rafmacalaba/data-use-mentions-extended huggingface dataset:
Himanshu (2019) documents trends in Indian inequality between the early 1980s and the present. He shows that while rates of poverty decline and economic growth were impressive during this period, particularly post-2000, measured real per-capita consumption inequality rose only modestly, with a Gini coefficient of just around 0.28 in 1993/94, rising to about 0.33 in 2011/12. The largest increase in measured inequality occurred between 1993/94 and 2004/05, rather than in the high-growth 2000s. Himanshu (2019) goes on to note the current debate in India, raised by recent analysis by Chancel and Piketty (2019), concerning the likely understatement of measured inequality in the NSS surveys due to under-representation of, and possible under-reporting by, the top segments of the consumption distribution in the NSS surveys. Chancel and Piketty (2019) employ a variety of adjustment methods to estimate present-day levels of income inequality in India, which compare to those prevailing at the height of the British Raj in the early 20th century. While the precise level of inequality is difficult to ascertain given the data constraints, the evidence suggests that some increase in inequality did occur alongside the growth in incomes and decline in poverty.

It is unlikely that the structure of economic growth in India from the late 1980s to the present took the form whereby all persons enjoyed rising welfare levels without seeing their relative position in the consumption distribution change. As Deng Xiaoping is reported to have noted in relation to the likely impact of the market-oriented reforms he launched in China, ‘some get richer before others’ (see discussion in Himanshu et al. (2018, p. 125)). This observation invites investigation along two fronts. First, to what extent is it the case that different households and population groups in India have participated to differing degrees in the generalized economic growth process? In other words, is there much relative income mobility, and has it increased over time? Second, are there particular distinguishing features of those population groups that have done
. Required savings are also subtracted from assets because they function as a reduction in loan size.

10).

_Outreach Depth._ Of the five programs reviewed, only P4K explicitly targets low-income households. The program claims that all beneficiary households have incomes below the poverty line. Limited data from PHBK indicate that about 20 percent of its client households have incomes below the poverty line. Income data on the other programs' clients were not available. One proxy for income commonly used to assess depth of microfinance outreach is

**Table 11** Depth of **Program Outreach**

||Average<br>Loan Size<br>(US$)|**Loan Size**<br>Percent of**GDP**<br>Per Caita|**Women**<br>**Percent of**<br>Borrowersa|
|---|---|---|---|
|**BKK**|<br>99|p<br>10|40|
|<br>**LKP (Dompu**<br>District)<br>|<br>75|<br>7|62<br>|
|**PLIBK**|71-130|7<sup>-</sup>1 3<sup>b</sup>|50|
|**P4K**|67|7|50|
|**BKD**|71|7|50|
|Range for 9<br>programs<br>worldwide|NA|16-136|20 - 90|

a] Women's share of loans was estimated by program managers or was based on a sample of program loans.

b/ Program managers' estimate of the average loan size per group membe **r** esults in loans per person equal to 13 percent of GDP per capita. Estimates of the number of members in each borrower group
is "With boys or men you have a sexual relationship with". Respondents were then asked to allocate beads into each circle in a way that represents time allocation in an average week. The data points were later converted into weekly hours using recorded total leisure time from the first exercise. Unwanted Sex is defined as, "Been involved in any sexual intercourse that you were not willing to do". Transactional Sex is defined as, "Receiving anything such as money, gifts, help with schoolwork or something else in exchange for sexual intercourse". Literacy is assessed based on respondent's ability to read simple things like labels on containers (basic), and reading comprehension and writing of complete sentence (advanced). The score representing respondent's proficiency with these tasks is then rescaled to range between 0 and 100. Pregnancy and HIV knowledge represent the number of correct answers to the following true/false questions: "A women cannot become pregnant at first intercourse or with occasional sexual relation"; "A woman who is breastfeeding can become pregnant."; "From one menstrual period to the next, there days when a woman is more likely to become pregnant if she has sexual relations."; "A person who has HIV is different from a person who is ill with AIDS"; "During vaginal sex, it is easier for a woman to receive the HIV virus than for a man."; "Pulling out the penis before a man climaxes keeps a woman from getting HIV during sex "; "A women cannot get HIV if she has sex during her period"; "Taking a test for HIV one week after having sex will tell a person if she or he has HIV"; "A Pregnant woman with HIV can give the virus to her unborn baby".
<!-- Start of picture text --> HAZ<br>-0.5 ian: B BoysGirls<br>ae<br>-15 \ e O eo i 4 O O T OD<br>- Ow e } I yh .2F 6 Te ' O OF Y |<br>-2 sds.<br>0 mos. 6 12 18 24 30 36 42 48 54 60<br>Age (completed months)<br>Source: Author's own calculation based on 2015 National Nutrition Survey (NNS). Notes: HAZ = Height-for-age z-<br>score. Point estimates for the mean and 95% confidence intervals are based on groups of children defined by<br>complete age in months. This analysis uses 2 month intervals where, for example, 1 represents children with between<br>0 and 2 completed months of age. age. The line represents a non-linear prediction of the the age-conditional HAZ fitted with<br>a generalized additive model (gam) using on all observations. The 95% confidence interval around the predictions<br>from the gam gam smoothed line is shown as a grey area.<br><!-- End of picture text -->

Source: Author's own calculation based on 2015 National Nutrition Survey (NNS). Notes: HAZ = Height-for-age z- score. Point estimates for the mean and 95% confidence intervals are based on groups of children defined by complete age in months. This analysis uses 2 month intervals where, for example, 1 represents children with between 0 and 2 completed months of age. age. The line represents a non-linear prediction of the the age-conditional HAZ fitted with a generalized additive model (gam) using on all observations. The 95% confidence interval around the predictions from the gam gam smoothed line is shown as a grey area.
Survey (MPS) is a component of the SHFS. The MPS collects weekly exchange rates and prices of a broad range of 91 products and services as well as exchange rates from 14 key markets across all Somali regions. 6 The Somali High Frequency Survey was implemented by Altai Consulting in coordination with the respective statistical authorities. The team worked closely with the Directorate of National Statistics, Ministry of Planning, Investment and Economic Development of the Federal Government of Somalia.

15
<!-- Start of picture text --> (a) Aggregate expenditure, by source (with (b) Aggregate expenditure, by source (w/o<br>Brazil)- 2010-17 Brazil) 2010-17<br>(c) Aggregate expenditure breakdown, 2010-2017<br>6%<br>a 5%<br>2 4%<br>be 3%2%<br>0%1%% —- =o eo |<br>& & ee & ro se & s ss &<br>ws s&s aS oe & oe Rts) e Ss<br><& r o><br>se 9<br><<br>mChina mPublic mPrivate mDonors mSOEs<br><!-- End of picture text -->
**Figure 4: Stochastic Dominance and official poverty lines**

<!-- Start of picture text --> Stochastic dominance and Official Poverty lines<br>Niassa Rural vs Maputo Urban Nampula Rural vs Maputo Rural<br>15.8 30.9 60 80 90 15.8 24.8 60 80<br>consumption per capita per day in MT consumption per capita per day in MT<br>Niassa Maputo Nampula Maputo<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br><!-- End of picture text --> High PL differentials significantly impact the calculation of poverty figures, and two specific examples corroborate this conclusion. The left-hand graph in Figure 34 above, compares the per capita consumption distribution of urban Maputo Province and rural Niassa and Cabo Delgado<sup>18</sup> (N-CD) Provinces, while the right-hand graph in Figure 3 compares rural Maputo to rural Nampula.<sup>19</sup> Per capita consumption in both urban Maputo and rural Maputo is stochastically dominant throughout the distribution, even though in these areas underestimation affects about 50 percent of the observations. Yet the official poverty figures imply the opposite: while urban Maputo registers a poverty rate of 63.7 percent, rural N-CD only registers a rate of 32.7 percent; rural Maputo, meanwhile, has an official poverty rate of 76.3 percent, while rural Nampula’s rate is 56.7 percent (see Table 2, column 2, below).

In both cases the cause of the difference is the differing PLs (the vertical lines in the two graphs). The PL for urban Maputo is 93 percent higher than that of rural N-CD, while the PL for rural Maputo exceeds the PL for Nampula by 73 percent (see Table 4, column 6, in Appendix). As
plant. Multi-establishment firms respond to the shock by increasing their production at other establishments. Related recent research based on micro data from India shows that flood events disrupt local supply chains and firms respond by diversifying across locations which has significant distributional consequences (Castro-Vincenzi et al., 2024).

Production networks are formed through a complex web of contracts between firms and these firms use public infrastructure to trade goods. During times of extreme weather, public goods such as logistics infrastructure that facilitate production networks and trade in goods faces disruptions due to the “Tragedy of the Commons” problem. In addition to direct effects, firms in unaffected locations are indirectly impacted through production networks. For example, suppliers and buyers suffer from reduced revenues as a result of suppliers’ exposure to extreme heat and flooding incidents. In Tanzania, floods destroyed the fragile supply chain infrastructure and led to a disruption in the access of necessary inputs for firms even in unflooded areas. This caused 30-50% of all supply and delivery delays in the region and a drop in sales. Such disruptions in production networks transcends national borders as evidenced in a storm that affected not only Chinese plants’ performance but also reduced their foreign transactions (Elliott et al., 2019). Japanese affiliates in Thailand experienced a drop in sales following 2011 floods, while imports from China and Japan increased (Hayakawa et al., 2015). Hurricanes-induced job losses occurring within multiplant firms were propagated across undisturbed distant regions in the US (Seetharam, 2018). For every job loss of the firm in an affected county, 0.19-0.25 jobs are also lost across other sites in unaffected regions.

Firms that buy key inputs can adapt to climate change risk by diversifying away from weather-stricken suppliers (Pankratz and Schiller, 2021). Firms may also adapt by establishing financial links to other regions; viewed as an effective risk sharing and consumption smoothing tool (Albert et al., 2021). Such adjustments also occur in an international context, as illustrated in a study where the
|
||(-0.656)|(-0.377)|
|Relative world prices of palm-soy/wheat-rice|<br>-2.382|<br>-2.919*|
||(-1.543)|(-1.901)|
|Relative world prices of palm/wheat|<br>0.757***|<br>0.619*|
||(2.992)|(1.939)|
|Intercept|<br>18.04***|<br>20.13***|
||(5.493)|(6.745)|
|Observations|1,292|1,292|
|Controls|||
|Year, Quarter, Product Fixed effects|Yes|Yes|
|Ramadan Dummies|Yes|Yes|
||||||uniform subsidies are<br>poorly-targeted|
|---|---|---|---|---|---|
|Kitagawa &<br>Tetenov (2018)|United States|1987-1989|Experimental data from the<br>National Job Training<br>Partnership Act (JTPA)<br>Study|Empirical Welfare<br>Maximization (EWM)<br>method and randomized<br>experiment|Shows how the uniform<br>convergence rate depends<br>on the richness of the class<br>of candidate decision rules,<br>the distribution of<br>conditional treatment<br>effects, and the lack of<br>knowledge of the<br>propensity score|
|Holland et al<br>(2016)|United States|2010-2012|Hourly emission and<br>electric consumption|OLS|Heterogeneity in the<br>environmental benefits of<br>PEV adoption depending<br>on the location|
|Davis (2019)|United States|2017|U.S. Department of<br>Transportation’s National<br>Household Travel Survey|Comparisons based on<br>survey data|Electric vehicles are driven<br>considerably fewer miles<br>per year on average than<br>gasoline-powered vehicles|
|Salvo & Huse<br>(2013)|Brazil|2000-2010|Survey|Natural experiment,<br>multinominal probit<br>estimatioms|Imperfect substitutability<br>between gasoline and<br>ethanol among flexible-<br>fuel motorists|
|Salvo (2018)|Brazil|2011-2012|193 “visit-days” to 52<br>different fueling stations to<br>collect information for<br>demand and price levels|Natural experiment|Energy demand is not very<br>price sensitive along the<br>extensive margin of which<br>fuel
Despite much higher consumption of gasoline by the rich, in relative terms the gap between different wealth groups narrows substantially. On average, Iranians spend about 2.1 percent of their household budget on gasoline, but the poorest and the richest 10 percent spend about only 1.8 percent and 1.6 percent respectively. It is the population in the middle of the distribution who spend the largest share of their budget on gasoline (Figure 5). This implies that while increases in gasoline prices will affect all households, it is those in the middle of the distribution who will be most affected, despite the highly skewed benefits from subsidies towards the richest. That is, the middle class will be more affected than both the poor and the rich, because the poor consume less gasoline and the consumption of the rich is still a relatively small amount of their total expenditure.

Figure 5. Expenditure on gasoline as a percentage of total expenditures by deciles, <u>%</u> <!-- Start of picture text --> 3.0<br>2.5 2.5 2.4 2.4<br>2.5 2.3 2.3 2.2<br>2.1<br>2.0<br>1.8<br>2.0<br>1.6<br>1.5<br>1.0<br>0.5<br>0.0<br>deciles<br>%<br><!-- End of picture text --> Source: Authors’ calculation using forecasted HEIS 2018/2019.

# 4. Impact of reform on poverty and government revenues without mitigation efforts

In this section we simulate the direct and indirect impacts of the reform on poverty and inequality without any mitigation steps by the government, as well as the associated impact on government spending.

- 4.1 Direct impact on poverty and inequality without mitigation efforts

To
Table 13c: Difference-in-difference Estimates of the Impact of PROSPERA on ENLACE Achievement Tests: 6<sup>th</sup> Graders Enrolled in Indigenous Primary Schools in 2008 Propensity score matching with exact matching on age and state

|Variables|7th grade- 6th grade|9th grade -6th grade|Increase in category<br>of score from 6<sup>th</sup>grade|
|---|---|---|---|
||score<br>_Pane_|score<br>_l A: Spanish_<br>|<br>to 12<sup>th</sup>grade|
|Girls|3.77|10.98*|0.011|
||(5.87)|(6.69)|(0.042)|
|Boys|7.21|8.16|0.07|
||(5.82)|(7.08)|(0.05)|
|Observations|2,387|2,474|1,326|
||_Panel B_|_: Mathematics_||
|Girls|1.64|0.522|-0.002|
||(6.18)|(8.58)|(0.043)|
|Boys|7.26|13.25|0.015|
||(6.1)|(8.53)|(0.053)|
|Observations|2,297|2,365|1,183|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Variables which enter the propensity score include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD,
# **Introduction**

School safety and classroom disciplinary climate have a direct impact on teachers’ ability to teach and students’ ability to learn. The more time teachers spend on classroom management, the less they are able to direct their efforts toward the teaching and learning practices. School safety and classroom disciplinary climates have been declining in the Middle East and North Africa (MENA) region, as will be demonstrated in this paper, thus creating disruptive learning environments that have a negative impact on student learning outcomes. This rise of disrupted learning environments is alarming given the already low levels of student performance outcomes in MENA, as measured by international assessments (World Bank 2018). However, learning outcomes are not the only concern. Rather, school safety and classroom disciplinary approaches can have emotional and psychological effects on the development of students, and they may affect future individual and social well-being. Thus, given the large individual and societal costs of disrupted learning environments, investigating the causes and impacts of school safety and classroom disciplinary climate is critical.

The objective of this paper is to analyze the determinants associated with disrupted learning environments, at the school and classroom levels, in the MENA region and to uncover the impacts these environments have on student learning outcomes. This information will provide policy makers evidence on disrupted learning environments while offering some recommendations on how these conditions can be improved. This paper aims to address the two following questions:

- What does the school safety and classroom disciplinary climate look like in MENA schools and how does it differ from other regions?

- What impact does poor school safety and classroom disciplinary climate have on the performance of students in the MENA region?

# **Data and Approach**

School safety and classroom disciplinary climates are the two components of focus in this paper. School safety is measured using the Progress in International Reading Literacy Study (PIRLS) and the Trends in Mathematics and Science Study (TIMSS) safety index, which describe the state of misbehavior and violence among students and school staff.
# **Abstract**

Concerns about the sustainability of conventional agriculture have prompted widespread introduction of integrated pest management (IPM), an ecologically-based approach to control of harmful insects and weeds. IPM is intended to reduce ecological and health damage from chemical pesticides by using natural parasites and predators to control pest populations. Since chemical pesticides are expensive for poor farmers, IPM offers the prospect of lower production costs and higher profitability. However, adoption of IPM may reduce profitability if it also lowers overall productivity, or induces more intensive use of other production factors. On the other hand, IPM may actually promote more productive farming by encouraging more skillful use of available resources. Data scarcity has hindered a full accounting of IPM’s impact on profitability, health and local ecosystems.

Using new survey data, this paper attempts such an accounting for rice farmers in Bangladesh. We compare outcomes for farming with IPM and conventional techniques, using input-use accounting, conventional production functions and frontier production estimation. All of our results suggest that the productivity of IPM rice farming is not significantly different from the productivity of conventional farming. Since IPM reduces pesticide costs with no countervailing loss in production, it appears to be more profitable than conventional rice farming. Our interview results also suggest substantial health and ecological benefits. However, externality problems make it difficult for farmers to adopt IPM individually. Without collective adoption, neighbors’ continued reliance on chemicals to kill pests will also kill helpful parasites and predators, as well as exposing IPM farmers and local ecosystems to chemical spillovers from adjoining fields. Successful IPM adoption may therefore depend on institutional support for collective action.

1
Policy Research Working Paper 6339

# **Abstract**

This paper uses unique survey data to provide, for the first time in the literature, direct evidence that vote buying in poor economies is associated with lower provision of public services that disproportionately benefit the poor. Various features of the data and the institutional context allow the interpretation of this correlation as the equilibrium policy consequence of clientelist politics, ruling out alternate explanations (such as, for example, poverty driving both vote buying and health outcomes). The data come from the Philippines, a country context that allows for measuring vote buying during elections and services delivered by the administrative unit controlled by winners of those elections. The data reveal a significant, robust negative correlation between vote buying and the delivery of primary health services. In places where households report more vote buying, government records show that municipalities invest less in basic health services for mothers and children; and, quite strikingly, as a summary measure of weak service delivery performance, a higher percentage of children are severely under-weight.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at skhemani@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