rafmacalaba/data-use-mentions-extended huggingface dataset:
perceived value of sons, irrespective of their economic value to parents. Across contexts, the common practices associated with son preference are patrilineal inheritance (inheritance through the male line) and patrilocal residence (co-residence with husband’s family after marriage). The more rigid these traditional kinship systems are, the less opportunity there is for girls to be valuable to parents. In these systems, sons cultivate the land, serve as primary care-givers for aging parents, and eventually inherit the land. Alternatively, daughters care for their in-laws after marriage. A daughter’s productivity is perceived as belonging to her husband’s household, and she is therefore deemed to be of little/no value to her parents (DasGupta et al. 2003; DasGupta 2010; Dyson & Moore 1983).<sup>5</sup> Traditional kinship systems that undermine the value of daughters versus sons are seen in all countries that have substantial sex selection. China, India, Korea, Albania, Vietnam, and the South Caucasus countries all have pre-industrial kinship systems that value sons more. As a reference, there are plenty of English language ethnographies of Asian kinship patterns (for China, India, Korea, see DasGupta et al. (2003); for Vietnam, see Guilmoto (2012c)). While English language ethnographies for South Caucasus are limited, the few studies that exist clearly document the predominance of male kin (Hayet 2002; Dragadze 1988; Ishkanian 2003). Moreover, recent qualitative studies in the South Caucasus region also illustrate the persistence of rigid patrilineal kinship systems. These studies find that for families in South Caucasus countries, sons have economic and symbolic value, that having a son is a source of security for mothers and a source of masculine affirmation for fathers, and that daughters, while helpful, are not as beneficial in the long-term as they prioritize their in-laws after marriage. While younger women in these countries are more likely to assert their desire to be economically productive, support parents after marriage, and inherit
Field, E., & Vyborny, K. (2022). _Women’s mobility and labor supply. Experimental evidence from_ _Pakistan_ (No. 655; ADB Economics Working Paper Series).

https://www.adb.org/sites/default/files/publication/792221/ewp-655-womens-mobility-laborsupply-pakistan.pdf Gobillon, L., Selod, H., & Zenou, Y. (2007). The Mechanisms of Spatial Mismatch. _Urban Studies_ , _44_ (12), 2401–2427. https://doi.org/10.1080/00420980701540937 Guzman, L. A., & Hessel, P. (2022). The effects of public transport subsidies for lower-income users on public transport use: A quasi-experimental study. _Transport Policy_ , _126_ , 215–224. https://doi.org/10.1016/j.tranpol.2022.07.016 ILO. (2017). _World Employment and Social Outlook: Trends for women 2017_ . ILO.

https://www.ilo.org/global/research/global-reports/weso/trends-for-women2017/lang-en/index.htm ILO. (2020). _Labour force participation rate by sex and age (%)_ . ILOSTAT.

https://www.ilo.org/shinyapps/bulkexplorer33/?lang=en&segment=indicator&id=EAP_DWA P_SEX_AGE_RT_A&ref_area=JOR Kain, J. F. (1992). The Spatial Mismatch Hypothesis: Three Decades Later. _Housing Policy Debate_ , _3_ (2), 371–460. https://doi.org/10.1080/10511482.1992.9521100 Klasen, S., & Pieters, J. (2015). What Explains the Stagnation of Female Labor Force Participation in Urban India? _The World Bank Economic Review_ , _29_ (3), 449–478. https://doi.org/10.1093/wber/lhv003 Lucas, K., & Porter
# Sources fo' benefit incidence numbers:

World Bank Poverty Assessments and country studies: Brazil, 1995; Colombia, 1994; Honduras, 1994; Kenya, 1995; Madagascar, 1996; Mongolia, 1996; Nicaragua, 1995; Tanzania, 1995; Uruguay, 1993; Vietnam, 1996 Madagascar Poverty Assessment 1996: Argentina, Brazil, 1985, Chile, South Africa, Malaysia Baker, Judy, 1997. Poverty Reduction and Human Capital Development in the Caribbean: A Cross-Country Study, The World Bank: Guyana, Jamaica, Trinidad and Tobago, St Lucia: Demery, Lionel, 1997, "Benefit Incidence Analysis" mimeo: Bulgaria, Ghana.

Van de Walle, Dominique, 'The Distribution of Subsidies through Public Health Services in Indonesia, 1978-87," The World Bank Economic Review, 8(2):279-309: Indonesia.

63
2012 2014<br>400 30<br>300<br>25<br>200<br>20<br>monthly rainfall in mm<br>100 monthly temperature in C<br>0 15<br><!-- End of picture text --> Notes: Box plot shows the distribution of commune observations for each climate zone. The boxes illustrate the 25 to 75 percentile with the median value represented by the line in the box. The whiskers indicate the lowest and highest adjacent value with the points outside below or above that identifying outlier observations. Values are measured as the mean of monthly rainfall levels and mean temperature in the last 12 months.

Source: Author’s calculation based on VHLSS 2010, 2012 & 2014 and CRU data.

Annual weather conditions vary between the zones and years. 2010 was the driest and warmest year with considerable variation between and within zones (Figure 5). Although on average 2012 is the wettest year in all zones, 2014 has a greater number of communes in the _Wet‐cold_ and _Wet‐hot_ zones which have rainfall levels far above the zone’s average. This skewed distribution reflects the heavy rainfalls that occurred in the Central Provinces at the end of 2013. These rainfalls resulted in severe flooding and livelihood damages (UN, 2013).

12
Small Area Estimation of Poverty in Four West African Countries by Integrating Survey and Geospatial Data _Ifeanyi Edochie_<sup>_1_</sup> _David Newhouse_<sup>_2_</sup> _Nikos Tzavidis_<sup>_3_</sup> _Timo Schmid_<sup>_4_</sup> _Elizabeth Foster_<sup>_1_</sup> _Angela Luna Hernandez_<sup>_3_</sup> _Aissatou Ouedraogo_<sup>_1_</sup> _Aly Sanoh_<sup>_1_</sup> _Aboudrahyme Savadogo_<sup>_1_</sup>

> 1 Poverty and Equity Global Practice, World Bank Group

> 2 Development Economics Data Group, World Bank Group

3Department of Social Statistics and Demography and Southampton Statistical Sciences Research Institute, University of Southampton

> 4Institute of Statistics, Otto-Friedrich-University Bamberg.

We thank Olivier Dupriez, Jed Friedman, Haishan Fu, Craig Hammer, Johannes Hoogeveen, Gabrela Inchauste, Talip Kilic, Johan Mistiaen, Pierella Paci, and Nobuo Yoshida for their support. This work was partially supported by the Knowledge for Change Program’s phase IV programmatic research project “Understanding Trends in Sub-National Differences in Economic Well-Being in Low and Middle- Income Countries” Program. The work of Tzavidis, Schmid, and Luna is supported by the Data and Evidence to End Extreme Poverty (DEEP) research program. DEEP is a consortium of the Universities of Cornell, Copenhagen, and Southampton led by Oxford Policy Management, in partnership with the World Bank - Development Data Group and funded by the UK Foreign, Commonwealth and Development Office. The work of Tzavidis is also supported by the UKRI-ESRC strategic research grant ES/X014150/1 for “Survey data collection methods collaboration: securing the future of social surveys”, known as Survey Futures. Survey Futures is a collaboration of twelve organizations, benefitting from additional support from the Office for National Statistics and the ESRC National Centre for Research Methods. Further information can be found at <u>www.surveyfutures.net.</u> Keywords: Poverty measurement,
**Table S1.** Comparison of SGRY and NREGA

|Description|SGRY|NREGA|
|---|---|---|
|Centrally Sponsored Scheme|Yes|Yes|
|Centre:State Cost Ratio|75:25|90:10|
|Female participation target|33%|33%|
|Districts covered|All|All|
|Primary Objectives:|||
|a) Wage Employment|Yes|Yes|
|b) Infrastructure Creation|Yes|Yes|
|Restrictions on public works:|||
|a) Ban on contractors|Yes|Yes|
|b) Ban on heavy machinery|Yes|Yes|
|c) Wage:Capital Cost Ratio|60:40|60:40|
|Implementing Authority|Village<br>Panchayat|Village<br>Panchayat|

_Source_ : Annual reports (2001 – 2010), Ministry of Rural Development, Government of India. _Note_ : The table compares various program attributes of SGRY and NREGA. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Employment Guarantee Act.

**Table S2.** Results with AWI data: Impact of public works on wages with binary treatment

||(1)|(2)|(3)|
|---|---|---|---|
|_NREGAd,t_|7.197<sup>_∗∗∗_</sup>|-2.302|-3.045|
||[2.319]|[1.946]|[1.989]|
|District effects|No|Yes|Yes|
|Year effects|No|Yes|Yes|
|Trend effects|No|No|Yes|
|Observations|1340|1340|1340|
|Adjusted_R_<sup>2</sup>|0.009|0.853|0.918|

_Source_ : Author’s
7

- simple multiple of per capita GDP, because "private" employees in the region also receive a variety of in-kind benefits. The highest government wages are found in the Middle East and North Africa, about one-third higher than comparable private compensation;

- at the risk of sounding pedantic, we must conclude this section with the same caveat as for employment: wage policy recommendations cannot be based on such aggregate evidence as presented here. However, the regional evidence does give some context to individual country data, and provides a basis for assessing broad trends over time -to which we now turn.

Table 2. Central Government Wages, 1990s

||_No. of_<br>_countries_|_Central Government_<br>_Wages and Salaries as_<br>_% of GDP_|_Average Central_<br>_Government Wage as_<br>_Multiple of per capita_<br>_GDP_|_Ratio of Public to_<br> <br>_Private sector_<br>_wages_|
|---|---|---|---|---|
|Africa|21|6.7|5.7|1.0|
|Asia|14|4.7|3.0|0.8|
|ECA|21|3.7|1.3|0.7|
|LAC|12|4.9|2.5|0.9|
|MENA|8|9.8|3.4|1.3|
|OECD|16|4.5|1.6|0.9|
|Overall|92|5.4|3.0|0.8|

**_Soume_ Table** A-3 **for** the **fit** twocolumns; various refs and World Bank pro documents, for about 16 counies, for the ratio of public and **privste** wages.
million lives<sup>1</sup> . In the past 30 years, the world has witnessed an alarming increase in the number of reported flood disasters, mainly driven by steady population growth and economic activities in flood-prone areas<sup>23,24</sup> . In many developing countries, the challenges associated with transport disruptions due to floods are aggravated by a lack of systematic urban planning, adequate flood control infrastructure (e.g., drainage systems), regular maintenance, technical capacity, and financial resources<sup>25</sup> . Without proactive flood risk management planning and adaptation, risks could continue to intensify as the absolute flood damage may increase up to a factor of 20 by the end of century<sup>26</sup> . When taking network complexity and cross-sector interconnectivity into consideration, such impacts will likely cascade through the networks causing systemlevel failures, severe delays, and prolonged socioeconomic damages.

To date, many studies have investigated the impact of floods on road networks in terms of hazard exposure to historical or modeled flood events<sup>27,28</sup> , travel delay as a result of flood disruption<sup>19,29</sup> , economic cost estimation<sup>2,30</sup> , and road network resilience evaluation<sup>31,32</sup> at local and regional scales. However, several challenges and limitations have so far prevented an analysis at a global level. First, the lack of publicly accessible high-resolution global flood maps capable of capturing flood events of varying intensities hinders a worldwide evaluation of flood hazard impacts on road transportation networks. Although the development of global flood models has been facilitated by advances in satellite data, numerical algorithms, computing

> 1 Historical flood records are extracted from the Emergency Events Database (EM-DAT22). Four flood hazard types are included: coastal flood, flash flood, ice jam flood and riverine flood. It should be noted that the number
15 in countries such as Poland, Turkey, Czech Republic, Bulgaria, Slovakia, and Croatia. Bank privatizations also took place in Latin America (Mexico and Brazil), East Asia (China, Philippines, Indonesia and Thailand), South Asia (Pakistan), and sub-Saharan Africa (Nigeria, Uganda).

# _Competitive sector_

24. Proceeds in competitive sectors, comprising largely manufacturing and service firms, had the largest number of transactions, registering at over 6,000. Manufacturing firms accounted for over two-thirds of competitive sector proceeds for the period as a whole, while service and other sectors accounted for the rest. The largest transactions in manufacturing were in steel, cement, fertilizer and other chemical sub-sectors. Countries in Europe and Central Asia were the most active in manufacturing, while Latin American countries divested the largest share in service and other sub-sectors. Proceeds in the competitive sectors declined from a 22 percent share of total proceeds in 1990-1999 to 9 percent in 2000-2003, reflecting in large part the shift to activity in infrastructure and finance.

# _Other sectors_

25. Energy sector proceeds (oil and gas, petrochemicals) remained more or less steady: transactions over the period as a whole were concentrated in a few countries—Argentina, Brazil, China, South Africa, Kazakhstan, Russia and Poland. The primary sector declined slightly, with most of the revenues concentrated in Latin America, Europe and Central Asia, and Sub-Saharan Africa.

# **III.    GOVERNMENT OWNERSHIP:  WHERE IS IT AT?**

26. The privatization of more, and more important, enterprises would tend to suggest that government ownership has declined over the years. But a systematic assessment is difficult in the absence of data on state enterprise sectors, in particular their share of GDP, investment, domestic credit, formal sector employment, and the like. Such data was last captured for industrial and developing countries for the period 1978-91 in the World Bank’s Bureaucrats in Business Report (1995). Since then the 2000 World Development Indicators updated
# **V: Extensions and applications**

We have used comprehensive off-chain transactions data from what has been the world’s largest peer-to-peer crypto exchange platform over the past five years. The analysis provides evidence that strongly suggests that Bitcoin is used actively as a vehicle currency in international transactions; in most countries it is also used extensively as vehicle for domestic currency transactions. Thus, it runs counter the oft-expressed view of crypto currencies as a purely speculative asset class. As we have emphasized, the nature of the data and, in particular, our ability to trace transactions price and currency is completely different than for research analyzing on-chain transactions.

Nevertheless, as computationally intensive as our exercise may be, the LocalBitcoins.com data set represents only a small share of the universe of off-chain crypto transactions when one includes centralized exchanges (where the exchange acts as financial intermediary).

In principle, our methodology can be applied for any more targeted investigation in any particular country/region, as well as to data from any exchange that identifies trades in terms of the fiat currency used to purchase crypto, as long as a number of minimum conditions are fulfilled (see Appendix A.6 for the precise description). Of course, when applying our methodology to data from other exchanges, one must account of their individual structure and features, including the average speed of clearing fiat money payments, and how to factor in pro-rata transactions fees. Also, in some centralized exchanges, the fee structure makes it more feasible to engage in very high-frequency speculative trading, although these almost invariably involve buying and selling in the same currency and one can thus exclude the impact of such trades on aggregates by exclusively analyzing international crypto vehicle currency trades.<sup>35</sup> Although data from many exchanges is private, regulators can typically access data from centralized exchanges in their own local jurisdiction, or potentially beyond that given sufficient

> 35 Similarly Wash trades, a fairly common and unregulated phenomenon in crypto markets (Cong et al, 2020
# **Figures and Tables**

**Figure 1: Children in early childhood education by country income and household wealth**

<!-- Start of picture text --> 90%<br>0.784<br>80%<br>70%<br>0.637<br>60% 0.557<br>0.482<br>50%<br>40%<br>30% 0.264 0.271<br>20% 0.155<br>10% 0.076<br>0%<br>Low Income Low-Middle Income High-Middle Income High Income<br>Bottom quintile Top quintile<br><!-- End of picture text --> _Notes_ : Data from the Multiple Indicator Cluster Surveys, reported in McCoy et al. (2018)
proposed, which assumes that a position in the highest occupational stratum grants greater access to life opportunities and, therefore, allows the identification of upward and downward movements of the youths. In table 4 our approach to occupational stratification is presented, which is ordered

> 41 In methodological literature, the causal inference methods based on matching are often introduced as (1) a quasiexperimental technique to contrast sample treatment and control cases, or otherwise as (2) a non-parametric method of fit for systematic patterns of assignment to treatment when it is not reasonable to rely on the estimators of a simple parametric regression (see Morgan and Winship, 2015).

> 42 The estimations based on inverse probability weights, introduced by Rubin (1985) and extended to the marginal structural models in Robins et. al (2000), have been extensively used to control the selection bias in treatment variables.

> 43 The Solís and Cortés classification (2009) was the first in the country validated with representative data at the national level, using the ENIGH 2004. Yaschine (2015) used and adapted this classification for the analysis of the ENCEL 2007 data.

24
produced)|47|0.05|0.07|63%|0.04|0.01|0.17|
|Collection rate (%)|48|68|80|19%|14.59|37.21|121.38|
|Meteringrate(%)|48|28|21|‐25%|33.96|‐|100.00|

Source: Author calculations based on IBNET data Pourashavas performed poorly when compared with global averages and with the WASAs and city corporations, except on indicators of efficiency.

_Efficiency:_ Pourashavas outperformed the WASAs and five city corporations on NRW. NRW in the pourashavas was 21.9 percent in 2010, which further decreased to 18.4 percent in 2016 compared to an NRW of 23 percent in 2016 for WASAs and city corporations during this period. Pourashavas also had lower NRW compared to the global average NRW, which was above 35 percent. While it is tempting to conclude that pourashavas performed well on this indicator, the low metering rates in the pourashavas may point to inaccurate measurement of NRW.

While staff productivity was higher globally at above 300 connections per employee throughout 2010‐ 2016, pourashavas performed better than the WASAs and city corporations during the period. Globally, there were more than 300 connections per employee throughout the period, while pourashavas had 163 connections per employee in 2016 increasing 26 percent from 2010. On the other hand, the average for WASAs and city corporations in 2016 was consistently lower than the pourashavas throughout the period and was only 101 connections per employee in 2016.

_Availability:_ Pourashavas performed poorly on indicators of availability – quantity of water supplied per person per day and duration of supply. Both global and WASA and city corporation averages performed better than the pourashavas. Consumption of water per person per day in the pourashavas was between 86 and 90 lpcd during 2010‐2016 compared to 100‐120 lpcd in the same period among the WASAs and city corporations. Global consumption of water
br>Power: 80%<br>Alpha: 0.05<br>Formal<br>employment at<br>baseline: 10%|_Region:_Caribe, Centro-Oriente,<br>Centro-Sur, Cafetero-Antioquia,<br>Pacifico<br>_HEI Administration_: Public, private<br>_HEI academic organization_:<br>University, technical center,<br>technological center.|National Information System of<br>Higher Education (_Sistema_<br>_Nacional de Información de la_<br>_Educación Superior_), 2017|
|**Dominican**<br>**Republic**|Census|209|n.a.|n.a.|All programs|Ministry of Economics, Planning,<br>and Development, 2019|
|**Ecuador**|Census|543|n.a.|n.a.|All programs|Secretariat of Higher Education,<br>Science, Technology, and<br>Innovation, 2019|
|**Peru**|Census|387|n.a.|n.a.|All licensedprograms|Ministryof Education,2019|

_Notes:_ For each country included in the WBSCPS, this table shows the sampling procedure (whether using the universe of programs or a representative sample) in column (1), size of program universes in column (2), replacement rate in column (3), and assumptions for power calculations in column (4). To determine the sample size, details on stratification levels used to calculate the number of programs to be included Colombia and Brazil samples and the source of sampling frame (universe or initial list of programs) are in columns (5) and (6). The assumptions to estimate sample size for Brazil were based on Almeida et al (2015). “n.a.” = not applicable.
making or expenditure and in practice remained largely dormant. In 2006, the Government of Sindh attempted to revitalize SMCs by providing annual grants worth PKR 22,000 (USD 220 equivalent in 2012) to support school improvement activities. The SMC was given control over its allocated funds and was empowered to withdraw these resources to implement activities in accordance with a School Improvement Plan (SIP), developed in consultation with the community. However, concerns persisted over the transparency and utilization of the grant funds. In 2009, as part of further reforms, the government distributed updated SMC guidelines to all public primary schools to avoid any fiduciary and audit concerns. These fiduciary controls, however, reduced the role of SMCs to that of drafting an SIP, maintaining auditable records for procurement of goods, implementing nominal repairs and maintenance, monitoring enrollments and ensuring that children in their community started school at the appropriate age. However, they retained no wider role of engaging the community to improve quality of teaching and learning in schools.

The reforms were successful in reinstating the transfer of SMC funds annually; as of June 2012, over 81 percent of SMC bank accounts were active and received grants from the government (World Bank, 2012). Evidence from the baseline survey for this study, conducted in 2012, suggests that the activity level of SMCs improved as a result of the reforms, but remained low overall: while 91 percent of schools had an SMC, fewer than half of these (45 percent) had held a meeting in the previous six to twelve months, and fewer than a quarter (21 percent) had held a village-wide meeting during the same time. Only 29 percent of head teachers reported that SMC members at their school were chosen by election. Underutilization of funds continued: in two of the three 13