rafmacalaba/datause-encoder-data huggingface dataset:
Alongside the large-scale, capital-intensive mining industry in Ghana, there is an artisanal and small-scale mining sector (ASM). ASM activities were legalized in 1984, when the state loosened its monopoly on gold mining. In Ghana, as in many other African countries, the sector is an important employer (ILO 1999). It is estimated that around 1 million people in Ghana support themselves with revenues from ASM activities.

The sector is associated with several hazardous labor conditions, however. This includes child labor, mercury exposure, and risk of mine collapse (Hilson 2009). The ASM and the large-scale mining sector sometimes thrive side by side, but sometimes competing interests lead to conflict between the two sectors, such as around Prestea, where domestic _galamsey_ miners (informal small-scale miners) have been in conflict with the multinational concession owner (Hilson and Yakoleva 2007).

In this analysis, we focus solely on large-scale mining. We understand, however, that smalland large-scale operations may be geographically correlated. Assuming that the start of a largescale mine does not affect the likelihood or viability of artisanal and small-scale mining, it is not a threat to our identifying assumptions. However, should ASM respond to large-scale activities, either by increasing or decreasing activity in the close geographic area, we will end up estimating the impact of these sectors jointly. In a later stage, should the opportunity arise, we encourage researchers to try to disentangle the effects of small-scale and large-scale mining.

# **3 Data**

To conduct this analysis, we combine different data sources using spatial analysis. The main mining data is a dataset from InterraRMG covering all large-scale mines in Ghana, explained in more detail in section 3.1. This dataset is linked to survey data from the DHS and GLSS, using spatial information. Geographical coordinates of enumeration areas in GLSS are from Ghana Statistical Services (GSS).<sup>2</sup> Point coordinates (global positioning system [GPS]) for the surveyed DHS clusters<sup>3</sup> allow us to match all individuals to one or several mineral mines. We do this in two ways.

First, we calculate distance spans from an exact mine location given by its GPS coordinates, and match surveyed individuals to mines. These are concentric circles with radiuses of 10, 20, and 30 kilometers (km), and so on, up to 100 km and beyond. In the baseline analysis where

> 2 The data was shared by Aragón and Rud (2013)

> 3 Both the DHS and GLSS enumeration area coordinates have a 1-5 km offset. The DHS clusters have up to 10km displacement in 1% of the cases.

7
I construct mean hourly wages for the three-month period prior to the survey by adding the "wage" and "allowance" components for the main job, and dividing this by the total number of hours worked. All wages are deflated with provincial-level price deflators-one for each of the 73 provinces in the country, as well as, separately, the 11 biggest cities (Balisacan 2000). I consider only men working in the wage-earning sector, rather than all employed men.<sup>8</sup> This is because very few self-employed men report "wages", and it is not clear what to make of the wage data for those who report being self-employed and earning wages on the same job. For similar reasons, the analysis in this paper is limited to the main job, because some men whose main job is in the wageearning sector have a second job where they are self-employed.<sup>9</sup> The education module in the APIS is also reasonably detailed. For the purposes of this paper, the relevant question asks about the highest grade completed by all household members aged 6 and older. With the exception of those _currently_ enrolled in school, the APIS provides no information about the kind of school attended by labor force participants, including whether it was public or private.

There are 21,882 men in the APIS who report all of the necessary information for the construction of hourly wages, including data on wages, allowances, days and hours worked on the main job, as well as information on own schooling. In addition, we can length of the consumption and income modules in the APIS and the FIES means that it is not possible to compare changes in income or consumption in any straightforward way.

8The APIS gives seven categories for class of worker: (i) worked for private household; (ii) worked for private establishment; (iii) worked for government / government corporation; (iv) self employed without any employee; (v) employer in own family operated farm or business; (vi) worked with pay on own familyoperated farm or business; (vii) worked without pay on own family-operated farm or business. The analysis in this paper is restricted to categories (i), (ii), (iii), and (vi) above.

9 Note, however, that only a tiny fraction (0.34%) of men in the sample who hold a wage-earning main job report holding a second wage-earning job, so restricting the analysis to the main job is unlikely to make much of a difference.

8
# **Table 8: Panel Regressions – Robustness of main result to alternative indicators: Volume of small transactions for all crypto-assets**

|<br>Dependent Variable: Log monthly on-chai<br>|<br>n crypto-assets volum<br>|<br>e (transaction size<br>|<br><US$10K), US dollars<br>|<br>|<br>|
|---|---|---|---|---|---|
|Variable|(1)|(2)|(3)<br>**All Countries**|(4)|(5)|
|**Change in broad money (yoy)**|0.663*|0.136|0.070|0.108*|0.066|
|Change exchange rate (mom)|-0.974|-0.045|0.873**|0.734|0.604|
|Change Bitcoin price (mom)||0.648***|0.570***|0.677***|0.527***|
|Change Ether price (mom)||0.276***|-0.070|0.009|-0.004|
|US 10-year Yield (inflation adjusted)|||-0.311***||-0.405***|
|**US 10-year breakeven inflation rate**|||0.990***||1.252***|
|**US BBB Corporate bond spread**||||-0.284***|0.131***|
|Change gold price (mom)||||-1.324***|-1.190***|
|Pandemic dummy||1.057***|0.842***|1.541***|0.530***|
|N of Observations|2258|2258|2258|2258|2258|
|N of clusters (countries)|102|102|102|102|102|
|Adjusted R squared|0.021|0.432|0.605|0.538|0.613|
|Country-Fixed Effects?|YES|YES|YES|YES|YES|
|Variable|(6)|(7)|(8)|(9)|(10)|
||||**EMDEs only**|||
|**Change in broad money (yoy)**|0.584*|0.136|0.062|0.105|0.059|
|Change exchange rate (mom)|-0.441|0.094|1.015***|0.886*|0.776|
|<br>Change Bitcoin price (mom)||0.607***|0.527***|0.634***|0.485***|
|Change Ether price (mom)||0.262***|-0.090|0.000|-0.016|
|US 10-year Yield (inflation adjusted)|||-0.372***||-0.475***|
|**US 10-year breakeven inflation rate**|||0.978***||1.232***|
|**US BBB Corporate bond spread**||||-0.284***|0.131***|
|Change gold price (mom)||||-1.558***|-1.463***|
|<br>Pandemic dummy||1.088***|0.821***|1.571***|0.496***|
|N of Observations|1689|1689|1689|1689|1689|
|N of clusters (countries)|78|78|78|78|78|
|Adjusted R squared|0.019|0.416|0.580|0.516|0.589|
|Country-Fixed Effects?|YES|YES|YES|YES|YES|

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the country level. All independent variables are lagged by one month, except the pandemic dummy. Bitcoin, ether, and gold prices expressed in US dollars. The gold price is expressed per Troy ounce. A constant is estimated, but not reported.
Equally important, YPs are drawn from a very large pool of applicants each year and are likely to be quite homogeneous with respect to omitted characteristics as well, relative to non-YPs.

After YPs have entered the common staff pool, however, a divergence in the salaries and grades of men and women emerges. This divergence could indicate that individual characteristics observed at entry by the program administrators, and on the basis of which YPs are chosen, (presumably a richer set of information than our model can account for) are not, after all, good predictors of subsequent performance. It is, however, implausible that this would only apply to women. Alternatively, women as a group may be making different internal assignment choices than men, for example to control levels of travel and stress in their jobs. However, this could presumably have been anticipated at time of hiring.

Another possible explanation is that biases slowly emerge which render the work environment less conducive to the progress of women. Men may tend to receive more mentoring, benefit from greater numbers of role models and be favored in task assignments that have higher potential for promotion. In contrast to that for YPs, annual pay raises for non-YP staff appear to gradually compensate over time for the large unexplained differences in entry level salaries. Indeed, while unexplained differences for YPs are greater for current salaries, for non-YPs they are greater at entry level.

The World Bank's YP hiring process can help test our model. As discussed above, our results for the total sample could be due simply to the influence of gender or nationality correlated variables that have been omitted from the model yet were observed at the hiring stage. In this case, we could be attributing to discrimination what are in actual fact returns to omitted variables. On the assumption that the YP process is non-discriminatory-in that it follows a 27
# **3 Empirical Findings**

## **3.1 Is there a rise in Markups?**

Using marginal costs measured by COGS, De Loecker and Eeckhout [2017] document a stunning rise in markups in the US over the past three decades. Traina [2018] however argues that COGS are a declining share of firm costs and once we use operating expenses that includes COGS and SGA, there has been no rise in firm markups. This is an important policy question as it also speaks to the discussion on the rise in industrial concentration and decline in labor share (see Grullon et al. [2017] and Autor et al. [2017]). Once we do take into account intangible capital, how have markups evolved over this period?

In Figure 5, we estimate the evolution of markups using capital adjustments, _Markups_ , in the US economy over our sample period. We see an upward trend only for the 90<sup>_th_</sup> percentile firms. To see if there is dispersion in markups by industry skill, we look at industries that rely heavily on routine manual tasks versus those that do not rely heavily on routine manual tasks and industries that have high vs low ratio of intangible capital/assets in Figure 6. We find that markups are higher in high skilled industries (low RMAN) than low skilled industries and in high ICAP industries than low ICAP industries for the top 10% of firms.

Overall, we see that there has indeed been a rise in markups once we adjust operating expenses for investment in intangible capital. While there is just a modest divergence between the top 10% of firms with the highest markups and the rest of the economy, we see these differences amplified in high skilled industries which use more intangible capital.

## **3.2 Future Performance of Stars**

We define a firm as a star firm in a given year if its return on invested capital is in the top 10% of firms in that year. It could be the case that there is a lot of churning in this top 10% of firms each year with different sets of firms randomly realizing high returns each year. In the first part of this sub-section we explore if these high returns are persistent and if being a superstar is associated with superior performance. To this end, we construct five non-overlapping panels: 1990-1995, 1995-2000, 16
unitary benefits after discounting households‘ out-of-pocket contributions inversely correlate with household consumption. Interestingly, the richest households contribute almost as much as they received from public education (both in primary and secondary school), a sign of systemic progressivity. In contrast, the net unitary benefits of tertiary education (Figures 13a and 13b) are deeply regressive and pro-rich. The richest quintile captures a disproportionate share of the benefits from tertiary education, but the second-richest quintile benefits substantially more from tertiary education than all other consumption quintiles, including the richest. This is due to the richest quintile‘s greater private contribution to public tertiary education.

This regressive and pro-rich distribution is the result of the dramatic overrepresentation of the top quintile in tertiary education. Students from the top quintile constitute about 89 percent of total enrollment in tertiary education, compared to 42 percent of total enrollment in secondary education. In other words, regressive benefits are driven by inequality of access, not necessarily by public spending itself (which is typical of a universal education system). The selection of scenarios does not change these results: the inability to determine the type of education establishment attended causes imprecision but does not call into question the regressive nature of the system.

Figure 10a and 10b: Aggregate Unitary Benefits of Education <!-- Start of picture text --> (a) (b)<br>1,500 3,000 1,500 3,000<br>1,094 2,500 2,500<br>1,000 1,603 2,000 1,000 832 2,000<br>1,500 1,374 1,500<br>500 335 339 345 409 489 472 494 590 1,000 500 308 313 301 359 471 463 462 561 1,000<br>500 500<br>301 278 250 248 534 439 388 358 357 782 275 255 212 212 346 421 377 325 331 572<br>0 0 0 0<br>(500) (500)<br>(500) (1,000) (500) (1,000)<br>(1,500) (1,500)<br>(1,000) (2,000) (1,000) (2,000)<br>Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5<br>Poorest Richest Poorest Richest Poorest Richest Poorest Richest<br>20% 20% 20% 20% 20% 20% 20% 20%<br>Aggregate Per student Aggregate Per student<br>Private (negative) Public Net Scenario 1 Private (negative) Public Net Scenario 2<br> Source: LCMS VI, MoE<br>Total Expenditure (ZMK billions) Total Expenditure (ZMK billions)<br>Expenditure per student (ZMK thousands) Expenditure per student (ZMK thousands)<br><!-- End of picture text --> 29
Table 2 shows the variations in the poverty and extreme poverty headcounts by area and division.<sup>10</sup> The main differences in poverty incidence come from rural and Sylhet when using the upper line for areas and divisions, respectively. While this also holds for rural areas with extreme poverty headcounts, Khulna presents the most significant gap in percentage points.

Table 2. Differences in 2016 poverty and extreme poverty incidence by area and division

||Po<br>|verty (upper li<br>|ne)<br>|Extrem<br>|epoverty (lo<br>|wer line)|
|---|---|---|---|---|---|---|
||Official<br>headcount<br>(a)|Simulated<br>headcount<br>(b)|Percentage<br>points<br>(b - a)|Official<br>headcount<br>(a)|Simulated<br>headcount<br>(b)|pp change<br>(b - a)|
|_Area_|||||||
|Rural|26.4|29.0|2.6|14.9|10.8|-4.1|
|Urban|18.9|19.4|0.5|7.6|5.3|-2.3|
|_Division_|||||||
|Barisal|26.5|30.7|4.2|14.5|12.0|-2.5|
|Chattogram|18.4|19.1|0.7|8.7|5.9|-2.8|
|Dhaka|16.0|19.2|3.2|7.2|4.8|-2.4|
|Khulna|27.5|28.4|0.9|12.4|6.7|-5.8|
|Rajshahi|28.9|27.8|-1.1|14.2|11.8|-2.5|
|Rangpur|47.2|45.8|-1.5|30.6|25.8|-4.7|
|Sylhet|16.2|22.8|6.6|11.5|6.8|-4.7|

Note: results for the Mymensingh division are available upon request. Source: Own estimations based on HIES 2016 and 2022 The imputation process has an impact on the poverty incidence levels within the divisions and their relative positions. However, it is expected that the relative positions of the divisions will not be significantly affected by this method. As expected, the results indicate only minor changes in the relative positions of divisions in both poverty and extreme poverty rankings. (Figure 4). However, these jumps are not significant enough to impact, for instance, the east-west regional divide. After imputation, Chattogram, Dhaka, and Sylhet are the divisions with the lowest poverty incidence, while Rangpur, Barisal, Khulna, and Rajshahi are in the opposite spectrum.

> 10 The simulated headcount was computed using the average of 1,000 simulations for per capita consumption with newly introduced items.

13
For the EU, differences in population size among nations are<sup>not as wide as for</sup> Asia. Although Luxembourg's population is extremely small<sup>(400,000),the largest</sup> country in the EU (Germany) has only 82 million people,<sup>which is minusculecompared</sup> to China's 1.3 billion.

Two observations can be made from the GNI figures. Firstly, there is the difference in the aggregate level of economic development<sup>between the tworegions -</sup> East Asia comprises ten developing economies, four NIEs<sup>and onedevelopedeconomy.</sup> On the other hand, the EU comprises 15 exclusively developed<sup>economies.</sup> Secondly, the uniform level of development among the EU countries<sup>is readily</sup> observable in the GNI per capita figures, which are mostly between<sup>$20,000 and $30,000</sup> with a few exceptions at both the low and high ends. The lowest<sup>WorldBank ranking for</sup> the EU economies is 4 9 th place for Portugal, although most<sup>countriesareamong the top</sup> 30 nations in the world in this category. Comparatively,<sup>mostof theEast Asian</sup> economies ranked between 80th place and 200t place. (There are<sup>slightly over 200</sup> countries in the ranking). Japan (7h), Korea (5 4th), Hong Kong<sup>(1</sup> 9 )), and Singapore (22nd) are exceptions. (Taiwan is not ranked.) 34
# **A3:  Health**

# **A3.1  Basic Methodology and Data Considerations**

The measure of health z in the description of the HCI in Section A1 should be interpreted as a scalar index of “latent” health that summarizes the aspects of health that matter for worker productivity. Latent health cannot be observed directly, and so implementing the HCI requires a mapping from unobserved “latent” health z to directly‐observable health indicator that serve as proxies, as well as an estimate of the corresponding “return” to health, γ . Weil (2007) proposes a strategy for doing so by recognizing that wages as well as observable summary indicators of health status such as adult height both respond to unobserved latent health. In the case of wages, this could reflect channels such as improved physical strength enabling greater work effort, as well as the effects of better health on better cognitive skills, both of which then are rewarded with higher wages.<sup>15</sup> A large literature has also argued that trends in average adult height within a country can serve as a proxy for trends in the overall health conditions in a country.<sup>16</sup> Poor _in utero_ and early childhood nutrition and health lead to stunting among children, which in turn is reflected in shorter adult height, as well as a greater incidence of poor health outcomes among adults.

A key advantage of adult height as an observable health indicator is that there are many micro‐ econometric estimates of the “return” to height obtained from extended Mincer regressions of log wages on education, height and other controls. Weil (2007) develops a latent variable representation of the relationship between unobserved latent health, wages, and height, and demonstrates that this can be used to replace unobserved latent health and its return, γ�z , with observed height and its estimated return, γ������ �HEIGHT , in the expressions for the health component of human capital in Equation (1). The interpretation in this case is not that height directly makes workers more productive. Rather, the correct interpretation is that if latent health improves in such a way that height increases by 1 cm, then this will lead to an increase in worker productivity of γ������ percent, i.e. height serves as an observable proxy for unobservable latent health.

> 15 In fact, Case and Paxson (2008) study data on test scores, height, and earnings in the US and the UK and conclude that all of the effect of height on earnings operates through cognition in their sample.

> 16 Considering within‐country over‐time trends in height is crucial here, since cross‐country differences in average stature also reflect cross‐country differences in genetic predisposition for height among different ethnicities.

37
# Table IV-1 (cont'd)

|Country.<br>Title of Project|Funder, Statucs<br>of Activity|Proect<br>SiM qry|Use Private<br>mltribution<br>System|Employe<br>leefit|OCaracteristics<br>o<br>Ctract<br>with Private<br>Protiders|fProject<br>Trainwin|Evaltion<br>Cpnt|
|---|---|---|---|---|---|---|---|
|D<br>81iNNW_ITD_|_11USIT-USCD_<br>SO<br>(|cont'd)||||||
|Bolivia||||||||
|Fmily Planning<br>_and_Health<br>Services for<br>Organized<br>Workers|Pathfinder Fwid,<br>Popalation CQM-<br>cil and USID<br>Operatlonal<br>Aeserch Project|Famly pluin<br>lnforuation<br>sevicee<br>will be provided to<br>20,000 minig famUil<br>In thre<br>towns. Service. lclude<br>piU,<br>IUD, fam<br>and condoms. Training<br>for mdlcal**staff**_md_promoters<br>In inlouded.<br>Ka_physicins_<br>_Will_<br>received<br>nan-mnetary<br>incetves<br>_foT f-aIY_<br>plaming eevicee-|_NO_|No||TeYe of Mu am<br>F;ly<br>Pl<br>clinic physicians,<br>auxilary<br>urve,<br>field eductorrs,<br>6<br>volunteer promoter|lb<br><br>s|
|Fmily Plar ing<br>Program through<br>Employers|USUD_EAterpriss_<br>Centro de lnveetl-<br>gaiones<br>Social.<br>(IS) Prom<br>Proposal|Integrated progras of reserchb<br>_education,_counseling ed direct<br>family_plaming_**_aerviceg_a*d**<br>**at**workin I<br>. ..:<br>eswith 150-<br>170 mloym<br>to be approached.|I/A|TYe|No|lb|ria<br>to**seem**<br>_<br>fit.<br>m<br>eving of<br>famly plumin<br>to<br>mloyers<br>surveys|
|Indonesia:<br>P.?. Cremsy<br>_Ojaja Family_<br>Planying Progrm|USAID Enterpris<br>Progrm Proposal|P..<br>COmy Dajej is a fitr with<br>1.30 employes0. The firm wl lk<br>renovate & Enterprise will cover<br>all expene. for a deily famly<br>plannin<br>clinic for 2 year.|No|Yee|No|**TM**|lb|
|Mexico<br>Fmily Plamig<br>Services at<br>Induatrial<br>Unides<br>S.A.S.E.P.<br>Quintero<br>Industril<br>Camplex|USAID Enterprlse<br>Progrm Propov l|uSA propo<br>to provide fadly<br>plannin<br>srvice<br>to Its 9,000<br>employes In_cany_<br>ElUlrce.<br>FEIDU (PV_supplies_contracep-<br>tives under a subcontract).<br>Objectlve li to reduc birth<br>rate amd teranty leave.<br>tAP<br>pro and post progra<br>planned.|No|Yes|Tee. for con-<br>traceptive|Tee forMm<br>end mdlcal<br>peremel.<br>Also trainn/<br>orientation<br> <br>for _layeWe<br>12 times a year|**Te,**through DI<br>monitorin<br>eva lntian<br>of<br>progrm by 2AfI<br>_<br>tasssemm<br>of net benfits<br>at proect<br>clne|
|Nigeria<br>LEN Employee<br>Clinic Family<br>Plannang Child<br>Spacing Program|USAID Enterprise<br>Program Proposal|Lover Drothers Nigerli Ltd. wnt<br>to eat up a fadly<br>planning pro-<br>gramnaexlsting covny<br>cilnics<br>servino white-collar<br>employees.<br>All methods will be provided and<br>referrals<br>mad for sterilization,<br>Will determ<br>desand through<br>focus Sroups, tAP surveys and<br>TIPPS project analyses.|No|Yes|Unclear. but<br>love plan<br>to<br>obtain con-<br>trsceptlve.<br>w sle<br>presumbly<br>after the<br>project end.<br>Sterilization<br>referrals|Te,<br>of<br>_dIcal<br>suff,<br>personnel and<br>voluntetr<br>tivatora|_NO_|