rafmacalaba/data-use-mentions-extended huggingface dataset:
restrictions to mitigate the new COVID wave. The Delta variant was first detected in India in December 2020. It spread to other countries and generated another wave of cases in the summer and fall of 2021.

Different regions also show very different COVID-19 severity patterns. At the beginning of the pandemic, the European countries and the United States saw a relatively fast spread of COVID-19 in March 2020, whereas Brazil and India did not reach their first peak until July 2020. In the winter of 2020, the European countries were ravaged by the so-called “second wave,” whereas Brazil and India did not see a significant winter spike. Later, in May 2021, India’s case

> 6 For instance, Italy was the first European country to impose a full lockdown measure; we can see that the stringency index for Italy rose before that of other countries. Another good example is India: during the Delta surge, India’s stringency index rose again. Note that even within the same continent, European countries often had stringency profiles different from those of other countries. We exploit this variation as an identification strategy to examine the impact of COVID-19 on electricity generation.

> 7 The World Health Organization declared COVID a pandemic on March 11, 2020.

8
Policy Research Working Paper 10441

# **Abstract**

This study examines the effects of communal violence, that is violence inflicted upon a particular group along ethnic or religious lines, on women’s marital outcomes. Communal violence, with its accompanying psychological and physical trauma, can have important socioeconomic consequences. The effects could be very different for women who are often disproportionately subject to sexual violence during such events. Using individual-level survey data from India and a difference-in-differences approach, the study shows that women’s age of marriage decreased, and the probability of getting married before the age of 18 increased after the Hindu-Muslim riots in Gujarat in 2002. The Gujarat riots stand out as one of the most significant and abrupt occurrences of communal violence in post-independence India, marked by widespread violence against women, thus providing a setting to study the impacts of such violence on women. Event study and synthetic control methods suggest that these effects are prominent two years after the riots and have increased over time. Women married after the riots also had fewer years of education and poorer social and economic status, such as a lower probability of employment and lower autonomy in household decision-making.

This paper is a product of the Development Research Group, Development Economics. 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 dghose@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 expressed in this paper are entirely those of the authors. They do not necessarily represent the views of
of that literature.

> 3 Two-thirds of bank regulators surveyed in 143 jurisdictions report that they have a mandate to promote financial inclusion (World Bank, 2014). And more than 50 countries have set formal goals and targets for financial inclusion (Demirguc-Kunt, Klapper, Singer, and Oudheusden, 2015; _Maya Declaration Commitments_ , Alliance for Financial Inclusion, http://www.afi-global.org/maya-declaration-commitments).

> 4 See Collins et al. (2009) for detailed evidence from financial diaries on the vast array of financial arrangements (both formal and informal) used by the poor in developing countries to manage their economic lives, and the lack of fit between many formal products and their needs.

> 5 Demirguc-Kunt, Klapper, Singer, and Oudheusden (2015).

2
examined: Bank Account Usage, Savings, Debit and Credit Card Usage, and Borrowing. While savings rates are low in Turkey, the use of bank accounts, credit, borrowing, and informal lending is high. In terms of the trends in financial inclusion, savings patterns increased while use of credit and debit cards diminished between 2011 and 2014.

An **Equity Adjusted Coverage Rate**<sup>2</sup> (EACR) in these four indicators is calculated for Turkey and compared to other countries across the world. The EACR index is a measure of the coverage of financial services adjusted for equity in the use of these services across ` _circumstances’_ . The characteristics or circumstances that are accounted for are gender, age, education, income quintile, and urban/rural. Differences in individual and household level data also yield interesting results, since characteristics at the individual-level are more descriptive of the population rather than characteristics of the head of household.

Using the EACR index, three types of analyses are presented:

1. The dissimilarity index in financial indicators is decomposed into composition, equalization, and scale effects.  The dissimilarity-index is a measure of dissimilar coverage rates across groups (gender, age, education, and income). At the individual level, gender accounts for large dissimilarity differences in Bank Account use as well as Debit and Credit Card usage. However, gender effects disappear when using household level data since characteristics are the head of household.

2. Turkey’s EACR index and its components are compared to nearest neighbor countries in terms of coverage rates. For example, in Bank Account Ownership, Turkey’s coverage rate is most similar to Brazil, Bosnia and Herzegovina, Italy, and the United Arab Emirates out of 145 countries.

> 2 See the appendix for an explanation of the methods used in this paper: the Equity Adjusted Coverage rate (EACR) calculation, calculation of components in differences in the EAC, and Shapley decomposition.

4
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 18. Urban versus rural access to water supply and sanitation, 2006**

<!-- Start of picture text --> a. Water supply b. Sanitation<br>80 100<br>70 90<br>80<br>60<br>70<br>50 60<br>40 50<br>30 40<br>30<br>20<br>20<br>10 10<br>0 0<br>Piped Standposts Wells and Surface water Flush toilets Improved Traditional Open<br>(public) boreholes latrines latrines defecation<br>Urban Rural Urban Rural<br>% population % population<br><!-- End of picture text --> Source: AICD water supply and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. Access figures calculated by AICD using data the 2006 Multiple Indicators Cluster Surveys published by WHO 2010a and 2010b The effects of low investment in the water supply and sanitation sectors are compounded by low budget-execution ratios. The discrepancy between the budget cycle and the cycle of projects makes it hard for the Ministry of Energy and Water Resources to spend up to 50 percent of its investment budget each year. As of 2010 a trust fund set up to finance sustainable development projects in the water and sanitation sector was not operating (Global Water Partnership 2010).

# Power

## **Achievements**

Access to power has steadily improved in Cameroon. National access to electricity increased from 37 percent in 1996 to 46 percent in 2002, and to 48 percent in 2007 (Helio International 2009; World Bank and IFC 2010), above the average for Africa’s resource-rich countries (table 10). Estimates of urban access suggest that between 65 percent and 88 percent of the urban population has access to electricity (USAID 2010; Nkama 2007). At 88 percent, access to
the inverse of the distance increased from 0.96 to 1.7. The respective weighted number of fatalities rose from 0.93 to 5.2.

For 2018, the descriptive statistics show that the women in our sample are, on average, 31.8 years old, 58 percent are Muslim, and 41 percent are Christian. About 44 percent have no formal education. The education level of the husbands is distributed in a similar way, with the exception of the percentage of men who report having no formal education, which is 25 percent. We observe similar descriptive statistics for men. The main differences are that men are about eight years older, a larger share of men are Muslim (52 percent), and a smaller share of men report having no formal education (25 percent). For men, we do not report the education level of their partners. Although it is possible to identify the partner in some cases, the information is missing for a large proportion of the sample, which means including this variable as a control would reduce the size of the sample considerably. Therefore, we refrain from including this variable. The average household size is six. Finally, 60 percent of women in the sample reside in rural clusters with a population density of 1,640 inhabitants within a 10 km radius.

# **4 Identification Strategy**

To examine spatial spillovers of violent conflict, we regress the outcome variables on the number of distant violent attacks in the five-year period before the DHS wave in 2013 and the wave in 2018. Our empirical strategy exploits that both the location and severity of distant attacks are exogenous to individuals and household clusters. We set up the following model: Here, _Yiclt_ indicates the outcome variable of individual _i_ from DHS household cluster (that is, village) _c_ located in local government area (that is, district) _l_ at time _t_ .

For the outcome variables, we focus on different financial indicators (employment, asset ownership, and decision-making autonomy)
**How Vulnerable Are Arab Countries to Global Food Price Shocks?**

# **1. Introduction**

There is a belief that households in the Middle East and North Africa<sup>1</sup> (MENA) should be largely insulated from fluctuations in global food prices. The region stands out among other developing regions for its extensive use of price subsidies and controls (Ortiz _et al._ , 2011). In addition, governments in MENA employ other policies aimed at managing and regulating food consumption, production and trade.<sup>2</sup> These include production subsidies, import protection cuts, and build-up of food reserves.

At the same time, high and volatile international food prices continue to be a major concern in the MENA region, and some have even cited them as a contributing factor in the Arab Spring (Breisinger _et al._ 2011; Zurayk, 2011). Food prices rose substantially during the second half of the 2000s, and even though prices plunged during the global economic and financial crisis, they recovered rapidly in 2010 before coming down somewhat in 2011 (Figure 1). At present, the aggregate food price index is above the levels observed in the mid-2000s, and prices of certain food items remain high (World Bank 2012a).

High international food prices feature prominently in public policy discussions in the Middle East and North Africa for a number of good reasons. Dependence on imported food is substantial and likely to grow in the future. Using data from the United States Department of Agriculture (USDA) this paper shows that the region meets through imports about half of its wheat and barley consumption requirements, 40 percent of its rice consumption, and nearly 70 percent of its corn consumption (Table 1). The region is the largest wheat importer in the world, and wheat prices increased by 70 percent in the second half of the 2000s. With expected high population growth rates and climate change that is likely to raise the frequency of draughts and
Figure 2: Establishment distribution: Manufacturing <!-- Start of picture text --> Burkina Faso (2015) Cameroon (2008) Ghana (2013)<br>Rwanda (2013) USA (2013)<br>1 − 4 5 − 910 − 1920 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>1 − 4 5 − 910 − 1920 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>100 100<br>80 80<br>60 60<br>40 40<br>Share of establishments (%) 20 20<br>0 0<br>100<br>80<br>60<br>40<br>Share of establishments (%) 20<br>0<br><!-- End of picture text --> _Source:_ Establishment censuses obtained from the statistical agencies of the select countries; see section 3. For the U.S., the data comes from the 2019 BDS dataset. _Note:_ The figure is constructed based on data for establishments in the manufacturing sector irrespective of their state or foreign ownership status. Establishments with missing employment data are excluded.

12
that many research designs are neither robust nor reliable. Furthermore,</mark> since migration is intrinsically a selective process, any causal inference analysis needs to check for the determinants of migration and have access to the necessary information for the identification of

8 Incidentally, we also note that the immobility issue is not specific to climate-induced migration. It has long been known that immobility often masks the inability of people willing to move to do so, due to liquidity constraints, lack of information, absence of networks, etc. When these obstacles are removed, migration increases. A recent example of international migration triggered by a program aimed at fostering migration through improved connection and information about employment opportunities (carried out as part of an experiment implemented in Mizoram, India) is provided in Gaikwad, Hanson and Tóth (2022).

> 9 <mark>Although there is a wide set of technical alternatives that could be employed to address this key issue, including, but not limited to, two-phase sampling, Heckman selection, Instrumental Variables (IV) and data-driven statistical methods, there is often limited credibility of the exclusion restriction of the proposed instruments.</mark> New data mining techniques (e.g., LASSO variable and instrument selection) can help in addressing the identification challenges connected with migration by drawing on the vast potential and richness of information of multi-topic household surveys (see, _inter alia_ , Belloni et al., 2014).

18
**Table 4: Proportion of households owning at least one appliance**

|Appliance|Ownership<br>share|Appliance|Ownership<br>share|
|---|---|---|---|
|Air cooler|68.9%|Television|73.5%|
|Air conditioner|7.4%|Refrigerator|60.1%|
|Room heater|1.2%|Water purifier|7.6%|
|Warm air blower|0.3%|Microwave oven|2.9%|
|Ceiling fan|92.8%|Mobile charger|97.2%|
|Table fan|16.3%|Desktop computer|4.7%|
|Immersion rod|4%|Laptop computer|10.7%|
|Water heater (geyser)|10.5%|Electric iron|33.2%|
|CFL|78.1%|Sewing machine|1.5%|
|Bulbs|39.6%|Water pump|37.4%|
|Tube lights|34%|Washing machine|19.2%|

Notes: Proportions are calculated using sampling probabilities

**Table 5: Proportion of households owning at least one appliance**

|Dependent Variable: log(daily energy services demand<br>based on surveydata)|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|log(observed daily consumption)|0.546***<br>(0.0557)|0.6***<br>(0.0624)|0.603***<br>(0.0636)|0.602***<br>(0.0656
is quite a large volume of water for a single household to use in just one month.

# **3. Data collection, sampling strategy, and fieldwork**

# **_Collection of customer billing records from electricity and water utilities_**

We obtained the complete set of monthly billing records from the Ethiopian Electricity Utility and the Addis Ababa Water and Sewerage Authority for their residential customers in metropolitan Addis Ababa for the one-year period from April 2015 through March 2016. Data from EEU included

> 14 The minimum monthly charge and the fixed monthly charge are both part of the algorithm used to calculate the monthly electricity bill for a connection. A fixed charge plus a charge for volumetric use are calculated for every connection (both private and shared). The total amount is then compared to the minimum charge associated with the volumetric use. If this total is _greater_ than the minimum charge, then the monthly water bill is the total (i.e., the minimum monthly charge does not affect the monthly bill). Yet if the total of the fixed charge and volumetric charge is _less_ than the minimum bill, then the connection’s owner is billed the minimum charge for that month.

7
6|46.7|
||1993|1,603.3|32.6|12.3|9.2|21.9|67.3|41.2|
||1990-92|1,629.9|38.8|13.1|10.1|26.4|61.2|34.7|
||1984-86|953.3|41.7|14.9|13.5|25.1|58.3|28.4|
||1979-81|1,021.0|44.0|9.8|8.5|33.6|56.0|30.6|
|MERCOSUR|1994|61,893.3|51.8|20.7|17.5|27.9|48.1|19.5|
||1993|<br>54,122.2|52.2|20.9|17.6|28.0|47.8|18.5|
||1990-92|<br>47,588.9|58.8|21.7|18.3|32.8|41.2|11.5|
||1984-86|<br>34,037.4|59.5|25.6|23.0|28.9|40.6|6.7|
||1979-81|29,203.6|53.9|18.2|14.9|32.7|46.1|10.7|

Source: All data compiled from United Nations COMTRADE records. Trade statistics for Mexico are included in the OECD totals.
or close to the WTO maximum binding rate, then the only WTO-permitted option to implement additional import protection for that product is through a TTB. Columns (5) and (6) report data from Bown (2012a) on the stock of temporary tariff barriers as a share of all imported products in 1995 and in 2010, respectively. A comparison of the data in these two columns indicates that there is considerable differentiation both across countries, as well as within countries over time, as to the economic importance of the import coverage of these TTB policies.

The complex interplay of broad trade liberalization commitments (as captured by WTO tariff bindings and applied tariff rates) and the potential unwinding of these commitments is summarized in the last three columns of Table 1. These columns provide two cuts of the data from imported products at the HS-06 level. For these three columns, we define an HS-06 product as “under WTO discipline” if it has an applied import tariff that is within 10 percentage points of its legally binding rate at the WTO; i.e., these are products for which governments have relatively little scope to further <mark>7</mark>
Based on data provided by the World Economic Forum<br>Global Competitiveness Report.|
|Banking and finance<br>(ief_banking)|1<br>Very low|Government involvement in the financial sector negligible;<br>very few restrictions on foreign financial institutions; banks<br>may engage in all types of financial services|1995-<br>2003<br>(annual)|**The Index of Economic Freedom, Heritage Foundation**|
||2<br>Low|Government involvement in the financial sector minimal; few<br>limits on foreign banks; country may maintain some limits on<br>financial services; domestic bank formation may face some<br>barriers||Based<br>on:<br>Economist<br>Intelligence<br>Unit,<br>Country<br>Commerce, Country Profile, and Country Report for 2001<br>and 2002; U.S. Department of State, Country Commercial|

39
million, that have the lowest poverty rates and that appear to be growing faster than the smaller statutory towns, with the share of the urban population in the big cities rising from 38 percent in 2001 to 42 percent in 2011 (Tripathi, 2013). Higher wage rates in larger cities will to some degree spill over to the towns and rural areas both through labor market adjustment and because they may also generate larger trade and remittance flows. Thus, it is theoretically ambiguous whether larger cities generate larger gains to the rural poor.

The Lanjouw and Murgai hypothesis that India may have experienced faster poverty reduction if smaller towns had grown as fast as the cities is consistent with evidence from other countries on the relationship between poverty and city size (Ferré, Ferreira and Lanjouw, 2012). However, it is difficult to test this hypothesis for India, or more generally to relate variation in growth of different types of cities to variation in rural poverty reduction. One difficulty arises because it is only once every ten years that city growth (in terms of population rather than economic output) is measured in India, using the census. A lot of the variation in rural poverty reduction occurs within a ten-year censual period and so would be missed by studies that rely on the census data to measure urban growth. Another difficulty is the absence of timely and spatially detailed (e.g. at city level) economic statistics.

This study uses a proxy for urban growth to test the hypothesis that it is the growth rates of India’s secondary towns, rather than the big cities, that matter most for rural poverty reduction. We use nighttime lights, as detected by satellite, to measure urban growth, distinguishing between growth on the extensive and intensive margins, and between the growth of cities and of secondary towns. These new measures of urban growth are econometrically related to sub-national poverty estimates that are formed at a finer spatial resolution than in the existing literature. Specifically, we use a division of India into 59 National Sample Survey
|2011|0.564|25,258|
|2012|0.364|26,571|
|2013|0.143|27,942|

_Note:_ This table presents, for each year in the period 2003-2013, the share of observations for which agricultural income is topcoded and the threshold for topcoding.

# **G Adjusting tax record data for 2003 - 2011**

In Table 1, we document that our dataset includes almost no information on withholding forms for the period 2003 -2010 - in particular, it includes no information on withheld wages, which represent a large share of income observed in the tax data. The reason for that is that withholding forms before 2011 were not electronic and have not 65
# **2** Previous Literature

The existing repertoire of literature examining World Bank project effectiveness is too numerous to be cited in full. Previous studies can generally be divided into three broad categories, examining region- and sector-specific work, the political determinants of project effectiveness, and the internal dynamics of project management influencing performance. Much of the aggregative work focuses on the importance of project design and management, whereas sector- and region-specific work emphasizes the impact of cross-national and industrial variation in determining whether a disbursement project is ultimately successful. Some of the more relevant work relating to these two camps will now be examined.

One of the earliest assessments of project effectiveness tested the rates of return from Bank projects at inception as opposed to completion, accounting for the divergences in productivity across a large sample of completed projects. Pohl and Mihaljek (1992) analyzed the World Bank’s project evaluation record using a sample of 1,015 projects via a comparison of rates of return at appraisal and completion (typically accounting for a ten-year project lifespan). They found that cost overruns and implementation delays have a small but negative impact on project performance, specifically in agricultural and industrial projects. The paper also noted a discrepancy between expected and actual rates of return, noting that World Bank appraisal estimates were often overly optimistic. Their analysis emphasized the importance of the macroeconomic environment, which they note to be more substantively significant than the other indicators considered and an under-emphasized element in project evaluation.

Similarly, Kilby (2012) uses a measure of economic vulnerability (as measured by the ratio of the recipient country’s short-term to long-term debt) to assess project performance. Projects with longer preparation times are expected to perform better, conditional on the degree of economic vulnerability: when project design takes into account the underlying economic conditions of the donor country, the initiatives tend to perform better.<sup>3</sup> These findings support both Kilby (2011) and Dreher et al.’s (2009) work on the political