rafmacalaba/data-use-mentions-extended huggingface dataset:
We also performed a mobile phone survey in April 2019. Households were first reached through random digit dialing and were given an initial automated interactive voice response (IVR) survey, where they were asked about the region in which they live and whether they would be willing to respond to an in-person mobile phone survey. Then, 750 households were given the final survey that included identical questions fielded in the internet survey. The share of households in the final survey that lived in DFA and IRGcontrolled territory was equal to their share in the population, and we further limited the share that lived in the largest cities in each region (Sana’a and Aden) to the share of the total population that lived in each city.

In both surveys, we include 11 identical questions in three broad groups, which are listed in Tables 1 and 2. First, we collect demographic information related to the likelihood of responding to an internet-based survey, including age, location, highest school grade completed, subjective assessments of income, and employment in the past month; second, we collect information on non-sensitive questions that are not obviously related to the likelihood of responding to an internet-based survey; and third, we collect information on sensitive questions to which respondents in traditional surveys might not answer truthfully.

For the sensitive questions, we vary the source of sensitivity- broken up into those potentially sensitive due to social desirability bias and those potentially sensitive due to both social desirability bias and safety concerns. For example, the question asking who the respondent held accountable for the violence they had witnessed is likely sensitive due to safety concerns or the potential judgement of others for favoring another party in the conflict; and the question on whether it could ever be morally justified to engage in violence is likely sensitive because it is not socially acceptable to all to support violence. The source of sensitivity for each sensitive question is listed in Table 2.<sup>13</sup>

> associated with the capital city in some countries. In order to get accurate
—with several noteworthy findings.

First, we find those with higher cognitive and SEM skills are more likely to migrate (at least within the country, if not outside). Second, accounting for cognitive and SEM skills lowers the difference in labor earnings of migrants versus non-migrants. However, a remarkable—and novel—finding in this sample, which we corroborate with data from Cambodia, is that the association of earnings with SEM skills is positive (and large) _only_ for non-migrants, and the correlation with years of schooling is higher for migrants. This result suggests that the returns to different skills vary by the migration status of the respondent. One possible explanation raised by Carranza et al. (2022) in South Africa is that this pattern reflects a lack of information among employers who rely on observable signals instead (years of schooling). They provide job seekers and employers with assessment scores for “non-specialist skills such as communication, numeracy, and grit” and find that both employers and job seekers react 30
|5|6|
|25-29|7|6|7|7|6|7|
|30-34|5|8|8|8|5|7|
|35-39|5|6|9|9|6|7|
|40-44|5|6|6|10|6|8|
|45-49|5|6|6|9|6|7|
|50-54|5|6|6|8|4|9|
|55-59|5|6|6|9|6|7|
|60-64|6|8|3|6|6|6|
|||**Ratio 7**|**5percentile/2 **|**5percentile**|||
|15-19|15-19|15-19|15-19|15-19|15-19|15-19|
|20-24|20-24|20-24|20-24|20-24|20-24|20-24|
|25-29|25-29|25-29|25-29|25-29|25-29|25-29|
|30-34|30-34|30-34|30-34|30-34|30-34|30-34|
|35-39|35-39|35-39|35-39|35-39|35-39|35-39|
|40-44|40-44|40-44|40-44|40-44|40-44|40-44|
|45-49|45-49|45-49|45-49|45-49|45-49|45-49|
|50-54|50-54|50-54|50-54|50-54|50-54|50-54|
|55-59|55-59|55-59|55-59|55-59|55-59|55-59|
|60-64|60-64|60-64|60-64|60-64|60-64|60-64|
|Source: Auth|ors’ calculations ba|sed on several h|ousehold surveys c|irca 2009.|||

11
Policy Research Working Paper 9019

# **Abstract**

This paper studies the relationship between conflict and household welfare by using a detailed panel data set of household victimization across the most conflict-affected regions in Nigeria between 2010 and 2017, during a time characterized by a sharp increase in conflict. The North East region has been hardest hit with the recent Boko Haram insurgency. The North Central region has seen clashes between herders and farmers over land and resources. Several militant groups operate in the oil-producing Niger Delta region, where their aim is to extract resources by disrupting oil production. By exploiting the plausibly exogenous variation in the timing, intensity, and spatial distribution of victimization, this study finds that becoming a victim of conflict leads to higher food insecurity and decreased consumption. Since different types of actors have different motivations for their actions, the consequences of victimization might vary depending on the perpetrator. The study finds that events perpetrated by insurgents are the most detrimental to consumption, whereas food insecurity increases as a consequence of insurgent and criminal activity. This is in line with the results being strongest in the North East, which also has the highest intensity of conflict. The study also finds that property-related events are more detrimental to consumption and food insecurity than are violent events. Likewise, it finds suggestive evidence that violent events, as well as events perpetrated by insurgents and bandits, are detrimental to mental health. The findings highlight the importance of collecting nuanced information on victimization in conflict-affected areas.

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 mazad@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
# **VIII TABLES**

Table 1. Descriptive Statistics – Remote Sensing Data

||Average|Std. Deviation|Observations|Years Available|
|---|---|---|---|---|
|Night Light Density [0 to 63]|0.732|1.159|71,760|1992-2021|
|Deforestation (Ha/Km2)|0.003|0.017|52,624|2001-2022|
|Land cover of dense-open forests (Km2)|0.035|0.153|47,840|2001-2020|
|Land cover of grassland (Km2)|0.089|0.193|47,840|2001-2022|
|Land cover of croplands (Km2)|0.460|0.441|47,840|2001-2024|

_Notes:_ The table reports the descriptive statistics of the outcomes for which we examine the impacts of the Cox’s Bazar refugee camps. Variables definition: (i) Night light density [0 to 63]: This variable represents the average light density at the grid level, obtained by averaging across pixels at the desired level of aggregation. Values range from 0 (no light) to 63 (maximum light density). (ii) Deforestation _Ha/Km_<sup>2</sup> : This variable corresponds to landsat images from the Hansen Global Forest Change, focusing on identifying forest loss defined as the transformation of forested areas into non-forested ones due to stand-replacement disturbances. The measure is expressed in hectares per square kilometer. (iii) Dense-open forests refer to areas with dense canopy cover (¿40% tree cover), characterized by high values of vegetation indices such as the Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI), indicating vigorous and dense vegetation. (iv) Grassland refers to vegetated areas dominated by grasses, herbs, and other non-woody vegetation, typically with sparse to moderate tree cover
# **I. Introduction**

# **India’s urbanization and the emergence of Census Towns**

India is still in the early stages of its urbanization process. According to the latest Census of 2011, the urbanization level in India has increased from 27.8% in 2001 to 31.2% in 2011, and for the first time, the absolute increase in urban population exceeded the increase in rural population. However, the statistical method used to measure urbanization in India can be interpreted as contributing to understating the share of India’s population living in urban areas. The definition of urbanization in India is rather restrictive. It is the only country to use population, density and economic character together as criteria. The agglomeration index (AI) - originally developed for World Development Report 2009: Reshaping Economic Geography provides a consistent basis for comparing urbanization across countries. By this measure, it was estimated that 52% of India’s population lived within an hour’s distance of large cities, compared to 36% in China, based on the census of 2001 and 2000 respectively.<sup>1</sup> In addition to India’s “hidden” urbanization, the 2011 census highlighted a new feature in its urban transformation. A total of 3,894 settlements were identified as Census Towns while there were only 1,362 such settlements in the 2001 census. Census Towns are part of India’s “visible” urbanization, since they satisfy the three criteria set by the Registrar General of India for defining an urban area: population of at least 5,000, density of at least 400 persons per square kilometer and workforce of at least 75% of male main workers in the non-farm sector. The remarkable increase in their number between 2001 and 2011 means that a large number of settlements were reclassified from rural to urban, because of the change in their demographic and workforce characteristics.

It is important to note that there are two dimensions to the distinction between urban
Policy Research Working Paper 10261

# **Abstract**

This paper develops a novel methodology to construct a harmonized cross-country database of the state’s footprint in markets: the Businesses of the State database. The methodology of the database is built on three criteria—(i) a harmonized definition of state-owned enterprises, (ii) identification of direct and indirect state ownership linkages at the national and subnational levels across the corporate sector, and (iii) classification of economic activities depending on their efficiency rationale—which conceptualize a framework to trace state presence in the corporate sector across economic activities. The database is constructed leveraging different firm-level data sources including the ORBIS Global Database, as the primary data source, which is then complemented with supplementary data sources (EMIS Intelligence, Factiva, Worldscope, Pitchbook, among others) to mitigate ORBIS’s data limitations across countries and regions. The Businesses of the State database identifies an unprecedented number of firms with state participation across countries and economic activities, as well as providing novel insights on financial performance, economic performance, and governance of state-owned enterprises. A deep-dive analysis of 36 countries within the Businesses of the State database shows that 69 percent of state-owned enterprises operate in competitive activities (low efficiency-rationale for state participation), 16% are in partially contestable industries (moderate efficiency rationale), and 15 percent are natural monopolies (strong efficiency rationale). Furthermore, this analysis suggests that performance-based productivity of state-owned enterprises (revenue per worker) is negatively correlated with government control variables, such as government shareholding percentage and direct versus indirect government ownership.

This paper is a product of the Finance, Competitiveness and Innovation 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 adallolio@worldbank.org, tgoodwin@worldbank.org
moderate and severe storms increase the debt-to-GDP ratio, although only the former is statistically significant.

Cabezon et al (2016) estimated the fiscal impact of natural disasters in five Pacific Island countries using a VAR covering years between 1970 and 2013. Disasters are scaled in terms of intensity (deaths and number of people affected relative to total population). Natural disasters which cause damages and losses equivalent to one percent of GDP cause an average fall in GDP of 0.7 percent in the year of the disaster and a deterioration in the fiscal balance of 0.5 percent of GDP (also in the year of the disaster), with tax revenues falling by 0.2 percent of GDP and spending rising by 0.7 percent of GDP (the impact on the fiscal balance is mitigated by a rise in grants).

In the following analysis, we use an event study methodology to examine the impact of hurricanes which caused large damages on islands in the Pacific and the Caribbean, on GDP growth, general government revenues and expenditures, the overall fiscal balance and (where data are available) public debt. The events we study are tropical cyclones that caused the largest damages in the 2010s; hurricanes Erika and Maria in Dominica, in 2015 and 2017 respectively, hurricane Irma in Antigua and Barbuda in 2017, cyclone Evan in Samoa in 2012 and cyclone Pam in Vanuatu in 2014.<sup>10</sup> The source of the data is the World Economic Outlook (WEO) database. For each tropical cyclone disaster, we compare the outturns in the affected country in the two years following the tropical cyclone with a counterfactual. The counterfactual we use is the projected outturn for these variables which was made just prior to the disaster, as shown in the applicable vintage of the WEO database. Hence, for example, the projection for outturns for Dominica before it was struck by hurricane Erika at the end of August 2015 are taken from the April 2015 WEO database. For each variable, we show the deviation of
16 other parts of the town. Inequality appears to be considerably more widely dispersed _within_ these two broad groups.

# **7. Three Robustness Checks**

We noted in Section 1 an important caveat that attaches to the broad findings in this paper, namely the possibility that there may exist important cost of living differences between urban conurbations of different sizes. The findings reported above have not attempted to adjust for such cost-of-living differences, because spatial price indices across city-size categories are not generally available. It is well recognized in the literature, however, that observed differences in poverty rates between urban and rural areas can be significantly attenuated once one corrects for the fact that the cost of living in urban areas may be much higher than in rural areas (generally because of the higher cost of food and housing). The possibility exists that our broad findings of lower poverty in small towns than in metropolitan areas might also be driven, at least in part, by our failure to allow for a higher cost of living in metropolitan areas.

While household survey datasets are not generally large enough in sample size to permit the construction of a cost-of-living index across different city-size categories, our survey data for Brazil constitute an important exception. We are able to draw on the 2002 POF data (see Table 1) to construct a cost-of-living index across the broad city size categories employed in this paper, and can check whether our findings for Brazil, reported in previous sections, are robust to this correction.

There are many ways in which spatial price indices can be constructed. We follow here the approach applied by Ferreira, Lanjouw and Neri (2003) to the construction of a _regional_ price index (that distinguished also between urban and rural areas) using 1996 PPV data for Brazil. This approach was subsequently applied in World Bank (2007) to produce a regional price index based on the 2002 POF data, and is based on unit-value information provided in the POF survey on food items, as well as a hedonic model of rent. We
# **2. Alternative hypotheses**

There is more than one way to interpret the finding that poorer countries tend to devote lower shares of their national income to SP (as seen in Figure 1). The “luxury good” interpretation assumes that public spending choices are reasonably responsive to the preferences of citizens. Then Figure 1 is seen to stem from a preferred policy hierarchy that simply reflects the preferences of citizens, most of whom (it is inferred) consider SP to be a luxury good. The take-away for policy is that the relatively low level of SP spending in poor countries should not be of much concern. If citizens of poor countries would rather see public spending going elsewhere, why should we care?

However, there are a number of alternative explanations for the rising SPEC, with different implications for development policy. These can be thought of as differences in the costs incurred by governments in supplying SP, as reflected in omitted country characteristics in a static cross-country comparison, such as Figure 1. This can be addressed in part by introducing country fixed effects into a SPEC estimated on a longitudinal data set. However, there are also concerns about omitted time-varying characteristics correlated with GDP. We can motivate thinking about those omitted characteristics in the form of a series of hypotheses, as follows. We note if the existing literature already includes what appears to be an adequate basis for rejecting the hypothesis and proceeding no further. For the rest, we proceed to further empirical testing. Our hypotheses can be grouped under four headings: relative prices, governance, distribution, technology and demographics.

The first hypothesis starts from the long-standing observation that Engel curves can shift with relative prices. While SP does not have its own “relative price” in any obvious sense, there are explicit prices for substitutes for SP within the gamut of public spending on human development. For 2017, the price data (described later) indicate that the price of health services relative to food and non-alcoholic beverages has a correlation coefficient of 0.
Here we mention a few caveats. The paper would benefit from panel data tracking women and their peer networks. Further, we are unable to extrapolate past autonomy and children’s food consumption and time use to welfare in general. In particular, there are unclear impacts on the extensive margin if daughters of participants are given more food, but spend more time on chores. The policy implications from this analysis must therefore involve caution, since we only consider one part of a much larger picture.

# **8 Conclusion**

This paper asked whether peers affect household decision-making. We used caste networks to identify causal peer effects and participation in a community-level women’s empowerment program, _Mahila Samakhya_ to identify shocks to female autonomy. We showed that women who do not participate in the program but have friends who do are more likely to leave the house without permission and work outside the household. But on most other outcomes of a woman’s autonomy, including perceived social norms, we found limited evidence of peer effects. This lack of effect may be indicative of the stickiness of social norms and is consistent with emerging evidence that a gap in aspirations and ability worsen outcomes, at least in the short run [Mukherjee, 2017]. Finally, we used mother fixed effects to examine investments in children. Here, our results showed a robust set of peer effect: having participant friends leads nonparticipants to feed their daughters a more protein-rich diet and ones presented above. However, program exposure is our preferred instrument because it relies on program rules and therefore is more likely to meet the exclusion restriction.

29
22 some manufacturers take advantage of this low price to export fertilizers rather than supply the domestic market to benefit local farmers.

# **Subsidies all too often create powerful incentives for commercial malpractice**

Setting different prices for the same fuel depending on use, large price differences between substitutable fuels, and large price differences across neighboring countries provide powerful financial incentives for commercial malpractice. Common examples are adulteration of higher‐priced fuel with lower‐priced fuel (such as adding kerosene to diesel); flourishing black markets combined with shortages of fuels sold at official prices to make purchase of subsidized fuels difficult, (much) higher actual prices paid by consumers, or both (examples being kerosene in India and Nigeria, and LPG in Egypt); and smuggling of fuels to neighboring countries (from Algeria, Indonesia, the Islamic Republic of Iran, Malaysia, Nigeria, and República Bolivariana de Venezuela to their neighbors). The archipelagos of Indonesia and Malaysia make it easy to use boats for fuel smuggling. This has posed special challenges to monitoring fuel subsidies provided to fishing vessels—fuel smuggling may be far more profitable than fishing. The Government of Malaysia in August 2012 even announced that a representative of the Malaysian Fisheries Development Authority would in the future verify each catch landed, and those with insufficient catch would no longer be permitted to buy lower‐priced fuel ( _Borneo Post_ 2012).

Some governments have tightened border controls to combat out‐smuggling due to low domestic prices, but without significant or lasting effects. Although data on fuel smuggling are difficult to obtain, the Government of Malaysia is reported as estimating that 3.5 billion liters a year (60,000 barrels per day) of diesel and 6 billion liters per year (100,000 barrels per day) of gasoline were being smuggled out of the country ( _Platts Oilgram Price Report_ 2014). In May 2014, the government launched an operation jointly conducted by the police, Customs Department, the Malaysian Maritime Enforcement Agency, the Malaysian Anti‐Corruption Commission
A C R O N Y M S ARF Agricultural Reform Fee ASYCUDA Automated System for Customs Data

- AVE Ad Valorem Equivalent

- CGE Computable General Equilibrium

- GATT General Agreement on Tariffs and Trade

- GDP Gross Domestic Product

- GMM Generalized Method of Moments

- HS Harmonized Commodity Description and Coding System

- IMF International Monetary Fund

- ISIC

- IV Instrumental Variables

- MFN Most Favored Nation

- LDC Least Developed Countries

- ROW Rest of the World

SAFTA South Asian Free Trade Area

- SL Sensitive List

TRIST Tariff Reform Impact Simulation Tool

- 2SLS Two-Stage Least Squares

- VAT Value-Added Tax

- WGI World Governance Indicators

WTO World Trade Organization v