rafmacalaba/data-use-mentions-extended huggingface dataset:
*|0.108***|
||(0.029)|(0.009)|(0.017)|(0.031)|(0.044)|(0.011)|(0.024)|
|Household size|0.010|-0.013*|-0.000|0.072|-0.004|0.000|-0.002|
||(0.013)|(0.004)|(0.001)|(0.011)|(0.008)|(0.002)|(0.001)|
|Region dummies|✓|✓|✓|✓|✓|✓|✓|
|Occupation dummies|✓|✓|✓|✓|☓|☓|☓|
|Observations|811|812|812|908|790|6,618|6,618|

_Notes_ . *** p<0.01, **<0.05, *<0.1 _._ A linear probability model’s coefficient estimates and standard errors are reported. This table uses panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. The sample is restricted to those aged at least 20 years old in 2012 and at most 59 years old in 2018. All control variables refer to 2012. Panel weights are used.

Table 2 shows the regression output on formality-sector transitions for men. We consider men who were employed either in the public sector or informally—which we define as the two absorbing states—at baseline and examine the transition probability to the public formal sector, private formal sector and private informal sector in 2018. Predictably, those better educated are found to be much more likely to secure formal employment, be it in the public or in the private sector. Having a secondary or a post-secondary level of education is strongly associated with moving out
# Figure 2. Composition of potential growth

A. Contributions to potential growth

## B. Contributions to potential growth in EMDEs

<!-- Start of picture text --> Percentage points<br>8 TFP Capital Labor Potential growth<br>6<br>4<br>2<br>0<br>World AEs EMDEs<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points<br>8 TFP Capital Labor Potential growth<br>6<br>4<br>2<br>0<br>EMDEs EMDEs excl. China<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> C. Contributions to potential growth in G7 and EM7 D. Contributions to potential growth in EMDE commodity exporters and nonexporters <!-- Start of picture text --> Percentage points Percentage points<br>8 TFP Capital Labor Potential growth 8 TFP Capital Labor Potential growth<br>6 6<br>4 4<br>2 2<br>0 0<br>EM7 G7 Exporters Non-exporters<br>Sources: Penn World Tables, World Bank.<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> Sources: Penn World Tables, World Bank.

Note: AEs = advanced economies; EMDEs = emerging market and developing economies. Based on production function approach, GDP-weighted arithmetic averages for a sample of 29 advanced economies and 53 EMDEs. G7 is the GDP-weighted arithmetic average of Canada, France, Germany, Italy, Japan, the United Kingdom and the United States. EM7 is the GDP-weighted arithmetic average of Brazil, China, India, Indonesia, Mexico, and Türkiye. Shaded areas indicate projections.
|(0.230)|(0.162)|
|Constant|<br>-0.208*|<br>-0.381***|<br>-0.570***|<br>-0.476***|<br>-0.777***|<br>-0.776***|<br>-1.706***|<br>-1.244***|<br>-1.520***|<br>-0.927***|
||(0.121)|(0.136)|(0.148)|(0.145)|(0.128)|(0.117)|(0.121)|(0.0906)|(0.221)|(0.195)|
|Observations|311,102,742|234,905,864|307,172,501|306,378,211|309,905,128|226,711,452|221,335,597|219,014,238|11,454,053|16,158,133|
||||Robust s|tandard errors|in parentheses||||||

*** p<0.01, ** p<0.05, * p<0.1

Source: Source: Authors’ estimates based on GHS Waves 1-3 Note: The highest education level completed among female household members above 18 years is used to proxy for the mother’s highest completed education level of existing household members in waves 2 and 3, as the GHS only asks in wave 1 about the mother’s education of all individuals, but in the later waves only for new household members. As the GHS data is unfortunately not an individual level panel, it is not possible
A one-time violation of these conditions typically results in a warning and the capping of enrollment figures for the subsidy payment until the next QAT round. A second violation results in the permanent disqualification of the school with immediate

> 14 Our own tallies based on the SLQAT data deviate slightly from the above numbers: we find that 796 and 856 schools took the SLQAT and, out of these, 511 and 432 schools achieved the minimum pass rate in phases 3 and 4, respectively.

13
Table 11 Backcasted estimates using different pass-through rates and sectoral GDP data at the US$1.90 and US$3.20 poverty lines

||Pover<br>|tyrate at US$|1.90povertyl<br>|ine<br>|Pover<br>|tyrate at US$ <br>|3.20povertyl<br>|ine<br>|
|---|---|---|---|---|---|---|---|---|
||Backcast,<br>pass-<br>through=0.<br>42|Backcast,<br>pass-<br>through=0.<br>75|Backcast,<br>pass-<br>through=0.<br>87|Backcast<br>, pass-<br>through<br>=1|Backcast,<br>pass-<br>through=0.<br>42|Backcast,<br>pass-<br>through=0.<br>75|Backcast,<br>pass-<br>through=0.<br>87|Backcast<br>, pass-<br>through<br>=1|
|2009|42.2|44.5|45.3|46.3|73.5|75.3|75.8|76.5|
|2010|40.7|41.9|42.4|42.8|72.0|72.9|73.1|73.5|
|2011|40.1|41.1|41.5|41.8|71.6|72.1|72.1|72.3|
|2012|39.5|39.9|40.1|40
292.43|512.41|76.65|134.31|369.08|646.73|
|South East Asia|559.77|983.40|147.31|258.79|707.08|1242.18|
|Western<br>Pacific,LMI|548.37|961.32|143.93|252.31|692.30|1213.63|
|Global|2328.58|4083.62|611.93|1073.14|2940.51|5156.76|

_Source : authors based on Bivins et al. (2017) and Rozenberg & Hallegatte (2015). * LMI = Low middle income countries_ Unreliable water network also impacts gender equality. When water services are limited, this vital resource needs to be collected from public standpipes, wells, bore holes, rivers or storage drums served by private tankers, with WHO data estimating that 72 per cent of this burden falls on women (Birch, 2011). Evidence in Lusaka, Zambia, shows that outages increase the time young girls spend at their chores, possibly at the expense of their education (Ashraf et al., 2017). In respect of sanitation, too, women living in poorly served settlements are characteristically responsible for disposing of fecal matter in their compounds, or accompanying their children to appropriate sites (Chant, 2007). Even if journeys are short in terms of distance, they may take long to execute where crossing inhospitable terrain, or queuing at outlets, is involved.<sup>8</sup> Furthermore, it is now widely reported in a range of settings that women and girls

> 8 Data on the time spent to fetch water are usually for rural households with no piped connection. No estimate of the time needed for connected households who experience a water supply outage could be identified.

14
# **4. Data description**

# **_Inequality measures_**

The first of the two large databases we use to derive inequality measures is that of Occupational Wages around the World (OWW). As mentioned before, the data cover the period from 1983 to 1999 and more than 150 countries. The coverage in all its dimensions, however, is problematic and fragmentary. Although there are 156 countries in total, each country does not provide data (occupational wages) for every year. The yearly country coverage varies between 48 and 76. Occupations included also vary from country to country. Moreover for a given country even when it does provide the annual data, the occupational coverage is not necessarily uniform for each year.<sup>20</sup> Furthermore, it should be noticed that each observation is an observation on “habitual” country-wide wages for a given occupation. Thus some averaging is already built into the data. That however need not be a problem since, for example, the differences in earnings by skill levels are also based on averaging. There is however a difference in that the latter are obtained through a statistical analysis that covers a well defined spectrum of wage earners (labor force survey) and controls for other relevant factors (gender, experience), while the ILO data represent a mish-mash of average “habitual” wages for different underlying populations: some countries—for some years— report monthly wage rates, others report collectively bargained wages, yet others report hourly wages. At times men and women are combined, and at times only wages for men are reported. Freeman and Oostendorp overcome the problems of data comparability by “calibration” which is essentially a process of finding the adjustment coefficients (based on a regression analysis) for the data given in a “non-standard” form, where the standard form is defined as the most common form being used in the dataset, _viz_ . monthly wages for male workers.<sup>21</sup>

> 20 For
a Public Sector Review in Zimbabwe. The data relates to the 1995/96 fiscal year. GDP is from IMF Staff Country Report and relates to 1994. Calculations of Average Public Sector wages and Average wage as percentage of GDP result from this data. Data on wages in manufacturing (monthly basis) are from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1994.

**_AS&a_**

Afghanistan Unemployment data are taken from the United Nation's Statistical Yearbook for Asia and the Pacific 1995 and refer to 1990.

Health sector employment is taken from WHO, 1993 and refers to the years 1989-1991. Employment is broken down as follows: 2,233 doctors, 267 dentists, 1,451 nurses, and 338 midwives.

Bangladesh Unemployment is taken from CIA Factbook and relates to 1993. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1990.

Data on Central Govemment employment, education, health and armed forces are taken from Syed Nizamuddin (SAlBG) paper entitled: A Review of the wage and non-wage composition of the recurrent Budget: Problems and issues in Government manpower and compensation policy (...) of February 27, 1996 and relate to 1992. Education corresponds to staff and officers of Education department, the bulk of which are primary education teachers. Health employment corresponds to staff and officers of Health and Family Welfare (91,573) and Public Health (4732) Employment in State-owned enterprises is taken from the Public Expenditure Review of July 31, 1996 and refer to 1996. Military employment data do not include personnel in paramilitary units, e.g., the Bangladesh Rifles (the border guard - 30,000 personnel), the armed police (5,000) and the Ansars (security guards -- 20.000).
Living Conditions (SILC), with 2016 income data, and 2016 budget and administrative data from the Statistical Office of Serbia, Ministry of Finance, Ministry of Education, Science and Technological Development, and Ministry of Health. It examines the following main questions: How much redistribution and poverty reduction are accomplished through social spending, subsidies, and taxation in Serbia? How progressive are revenue collection and government spending? Based on the data available, the analysis covers fiscal components that represent about 65 percent of direct taxes (83 percent of contributions), 50 percent of indirect taxes revenues, and 82 percent of spending. As with many other CEQ studies, certain categories such as corporate tax and spending on infrastructure are excluded due to the difficulty of assigning the benefit or burden to individual households based on the available information.

Our findings show that Serbia’s fiscal system reduces inequality, though it has a povertyincreasing effect. The system is redistributive, reducing the Gini coefficient of income from 0.38 at market income to 0.35 at final income, including the benefit of public expenditures on education and health. However, poverty, measured as per-capita income below $5.5/day in 2011 PPP, is estimated to increase by about 7 percentage points, from 15.3 percent in market income (including pensions as deferred income) to 22.4 percent in consumable income after reflecting direct and indirect taxes and transfers. Serbia’s social benefits are inequalityreducing. Direct taxes in Serbia are overall slightly progressive, and their impact on lowering inequality is smaller than that of direct transfers and the impact observed in other countries. The flat personal income tax gives little progressivity while regressivity of Value-Added Tax (VAT) with respect to current income increases inequality. With respect to consumption when looking at welfare over the life cycle, the incidence of VAT is much less regressive. Future analyses can assess the impacts of potential changes to a tax or a benefit, or a combination of several changes, in the system. Simulating such
holds from which individuals were matched<sup>17</sup> with the official demobilization registers ( _Di_<sup>_FNL_2</sup> and _D_<sup>_CNDD_</sup> _i_<sup>+2</sup> ). The third definition is based on self-reported data, and includes anyone who declared to be demobilized, that is, to have received the cash ( _Di_<sup>_FNL_3</sup> and _D_<sup>_CNDD_</sup> _i_<sup>+3</sup> ). The last definition is more likely to contain measurement errors as it is based on self-reports; its associated coefficients will presumably be biased towards zero (Hausman, 2001). The three different indicators will be compared in the empirical analysis.<sup>18</sup> Table 2: Construction of ex-rebel household variables

||CNDD+|FNL|Total|Variable o|f interest|
|---|---|---|---|---|---|
|||||CNDD+|FNL|
|**Member declared having ties with the factions**||||||
|but did not receive anything|8|10|18|||
|and to be GdP/MC/AA|6|4|10|||
|**Demobilized ex-combatant member**||||||
|Member declared to be demobilized|14|9|23|_D_<sup>_CNDD_+3</sup><br>_i_|_D_<sup>_FNL_3</sup><br>_i_|
|And not recorded in ofcial registers|8|5|13|||
|And recorded in ofcial registers|6|4|10|_D_<sup>_CNDD_+1</sup><br>_i_|_D_<sup>_FNL_1</sup><br>
only minimal refinements have occurred over the past four decades.<sup>15</sup> This finding ensures that ex post data revisions do not reflect the evolution of reporting guidelines; rather, they can be traced back to changes in compliance with a stable set of rules over time.

**Sources and digitization:** Our new database combines more than 50 different editions (“vintages”) of the World Bank’s International Debt Statistics (2013–2023) and its prede-

> 14See Appendix Section B.2 below for a discussion of ex post foreign exchange rate revisions. We show that they are minuscule in size and cannot explain the revision patterns that we document.

> 15Appendix Section B.1 documents these minor refinements. Our results remain unchanged when eliminating vintages that introduced changes to the reporting guidelines.

9
employees to align the data set with the survey’s stated intent of sampling firms with five or more employees. The final data set contains information on 52,231 firms operating in 70 countries.

Second, we use data from Orbis, a commercial database distributed by Bureau van Dijk over the period 2004-2011. We focus on those firms that report data on employment. We restrict our analysis to developing countries that had a minimum of 25 firms and furthermore to firms that have a median of five or more employees over the period 2004-2011, to be consistent with the ES sample.<sup>11</sup> Overall, the data we use from Orbis includes information for over one million unique firms operating across 29 developing countries. One advantage of using Orbis is that it includes large, small, listed and unlisted firms. Since most firms are followed through time, the data set also introduces a panel dimension to our analysis.

The use of these two data sets in our estimations aids with the analysis because of their complementary nature. With the ES we get a more comprehensive coverage of developing countries, and the random survey sample is nationally representative. However, the total number of firms included in the analysis is just over 50,000. In contrast, the Orbis data set does not cover as many countries, but provides widespread coverage of both listed and unlisted firms in countries where data are collected. In all, estimations using the Orbis data set include over 4.3 million observations. In addition, as most firms are followed through time in Orbis, we are also able to construct a panel data set following over one million unique firms through time. The ES does include a panel component, but one that is too small for a meaningful cross-country exploration in the context we wish to analyze. As a survey, the ES does offer more insight into firm characteristics which allow the use of more control variables in our regressions analysis.

> 11 We impose the minimum number of firms filter before dropping firms
the foreign exchange parallel market premium. Source: Author's calculations.

We have examined case studies of the importance to economic growth of the distribution of education and of the interaction of policy reform and education. We turn next to theoretical models and empirical investigations of the distribution of education and the interaction of policy relForms and education.

> 3 The cross-country project-level data set includes variables on education, per capita income, openness, government expenditure, and project performance. The project data cover 3,590 lending projects in 109 countries evaluated by the Operations Evaluation Department (OED) in 1974-94, with the OED rating of overall performance (satisfactory/not) and economic rates of return.

9
country-invariant,<sup>8</sup> while the Enterprise Survey indicators are city-industry–level indicators that capture more local BE variations. Second, in contrast to the objective nature of the _Doing Business_ indicators, some Enterprise Survey indicators are subjective and are based on whether the entrepreneurs consider certain aspects of the business environment to be a relatively severe obstacle. Third, the Enterprise Survey indicators capture the real actions that firms take and the real conditions firms face. For example, while the Global Competitiveness indicators measure the level of internet infrastructure by proxying a country’s broadband quality , the Enterprise Survey indicators measure the same aspect by proxying the share of firms using websites to conduct business; compared with measuring the ease of getting credit in _Doing Business_ , the Enterprise Survey indicators directly measure the share of firms having an overdraft facility.

Table 4 reports how the Enterprise Survey indicators are related to the Global Competitiveness and _Doing Business_ indicators, In particular, we run a simple regression in which the dependent variable is an Enterprise Survey indicator and the key explanatory variable is either a Global Competitiveness or a _Doing Business_ indicator. The regression is at the cityindustry level, the level that an Enterprise Survey indicator is measured uniquely.<sup>9</sup> In the regressions, we control for basic country-level characteristics (that is, log GDP per capita, log population, and log population density), along with the industry fixed effects. We cluster the standard errors at the country level because some of the key variables are at this level.

The Enterprise Survey indicators and those country-level indicators are significantly correlated, with some exceptions. The following pairs are reasonably correlated: (1) the Enterprise Survey indicator on Corruption and the Global Competitiveness indicator on Ethics; (2) the Enterprise Survey indicator on Security Costs and the Global Competitiveness indicator score on Police System; (3) the Enterprise Survey indicator on Web intensity and the Global Competitiveness indicator on Broadband; (4) the Enterprise Survey indicator on Power Outage and the Global
.589***|0.036||0.762***|0.179|
||(0.048)|(0.109)||(0.053)|(0.122)|
|**Average **|**0.121**|**-0.117**||**0.385**|**0.201**|

Notes: Each column reports the coefficient in the regressions of ln (monthly/hourly earnings) on (a dummy for) a type of teachers with controls. The comparison group are other wage workers. Controls include gender, age, agesquared, urbanicity and (a dummy for) post-secondary education. Sample is restricted to workers with secondary education or more. Surveys in The Gambia, Ghana, Liberia, Malawi, and Nigeria did not report whether a worker was on a permanent or fixed term/temporary contract. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Source: Surveys listed in Table 1.

43
The rest of the paper is organized as follows. Section 2 documents trends in wage inequality during 1995–2015. Section 3 describes the evolution of the schooling and experience premiums in the region and the linked changes in labor supply. Section 4 discusses the role of technological change and polarization in relative wages and employment. Section 5 decomposes the changes in wage inequality into between- and within-skill group components and betweenand within-sector occupations. Section 6 examines the role of informality in the evolution of inequality. Sector 7 explores the role of firm heterogeneity in the observed trends, decomposing levels and changes in the wage variance into worker and firm components and betweenand within-firm components among formal workers. Section 8 discusses possible drivers of the observed trends. Section 9 concludes.

# **2. Wage Inequality: New Trends in the New Millennium**

This section presents the evolution of wage inequality in Latin America during 1995–2015. It measures wage inequality and characterizes its evolution using household data for 16 countries harmonized by the Socioeconomic Database for Latin America and the Caribbean (SEDLAC), World Bank and the Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS). The earnings measure this paper uses, unless noted otherwise, is hourly earnings in the main job. This measure was obtained by harmonizing the earnings and hours of work information from the different household surveys.<sup>6</sup> Supplementary online appendix S1.1 provides the details. The sample includes all workers 16 to 65 years-old who are employees or self-employed. Hence, unpaid family workers and employers are excluded from the sample. Sampling weights are used for all the reported statistics. Regional averages are unweighted.<sup>7</sup> The data include more than 14,908,680 observations and represent 87.54 percent of the region’s working-age population in 2015.

Figure 1 depicts the evolution of wage inequality since 1995. Wage dispersion had a very

> 6 Earnings are take-home pay, after social
<br>(0.0271)|
|_FVADP_|0.239***|0.250***|0.264***|0.233***|0.235***|0.259***|
|_DVAR*Basic ILO conv._|(0.0261)<br>0.0115*<br>(0.0061)|(0.0234)|(0.0275)|(0.0259)|(0.0298)|(0.0247)|
|_DVAR*Vulnerable Empl._||-0.0010*<br>(0.0005)|||||
|_DVAR*Child labor_|||-0.0027**<br>(0.0012)||||
|_DVAR*Female workforce_||||0.0016<br>(0.0020)|||
|_DVAR*Wage dispersion_|||||-0.1200**<br>(0.0554)||
|_DVAR*Minimum wage_||||||3.2e-05<br>(2.8e-05)|
|_Constant_|4.899***|4.715***|5.071***|5.162***|4.886***|<br>4.980***|
||(0.180)|(0.155)|(0.212)|(0.184)|(0.222)|(0.180)|
|Observations|7,045|6,580|5,149|7,164|2,997|5,502|
|R-squared|0.875|0.875|0.861|0.872|0.886|0.886|

Source: OECD ICIO database
<!-- Start of picture text --> Source: Own estimates based on Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 -2010.<br>Figure 11. Changes in the Structure of Working Age Population<br>A. Education levels B. Urban Population as a Share of Total<br>(percent of working age population) (percent of working age population)<br>70 70.0 64.5 63.5<br>60<br>60.0<br>50<br>40 50.0<br>30<br>40.0<br>20<br>31<br>10 30.0 28.0 29<br>21.8<br>0<br>20.0<br>2000 2010 2004 2010 2000 2009<br>10.0<br>Bangladesh Peru Thailand<br>0.0<br>Illiterate & Incomplete primary 2000 2010 2004 2010 2000 2009<br>Complete primary & lower secondary<br>Bangladesh Peru Thailand<br>Higher secondary & Tertiary<br>Source: Own estimates based on Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 -2010.<br><!-- End of picture text --> 27