rafmacalaba/data-use-mentions-extended huggingface dataset:
The last two columns in table 9 show the formal-informal earnings differentials. The observed one is positive and increasing in educational levels. The differential narrows in the counterfactual scenario and proportionately more for workers with more education: for workers with incomplete primary education, it falls from 1.21 to 1.11 percent vs. 1.62 to 1.26 percent for those with university education (and from 1.58 percent to 1.35 percent for the full sample). The counterfactual earnings differentials are interpreted as a result of other differences in firm characteristics that are, in principle, observable (such as unionization or job tenure), but that are not considered in our earnings regressions because they are not available for every year in the ENE-ENOE data.

Figure 5 depicts the observed and counterfactual earnings distributions of employees with complete primary, university education, and all educational categories together (note that the scales vary). Panels on the left refer to informal employees, while panels on the right refer to formal and informal ones. Considering only the former, it is clear that the mean and the dispersion of earnings increase in the counterfactual scenario; furthermore, the contrast between the two educational levels is notable. These contrasts are reduced considering the sum of formal and informal employees, in the right-hand panels, because the share of informal employees with complete primary in all employees of that educational level is higher than that of employees with university education. Still, the mean and the dispersion are larger in the counterfactual scenario for both groups. This holds for all educational groups combined, as can be seen in the bottom graphs (and verified in table 8).

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the Environmental Performance Index 2020 (Yale Center for Environment Law and Policy, 2020) finds that good policy results are associated with wealth (GDP per capita). Poor environmental regulations combined with large informal sectors might further exacerbate the challenges related to emissions. At the same time, informal firms are excluded from public incentives to reduce emissions such as subsidies to adopt energy efficiency and their workers lack opportunities to access training and skills development programs (International Labor Organization, 2018a). Therefore, understanding the relationship between informality and emissions across countries is critical for informing environmental policies in various countries that aim at reducing emissions both locally and globally.

The paper studies the relationship between the informal economy and CO2 and non-CO2 emissions in developing countries using informal employment as a proxy for the informal economy and emissions per value added. This measure of the informal economy is constructed using data collected through labor force surveys and is available for a majority of developing countries. However, it deviates from the literature, which predominantly uses the share of GDP contributed by the informal sector when studying the relationship between informality and emissions. The share of informal workers in total is data-driven as opposed to measurement of the informal sector using GDP, which is based on various estimates offered in the literature and is thus greatly dependent on the chosen estimation approach and assumptions (e.g. see Schneider (2005), Elgin and Oztunali (2014), or Orsi et al. (2012)). In nearly all developing countries, micro surveys – household and labor force surveys – include questions related to informality such as whether a worker has a contract or employment benefits, belongs to a union, contributes social security payments, or receives renumeration. However, these informality-related questions vary considerably across developing countries. To ensure consistency in the measurement of informality across countries, this paper follows Bürgi et al. (2020) and Loayza et al. (2011) and considers as “informal” only those who are self-employed and non-paid workers. While some surveys include additional information
values of the Herfindahl-Hirschman index imply greater concentration of sales/output among firms in the industry and therefore lower competition.

Next, we control for supply-side factors. These include the proportion of women in total population and the fertility rate. The data source for these variables is WDI, World Bank. Fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with current age-specific fertility rates. High fertility rates have a direct effect on women’s involvement in the labor market via less time available for working (see for example, Bloom et al. 2009; Morrison et al. 2007; World Bank 2011). Regarding the proportion of women in total population, a higher proportion implies a larger supply of women workers and therefore proportionately more women in the workforce.

Labor laws that discriminate against women vs. men can have significant impact on women’s participation (relative to men’s) in the formal labor market. For instance, using firmlevel survey data for a cross-section of countries, Amin and Islam (2016) find that the share of women in total workers among registered private firms is significantly higher in countries that have implemented laws that prohibits discrimination against women in hiring practices. Islam et al. (2019) reach a similar result for an overall measure of gender-based disparity in the laws. Following this body of work, we control for an overall measure of disparity in the laws as they apply to women vs. men in the area of starting a job. We control two additional variables for disparity in the laws that we believe may alter the strength of the relationship between exports and female workers. These variables are a dummy variable equal to 1 if women can work in the same industries as men and 0 otherwise, and a dummy variable equal to 1 if women can work the same night hours as men and 0 otherwise. Data for all the controls for legal gender disparity are from Women,
per worker.<sup>22</sup> Data on other macroeconomic aggregates such as GDP are from the World Bank’s World Development Indicators (WDI) database, with data on employment from the Conference Board’s Total Economy Database (TED), complemented by data from the International Labour Organization (ILO) where TED data is incomplete. Data are available on a consistent basis since 1970 for a sample consisting of 29 advanced economies and 74 EMDEs. Six oil-exporting EMDEs which had productivity levels above those of the United States in the 1970s are excluded from the statistical analysis of beta and club convergence. For the initial overview of the current distribution of productivity levels, the sample is expanded to 126 EMDEs and 35 advanced economies.

> 20See Romer (2011) and Barro and Sala-i Martin (2004) for a textbook treatment of the Solow-Swan model of growth.

> 21 A zero-saving rate ( _s_ = 0) implies that the capital stock per worker declines at the effective rate ( _δ_ + _n_ ), reflecting both capital depreciation and population increase.

> 22Hours worked per employee can fluctuate over time. For example, in the average OECD country since 1990, average hours per worker has fallen by 6 percent. Within the OECD, average hours per worker ranged from 1,363 in Germany to 2,148 in Mexico in 2015.

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-Teaders-Non-teadwers Pay DIfferers 8-

The results reveal an interesting systematic pattern between government workers (Equations 2 and 4) and all workers 'Equations ' and 3). The returns to schooling are lower in the government sector than in the economy as a whole, but a large premium is paid in the government sector for having earned a degree; this suggests a strong presence of credentialism(3) in the government sector only. The impact of experience on earnings, on the other hand, is twice as high in the complete sample than in the government subsample. These observations can be easily explained -)y the more compressed distribution of earnings in the gover.X sector thin in the private sector and by the fact that productivity is not a major critarion of remuneration (if one at all) in the government sector. For example, the non-significant coefficient of experience-squared in the government subsample reflects the government's habit of granting workers fixed increases (about 6%) each year, regardless of amount of experience.

The monthly number of hours worked does not appear to be a strong determinant of earnings neither in the government nor in the total sample. The Z-variables included in the equation are generally not significant: no remuneration differences are apparent due to nationality or place of work. Government workers as such do not receive a premium over private sector workers. This is perhaps surprising in view of the differences in the way the two sectors reward human capital as we discussed above, but of course, it is not inherently inconsistent with this. The results in Table 2 do suggest remuneration differences between the sexes, ranging from 20 to 57Z in favor of men (4).

However, as a recent study by Grootaert (1987) also based on the Cote d'Ivoire Living Standards Survey data has shown, sex is an important determinant of selection into different types of employment in Cote
financial systems, discussed in the remainder in this section. Principal component analysis was used to double-check the choice of variables and their assignment to the 4x2 categories from the previous research.

The Global Financial Development Database is based on this 4x2 framework. It builds on, updates, and extends previous efforts, in particular the data collected for the ―Database on Financial Development and Structure‖ by Beck, Demirgüç-Kunt, and Levine (2000, 2010). The database also incorporates data from the Financial Access Survey (http://fas.imf.org), the Global Findex (www.worldbank.org/globalfindex) and Financial Soundness Indicators <u>(http://fsi.imf.org). Appendix I provides a further description of the data sources.</u>
3 world oil price was high, total global fossil fuel subsidies exceeded US$500 billion, with oil accounting for about US$300 billion, gas and electricity accounting for more than US$100 billion each, and coal making a negligible contribution (see figure 9.1 in IEA 2014). The IEA estimates that global fossil fuel subsidies fell to US$493 billion in 2014, and that the total would have been US$117 billion higher without the reforms that had taken place since 2009 (IEA 2015).

Subsidies identified by the price‐gap approach are disproportionately concentrated outside high‐income OECD countries. For this reason, the IEA’s list of subsidizing countries does not include any OECD member other than Mexico. Not all of the price gaps are paid for by the government budget: upstream oil and gas producers in some countries are required to sell hydrocarbons to domestic consumers at prices that are far below the opportunity cost, and in a handful of cases even below the cost of production. Non‐OECD net oil exporters, current or historical, are far more likely to subsidize oil and gas than countries that are net importers (see table 8 in Kojima 2012).

By contrast, OECD member countries do not provide generalized price subsidies as identified by the price‐gap approach, and instead provide producer subsidies or targeted conditional cash transfers (or their equivalent) without lowering end‐user prices measurably. The OECD database, _Inventory of Support Measures for Fossil Fuels_ , and its companion report (OECD 2015) provide detailed qualitative and quantitative data in a consistent manner for measures that support the production or use of fossil fuels in the 34 OECD member countries and six large emerging economies (Brazil, China, India, Indonesia, the Russian Federation, and South Africa). About two‐thirds of the reported support measures take the form of tax expenditures, such as tax reductions or exemptions, accelerated depreciation and capital allowances, and royalty concessions in upstream oil and gas. Another widespread form
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salaries, expenses for utilities and communications, and scholarships. The salaries for individual types of institutions were therefore calculated on the basis of the number of teachers (see Table 23 in the main text). Utilities and communication expenses and scholarships were allocated on the basis of the number of students. As a result, the figures for each type of institution may have a wider margin of error than the figures for the entire secondary VTE system.

The budget data on salaries also do not include the important housing allowances. These vary from teacher to teacher, but an inspection of actual payroll data showed that they were on average 12% of base salaries and this figure was used in the calculations.

Salaries of expatriate technical assistants are also excluded from published budget data. They were therefore estimated on the basis of the number of expatriate teachers in each type of institution. For regular "cooperation contracts" we used published French civil servant salary levels, to which an expatriate premium and a housing allowance were added. This yielded an average annual figure of 14,926,320 CFAF, of which C8te d' Ivoire pays 71.5%, i.e. 10,672,319 CFAF. Some technical assistants are directly hired by the C8te d'Ivoire government (so-called "local contracts"), and are paid according to a special salary scale. Our figures are based on the midpoint of that scale, to which also a housing allowance was added. An average local contract cost 6,435,672 CFAF per year.
These latter examples show that cross-survey imputation methods can be used effectively to improve both the levels of _disaggregation_ and the _frequency_ at which statistics of interest may be obtained. This is of great relevance to both research and policy making. Imagine a country that may have been affected by the 2008 global financial crisis and the 2001 crisis before that. Suppose however that the only official poverty estimates that are available over this extended period are for the years 2001 and 2007, while the next official poverty estimates will not become available until 2014. This means that until 2014 policy makers will have no data on how poverty has evolved post-2007, i.e. there is no way to tell whether the recent global crisis has had any impact on poverty. By 2014, when the new official poverty estimates are published, the effects of the 2008 crisis may not be visible any longer and the window for response may have passed. The same argument can be applied to macroeconomic shocks that have occurred prior to 2007, which include the 2001 financial crisis. Relying solely on official poverty estimates derived from these household surveys does not allow us to identify any responses in poverty between survey years, which can be far apart. And poverty may exhibit important seasonal fluctuations that annual poverty estimates cannot reveal.<sup>4</sup> In this paper, we build on the existing cross-survey imputation literature to improve the _frequency_ of poverty estimates, i.e. to provide up-to-date estimates of poverty when official estimates are deemed outdated. We do this using quarterly Labor Force Surveys (LFSs) from Morocco, a country that represents an ideal natural experiment. Morocco implemented consumption surveys in 2001 and 2007 and is expecting to complete the next survey in 2013. Note however that much has happened since 2007 as well as between 2001 and 2007; two global financial crisis (2001 and 2008) and a number of domestic shocks which include favorable rainfall that has boosted agricultural output. Any one of these events may have had a significant impact on poverty levels.
if_ _(ii) There exist societies A and B for which PALEθ_ ( _A_ ) _≤ PALEθ_ ( _B_ ) _for all θ ≤_ 1 _even though H_ ( _A_ ) _< H_ ( _B_ ) _. These societies are such that H_ ( _A_ ) _< H_ ( _B_ ) _and LE_ ( _A_ ) _< LE_ ( _B_ ) _._ _Proof._ See Appendix J.

In Appendix K, we study the conditions under which comparisons by _EDθa_ ˆ are robust to the plausible values for its parameters.

# **4 Real world implications**

We now turn to data on poverty and life expectancy spanning the period 1990-2019. The data come respectively from the World Bank’s Poverty and Inequality Platform (World Bank, 2023) and the Global Burden of Disease Project (Global Burden of 15
the two sets of control variables. Note that in case of adding the OECD DSTRI instead of the ECIPE DTRI, the results would omit a large number of developing countries.<sup>26</sup> The result that now stand out is the negative coefficient results for the

> 26 Interestingly, the panel results when using the OECD DSTRI would be in line with the results found in Table 6, particularly with respect to the Digital sector (column 2). However, the years covered by the OECD DSTRI index

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# **3.1 Sample**

– We conduct the statistical analysis using a sample of 98 developing and developed countries for the period 1985 2014. They are selected from the larger sample of countries featured in the Penn World Table (PWT) 9.0 and the World Bank World Development Indicators (WDI) databases. We exclude countries that do not have a minimal set of historical data for statistical analysis, countries that depend heavily on oil production (because the contribution of oil to output could result in a large overestimation of TFP growth),<sup>1</sup> and small countries, defined as those with population less than 2 million (in 2016) (World Bank 2017m).

For the descriptive analysis of TFP growth across regions and decades (in section 4.1), we add 16 countries for which data on the share of labor in income is missing in PWT 9.0 but available from the Global Trade Analysis Project (GTAP) 9.0 (Aguiar, Narayanan, and McDougall 2016). For the descriptive analysis of TFP determinants (in section 4.2), we additionally include 22 countries, which, though not having information to obtain TFP estimates, do have data for the proposed determinant indicators. For growth projections in the Long-Term Growth Model (LTGM), we add back small countries, heavily oil dependent countries, and those for which we can complete missing data from other sources and additional assumptions; thus, the TFP extension of the LTGM can be applied to about 190 countries for growth projections.

We classify high-income countries that have been members of OECD for more than 40 years as the OECD group. The rest of countries are classified by region and income. We use the average of GDP per capita (World Bank 2017e) over 1985–2014 to break the sample into income quintiles. Table B.1 shows the country list by region and income quintile groups, indicating their inclusion in the samples by type of analysis (descriptive and
[_0.0047_]|(_0.0081_)|[_0.0076_]|(_0.0122_)|[_0.0058_]|(_0.0101_)|
|Employed|0.7313|-0.0108|0.7514|-0.0164|0.7238|-0.0087|
||[_0.0054_]|(_0.0097_)|[_0.0075_]|(_0.0140_)|[_0.0069_]|(_0.0122_)|
|Number of districts|119|119|119|119|119|119|
|Number of EAs|2,828|2,828|2,828|2,828|2,828|2,828|
|Number of households|16,530|8,220|16,530|8,220|16,530|8,220|

> a This table reports tests for covariate balance using household-level demographics (Panel A) and demographics of the household head (Panel B). Household is the unit of analysis. Columns (1), (3) and (5) report statistics for the _baseline sample_ , which consists of households excluded from the recall-module experiment. Means and standard deviations (in square brackets) are reported for variables listed at left. The _treatment sample_ consists of households randomized to the recall-module experiment. For these households, both diary and recall measurements are available. Columns (2), (4) and (6) report coefficients from regressions of each variable on the treatment dummy and a full set of dummies for strata (enumeration areas, EAs) used in the randomization design, pooling data from the two samples. Standard errors for the coefficient on the treatment dummy (in round brackets) are clustered on EAs. * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01
their firms. Panel D of Figure 1 and column 5 show a small and insignificant impact on the number of employees, which has stayed constant over time. Table A8 shows this remains the case if we examine employees in all firms (only measured in the long-run survey), as well as if we restrict attention to paid employees. This lack of labor impact is consistent with the meta-analysis of shortterm training impacts in McKenzie et al. (2023), who find a relatively precise zero impact of business training on employment in small firms. This may arise because the existing workforce (including the owner) has some slack and scope to work harder without the firm needing to hire more workers (Egger et al, 2024), as well as firms being able to use more capital in place of additional labor.

Column 6 shows that personal initiative training instead caused large increases in the capital stock of firms operated by men, with a 2-year impact of $1,298, and 7-year impact of $3,627. This 7- year impact represents an 81% increase on the control mean of $4,461, and the long-run effects are significantly larger than the short-run. Figure A1 shows that firms run by women had much lower capital stock to begin with, and that women increase capital stock much less over time. The treatment effect at 7-years of $1,166 is less than one-third of that of men, is not statistically significant, and we can reject equality with the impact for men (p=0.100). As with profits, the quantile treatment effects for capital stock are much larger for men across the distribution (Figure A1). Table A9 examines impacts on the different types of capital stock, and shows that the largest increases, and biggest gender differences, are in the amount of machinery and equipment, and vehicles.<sup>3</sup> In addition to building up more capital, there is also a positive treatment effect for men on the amount
While suggestive, these correlations are not necessarily informative on the role of human capital for economic development. On one hand, the relationship might not be causal in nature, as third factors might explain cross-country differences in both human capital and GDP per worker, and the latter might also explain the accumulation of the former. Moreover, several dimensions of human skills are inherently difficult to measure, and the proxies considered here might miss an important part of the picture. Finally, even a positive and causal relationship between average human capital and development would not be fully informative on the potentially complex role of human skills in the production process.

The approaches reviewed in this paper attempt to overcome these problems through different strategies. As a background for the discussion of these methodologies, I start by sketching in Section 2 a simple production function framework which highlights how human capital can affect aggregate economic performance.

Section 3 reviews results from the cross-country regressions approach, which consists of relating measures of economic performance and human capital in a regression model. Section 4 discusses development accounting, a methodology based on the direct calibration of an aggregate production function (rather than its estimation). Here I also replicate some of the key results in this literature using data from 2010. Section 5 reviews some of the key insights from quantitative models on the accumulation and allocation of human capital. Finally, the last Section concludes by drawing general lessons and highlighting avenues for future work.

# **2 A General Framework**

The starting point for most macroeconomic analyses on the effects of human capital is the postulation of an aggregate production function, The aggregate production function relates GDP ( _Y_ ) to the amounts of physical capital ( _K_ ), human capital ( _H_ ) and technology ( _A_ ) available in the economy. In the simplest specification, aggregate human capital is the sum of the human capital of all workers, that is _H_ “ _hL_ where _h_ is the human capital of a representative worker and _L_ is the number of workers. Alternative specifications assume that _H_