rafmacalaba/data-use-mentions-extended huggingface dataset:
- For The Million Death Study Collaborators. (2010). Causes of Neonatal and Child Mortality in India: Nationally Representative Mortality Survey. _The Lancet_ , _376_ , 1853-1860.

- Freedom House. (2014). _Freedom in the world, 1973-2014._ New York: Freedom House.

- Gupta, S., Clement, B., Guin-Siu, M. T., & Leruth, L. (2002). Debt Relief and Public Health Spending in Heavily Indebted Poor Countries. _Bulletin of the World Health Organization_ , _80_ (2), 151-157.

- Gyimah-Brempong, K. (2015). Do African Countries Get Health from Health Aid? _Journal of African Development_ , _17_ (2), 83-115.

- IMF. (2014). _Heavily Indebted Poor Countries (HIPC) Initiative and Multilateral Debt Relief Initiative (MDRI) - Statistical Update December 2014._ Washington, D.C.. Retrieved from `https://www .imf.org/external/np/pp/eng/2014/121214.pdf`

- IMF. (2015a). _Debt Relief Under the Heavily Indebted Poor Countries (HIPC) Initiative - Factsheet._ Washington, D.C.. Retrieved from `http://www.imf.org/external/np/exr/facts/ hipc.htm`

- IMF. (2015b). _The Multilateral Debt Relief Initiative - Factsheet._ Washington, D.C.. Retrieved from `https://www.imf.org/external/np/exr/facts/mdri.htm`

- Johansson, P. (2010). Debt Relief, Investment and Growth. _World Development_ , _38_ (9), 1204-1216.

- Jones, G., Steketee, R. W., Black, R. E., Bhutta, Z. A., Morris, S. S., & the Bellagio Child Survival Study Group. (2003). How Many Child Deaths Can We Prevent
Policy Research Working Paper 9468

# **Abstract**

Regulatory compliance is key in the fight against climate change and other environmental challenges. But regulatory agencies, especially in developing countries, are often hampered by their capacity to monitor and enforce standards and regulations against recalcitrant firms. There is now a big push toward self-reporting whereby the firms monitor and report on their compliance levels vis-à-vis the standards. This is seen as a way around the costs that agencies must incur if they were to scale up their inspections. In this paper, extensive firm-level data from India are used to compare the compliance level of firms when they are inspected by agencies versus the times when they self-report. Other factors that may determine regulatory compliance, such as age, size, sector, location, and so forth, are also examined. The results indicate that compliance rates are higher in the case of self-reporting than in the case of inspection, suggesting that there is a need to reform the self-report mechanism. Newer and privately owned firms are more compliant. There are also differences between complying with air and water pollution. Finally, the paper examines whether environmental monitoring through inspections leads to improvement in compliance levels, to assess the effectiveness of the regulations and inspections. The findings suggest that the increase in compliance is limited to a few industries.

This paper is a product of the Office of the Chief Economist, South Asia Region. 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 sfranco2@worldbank.org and mmani@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers
. For the drought in Ethiopia in 2015-2016, the micro-data crop loss estimates are much larger than the total economic damage reported in the EM-DAT. For the 2014 floods in Niger, the estimated value of crop losses exceeds the total damage reported in the EM-DAT data by almost USD 78 million (in 2022 USD values). In the other years there is no damage estimate in EM-DAT, but our survey micro-data documents even some large disaster impacts, such as in Niger in 2011 and Ethiopia in 2018-2019 with estimated losses of USD 1.6 billion and USD 1.4 billion, respectively. Taken together, we estimate that across the countries and years captured in the microdata, there were USD 5.1 billion in drought and flood damages unaccounted for in the EM-DAT data (Table 18).

What explains these discrepancies? Disaster inventories such as EM-DAT and survey microdata differ in a number of meaningful ways. Most importantly, disaster inventories do not measure shock impacts themselves but instead aggregate data from government sources, humanitarian organizations, the media, and others. They therefore rely on the comprehensiveness and accuracy with which shocks due to natural hazards are covered by one or more of these sources.<sup>30,37</sup> Less salient events as well as those affecting marginalized population groups are less likely to be reported on and less likely to have detailed information on the affected population or economic and welfare impacts.<sup>37–40</sup> This is particularly acute in LMICs where the density of information for disaster repositories to draw on is much lower and a large share of damages is uninsured.<sup>30,37</sup> Shocks in LMICs in general and smaller events (in terms of intensity or the population affected) in particular are more likely to have incomplete or inaccurate information in disaster repositories or are not covered at all.<sup>30,37,41,42</sup> Microdata such as the LSMS-ISA measure shock impacts on smallholder farmers where
business owners with different characteristics evolved throughout the pandemic, so we use the household head definition. This allows us to add 14 countries to the sample, which had been excluded from the short-term analysis due to a lack of data on variables necessary for those exercises. The 27 countries included in the larger sample are home to more than 78 percent of all Sub-Saharan Africans.<sup>14</sup>

# **2. Methodology**

**The analysis focuses first on the initial impacts of the crisis and then on the medium-term effects** . First, using samples derived from both the household business and household head definitions, we explore how they fared during the early period of the pandemic by looking at changes in income, the use of coping strategies, and the extent to which use of coping strategies are associated with income declines.<sup>15</sup> Second, using a sample generated by the household head definition,<sup>16</sup> we look at the medium-term impacts of the pandemic by exploring the prevalence of household enterprises, income decline and coping strategies changes during the first year of the pandemic, namely April 2020 through April 2021. In both exercises, we pay particular attention to differential effects by household welfare, and when using the household head definition, by gender and age of the head. Since we are working with cross-sectional, recall data, our results are limited to correlations of events occurring within a defined time period.

**Table 1: Data from the first wave of the survey in each country, and month of the first pandemic-induced lockdown (2020)**

|Country|Month of<br>first<br>lockdown|Month of<br>first survey<br>wave|Share of<br>respondents who<br>are heads(%)|Included in<br>HH business<br>sample|Included in<br>household<br>head def|
|---
ONLINE APPENDIX The Entertaining Way to Behavioral Change: Fighting HIV with MTV Abhijit Banerjee Eliana La Ferrara Victor Orozco MIT Bocconi University Oxford University and IGIER and LEAP The World Bank This version: April 2019

# **A. Sample selection**

## **Locations**

The study sites were 80 urban and peri-urban locations chosen in 7 towns across three states of South-West Nigeria. The distribution of locations across states and towns is as follows. Oyo state: Ibadan (50 locations), Ogbomosho (6 locations), Oyo (4 locations). Osun state: Ile-Ife (3 locations), Ilesha (4 locations), Osogbo (7 locations). Ondo state: Akure (6 locations). The selection of these towns balanced competing requirements such as: (i) excluding states earmarked by MTV as priority states for marketing Shuga (to avoid contamination of the control group); (ii) excluding areas where the integrity of the evaluation could have been compromised by security risks; (iii) choosing contiguous states to facilitate the logistical implementation. Locations were defined by drawing a 2-mile radius around each screening center where the intervention was implemented, and randomly selecting households within this radius. We ensured that there were buffer zones between communities to minimize the risk of contamination across study groups. These locations constitute our unit of randomization. Appendix Figure A1 shows the geographic distribution of treatment and control locations.

## **Main study participants**

To identify study participants, we adopted a three-step recruitment strategy. First, enumerators visited a random selection of 200 _−_ 225 households in each location and collected basic demographic information about all young people aged 18 to 25 residing in those households.

i
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# **Introduction**

<mark>Over the last 30 years, earth observation remote sensing for the monitoring of land use and agricultural development has grown significantly. Remote sensing-based cropland monitoring is rapidly becoming operational in different parts of the world (Gumma et al. 2014; Ozdogan 2010; Ozdogan and Gutman 2008; Thenkabail and Wu 2012). A number of agricultural monitoring systems started forecasting crop yields and production. But the advances have been geographically uneven, and the Sudan-Sahel region of Sub-Saharan Africa is one of them. Beginning with Tucker et al. (1985), a number of initiatives have studied land-cover and land-use (LCLU) trends in the region using Earth observation data (Brink and Eva 2009; CILSS 2016, 219; Fensholt et al. 2013; Herrmann, Anyamba, and Tucker 2005; Knauer et al. 2014; Spiekermann, Brandt, and Samimi 2015). But the use of remote sensing monitoring systems in the region has been sluggish. Availability and quality of land-use data have therefore been limited due to limited agricultural production, technical challenges, and slow adoption of remote sensing monitoring systems.</mark> Despite its relevance for human development and poverty alleviation, there is a lack of basic information on the distribution of cultivated land and the main land processes in the Sudan-Sahel. The region is expected to experience major changes in the near future, and land-use information remains crucial because agriculture represents the main source of livelihoods. The region’s fertility rates are among the highest in the developing world (Bertoni et al. 2016), and rural population growth will accelerate the expansion of cultivated land for subsistence farming. Likewise, the steady growth of the urban population will fuel the demand for agricultural products, which is a major driver of land-use change (DeFries et al. 2010). The consequences of climate change will likely disrupt regional agricultural practices and production. Expected temperature increases by 2050 will shorten crop-growing cycles, leading to severe yield reductions and threatening food production systems (
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

|Indicators|Units|South Sudan|East Africa|Low-income,<br>nonfragile<br>countries|Middle-<br>income<br>countries|Resource-<br>rich<br>countries|
|---|---|---|---|---|---|---|
|Collection rate|% billing|40|94|92|91|70|
|Revenue per unit|US cents/ kWh||10.5|14|13|13|
|System losses|% of generation|50|23|24|20|52|
|||South Suda|n<br>Predom|inantly thermal|Other devel|oping regions|
|Average effective tariff|US cents/ kWh|18|–29|14.5||5.0–10.0|

_Source:_ All data unless specified are for 2005 and based on AICD calculations; Data for South Sudan access to electricity are from 2010 and were obtained from World Bank (2011); data for Installed capacity for South Sudan are from 2009 and were obtained from Platts (2009); data for power outages were derived from Vennemo and Rosnes (2009); data for emergency generation from Eberhard and others (2008); data for firms that find power to be a constraint and firms with own generators are from 2007 and were obtained from World Bank (2009b); data on Sudan collection rate, revenue per unit, and system losses are for 2010 and were taken from World Bank (2011); data for average effective tariff for South Sudan are taken from World Bank (2011). For the thermal benchmark, data represent primarily residential users. _Note:_ Access to electricity data for urban and rural access in South Sudan are estimates based on calculations from the AICD economic model for power investment needs. Installed capacity per million
Hanna (2014), from a combination of CPCB online and print records. The dataset spans the whole of India and runs from 1986-2004; an observation is a pollution monitor-month.

The national dataset contains information from 470 monitors situated along 162 rivers in 28 states. Excluding all areas outside the Ganga Basin leaves us with 101 unique pollution monitors along 29 rivers, in 62 districts representing 10 states; these are mapped in Figure 2. Over our 19-year period of observation, as many as 46 different measures of water quality are recorded at these monitoring stations, but only a few measures are consistently recorded over the entire sample time frame. The data are noisy, suggesting significant measurement error, and the panel is imbalanced. To mitigate these problems, we collapse monitor-level observations to the district level and construct moving averages of the data over a four-month window. These steps produce 8,725 district-month observations in the Ganga Basin with at least one non-missing value from our pollution measures of interest.

Our main indicator of river quality is BOD. This common, broad-based indicator of water pollution captures the amount of dissolved oxygen needed by water-borne, aerobic organisms to break down organic material present at a certain temperature (usually 20 degrees Celsius) and over a specific time period (usually five days). Its units are milligrams of oxygen consumed per liter (mg/l). Reduction of BOD is the primary goal of waste treatment plants in general (Brown and Caldwell 2001), but BOD is a particularly good choice for pollution measurement in the setting of Kanpur. Pollution from the tanning process primarily comes from two sources: the animal hides themselves, and the chemicals used to tan them. Both of these sources contain large amounts of organic matter, resulting in abnormally high BOD levels in tannery effluent. According to the United Nations Industrial Development Organization (UNIDO), effluent discharge into surface water typically is required to have BOD below 30-40 mg/l, while the typical BOD in _raw_ tannery effluent is approximately 2,000 (UNIDO 2011
infrastructure, internet infrastructure, the prevalence of spoken English in the regressions, but these variables were all insigni...cant.<sup>35</sup> To understand which sectors within the economy are driving the aggregate results presented in Table 1, we estimate Equation (1) separately for each of the backward GVC participation measures by broad sector - agriculture, mining, manufacturing, and services.

> pass-through and disconnect observed in the data is due to the fact that large exporters are simultaneously large importers, with high-market-share and high-import-intensity.

> 34Since many countries change income status during our sample period, we use a time-varying World Bank income classi...cation to identify high-income countries.

> 35These results are available upon request. A di¤erent type of robustness checks uses GVC participation measures based on alternative TIVA and WIOD databases. These databases’time, and importantly, country coverage is much more limited than EORA’s, as they focus on high- and middle-income countries. Thus, the cross-country variation in GVC participation and in determinants is much smaller. But the broad patterns of results are similar to those in Tables 1 and 6 though weaker in signi...cance. The estimates that remain signi...cant for backward GVC participation are the negative impact of natural resource endowments, distance to GVC hubs and domestic industrial capacity and the positive impact of political stability.

17
Policy Research Working Paper 9376

# **Abstract**

Smallholder farmers’ investment decisions and the efficiency of resource allocation depend on the security of land tenure. This paper develops a simple model that captures essential institutional features of rural land markets in Ghana, including the dependence of future rights over land on current cultivation and land rental decisions. The model predictions guide the evaluation of a pilot land titling intervention that took place in an urbanizing area located in the Central Region of Ghana. The evaluation is based on a regression discontinuity design combined with three rounds of household survey data collected over a period of six years. The analysis finds strong markers for the program’s success in registering land in the targeted program area. However, land registration does not translate into agricultural investments or increased credit taking. Instead, treated households decrease their amount of agricultural labor, accompanied by only a small reduction of agricultural production and no changes in productivity. In line with this result, households decrease their landholdings amid a surge in land valuations. The analysis uncovers important within-household differences in how women and men respond differentially to the program. There appears to be a general shift to nonfarm economic activities, and women’s business profits increased considerably.

This paper is a product of the Gender Innovation Lab, Africa Region. 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 nbuehren@worldbank.org and mgoldstein@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions
that liquid wealth seems to be more relevant in dealing with a ...nancial shock in this part of the world.

# 2.2. Wealthy HtM individuals: Does this kind also exist in emerging markets?

We now explore the relevance of wealthy HtM arguments outside the developed world. Unfortunately, we do not have rich portfolio data. However, we can identify whether HtM individuals have a real estate property loan or not. While not ideal, this is very relevant information because, as discussed in great detail in Kaplan et al. (2014), housing wealth is an important source of wealth behind wealthy HtM and because between 50 and 70 percent of owner-occupied homes have a mortgage in developed countries.<sup>40</sup> For example, Kaplan et al. (2014) ...nd that HtM households due to “only house wealth”is about 6 percent of all households in the United States and 2 percent in Germany.

Panels A and B in Figure A.2.2 split the share of HtM individuals reported in Figure 2, into those who have a standing property loan and those who do not. Making a stretch in the use of the words, we refer to the share of wealthy HtM (�W ) and poor HtM (�P ). Panel A shows that, like in the developed world, wealthy HtM individuals are also present in emerging markets. On average, the share of wealthy HtM is 4.1 and 3.6 in developed and emerging markets, respectively.<sup>41</sup> Interestingly, yet not surprising, Panel B in Figure A.2.2 shows that while wealthy HtM constitute a relevant share of the population in developed countries, it is less so in emerging markets where the HtM phenomenon is largely driven by poor HtM individuals. On average, the share of poor HtM is 17.1 and 39.1 in developed and emerging markets, respectively.

# 2.3. Main
pay monthly contributions on a graduated scale up to a ceiling of 11 per cent, with employers contributing a further 20% of payroll. Membership of an institute provides defined benefit pension provision (up to an upper ceiling), permanent disability benefits and life insurance (survivor) benefits. Workers without social security institute membership qualify for a meager flat rate old age pension equal to the minimum wage.

Our investigation of informality draws on detailed information contained with the Brazilian household survey (PNAD). From 1992 the consistent questionnaire structure adopted by the survey allows separate identification of contract registration (labor card) and social protection across an individual’s main and supplementary jobs, alongside detailed information on the type of employment and the size and activity of the business. Surveys were conducted annually with the exception of 1994 (because of austerity associated with the introduction of the Plano Real) and 2000 (the year of the decennial census).

Table 1 reports summary information on economic activity in main employment for all workers aged between 18 and 65 for each available year. All descriptive analysis makes use of individual sample weights provided by IBGE. In any year just over half of the economically active are engaged in paid employment. This proportion seems quite stable although there is some evidence of an upturn in the proportion towards the very end of the period. However the proportion of public sector workers, who by definition have registered contracts, falls from 27.4% of employees in 1992 to 23.6% in 2001. The next largest group are the self-employed (own-account workers), comprising almost of quarter of the economically active. A further four percent are entrepreneurs employing others. There is some slight evidence that employers as a 14
.118|***|
|**Education**|0.040|***|0.039|***|0.041|***|
|**Textiles and apparel**|0.196|***|0.050|***|0.082|**|
|**Hazard**|0.525|***|0.790|***|0.528|***|
|**Constant**|6.070|***|5.742|***|6.181|***|
|**Number of observations**|12,213||17,828||14,998||

_Source:_ Authors’ calculations based on the Sri Lankan 2002 and 2006 Household Income and Expenditure Surveys and the 2002 and 2008 Labor Force Surveys.

_Note:_ The grand mean effects of the industries are calculated; additional controls include age squared, industry, and occupation dummies. Hours worked were not available in the Sri Lankan surveys.

# _4.4. Working Conditions_

Working conditions directly impact a worker’s well-being through the number of hours that are worked overtime, a hazardous work environment, social benefits, or workplace 14