rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

Household consumption is typically the core concept at the center of any attempt to measure living standards, inequality and poverty in the developing world.<sup>1</sup> Measures of consumption are derived almost exclusively through survey, however a review of survey practice will quickly reveal extensive variation over several dimensions, such as the method of data capture (diary versus recall), the level of respondent (individual versus household), the reference period for which consumption is reported (anywhere from 3 days to one year) and the degree of commodity detail (from less than 20 items to over 400 items). Variations occur both across countries and over time as statistical offices alter survey designs, with little understanding of the implications of such changes for accurate and consistent measurement of consumption. Such variation hampers both crosscountry studies of poverty and inequality measures as well as the measurement of welfare trends within a country (Lanjouw and Lanjouw, 2001).<sup>2</sup> For example, the national living standards survey in South Africa found a decline in real food expenditure from 2000 to 2005 even though the same data sources indicated increases in overall consumption and declines in self-reported hunger/food insecurity. This contradictory finding is largely attributed to a change in survey design from recall to diary for food consumption measurement in 2005 (Yu, 2008).

Changes in survey design, if they matter, need to be considered in analysis of trends in consumption or poverty over time. By extension, analysis that pools surveys of different types to study economic growth and inequality should take these differences seriously. Consider, for example, the widely used UNU/WIDER World Income Inequality Database (WIID) on income distribution and inequality. Concerns about consistency in such cross-country databases focus on whether the source surveys are income or consumption surveys (such as Atkinson and Brandolini, 2001, and Pinkovskiy and Sala-i-Martin, 2009), and the comprehensiveness of the consumption measure (Chen and Ravallion, 2008)
Policy Research Working Paper 7776

# **Abstract**

In the 2000s, global inequality fell for the first time since the Industrial Revolution, driven by a decline in the dispersion of average incomes across countries. Between 1988 and 2008, a period of rapidly increasing global integration, income growth was largest for the global top 1 percent and for country-deciles in Asia, often in the upper halves of the national distributions, while the poorer deciles in rich countries lagged behind. Although within-country inequality increased in population-weighted terms, for the average developing country the rise in inequality slowed down in the second half of the 2000s. However, like any analysis based on household surveys, these results could miss important increases in inequality if they are concentrated at the top. These data constraints remain especially serious in developing countries where only very limited information on the top tail exists, especially regarding capital incomes.

This paper is a product of the Poverty and Inequality 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 clakner@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
.11, 6.13, ETI 1.01-4.02)<br>•<br>Average time to clear exports through customs / time to export/import (WDI)<br>•<br>Documents to export/import(WDI)|
|**Incr**<br>**con**<br>**the**<br>**and**<br>**tele**|**easing the accessibility and**<br>**nectivity of the domestic market, and**<br>**security, predictability, reliability**<br>**efficiency of transports/logistics,**<br>**communications and ICT**|•<br>Logistics performance index and its indicators – quality of transports and IT<br>infrastructure, international transport costs, logistics competence, trackability and<br>timeliness of shipments, domestic transportation costs (WDI, LPI, WTI 4.1)<br>•<br>Trading across borders – Doing business (IFC, WTI 4.2)<br>•<br>Trade Enabling and Global competitiveness indexes – infrastructure: quality of<br>infrastructure overall roads railroads orts air transort available seats fixed|
|•|Reforms of the telecommunications<br>sector, including infrastructure,<br>regulation, competition, and access for<br>all segments to include fixed lines and<br>mobiles|, , , p, p, ,<br>telephone lines/100, mobile phone subscriptions/100, availability and quality of<br>transport infrastructure and services, availability and use of ICTs (WEF GCI 2.01-2.09,<br>WEF TEI 4.01-7.05, WDI);<br>– technological readiness (WEF GCI 9.01-9.06)<br>•<br>Africa infrastructure country diagnostic (AICD)
along OD route). Control 3: Control 1 plus ln (average nightlight luminosity along OD route). The sample for column 2 is smaller because diesel price data is available only for 2019. Standard errors are clustered at the destination level. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

**Table 8: Estimation for Extensive margin of trade**

||Ln (Freight Rate)<br>OLS|Ln (numb<br>OLS|er of trips)<br>IV|
|---|---|---|---|
||(1)|(2)|(3)|
|Av. VAT rate at next probable destinations|10.485***<br>(1.672)|||
|Ln (Freight rate)||-0.175***|-1.232***|
|||(0.045)|(0.409)|
|Kleibergen-Paap F statistic|||39.31|
|No. OD pairs|24,445|24,445|24,445|

Note: Each column represents a coefficient estimate from separate regressions. The column heading corresponds to the dependent variable. All regression includes the following controls: ln (distance between origin and destination (OD)) and ln (average VAT in potential next destinations*crude price), ln (night lights at origin), ln(night lights at destination), ln(night lights at the route level). Standard errors are clustered at the destination level. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

29
**Figure 22: The 1999 Trends in International Mathematics and Science Study and National Income Levels**

# A. Mathematics Test Scores and 2003 Per Capita GNI

<!-- Start of picture text --> SGP<br>KOR HKG JPN<br>BEL<br>RUS MYS SVK HUNCZE SVN AUSCANNLDFIN<br>BGR LVA NZL GBR USA<br>MDA THAROM LTU CYP ISR ITA<br>MKDTUN<br>JORIRN TUR<br>IDN<br>CHL<br>PHL<br>MAR<br>ZAF<br>6 7 8 9 10 11<br>Log of PC GNI 2003<br>95% CI predicted Maths<br>Other TIMSS Countries Thailand<br>600<br>500<br>400<br>300<br>95% CI/predicted Maths/Test Score Maths<br><!-- End of picture text -->

- B. Science Test Scores and 2003 Per Capita GNI

<!-- Start of picture text --> SGP<br>RUS SVK HUNCZE SVNKOR AUSCANHKGBELNLDFINGBRJPN<br>BGR NZL USA<br>LVA<br>THA MYSLTU ITA<br>ROM ISR<br>MDA JORMKDIRN CYP<br>IDN TUNTUR<br>CHL<br>PHL<br>MAR<br>ZAF<br>6 7 8 9 10 11<br>Log of PC GNI 2003<br>95% CI predicted Science<br>Other TIMSS Countries Thailand<br>600<br>500<br>400<br>300<br>200<br><!-- End of picture text --> 54
- Student-staff ratio - the total number of students in the school divided by the total number of teachers and non-teaching staff in the school (these numbers are based on headcounts, not full time equivalents).

We also have two measures of the group composition of the exam takers of each school.

- Language – the percentage of EIT exams (other than the Ukrainian exam) taken in Ukrainian. Note that this reflects the language choice of the students taking the test and hence can differ from the language choice of the whole class.

- Gender Composition – the share of boys among those taking the EIT exam. As not all students take the exam, this can differ from the share of boys in the class.

And we have two measures of the economic situation in the area (rayon) where the school is located.

- The unemployment rate at the end of 2009.

- The average wage at the end of 2009.

Finally, we have one measure at the level of the village where the school is located, that control for the possibility that bigger villages can have both bigger schools (because they have a bigger population) and better schools (because better teachers or „better‟ parents might move to bigger villages with more amenities).

- The population in the village where the school is located based on the 2001 population survey. For the big cities, we typically have population data for the district where the school is located.

Descriptive statistics are provided in Tables 3a (rural schools) and 3b (urban schools).

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tion strategies against the socioeconomic crisis induced by COVID-19 may increase child marriage and child labor (UNICEF, 2021a).

The facts mentioned above make the context of Nigeria all the more relevant to our study. It is a Sub-Saharan African country with a Poverty Headcount Index of 40.10%.<sup>3</sup> Furthermore, _Demographic and Health Survey_ (DHS) evidence shows a 43% prevalence of child marriage in Nigeria among married women aged 20 ́ 24. Additionally, data from the Multiple Indicator Cluster Survey (MICS) indicate a high prevalence of child labor among children aged 5 ́ 17 in Nigeria. For example, in 2007, the prevalence of child ́ labor among children aged 5 14 years was 28.9%, while in 2011, this share was 57.1% for children aged 5-11. In 2016-2017, around one in two children aged 5 ́ 17 was involved in child labor (50.8%) of which 54.3% were boys, and 47.2% were girls.<sup>4</sup> Both these figures are well above the Sub-Saharan Africa region’s average of 37% for child marriage and 29% for child labor. They underscore the importance of education in Nigeria.

To identify the effect of nationwide COVID-19 lockdown measures on children’s probability of school attendance after schools’ reopening, we use a panel of school-age individuals observed just before schools’ closure and just after schools’ reopening and rely on the quasi-randomness of the occurrence of the pandemic. We draw from Mahmud and Riley (2021) by estimating a fixed-effect linear probability model. However, despite the plausible exogeneity of the COVID-19 shock, the potential simultaneous occurrence of other covariate shocks (e.g., climate shocks) can confound the identification of its effect on children’s school attendance. Indeed, in the context of Nigeria, whose territory is partially located in the drought-prone western Sahel region, the concomitant occurrence of climate shocks such as drought
**Figure 3.3:   Poverty Gap (P1) in Mexico** **<u>Selected years 1992-2002</u>**

<!-- Start of picture text --> 30.0<br>National<br>Urban<br>25.0 Rural (0-15,000)<br>localities with 1 to 2,500<br>localities with 2,501 to 15,000<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>1992 1996 2002<br>Source: Calculations based on selected years of INIGH in 1992-2002.<br><!-- End of picture text -->

**Figure 3.4: Extreme Rural Poverty Depth (P1) in Mexico** **<u>By Region, Selected years 1992-2002</u>**

<!-- Start of picture text --> 35.0<br>1992<br>30.0<br>1996<br>2002<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>Mexico Norte Capital Golfo Pacifico Sur Centro-Norte Centro<br>Source: Calculations based on ENIGH 1992, 1996 and 2002. Rural: areas population with <15,000.<br><!-- End of picture text --> 14
- **_Treat households as freight attractor nodes in mobility surveys_** . Freight transport studies usually focus on studying trip generation and attraction by production, logistics and sales nodes, including only retail as the final destination point and without considering households as a point of attraction for freight trips. Household mobility surveys can include basic questions about online shopping habits without significantly increasing the response time.

- **_Manage e-commerce as a mobility-generating challenge in the short-to-medium term_** . The existing pressure on mobility planning to find ways to reduce externalities in the transport sector means that “virtualization” of activities is routinely seen as a powerful tool to reduce mobility demand (Pettersson et al. 2018). In the case of e-commerce, international evidence and the results of the analysis conducted this study suggest that this is not the case; as of today there is no substitution effect of shopping trips that compensates for the increase in freight vehicle traffic. In this sense, e-commerce differs from the effects of other virtualization processes (telework, online leisure, etc.) in which there may be more opportunity to reduce mobility demand without generating other types of trips. In any case, the effect of each social trend on mobility demand has to be carefully analyzed prior to pinning hopes on it in terms of mobility reduction. For example, the available evidence on the effects of teleworking suggests complementarity effects with other travel purposes and increases in distances traveled (Ravalet and Rerat 2019).

- **_Focus on the optimization of distribution routes and the electrification of fleets_** . The optimization of distribution routes is the measure that can most effectively mitigate the impacts of e-commerce on mobility, as it can reduce the number of kilometers traveled by freight vehicles in the city, with all that it entails in terms of reducing emissions, mitigating road accidents associated with this type of vehicles and containing congestion. The co-creation of more sustainable alternatives for distribution together with the sector stakeholders must be a priority for the transport authorities. This optimization includes the implementation of freight
14 KPP reconsider VAR analysis around two facts: a) shocks to different variable are not independent b) shocks are correlated across time'<sup>3</sup> . These two issues led them to examine system-wide shocks (as opposed to variable-specific) and to deal in some manner with future shocks given their autocorrelation. The way they did this was by means of the expectations operator conditional on history and/or shock. The constructed response averages out future shocks, hence it is an average of what might happen in the future given the past and present.

The key features of the Generalized Impulse Response analysis are: a) Impulse response functions and variance decompositions are independent of the ordering of the variables (compositional problem) or any other a-priori restrictions on the variance-covariance matrix of the reduced form residuals b). The analysis describes how the system behaves after a specific historical shock, taking into consideration the correlation among shocks. Since historical shocks are not orthogonal, they do not add up to 1 00%.

# D. **Results for 1992-1998: Generalized Variance Decomposition of Output and Impulse-Response Functions**

The country-specific GVAR models were estimated using monthly data for the same set of five variables (industrial output growth, real interest rates, real exchange rates, sovereign spreads and terms of trade<sup>14</sup> ) during the period 1992-1998. To capture international capital markets dynamics we used the Brady Bonds sovereign spread and if the country did not have one the regional average was used. Appendix 3 contains a detailed description and source of each variable).

The lag-selection process was done with different information criteria. The Akaike criterion yielded almost always twelve lags, while the Schwarz criterion pointed at lag one. The likelihood ratio test between the two orders indicated that the longer lag length was preferable'<sup>5</sup> . (Appendix 3 presents a summary of the VAR estimates for each country and the coefficient of variation of each of the variables during the sample period
The first approach consists of examining the co‐movement of prices for homogeneous products (both the co‐movement among spatially separated markets within India, and between Indian markets and international markets), purging the price series from their irregular components. Thus, this analysis focuses on co‐movements of substantial price changes. Since the raw series for prices are highly volatile, a smoother is applied that preserves cycles but reduces the noise in the series. For each city‐ commodity, the Christiano and Fitzgerald (2003) filter is applied to isolate cycle‐components. This filter has the property of approximating the optimal linear smoother, which can only be applied to an infinite time series. The filter minimizes the mean squared error between the filtered series and the series filtered by an ideal band‐pass filter. We select periodicities between 12 and 36 months.<sup>7</sup> The choice of these periodicities was driven by visual inspection of the peak‐to‐peak analysis of price data.

This approach allows visualizing the extent of transmission of substantial price changes, as well as other patterns, such as the symmetry of transmissions from peak‐to‐peak and from bust‐to‐bust.

The second approach directly tests the relative version of the LOP. Relative LOP establishes that the growth rate of domestic prices reflects the growth rate of international prices and of the nominal exchange rate. The absolute LOP postulates that in the long‐run there are no arbitrage opportunities, because the price of a homogenous good in spatially separated markets needs to be equal. Given the price in market i, Pi , the price in market j, Pj, the absolute LOP postulates that:

# Pi�Pj

In growth rates (denoting log differences in minuscule), the relative PPP can be defined as: Empirically, to assess the extent of spatial integration we analyze how the prices in i and j co‐move.

A natural way of testing the relationship for India is to estimate a vector error correction model
of economic growth and employment creation as one of its four pillars. Labor force participation as a share of the working-age population (15–64 years) increased from 48.3 percent in 2009 to about 53.5 percent in 2014, while the unemployment rate decreased slightly to 11.3 percent in 2014 from 12.5 percent in 2009 (World Bank 2019c). The labor force participation rate is 76 percent among men, compared to 33 percent among women (World Bank 2019b). Agriculture and services are the main sources of employment in Sudan, and employment in agriculture is more common among women.<sup>2</sup> The overall unemployment rate for women is more than twice that of men (19 percent and 8 percent, respectively), and being a young woman is even more detrimental: 27 percent of young women are unemployed, compared to 20 percent of young men.

Understanding gender- and youth-specific issues can help identify entry points for greater employment opportunities for women and youth in Sudan and shape actions for enhanced growth and sustainability. The objective of this mixed methods study is to contribute to a better understanding of the current situation, challenges, and constraints that women and youth face in accessing employment opportunities. The focus is on women, with age-related data and information provided wherever possible. The study reflects the types of barriers that may prevent women and youth from capitalizing on existing economic opportunities. The study also contributes to enhancing knowledge on women- and youth-related issues in Sudan, areas in which analytical work is scarce. The findings from this study are expected to be a tool

> 2 Agriculture accounts for 60 percent of all employment among women (and almost all employment in rural areas), compared to 39 percent among men. Women are mostly engaged in subsistence farming and their agricultural activities are key for both income generation and food security for the family.

2
dialing (RDD) in 29 countries, and a list of phone numbers typically obtained from mobile phone operators in 5 countries. The RDD design samples from all active mobile and landline phone numbers, such that RDD surveys are representative of the population aged 18 and over with an active phone number, conditional on response and survey completion. Sampling weights were then constructed to correct for selection bias due to the inability to contact households that did not participate in the survey either due to non-response or lack of access to a phone, with the goal of obtaining estimates that are as close to being nationally representative as possible. Information was collected from one respondent per household. In the case of countries where the sampling frame was derived from a previous survey, this was typically the household head. In other cases, such as when the sample utilized RDD, the respondent was more representative of individuals within the household. While the non-random selection of individuals in the former cases is not ideal, in an earlier analysis of labor market indicators derived from phone surveys, Kugler et al. (2021) find little evidence of differences in employment outcomes or trends over time being affected by the oversampling of household heads with respect to variables such as level of education, gender or location, the main source of bias being for age comparisons. Within the pooled data set, the household sampling weights are scaled such that countries are weighted equally, such that estimates of vaccine hesitancy reported in this study are averages of country averages.<sup>4</sup>

# _2b. Outcome measures_

<mark>The questions asked about vaccine hesitancy in the surveys varied</mark> depending on whether a vaccine was available in the country at the time of the survey. In surveys that took place before the vaccine rollout had

> 3 South Asia is not represented due to lack of available data. The Middle East and North Africa includes two countries—Iraq and Lebanon.

> 4 Using population weights would have resulted in the results being driven by a
: Estimates are based on the synthetic control method using forest-month level data. We use fires with 30% or better confidence to construct the outcomes. Treatment start from October 2014. P-values from the placebo analyses, in which treatment effects are standardized by pre-treatment RMSPE, are in brackets. Average fire spread is measured in km<sup>2</sup> . *** p _<_ .01, ** p _<_ .05, * p _<_ .1 of grid cells affected by fire in the post-harvesting month in the period 2015-2017 as it was in reducing overall forest fire occurrences. Indeed, the percentage-point reduction in the share of grid cells affected by fire in the 2015-2017 November months ranged between 8 _._ 2% and 10 _._ 1%. From Columns (2) and (3) we can infer that the main reason why the intervention was effective was that it resulted in a better containment of forest fires. As shown in Column (2), fewer fires were started throughout the post-intervention period, but these reductions are not significant in any of the post-intervention years except for 2016. Column (3), however, shows that the intervention resulted in a reduced spread of forest fires over neighboring grid cells in both 2016 and 2017: the 2 _._ 10 km<sup>2</sup> and 1 _._ 83 km<sup>2</sup> decreases in the size of fire events translate into a 38% and 29% decrease, respectively. We thus find that the reductions documented in Table 1 are more likely the result of improved forest fire containment than of a sizeable and long-lasting reduction in the number of fires started. Also note, however, that while the point estimate with respect to the number of fires started in 2018 was negative and sizeable (see Column (2)), this effect is not statistically significant, and we observe a sizeable _increase_ in the 24