rafmacalaba/data-use-mentions-extended huggingface dataset:
deterioration of per capita consumption levels during the recession (2008:Q4 through 2009:Q3), or any real rise subsequently. Indeed for most of the regions there is no discernible trend in these numbers.

The bottom panel of Figure 6 shows the evolution of real per capita expenditure for a selection of deciles, with very little apparent variation over time in the bottom decile (decile 1) or the fifth decile, and some variation but no apparent trend in the top decile. The SES data for 2007-2010 are combined; the measures we used are based on a consistent set of survey questions and protocols that did not change over the period in question. Households are grouped into deciles based on the combined data, so that the deciles represent consistent levels of per capita expenditure over time.

Figure 7 presents two other series. The top line in the upper panel shows the evolution of urban expenditure per capita, which again shows no trend after the beginning of 2008. When expenditure per capita is disaggregated by household size it is clear that larger households have lower levels of per capita expenditure, but they were neither more nor less affected by the crisis than urban households.

The bottom panel of Figure 7 compares real with nominal expenditures per capita for the population as a whole; while nominal expenditures trend upwards, the level of real spending, as we have seen, has remained remarkably steady. This graph also shows nominal income per capita for 2007 and 2009, the two years for which the numbers are available. Not only is expenditure less variable than income – evidence of consumption smoothing – but there is some evidence of higher saving rates in 2009 than in 2007.

While the graphical evidence is striking, it is not completely compelling because it does not include controls. To address this problem, we ran a series of regression equations for which the dependent variable is always real per capita expenditure, and the independent variables include geographic and household variables, as well as dummy variables for each quarter (with 2007:Q1 being the
payment behavior on a daily basis. Notably, men reacted more quickly to the tax letters, with the treatment effect appearing earlier and stabilizing after the due date. In contrast, treated women continued to make overdue payments in the days following, rapidly closing the initial gender gap in compliance. Second, the impact of the personalized tax letters extended beyond the targeted October 2020 bill, but the response is similar for men and women. Despite receiving the letters in early October, the intervention prompted some treated taxpayers to clear past-due debt from previous months and also pay their November and December bills. Third, while women present homogeneous responses across quintiles of property valuation, men exhibit a slightly negative gradient. Notably, in the lowest quintile, the personalized tax letter boosted timely payments of men by 8 percentage points—twice the increase observed for women.

Lastly, the inclusion of the recent pandemic in our analysis period allows us to examine gender-specific responses to a macroeconomic shock.<sup>3</sup> Although payment rates dropped significantly—by approximately 25 percentage points—at the onset of the COVID-19 pandemic, we observe no noticeable gender-based differences in response. This indicates that macroeconomic shocks seem to affect property tax delinquency behavior similarly for both men and women.

Overall, our findings suggest that gender-targeted strategies may have limited potential for improving tax compliance in the context of property taxes, as men and women exhibit similar baseline payment rates and respond in comparable ways to personalized tax letters.<sup>4</sup> Notwithstanding, the pronounced gender disparities in higher-value property ownership documented in our analysis raise important questions about wealth inequality and underscore the need for more research into the underlying causes and broader economic implications (e.g., differences in credit access).

We contribute to several strands of the literature studying asset ownership and the link between wealth taxation (including property taxes) and gender. Many countries recently highlighted the need to improve the collection of gender-disaggregated data on taxation in general, and on men’s
Based on qualitative research commissioned for this paper, we find that in spite of the subsidies, low‐ and lower‐middle income households in Pakistan struggle to afford their basic electricity needs. Some households reported being forced to reduce expenditure on food, health and childcare in order to afford electricity bills. Such coping mechanisms particularly affect women, who are the main consumers of electricity at the household level. Aside from affordability, residential electricity users are most concerned about the reliability of supply and quality of customer service. Electricity is central to the lives of modern Pakistani households, and their lives are disrupted by long hours of load shedding common in recent years. The stress of coping with the high cost and low reliability of electricity is taking a toll on family life, health, education, and economic activities. Low‐income households—in particular urban slum residents and beneficiaries of the country’s main welfare program—mentioned resorting to illegal electricity connections because they could not afford to pay their bills. In addition, the qualitative research revealed a general lack of trust in electricity service providers and a poor perception of governance in the sector.

Households need support to manage rising and volatile energy costs, but assistance can be designed in a way that is less distortionary to prices and incentives, and more effectively targeted to the poor than the existing price‐based subsidies. Pakistan has state‐of‐the‐art targeting and registration mechanisms available to deliver better targeted assistance. A national proxy means test (PMT) database developed for the Benazir Income Support Program could be used to target assistance to households based on their welfare. An alternative would be to use exclusion filters like land or vehicle ownership, or tax records. Targeting subsidies would help ensure that assistance goes to those households that most need it, while excluding the richest consumers would save considerable resources. Providing bill credits in place of price‐based subsidies would make billing simpler and provide equal assistance to all targeted households. Other forms of assistance can help encourage consumers to
Table 5. Multinomial Logit Model using Data for Argentina and Brazil , Men, 15 to 18 y.o

||_BRAZIL -_|_Males 15-18 (4fir_|_st interviews)_||_ARG_|_ENTINA - Males_|_15-18_|
|---|---|---|---|---|---|---|---|
|_Variables_|_Nini_|_Study Only_|_Work only_|_Variables_|_Nini_|_Study Only_|_Work only_|
|Index Study Only|-0.007|0.032***|-0.025***|Index Study Only|-0.038|0.134***|-0.096***|
||(0.009)|(0.009)|(0.003)||(0.033)|(0.036)|(0.011)|
|Index Nini|0.171***<br>|-0.171***<br>|0.001<br>|Index Nini|0.250***<br>|-0.259***<br>|0.009<br>|
||(0.014)|(0.014)|(0.001)||(0.037)|(0.040)|(0.008)|
|Index Work Only|0.140***<br>(0.016)|-0.165***<br>(0.017)|0.025***<br>(0.004)|Index Work Only|0.296***<br>(0.045)|-0.378***<br>(0.050)|0.082***<br
locations with above median location premia (Appendix Figure A1).

> 19 This is consistent with the increase in the difference between the location premium in the leading areas ( ) and the median premium ( ) in Colombia, Honduras, and Panama in Table 8. In the cases of Brazil and Mexico, the periods shown in Table 8 do not align with the periods shown for the two countries in Figure 4, because the table reflects γγ<sup>∗</sup> ll50 information from the IPUMS census data, while the figure shows data from the SEDLAC household surveys.    γγ

27
Cells are indexed by _i_ and years by _t_ . Thus, the unit of observation is the grid cell-year. Here N i is net primary productivity in gridcell i in year t. NPP, which can be measured from satellite imagery, is used to NN measure agricultural performance following the past literature since it provides a common unit of productivity across different crop types (Zaveri, Russ, and Damania, 2020). NPP is linearly related to the amount of solar energy that plants absorb over a growing season and is measured in grams of carbon per square meter. NPP is combined with a land cover data set developed by the European Space Agency’s Climate Change Initiative, which provides information on 37 land cover classes globally at a 300-meter grid. This ensures that plant productivity as measured by NPP is only captured in grid cells that contain significant amounts of agriculture and avoids attributing impacts to forests or other natural habitats.

Data on average annual nitrogen fertilizer used per hectare of cropland is sourced from Lu and Tian (2017). Previous work by McArthur and McCord (2017)<sup>13</sup> finds that a 1 kg/ha increase in fertilizer causes an 8-9 kg/ha increase in yields. This translates to a 10 percent increase in nitrogen fertilizer use, boosting global yields by about 29-32 percent. However, a continuous measure of nitrogen usage can mask heterogeneity in impacts on yields due to the presence of subsidies. This is because although fertilizer subsidies may have enabled fertilizer usage and hence agricultural productivity, subsidies can incentivize farmers to apply more than they need such that the beneficial impacts of nitrogenous fertilizers on productivity can begin to wane beyond a point. To demonstrate the non-linear effects of nitrogen use on agricultural productivity at the global scale, a continuous measure of nitrogen fertilizer use is replaced with indicators for whether the log values of nitrogen usage lie within nine different quantile bins i to assess impacts across the global fertilizer distribution. The quantile coefficients, , are our coefficients of interest. The first quantile is
careful policy decisions in the port, road, rail, and river transport sectors. Following the approval of the National Transport Plan, major road sector investments are underway to restore connections between Pointe-Noire, Brazzaville, and the north of the country. A road fund has also been established to provide for maintenance, although so far resources have not been adequate. Significant performance improvements are needed at the PointeNoire port and on the CFCO rail link if the competitiveness of that corridor is to be restored. Constructing a bridge linking Brazzaville to Kinshasa would help to increase traffic through Pointe-Noire and improve overall route viability. Improving navigability on the Congo-Oubangui-Sangha rivers would also help to restore a highly cost-effective transport mode of strategic significance to the north of the country.

The Republic of Congo performs relatively well on service coverage in the water and sanitation sector. The country’s access statistics are substantially ahead of those in its peer group, particularly with regard to piped water, standposts, and improved latrines. However, access to services is much greater in urban areas than in rural areas. Furthermore, underpricing of water has hurt the financial soundness of the water utility, even though analysis suggests that cost recovery tariffs would be affordable to consumers.

The Republic of Congo needs to spend $0.95 billion per year through 2015 to catch up on infrastructure quantity and quality. This is equivalent to 16 percent of GDP, comparable to the share
(or nearest equivalent)**|First difference|Haver Analytics||
|**EMBI+ spread**|First difference|J.P. Morgan||
|**Primary balance (general government or**<br>**central government) to GDP**|NA|Haver Analytics||
|**Revenue (general government or central**<br>**government) to GDP**|NA|Haver Analytics||
|**Expenditure (general government or central**<br>**government) to GDP**|NA|Haver Analytics||
|**Gross debt (general government or central**<br>**government) to GDP**|First difference|Haver<br>An<br>Quarterly Public<br>Debt Database,<br>Bank|alytics;<br>Sector<br>World|
|**Foreign-currency government debt as a ratio**<br>**to total debt**|NA|Quarterly Public<br>Debt Database,<br>Bank|Sector<br>World|
|**Short-term (less than one year maturity) debt**<br>**as a ratio to total debt**|NA|Quarterly Public<br>Debt Database,<br>Bank|Sector<br>World|
|**Debt held by external creditors as a ratio to**<br>**total debt**|NA|Quarterly Public<br>Debt Database,<br>Bank|Sector<br>World|

44
0.010)<sup>_∗_</sup>|(0.007)||
|||[0.887]|[1.000]|[0.710]||
|(12) Staff attitude|3.735|0.043|_−_0.011|0.063|2845|
||(0.580)|(0.029)|(0.038)|(0.031)<sup>_∗∗_</sup>||
|||[0.887]|[1.000]|[0.710]||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Multiple-inference corrected q-values that adjust for the false discovery rate within treatment arm are shown in square brackets.The absenteeism index is composed of an indicator of whether patients had ever found no staff present when visiting the clinic and the waiting time at the last visit. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A19
example, much attention in the literature is devoted to analysis of enforcement by third parties, such as social or merchant groups, or communities at large. While the inner workings of thirdparty enforcement are often investigated in detail, they are not usually analyzed as one of several enforcement options that firms may choose from. Moreover, first- and second-party enforcement often appear in separate branches of the literature (e.g., in sociology and economics, respectively), not directly presented in the specific contexts of enforcement of business agreements between firms.

In general, firms rate their governance structures as less effective for customer relations than for supplier relations. (This follows from a quick visual comparison of the figures accompanying Tables 2a and 2b.) This characteristic is epitomized in the two class-4 names— _strong comprehensive governance_ and _weak comprehensive governance_ .

The estimation and naming of the classes not only reveal which governance structures are important, but also which are absent. All governance structures rely, at least in part, on bilateralism: no firm relies solely on a combination of third parties and formal institutions. This is also a finding of Mike and Kiss (2019) in the data from Hungary, leading the authors to state that "Law never stands alone", but it is inconsistent with many claims in the literature that characterize development as a process of escaping personalized interaction and moving to a rule-based, impersonalized set of interactions.<sup>64</sup> A very widely cited version of this view can be found in Peng (2003: 276), which claims that the most important transition for emerging economies is the process of moving "from a relationship-based, personalized transaction structure calling for a networkcentered strategy to a rule-based, impersonal exchange regime". While our data do not capture the

> 64 Mike and Kiss (2019) characterize this as the classical view, and give many references to its use.

35
br>20 Mining<br>15<br>10<br>5<br>0<br>-5<br>2013 2014 2015 2016YTD<br><!-- End of picture text -->

## **D. Change in doing business’ distance to frontier rankings from 2010 to 2016**

<!-- Start of picture text --> Index<br>20<br>Overall change in score<br>15<br>10<br>5<br>0<br>-5<br>-10<br>Protection Getting Credit Registering Property Trading across Border Resolving Insolvency Paying Taxes<br>of Minority Investors Getting Electricity Enforcing Contracts Starting a Business<br><!-- End of picture text --> Sources: China Economic and Industry Data Database, China’s National Statistical Office, Haver Analytics, The Conference Board, World Bank.

A.B. “Enterprises with state participation” includes enterprises that are state‐owned or those with state participation. Investment is defined as fixed assets investment, which differs from gross fixed capital formation in the national accounts by including land sales. Six‐month moving averages (6mma) of year‐on‐year growth rates. Latest observation is October 2016. See Footnote 30 for the definition of private (“minjian”) investment.

C. China industrial enterprise survey of 5000 leading enterprises to rate their perception on selected topics. Index higher than 50 indicates improvement. Latest observation is 2016Q3.

D. Distance of China to the “frontier”‐best performers whose score is 100. An increase in scores indicates improvement; a decrease deterioration.

53
adjustment has significant impact on the welfare ranking of households, individuals and areas. These rankings are important for designing policies to alleviate extreme poverty and promote shared prosperity. Unadjusted welfare indicators will likely misguide the identification of the extreme poor and the poorest 40 percent of the population.

# **3.3. Shared Prosperity Using Income Data**

As mentioned, various factors in the poverty measurement literature point to consumption being a better proxy to measure welfare than income. Nevertheless, many countries in the world measure welfare using income. In particular, European countries measure welfare using income data from Eurostat on Income and Living Conditions (EUSILC) including negative and zero income. Countries from LAC also use income to measure social‐economic well‐being, but recode negative income to missing values. In this section, we test the impact of negative income data on shared prosperity using EUSILC data and the impact of zero incomes on shared prosperity using selected countries from the Latin American and Caribbean region.<sup>13</sup>

## **3.3.1 Data with negative numbers**

The shares of households with negative income across EUSILC surveys are minimal, with a maximum of 1.2 percent for Greece in 2011. For most countries collecting EUSILC data, the share is less than 0.5 percent. To observe the impact of negative numbers of income, we run three simulations: (1) keeping negative values; (2) replacing negative values with zero; and (3) excluding negative values. The results of the shared prosperity numbers using these three scenarios are reported in table 5. The differences in estimates for the proposed scenarios are reported in percent and percentage points. In relative terms, the difference in estimates can vary substantially reaching more than 100 percent; however, this is mainly for countries with low numbers on shared prosperity. In absolute terms, the differences are less dramatic ranging from 0 to 0.7 percentage points.

Though not strictly comparable (as different sets
The DHS are nationally representative random samples of households, from which a woman of reproductive age was interviewed<sup>5</sup> . The data used here are from 69 surveys from 52 countries carried out between 1985 and 1995, with sample sizes varying from 1,623 (Nepal 1987) to 28,168 women (Indonesia 1994). The measures of gender disparity are derived from the Final Report issued for each survey. The questionnaire has been revised several times and not all questions are asked in all countries, so that jiot all data are available for all countries. Moreover, certain of the statistics are reported for slightly different populations; for example, the coverage for Acute Respiratory Infections ranges from children under three years old in some countries to children under five in others. While these may affect levels, we hope that these differences do not substantively affect the comparisons of female to male _ratios_ of the indicators. In general, however, these are some of the most comparable data of this type, and certainly the most widely available.

The NFHS were carried out in 26 Indian States between 1992 and 1993 using a format very similar to that of the DHS. The sample sizes in the states range from 882 (Arunachal Pradesh) to 8,722 women (Uttar Pradesh). Although the survey instruments were very similar across states, some of the Final Reports do not report gender-disaggregated results for certain indicators (primarily because of small sample sizes). Hence, even within India, not all the data are available for all states.

National representativeness is usually achieved through weighting as the samples are often stratified random samples.

6 The sample used in Arnold (1990) includes 26 surveys and countries, that in Hill and Upchurch (1995) includes 38 surveys from 35 countries.

_5_
Policy Research Working Paper 9455

# **Abstract**

This paper analyzes the relationship between poverty and conflict in the macro and regional data, including a detailed case study of Uganda. The paper relies on a large and growing literature that provides evidence on the devastating impact that conflict has on health and expectations. Based on this evidence, it develops a statistical framework to track the cumulative long-term impact that armed conflict has on poverty, which the paper calls conflict debt. The data confirm that contemporaneous conflict leads to a conflict debt which is only recovered slowly. The empirical model is not only a good description of the cross-country aggregate poverty time-series data, but also regional cross-sectional data. A new aspect in the model is that armed conflict can prevent poverty reduction and, once it is over, allow for strong catch‐up effects as they exist in the data. But in the most conflict-ridden countries, repeated cycles of violence prevent poverty from recovering. According to the most conservative estimates, these countries and regions would have 5–10 percentage points lower poverty rates without their conflict debt.

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 h.mueller.uni@gmail.com.

_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