rafmacalaba/data-use-mentions-extended huggingface dataset:
- 10 -

18. For the spatial weighting matrix, inverse distances between two locations _i_ and _j_ are used. The distance is calculated using the Euclidean distance between the two locations. The intuition is that two locations are more closely related to each other, if they are located closely. This follows Tobler’s first law of geography: “everything is related to everything else, but near things are more related than distant things (Tobler 1970).”

# **IV. DATA**

19. Our unit of analysis is fokontany, which is the fourth-level of administrative boundaries, following regions, districts, and communes. In Greater Antananarivo, there are 556 fokontany in 4 districts. From the analysis, 8 observations are excluded, of which some data are missing. The summary statistics are shown in ( **Table 1** ). The average land prices were collected at each fokontany in 2018. The prices vary substantially between MGA 110 and MGA 1.3 million, with an average of about MGA 64,000 or US$18 per m<sup>2</sup> . Travel time is estimated using spatial software based on the road network data with road conditions and bus traffic attached (see **Figure 7** above). It takes 42 minutes on average, but from some fokontany, it may take more than an hour and a half.

20. As discussed, the JAI is calculated based on the size of job opportunities at each destination and the travel time between the origin and the destination. To identify and quantify the size of job opportunities, satellite imagery was used. The following areas were considered: Administrative Zone; Commerciale Zone; Industrial Zone; Cultural Facilities; Hotel Buildings; Market Area; and Office Buildings. In total, 1,094 land parcels were identified ( **Figure 11** ). The size of each parcel of land was measured as a proxy of the number of job opportunities.<sup>2<
# **3.1 Globalization Dynamics**

I start by estimating the globalization dynamics at the sector level. Figure (2) plots the results for 1965-2021. Note that these dynamics are obtained with different estimations by sector and period.<sup>7</sup> It shows that globalization started earlier for manufacturing, with positive dynamics already in the 1960s. Agriculture started its globalization process in the early 1980s, and services only took off in the second half of the 1990s. From figure (2) (a), we see that by 2015, international trade in manufacturing grew 314% ([exp(1.42) _−_ 1] _∗_ 100%) with respect to domestic trade since the year 1965. This growth is equivalent to a tariff reduction of 29.97% ([exp(1.42/(1 _−_ 5)) _−_ 1] _∗_ 100%), as shown in figure (2) (b), when taking a value for the elasticity of substitution for (1) from the literature, _σ_<sup>_k_</sup> = 5.<sup>8</sup> Note that the 316% growth of international trade with respect to domestic trade during 1965-2015 results from an average annual growth rate of 2.88% over 50 years or an annual equivalent tariff reduction of 0.71%.

During the same period, agriculture grew 172%, or had an equivalent tariff reduction of 22.15%. While services grew 63.3%, or had a tariff equivalent reduction of 11.5%. Note that services display a positive trend after the mid-1990s, but agriculture and manufacturing show a positive trend before the 1990, with the trend being very similar for the periods 1965-1990 and 1990-2015. These similar trends show that using the long WIOD data until 1990 and EORA after that year (as explained in the data section) allows for a smooth
and Bhatt (2015) and Azam (2016). Our sample includes not only the non-resident fathers but also other non-resident family members, and non-resident children of household heads in particular.

The data for Indonesia come from Indonesia Family Life Survey (IFLS) 2014 wave. IFLS's household roaster, nonresident parents module, and mother's marriage module allow us to construct father-children pairs whose education information is not subject to truncation bias. More details about the sample construction procedure, readers are referred to Ahsan, Emran, Shilpi (2021) and Mazumder et al. (2019).

The summary statistics for our main estimation samples are reported in Table 1. We rst report the average years of schooling for both father and children in our full sample born between 1950 and 1989 across three countries respectively. The average years of schooling for fathers is 4.24 in China, 3.63 in India, and 6.21 in Indonesia. The average years of schooling for children is 7.52 in China, 6.50 in India, and 9.52 in Indonesia. Therefore, Indonesia has the best education outcome for both fathers and children among the three countries while India has the lowest mean education for both generations.

We also report the summary statistics for our four main sub-samples: urban, rural, sons, and daughters in the following panels respectively. In each country, there is consistent ruralurban gap in education for both generations. Children in urban China and urban India have about 3 more years of schooling than children in rural areas, while the gap is smaller in Indonesia (2 years). All three countries exhibit dierent degree of gender gap in schooling among children: 1.3 years in China, 2.3 years in India, and 0.6 years in Indonesia. Gender gap in Indonesia is much smaller, consistent with a large literature showing that girls in Indonesia do not face any signicant disadvantages compared to the boys.

# (4) Evidence on Conditional Variance
|_−_23_._00|_−_1_._00|
|Husband works in garment industry (if married)|208|0_._66|0_._47|0_._00|1_._00|
|Woman’s share in household income|238|0_._48|0_._18|0_._00|1_._00|

Notes: Panel A uses data from the baseline survey of the promotion program, conducted in the factory. Because there is no baseline for the exposure analysis, Panel B uses data from the household surveys only from the non-exposed comparison group.

73
0.136|0.148|-2.597|-2.776|-2.719*|-2.377|-0.104|-0.115|0.033|0.102|-2.501***|-2.502***|-2.465***|-2.397**|
||(0.258)|(0.327)|(0.262)|(0.265)|(1.653)|(1.689)|(1.640)|(1.625)|(0.951)|(0.953)|(0.856)|(0.915)|(0.965)|(0.948)|(0.931)|(0.940)|
|Number of observations|262|262|262|262|223|223|223|221|264|264|264|264|261|261|261|258|
|r2_w|0.398|0.405|0.377|0.378|||||||||||||
|r2_b|0.008|0.001|0.008|0.008|||||||||||||
|r2_o|0.023|0.010|0.026|0.025|||||||||||||
|rho|0.850|0.884|0.833|0.835|||||||||||||

Notes: This table reports panel regressions with individual firm fixed effects for the El Salvador COV-ES data. Sector information used as the interaction terms is taken from the baseline ES. Columns 3 through 8 report coefficients from logistic regressions. Sampling weights from the
# **I. Introduction**

Consumption or income, valued at prevailing market prices, is the workhorse metric of human welfare in economic analysis; poverty is almost universally defined in these terms. In low- and middle-income countries, these measures of household resource availability are typically assessed through household surveys. The global diversity in survey approaches is vast, with little rigorous evidence concerning which particular approach, in conjunction with which context, yields the most accurate resource estimate. Many other key dimensions of welfare, such as nutrition intake and hunger, are also widely assessed through household consumption surveys (Fiedler et al. 2008). While levels of hunger and nutrition covary with household resource availability, the role of resources relative to other driving forces is debated (Deaton 1997). The evidence cited in this debate has been influenced by the reliability of measures of food consumption and economic resources (Bouis, Haddad, and Kennedy 1992; Gibson and Kim 2013).

This paper focuses on the measurement of food consumption. It leverages a recent survey experiment to study the performance of commonly used consumption survey modules to shed light on the nature of reporting errors in consumption data. The experiment involved randomly allocating one of eight consumption survey modules to a nationally representative sample of Tanzanian households. An individual diary supervised on a daily basis has been taken as the benchmark, or gold standard, survey approach. This approach was adopted because of the scope of the resources and the care teams devoted to the survey (see below). The accuracy of the other modules is assessed with respect to this benchmark. Previous work associated with the same experiment has explored the relative performance of the eight modules in terms of mean consumption, inequality, poverty, and the prevalence of hunger (Beegle et al. 2012; De Weerdt et al. 2016; Gibson et al. 2015). These studies concentrate on total household-level consumption aggregates and do not consider variations in performance among individual items, as is done here. Moreover, variations in mean consumption by
broader public attitudes is limited (Munoz, 2021, p. 1). Using a similar, convenience sample approach a UNICEF U-report poll found less than a third of participants were willing to be vaccinated (UNICEF, 2021).

# **4. Methodology**

This section provides details about the data collection methodology and empirical analysis we conducted for the phone survey and online randomized survey experiment.

# <u>4.1 Phone survey data collection and analysis</u>

To collect information about levels of vaccine hesitancy and what factors could be contributing to it, a phone survey covering 2,533 households was in the field from May 26 to June 6, 2021. The most recent data available for mobile phone penetration estimates usage at 32 percent of the population (World Bank, 2021). The phone survey used geographic quotas based on the most recent tower that mobile phones had been connected through to ensure there was coverage across all provinces and districts in PNG. To address concerns that the population with access to mobile phones may be disproportionally wealthier, efforts were made to oversample phone users that do not send text messages and/or rely on others to transfer them credit to pay for their phone use. These attributes have been shown to be far more common among poorer phone users in PNG (Himelein and McPherson, 2021).

The responses provided by participants in the phone survey were weighted to match the characteristics of the 2016–18 Demographic and Health Survey (DHS) in PNG (National Statistical Office of PNG and ICF, 2019). The DHS is the most recent nationally representative data set including a measure of welfare and therefore this survey was used as the base for the reweighting. The mobile phone survey weights were designed to follow the distribution of the sample by province and urban/rural location. The weights were calibrated to the DHS distribution. This addressed issues with oversampling related to the number of calls but did not adjust for differences in the distribution of the wealth index or for differences
college varies substantially within and across the public and private sectors (Carranza and Ferreyra, 2019).

## **_Data sources and sample_**

Our sample draws from two datasets. The first one is the universe of high school students who took Saber 11 between 2000 and 2009. For these students, we observe test scores (which we normalize by semester) as well as a rich set of characteristics such as gender, ethnic origin, and parental income. Importantly, this data set records the location of the high school attended by the student. The second data set is administrative data from the Labor Observatory of Education on college graduates. For these individuals we observe hei, degree, and major. For those who work as employees in the formal sector between 2007 and 2013, we also observe wages and work location.

The combination of these two datasets yields a dataset of young college graduates ( _n_ = 729,726) for whom we observe hei, degree, major, Saber 11 standardized score, background characteristics, and importantly, location when finishing high school, attending college, and beginning to work. We first restrict the data to those who obtained a bachelor’s degree ( _n_ = 473,775). We further limit the sample to individuals who had graduated from college by 2012 (since the last year we observe a wage is 2013), were aged between 20 and 30 at college graduation, took the Saber 11 test between the ages of 14 and 24, had at least 4 years between taking Saber 11 and completing 4A department is similar to a us state and is a relatively autonomous country subdivision governed by a Governor. Bogota is the Capital District and not one of the 32 departments.

4
# **3. SDG gender indicator availability and country statistical performance**

Next we assess how a country’s overall statistical performance relates to the availability of gender data, and identify countries that may have strong systems overall but are underperforming on gender statistics. To do so, we compare the availability of gender statistics to scores on the World Bank’s Statistical Performance Indicators (SPI) (Dang et al 2023).

The World Bank’s Statistical Performance Indicators (SPI) measure statistical performance for 174 countries covering over 99% of the world population. The indicators are grouped into five pillars: (1) data use, which captures the demand side of the statistical system; (2) data services, which looks at the interaction between data supply and demand such as the openness of data and quality of data releases; (3) data products, which reviews whether countries report on global indicators;<sup>8</sup> (4) data sources, which assesses whether censuses, surveys, and other data sources are created; and (5) data infrastructure, which captures whether foundations such as financing, skills, and governance needed for a strong statistical system are in place. Within each pillar is a set of dimensions, and under each dimension is a set of indicators to measure performance. The indicators provide a time series extending at least from 2016 to 2020 in all cases, with some indicators going back to 2004.<sup>9</sup> The indicators are summarized as an index, termed the SPI overall score, with scores ranging from a low of 0 to a high of 100. We use the SPI data for 2019.

> 8 The data products pillar measures whether countries have recent SDG indicators across the 17 goals available in the UN Global SDG Indicators database.

> 9 The data for the indicators are from a variety of sources, including databases produced by the World Bank, International Monetary Fund (IMF), United Nations (UN),
Sector of household head employment||||X|
|Share of household members engaged in wage employment||X|||
|Share of household members engaged in informal (ganyu)<br>employment||X|||
|Copingstrategies index||X|||
|Mobilephone ownership|||X||
|Number of months household head was away from the<br>household||||X|

Following the bias adjustments, the weights were trimmed at the 2<sup>nd</sup> and 98<sup>th</sup> percentiles to prevent outlier weights that would result in a substantial increase in the variance of the weights. Lastly, the weights were post-stratified to match population totals in each country (following the same poststratification approach utilized in the LSMS-ISA weight calculation). For full details on the sampling and weight calculation for Ethiopia and Nigeria, see Ambel et al (2020) and McGee et al (2020), respectively. Full documentation as well as the microdata for these four surveys can be found on the World Bank’s Microdata Catalogue<sup>10</sup> or on the <u>LSMS website.</u>

> 10 Ethiopia HFPS: <u>https://microdata.worldbank.org/index.php/catalog/3716</u> Malawi HFPS: <u>https://microdata.worldbank.org/index.php/catalog/3766</u> Nigeria HFPS: <u>https://microdata.worldbank.org/index.php/catalog/3712</u> Uganda HFPS: <u>https://microdata.worldbank.org/index.php/catalog/3765</u>

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how students can improve (Teixeira et al. 2020).

A supportive school environment is important for boys’ learning, but support for learning in the home is also crucial. By the time students begin attending school, they have been growing, developing, and learning at home and in the community for several years already. The TIMSS data show that boys in Saudi Arabia tend to begin school with weaker early literacy and numeracy skills than girls. More than that, the models indicate that boys’ science achievement is more strongly associated with their early literacy and numeracy skills compared to girls’. Boys who begin school with weak early literacy and 32
Figure 2: Use of crop or livestock insurance is low <!-- Start of picture text --> Adults living ina household where growing crops or raising livestock is the main source of<br>household income (%), 2017<br>Congo, Dem. Rep. ——<br>pl Em nn<br>cea [TR<br>aw<br>aySTS<br>randall<br>zara. il<br>0% 10% 20% 30% 40% 50% 60%<br>@ Had crop or livestock insurance in the © Did not have croporlivestock insurance<br>past 5 years in the past 5 years<br><!-- End of picture text --> Source: Global Findex database. Note: The total length of the bar represents the share of adults living in a household where growing crops or raising livestock is the main source of income. They survey asked respondents if their household got crop or livestock insurance in the past five years.
to account for potential sample selection issues in our model and econometric methodology.

We use 2005 GDP measured in constant (2000) US Dollars from the World Development Indicators. As will become clear, each country’s GDP share is an important component of our analysis. We calculate each country’s share of world GDP by dividing its GDP by world GDP, where world GDP is the sum of the 116 countries in our sample.

The measure of bilateral distance that we use captures the internal distance in a country, accounts for the distance from a number of major cities and is constructed by CEPII.<sup>9</sup> Border data are also sourced from CEPII. Our control variables, including dummies for whether or not two countries share a common language,<sup>10</sup> a common colonizer or were once the same country, as well as a dummy for a landlocked country, are sourced from CEPII. Additional variables for identi...cation as ...xed export costs or instruments are taken from the World Bank Doing Business database. These data include the number of procedures needed to start a business, the number of days it takes to start a business and the cost of registering to start a new business in a country. We also considered data on the number of documents required to export or to import goods. Finally, we took the newly recoded data on religious similarity used by Helpman, Melitz & Rubinstein (2008) from Elhanan Helpman’s website.

> 7 The 2009 version of this paper used the full LPI. That paper also restricted the sample to developing countries that are not islands or are not classi...ed as having neighbouring countries su¢ ciently close to their major cities.

> 8 Further discussion of why we drop rich exporters is postponed until Section A.3.

> 9 The distance measure used is distw and is described at http://www.cepii.fr/anglaisgraph/bdd/distances.htm
gives 290 hits of academic articles that have been prepared using this data set (checked on 23 Feb 2021). The inflow of research with new data strengthens the analytical capacity of the national statistical system and has huge marginal gains especially for lower income countries that are less likely to conduct household surveys.</mark><sup>2</sup> <mark>The second reason to focus on publicly releasing microdata is that by not doing so, the public use value of the data in research is foregone. This value can be significant. The cost of collecting the data is sunk (taxpayers have already paid for it) and the marginal cost of creating another copy of the data base is negligible. The benefits on the other hand can be substantial. I</mark> ncreased accessibility to data has been related to the MENA region’s chronic low-growth syndrome and Arezki et al. (2020) estimate that the region’s lack of data transparency has resulted in losses of income per person ranging between 7% and 14%.

The third reason to focus on the availability of microdata is because it demonstrates a credible commitment to transparency. Between 2005 and 2008 MENA, was the only region globally to

> 2 Dang et al. (2019) provide evidence that countries with higher incomes more frequently implement household surveys.

2
providing a good proxy for poverty. Moreover, given the enrollment numbers supplied by the ministry, the poor 65 years of age or older should be largely covered.

**Table 1: Estimates of the Target Population according to ENCV 2013**

|Total beneficiaries|828,738|
|---|---|
|_Age eligible_||
|Total|7,383,638|
|Sisben eligible|2,083,818|
|Poor|2,066,330|
|Indigent|699,141|
|_60 years of age or older_||
|Total|4,947,172|
|Sisben eligible|1,669,819|
|Poor|1,476,454|
|Indigent|525,100|
|_65 years of age or older_||
|Total|3,414,864|

> 3 See “Estadísticas por Tema: Pobreza y Condiciones de Vida” (database) (accessed August 4, 2015), National Administrative Department of Statistics, Bogotá, Colombia.

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<0.01, ** p<0.05, * p<0.1. ‘_i’ and ‘_j’ stand for, respectively, migrant inventor’s sending country and migrant inventor’s receiving country. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia, and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia, and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800.

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# Is Inequality Underestimated in Egypt? Evidence from House Prices

Roy van der Weide, Christoph Lakner and Elena Ianchovichina<sup>_∗_</sup> _JEL classification_ : D31.

_Keywords_ : inequality; top incomes; house prices; Egypt.

> _∗_ All authors are with the World Bank. Contact information: rvanderweide@worldbank.org, clakner@worldbank.org and eianchovichina@worldbank.org. This is a background paper for the report entitled “Inequality, Uprisings, and Conflict in the Arab World” led by the World Bank’s Chief Economist Office for the Middle East and North Africa region. The authors wish to thank Guoliang Feng and Youssouf Kiendrebeogo for excellent research assistance. We would like to thank Facundo Alvaredo, Francisco Ferreira, Nadine Ghobrial, Vladimir Hlasny, Aart Kraay, Peter Lanjouw, Branko Milanovic, Thomas Piketty, Martin Ravallion, Paolo Verme and participants of the World Bank workshop on the Arab Inequality Puzzle and the IARIW-CAPMAS Conference “Experiences and Challenges in Measuring Income, Wealth, Poverty and Inequality in the Middle East and North Africa” for useful comments. We are grateful to the UK Department for International Development for financial assistance through its Strategic Research Program.