rafmacalaba/data-use-mentions-extended huggingface dataset:
a reduction in inequality is a consequence of the imposition of direct taxes and direct transfers, and that the effects of indirect taxes (from disposable to consumable income) appear to be small. Considering inkind transfers (due to publicly provided education and health services), the reduction in the Gini is 15.6 points. Importantly, we document that the inequality reduction from MIPP to consumable income is among the highest in an international comparison considering the countries of the CEQ database, only trailing behind South Africa and Argentina. We also find that inequality reduction due to fiscal policies was higher in 2019 than in 2009: 6.4 versus 3.1 Gini points (comparing MIPP with consumable income). To be sure, the post-fiscal Gini coefficient is still very high (0.521) and among the top 9 in a subset of 52 countries of the CEQ database and in the top third of 18 upper-middle income economies.

Our results also indicate that direct transfers and public services typically benefit mostly the poorest. We document that half of the direct transfers go to the first income quintile, which includes the population in extreme poverty and part of those in moderate poverty. Bolsa Familia is the program with the highest progressivity, followed by rural pensions and Benefício de Prestação Continuada (BPC). They represent an important share of MIPP for the first quintile of the income distribution as measured by the pre-fiscal income. The results also suggest that the first and second income quintiles are net cash beneficiaries of the fiscal system, while the third, fourth and fifth are net payers. When considering the total fiscal position, which includes the education and health in-kind benefits, the results show that the first three quintiles are net receivers, while the last two are net payers of the fiscal system. At the same time, we document a large heterogeneity in the distribution of government expenditures. The first quintile of the MIPP income distribution receives about 70% of Bolsa Familia’s transfers, 55% of rural pension, 45% of BPC
5|
|Income per AEU|52.5|_56.0_<br>61.1|

# _Source: GLSS 1987_

The differences in incomes between urban and rural areas, as reported in 1987, are summarized in Table 6.2. We differentiate between Accra and other urban areas. It is seen that income per earner is somewhat higher in other towns and even higher in Accra, though the extent of the difference is almost certainly not sufficient to compensate for the cost-of-living difference between town and country. But even the nominal difference is reversed when we consider household incomes. The data show the perfectly understandable fact that rural households have more earners, because of the greater opportunity of secondary members (wives, children etc.) to participate in incomeearning opportunities in and around the farm. When we move to the statistics on income per capita, the levels of income in nominal terms is almost the same in the three sectors-showing the higher burden of dependency of rural households. This is a fairly general phenomenon in developing countries, reflecting the greater incidence of single member or smaller households in the urban setting. In fact, because of the limited effect of circulatory migrants in Ghana, the quantitative importance of this effect is much 40
locality that participated in these applications, the number of applications led by women by locality, and the number of applications by locality whose teams are all women. Secondary outcomes of relevance are the number of applications selected for grants by locality and the total number of people by locality that are part of an application. For these outcomes, we used OLS regressions with treatment status of the locality, controlling for the total adult population in the locality in 2010, the proportion of adult woman in the locality in 2010, the proportion of females three years or older speaking an indigenous language in 2010, the localities’ continuous marginalization index in 2010, the number of grants awarded from CONAFOR between 2007 and 2017, and a dummy variable for state entity. We clustered the standard errors by agrarian unit to account for the randomization.<sup>11</sup> The main estimates of interest were the coefficients for the treatment variable, particularly the difference between T0 versus T1 and T1 versus T2 to capture the additional effects of the componentes that were added in each group. We also looked at T0 versus T2 given the limitations we have on power, capturing the full package of additions to the standard outreach strategy. These represent Intention-to-Treat (ITT) estimates.

Additionally, the survey data provided us with individual-level outputs, namely whether women had heard about the WB project and call for proposals, and whether they recognized any of the

> 11 The conclusions do not change if we analyze them without clustering.

10
were national in scope, with the exception of Nigeria, where surveys representative at the state level were implemented in four states (Anambra, Bauchi, Ekiti, and Niger).

Representative surveys of between 150 and 760 schools were implemented in each country or Nigerian state using a multistage, cluster-sampling design. Primary schools with at least one fourth-grade class formed the sampling frame. The samples were designed to provide representative estimates for teacher effort, knowledge, and skills in public primary schools, broken down by urban and rural location. For five of the six non-pilot surveys, representative data were also collected for private primary schools. Across the eight surveys, the SDI collected data on 2,600 schools, over 21,000 teachers and 24,000 students in Sub-Saharan Africa.<sup>2</sup> The surveys collected a broad set of school, teacher, and student specific information, with an approach that relies as much as possible on direct observation rather than on respondent reports. Data were collected through visual inspections of fourth-grade classrooms and the school premises, direct physical verification of teacher presence by unannounced visits, and teacher and student tests. We focus here on the data on teacher behavior and knowledge.<sup>3</sup>

# **3. The starting point: Learning outcomes in primary schools in Africa**

In the last decade, the major International Association for the Evaluation of Education Achievement (IEA) and OECD testing programs have expanded dramatically, with more than 100 participating countries in at least one of these assessments in 2012 (Hanushek and Woessmann 2015). However, only one Sub-Saharan African country (Botswana) participated in the last IEA mathematics tests at the primary level, and only three countries participated at the secondary level (Botswana, Ghana, and South Africa).<sup>4</sup> Average test results at the secondary level for these three countries suggest _average_ test scores below the lowest _5th percentile_ score in the US. A significant share of students,
described above, HSP households received not only full rental subsidies for 9 to 18 months but also physical upgrades to the home environment. These proximate outcome measures can be seen as capturing successful program implementation.

To measure physical improvements to the shelter, we constructed an index of self-reported floor, roof, and wall quality; electricity and water access; and crowding. Housing expenditures were measured in two ways: First, in the midline phone survey, respondents reported their total monthly housing expenditures, including the payments provided by the implementing organization. Second, in the endline in-person survey, once assistance had ended, participants reported their monthly out-of-pocket housing expenditures (excluding the amount paid by the implementer). The latter is our preferred measure of the direct effect of the program on housing expenditures, because it allows us to test whether treated households experienced rental savings. Because the endline measure was collected after assistance ended, any observed expenditure reductions would most likely be an underestimate of the benefits experienced during the program. Recall that HSP also required the landlord to not raise rent for an additional year after the assistance ended, which could help account for any persistent effects on housing expenditures in the treatment group.

2. _Primary well-being outcomes:_ This category includes three main outcomes that seek to capture refugee household well-being, including household consumption, mental health (measured through the Center for Epidemiological Studies Depression Scale, CES-D), and an index summarizing the 25-item child Strengths and Difficulties Questionnaire (SDQ) that measures emotional and conduct problems, inattention, peer relations, and prosocial behaviors. Both the CES-D and SDQ are validated tools that have been utilized in a variety of international contexts (Park and Yu 2021, Woerner et al. 2004). In each of the three survey rounds, the primary respondent completed the CES-D depression screening. The SDQ was completed by an adult respondent regarding a randomly selected child aged 3 to 8 years old in the endline survey, as well as for the same child in the one and a half year follow-up.
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>Nominal Real - Decile Specific Inflation<br><!-- End of picture text --> _Source_ : World Bank calculations using data from TUIK

||Table1:Pove<br>|rtyrate and s<br>|ensitivity of<br>|poverty to us<br>|ing the price <br>|index forthe1|st decile<br>|
|---|---|---|---|---|---|---|---|
||**$5.50 Poverty**<br>**Line (2011**<br>**PPP) and**<br>**Official CPI**<br>(1)|**Poverty**<br>**Rate with**<br>**$5.50 Line**<br>(2)|**Poverty**<br>**Line -**<br>**1st-decile**<br>**CPI**<br>(3)|**Poverty**<br>**Rate - 1st-**<br>**decile price**<br>**index**<br>(4)|**Sensitivity**<br>**of the**<br>**poverty**<br>**rate**<br>(5)|**Population**<br>(6)|**Additional**<br>**Individuals in**<br>**poverty with**<br>**1st- decile CPI**<br>(7)|
|2011|189.8|13.0|189.8|13.0|-|||
|2012|206.7
contribution of demographic change towards output-per-worker growth and translate the results into per capita terms. The results suggest that over the period 1960-1995 demographic change, particularly due to changes in the youth dependency ratio, accounted for approximately 20 percent of per capita output growth impacts, with larger shares in Asia and Europe.

**Figure 3 A rising working-age population share is positively correlated with GDP per capita growth**

<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>-2<br>-10 -5 0 5 10 15 20 25 30<br>-4<br>GDP per capita, 1960-2014<br>Average annual percentage point change in real<br><!-- End of picture text --> Percentage point change in the share of the working-age population, 1960-2014 _Source_ : Authors’ estimates _Note_ : Data from World Development Indicators 2015 and UN (2015).

Overall, the positive effect of a larger share of working age population on growth is widely supported in the literature (Bloom and Williamson, 1998; Higgins and Williamson, 1997; Eastwood and Lipton, 2011; Kelley and Schmidt, 1995, 2005, 2007), including its important role in Asia’s growth between 1965 and 1990 (Bloom et al., 2000) and improvements in the accuracy of growth projections by taking age structure into account (Bloom et al., 2007). The evidence of the positive impact of a larger share of working age population on growth is not limited to crosscountry analysis. Mody and Aiyar (2011) suggest that a one standard deviation increase in the working age ratio is associated with an increase of about 0.6 percentage point in per capita income growth across states in India.

As part of the second demographic dividend, national private savings rates have been found to depend on the age composition of the population: individuals are typically net savers when they are working-age and
006|0.021|0.008|0.000|0.000|
|_Scenario 2_<br><br><br>|_The first outco_|_me variable prop_|_ortionally increa_|_ses from 100 in P_|_eriod 1 to 120 i_|_n Period 2_|
|M(x�)<br><br>|0.270|0.284|0.281|0.296|0.400|0.409|
|M(x)<br><br>|0.158|0.171|0.170|0.185|0.311|0.323|
|A(x)<br><br>|0.302|0.316|0.302|0.316|0.400|0.409|
|R(x)<br>|0.112|0.113|0.111|0.111|0.089|0.086|
|S(x)<br>*) The simulate<br>mean 30, Gini|0.032<br>d distributions ha<br>0.3; Correlation: p|0.032<br>ve the following<br>eriod 1: 0; period|0.021<br>parameters: First<br>2: ≈ 0.3.|0.020<br>outcome: mean|0.000<br>100, Gini 0.5; Se|0.000<br>cond outcome:|

We now apply the methods to analyze changes in multidimensional inequality in Azerbaijan from 2016 to 2018. We use data from the second (2016) and the fourth (2023) rounds of the Life in Transition Survey (LITS). The LITS is a survey run by the European Bank of Reconstruction and Development and the World Bank covering the so-called “transition countries” of
Policy Research Working Paper 10533

# **Abstract**

The recent global crises, such as the COVID-19 crisis, remind us of the importance of efficient transportation and logistics. Notably, however, even before the crises, some regions were already experiencing a gradual increase in freight costs, with more and more empty trucks observed. The paper recasts light on the question of how road freight costs are determined using large, unique shipping data from Eastern European and Central Asian countries. It finds that economies of scale are significant in both freight weight or load factor and distance. The elasticity with respect to freight weight is particularly high at about 0.3 to 1.0 in absolute terms. Thus, to contain trucking costs, it is important to maximize the load factor through freight consolidation at origins and destinations. The elasticity with respect to distance is relatively modest at 0.04 to 0.16 in absolute terms but still statistically significant, indicating that distance may not necessarily be a constraint on trade and regional integration. Trucking costs also decrease with driving speed, a proxy for efficiency of movements or road conditions. The elasticity is significant for food products (−0.03) and other consumer goods (−0.11). Finally, the paper finds that border crossing adds 3–4 percent to freight costs.

This paper is a product of the Transport 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 author may be contacted at aiimi@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
--|---|
|_Southern Zone: 3 communities (Palo_|4 Palo Marcado|5 Palo Marcado|2 Palo Marcado|
|_Marcado, Las Tablas & Las Crucitas)_|8 Las Tablas|4 Las Tablas|1 Las Tablas|
|_Project ACS -PROSASUR_|8 Las Crucitas|4 Las Crucitas|1 Las Crucitas|
|_Western Zone: 4 Cooperatives of_|8 Cooperative|4 Cooperative|1 Cooperative|
|_the World Bank project COMRURAL_|COCAFCAL- Capucas|COCABEL, Belén.|COCABEL, Belén|
||8 Cooperative|4 Cooperative|1 AMPROCAL, La Labor|
||COPRAUL-Plan del|COPRAUL-Plan del||
||Rancho|Rancho||
|_TOTAL_|<br>36|21|6|

Interviews and focus groups were conducted in Spanish language and fully transcribed and coded in a coding matrix consisting of key themes and codes identified in early engagement with incoming data which was adapted throughout the analysis process. To enhance consistency in the coding process, a subset of transcripts were coded twice by different research team members thereby facilitating quality checks and mutual feedback processes between team members. The coded matrix containing quotations and themes organized by sample group and topic was then used as the principal basis for analysis and write-up of a draft version of the paper. Preliminary findings were discussed within the research team before a second deep-dive into the qualitative data to further explore specific themes before finalizing the paper.

12
effective in reducing the bias. In fact, our study finds that the performance of reweighting in estimating poverty can be improved when combined with poverty imputation methods such as SWIFT.

# _Experiments_

This paper examines whether, and if so, to what extent, a combination of reweighting techniques and the SWIFT poverty projection methodology can eliminate sampling biases in poverty estimates based on biased survey samples. To see this, we first use reference household surveys in Rwanda, St Lucia, and Uganda and construct subsamples by selecting households with at least a mobile or landline phone. These samples are subject to sampling bias by construction. Without reweighting and SWIFT poverty projections, the poverty rates in these subsamples of phone owners are lower than those in the full samples. We then examine whether reweighting and SWIFT poverty projections can correct for the abovementioned bias.

Phone and web survey data collections face sampling and nonresponse biases, but the abovementioned experiments only focus on sampling biases that arise from uneven phone ownership. To understand the ability of the SWIFT and reweighting techniques to adjust for nonresponse bias, this paper conducts an additional experiment using the sample of Ethiopia High-Frequency Phone Survey round 7 (HFPS7), which is a subsample of the 2018/19 Ethiopia Socioeconomic Survey round 4 (ESS4). Since this subsample of ESS4 includes only phone owners, it is subject to sampling bias. Also, since it includes only households of ESS4 that responded to the HFPS7, it is also subject to nonresponse bias. Using this subsample, we conduct the same analysis as above to assess whether reweighting techniques and SWIFT can correct the bias in poverty estimates arising from both sampling and nonresponse biases.

This paper is organized as follows. Section II introduces the SWIFT poverty projection methodology and reweighting techniques, and Section III displays results from four experimental studies (Saint Lucia, Rwanda, Uganda, and Ethiopia). Section IV concludes with an assessment of a combination of reweighting and the SWIFT-based poverty projection model in eliminating the bias in poverty estimation based on the four case studies.

# *
they are designated "non-market economies". Table 4 presents recent evidence from the WTO data base on the frequency of the initiation of anti-dumping investigations and the imposition of definitive measures relative to countries' shares in world exports. The ratio of investigations or definitive measures to the share of total trade (Rad) is a measure that shows the tendency of a country's exports being subjected to antidumping actions relative to its share of world exports.

Table 4 shows that Rad is much higher for non-WTO members than for members for both antidumping investigations and the imposition of "definitive" measures-usually antidumping duties. It also shows that the so called non- market economies which are also not WTO members, including China, Russia and the rest of the FSU, except the Baltics, are much more likely to be the targets of antidumping investigations-which themselves have been shown to have an adverse impact on exports-relative to their share in total world trade, by comparison to any other group of countries, developed, developing or transition. And they are also even more likely to be the targets of definitive anti-dumping duties.

_Table 4 Antidumping: Share of Affected Economies in Total Cases Relative to Share in World Exports_ _(in % and ratios)_

|**_Affected Economies_**|**_Share in World_**<br>**_Exports%_**|**_Share in Total_**<br>**_Anti-Dumping_**<br>**_Investigations%_**|**_Share in Total_**<br>**_Definitive Measures%_**|**_Rad_**<br>**_Investigations_**|**_Rad Definitive_**<br>**_Measures_**|
|---|---|---|---|---|---|
||**_1995-1997_**|**_1995-1997_**|**_1995-1997_**|**_1995-1997_**|*
the percent increase in column 4 - the share that did not care about the targeting of humanitarian assistance as long as they themselves received assistance- was especially large. Essentially nobody in the mobile phone survey admitted to having this preference. But a non-negligible share - 9.9 percent- admitted to it in the internet survey, which represents a 990 percent increase. Thus, even in the case where the percentage point difference between the surveys was one of the smallest among sensitive questions, the percent increase was the largest.

Third, the difference between the modalities in the sensitive questions was significantly larger than the difference in non-sensitive questions. The average difference between the modalities for sensitive questions in columns (1)-(4) was 3.4 times greater than the average of the absolute difference in columns (5)-(8)<sup>23</sup> ; the largest percentage point difference in the sensitive questions (column 1) was over twice as large as the largest difference for the non-sensitive questions (column 6); the smallest difference in the sensitive questions was still 9.9 percentage points (columns 3 and 4) while the smallest difference for the non-sensitive questions was essentially zero (columns 5 and 8); and the differences in the sensitive questions were all statistically significant at the 1 percent level, while two of the four differences in the non-sensitive questions were not statistically different from zero at conventional significance levels and were very low in magnitude.<sup>24</sup> And fourth, despite the strong average difference reported across sensitive questions in Table 4, the results also illustrate that the difference between modalities was not equal across the sensitive questions. The largest difference between the modalities was in the question that was potentially sensitive due to social desirability bias and the possibility of the respondent’s safety being threatened- the question asking respondents to name the party most responsible for the violence they witnessed. The point estimate was almost twice as large as the sensitive question with the
This version: 15 Feb 2024 38 _have RM 10 or so with me. I consider myself rich because I have land and I can get food from the nature around me. We don’t have to buy anything with money. I think I am rich. I don’t think that I am poor based on the government’s definition. I can still survive today as long as I have land. All my cats and dogs sleep with me. I’m alright. I’m not ashamed. I’m not going hungry. When you don’t have your land, you lose everything._ [Rural Sabah]

# **4. Discussion**

Our in-depth qualitative assessment of life quality across multiple ethnic groups and geographic locations has produced nine themes that are shared across all groups. These include three positive and six negative themes. Among common positive themes, across all regions and ethnic groups, participants agreed that there were more types of jobs, requiring less physical labor and providing more income. Second, across all regions and ethnic groups, respondents also agreed that there have been substantial improvements in educational levels, when respondents compared themselves to their parents. Third, participants agreed that there were significant improvements in communication infrastructure development, particularly on road networks, and better transportation facilities, including the ability to afford a car.

All these findings are consistent with the quantitative data on Malaysia. Yet when asked to reflect on the quality of their life compared to the past, their verdict was very different. Fifty-eight of FGDs in our sample agreed that their life is harder compared to five years ago and this was also true across ethnic groups for the following reasons: 38