rafmacalaba/data-use-mentions-extended huggingface dataset:
-1.6<br>Gov't eff. Reg. quality Rule of law Control of<br>corruption<br>2018 2018 2018<br>1998 1998<br>1998 2007 2018 1998 2007 1998 2007 1998 2007<br>1998 2007 2018 2007 2018 1998 2007 2018 2007 2018<br><!-- End of picture text -->

# D. Low-income countries

Source: World Bank Worldwide Governance Indicators.

Note: Based on indicators from the Worldwide Governance Indicators measuring aspects of governance. WGI defines governance as “the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.” The four indicators are government effectiveness, regulatory quality, rule of law, and control of corruption.

A. A country significantly improved its rating if it saw a two standard deviation improvement in one of four indicators between 1996 and 2018. The standard errors are the average between two observations.

B.-D. Annual observations are unweighted averages. EM7 are the seven largest EMDEs including Brazil, China, Mexico, India, Indonesia, Turkey, and Russia. EM22 are the 22 largest EMDEs. LICs are Low-income Countries and includes data for 26 economies. Error bands are 1 standard deviation.
## 3.3 Main independent variables: Macro-financial variables and other country

## characteristics

Table 2 provides definitions and sources of the main independent variables used in our empirical analysis. Table 3 and Table 4 provide summary statistics and correlation matrixes, respectively. In addition to the crypto-asset data from Chainalysis, we use countries’ financial and macroeconomic data from various sources. We focus on a parsimonious set of monthly indicators to ensure broad country coverage. Our monthly indicators are derived from three data sets: 1) IMF’s International Financial Statistics (e.g., country-month inflation and exchange rates); 2) IMF’s World Economic Outlook (e.g., country-year nominal GDP); and 3) US Federal Reserve economic data for global or U.S. asset prices, interest rates, risk appetite measures, and inflation expectations. The bitcoin and ether data are sourced from Coinbase, a large U.S.-based crypto-assets exchange.

Our set of broader annual country-level variable include GDP per capita, financial development indicators such as domestic bank credit to GDP, financial account ownership, remittances to GDP, and ICT development indicators (e.g., mobile phone subscriptions and fixed broadband subscriptions). We also include indicators on countries’ institutional frameworks proxied by economic freedom indicators. These indicators are taken from the World Bank World Development Indicators and the Heritage Foundation’s Economic Freedom Index, respectively.

# 4 Empirical approach

First, we start by running monthly panel regressions focusing on a set of country and global macroeconomic and financial factors with country-fixed effects. We do not include time-fixed effects since we are studying the global macro-financial factors. Second, we run basic cross-sectional regressions to explore the role of a broader set of annual variables. All estimations involve robust standard errors clustered at the country level. In light of the relatively small size and impact of crypto-assets activity on country and global macro-economic variables to date, we are not very concerned about endogeneity challenges.

We exploit within-country variation by deploying panel
Adults living in Baghdad are significantly less likely to be satisfied with the quality of education and water in their area. However, this might reflect the relatively higher income and education of adults living in Baghdad, who might have higher expectations of government services. The perceptions of government services among Kurds are typically higher. The Kurds, with an autonomous history of local governance, are satisfied with these services throughout the decade, and even during the period of ISIS control. Perceptions of the quality of health care delivery are not significantly different between ethnoreligious groups. This is echoed by the findings of the World Bank’s Iraq Human Development Public Expenditure Review (2021) that found deteriorating outcomes in health care services across the board in the past two decades and limited availability of quality services, even for upper income groups (Panel C).

# **7. Conclusion**

The history of Iraq over the past decade is a stark example of increasing vertical and horizontal inequalities, preceding and following a period of violent conflict. This paper uses cross-sectional individual-level nationally representative survey data to study how adults in Iraq report confidence in national institutions, quality of life, and quality of service delivery. The analysis finds that positive perceptions across these categories declined during ISIS-control of parts of Iraq and are slowly picking up in recent years. Over the last decade, poor adults are more likely to be food insecure and have a weaker outlook on the standard of living. Men are more likely than women to report lower country leadership approval ratings, a lower standard of living, and weaker job prospects in the post-ISIS period. The findings show that Shias, Kurds, and adults in living in Baghdad have significantly deteriorating quality of life, over time, and a lower quality of life than Sunnis in the post-ISIS period. This is largely driven by the marginalization of the Sunnis in the last couple of decades (Bormann et al. 2021). Nearly all ethnoreligious groups perceive the government to be corrupt. Additional research (Dodge and Mansour 2021)
levels of consumption in each country. Therefore, official poverty lines and the subsequent national poverty rates generated using these lines are not comparable across countries (national poverty rates from now on). To generate international poverty estimates, the World Bank has created an international poverty line, which is applied consistently to all countries to monitor global extreme poverty. A line of $1 per day was first estimated in 1990 and has been repeatedly updated to take into account the changes in the purchasing power parity of countries (Ravallion et al., 2009).</mark><sup>3</sup> <mark>The last update of the international poverty line was done in 2015, when it was raised to $1.90 per person per day according to the 2011 values of purchasing power parity exchange rates (Ferreira, et al, 2016).</mark> <mark>The extreme poor are concentrated in Sub-Saharan Africa and South Asia. Table 1 shows the number and proportion of the international extreme poor in 2013 by regions. On average, 12.6 percent of the world’s population, or about one in eight of the world’s population lives in international extreme poverty. Sub-Saharan Africa and South Asia have the highest and second highest number and proportion of international extreme poor, with 50.7 percent and 33.4 percent of the world’s international extreme poor living in these two respective regions.</mark> <mark>A recent report by the Commission on Global Poverty to improve global poverty monitoring highlights the considerable uncertainty in global poverty point estimates. Since adopting the twin goals, the World Bank has devoted considerable amount of attention to improving its measure of</mark>

> 2 <mark>This line was calculated in the following way: (i) the national poverty lines of 15 poor countries was selected, (ii) those poverty lines were inflated to 2011 levels using the CPI of those countries, (iii) the inflated poverty lines
- OECD. (2022). _Gender wage gap (indicator)_ . Retrieved April 7, 2022, from OECD Data: https://data.oecd.org/earnwage/gender-wage-gap.htm

- Parr, N. J. (1995). Pre-Marital Fertility in Liberia. _Journal of Biosocial Science, 27_ (1).

- Peet, E. D., Günther, F., & Fawzi, W. (2015). Returns to education in developing countries: Evidence from the living standards and measurement study surveys. _Economics of Education Review_ (49), 69-90.

- Pennings, S. M. (2022). _A Gender Employment Gap Index (GEGI) : A Simple Measure of the Economic Gains from Closing Gender Employment Gaps, with an Application to the Pacific Islands._ Washington, DC: World Bank.

- Pratap, S., & Quintin, E. (2006). The Informal Sector in Developing Countries: Output, Assets and Employment. _WIDER Working Paper Series_ (RP2006-130).

- Roemer, P. (1996). Increasing Returns and Long-Run Growth. _Journal of Political Economy_ (94), 1002-1037.

- Rosenzweig, M. R., & Zhang, J. (2009). Do Population Control Policies Induce More Human Capital Investment? Twins, Birth Weight and China’s “One-Child” Policy. _The Review of Economic Studies_ , _76_ (3), 1149–1174.

- Rubiano-Matulevich, E., & Viollaz, M. (2019). Gender Differences in Time Use: Allocating Time between the Market and the Household. _Policy Research Working Paper_ .

- Schmidhuber, J., Bruinsma, J., & Boedeker, G. (2009). _Capital requirements for agriculture in developing countries to 2050. In FAO Expert Meeting on “How to Feed the World in 2050.”._ Rome: FAO.

- Schultz, T. P. (2002). Wage gains associated with height as a
Policy Research Working Paper 9003

# **Abstract**

This paper examines the impact of the recent recession in Belarus on poverty and broader measures of household welfare and compares the recent recession episode to previous economic crises in Belarus. The paper constructs a measure of vulnerability to poverty, based on an estimated probability of falling below the national poverty threshold not exceeding 10 percent, which is estimated for each year of the household survey data between 2014 and 2017. The analysis finds that the recession of 2014–16 was qualitatively different from earlier recent crisis episodes (2008–09 and 2010–11) in that it affected low-income households to a much greater extent, and the negative welfare effects lingered. The paper also documents that although the recession did not result in a substantial increase in absolute poverty by the official definition, it led to a considerable increase in the share of households that are vulnerable to poverty. The greater degree of vulnerability is also evident from the growing share of the population that faced a risk of poverty within a year (going in and out of poverty). Household types for which the impact of the recession was most pronounced are households with multiple children, single-parent households, residents of rural areas, as well as those who were not employed, partly employed, or low-skilled employees. Coping strategies that were employed by the population were largely related to reducing expenditures and, among vulnerable households, food expenditures in particular, as well as drawing down on their savings.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bornukova@beroc.by, acojocaru@worldbank.org, mmatytsin@worldbank.org, and shymanovich@research.by.

_The Policy Research Working Paper Series
members’ behavior. It may also offer opportunities to improve enforcement of pastoralists’ use rights and right holders’ ability to enter contingent contracts building on such rights, something that can increase flexibility of and most likely also effectiveness of PES, e.g., by including upfront payments (Jack _et al._ 2022) or tailoring interactions to the specific circumstances of target groups.

The combination of increased benefits and reduced cost of documented land rights creates opportunities for African countries to reap benefits that can translate into greater transparency, effectiveness, coverage, and fiscal impact of their land institutions through appropriate regulatory change. Such change would include (i) adjusting their regulatory and institutional frameworks to deliver services at scale by allowing use of digital technology (e-signatures, mass valuation, digital lodging of surveys, etc.), interoperability, and public monitoring; (ii) replacing high and distortionary transaction taxes by a recurrent land tax based on mass valuation techniques and public access to assessed property values; (iii) using access to registry information to catalyze operation of financial and other factor markets; (iv) clarifying modalities for registering group and secondary rights, including ways to decide on management and conflict resolution consensually and transparently; (v) establishing standards for transfer of use rights to public land to which no formal rights exist or for public land acquisition, in a transparent and competitive way; and (vi) allowing land users to capitalize on new opportunities to provide local and global public goods through climate change mitigation and adaptation by providing national land use monitoring to allow documentation and quantification of such contributions.

# **3. Land rights to help address challenges of structural transformation**

Institutions that clearly define rights to agricultural land and allow such land to be transferred easily can help reduce transaction cost in land markets and support functioning of other factor markets linked to land markets (e.g., those for insurance and credit). Africa is also richly endowed with land that, while traditionally often considered to be marginal,
and older plants increase labor productivity by 16.4%.<sup>17</sup> Older plants may be growing or gaining higher rents à la de Loecker et al., while shedding some labor, relative to younger plants that hire as they expand.

# _iii._ **_The role of initial plant capabilities/characteristics_**

Table 5 splits the regressions presented in Table 4 by plant position above or below median values of four pre-GQ levels of plant capabilities: skill intensity defined as the average plant wage per employee; capital intensity measured as the ratio of the value of tangible assets to total revenue; labor productivity; and TFPR.

The impact of proximity on exit follows the same decreasing gradient with age found previously although surprisingly, there appears little relationship with initial plant capabilities. Except for capital intensity for young plants, in general more able plants do not seem to survive better with proximity than those less so.

However, there are important differential effects on output and plant size that are consistent with findings in the aggregate data, that again suggest two types of adjustments for young and old plants. These adjustments indicate that exit may be a longer-term process as noted in Figure 1. With GQ proximity, young plants with above median skill intensity show a 10% increase for output compared to those below which show a negative but insignificant response. Older plants show the same pattern but where skill now mitigates contraction: output in below median skills plants fell by 11.8% and those with above median skills showing no significant effects.

A similar pattern emerges with capital intensity which is consistent with the industry characteristic effects discussed earlier: output in young plants with above median capital intensity increases by 13%, while those below the median contract by 9%; older plants with above median capital intensity show no effect while those below show a contraction of 8.5%. More sophisticated young plants grow with proximity to the highway network, while sophisticated older plants, contract less. Plant size (employment
vulnerability of mangroves due to sea level rise in a changing climate; in Section 5, we address the coastal protection services of mangroves at risk in a changing climate; and in Section 6, present the limitations of our analysis. Section 7 concludes with a brief discussion of the results.

# **2. Area Estimates of Coastal Mangroves in Developing Countries with Previous Exposure to Tropical Cyclones**

For our analysis, we used information provided by Giri et al. (2010) on the extent and distribution of mangroves from the global mangrove databases of the USGS: Earth Resources Observation and Science Center. In this database, the status and distributions of mangroves were mapped using the 30‐m resolution Global Land Survey (GLS) data for 2000 supplemented by Landsat archives. The GLS 2000 mosaics were prepared using images acquired from 1997 to 2000. Landsat imagery from the USGS archives was used if GLS data were cloudy. While mapping, each image was normalized for variation in solar angle and earth‐sun distance by converting the digital number values to the top‐of‐the‐atmosphere reflectance. The results were validated with other existing global, regional and local data sets (for details, see Giri et al. 2010). The USGS database includes a presence or absence grid cells showing the exact location, size, and shape of the mangroves.

In order to estimate coastal mangrove areas by country, we extracted vector coastline masks from SRTM version 2 Surface Water Body Data provided by NASA, and used the country and region identifiers used by the World Bank. Country boundaries along with mangrove data were used to estimate the extent of coastal mangrove forests, by country. We restricted our analysis to countries with previous exposure to tropical cyclones (UNEP/GRID 2009). A total of 46 countries meet the criteria for inclusion in this study (for country coverage, see Box 1). While other countries have mangrove forests, the absence of cyclones makes their storm protection service less important.

5
|Dimension|Brief Description|
|---|---|
||This set of indicators is based on countries’ use of internationally accepted and<br>recommended methodologies, classifications and standards regarding data integration.<br>These indicators help facilitate data exchange and provide the foundation for the<br>preparation of relevant statistical indicators. The following methods and standards are<br>considered: System of national accounts in use, National Accounts base year,|
|Dimension 5.2:<br>Standards and Methods|Classification of national industry, CPI base year, Classification of household<br>consumption, Classification of status of employment, Central government accounting<br>status, Compilation of government finance statistics, Compilation of monetary and<br>financial statistics, Business process. Further work could improve the validity of this<br>indicator and reduce the risk that countries may be incentivized to adopt only<br>traditional standards and methods and neglect innovative solutions that may be more<br>valid in the current context.|
||Not included because of lack of established methodology or suitable data sources. A|
|Dimension 5.3: Skills|new indicator drawing on PARIS21 indicators such as statistical society presence and<br>data literacy could be developed and is an area of future work.|
||Not included because of lack of established methodology or suitable data sources. A|
|Dimension 5.4:|new indicator based on textual analysis of NSS reports/websites for references to|
|Partnerships|partner organizations could be developed. This is an area of future work.|
||The indicator is based on PARIS21 SDG indicators (SDG 17.18.3 (national statistical|
|Dimension 5.5: Finance|plan that is fully funded and under implementation). It is included in dashboard, but not<br>in the overall SPI score or index because of insufficient country coverage.|

39
0|3.5|-7.5***|13.4|3.4|-10.0***|19.8|14.9|-4.9***|

Note 1: + p<0.10 * p<0.05 ** p<0.01 *** p<0.001 We observe that PE officials have an overwhelming positive perception of the impact of the e-procurement system. On average they reported a positive effect of the transition on evaluation periods whereas administrative data shows a negative impact. Moreover, 22% of PE officials mentioned the lower turnaround time required for processing tenders as one of the two main advantages of the e-procurement system. Similarly, 59% of bidders reported preparation times of tenders as one of the main drawbacks of the paperbased system that is alleviated by the transition to the electronic system.

# 2. Access Indicators

The analysis using administrative data of access indicators showed a positive impact of the introduction of e-procurement. We observed a significant increase in the number of non-local winners of contracts. This finding was corroborated by bidders in the firm survey. Bidders were asked about their estimation of the percentage of tenders they participated in and won outside their district. On average, bidders reported a 16.6% increase in the proportion of tenders they participated in and a 14.8% increase in the proportion of contracts won outside their district.

When bidders were asked about their how confident they were in bidding outside their district, 164 reported they had become more confident in bidding outside their districts while 58 said they had become less confident as result of the transition to the e-procurement system, with 361 bidders reporting no change in how confident they were bidding outside their district. It’s worthwhile to note 100 bidders (out of the 583) reported becoming equally confident in bidding in-and-out of their respective districts as opposed to being more confident in their own district previously. The table given below tabulates the
the probability of working, either exclusively or in combination with school attendance, increases (top panel in Figure 6). The average number of hours that a child works steadily increases with the number of animals (bottom panel in Figure 6). Consistent with previous results for _Progresa/Oportunidades_ (Gertler et al., 2012), using evidence follow-up survey we find that among households with at least one child aged 8-13, PAL increased the number of animals owned, with an effect that is statistically larger for cash than in-kind ones (column 1 in Appendix Table A8). Households from both treatment groups are more likely to report being involved in agricultural activities after the program inception (column 2 in Table A8), with the effect being statistically larger among those from cash localities than in-kind ones. We also find that households in the cash group are more likely to report a higher number of family members involved in agricultural activities, and a higher number of hours farming, but in both cases differences are not statistically significant at conventional levels (columns 3 and 4 in in Table A8). Results presented in Skoufias et al. (2013) find that PAL did not lead to any change in the overall labor supply of adult males and females, but adult males substituted agricultural activities with non-agricultural ones. Also, Tagliati (2019) finds that among older children (age 15-16 at the baseline) both transfers increased child labor, with a stronger impact for cash transfers. Taken all together, the available evidence is consistent with the hypothesis that at least in households with primary school age children, the transfer contributed to a partial reallocation of agricultural tasks from adults and older children to younger ones.

In summary, the results in this section are highly suggestive that the PAL transfer, especially when distributed in cash, led to an increase in the return to labor of young children and a higher probability of combining work and school attendance. The fact that children in inkind localities, despite the improved nutritional intakes, do not reduce