rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Classification of Households into Poverty Groups**

Households are classified into poverty groups in two ways: first using consumption expenditure and second using an asset-based wealth index.<sup>6</sup> Both consumption expenditure and the assertbased wealth index serve as proxies for the long-term economic status of households. The theoretical underpinning of using both variables as measures of long-term economic status of households has been established in the literature (Deaton and Muellbauer, 1980; Deaton 1997; Deaton and Zaidi, 1999; Filmer and Pritchett, 2001). Both measures are used for the classification of households into poverty groups in order to consider all the possible empirically proven ways (expenditure and asset ownership) by which the poverty status of households may be expressed.

The consumption expenditure variable represents aggregate annual expenditure on food and non-food products. The food expenditure component was constructed by adding up expenditure on all food items consumed by the household at home and away from home over the past seven days. The non-food expenditure component consists of expenditure on utilities such as kerosene and electricity, health, transport, communications, recreation, education, furnishing, personal care, durable assets and housing. A more elaborate description of the construction of the consumption expenditure variable can be found World Bank (2013). Using the official poverty and ultra-poverty lines of MKW 85,852 and MKW 53,262, respectively, households are classified into non-poor, poor and ultra-poor households.

Following Filmer and Pritchett (2001), the asset-based wealth index is measured as a linear index generated from indicators of household asset ownership and housing condition using principal-components analysis (PCA) to derive weights. PCA is a statistical technique used to

> 6 Consumption expenditure and asset-based wealth index are chosen over income for the poverty classification because both variables reflect smoothing and are easier to measure than income in rural settings.

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8 electricity. Countries with more imported natural gas in their energy mix before the crisis were more exposed to the natural gas price shocks that followed the Russian invasion of Ukraine because of supply disruptions to global gas markets. In comparison, countries with little gas consumption were less affected by supply decreases, as were countries able to rely on domestic gas production. This exposure measure is supported by the data in Figure 2 demonstrating the positive relationship between country-level gas import dependence in 2019 and the rise in electricity prices between 2019 and 2022.

Establishment-level pre-crisis energy intensity is constructed using data on firm-level energy consumption from the Carbon Disclosure Project (CDP) divided by revenue data from Factset in 2020.<sup>9</sup> Energy consumption is measured as fuel and purchased steam, heat, electricity, and cooling consumption in megawatt-hour (MWh) equivalents and revenue is measured in millions of 2020 US dollars. Establishments that belong to the same firm, but are located in different countries, have the same energy intensity in our data.

# **2.3 Firm-level data and matching of datasets**

We match establishment-level job vacancies data to firm-level data on energy consumption from CDP and balance sheet information from Factset (more details are provided in Supplementary Appendix Section A.3). Our final dataset is at the level of establishments defined as firm-countrysubnational region triplets, following Acemoglu et al. (2020).<sup>10</sup> CDP includes information on energy consumption during 2020 for 3,598 firms operating in the 16 countries in our sample. We use information available from CDP on each firm’s ISIN or CUSIP identifier code to match these firms to Factset. Factset is a private company that collates data on ‘firm-level entities’ from financial statements and other sources for publicly traded companies as well as data on entities’ supply chain relationships.<sup>11</sup> We use fuzzy matching methods to identify Factset entities in the Lightcast data on job open-

> 9Annual energy consumption is
the retail chain level, weighted by store revenue in the pre-period of 2006, excluding stores in the same state. We find that the estimated coefficient for state-level benefits per population is only reduced by about 20% and the sum of the two coefficients is similar to our previous estimates. This finding is consistent with our earlier discussion that a large amount of grocery stores belong to chains that price flexibly or chains that are located only in a few states, which implies that most grocery stores engage in regional pricing.

Furthermore, we conduct a series of additional robustness checks in Appendix Table N5 to show the estimated price response is robust to using log benefits with or without population controls; weighting by store revenue or county population; regressing with state-level observations; using county-level benefits; using the full sample from 2006 to 2015, which includes the recovery periods; removing SNAP disaster payments; including a set of government transfer policy controls at the state-year level; and using states with each of the two main methodologies in setting the SUA. Note that we can observe how states that use lagged CPI are setting the SUA by using CPI data, and we control for contemporaneous 19
Indonesia and China (compared with Jirasavetakul and Lakner, forthcoming).

> 27 Furthermore and in contrast to the results for Africa, the EAP Gini exceeds all of the within-country Gini indices in the region.

> 28 The right-hand panel zooms in at the very bottom of the Lorenz curve. From this graph it appears that there might be some crossing at the very bottom which we ignore here.

> 29 To be precise, the Lorenz dominance result applies to inequality measures that satisfy the Pigou-Dalton transfer axiom. An inequality measure satisfying this axiom would increase after a transfer from a poor to a rich person.

9
Policy Research Working Paper 8828

# **Abstract**

Electronic reverse auctions are the most used competitive method for procurement of goods and non-consulting services by the Federal Government of Brazil. These auctions are closed randomly, which perfectly satisfies fairness considerations but may be suboptimal from an efficiency perspective. There are concerns that tenders are closed too early and randomness favors bidders with algorithmic bidding software, leading to high prices. Hence, this paper investigates what would happen if the random closing rule was replaced by another rule. The paper uses the complete data set of completed electronic actions in 2015–17 comprising 112 million bids for 0.9 million items purchased. Exploiting the random closing rule, simple OLS models are run with a wide set of fixed effects as well as covariates capturing competition. The findings point at alternative strategies to optimize auction design: simple actions such as increasing the average and minimum length of the random phase can result in 2.8 and 0.6 percent price savings, respectively, or R$540 million and R$116 million per year; or more complex designs such as setting the length to the maximum for the random phase if there are 15 bidders or more can yield 2.6 percent or R$ 500 million a year in price savings, or doing the same if a large discount is placed within three minutes to closing can yield 1.1 percent lower prices or R$ 210 million a year in savings.

This paper is a product of the Governance Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aoliveira@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
as a basic change in the power relationships between central and local governments. While deconcentration is an important development our underlying assumption is the traditional one: "he who pays the piper calls the tune'.

_Health employmen._ covers employees of government hospitals and other govemment health institutions at all levels of government. It includes medical and paramedical staff (doctors, and nurses), laboratory technicians and workers in technical facilities. Wherever possible, administrative employees working in the health sector have been placed in Govemment administration and not in health employment.

_Education employment_ covers primary, secondary and university education employment. Where we were able to identify them, administrative employees either working in the Ministry of Education or in the school system were placed in Govemment administration.

Defense: includes all enlisted personnel (including conscripts) and professional military. Where we were able to identify them, administrative employees of the Ministry of Defense were considered to be part of Govemment administration. Since defense is not within the scope of this study, we will focus essentially on _Civilian Govemment_ Reference is occasionally made to _Consolidated Central Govemment -_ this definition, consistent with data originating from the IMF, corresponds to Govemment administration, education and health employment.

In the public sector, but outside of General Government, are _Public_ **_Enterprises,_** majority-owned, directly or indirectly, by the state. Employment in public enterprises is also not discussed in this study as it raises very different issues from government employment.

# Government Wages

Certain concepts require further explanation: Average Government wages: Represents the ratio between Central government's wage bill and the number of central govemment employees. It is important to note, in this instance, that the difference in functions that each individual country imparts onto its government complicates the calculation of this data. Issues of how to classify government employees, are relevan **t** o this discussion. Where a specific source is listed for this item, the authors have used already available figures, thereby considerably reducing any doubt as to
funded education projects. Unfortunately, this is not always the case. Consider, for example, households in rural areas who only have children attending primary school. In 4 rural communities in the sample, all such households reported that FONCODES had financed improvements to the local school, and in a further 107 all reported that FONCODES had not financed improvements. In 46 rural communities, however, there was no agreement, with different households providing different responses. These results, and similar ones for the FONCODES school breakfast program, suggest that households in the 1996 INEI survey do not always report program benefits accurately. Measurement error of this sort will bias the estimates of program incidence if it is correlated with income, so that richer (or poorer) households are more (or less) likely to report that they benefited from FONCODES.

A second problem arises if program participation has an impact on per capita income. Ideally, to estimate program incidence, one would rank households by the per capita income they would have had in the absence of the program. But this income is, by definition, unobservable. We believe that in the case at hand the impact of program participation on household income is not a major source of concern because the monetary value to a household of FONCODES investments in education should only be a small fraction of total household income. Throughout the analysis, we simply rank households by their observed per capita income." As before, we present nonparametric regressions for educational infrastructure and school breakfast programs.

Figure 5.7 presents a nonparametric univariate density estimate of the distribution of the log of per capita income. A very small fraction of the population in Peru lives on a (log of) per capita income of less than 6 soles or more than 10 soles per year. The graphs for the nonparametric regressions trim these households at the top and bottom 1% of the income distribution.

Figure 5.8 compares the household-level incidence of FONCODES investments in school infrastructure with those made by INFES, and the Parents' Committees
on manufacturing firms in a broad set of countries (mainly in Eastern Europe) found that the negative correlation between management practices and energy (expenditure) intensity was much higher in magnitude when energy prices are low (subsidized) compared to when they are high (Schweiger and Stepanov, 2019). The basic argument is that management’s effect on energy intensity depends on factor costs; better managed firms conserve in the face of high prices but intensify use to take advantage of lower prices.

Firms in developing countries, where the energy-management nexus has been rarely explored, may show a different relationship when compared to their counterparts in the developed world. _First_ , industry composition often varies with the level of development. Developing countries may possess inexpensive labor or natural resources, which may encourage relatively energy-intensive industrial production and discourage energy-saving investments. _Second_ , firms in developing countries face more heavily subsidized costs of fossil energy. Nine out of the top ten countries that accounted for 61% (US $272 billion) of the total fossil fuel subsidies globally in 2017 were developing or emerging markets (Taylor, 2020). _Third_ , firms in developing countries may prioritize growth over environmental protection, although they may also be more sensitive to input costs. Evidence from a randomized controlled trial in Shandong Province, China, suggests that firms rarely considered energy efficiency interventions with a payback period longer than one year (Karplus and Zhang, 2020). In Ryan (2018), an experiment that raised energy productivity of Indian manufacturing plants led to longer operating hours and greater demand for skilled labor and electricity, which given the country’s high reliance on coal generation likely increased environmental impact.

The purpose of this study is to develop a deeper understanding of the link between management practices (both generic and energy-centric) and energy intensity in a large and diverse sample of firms operating globally. We use the data collected in a recent wave of World Bank Enterprise Surveys (WBES), which provides a representative cross-section of firms in 31 countries. We observe complete
# 5. Conclusion

The need to expand the assistance and protection for conflict-affected populations beyond humanitarian aid cannot be overemphasized. The debate on how to expand government supports to IDPs has been ongoing for several years. Although certain frameworks are available in the literature, there is limited evidence of practical application of frameworks in a real-world setting. This paper adds to this ongoing debate and goes beyond framework development to a demonstrated application on this topic in Iraq.

This paper investigated a possible approach to bringing the humanitarian caseload into the government SP programs by exploiting data from a field test. As part of the CLC's recertification and enrollment process in the Northern conflict-affected governorates, a field survey asked the complete set of questions required to construct a full set of variables used in both the humanitarian and government cash transfer targeting models from the same households. It also collected information on perception and willingness to be referred to the government’s program. Just because a household is likely eligible does not necessarily mean it is willing to be referred for assistance from the government. A combination of likely eligibility and willingness will ultimately determine how many potential MoLSA beneficiaries could come from the humanitarian database.

Analyzing the field-test data, this paper reconfirmed the findings from the desk review that a significant number of households that qualify for the humanitarian MPCA assistance are also eligible for the government’s cash transfer program. Among households identified as <mark>extremely vulnerable by</mark> the SocioEconomic Vulnerability Assessment Tool (SEVAT) used by the MPCA since 2019, 40 percent of the beneficiaries would be eligible for the government’s program when the targeting is limited to those below the national poverty line (a medium-small program). The referral rate would increase to <mark>70 percent when the program expansion included everyone in bottom 35 percent nationally or within 1.36 times the poverty line (a large program). Under the same two expansion scenarios, 20 and 48 percent of the extremely vulnerable group identified by the old MPCA targeting tool, Legacy-VA,
delve more in depth into these issues of potential omitted variable biases, and expand the database to account for additional institutional variables at the city level. Similarly, in future research it will be desirable to include proxies for economic output or productivity at the city level (there is a limited database for city product per capita by the UN, which suitably expanded and updated could be used for instance. Simple plotgrams with these limited dataset does suggest that city product does improve with globalization, for instance (see Léautier, Mehta, and Saito, 2004).<sup>20</sup>

# **7.3 Policy Implications**

The analysis we have done has provided empirical evidence on many observations and statements made in the past by experts in the field, prior to the gathering and analysis of a data. From this perspective, a number of policy conclusions emerge.

First, our results are suggestive on the importance of focusing on country-wide governance, thereby tapping into economic benefits for the country at large but also providing a platform for cities to benefit from such environment-wide improvements. This suggests that reformist majors have an incentive to promote good governance at the national level (given the large externalities for the city). Further, International Financial Institutions (IFIs, such as the World Bank) and donors ought to continue to monitor and assess in some detail, how countries are faring with respect to governance at the national level. This makes sense not only from a country-wide performance standpoint, but also to point to ways of improving city-level performance.

Second, our framework and evidence also points to the fact that improving governance at the city level allows cities to translate global opportunity into local value for their citizens. This implies that reformist city leaders do have important local policy and institutional levers at their disposal: governance at the local level matters significantly for urban performance. Similarly, this finding suggests that donor agencies could focus more on supporting improvements in city level governance.

Finally, it is important to work with cities to
This paper investigates the effects of the 2006 UN sanctions against Iran to identify the impacts of sanctions on children’s education. For estimation of the sanctions effects on children’s education, the Iranian setting is well suited for two reasons. First, other factors that affect children’s education (e.g., political stability) arguably remain unchanged after the sanctions (Borszik (2016)). Second, there are rich data, Iranian Household Income and Expenditure Surveys (HIES), that roughly span the four decades from the 1980s to 2010s (before, during, and after the sanctions). These surveys collected detailed information on the children’s years of schooling and their family income and expenditures including spending on education.

On 23 December 2006, the UN Security Council passed Resolution 1737 and imposed economic sanctions after Iran declined to suspend its uranium enrichment program. The UN sanctions include trade and financial restrictions. Trade restrictions targeted specific firms and individuals including oil and gas production and shipping companies, nuclear research and production companies, and military and security services companies owned, controlled, or performing on behalf of the Islamic Revolutionary Guard Corps (IRGC) or Setad Ejraie Foundation. Overall, these sanctions mostly targeted investments in and export of oil and gas. Financial restrictions entail any transactions with the Central Bank of Iran, disconnecting Iranian banks from the SWIFT, and freezing assets of specific firms and individuals. As a consequence, crude oil exports declined to less than one million barrels per day and the growth rate sank to -6% in 2012. The targeted sanctions were associated with large, sudden reductions in households’ income and consumption. As Figure 1 shows, very shortly after the implementation of the sanctions, the average real income of Iranian households decreased and the decreasing trend lasted for seven years. During 2007-2013, households’ real income on average decreased by 35%, resulting in cutting off their spending on education by 43%. The reduction in education spending reflects both young children not attending school and
Figure 6 explores how the slope of the age-mortality gradient for each country varies with its level of income. More precisely, it shows the exponential rate of increase in mortality by age in percent per year starting at age 45 against 2019 GNI per capita. The left panel presents this plot for the 64 countries with official COVID-19 deaths data and the right displays the corresponding plot for the 33 countries with excess deaths estimates available for a consistent set of age-groups as follows: 45-54, 55-64, 65-74,75-84, 85+. Using all available data, we find that the increase in mortality risk with each additional year of age above 45 typically ranges (IQR) from 7.9 to 12.2 percent (median = 10.9) using excess deaths and 6.6 to 12.6 percent (median = 9.2) using official deaths.

Both plots show a positive association between income per capita and the age-mortality gradient, indicating that COVID-19 mortality increases more steeply for older age groups in richer countries. Using official death rate data, across countries the rate of increase of mortality risk per year of age rises by 0.14 percent for each US$1,000 of GNI. Using excess mortality data, it rises by 0.21 percent for each US$1,000 of GNI.

To verify that this observed association is not driven by the composition of both sets of countries/economies, Figure 7 repeats the same exercise with the common set of 25 countries/economies. The conclusions taken from Figures 6 and 7 are similar: COVID mortality is more concentrated among older individuals in richer countries. With the more limited set of countries the gradient is less steep and more consistent between the methods for estimating mortality. Using official death rate data, across countries the rate of increase of mortality risk per year of age rises by 0.12 percent for each US$1,000 of GNI. Using excess mortality data, it rises by 0.10 percent for each US$1