rafmacalaba/datause-encoder-data huggingface dataset:
> DATA AND METHoos bomil

# Figure Al Distribution of countries by year of latest census

<!-- Start of picture text --> 100<br>90<br>so<br>70<br>0<br>40-<br>70 - or 90o oe 17-99 t8-18 95o ae<br>30-<br>20-<br>10<br>0 N<br>Before 1970 or none 1970- 1979 1980- 19e4 1985 or later<br><!-- End of picture text --> censuses, offlcial estimates, USBOC, Eurostat and World Bank estimates. The mortality risk for children under age _5_ for the current period is deriv'i from a model described below. Earlier estimates are often taken from the U.N. publication _Mortality of Children Under Age 5._

## _Ferility data_

Total fertility rates are derived from similar sources as mortality rates, including the _U.N.'s World Population Prospects 1990,_ surveys, censuses, and the U.S. Bureau of the Census. The DHS survey are a particularly useful source of data. Other sources include official estimates, Bank estimates, and Eurostat. Because fertility rates are not always available for the specified period or are in terms of another parameter such as the crude birth rate, total fertility rates are sometimes obtained by projections of earlier estimates or approximated using available data on age-sex structure and agespecific fertility.

## _Migration Data_

Estimates of rn,,t international migration are obtained from a variety of sources including statistics from national immigration agencies, statistical organizations, official sources, interagency task forces and research institutions. Arnold (1989) compiled all these data. Updates of this information have been made as more data has become available.

In developing these estimates, migrants are considered persons who have resided in countries different from their previous residence for at least a year or who are believed Ukely to remain permanently in receiving countries. Persons are not counted as migrants when they are refugees living in officially designated camps. Only net estimates are made by quinquennium; immigration and emigration are not estimated separately, and single-year figures were not assessed.
# **Table IV:** Program impacts on labor market outcomes and empowerment (beneficiaries)

|**Panel A: Baseline Dropouts**|Lab|or Market Outc|Proportion of<br> <br>omes||Change in<br>Empow|<br>erment|||
|---|---|---|---|---|---|---|---|---|
||||H St|SId f||SId f|SId f|Mid Id|
||Opportunity<br>Cost of Time|Typical Wage<br>in Past Three<br>Months|ours pen<br>in Self-<br>Employment<br>or Paid Work|uper-nex o<br>Overall<br>Empowerment<br>(Standardized)|Subjective<br>Wellbeing from<br>Five Years Ago<br>|uper-nex o<br>Unmarried<br>Empowerment<br>(Standardized)|uper-nex o<br>Married<br>Empowerment<br>(Standardized)|arre nex<br>of Economic<br>Control<br>(Standardized)|
||||in Past Week|Two Year|to Today<br>s After Program||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|=1 if Conditional Schoolgirl|-0.037|-0.140**|-0.011|-0.083|-0.032|0.018|-0.113|-0.118|
||(0.079)|(0.068)|(0.009)|(0.074)|(0.232)|(0.112)|(0.102)|(0.096)|
|Mean in Control Group|0.707|0.375|0.061|0.000|1.120|0.000|0.000|0.000|
|Sample Size|718|743|744|744|744|289|455|455|
|**Panel B: Baseline Schoolgirls**|||||||||
|=1 if Conditional Schoolgirl|-0.051|-0.011|0.003|0.049|0.276|0.111|0.068|-0.107|
||(0.101)|(0.058)|(0.005)|(0.082)|(0.187)|(0.098)|(0.095)|(0.108)|
|=1 if Unconditional Schoolgirl|-0.115|0.036|0.002|-0.159*|0.176|-0.094|-0.342***|0.147|
||(0.074)|(0.104)|(0.008)|(0.081)|(0.190)|(0.109)|(0.099)|(0.307)|
|p-value UCT vs. CCT|0.550|0.665|0.842|0.052|0.650|0.120|0.001|0.406|
|p-value Treatment|0.297|0.910|0.784|0.101|0.306|0.287|0.001|0.484|
|Mean in Control Group|0.897|0.212|0.029|0.000|0.906|0.000|0.000|0.000|
|Sample Size|2,002|2,048|2,045|2,049|2,049|1,271|776|774|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. Opportunity cost of time is calculated by taking the minimum daily wage the respondent would take for one year of work in her village. Detail on the construction of the super-indices can be found in Appendix A and Appendix B. The change in subjective wellbeing asks the respondent where she sees herself on a 10-step ladder comparing five years ago to today, where zero represents the worst possible life she could have and 10 represents the best possible life she could have. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
# They Got Mad Skills: The Effects of Training on Youth Employability and Resilience to the Ebola Shock

Nina Rosas,<sup>*</sup> Maria Cecilia Acevedo,<sup>†</sup> and Samantha Zaldivar<sup>‡</sup> Keywords: Social protection, skills, capital, impact evaluation, youth, employment, Sierra Leone, gender, Ebola JEL Codes: E21, I38, J13, J16, J24, J46, L26, O15, O20

> * Senior Economist, World Bank. E‐mail: nrosas@worldbank.org.

> † Senior Economist, Inter‐American Development Bank. E‐mail: mariaace@iadb.org.

> ‡ Social Protection Specialist, World Bank. E‐mail. szaldivar@worldbank.org.

We thank Statistics Sierra Leone, which conducted the data collection and ensured data quality. Special thanks go to the staff of HELP‐SL, the National Youth Commission, and the Ministry of Finance and Economic Development’s Integrated Project Administration Unit for their efforts and collaboration, which have made this impact evaluation possible. We are grateful to Shwetlena Sabarwal for her invaluable contributions to the evaluation design and analysis, Andrea Martin for supporting the communications campaign, public lottery process, and electronic data collection, and Mariana Pinzon‐Caicedo for excellent research assistance. Markus Goldstein, David McKenzie, Patrick Premand, Shubha Chakravarty, and Thomas Bossuroy (World Bank) provided comments that improved the quality of the study. We are also grateful to Suleiman Namara and John Van Dyck, the Task Team Leaders of the Youth Employment Support Project, for their support throughout this task. We also appreciate the support of the U.S. Agency for International Development, which provided funding through the Women’s Leadership in Small and Medium Enterprises Program.
# **Figure 7—Jobless Industrialization**

Share of employment in industry vs. GDP per capita late 80s, late 90s, late 00s <!-- Start of picture text --> 7 8 9 10 11<br>ln(GDP per capita, const USD @ PPP)<br>1987-1990 1997-2000 2007-2010<br>Source: Authors' calculations using WDI. Balanced country panel formed by using latest available data from range.<br>Employment share regressed on ln(GDP per capita, const USD @ PPP) and its square. All definitions based on WDI.<br>50<br>40<br>30<br>20<br>Industry Employment Share<br>10<br><!-- End of picture text -->

# **Figure 8—Job Growth in Services**

<!-- Start of picture text --> Share of employment in services vs. GDP per capita<br>late 80s, late 90s, late 00s<br>7 8 9 10 11<br>ln(GDP per capita, const USD @ PPP)<br>1987-1990 1997-2000 2007-2010<br>Data source: WDI. Balanced country panel used.<br>Employment share regressed on ln(GDP per capita, const USD @ PPP) its square.<br>100<br>80<br>60<br>40<br>Industry Employment Share<br>20<br><!-- End of picture text --> 12
||0.08|0.06|0.10|
|---|---|---|---|
|**Private investment**|-0.11|-0.15|-0.05|
||0.06|0.12|0.12|
|**Public investment**|0.13|-0.10|-0.23|
||0.11|0.23|0.23|
|**Public expenditures**|0.05|0.06|0.01|
||0.07|0.06|0.10|
|**Public revenues**|-0.07|0.08|0.15|
||0.11|0.07|0.12|

Note: Standard errors below statistics. * p<0.10, ** p<0.05, *** p<0.01 Table **13** summarizes our results for persistence of the macro variables, separately for countries that have fiscal rules in place, and countries without fiscal rules. Several variables show significantly higher persistence coefficient in countries with fiscal rules. These are current account, fiscal balance, and inflation. For all other variables the differences in persistence are not statistically significant.

|**Table 13.** **Persistence o**|**f key macro aggreg**<br>**no fiscal rule**|**ates, by the presence of**<br>**with fiscal rule**|**a fiscal rule**<br>**diff**|
|---|---|---|---|
|**GDP**|0.54|0.57|0.03|
||0.02|0.02|0.03|
|**GNI**|0.50|0.52|0.02|
||0.04|0.02|0.04|
|**Trade balance/GDP**|0.40|0.45|0.05|
||0.02|0.02|0.04|
|**Current account/GDP**|0.20|0.34|0.14***|
||0.03|0.02|0.04|
|**Real int rate**|0.21|0.19|-0.01|
||0.05|0.03|0.06|
|**Terms of trade**|0.28|0.34|0.06|
||0.03|0.03|0.05|
|**Fiscal balance/GDP**|0.16|0.38|0.22***|
||0.07|0.04|0.07|
|**Inflation**|0.20|0.28|0.08**|
||0.03|0.02|0.04|
|**Total investment**|0.51|0.55|0.04|
||0.03|0.02|0.04|
|**Employment**|0.54|0.62|0.09|
||.|0.03|.|
|**Government consumption**|0.50|0.54|0.04|
||0.03|0.02|0.03|
|**Private consumption**|0.50|0.52|0.03|
||0.03|0.03|0.04|
|**Exports**|0.43|0.44|0.02|

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**Table 1:  Share of the Employment Stock**

|100 emp|Based o<br>. cutoff|n Initial<br>250 emp|. cutoff|100 em|Based on<br>p. cutoff|Average<br>250 emp|. cutoff||||
|---|---|---|---|---|---|---|---|---|---|---|
|SME|Large|SME|Large|SME|Large|SME|Large|Young<br><=5 yrs.|Mid-age<br>6-10 yrs.|Mature<br>11+ yrs.|
|57.2|42.8|75.4|24.6|60.3|39.7|79.0|21.0|17.9|19.8|62.3|

_Age and initial size defined by the re-call period prior to the survey. Average-period categories defined by average establishment size between t and t-d. For age cuts the share of period average employment is reported._ Table 2 shows these results further divided by establishment size-age categories, where establishments are divided between SMEs and large and by age categories (young, mid-age, and mature).<sup>26</sup> Following the methodology used by ADM and elsewhere, both age and size groupings are defined by the initial period recorded in the survey. Where the period average is used to define size cuts, age remains defined by the initial period. In line with findings from ADM, mature establishments constitute the largest single share of employment stock by all size groups and definitions. As a departure from their findings though, we find that mature SMEs are the largest plurality, though very closely followed by large mature establishments; their findings indicate that these two groupings are likewise the largest source of employment, but in the reverse order.

**Table 2: Share of the Employment Stock – By Establishment Size-Age**

|||Based on|Initial|||Based on|Average||
|---|---|---|---|---|---|---|---|---|
||100 emp|. cutoff|250 em|p. cutoff|100 em|p. cutoff|250 em|p. cutoff|
||SME|Large|SME|Large|SME|Large|SME|Large|
|Young|11.5|4.0|13.6|1.8|13.7|4.2|16.2|1.7|
|Mid-age|13.4|6.6|16.1|3.9|13.7|6.1|16.7|3.1|
|Mature|32.4|32.2|45.6|19.0|32.9|29.4|46.1|16.2|

_Age and initial size defined by the re-call period prior to the survey. Average-period categories defined by average establishment size between t and t-d._

# 2. <u>Job Growth Rates: What Establishments Are Creating or Contracting Jobs?</u>

Among developing economies, the net employment growth rate, i.e., the rate at which the stock of employment is growing, is 5.2% per annum. On net, this rate is driven by SMEs, supporting the results from

> 26 It should be noted however that the ES data while stratified by criteria for size, sector, and location, the methodology is not stratified by firm or establishment age and thus does not ensure representativeness on that dimension beyond the random sampling within the specified strata.

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# **3. The urbanization of poverty 1993-2002**

Tables 3 and 4 give our aggregate results. Consistently with Claim 2, we find that rural poverty incidence is appreciably higher than urban. The “$1 a day” rural poverty rate in 2002 of 30% is more than double the urban rate. Similarly, while we find that 70% of the rural population lives below $2 a day, the proportion in urban areas is less than half that figure. The rural share of poverty in 2002 is 75% using the $1 a day line, and slightly lower using the $2 line. This is higher than the widely-used estimate of 68% obtained by Ravallion (2002) using a population-weighted aggregate of the national poverty measures. This is a non-negligible difference, representing the reclassification of over 80 million poor people from urban to rural.

Over the period as a whole, we find a 5.5% point decline in the “$1 a day” poverty rate, from 27.8% in 1993 to 22.3% in 2002. This was sufficient to reduce the overall count of the number of poor by about 100 million people. However, there is a marked difference between urban and rural areas. The rural poverty rate fell much more than the rural rate. The count of 106 million fewer poor by the “$1 a day” standard is the net effect of a decline by 153 million in the number of rural poor and an increase of 47 million in the number of urban poor. Similarly, the progress in reducing the total number of people living under $2 a day in rural areas by 117 million came with an increase in the number of urban poor of 63 million, giving a net drop in the poverty count of only 54 million (Table 4).

Our aggregate results point to a somewhat higher overall poverty rate, and a slightly lower rate of poverty reduction than found in Chen and Ravallion (2004). On comparing our results for 1993 in Table 3 to the Chen-Ravallion estimates, using essentially the same methods but without allowing for an urban-rural differential in the cost-of-living, we find that a $1 a day headcount index that is about about 2.2% points higher in 1993 (27.8% versus 25.6%) and that it declines at a rate of about 0.6% points per year, as compared to 0.7% points. The higher level is unsurprising (given that we have allowed for a higher poverty line in urban areas). The lower pace of overall poverty reduction reflects the fact that the urban headcount index for $1 a day shows no trend decline (Table 3). Thus, we find that past methods that have ignored the urban14
This paper focuses on the situation of immigrants in the South African labor market. At the end of _apartheid_ , South Africa opened up to migration. The total number of documented immigrants increased by 60% from 1994 to 2003 and the share of Africans among immigrants increased from 25% to almost 50% over this time. In 2002, amidst increasing unemployment rates, the government passed the Immigration Act to facilitate easier access for South African employers to foreign skills while limiting labor market access for unskilled and semi-skilled immigrants. Employment opportunities in the formal sector became scarcer for immigrants due to “South Africans first” legislation, most notably in the mining sector. As a result, the proportion of foreign miners fell from 51% in 1997 to 38% in 2006 (DOL (2007)). Yet, rather than deterring immigration, many believe that the new migration policy led to a shift from legal to undocumented migration and employment of immigrants in the informal rather than formal sector. Undocumented immigration in recent years was further spurred by the political and economic crisis in neighboring countries, most notably Zimbabwe.

It is difficult to measure the number of immigrants living in South Africa. According to the 2001 census, 688,000 foreigners lived in South Africa, yet the number of irregular immigrants is likely much higher. The government estimated the number of undocumented immigrants to be around 600,000 but they cannot rule out that the true figure might be as high as three million (DOL (2007)). The government’s response to the influx of undocumented immigrants was increased deportation: more than 3 million undocumented immigrants have been deported since 1990 (97% from SADC countries including 90% from Zimbabwe and Mozambique) and the annual number of cases tripled between 2002 and 2007 (Crush (2011)).

Large immigration flows combined with high unemployment rates have fostered xenophobic attitudes among South Africans as revealed in a survey of South Africans’ attitudes towards migrants and refugees (Crush (2008)).<sup>11</sup> Yet, the South African Department of Labor (DoL) concludes that “ _although a systematic survey of employers has not been conducted, case study evidence suggests that there is widespread preference for non-South African workers._ ” (DOL (2007)). If true, this poses a puzzle: Do employers prefer hiring immigrants despite widespread xenophobic attitudes? What role does the legal status and exploitability of immigrants play? And how does discrimination vary be-

> 1184% of respondents believe that South Africa lets too many foreigners into the country, 74% favor deporting people that are not contributing economically, and two-thirds agree that rights to legal protection and police protection should never be granted to undocumented immigrants. Hostile attitudes are routed in beliefs that foreigners pose a criminal threat (48%), compete for jobs (37%), and bring diseases (29%). Xenophobic attitudes only differ slightly by employment status and tend to be somewhat stronger among whites across all income brackets (Crush 2010).

6
responsibility, the SEC requires public companies to disclose material financial and other information to the public, thereby providing information for an investor to use in judging if a company's securities are a good investment.<sup>16</sup> Crucial to the SEC's effectiveness is its enforcement authority. Each year, the SEC brings between 400-500 civil enforcement actions against individuals and companies for violations of the US federal securities laws. Typical allegations include insider trading, accounting fraud, and providing false or misleading information about the securities that are being offered or sold. The SEC can bring a civil action in US federal district court, seeking an injunction, which is an order that prohibits future violations; a person who violates an injunction is subject to fines or imprisonment for contempt. The SEC also may seek civil monetary penalties and the disgorgement of illegal profits. Such penalties can be substantial, in some cases up to three times the amount of illegal profits obtained. In addition to its civil enforcement authority, the SEC may bring a variety of administrative proceedings, which are heard by administrative law judges and by the SEC Commission itself. Finally, with the passage of the Sarbanes-Oxley legislation of 2002, the SEC may also use administrative proceedings to bar or suspend individuals from acting as corporate officers or directors.

# **D.  OFFICE OF FEDERAL HOUSING ENTERPRISE OVERSIGHT (OFHEO)**

The Office of Federal Housing Enterprise Oversight (OFHEO) was established as an independent entity within the US Department of Housing and Urban Development in 1992.<sup>17</sup> OFHEO's primary mission is to ensure the capital adequacy and financial safety and soundness of two GSEs, the Federal National Mortgage Association (Fannie Mae) and the Federal Home Loan Mortgage Corporation (Freddie Mac).<sup>18</sup> These two entities are the nation’s largest housing finance institutions and are responsible for creating a ready supply of mortgage funds for US homebuyers.<sup>19</sup> OFHEO is responsible for issuing regulations concerning capital adequacy and enforcement standards, conducting oversight examinations and risk-based capital stress tests of Fannie Mae and Freddie Mac, and taking necessary enforcement actions. OFHEO is funded through assessments on Fannie Mae and Freddie Mac.

> 16 _See_ SEC website, www.sec.gov.

> 17  Congress established OFHEO by the Federal Housing Enterprises Financial Safety and Soundness Act of 1992 (Title 13 of P.L. 102-550).

> 18 Ginnie Mae is not regulated by OFHEO.  It is, itself, a government corporation within the US Department of Housing and Urban Development.

> 19 The Federal Housing Finance Board (formerly the Federal Home Loan Bank Board) plays a similar role as OFHEO with respect to the 12 federal home loan banks that were created in 1932 to serve as a central credit system for savings and loan institutions and to help increase the supply of funds to finance home mortgage loans.  The Board is an independent federal regulatory agency with a five member board. _See_ www.fhfb.gov.

7
costs, as in Bloom (2009) and Cooper and Haltiwanger (2006), which makes the investment decision dynamic. At time _t_ capital is predetermined; investment made at _t_ transforms into working capital in _t_ + 1 .

We make two simplifying assumptions regarding participation. In our baseline specication, we do not model the decision to enter or exit the domestic market. That is, the number of rms is xed and there are no xed costs of production so that even the least productive rms nd it protable to produce. In the simulations (section 4), however, we explore an extension of our baseline model that allows for entry and exit of rm. We also do not model the decision to export. Since rms face a perfectly elastic demand, the decision to export does not play any role in this model.

We start by describing the production technology and the static prot maximization. The production function is a Leontief combination of materials and a Cobb-Douglas index of labor and capital given by where _Yijt_ is output, _Lijt_ is labor, _Kijt_ is the capital stock, and _Mijt_ is materials. The variables _b_<sup>0</sup> _jt_<sup>and</sup><sup>_Aijt_aresector-levelandrm-levelproductivityshocks,respectively.BothareHicks-neutral</sup> in the three inputs. The parameters _αK_ and _αL_ are the Cobb-Douglas output elasticities and _vM_ is the unit input requirement for materials. The intuition behind the functional form is that there is substitution between labor and capital, with the capital-labor intensity being chosen by rms, whereas materials transform into output in xed proportions and cannot be substituted for with labor or capital.

We furthermore assume that rms source materials from their own sector only. Under these assumptions we can write instantaneous prots as a function of labor and capital as where _wjt_ is the sector wage and _pjt_ the sector domestic price including taris. Notice that the price and productivity shocks enter instantaneous prots multiplicatively. We combine the two sectorlevel shocks into one prot shock, denoted by _bjt_ = _pjtb_<sup>0</sup> _jt_<sup>.Thisprotshockincludespriceshocks</sup> and technology shocks that are common to all rms in a sector. It can also include uncertainty shocks to taris or trade policy (Handley and Limao, 2015). Firm-level shocks are only productivity 7
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