rafmacalaba/data-use-mentions-extended huggingface dataset:
in collaboration with other international agencies, it seeks to reduce the untimeliness of data and poor data quality with a less expensive alternative optimizing the sampling procedure and the structure of the questionnaire (Ajayi, 2006; Zoyem et al, 2008). Besides the core modules on household characteristics and consumption behavior, each country can implement additional modules according to its specificities. In the QUIBB for Burundi, questions about displacement and return were included. The sample consists of 6,700 households. The QUIBB sample is characterized by two-stage cluster sampling. In the first stage, 548 sous-collines<sup>11</sup> were sampled and in the second stage, 15 households were sampled in each primary unit.<sup>12</sup> From the original data set, we applied a standard data cleaning process, where missing observations, data entry errors, potential measurement errors, distillation errors and outlier from unusual consumption were deleted.

The second round, undertaken by the authors in April 2010, only retained three provinces of the QUIBB sample: Bubanza, Bujumbura Rural, and Cibitoke, located in the North-West of the country. The choice of these provinces is justified by the concentration of ex-combatants in these three provinces in combination with high level of violence in the region over the last years, as well as by budgetary constraints. We traced 1284 households which were interviewed for the QUIBB survey in the three provinces of interest. A total of 1064 (or 83%) households were found and interviewed in 85 collines.<sup>13</sup> We examine issues related to attrition more closely in section 3.2.

In order to maximize the reliability of the data, we trained interviewers for a week. Interviews were conducted in Kirundi, the local language. After the training, interviewers were selected on the basis of an exam and simulated interviews. The questionnaire was then tested during a pilot study in a colline that was not included in our sample. We assigned teams of five interviewers, each including a
Table 9: How did obstacles affect your business? Choose 3 options Fraction of owners that chose the following options:

||ICT|non ICT|
|---|---|---|
|Reduced demand from costumers/clients|13%|12%|
|Delays from suppliers|12%|19%|
|Transportation cost|12%|9%|
|Access to costumers/clients|5%|5%|
|Workers delays to get to work/reach their homes|5%|6%|
|Delays to supply product to clients|3%|9%|
|Difculty to obtain supplies|1%|4%|
|None of the above|48%|31%|

_Notes:_ The options available were: access to clients; reduced demand from clients; difficulty to obtain supplies; delays from suppliers; delays to supply product to clients; workers delays to get to work/reach their homes; transportation cost; Other; None of the above. _Sources:_ Authors’ survey.

_x_ ˆ _≡ x_<sup>_′_</sup> _/x_ . The advantage of this approach is that it allows us to obtain estimates for the productivity levels for each industry and each location. In particular, the following holds:

**Proposition.** Given parameter values for the productivity dispersion _θ_<sup>_j_</sup> , the expenditure shares _α_<sup>_j_</sup> , and the input shares _β_<sup>_j_</sup> , and given values or estimates for the bilateral trade shares _π_<sup>_j_</sup> _ni_<sup>, employment by industry and location,</sup><sup>_Lj_</sup> _i_<sup>, and wage rates in each location</sup><sup>_wi_, and given</sup> exogenous changes in trade costs _τni_<sup>
across districts. Therefore, our second source of data is information on construction planned under INPRES reported in Duflo (2001), which identifies the number of schools to be built in each district after 1974. We merge this district-level data to our working sample of 2,118 mothers in the ECED data. Appendix Table A1 reports descriptive statistics for the districts on Duflo (2001) data as well as for districts of birth captured in the working sample.

It is clear from these figures that our working sample represents a set of districts that had a 6
perceptions of the prevalence of unofficial payments<sup>3</sup> and then about their household‘s utilization, satisfaction and actual unofficial payments.

**Utilization:** Respondents were asked if any household member had interacted with, or used, each of the public services.

**Satisfaction with service delivery:** All respondents who indicated that a household member had used the service during the past 12 months were asked if they were satisfied with the quality and efficiency of the service interaction<sup>4</sup> . Satisfaction data can be a proxy for measuring actual quality of services, as well as an indicator of the extent to which services are responsive to the needs and preferences of clients. They can also help to assess the effects of service delivery reforms (see section 3.2).

**Unofficial payments:** Respondents were asked if they had to make unofficial payments and why – specifically whether the payment was requested, expected, offered or given as a gift – allowing for a more detailed analysis of the incidence and causes of informal payments than is usually possible from household surveys.

For education and public services, the 2010 LITS survey provides more in-depth information on perceptions of service quality and grievance redress mechanisms. Regarding education, the survey inquired about any lack of textbooks and supplies, poor teaching, teacher absenteeism, overcrowded classrooms and poor conditions of facilities. Similarly for health, it asked about doctor absenteeism, treatment by staff, availability of medicines, waiting times and cleanliness of facilities. The answers to these questions provide a snapshot of people‘s experiences with services and can act as another measure of service quality. Another new feature of the 2010 LITS is a set of questions related to grievance redress mechanisms in health and education. The survey asks whether people know where to file a complaint if they were unsatisfied with education and health services, whether they filed a complaint, received a response, or were satisfied with the response.

Finally, respondents were
heterogeneity of program impacts using the data at hand. Second we rely on quantitative and qualitative data from a sub-sample of head teachers about recruitment of new students.

We explore the likely nature of the selection observed by testing whether the observed expansion in enrollment or student characteristics varies by attributes associated with a lower cost of school-driven selection. These attributes include (i) number of secondary schools within 10 km (number of competing schools in the market of the school) and (ii) age of the school in years. We argue that these attributes are likely to reduce the cost of selection either through proximity to higher quality students, higher demand for strong reputation signals and well established and arguably better schools (see Melitz (2002)).<sup>16</sup> We run the standard model:<sup>17</sup> where ��,� captures the heterogeneity attribution (competition or age); and ��,� represents enrollment (by grade) and, for check number 3, school average test results in the PLE. Table 11 shows the results of this exploration. The table only shows the coefficients �� and ��. The main effect of the attribute and other controls (region fixed effects) are not shown. Each panel corresponds to each survey round. In panel C, we include a measure of student quality- the school average score on the national primary leaving examination – as the dependent variable.

The presence of nearby secondary schools and age of the schools seems to negatively affect changes in student enrollment in treatment schools. All coefficients on the interaction between treatment and the attribution are negative, and all but one estimate is not statistically significant. In short, the higher the number of secondary schools in the vicinity, the lower the number of students that treatment schools can attract. Likewise, the older the treatment school, the lower the growth in student enrollment. These results also hold for the secondary school entrance exam scores measured at Check 3.

Next,
61.34|74.22|
|100-500|84.31|33.18|62.44|64.74|
|> 500 ha|82.70|44.79|48.01|74.45|
|**Use seed (%)**|||||
|Smallholder farmers|24.92|0.40|4.29|7.17|
|Commercial farmers|66.81|15.19|69.32|58.19|
|< 20 ha|51.99|17.58|85.44|65.79|
|20-50|57.87|18.66|76.23|58.25|
|50-100|69.57|15.88|58.66|74.22|
|100-500|86.21|4.21|45.86|40.32|
|> 500 ha|80.30|42.08|40.93|74.45|
|**Observations**|||||
|Smallholder kebeles|1,368|910|955|634|
|Commercial farmers|1,659|3,077|826|464|
|< 20 ha|358|295|291|162|
|20-50|479|1122|212|109|
|50-100|351|833|124|28|
|100-500|382|724|165|128|
|> 500 ha|89|103|34|37|

_Source:_ Own computation from 2013/4 CSA large farm and smallholder farm surveys 18
Machinery and equipment: industrial and non-agro<br>machinery, agro-machineryand equipment|A single accounts receivable|
|Inventory, i.e.,goods for sale|Multiple accounts receivable|
|Motor vehicles|Investment property (stocks and securities, options and<br>futures, bonds, derivativeproducts, etc)|
|Agricultural products: fertilizers, crops and other<br>agriculturalyields, livestock, fish farm, etc.|Intellectual property (e.g., patent rights, trademarks)|
|Consumer goods: office equipment, computers, servers,<br>screens,projectors, furniture, etc.|Insurance policies|
|Minerals, hydrocarbons, timber|Membership and partnership interests in business<br>entities and cooperative shares|
|Medical equipment|Futures (e.g., crop futures, future acquisitions of<br>collateral described in the agreement, and unborn<br>livestock)|
|Jewelry,precious stones|Bank accounts|

In MENA, immovable property is still used as the prevalent form of collateral. The majority of banks in the MENA region accept movable property as collateral. A recent World Bank survey to financial institutions in the MENA region shows that on average 67% of banks accept 13
We find that COVID vaccination hesitancy on Twitter in the Middle East is estimated at just below 20 percent, which is of a comparable magnitude as hesitancy rates estimated from survey data, provided that the appropriate filters are applied. At first glance, Twitter-based estimates of hesitancy appear notably downwards biased; when anti-vaccine attitudes are evaluated over the full population of Twitter accounts who engaged in the discourse on COVID vaccination, hesitancy is estimated at around 3 percent. This bias is fully accounted for, however, when we: (1) evaluate hesitancy over Twitter accounts belonging to individuals (i.e. filtering out accounts belonging to organizations who make up a significant share of the discourse on Twitter), and (2) adjust for the fact that the population of Twitter users is biased towards more educated individuals.

With regards to effective messengers and messages in the discourse on COVID vaccination, we find that the most effective tweets on the anti-vaccine side highlight claims that the vaccine causes serious life-threatening side effects -- while on the pro-vaccine side, tweets showing public figures receiving the vaccine are found to be most effective. Finally, state-news accounts are found to receive the overwhelming majority of retweets in our database making them the most effective messengers.

2
occupations such as business ownership, qualified self-employment, and white collar jobs. In conjunction with our findings in Section 3.1, this indicates significant limitations on upward mobility for individuals hailing from gender and social minorities.

# **3.5.2 Heterogeneity by Education and Bank Account**

Section 3.5.1 established the positive and significant impact of education on rural workers’ likelihood of being engaged in non-farm activities. Alternatively, it showed that women and minorities are significantly less likely to be engaged in such activities. Similar to the historical data from the NSS, only 25 (28) percent of rural women (minorities) aged above 20 have completed secondary education, raising concerns as to whether these individuals are unable to engage in non-farm activities due to the lack of human capital skills (as measured by education), or due to structural barriers which impede their participation. We conclude our empirical analysis by testing for heterogeneity across educational attainment using 2015 data to understand whether education can ameliorate the social disadvantages faced by these 29
Such an adjustment can be done for a particular year, or by taking the average ratio for a country over a longer time period. We estimate such country specific IPLs for use with national accounts for 1990, ensuring that poverty estimates from national accounts and surveys are aligned in that year. The results for global poverty are shown in Panel C of Figure 5. While the poverty estimates are equal (by design) in 1990, there is considerable variation over time, and, in global measures the rate of decline is much larger, due to the faster growth rates of national accounts means compared with survey means.

Across all the methods which involve substituting survey means with national accounts means, poverty is estimated to be lower and falling faster when compared to traditional survey measures. The more rapid decline seen in the poverty measures using national accounts points to a concern with current use of national accounts growth rates in extrapolating household survey estimates for years with missing surveys. Even if household survey means are used for poverty estimation for survey years, national accounts growth rates are used to interpolate such estimates to non-survey years and for nowcasts and projections of poverty in the future. Current methods use actual or projected national accounts growth rates to align poverty estimates to non-survey years for global aggregation. Removing the bias implicit in this method suggests a slower global decline in poverty than the World Banks official poverty numbers.

Because India, Indonesia and China, countries which historically have been the home of a large share of the global poor, typically have household surveys for most reference years for which the World Bank reports poverty, the effect on global numbers is of less concern. However, the lack of recent surveys available for India, the home to a large share of the world’s poor, has generated greater uncertainty about poverty estimates from national accounts based extrapolation of the latest available survey.<sup>31</sup> Extrapolations or projections of poverty beyond the World Bank’s latest reference year that use national accounts growth rates commonly use
(spousal) unemployment is associated with a higher incidence of violence, although this is not statistically significant in every specification.

# **5 Discussion and Conclusion**

The literature has considered alternative explanations for why men perpetrate violence against women partners. Among these are that men gain self-esteem or an enhanced sense of control from committing violence (Farmer and <mark>Tiefenthaler, 1997)</mark> ; that they have strategic motivations, such as to use violence to extract resources from the woman or her family (Bloch and Rao, 2002); and that violence occurs almost inadvertently in response to emotional cues (Card and Dahl, 2011). Other studies have highlighted psychological drivers of abuse such as a history of childhood trauma, personality and relationship dysfunctionality (Heise and Kotsadam, 2015), or early life exposure to domestic violence (Pollak 2004). Finally, there are studies showing that regional variation in IPV today reflects cultural factors rooted in historical customs (Alesina et al. 2016; Tur Prats 2015).

This paper used comparable and representative repeated cross-sections of survey data from up to 31 countries containing women’s reports of violence committed against them in the preceding year. It identified a systematic contemporaneous association of unemployment shocks with interpersonal violence. The study finds that changes in male and female unemployment rates have opposing association with IPV; these associations are large, at between 2.75 – 2.86% of the mean IPV prevalence rates in the sample. However, they are smaller than the estimates obtained by studies on the impact of alcohol on IPV (Luca et al., 2017). Interpersonal violence is higher when men are more likely to be unemployed and, holding constant male unemployment rates, it is higher if women face improved employment probabilities.

These findings contrast sharply with recent results for the US and the UK (Aizer 2010, Anderberg et al. 2015) where IPV has been seen to be decreasing in the gender wage gap and in increased employment rates of women respectively.
Policy Research Working Paper 9046

# **Abstract**

Workers in developing countries are subject to frequent health shocks. Using 10 weeks of high-frequency labor market data that were collected in urban Ghana, this paper documents that men are 9 percentage points more likely to work in weeks in which another worker in the household is unexpectedly ill. The paper provides suggestive evidence that these effects are strongest among very risk averse men, men in poorer households, and men who are the highest earners in their household. By contrast, women display a net zero response to another worker’s illness, even women who are the highest earners in their household.

This paper is a product of the Development Research Group, Development Economics and 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 rmheath@uw.edu, gmansuri@worldbank.org, and brijkers@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 the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
secondary|1.2511***|0.9885***|0.2630***|0.1745***|
|education|(0.0631)|(0.0540)|(0.0200)|(0.0167)|
|=1 if Head has higher education|1.5440***|1.1224***|0.3333***|0.1747***|
||(0.1276)|(0.1304)|(0.0233)|(0.0346)|
|Constant|0.1064|-0.0388|0.3697***|0.4528***|
||(0.0983)|(0.0696)|(0.0292)|(0.0251)|
|Panel variable|District|District|District|District|
|Age at Interview dummies|Yes|Yes|Yes|Yes|
|DHS 2006 x Region x Age<br>dummies|Yes|Yes|Yes|Yes|
|Observations|11798|12117|11798|12117|
|No. of Clusters|69<sup>a</sup>|69<sup>a</sup>|69<sup>a</sup>|69<sup>a</sup>|
|R-Squared|0.5158|0.6101|0.1597|0.1381|

All specifications are estimated using the panel fixed-effects estimator. District casualties are expressed per 1000 inhabitants. Sample only includes individuals surveyed in the Nepal DHS 2001 and 2006 and aged 5-14 at the time of the survey.<sup>a</sup> DHS data collection was somewhat affected by the conflict in 2001, and so a number of districts were not covered, namely Dolpa, Jajarkot, Rolpa and Rukhum. The small districts of Manang and Mustang were not surveyed either
long‐run objectives of the policy) is threatened when debt is high and of moderate maturity (between 2 and 7 years), which is indeed the maturity for most European countries.<sup>3</sup> This is a crucial policy message because most European countries (with the notable exception of the United Kingdom) feature high debt levels and average debt maturities falling within this range. Eusepi and Preston (2018b) show that, even under an aggressive monetary policy, debt levels and maturities comparable to the European ones would have prevented the US economy from the Great Moderation. Turning to the VAR empirical side, Rossi and Zubairy (2011) and Canova and Pappa (2011) show that fiscal multipliers tend to be larger when positive spending shocks are accompanied by a decline in the real interest rate.

A number of studies have looked at the size of multipliers under different macroeconomic conditions. After analyzing a quarterly data set on government expenditures for 44 countries (20 high-income and 24 developing) from 1960 to 2007, Ilzetzki _et al._ (2013) conclude that the fiscal multiplier is relatively large in economies operating under a predetermined exchange rate, but it is zero in economies operating under flexible exchange rates. They also find that fiscal multipliers are smaller in open economies than in closed economies and are zero in high-debt countries. The evidence, despite being intuitive, as a fixed exchange rate and the limited openness to trade may reduce potential crowding out effects, is however based on comparison between countries with different regimes rather than implying evidence _ceteris paribus_ on the shift from one regime to another for the same country.

Unfortunately, there is a substantially smaller number of studies that have focused on the determinants of the size of the public investment multipliers, particularly when it comes to developing countries, for which lack of long time series poses formidable challenges. Accordingly, crucial questions such as whether multipliers in lower-income countries should be expected to be higher or lower than in high-income ones remain still unaddressed (Batini _et al.
5 information on these topics. With respect to migration, the survey collected no information on the characteristics of the migrant: age, education or income earned away from home. On the issue of remittances, the survey only asked three basic questions: (1) Does your household receive remittances from family or friends?; (2) Where do these people sending remittances live?;<sup>v</sup> and (3) How much (remittance) money did your household receive in the past 12 months?

The lack of data on migrant characteristics in the Guatemala survey is unfortunate, and makes it virtually impossible to control for possible selectivity bias in the decision to migrate. However, the focus of this paper is on remittances, not migration. Since remittances are fungible like any other source of income, the presence of possible selectivity bias in the decision to migrate should not be such an issue, because the conventional argument is that since “a dollar is a dollar” households will spend their income without regard to the source of that income. In other words, most economists would argue that remittance income – received by either rich or poor households – will be spent just like any other income. One underlying goal of this paper, therefore, is to challenge this conventional way of thinking by showing how the marginal spending patterns of households receiving remittances are qualitatively different from those households which do not receive remittances.

Table 1 presents summary data from the Guatemala survey. This table shows that 5,665 households (77.8 percent of all households) received no remittances, 1,063 households (14.6 percent) received internal remittances (from Guatemala) and 593 households (8.1 percent) received international remittances (from USA). About 88 households received both internal and international remittances and, since there is no way