rafmacalaba/data-use-mentions-extended huggingface dataset:
3.4. The predictions again work well; true estimates lie within the estimated bounds. For example, the model that includes interaction terms suggests that between 11.09 and 21.78 percent of households escape poverty in Chile between the first and the second round of the survey, while the actual panel dataset suggests that actual upward mobility is somewhere between 16.87 and 22.29 percent. Similarly, true downward mobility lies between 1.79 and 4.11 percent in Chile, while the model with interactions predicts that between 2.25 and 5.13 entered poverty between first and second rounds of the survey.

As before, predicted power increases rapidly when moving from the first specification towards the fourth specification; bounds are also reduced considerably when increasing the number of variables. This is especially the case for the specifications that include interaction terms and retrospective variables. These results suggest that the use of the most complex model available is recommended.

As expected, the correlation of disturbance terms between the first and second rounds is always positive. The results also show that, as in the case of Lanjouw et al. (2011), this residual correlation declines when moving from the first toward the fourth specification; presumably because the additional explanatory variables are better able to capture the effect of shocks and fixed effects. For example, the model that includes regional controls and fixed effects reduces the residual correlation from 0.69 to 0.64 in Peru. Correlation is then slightly reduced to 0.62 when we include interaction terms and further reduced to 0.56 when we include retrospective variables.

12
The nature of the simulation in this section is different. We are pretending we are in 2045, so for instance _Aic_ should be the access to internet in 2020 of those adults in 2045 who were students during the pandemic.<sup>14</sup>

> 14 Since retrospective questions are not included in most household surveys at our disposal, we do not know the parental background of these adults. Anyway, it is not clear that retrospective questions would be very helpful. For instance, retrospective questions on access to internet in childhood would be senseless for adults in 2019 who were children in the late 1980s, when internet was unknown for most people.

17
Gurgel et al. (2007) use the MIT Emissions Prediction and Policy Analysis (EPPA) model to analyze an expansion of second-generation biofuel based on biomass production. The EPPA model is calibrated on 1997 data from GTAP and other sources and is rather aggregated. There is only one aggregate agricultural crop sector and many large countries are aggregated in regions. For example, Brazil and Argentina are part of the Central and South America region. Land is categorized in 5 types: crop land, pasture land, harvested forest land, natural grass land, and natural forest land. The crops sector and two biomass sectors compete for cropland. Pasture land is specific to the livestock sector, and so is harvested forest land for the forest sector. The EPPA model collapses the biomass production and biofuel transformation into a single sector. Two specifications are used for land allocation. Land moves across activities according to a CET transformation -land supply is price responsive- in a first specification, and under an alternative specification, non-agricultural land enters crop land according to an explicit cost of conversion into agricultural use. EPPA’s approach is not well suited to analyzing the food-bioenergy tradeoff because of its lack or explicit representation of agriculture, its focus on second- generation cellulosic biofuel, and the high level of aggregation.

# **_2.3 Land Module for Biofuel Modeling_**

# **_2.3.1 Biofuels, feedstock, and major biofuel producers_**

Ethanol and biodiesel are the two major commercial biofuel considered in our model. The major feedstocks used for ethanol are grains, sugar beet, sugarcane, and molasses. For biodiesel, the major feedstocks are vegetable oil from rapeseed, soybean, and palm oil. The major feedstock for ethanol in warmer countries is sugarcane, whereas grains are used in more temperate climates (corn, but also wheat, and barley), and to a much lesser extent sugar from sugar beet (Koizumi and Ohga (2007); Elobeid and Tokgoz (2006), FAPRI (2009))
descriptive statistics. Section 5 presents our results. Section 6 discusses our findings. Section 7 concludes.

# **2 Context**

The North-east of Nigeria, comprised of the six states of Adamawa, Bauchi, Borno, Gombe, Taraba and Yobe ( **Figure 1** ), has been afflicted by multiple crises including from the humanitarian and security perspectives.<sup>3</sup> Conflict and violence in the Lake Chad region (and the Sahel more generally) have had a negative impact on opportunities for development and further depleted scarce resources for livelihoods. It is a mostly rural setting, characterized by alarming levels of poverty and food insecurity (Baliki, Brück and Stojetz, 2018). An estimated 80% of the region’s total population of about 20 million rely on agriculture, fishery and livestock for their livelihoods, but the conditions for agriculture have worsened (World Bank, 2016). Among other issues, access to input factors for farming has deteriorated, including access to land and irrigation, and market prices have increased, including for transportation costs and input purchases.

Since August 2016, North-east Nigeria has been classified as a Level-3 emergency by the World Food Programme (WFP), with an estimated 9.2 million people being food insecure (WFP, 2021). For example, the situation forces many to take drastic measures such as reducing meals or portion sizes and having to buy most of their food from the market (World Bank, 2018). Violent conflict has disrupted social networks, social cohesion and value chains, and caused conflict-induced forced displacement, which puts already fragile displaced and host communities under additional stress. The broader country and region face systemic economic, social and political fragility, and volatile climate and weather conditions (World Bank, 2020; ACAPS, 2022), including recurring droughts (Okpara et al., 2016; Nagarajan et al., 2018). As a result, multiple, extreme forms of adversity have sharply increased the population’s
The role of gender is also of interest in this context. We know very little about interpersonal inequality in living standards, including between men and women, given that survey data on consumption are almost invariably collected at the household level. Since adult women are generally married, it is difficult to separate their own welfare from that of their husbands on the basis of household data. However, gender dimensions of inequality are considered important. Male control over land and its inheritance has long been a prominent gender issue in development studies.<sup>4</sup> Maternal education and work experience might also be expected to play a role. It has often been argued that maternal education has an important influence on children‘s health, nutritional status and schooling.<sup>5</sup> There might also be implications for adult welfare and economic activity, though there has been less research on this intergenerational linkage.

In an effort to yield some new knowledge on these issues, this paper takes advantage of an unusual dataset that measures consumption at a relatively disaggregated level within the household, so that we can build an ―individualized‖ consumption-based welfare measure. This allows us to distinguish inheritance and its economic welfare impacts by gender. The data were collected in Senegal—one of many developing countries where these issues have salience.

We document new evidence on the extent of inequality in living standards and its origins, paying special attention to the role of inheritance. Our results lead us to question the traditional model of a developing economy with imperfect credit markets in which privately-owned land is a marketable asset generating an income stream exclusively for the designated individual owner. But our results are easier to understand in the light of richer models of underdeveloped rural economies. We already know from the literature (in anthropology as well as economics) that market failures and non-market allocation processes play an important role in how land is used. As anthropologists have emphasized, agricultural land inheritance in much of Africa is typically filtered through customary land allocation processes involving kinship
semi-elasticity _α_ . We solve this equation to obtain: _α_ ˆ = 3 _._ 3 _×_ 10<sup>_−_5</sup> . To estimate crop-specific international trade costs ( _{βk}k_ ), we use FAO data on each crop’s producer price in the RoW ( _pRoW,k_ ), in USD/kg, and data on each crop’s “benchmark price” at the

> 114 The data is available at: https://ceypetco.gov.lk/historical-prices/.
# **4.1 Data**

**Observed deforestation for Brazil’s Legal Amazon** We use deforestation data from PRODES9 for the Legal Amazon between 1989 and 2021 for the annual model (Figure 4), and from DETER10 between May 2015 and January 2021 for the monthly model (see Appendix A). This serves as the outcome variable, _yt_ . We explore sub-annual and sub-national deforestation data using remotesensed data with ex-post attribution of deforestation drivers in subsequent research.

As discussed in Section 2.4, benchmarked KPIs are defined with respect to the benchmark outcome, which is an estimate from the benchmark model. The main ingredient of the approach in this _X_ . application is a set of exogenous factors From the literature, we can identify a set of possible factors that are both exogenous to the Brazilian economy as well as predictive of the deforestation dynamics. However, it is harder to know ex-ante exactly which factors should be included and in what form. We describe the candidate variables below.

**Real effective exchange rate (REER)** As the exchange rate determines a country’s competitiveness on the world stage, a weakening exchange rate will accelerate deforestation for wood production and the export of timber products, and/or advance the forest to land conversion for agricultural exports (Arcand et al., 2008; Hanusch, 2023; Richards, 2021).

**Global commodity prices.** Globally, Curtis et al. (2018) estimate that "27±5% of all forest disturbance between 2001 and 2015 was associated with commodity-driven deforestation". For our application, we use global market prices of beef, coffee, soy, corn, sugar, soy oil, hardwood logs, and iron ore in Brazilian real. In line with the previous point, higher commodity prices will drive Brazilian deforestation. However, the reverse does not hold, i.e. it is very unlikely that Brazilian forest loss will drive global commodity prices, esp. in the short-run (Arcand
certain items, the values are either simulated or imputed. Because the survey data is being used for the assessment of taxes and benefits, the annual amounts of tax revenues and social spending do not necessarily coincide with those found in other sources, in particular National Accounts.<sup>a</sup> We focus on the redistributive role of tax and benefit policies as one of the underlying forces behind the disparities in poverty and inequality rates across the FSU countries. Poverty is measured using the LowerMiddle-Income Class Poverty Line of the World Bank. Depending on the year in which a CEQ study for each country was conducted it is equal to either $2.5 or $3.2 per person per day at 2011 PPP values.<sup>2</sup> Inequality is measured using the Gini coefficient. The _distributive impact_ of a tax or a transfer is measured as the marginal reduction in inequality/poverty due to a tax or a transfer, i.e. the absolute change in Gini index/poverty headcount due to a removal of the transfer or the tax from household income (Reynolds and Smolensky 1977). Therefore, a positive value indicates that a tax or a transfer contributes to a decline in inequality/poverty, and otherwise. The extent of fiscal redistribution depends on the size of taxes and social spending (as share of the GDP) and on their progressivity.<sup>b</sup> _Progressivity_ refers to the degree to which tax burdens and benefit entitlements rise or fall with household income. The summary measure of progressivity is the Kakwani index (Kakwani 1977). The Kakwani index is equal to the difference between the Gini index for a tax/transfer, and the Gini index for incomes before application of the tax/transfer. Theoretically, the Kakwani index can vary between -1 to 1; the larger the index is, the more progressive is the expenditure or tax.

The data for comparative analysis comes from the CEQ analyses for separate countries produced by country teams
# **Mind the Gap**

## **Disparities in Assessments of Living Standards Using National Accounts and Household Surveys**

Espen Beer Prydz<sup>aϯ</sup> , Dean Jolliffe<sup>ab</sup> , Umar Serajuddin<sup>a</sup> _Keywords_ : National Accounts Systems, Household Income and Expenditure Surveys, Poverty, Inequality.

_JEL Codes_ : I3, I32, E31, F01.

_Acknowledgements:_ The authors appreciate the feedback from Daniel Mahler, Christoph Lakner, Marshall Reinsdorf and Xavier Mancero, as well as conference participants at the Special IARIW-World Bank Conference, Washington, DC November 7-8, 2019. The authors gratefully acknowledge financial support from the UK Government through the Knowledge for Change Program and the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Program. They also thank for financial support the Republic of Korea, acting through the Korea Development Institute School of Public Policy and Management (KDIS) and its Knowledge Partnership with the World Bank. The analysis in this paper would not be possible without data from national accounts and household surveys, and publicly available compilations by international agencies made available from the World Development Indicators database, PovcalNet and others. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of any of the funders, author affiliations, or the World Bank Group and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

ϯ Corresponding author. Email addresses: eprydz@worldbank.org (E. B. Prydz), userajuddin@worldbank.org (U. Serajuddin), djolliffe@worldbank.org (D. ~~Jolliffe)~~ a The World Bank, Development Economics Data Group (DECDG) b Other affiliations include, adjunct faculty at Johns Hopkins University School of Advanced International Studies (SAIS), Research Fellow with the Institute for the Study of Labor (IZA), and Fellow
that individuals who do worse in the labor market (for a given school attainment), are those individuals who received lower quality schooling (World Bank 2008). This is a difficult proposition to test without adequate information. However, there are several international student assessments that would allow us to look at the question of achievement and inequality. The assessments can be used to rank countries; but they can also be used to estimate equality in learning as well, such as the dispersion in learning outcomes. Is there a relationship between dispersion in learning outcomes and dispersion in returns?

There appears to be an association between dispersion in learning outcomes and dispersion in returns. Figure 11 shows dispersion in returns measured as the difference between the 90<sup>th</sup> and 10<sup>th</sup> quantile and dispersion in learning measured as the difference in 90<sup>th</sup> and 10<sup>th</sup> percentiles. There is an apparent association. The lower the dispersion in test scores, the lower the dispersion in returns. Despite the limited sample of countries, since we are limited to countries that participated in PISA and for which we have quantile return estimates, there is a clear link. In fact, all East Asian countries in our sample exhibit low dispersion in test scores and low dispersion in returns.

Korea is known as a country that excels in ensuring that all students learn. Efforts are made to equip students with the teachers and resources they need to succeed. At the middle school level, a national lottery is held to assign students to private schools. That is part of the Government’s high school equalization policy, in effect since 1973. Initially a response to growing demand for better schools and rampant private tutoring, under this policy the competitive entrance exams were substituted with random assignment of students for all secondary schools (both private and public). In addition, the government subsidizes private schools. Under the equalization policy, all schools
# **2 Data and context**

## **2.1 Data**

We use administrative data from the Rwanda Revenue Authority (RRA) to study the market for masks at the outset of the pandemic in Rwanda. Our study sample begins in November 2019 and extends through August 2020, six months after the first positive COVID-19 test in Rwanda. We leverage administrative data from EBM receipts, customs, and firm registration, and complement these with census data.

**EBM** Our primary source of data is the universe of digitally signed and timestamped software issued EBM receipts collected by the tax authority between November 2019 and August 2020, allowing us to track product-level sales between firms and final consumers. EBMs record receipts issued by businesses to support the collection of VAT in Rwanda, and is mandated for use by VAT taxpayers. In November 2019, these data recorded 81.8 billion Rwandan Francs (RwF) of value added, equivalent to 10.4% of monthly GDP or 16.9% of VAT declared turnover.

We leverage these receipts to construct firm-product data on prices and quantities at the transaction level. First, each EBM is identified by a unique Sales Data Controller (SDC) identifier, which we link to Taxpayer Identification Numbers (TIN) and, in turn, firm registration data to identify selling firms. When a receipt is issued for a sale to other VAT taxpayers, purchasing firms must provide their TIN. To construct the sub-District of both buyers and sellers we assume that non-VAT taxpayer final consumers are located in the same sub-District as the selling firm. For the majority of our analysis, we aggregate data to the buyer sub-District, which constitutes credible markets; the average sub-District has a population of 32,000 citizens and 5
effects hold across most continents, reassuring that a few countries and regions do not drive the average impacts. In particular, these findings confirm that our search terms and languages are relevant to all regions and hence can capture demand for the important sectors and services we are studying.

# **7. Concluding Remarks**

To uncover the impacts of the COVID-19 pandemic on various services, we assemble Google search data that capture the temporal and spatial evolution of consumer demand for a carefully selected list of services. These novel data provide a unique opportunity to estimate the impacts of the pandemic in contexts where other types of conventional data sources are not available. These data are used to predict human preferences, and responses to fast-shifting events and pandemics (Choi and Varian, 2009; Goel et al., 2010; Pavlicek and Kristoufek, 2015). We use fixed effects specifications to quantify the impact of the COVID-19 spread across countries. We exploit temporal variations in the spread of COVID-19, both confirmed cases and deaths, across 182 countries that are currently affected by the pandemic.

We find that demand for some services has significantly increased because of the pandemic, while some services experienced significant loss. Most importantly, we quantify the impact of the additional spread of the burden on winning and losing sectors economies. More specifically, we find that the spread of the pandemic has significantly reduced demand for services that require face-to-face interactions while boosting demand for services involving less in-person interactions. The size of the impacts is quite large. For instance, countries with at least 10 confirmed cases are likely to experience a 63-79 percent reduction in demand for hotel and restaurant services while enjoying a comparable increase in demand for ICT services. Most of these patterns are observed in all four continents (Africa, Americas, Asia, and Europe) in our sample. Our estimates suggest that the impacts are driven both by supply-side shocks due to social distancing and lockdown measures and demand-driven contractions (expansions), with the latter dominating in most of the services we study.
may be unwilling to respond to a follow-up. Therefore a listing stage was done which did not involve talking to the firm owner.

# **4.1. Listing Survey**

We started with a list of all 2,563 census blocks in Belo Horizonte. Using information from official government lists of registration, as well as a list of partially informal firms (firms who had acquired a license to display a sign outside the firm, but had not registered for a municipal or tax license according to the databases), we found that the number of formal and informal firms were reasonably highly correlated across blocks (0.67), reflecting that some census blocks are residential neighborhoods with few firms and others have more firms. Based on this, we dropped census blocks with below the median number of formal firms (11 formal firms) since these were likely to be mostly residential. This dropped 1,236 blocks. We then also dropped blocks in the top 5 percent of formal firm density, since high density blocks indicated high rise buildings with mainly formal firms and in which surveyors would not be able to enter without permission. We then stratified the remaining 1,260 blocks by sub-district, and randomly selected 600 census blocks to be listed, along with substitutes to be used in case some of the census blocks did not contain any informal firms.

The survey firm Gauss Estatística & Mercado was then hired by the Minas Gerais government through a public procurement process to undertake this listing survey. Listing consisted of enumerators visiting every firm operating out of a fixed building in the census block. It excluded individuals operating informally on the street since our interest was in larger informal firms, and excluded transportation firms since the rules for formalizing are different for them. Enumerators recorded basic information about the firm that could be observed without talking to the firm owner – the full street address, the business sector, the “fantasy name” of the firm (the name on a sign outside the firm if they had one),