</sup> In the literature, this is often referred to as “over-education”, though a better description is “underutilization” as it is the lack of jobs and not the level of education that is cause of the mismatch. In some regions, especially the Middle East and North Africa, underutilization is associated with selfemployment, for example while queuing for a formal sector job.<sup>16</sup> To address this, the full UHCI introduces a concept of _better employment,_ which is designed to capture the employment categories where people can better use their human capital (subject to available data). More specifically, _better employment_ is defined as non-agricultural employees, plus employers. This definition is not intended as a value judgment, but rather is based on the type of jobs that are relatively rare in low-income countries, but are common in high-income countries—suggesting they are associated with higher productivity. The share of employment in better jobs increases from about 20 percent in low-income countries to 80 percent in high-income countries (Merotto et al. 2018, Figure 0.8). The main categories excluded from the definition are subsistence ownaccount/family agriculture, small-scale traders, and landless agricultural laborers, as these employment types are only common in low-income countries—suggesting they are more likely to have lower productivity.<sup>17</sup> By using a narrower definition, better employment also reduces variation in employment rates across low-income countries (variation in basic utilization is are often caused by variation in the employment definition used).

The definition of better employment is based on the way that the work is organized, rather whether the job is formal or informal. For example, non-agricultural employees could be formal or informal.<sup>18</sup> Better employment involves work organized in a team consisting of at least an employer and employee, where employees are paid for their work (rather than out of familial obligation). This allows a minimum degree of specialization and organization, which helps to boost productivity and for people to use their skills.<sup>19</sup> A second conceptual issue with the basic measure is that utilization should be _relative to potential_ , which will depend on how much human capital there is to underutilize. That is, a doctor working as an agricultural laborer results in severely underutilized human capital, whereas the human capital of a worker with no education doing the same job is closer to being fully utilized. This means the utilization scores of countries with higher levels of human capital should be more heavily penalized by a lack of better employment.

The final, and more technical, conceptual issue that some of the income gains in Equation 10 are from utilizing people’s time, rather than utilizing their human capital: even in a country with no human capital in both status quo and alternative worlds (h=1, raw labor), an increase in basic utilization will still increase output.

14 On average, those with a complete secondary education were only earning 60 percent more than those with no education, which is the equivalent of fewer than 6 years with an 8 percent return to education. Omitted variables such as parental income and ability mean that the 6 years is likely an overstatement.

15 See Battu and Bender (2020) for a survey. Another cause of the mismatch can be poor education quality, where those with a qualification are not able to perform the functions required. In this case, the cause of the engineer driving a taxi is because of he or she is not able perform the tasks of an engineer due to poor quality education.

Handel et al (2016) find that in 12 low- and middle-income countries, the overeducation/underutilization rate is 36 percent. Overeducation/underutilization rates vary across countries and can depend on how it is measured.

16 See Handel et al (2016) and Gatti et al (2013).

17 The categories here are based on "International Classification by Status in Employment, 1993 (ICSE-93)”, and also exclude workers not classifiable by status.

18 The definition of formal employment varies across countries, but generally refers to the coverage of the worker with respect to benefits like unemployment insurance, pensions, and sick or annual leave.

19 Better employment is different from “decent work” (ILO) and “Good jobs for development” (WDR 2013).

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Putting these concerns together first suggests that the full UHCI should depend on the better employment rate (BER) (better employment as a share of the working-age population), rather than raw employment rates. However, the utilization rate is not simply the BER, because this fails to adjust for how much human capital there is to underutilize. Instead, utilization rates for those without better employment should depend _inversely_ on the HCI (relative to a natural minimum). Finally, there should be some adjustment for utilization of people’s time. The full utilization measure and full UHCI capture all of these concerns. <u>Deriving the full UHCI as the long run income gains from complete HC and complete utilization</u> The full UHCI is derived in the same way as the basic UHCI, as the ratio : future GDP per capita under statusquo policies, as a fraction of future GDP per capita in an alternative complete-HC-complete-utilization world. Per capita GDP for the full UHCI in the complete HC-complete utilization world is the same as in basic UHCI. yy/yy<sup>⋆</sup> yy<sup>20</sup> The yy<sup>⋆</sup> differences between the full and basic UHCI depend on how the labor contribution of status-quo GDP per capita is formed, which I explain now. Let B be the number of workers in better employment. I assume these workers are as productive as their human capital allows—in other words, their human capital is fully utilized (utilization rate of 1). The per worker boost to LL productivity is just their human capital h, and so the effective contribution of all workers in better employment to GDP is B . hLL is the total number of people employed (in better employment and other types of employment). I assume that those employed, but without better employment B ) , are only as productive as raw labor ( hm =1). So, LL their contribution GDP will be hm B ). As their have more human capital than that ( h> 1 ), it is being mm underutilized. The degree of underutilization is their productivity relative to potential under current policies 1/ (LL−LL h . mm (LL−LL Finally, is the potential labor force (equal to the working age population, as before). Moving to a world of complete utilization involves an expansion of employment of . To prevent misclassifying the income gains LL<sup>⋆</sup> from increase in utilization of time (raw labor) as utilization of human capital, I add the raw labor contribution hm to status quo GDP. LL<sup>⋆</sup> −LL mm (LL<sup>⋆</sup> −LL) For the derivation, we will also have to use the definition of the HCI: human capital h relative to its theoretical maximum h<sup>⋆</sup> (HCI= h/h<sup>⋆</sup> ). The minimum HCI is defined in the same way, but with hm = 1 rather than h : H m = 1/h<sup>⋆</sup> ≈0.2 (see Box 1). mm mm HH

> 20 Except full employment is in better jobs.

21 A minor complication is that data on employees and employers are often for all age groups, whereas the employment-topopulation ratio is based on the working age population. This is why the BER is based on the product of these two ratios.

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As mentioned above, the labor contribution to future GDP per capita in the status-quo world is different from that in the basic UHCI. Specifically, in Equation 8 is replaced by three terms: • The contribution to GDP of workers in better employment is B (blue). This is similar to that in the basic UHCI, but only covers workers in better employment hLL B , rather than all workers . • The contribution to GDP of the raw labor of those employed, but outside better employment is hLL hm B ) (green). Recall that these workers have human capital LL h> hmLL , but cannot use it to increase productivity in their jobs. mm mm • An additional term to adjust for income gains from greater utilization of people’s time (raw labor) in full (LL−LL employment, hm (purple). This boosts GDP in the status quo world, to make sure that GDP per capita in the status quo and alternative worlds are measured on a consistent basis. mm (LL<sup>⋆</sup> −LL) As such, future GDPPC in the status quo world becomes Equation 13 (recall is TFP, ⁄ is the capital-to-output ratio and N is population – all of which are the same in the status-quo and complete HC-complete utilization world). AA KK<sup>�</sup> YY<sup>�</sup> (13) ⁄ )<sup>(1−ββ)/ββ</sup> ( B + hm B ) + hm mm mm yy= AA<sup>1/ββ</sup> (KK<sup>�</sup> YY<sup>�</sup> hLL (LL−LL (LL<sup>⋆</sup> −LL))/NN The full UHCI can be simply derived as the ratio (recall is future GDPPC in the in the complete HC-complete utilization world): yy/yy<sup>⋆</sup> yy<sup>⋆</sup>
labor relative to its theoretical maximum. For the full UHCI, I assume workers without better employment are only as productive as raw labor.

The minimum HCI score can be calculated by substituting the minimum possible scores for education and health into the standard HCI Equations 9-12 in Kraay (2018): zero expected quality-adjusted year of schooling (QAYS=0), complete stunting (not stunted (NS) rate of 0), and zero chance of adults surviving to age 60 (ASR=0). The probability of survival to age 5, which is part of the HCI but not in a growth accounting framework, is assumed to be 1 as this does not affect the growth calculations.

be 1 as this does not affect the growth calculations. (16) H m m m mm mm mm HH ≡SSffooSSbbSSbbff× SSbbho<sup>mm −14)</sup> ff × HHRRbbffUUh<sup>mm−1)+0.35×(AASSRRmmm−1))/2</sup> = 1 × RR≈0.2<sup>0.08×(QQAAYYSSm</sup> × RR<sup>(0.65×(NNSSm</sup> = 1 × RR<sup>0.08×(0−14)</sup> × RR<sup>(0.65×(0−1)+0.35×(0−1))/2</sup> In Appendix 4, I calculate an alternative H m assuming that raw labor (for those without better jobs) makes use of health human capital, but not education human ca AAffAA <u>pital. The results are broadly similar.</u> mm HH

# <u>Example full UHCI scores</u>

The full utilization rate and full UHCI change with both the better employment rate (BER) and the HCI score of a country.

**Figure 1: Theoretical Full Utilization — by BER and HCI Figure 2: Theoretical Full UHCI — by BER and HCI**

<!-- Start of picture text -->
1.0 1.0<br>0.9 0.9 HCI=1<br>0.8 0.8 HCI=0.75<br>UHCI=0.5<br>0.7 0.7<br>HCI=0.25<br>0.6 0.6<br>HCI=1<br>HCI=0.2<br>0.5 0.5<br>HCI=0.75<br>0.4 0.4<br>HCI=0.5<br>0.3 0.3<br>HCI=0.25<br>0.2 0.2<br>HCI=0.2<br>0.1 0.1<br>0.2 is the minimum HCI (productivity of raw labor) 0.2 is the minimum HCI (productivity of raw labor)<br>0.0 0.0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Better Employment Rate (Better Emp/Working Age Pop.) Better Employment Rate (Better Emp/Working Age Pop.)<br>Full UHCI<br>Full Utilization Rate<br><!-- End of picture text -->

The lowest full utilization rates will be in countries with _both_ a small fraction of potential workers in better employment AND a high HCI, as this combination wastes the most human capital (blue solid line in Figure 1). This corresponds to the “engineers driving taxis” anecdote mentioned above. The utilization rate will be _highest_ (close to 1 with ), in two polar opposite cases. First is where better employment rates are very high ( 1 , right of Figure 1). This yields a high utilization rate regardless of the value of the HCI, but in practice only happens UUH HH≈H HH U <u>m m</u> BBEERR≈ in high income countries. The second is in countries with little human capital to underutilize ≈1 (blue line U UU with squares in Figure 1), regardless of the value of BER. In these second group of countries, the income gain from UU being in better employment is negligible because productivity is constrained by human capital, not by an absence of better jobs (“taxi drivers driving taxis”).

10
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
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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.

13
# ~~———————~~ x (1 —@x ~~—__—_____—)~~ + ~~—__—~~

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<!-- Start of picture text --> 2<br>2<br>o-<br>2) ones<br>om e@syc ear<br>=<< ONstst OOEo Ee cHE cHE<br>=SoSo oncreSRUS AirsSRUS Airs Airs<br>3, OKAZ eR 0g: @LUX<br>o% e oeyRE<br>s @UKR emu.” HS<br>oLuLu SRPonon OReleseeeleseeee L ITA<br>. oSOROTIIS<br>6G Sgn Fire<br>gs ech I ero<br>= eKcz CU @TUR<br>5raoanaocraoanaocoanaocaoc ovDeaptBNA,NMCeaptBNA,NMCeBNA,NMCeNMCeCe 3 anBFRY ZA,BFRY ZA,GABoyoy GABoyoy @SAU<br>o OOO ecm ew ESO<br>5 6 7 8 9 10 11 12<br>log GNI per capita (Altas)<br><!-- End of picture text --> <!-- Start of picture text --> @QAT<br>go enn @ARE<br>S @oMn f®KWT<br>Be ; @Mac<br>D enru °SAY egies? CHE CHE<br>zoNoN ORBHGESEQUILTQUILT @ @REDMNOREHSEHS<br>Fo55 @BLRSEORURE Se‘mca KOSSEORURE Se‘mca KOS Se‘mca KOS‘mca KOS KOS EETS Lux<br>5 eguseMb MRRP @ESP<br>Lio @TJK ep y OITA<br>cS * @UKR °<br>§22 @KHM OSCEeeee 5RARA VEN<br>Lt ecth —— SURIERRUR SURIERRUR TREN<br>5 enpL © PARBIZE BAe ECU ECU<br>g Sh evr ery ery<br>EN Age @ VUT VUT<br>wi”rat5.rat5.5. @BD)noteF LSNN BRIND@LAO@LAO @LBN<br>oan @NE<br>o o @Lso<br>5 6 7 8 9 10 11 12<br>log GNI per capita (Altas)<br><!-- End of picture text --> <!-- Start of picture text --> 2 @QAT<br>2 go enn @ARE<br>o- S @oMn f®KWT<br>2) ones Be ; @Mac<br>om e@syc ear D enru °SAY egies? CHE CHE<br>=<< ONstst OOEo Ee cHE cHE zoNoN ORBHGESEQUILTQUILT @ @REDMNOREHSEHS<br>=SoSo oncreSRUS AirsSRUS Airs Airs Fo55 @BLRSEORURE Se‘mca KOSSEORURE Se‘mca KOS Se‘mca KOS‘mca KOS KOS EETS Lux<br>3, OKAZ eR 0g: @LUX 5 eguseMb MRRP @ESP<br>o% e oeyRE Lio @TJK ep y OITA<br>s @UKR emu.” HS cS * @UKR °<br>oLuLu SRPonon OReleseeeleseeee L ITA §22 @KHM OSCEeeee 5RARA VEN<br>. oSOROTIIS Lt ecth —— SURIERRUR SURIERRUR TREN<br>6G Sgn Fire 5 enpL © PARBIZE BAe ECU ECU<br>gs ech I ero g Sh evr ery ery<br>= eKcz CU @TUR EN Age @ VUT VUT<br>@SAU noteFF BRIND@LAO@LAO @LBN<br>oc ZA, wi”rat5.rat5.5. @BD)noteF LSNN<br>5raoanaocraoanaocoanaocaoc ovDeaptBNA,NMCeaptBNA,NMCeBNA,NMCeNMCeCe 3 anBFRY ZA,BFRY ZA,GABoyoy oan @NE<br>o OOO ecm ew ESO o o @Lso<br>5 6 7 8 9 10 11 12 5 6 7 8 9 10 11 12<br>log GNI per capita (Altas) log GNI per capita (Altas)<br>° |aa HC! MEE Basic Util. (Emp/Pop) (Emp/Pop)<br>ME Basic UHCI MEE Full Utilization<br>Full UHCI oss © HCImin/HCI<br>* ||EEEEEE Better Employment Rate<br>© 0.59<br>0.55 0.57 67<br>0.64 0.65 06 0.6 ;<br>o 0.48 3 27 27 oO soe soe “ a<br>=SySy ; 450.470.47 3QeQe ov. 51 05 0.54 0.5 0.53<br>2 | 040 355 34 34 0.38 .380.38 2ss 42 0.43 oo<br>230.24 240-26 = . 2:<br>n 0.23<br>N 0.17<br>o o<br>SSA SA LAC MENA EAP ECA SSA SA LAC MENA EAP ECA<br><!-- End of picture text -->

<!-- Start of picture text --> ° |aa HC!<br>ME Basic UHCI<br>Full UHCI oss<br>© 0.59<br>0.55 0.57<br>o 0.48<br>=SySy ; 450.470.47<br>2 | 040 355 34 34 0.38 .380.38<br>230.24 240-26<br>n<br>o<br>SSA SA LAC MENA EAP ECA<br><!-- End of picture text -->

<!-- Start of picture text --> MEE Basic Util. (Emp/Pop) (Emp/Pop)<br>MEE Full Utilization<br>© HCImin/HCI<br>* ||EEEEEE Better Employment Rate<br>67<br>0.64 0.65 06 0.6 ;<br>3 27 27 oO soe soe “ a<br>3QeQe ov. 51 05 0.54 0.5 0.53<br>2ss 42 0.43 oo<br>= . 2:<br>0.23<br>N 0.17<br>o<br>SSA SA LAC MENA EAP ECA<br><!-- End of picture text -->
<!-- Start of picture text --> 0.25 0.26<br>N: | | 0.16L 0.21] 0.16| 0.12<br>nN<br>‘ 42 36 37 3670.31<br>045 "a8 0.41<br>© 8 52 055<br>' -0.60 -0.65 -0.62<br>0.68<br>2 -0.77 -0.76<br>SSA SA LAC MENA EAP ECA<br>MEE Overall UHCI basic gap =MEN Basic Utilization gap<br>Human Capital gap MEE Basic Covariance Gap<br><!-- End of picture text -->

<!-- Start of picture text --> 0.24 0.24<br>N | | 0.17]: [|0.15 0.16|] 0.11[|<br>nN<br>~ 7 . 35 36 59-31<br>al "045 0.43 ~0.<br>© 0.52 0.53<br>‘ -0.60 -0.62 -0.62<br>-0.66<br>2 -0.76 0.74<br>SSA SA LAC MENA EAP ECA<br>MEE Overall UHC! full gap += Full Utilization gap<br>Human Capital gap MEE Full Covariance Gap<br><!-- End of picture text -->
<!-- Start of picture text --> “ 0.57 |L____] HCI- Male ME HC! - Female<br>0.54 [J Basic UHCI- Male EEE. Basic UHCI - Female<br>|__| Full UHCI-Male = ["____] Full UHCI - Female<br>0.450.45<br>0.44<br>t 0.38039<br>rs) 0.35<br>= 0.32 O88<br>2 0.29)<br>(6) 0.27<br>= oo. 0.25 ozo! gag 022<br>N<br>0.13<br>o<br>Latin America & Caribbean South Asia Sub-Saharan Africa<br><!-- End of picture text -->

<!-- Start of picture text --> ~7|( 7 utilization BASIC - Male [EEE Utilization BASIC - Female<br>[Utilization FULL- Male EE Utilization FULL-Female<br>© 0.77 0.75<br>0.65 0.66<br>2 0.60 0.63<br>© © 0.56 0.56<br>E 0.51 0.50 0.52<br>8<br>5+<br>0.29<br>N<br>Co<br>Latin America & Caribbean South Asia Sub-Saharan Africa<br><!-- End of picture text -->

<!-- Start of picture text --> 2 {-] HCI- Male ME HC! - Female<br>071 |[- ~~) Basic UHCI-Male [EE Basic UHCI - Female<br>0.67 |__| Full UHCI-Male = [_____] Full UHCI - Female<br>© 0.60 0.59<br>° 0.57 0.55<br>0.48 = 0.470.47<br>0.43 0.41 oar<br>t - 0.39 -<br>. 0.36<br>0.33 0.33<br>“ 0.19<br>o<br>Europe & Central Asia East Asia & Pacific Middle East & North Africa<br><!-- End of picture text -->

<!-- Start of picture text --> ~7|(- 7 utilization BASIC - Male [EEE Utilization BASIC - Female<br>[] Utilization FULL- Male EEE Utilization FULL- Female<br>© 0.73 0.77<br>071 070 0.71<br>0.65 0.66<br>s 0.59<br>“ro = 053 0.57<br>5 : 0.53<br>8<br>3+<br>0.29<br>N<br>Co<br>Europe & Central Asia East Asia & Pacific Middle East & North Africa<br><!-- End of picture text -->
<!-- Start of picture text --> =[_} SEBJ - Male MG SEB, - Female<br>[7] BER - Male MG «BER - Female<br>[) HCImin/HCl- Male =] HCImin/HCI - Female<br>(6)<br>x<br>: ©<br>50.61<br>eo, 058<br>ui 0.539 54<br>5 10.44. 0.450.45<br>ao * o :035 a 0.28 0.33<br>~ 0.24 10,200.21<br>0.08 10.09<br>o<br>Latin America & Caribbean South Asia Sub-Saharan Africa<br><!-- End of picture text --> <!-- Start of picture text --> = [_} SEBJ - Male MG SEB, - Female<br>[7] BER - Male MN BER - Female<br>|) HCImin/HCl- Male = ["___] _HCImin/HClI - Female<br>(6)<br>x 0.83 0,830.83<br>E wo | 079<br>5 0.629 60 0.63<br>ee 0.56<br>ui 0.51<br>5 10.45<br>a * 0.33” 0.34 237 9.34<br>o 0.309 28 0.28<br>~<br>o<br>Europe & Central Asia East Asia & Pacific Middle East & North Africa<br><!-- End of picture text -->
productivity, and should not be interpreted as a value judgment. In order to keep the index simple and country coverage broad, I have made simplifying assumptions about what constitutes better employment, with a focus on the most important broad types of employment. Naturally, there are many examples of jobs where people can utilize higher levels of human capital but fall outside this definition: formal self-employed workers (many doctors, consultants, tradespeople) and farmers in developed countries are good examples. In reality, the extent to which people can use human capital to boost productivity is a continuum across different job types, rather than the stark in-or-out categorization adopted here for simplicity.

This suggests that a fruitful area for future research might be a more empirical and nuanced definition of better employment, with different utilization rates across employment categories and sectors. For example, it could be that education is better utilized in informal trading (ability to do basic accounts) and subsistence agriculture (reading instructions for the application of fertilizer and pesticide) than in manual agricultural wage labor. These differences could be based on the development literature, or on observed earnings (conditional on human capital) across different employment types.

One other promising area for future work is to allow for a greater disaggregation of human capital and utilization rates. The analysis in this paper implicitly assumes that a representative agent has average human capital and average utilization rates. However, it could be that better educated/healthier subgroups of the population are more likely to work or are more likely to work in better jobs. This positive correlation between HCI and utilization measures would result in a higher UHCI. Conversely, a negative correlation (“unemployed engineers”), would reduce the UHCI. Appendix 3 shows that if the disaggregation is by gender—the only level for which aggregate data exist—the total UHCI is very similar, but this might not be the case for disaggregation at other levels.

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