rafmacalaba/data-use-mentions-extended huggingface dataset:
In addition, there is some indication of a positive pre-trend in violence before the completion of roads. Remember that we matched observations by the UCDP violence risk in period -1. What we, therefore, find here is that treated cells might have suffered a slight escalation of violence before road completion. What could explain this? Armed forces brought in by the government or UN to secure a project perimeter may meet armed resistance or engage in violent wealth extraction to finance their deployment. It may also be that the roadworks attracted attempts of resistance by local armed actors or that the rents that the project brought led to fights. Impending roadworks may also turn stationary bandits roving, i.e. disturb entrenched and predictable armed group revenue generation strategies such as roadblocks, leading to violent transitioning to alternative forms of wealth extraction (see (Schouten et al. 2022)). According to _ibid_ and expert interviews, it is also true that road project announcements often lead to land grabbing in the area. This might cause violence to increase before the actual start of the project. However, the timing here is important - road projects usually go on for several years, and the sudden increase of violence before completion is not consistent with land grabbing at the start of the project. An alternative interpretation is that violence is causally linked to the government showing its strength with the increased connectivity.

# **5 Robustness Checks and Potential Channels**

## **5.1 Different Types of Violence**

In the previous regressions, we only focused on the total number of events and fatalities. However, such aggregates cover very different types of violent events, and some of them, like protests, are not the focus of this paper. We, therefore, run the OLS regressions again using different subsets of violent events. Different measures of violence as dependent variables either in sensitivity tests or in order to shed light on potential channels of causation have been used (Besley and Persson 2011; Bazzi and Blattman 2014; Harari and
59.6|42.7|41.4|
|||Between 15-24|Not married|34.4|26.9|30.4|19.7|17.1|
||Top 60%||Married|44.3|36.2|39.6|28.0|26.0|
|||Between 25-65|Not married|43.2|40.3|43.7|43.2|40.5|
||||Married|52.2|47.2|52.2|41.8|40.3|
|||Between 15-24|Not married|25.6|19.1|27.5|19.7|20.9|
||Bottom 40%||Married|21.9|18.3|20.8|13.7|10.4|
|Urbn||Between 25-65|Not married|36.9|33.6|38.9|33.3|34.4|
|a|||Married|32.0|30.1|34.2|22.9|23.9|
|||Between 15-24|Not married|16.1|15.4|19.3|13.6|14.1|
||Top 60%||Married|13.3|13.0|16.7|12.0|12.7|
|||Between 25-65|Not married|31.1|29.9|31.9|28.9|31.3|
||||Married|19.8|19.3|22.6|18.5|19.5|

Source: Authors’ own estimate based on NSSO data. Note: ‘Married’ refers to currently married females and ‘not married’ refers to females who are
# **Figure 2 Return on average assets of infrastructure SOEs with and without adjustment for operating subsidies, by sector**

<!-- Start of picture text --> 5<br>0<br>-5<br>-10<br>-15<br>Power Airlines and airports Roads Railways Average<br>Return on average assets Adjusted return on average assets<br>Percent of average assets<br><!-- End of picture text --> _Source_ : Authors, based on data from the World Bank Infrastructure SOEs Database. _Note_ : Data are for 2009–18.

**Figure 3 Return on average assets for partially privatized and fully owned infrastructure SOEs, with and without adjusting for operating subsidies, by sector**

## **a. Partially privatized infrastructure SOEs**

<!-- Start of picture text --> 5<br>0<br>-5<br>-10<br>-15<br>Power Airlines and airports Roads Average<br>Percent of average assets<br><!-- End of picture text --> 9
- Martin, W., D. Mitra (1999). _Productivity Growth and Convergence in Agriculture and Manufacturing_ . World Bank Policy Research Paper 2171.

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Figure 4 summarizes the breakdown of spending between durable and tradable categories. Each rectangle corresponds to one unit of spending. The blue rectangles towards the right, denoted as uncategorized, correspond to the 10% of spending that cannot be categorized. Among the transactions that can be categorized (90% of all spending), 31% are categorized as tradable and the remaining 69% as nontradable. Among the transactions that can be categorized, 32% are durable and 68% nondurable. Figure 4 also shows substantial orthogonal variation between the two categorizations. That is, not all tradables are durables and vice-versa.

Table 2 also shows average spending statistics for each key spending category used in our analysis. Specifically, column (2) shows that customers who participated in the experiment used their credit cards to spend average monthly amounts of $371 (33% of the $1,135 total credit card spending) on durables and $275 (24% of total spending) on tradable durables. On average, subjects had $1,805 in outstanding credit card debt, equivalent to 1.5 months of spending.

# **6 Results**

## **6.1 Spending: Survey versus Administrative Data**

Existing research has generally studied the impact of economic expectations on consumption using survey data, which may suffer from a number of well-known limitations, such as measurement error, selection problems, and surveyor demand effects. To assess whether this is a source of concern in our study, we explicitly test the relationship between self-reported consumption plans and _actual_ future consumption in our data. If survey measures of consumption track actual consumption closely, measuring consumption in administrative data has few benefits. If, however, there is a disconnect between survey responses and actual spending, this would suggest that using administrative data could be crucially important to avoid spurious results.

Our survey elicited expectations about future credit card spending by asking respondents whether they expect their credit card spending to increase, decrease, or remain the same. Comparing these self-reported
Global Trends in Child Monetary Poverty According to International Poverty Lines Daylan Salmeron-Gomez<sup>*†</sup> Solrun Engilbertsdottir<sup>+</sup> Jose Antonio Cuesta Leiva<sup>*</sup> David Newhouse<sup>*</sup> David Stewart<sup>+</sup>

* World Bank Group, Washington DC

- UNICEF, New York, NY.

The authors gratefully acknowledge support received from the Government of Iceland for the analysis and development of this paper.

The authors thank Alexandru Cojocaru, Natalia Winder Rossi and Enrique Delamonica for helpful comments and guidance, Rose Mungai and Nishant Yonzan for assistance with the data, and Joao-Pedro Azevedo, Benu Bidani, Louise Cord, Carolina Diaz-Bonilla, Haishan Fu, Gabriel Lara Ibarra, Luis-Felipe Lopez-Calva, and Daniel Mahler for overall support.

JEL Codes: I32 Keywords: Poverty, Children
0.83|0.50|0.75|
|Deviation in No Rain Days|0.25|0.58|0.17|0.42|0.50|0.83|
|% Rainy Days|0.75|0.67|0.25|0.83|0.83|0.92|
|Deviation in % Rainy Days|0.25|0.58|0.17|0.42|0.50|0.83|
|Longest Dry Spell|0.33|0.67|0.42|0.50|0.42|0.25|
|Observations|168|168|168|168|168|168|

_Note:_ The table presents the share of statistically significant rainfall coefficients in Ethiopia.

Table E2: Share of Significant Rainfall Coefficients in Malawi

||CHIRPS|CPC|MERRA-2|ARC2|ERA5|TAMSAT|
|---|---|---|---|---|---|---|
|Mean Daily Rainfall|0.75|0.67|0.33|0.67|0.67|0.75|
|Median Daily Rainfall|0.17|0.42|0.92|0.42|0.50|0.58|
|Variance of Daily Rainfall|0.33|0.33|0.42|0.17|0.25|0.33|
|Skew of Daily Rainfall|0.92|1.00|0.42|0.67|0.08|0.58|
|Total Rainfall|0.75|0.67|0.42|0.67|0.67|0.75|
|Deviation in Total Rainfall|0.17|0.50|0.33|0.25|
Policy Research Working Paper 10505

# **Abstract**

This paper uses data from a survey of 116,061 households in India to study people’s beliefs about inequality and demand for redistribution. The findings show that a household’s beliefs about inequality, implied by the perception of their position on the income distribution, is negatively correlated with support for reducing inequality. This is relevant since there are significant differences between where individuals believe their household stands and their actual position, with the gap between perceived and actual position exceeding two deciles on average. Despite these large differences, informing individuals of their household’s position on the income distribution has no discernible effect on support for reducing inequality. The paper posits that demand for redistribution may be unresponsive to this information because it is based on exclusively on household’s income and does not account for the sharing of resources within communities. In communities where group-specific public goods, such as religious and social goods, are present, class antagonism and redistribution are mitigated by community solidarity. Households benefit from these goods, and such benefits alter the individuals’ beliefs of inequality. Consistent with this prediction, the average individual perceives their household as richer in districts with a greater supply of religious or social goods. The sharing of resources within religious or ethnic groups can shape perceptions of the income distribution and reduce support for redistribution within these groups, and thus requires serious consideration in studies of inequality.

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 mbussolo@worldbank.org and at akshay_dixit@g.harvard.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
education which is slightly lower than the one of up to incomplete primary education (excluding no schooling at all). For example, consumption of households headed by someone with some primary education is about 19 percent higher than consumption of households headed by someone with no education. Households headed by someone with post-secondary education or above have a consumption level about 91 percent higher than the consumption level of households whose head has no schooling. The household size and composition also plays a role. Consumption level, measured in per adult equivalent terms, reduces with the size of the household, and it is negatively correlated with the share of young household members and positively associated with the share of members in working-age. It is also remarkably higher in households with a larger share of employed members and it is lower the larger the share of family members without a job. Controlling for all the characteristics of households and households’ heads, geographical location still plays a relevant role: urban households have a consumption level about 32 percent higher relative to their rural counterparts, and households originally from (or still residing in) Inland Ghana, notably in the remote regions in the North of Ghana (Norther, Upper East, and Upper West), suffer a considerable consumption loss (up to -83 percent) relative to households in the Greater Accra region.<sup>16</sup> Table 5 presents heterogeneous effects by year of migration. The hypothesis we want to test is whether the effect of migration on current consumption levels fades away over time. For this purpose, we consider two periods of time: the first goes from 2000 to 2011, the second spans between 2005 and 2011, in other words we compare non-migrant households with households that migrated up to a maximum of 11 (7) years before the survey and between 16 and 27 (21 and 27) years before the 1984 census in the first (second) case. We do not find large differences by year of migration. The average migrant household migrated between 2000 and 2011
|**Adjusted R**<sup>**2**</sup>|0.194|0.323|0.435|0.483|0.496|
|---|---|---|---|---|---|
|**Observations**|1001|1001|1001|1001|1001|
||Note: Robust standard er|rors inparenthe<br>**Fuel Pr**|ses. *p< 0.1,**<br>**ice Increase=**|p< 0.05,***p|< 0.01|
||**60%**|**70%**|**80%**|**90%**|**100%**|
|**Constant**|23.2495***(1.914<br>2)|27.1896***<br>(2.2518)|31.7407***<br>(2.6005)|33.3762***<br>(2.7276)|37.9368***<br>(3.1221)|
|**Baseline Accessibility**|-0.0234**<br>(0.0098)|-0.0231**<br>(0.0114)|-0.032**<br>(0.0131)|-0.0328**<br>(0.0137)|-0.034**<br>(0.0157)|
|**Tl i lbd d**|0.0045|0.0077**|0.0063*|0.008**|0.0094**|
|**rave
- 54 -

Seychelles Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1990.

|Sierra Leone|<br>Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers<br>to 1989.|
|---|---|
|South Africa||
||Population data are taken from IMF Government Finance Statistics and are for 1994. Unemployment rate is a CIA<br>Fadtbook 1995 estimate from 1994. Underemployment is estimated at 11%. Paid employment in non-agricultural<br>activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1993.<br>Data are taken from the South African Statistical Yearbook of 1992, and refer to 1991. Local Government<br>Employment relates to both Provincial Govemment employment and Local authorities and self governing territories.<br>Education and Health data are taken from UNESCO and WHO sources respectively and are for 1991. Regarding<br>military employment, an estimated 35,000 people from other forces, including MK and the Homelands, are being<br>absorbed into the new South African National Defense Force. Of these 11,000 are already members.<br>GDP estimate is taken from IMF Govemment Finance Statistics 1995 and is for 1994. Wages and salaries of<br>Consolidated Central Government are from IMF Govemment Finance Statistics 1995 and are for 1994 (provisional).<br>Data on wages in manufacturing are from the International Labor Office's Yearbook of Labor Statistics 1995 and<br>refer to 1993.|
|Sudan|Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995, refer to 1992 and source l1l, Code<br>2:_employment_office_statistics_and include persons in employment who are seeking a change of job or extra work<br>and are therefore also registered at employment offices. This clearly underestimates the number of unemployed<br>people
043<sup>_∗∗_</sup>|-0.001|-0.004<sup>_∗∗_</sup>|0.010|-0.001|-0.020|
||(0.015)|(0.022)|(0.021)|(0.016)|(0.026)|(0.017)|(0.016)|(0.031)|(0.027)|(0.026)|(0.021)|(0.002)|(0.002)|(0.010)|(0.008)|(0.014)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|<br>Observations|30947|30947|30947|30947|30947|30947|30947|30947|30947|30947|30947|30947|30947|30947|30947|30947|
|_R_<sup>2</sup>|0.026|0.055|0.041|0.040|0.034|0.029|0.021|0.061|0.069|0.059|0.045|0.091|0.025|0.041|0.028|0.035|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. All regressions include the set of controls described in Table 1. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
Africa over a comparable time period. The regional Gini indices increased in most other regions, with the major exception of LAC where it fell from 56.4% in 1998 to 52.2% in 2008. Therefore, in terms of the regional Gini index Africa has overtaken LAC.

The finding of increasing African inequality is robust to choosing alternative measures of inequality. Figure 2 shows the Lorenz curve for the African distribution of consumption expenditure. The Lorenz curve for 2008 is uniformly to the right of the 1993 curve without crossing.<sup>28</sup> Hence, inequality has increased between 1993 and 2008 irrespective of the choice of inequality measure (Atkinson, 1970).<sup>29</sup> Consistent with this, all the alternative GE inequality measures increase over this period (Table 1, Panel A). The GE(2) measure is most sensitive at the very top of the distribution (Cowell, 2009), so it appears that inequality increased most strongly in this part of the distribution. As Anand and Segal (2015) point out, the estimation of standard errors for the regional Gini index (and other inequality measures) is not straightforward. This is because the Gini index is estimated from combining a set of country-distributions, not from a single regional household survey. However, while changes between some of the intermediate benchmark years are small, the change between 1993 and 2008 of four Gini points appears substantive.

> 25 These countries are Botswana, Central African Republic, Namibia and South Africa.

> 26 All global and regional inequality estimates in this paragraph use the 2011 PPPs, although Lakner and Milanovic (2015, Table A.3) use decile groups, and a slightly different spatial price adjustment for Indonesia and China (compared with Jirasavetakul and Lakner, forthcoming).

> 27 Furthermore and in contrast to the results for Africa, the EAP Gini exceeds all of the within-country Gini indices in the region.

> 28 The right-hand panel zooms in at the very bottom
subjective well-being, mental health and welfare. _Journal of Public Economics_ , _135_ , 4760.

- Di Maio, M., & Nisticò, R. (2019). The effect of parental job loss on child school dropout: Evidence from the Occupied Palestinian Territories. _Journal of Development Economics_ , _141_ , 102375.

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23
Policy Research Working Paper 10610

# **Abstract**

This paper studies the impact of point of sale technology adoption on local tax compliance by firms. The paper exploits administrative data on monthly restaurant and hotel tax payments in the Indonesian districts of West Manggarai and Gorontalo and combines this with information on the point of sales distribution timeline from 2018 to 2022. The findings show that certain point of sales treatments led to a substantial increase in restaurant tax payments, ranging from 55 to 180 percent, while others did not increase tax revenues. The paper discusses the underlying drivers of the results and argues that the effectiveness of point of sales technology is conditional on (i) the devices’ ease of use, (ii) the recipients’ technological aptitude, and (iii) the presence of information accessible by third parties.

This paper is a product of the Governance 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 mchattha@worldbank.org, ptyas@worldbank.org, nsoko@worldbank.org, and rfadilla@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
The project is a collaborative effort between GFDRR and the Poverty GP. This component improves the understanding of how disasters and poverty are related, to be able to (i) effectively guide policy on how to strengthen resilience; (ii) capture synergies between risk management and poverty reduction.

While other components under TURP focused only on priority areas, the Disaster-Poverty survey included all of Dar es Salaam in the project design and analysis. Creating a baseline with data from the entire city enables comparison between priority areas and the rest of the city in terms of exposure, vulnerability and socioeconomic resilience. It allows the benefits from TURP interventions to be monitored, and can be used to inform the allocation and prioritization of future TURP investments and public policy.

# 2.2 Sampling and data collection

The selection of households in the survey design had two objectives. First, to select a sample that represents the population of Dar es Salaam and second, to interview enough people who had experienced floods to be able to detect patterns in their socio-economic characteristics.

A large enough sample size was selected to confidently represent the population of Dar es Salaam given the income level and income distribution. A database of all enumeration areas (EAs) in Dar es Salaam was provided as a sampling frame, by Tanzania’s National Bureau of Statistics. After data cleaning, it included 14,987 EAs. Sample size was chosen based on the combination of number of EAs and number of households per EA that produce acceptable relative standard errors (lower than 25%) given the budget constraint for data collection. We found that this could be obtained by selecting 105 EAs and 10 households per EA. Then, we randomly selected 10 households from each EA using satellite imagery. Later, we added 28 EAs to the original sample as part of an additional round of data collection. These were selected using a similar methodology with the objective to include more households from risk prone areas.

To capture enough households that had experienced floods, flood risk stratums were designed using the