rafmacalaba/data-use-mentions-extended huggingface dataset:
, which was intended to monitor the world’s progress toward the institution’s self-assigned “twin goals” of eliminating poverty and promoting shared prosperity. There have now been three such reports (World Bank, 2016, 2018 and 2020), all jointly produced by some combination of the Research and Data Departments, in partnership with the Bank’s central operational hub for poverty work, which during this time has been known as the Poverty and Equity Global Practice. Although these reports focus explicitly on poverty and “shared prosperity”, they inevitably discuss inequality as well since, as noted above, it is very hard to discuss changes in either without a broader understanding of changes in the distribution. Indeed, the first PSPR (2016) was entitled “Taking on Inequality” and made the point that growth alone – even if it continued at the relatively high rates of the previous two decades - would be insufficient for the world to reach the 3% target for extreme poverty by 2030. Doing so would require a reduction in inequality as well.<sup>32</sup> Third, a lot of the work summarized immediately above builds on one of the Bank’s key comparative advantages, namely its unique ability to assemble, curate and analyze microdata from a very large number of countries in the world. These data underpin the work on global inequality; on international comparisons of economic mobility; and the monitoring exercises reported in the Poverty and Shared Prosperity reports. Summary statistics from the data sets and, increasingly, the ability to access the microdata directly to produce simple calculations on the fly, are made available through a website known as Poverty and Inequality Platform, which has succeeded the online platform PovcalNet. These websites have been important public goods provided to the academic and policy communities around the world by the Bank’s Research (and now Data) Departments. PovcalNet owes its origins to the early work on global poverty measurement started by Martin Ravallion and colleagues such as Shaohua Chen and Prem Sangraula. Today
evidence by Hall (2011) and Mohnen and Hall (2013). In addition, we include a variable for age to control for the different propensity of firms to innovate over their life cycle (Huergo and Jaumandreu 2004). Finally, we control for ownership as, especially in developing countries, being foreign owned may facilitate acquisition and adoption of foreign technologies. Size, age, and ownership have been also recognized as determinants of financing constraints (Beck et al. 2006). The more expensive it is to finance innovation externally, the more detrimental to innovation are the effects of financial constraints. Descriptions of the main variables employed in the estimations are reported in Table 1.

**Table 1: Variables definition**

|**Variable**|**Description**|
|---|---|
||**_Dependent variables_**<br>|
|**Innovation score**|It is computed using the following 4 questions from the WB ES: (1) "Does this<br>establishment at present use technology licensed from a foreign-owned company,<br>excluding office software?" (2) "During the last three years, has this establishment<br>introduced new or significantly improved products or services?" (3) "During the<br>last three years, has this establishment introduced any new or significantly<br>improved process?" (4) "During the last fiscal year, did this establishment spend on<br>formal research and development activities, either in-house or contracted with other<br>companies, excluding market research surveys?". The score is then rescaled using<br>the total number ofquestions for which an answer is available.|
|**Innovation dummy**|It takes a value of 1 if the innovation score isgreater than 0,0 otherwise.|
|**Adoption score**|The adoption score uses the questions (1) to (3
. The effect is stronger for the lower poverty lines – for the international line of 3.2 USD/day in 2011 PPP the poverty rate goes down by 64 percent. For the international line of 5.5 USD/day in 2011 PPP and national lines the effect is less pronounced, but still high and reaches 20 percent. Like in the case of inequality, the biggest relative reduction of poverty happens through pensions. Direct transfers minus direct taxes still contribute to a notable absolute reduction in poverty incidence (Table 12).

The results for the alternative scenario, when pensions are treated as deferred income, are much more moderate. The Gini index falls from market income plus pensions to final by 2 percentage points or 3.4 percent. The poverty headcount under national and international lines actually increases by 16 percentage points due to the strong effect of indirect taxes, which overcompensates the positive contribution from direct transfers minus direct taxes.

**<u>Table 12. Poverty and inequality at various levels of income in Moldova.</u>**

||||market ma|rket +pensio<br>Inco|ns disposable c<br>me concept|onsumabl|e final|
|---|---|---|---|---|---|---|---|
|Inequality|Gini index||48.0|34.1|30.5|32.0|29.6|
||National||39.4|25.2|18.6|30.5||
|Poverty|International|(3.2 USD/day)|28.0|9.1|6.0|9.9||
||International|(5.5 USD/day)|43.2|27.9|22.2|33.3||

Notes: National - below the national poverty line
Figure 2. The Percentage of Firms Owned/ Managed by Female integrating into GVCs, by size <!-- Start of picture text --> 100%<br>38.0% 52.5% 51.2% 63.3% 33.0% 49.2% 54.5% 63.7%<br>90%<br>80%<br>70%<br>60% 36.0%<br>36.8%<br>50%<br>40% 34.4% 32.1% 33.0% 29.8%<br>30% 27.3% 25.2%<br>31.0%<br>20%<br>25.2%<br>10% 16.7% 17.8% 15.7%<br>0% 13.1% 9.4% 11.1%<br>GVC1 GVC2 GVC3 GVC4 GVC1 GVC2 GVC3 GVC4<br>(a) Owned (b) Managed<br>Small Medium Large<br><!-- End of picture text --> Source: Constructed by the authors using the WBES.

Note: GVC1 refers to firms that export and import simultaneously, GVC2 = GVC1 + international certification, GVC3= GVC1+ share of its capital owned by a foreign firm, GVC4 combines the four criteria altogether.

Figure 3. The Percentage of Firms Owned/ Managed by Female integrating into GVCs, by region <!-- Start of picture text --> 100.0%<br>90.0%<br>80.0%<br>51.2%<br>70.0%<br>60.0% 69.6% 64.0%64.3%72.3%65.7%71.1% 62.5% 74.7%81.8
# **<mark>2. Sector context</mark>**

While infrastructure investment in the region overall has been strong (Table 1), there is wide variation across countries in the quality and quantity of infrastructure (Table 2). The high-income Gulf Cooperation Council (GCC) group has the best infrastructure endowments and services in the region, reflecting advanced stage of development and commitment to infrastructure investments financed by oil revenues. However, infrastructure deficiencies in developing MENA remain a concern.

**Table 1: Infrastructure endowments in the developing world (averages 2005-2008)**

|**Sector**|**EAP**|**ECA **|**LAC**|**MENA **|**SA **|**SSA **|
|---|---|---|---|---|---|---|
|**Density of paved road network**<br>**km / 1,000 km2 of arable land**|1,128|1,051|2,965|**2,179**|467|1,095|
|**Telephone Density**|||||||
|**Fixed and mobile subscribers per**<br>**1,000 people**|400|929|839|**537**|353|273|
|**Electricity generating capacity**<br>**Million KWh per million people**|0.30|0.92|0.44|**0.30**|0.31|0.11|
|**Access to electricity**<br>**% of population with access**|62|NA|86|**91**|48|31|
|**Improved Water**<br
|**Credit Constraints**|||||
|---|---|---|---|---|
|No need for external finance|0.244***<br>(0.060)|0.231***<br>(0.057)|-0.363***<br>(0.029)|-0.355***<br>(0.033)|
|constant||0.537***||0.181|
|||(0.158)||(0.118)|
|**Pseudo R-Squared/R-**<br>**Squared**|0.085|0.107|0.235|0.240|

***,**, * Significant at 1,5, and 10 percent level.  T-statistics in parentheses. Source: Authors‘ calculations based upon data from the World Bank Enterprise Survey,  BEEPS data and from other sources Note:   Since dependent variables are dummy variables, probit regressions are used.  Performance variables used in analysis in Table 3 are omitted because they were not available for all years.  All regressions include dummy variables indicating sector.  Robust standard errors are in parentheses. Errors are clustered at country level.  Marginal effects calculated at sample means for each variable are shown. Dependent variable is a dummy variable indicating that firm manager responded that access to finance was no obstacle to firm operations.

44
countries on the employment buffering role of SOEs during crises. The study found that SOEs in Latvia shed jobs at a slower rate than POEs during the 2008-09 global financial crisis, thus mitigating the negative effect on jobs during the recession. In Serbia, the SOE sector shed jobs faster than the POE sector over the entire 2007-16 period, irrespective of the economic cycles. While governments may not, as a policy goal, be actively using SOE employment to cushion the adverse job effects of economic crises, SOEs may be a passive means of maintaining stable jobs for their citizenry. Szarzec, Dombi, and Matuszak (2021) found that while domestic and foreign-owned firms reacted to the 2008 global financial crisis by decreasing their net job creation, Hungarian SOEs did not reduce their net job creation. During the COVID-19 pandemic, in most countries, SOEs did not furlough or fire employees (IMF 2021). A household survey conducted by the EBRD (2020) in August 2020 revealed that employees of state firms were less likely to lose their jobs or see their income reduced in the early months of the COVID-19 crisis. This is consistent with findings for the global financial crisis. Overall, it is worth noting that data limitations often prevent insights on the buffer effects of SOEs during crises.

**Finally, some studies have examined how a greater SOE footprint in markets affects competition and market outcomes, such as business dynamism and allocative efficiency** . Business dynamism matters for aggregate productivity improvements and economic growth. In a study on China, Cerdeiro and Ruane (2022) found that in provinces where SOEs account for a larger share of the capital stock, manufacturing sector business dynamism in those provinces tended to be weaker. Other studies have highlighted that the presence of the state in markets is associated with entry barriers and, thus, lower entry rates of new firms. Using 1995, 2004, and  2008 data from the Chinese Industrial Census, Brandt, Kambourov, and Storesletten (2020) indicate that a key factor underlying the dispersion and dynamics
-|---|---|---|---|---|
||**female**|**male**|**female**|**male**|**female**|**male**|
|**2019/20**|17|71.1|14.02|64.69|17.53|9.01|
|**2020/21**|13.9|68.66|11.72|62.9|15.65|8.39|
|**_Difference (yoy)_**|_-3.1***_|_-2.44***_|_-2.3***_|_-1.79***_|_-1.88***_|_-0.62***_|
|**_Differential covid impact (F/M)_**|_1.27_|_***_|_1.28_|_***_|_3.03_|_***_|

Note: Notes: Stars indicates levels of significance of t-test on equality of means between the two years: * p<0.1, ** p<0.05, *** p<0.01. Survey weights used in the analysis Source: LFS 2019-20, 2020-21

**_Table 4: Changes in work hours and underemployment 2019-20/2020-21, by_** **_<u>gender</u>_**

||**Hour worke**<br>|**d (total)**<br>|**Hours w**<br>**(mainj**<br>|**orked**<br>**ob)**<br>|**Under-em**<br
|UNHCR/WB|
|topic_id|Numerical identifiers for which topics are covered in the survey based on nlp4dev<br>(https://www.nlp4dev.org/)|Authors|
|topic_words|Text identifiers for which topics are covered in the survey based on nlp4dev<br>(https://www.nlp4dev.org/)|Authors|
|topic_main|Indicator of the main topic covered in the survey based on nlp4dev<br>(https://www.nlp4dev.org/)|Authors|
|project_specific|Binaryindicator of whether the dataset isproject specific or not: Yes or no|Authors|
|idp|Binaryindicator of whether the dataset includes IPDs or not: Yes or no|Authors|
|Longitude|Longitude of countryof data collection|Authors|
|Latitude|Latitude of countryof data collection|Authors|
|Region|Name of the region where the survey took place: Middle East & North Africa, Sub-<br>Saharan Africa,South-Asia,Latin America & Caribbean|Authors|
|income_group|The income status of the country: Low income, Lower middle income, Upper middle<br>income|Authors|
|country_of_birth|Binary indicator of whether the survey asks a respondent their country of birth: Yes or<br>no|Authors|
|country_of_citizenship|Binaryindicator of whether surveyasks countryof citizenship: Yes or no|Authors|
|acquisition_of_citizenship|Binaryindicator of whether surveyasks aquisition of citizenship: Yes or no|Authors|
|date_arrival|Binary indicator of whether the survey asks a respondent their date of arrival: Yes or<br>no|Authors|
|reason_for_migration|Binaryindicator of whether the surveyasks their reason for migration: Yes or no|Authors|
.04]|
|Observations|10342|10225|10005|10013|10019|10005|9943|9912|9835|9796|9738|

Source: Authors' calculations using historical tax declaration records. Random effects model estimated. Robust standard errors in parenthesis *** p<0.01, ** p<0.05, * p<0.10 _ii. Longitudinal impacts of letters on firm payroll declarations_ The illustrative evidence in Figure 1 above is further supported by the estimation of equation (3), which accounts for both time-variant and time-invariant factors. Figure 2 below (full results in table A1 of the Annex) reports the treatment effects from the random assignment of firms to the soft or strong tone letters (relative to the control group) on the log wage bill (as such, coefficients capture the percent difference in the total wage bill). The coefficients of interest here are the interaction between the month of declaration and the random assignment, which capture the impact of each letter at a specific declaration period relative to the control group at baseline (January).<sup>11</sup> We observe large, statistically significant impacts of the soft letter across all firms in March (6.9% impact on the wage bill relative to no notification in January , p<0.01) and April (10.2% impact on the wage bill relative to no notification in January, p<0.05), but no impacts from the strong letter.

> 11 Not shown in the table are the time effects or the time invariant treatment effects, which highlight that there are no statistically significant differences in payroll across treatment assignment at baseline (January), and that there is a statistically significant drop in payroll declared (relative to January) that persists through November. Full results are provided in the annex.

12
# **References**

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# **1. Introduction**

A 2012 report by the World Bank on teenage pregnancy stresses the correlation between teenage childbearing and socioeconomic variables including poverty, inequality, public health expenditure and female labor force participation. The report shows that, despite substantial reductions in teenage pregnancy rates in virtually all countries, we continue to see rates differ vastly between high- and low-income countries, with Brazil—as a middle-income country—being placed somewhere in the middle of the distribution (World Bank 2012).

Improved access to education, not explored in detail in the World Bank report, potentially provides an important channel through which teenage pregnancy and the above socioeconomic variables are correlated. Observational evidence shows a strong negative relationship between school availability and teenage childbearing in Brazil. We plot this relationship over time for Brazil in Figure 1. Between 1997 and 2009,<sup>1</sup> 9,402 secondary schools where introduced in Brazilian municipalities (a 57% increase), raising the average school density in municipalities from 1.06 per 100 teenagers to 1.54 per 100 teenagers. Over the same period, the birth rate for teenage girls<sup>2</sup> decreased by 21% from 8.1 births per 100 to 6.4 births per 100. This suggests an additional secondary school per 100 teenage females is associated with a decrease in births of 3.33 births per 100 teenage females. Evidence based on cross-sectional data shows a very similar picture. In Figure 2, we present the state-level relationship between school density and the rate of teenage childbearing. Based on this clear negative relationship we calculate that an additional secondary school per 100 teenage females is associated with a larger decrease in births of 17.9 births per 100 teenage females.<sup>3</sup>

> 1 Section 3 provides the reasoning for the chosen period of 1997-2009 of this study.

> 2 In this paper we define ‘teenage’ as ages 15–19 to match with the target entry age
, which sums the following appliances: blender, refrigerator, electric grill, gas stove or microwave oven; (4) _Basic services_ , which consists of the availability of the following services: firm floor, electricity, drainage and water access in the property; (5) _Years of completed formal schooling_ ; (6) _Pos income_ indicates household per capita income greater than zero; (7) _log(income)_ , is total individual income; and (8) _log(imp cons)_ , is the imputed measure of non-durable expenditure on food personal products, and clothing. Data employed comes from 1997 ENCASEH and 2017-18 ENCEL. Panel A shows a DiD estimation. Panel B estimates the treatment effect for different HH types: original and new. Panel C explores differences in the treatment effect by groups of age formed based on the ages associated to critical schooling stages. Panel D shows differences by health shocks status (health shocks are defined as having at least one household member sick or injured during the two weeks before the survey). Panel E looks into the treatment effect by migration status (Metropolis includes Mexico City, Guadalajara and Monterrey). Controls include (from ENCASEH 1997): HH head age, education, speaking a dialect, HH size, % members under 15, dummies for home, productive land and draft animals ownership, and locality marginalization index. Standard errors clustered at the locality level. Asterisks indicate significance at the ***1%, **5%, and *10% level.

29
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- Mogstad, M. and Torsvik, G. (2021). Family Background, Neighborhoods and Intergenerational Mobility. NBER Working Papers 28874, National Bureau of Economic Research, Inc.

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- Neilson, C. and Zimmerman, S.
projects in 2014. This was followed by financial services (18.1 percent) and consumer products and retail (14.1 percent). The natural resource sector (coal, oil, and natural gas) ranked 9<sup>th</sup> and accounted for only 3.5 percent of all Greenfield projects in Africa in 2014. As shown in Table 1 above, consumer products and retail (33.6 percent), and real estate, hospitality and construction (33.6 percent) sectors are the biggest creators of Greenfield FDI-related jobs; yet, these sectors are ranked 3<sup>rd</sup> and 4<sup>th</sup> on the list of attractive sectors for FDI in the region (based on fDi Market surveys). The two most attractive sectors to foreign investors are technology, media, and telecommunications, and financial services created only 6.4 and 2 percent respectively, of Africa’s Greenfield FDI-related jobs in 2014. Overall, US, UK, and France’s direct Greenfield investments tend to be concentrated in: technology, media and telecommunication, transport and logistics, consumer products and retail, business services, and financial services sectors. Chinese FDI is also predominantly in technology, media and telecommunications sector, where approximately 33 percent of its projects in Africa were concentrated in 2014 (EY 2015).

Many analysts have tended to assume that much of FDI to Africa is in the natural resource sector. However, the preceding and recent evidence on Greenfield investments suggests that there has been a change over time. In the past, foreign investors may have been attracted to Africa solely because of its abundance of natural resources, especially in oil and ores. However, recent evidence puts rising urbanization and an expanding consumer base as some of the major determining factors for FDI to Africa. As a result, the large economies of the continent: South Africa, Nigeria, Kenya, Ghana, Egypt, Morocco and recently, Ethiopia and Mozambique