rafmacalaba/data-use-mentions-extended huggingface dataset:
Bank 2006) and as low as 12 percent by 2010 (World Bank 2013),<sup>6,7</sup> far exceeding the optimistic scenario envisaged in the early 1990s. This impressive stretch of poverty reduction record led the government to re-evaluate the poverty line established in 1995 in order to reflect the evolving living standard in today’s Nepal. Even with the revised poverty line that increased in its real value by 35 percent from the previous line,<sup>8</sup> the poverty rate in 2010 was estimated at 25 percent (World Bank 2013). The pace of recent poverty reduction in Nepal has been so spectacular that one global poverty reduction benchmarking exercise puts Nepal in the 96th percentile of all available country episodes.<sup>9</sup> Many non-income dimensions also exhibited significant improvements in terms of both levels and rankings, often at a pace faster than neighboring countries such as India and Bangladesh (Table 1 and Figure 1). Life expectancy at birth was 68 years as of 2011, an improvement by 18 years since 1985. Nepal’s infant mortality rate (per 1,000 live births) was much higher than India, Bangladesh or Sub-Saharan Africa until the early 1980s, but converged to the world average by the late 2000s. Access to improved sanitation facilities is still low at 40 percent and ranks 154<sup>th</sup> out of 195 countries in 2011, but a considerable improvement from 4.5 percent in 1990. Nepal’s primary completion rate was as low as Sub-Saharan Africa on average in the early 1990s, but it exceeds the world average in 2014. The adult literacy rate tripled from 21 percent in 1981 to 60 percent in 2011. The youth literacy rate (15 to 24 years old) increased by 55 percentage points, from 30 percent in 1981 to 85 percent in 2011. National Account indicators, in contrast, show much slower progress. The share of agricultural output in GDP declined to 38 percent
four sectors and divide each by the country’s total exports. Using these alternative measures by broad sector sheds light on which sectors are driving the aggregate cross-country results. Finally, both types of GVC participation measures are also available at the country-sector level for an analysis of cross-country cross-sector determinants of GVC participation.

In addition to studying the “intensity”of GVC participation, as measured by the shares in gross exports, we are also interested in the levels of GVC participation (i.e., the numerator of the “intensity”) and in the levels of gross exports. Comparing the factors that a¤ect GVC participation shares with their in‡uence on GVC participation levels and on export levels indicates which determinants matter beyond traditional exports.

The main data source for the GVC participation measures is the EORA database described in Lenzen et al. (2013), which covers 190 countries over the period 1990-2015 using a 26-sector harmonized classi...cation.<sup>7</sup> The decomposition of country-level GVC participation measures into agriculture, mining, manufacturing, and services also draws on the EORA database and our cross-country cross-sector analysis relies on GVC participation measures for eight manufacturing sub-sectors in that database, listed in the Appendix.<sup>8</sup> There are several limitations to using international input-output tables to measure GVC participation. A major limitation is related to the distorted statistics that international input-output tables are based on.<sup>9</sup> In particular for lower-income countries that often do not produce national supply-use tables, the EORA data are based on interpolations and estimations, and therefore subject to measurement errors. There is also a concern regarding the lack of ...rm-level heterogeneity in the conceptual measure of GVC participation which may also lead to errors.<sup>10</sup> All these measurement errors in the EORA GVC data may bias

> 7The number of countries in our estimating sample is at most 121 due to missing data on determinants and to the exclusion
Policy Research Working Paper 9902

# **Abstract**

This paper examines the short-term implications of the COVID-19 pandemic for inequality in developing countries. The analysis takes advantage of high-frequency phone survey data collected by the World Bank to assess the distributional impacts of the pandemic through the channels of job and income losses, food insecurity, and children’s education in the early days of the pandemic and subsequent period of economic recovery leading up to early 2021. It also introduces a methodology for estimating changes in income inequality due to the pandemic by combining data from phone surveys, pre-pandemic household surveys, and macroeconomic projections of sectoral growth rates. The paper finds that the pandemic had dis-equalizing impacts both across and within countries. Even under the assumption of distribution-neutral impacts within countries, the projected income losses are estimated to be higher in the bottom half of the global income distribution. Within countries, disadvantaged groups were more likely to have experienced work and income losses initially and are recovering more slowly. Inequality simulations suggest an increase in the Gini index for 29 of 34 countries in the sample, with an average increase of about 1 percent. Although these shortterm impacts on inequality appear to be small, they suggest that projections of global poverty and inequality impacts of COVID-19 under the assumption of distribution-neutral changes within countries are likely to underestimate actual impacts. Finally, the paper argues that the overall inequality impacts of COVID-19 could be larger over the mediumto-long term on account of a slow and uneven recovery in many developing countries, and disparities in learning losses during pandemic-related school closures, which will likely have long-lasting effects on inequality of opportunity and social mobility.

This paper is a product of the Poverty and Equity Global Practice and the Development Data Group, Development Economics. 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 authors may be
status group. To the extent that the latter group dominates the community (cf., Anderson, 2011), it may be able (and willing) to exclude the former group from accessing local public services. Thus, for low-caste households, sending a child, male or female, to a school dominated by higher castes is more costly than sending him or her to a caste-concordant school.

We examine these issues using a novel data-set that explicitly recognizes the geographic structure of villages and the social structure of their constituent settlements. Our evidence shows that, controlling for distance to school, the odds of ever enrolling are substantially lower for girls who would need to cross settlement boundaries to attend, an effect not present for boys and one that does not appear to be an artifact of school allocation decisions. Once we distinguish children by their caste, however, the crossing-boundary effect remains relevant only for highstatus girls; among low-castes, neither boys nor girls benefit from having a school located within their own settlement. This phenomenon can be attributed to the fact that many low-caste children reside in high-caste dominant settlements, where their families originally settled to provide services to the landowning class. In these settlements, low caste children are stigmatized and face high psychic costs of attending school. But, when the most convenient school happens to be in a low-caste dominant settlement, we find low-caste children are much more likely to enroll.

Experiments conducted in India by Hoff and Pandey (2006) suggest that castebased stigma plays a powerful and early role in human capital formation. Low-caste children perform poorly on cognitive tests when in the presence of their high-caste counterparts, but only if their caste status is made public; if caste is kept private, both groups do equally well. Munshi and Rosenzweig (2006), meanwhile, argue that low-caste households, who tend to have looser ties to ancestral kin networks, perceive a greater return to education (see also Luke and Munshi, 2007). Our findings imply that once the playing field is level—i.
estimated that 228 million cases of malaria occurred worldwide in 2018 and 405,000 people died from malaria (WHO 2019). Africa accounts for about 94 percent of global malaria deaths. Nigeria accounts for 24 percent, followed by 11 percent in the Democratic Republic of Congo, 5 percent in Tanzania and 4 percent in Mozambique.

From the methodological point of view, the paper also contributes to exploring the way of combining rich micro data in the health sector with emerging spatial data, which can be used
# **Introduction**

Despite substantial investments in infrastructure and teachers, many low-income countries still struggle with large class sizes in primary school, particularly in lower grades. The typical primary school in Sub-Saharan Africa has more than 30 pupils per class, but average sizes rise to above 50 in countries including the Democratic Republic of Congo, Tanzania and Chad (Bashir et al., 2018). In Malawi, the context for this study, class sizes are particularly severe with a typical school having more than 125 students to a class (ibid.). These poor conditions stem from the failure of national public education systems to keep up with rapid growth in school enrollments, the result of rapid population growth and rising enrollment rates over more than two decades since the introduction of free primary education by the majority of low-income countries in the 1990s (ibid.).

These poor conditions contribute to low rates of learning in lower grades. In Malawi, fewer than 25 percent of students in Grade 2 achieve minimum proficiency levels in reading according to the Early Grade Reading Assessment (EGRA) conducted by the United States Agency for International Development (USAID) (USAID, 2013). These poor levels of learning in early grades contribute to high rates of repetition, further exacerbating large class sizes, a case of ‘early grade bulge’ (Bashir et al., 2018). In the case of Malawi, repetition rates are typically above 25 percent in lower grades (Ministry of Education, 2022). Faced with these poor conditions, many students simply drop out. Fewer than three-quarters of remain in school until Grade 5 in a number of countries in Sub-Saharan Africa, including Uganda, Burkina Faso, and Mozambique (World Bank, b). In Malawi, around one in four students drop out prior to Grade 5 (Ministry of Education, 2022).

Large class sizes may prove detrimental to learning by reducing the level of attention received from teachers by each student, reducing students’ ability to ask and answer questions and
scaled. The evolving social contract also includes the responsible use and reuse of data for development purposes, built on trust that encourages individuals and firms to contribute that essential data. When used in an effective and trustworthy way, data and digital technologies effectively can be crucial enablers for improving service delivery, the accuracy of decisionmaking, fighting corruption, and increasing transparency.

There are important roles for governments, the private sector and civil society to enable data to create value. Governments play a key role in developing strategies and rules to govern data, with input from civil society and the private sector. Governments also play a key role in producing the data and statistics to inform about developmental progress. The private sector’s role as a producer and user of data is also vital to enable the region to compete within the global digital economy. For that to happen, bottlenecks must be removed to enable access to quality and freely available traditional data sources such as surveys and censuses with open licenses for reuse. Investments in the production of statistics are much needed. There needs to be strong commitment to traditional data (which the WDR 2021 terms “public intent data”) – the kind of data that is used for public good, that improves service delivery and that measures progress specifically for the most vulnerable. This kind of data holds governments accountable and has the potential to empower individuals and groups in fragile and conflict-affected settings where traditional micro data has been sorely lacking. Empowering civil society organizations and academia to research and advocate for the better use of data and the protection of data rights may increase trust in the data economy.

International institutions such as the World Bank can play an important role in developing a social contract around data. One way forward for the World Bank, especially with improving traditional sources of data, would be to create a Statistics Compact for the region.<sup>6</sup> The goal of the Statistics Compact is twofold: (i) close the microdata gap (data collection and sharing) and
coefficient at horizons, 1, 5, or 10 years.

**_Unrelated low-frequency trends in productivity and employment_** The second source of bias that could be present in advanced economies and EMDEs is the effects of unrelated longrun trends in productivity growth and employment Fernald (2007). In the US context, long-run trends in these variables are found to change the sign of the employment effect of technology shocks. Tests for the presence of structural breaks in unspecified time periods in the employment and productivity growth series suggests the presence of multiple breaks for all economies in the sample (Bai and Perron, 1998). The multitude of structural breaks could suggest the presence of low-frequency unrelated trends across both advanced economies and EMDEs. To ensure that these potentially unrelated trends are not driving the negative employment effect, an HP filter is applied to the log employment series, removing low-frequency trends a leaving only higher frequency movements in the employment data. The panel-estimation result continues to be a negative response of employment in both advanced economies and EMDEs (Figure 4). By construction, the removal of low-frequency variation in employment will reduce the size and persistence of the IRFs in both groups, with both IRFs now no longer significantly different from zero in the second year. The presence of low-frequency correlations between technology and employment cannot be ruled out as unrelated. However, the presence of a range of structural characteristics associated with the degree of persistence of the IRFs suggests that the relationship is unlikely to be spurious.

**_New Keynesian explanations of negative employment response to technology shocks_** The initial technology shock SVAR literature focused on determining whether the economy reacted to technology shocks consistent with an RBC or New Keynesian description of the economy. In the New Keynesian explanation, all factor inputs to production should contract initially following a technology shock, as prices are inflexible in the short run, restricting increases in production. And the same quantity of production could be achieved with fewer inputs. This framework predicts that both investment and employment or hours should fall in the short
# 2 Gender Dimensions in Fiscal Policy

Early studies investigating the differential impacts of fiscal policy across sex and gender<sup>5</sup> looked at the expenditure or tax sides of fiscal policy in isolation. For example, Selden and Wasylenko (1995), Demery (1996), Sahn and Younger (2000), and Pritchett and Filmer (1999) examined the incidence of education and health expenditures separately for men and women using a sample of 57 Demographic and Health Surveys (DHS) from 41 countries. Glick, Saha, and Younger (2004) developed a more systematic attempt to “engender” benefit incidence analysis, examining gender gaps in benefits from health, education, and infrastructure (water supply, electricity, and roads).<sup>6</sup> Using household survey data from Bulgaria, Ghana, Jamaica, Madagascar, Mauritania, Pakistan, Peru, Uganda, and Viet Nam, they calculated the share of benefits as the proportion of males/females in any given quintile that benefit from a particular public service in these sectors and documented large gender gaps in access to these services.

Grown and Valodia (2010) investigated the differential burden of taxes across genders via household types.<sup>7</sup> For example, households might have male, female, or joint income earners; or male or female adult majorities; or younger- or older-age dependents (or both). Tax incidence<sup>8</sup> is estimated and compared across the different household types and across the post-tax expenditure distribution. More recently, Komatsu and others (2021) used sociological, cultural, and institutional information regarding the role of women in agricultural production in Ethiopia to better illuminate why a statutorily _neutral_ agricultural income tax and rural land use fee nonetheless created larger economic burdens for households distinguished by having female agricultural landowners.

Greenspun (2019) used the Grown and Valodia (2010) household categories but brought both the expenditure and tax sides together within the CEQ assessment FIA framework
# 3. Data and methodology

## 3.1. A dedicated firm survey

This study presents evidence from a dedicated Tanzanian firm survey conducted with 837 businesses in Dar es Salaam and the provinces of Tanga and Dodoma. The survey explores the real costs of disasters on firms—both through direct damage to assets and operations, and the indirect costs of perpetuated economic inefficiencies and coping measures. The survey’s target population was all 58,959 firms in Dar es Salaam, Tanga, and Dodoma that were registered with the National Bureau of Statistics’ (NBS) in 2015. This excluded informal firms, which contribute significantly to the Tanzanian economy (Adams et al. 2013). Firms listed in the NBS registry without a contact telephone number were also excluded from the sampling frame. Hence, all survey results should be interpreted as representative of more formal business activities.

To ensure that robust estimation results can be obtained for different subpopulations, the survey used a dedicated sampling strategy. Based on information from the NBS, all registered firms were divided into five distinct strata, depending on their reliance on transport systems. Ordered from low to high transport reliance, these strata contain firms from the following sectors:

1. Accommodation and food service activities, Construction

2. Communication and other services 3. Manufacturing, Mining, Water, Energy, Agriculture 4. Transportation and storage 5. Wholesale and retail trade, Repair of motor vehicles and motorcycles The sample selection was completed in one stage, with firms selected through a systematic random sampling method from each stratum. In terms of regional distribution, the sample contains 623 firms from Dar es Salaam, 101 from Dodoma, and 113 from Tanga. For a full overview of the total and sampled number of firms by region and strata, see Table A.1 (appendix A). The survey was implemented over 50 days between Tuesday 25<sup>th</sup> September and 13<sup>th</sup> November 2018. About 10 percent of listed
**Figure 6. Change in Exports Receipts (in percent)**

<!-- Start of picture text --> ‐14.8 ASS<br>‐30.7 Nigéria<br>‐2.2 Faible Revenu<br>‐9.9 Guinée Equatoriale<br>‐28.3 RDC<br>‐12.5 Cameroun<br>‐37.7 Botswana<br>‐50.0 ‐40.0 ‐30.0 ‐20.0 ‐10.0 0.0<br><!-- End of picture text --> Source: calculated using data from UNCTAD database.

10. **The collapse in export receipts dragged down GDP growth, hence underlying the vulnerabilities related to product concentration in DRC’s exports.** Economic growth rates plummeted as export incomes fell. For instance, for six consecutive years prior to the 2009 crisis, DRC’s economy grew by an average rate of 6 percent, while in 2009 growth dropped to 2.8 percent. Dependency on exports and, more importantly, high product concentration have been the main drivers of the negative growth impact of the collapse in the prices and volumes of exports (see Box 1).

# **Box 1: The Relation between Export Dynamics & GDP Growth – A Matter of Dependency or of Concentration** ?

Although export dependence increases an economy’s susceptibility to external shocks, the size of the loss in trade revenue depends on each country’s mix of exports and main trading partners (i.e., on its degree of export concentration). This, then, explains the difference in the size of loss that different countries and regions experience. It explains why East Asia, despite being the most export-dependent region but one with the most diversified export portfolio, lost the least amount in export revenues compared to regions like SSA that, despite being relatively less exposed, had an export basket that was highly concentrated
page 28 then standard allocation rules can be used as suggested in deFeranti (1983, pp. 31-33). However if joint costs are so large chat different allocation rules alter the choice between the fixed and mobile strategy, then the scope of the model must be expanded to include a vector of these other activities in the objecdLve function as veU as in the cost functions.

- (7] In fact h(e, v, x) is likely to be nonlinear both because the health impact of any given intervention typically diminishes with increased coverage or intensity and because a reduction in the morbidity or mortality from one disease typically influences the morbidity and mortality from other diseases. Barnum et al (1980, Chapters 2, 3) specify a programming model with a nonlinear objective function to capture these problems, though for lack of appropriate data they are forced to estimate its 221 parameters from the survey responses of 16 experts. Section V and its notes discuss a nonlinear version of h(e, v, x) in the present modeL

- (8] The functional form of equation (2) is that of tae constant elasticity of substitution production function with the sign of its exponent, and thus its curvature, reversed. The elasticity of product transformation (or elasticity of complementarity) is given by o - 1/(3 - 1) and can be interpreted as the percentage increase in the optimal ratio of vaccinations to encounters (v/e) resulting from a one percent increase in the ratio of the effectiveness of vaccinations to that of encounters (b/a). By assumption the cost function is separable in prices and output.

- L9] Under this hypothesis:

Thus for given p, Af(j), A (2), s and s , V is an ifcreaFng function of C Under constant returns to scale A4j) - 2 Am(5) and V - C /Af(p).

- (10] For example, Walker and Gish (1977) found mobile