rafmacalaba/data-use-mentions-extended huggingface dataset:
consumption is independent across items and goods are acquired once a week ( _Ni_<sup>_∗_= 1),thevarianceof</sup><sup>_Yd_mustbeatleasttwiceaslargeasthevarianceof</sup><sup>_Y∗_.</sup> Equation (2) may still be valid if survey participation affects the purchasing behavior of respondents, or with misreporting. For example, recorded cash outlays tend to be higher at the beginning of the diary period (in the first day in particular; see Lyberg and Kasprzyk, 2004, and Silberstein and Scott, 2004). If this pattern arises because of antedated expenditures that would normally take place later in the same week, equation (2) is still valid. Moreover, acquisition _Y_<sup>_d_</sup> is still an unbiased estimate of _Y_<sup>_∗_</sup> under more general forms of manipulation or misreporting (e.g., overspending on goods linked to social status or underreporting due to an embarrassment motive) that result in any zero-mean, possibly non-classical, disturbance term at the right hand side of _Y_<sup>_d_</sup> in (3).

# **3 Data, Descriptives and Graphical Analysis**

## **3.1 The Iraq Household and Socio-Economic Expenditure Survey**

Iraq is an upper middle income, resource-rich, fragile, and conflict-affected country facing development challenges in line with those of far poorer countries. The median education level in 2012 was primary schooling. While infant mortality rates remain below the norm for similar countries, a third of Iraqi children aged 0-5 are stunted, 58 percent of adult males of working age are employed, and only one in 8
than through-the-cycle (TTC) estimates.<sup>38</sup> The issue is also important because the effectiveness of the countercyclical capital buffer (such as the one devised under Basel III, which will be discussed in the next section) will depend on the degree of procyclicality of the minimum capital requirement, on top of which the buffer is built.

Under the standardized approach, minimum capital requirement relies on external ratings, which tend to be backward-looking and procyclical. The issue of external ratings is many-faceted, requiring policy changes on multiple fronts. One of the measures that have been proposed in the international debate, in particular at the Financial Stability Board (FSB), is to reduce or remove

> 36 For example, currently the U.S. Generally Accepted Accounting Principles (US GAAP) allows for more netting than IFRS, resulting in the same financial institution having larger balance sheet under IFRS than under US GAAP, which has implications for the calculation of leverage ratio if left unadjusted. This is expected to be resolved in the near future, as IASB and US FSAB are jointly developing a converged standard on balance sheet netting. 37 The results of 2010 Financial Stability Institute (FSI) Survey on the Implementation of the New Capital Adequacy Framework indicate that 112 out of 133 jurisdictions that responded to the survey have implemented or are planning to implement Basel II. See FSI (2010) for more details.

> 38 Using data for Spanish banks from 1987 to 2008, Repullo, Saurina, and Trucharte (2010), for example, show capital requirements calculated using point-in-time estimates of PDs move significantly along the business cycle, ranging from 7.6 percent to 11.9 percent per unit of loan from peak to trough.

21
to fall below 1.1 percent to register a contraction in per capita GDP given the population growth in 2019, but, of course, population growth is time variant with substantial changes from one decade to another.<sup>13</sup> The judgmental method is applied at the global level by looking at movements in several indicators of global activity—real GDP per capita, industrial production, trade, capital flows, oil consumption, and employment. This method also results in the same four dates as the years of global recessions: most of these indicators point to an obvious contraction in global economic activity in these years, after a peak in the preceding year. The behavior of the indicators during the global recessions is discussed below.

The turning points of the global business cycle identified using the quarterly data are consistent with those from the annual data series. The statistical approach identifies four global recessions in the quarterly series since 1960: 1974:1-1975:1, 1981:4-1982:4, 1990:41991:1, and 2008:3-2009:1 (Figure 6; Table 1). With the quarterly data, the average duration of global recessions was slightly less than one year. In addition to these four recession episodes, global per capita output contracted in 1970:4 (-0.7 percent), 1980:2 (- 4.8 percent), 1981:2 (-0.3 percent), 1998:1 (-0.2 percent), and 2001:3 (-0.5 percent).<sup>14</sup> These contractions lasted for only a quarter without translating into global recessions. However, some of these short-lived global contractions were associated with recessions in major economies that took place ahead of global recessions (1982) or coincided with global downturns (1998 and 2001), as discussed below.<sup>15</sup> Global downturns. In addition to the four global recessions, the global economy experienced low
have been excluded.<sup>2</sup> Most common access technology in Senegal is a solar panel; there are also very few car batteries and generators.

The Senegal data are random samples of households in villages that the Senegalo‐ German Energy Access Program PERACOD<sup>7</sup> selected for electrification through solar‐diesel village grids or Solar Home Systems (see Bensch et al. 2011). All the households were asked about their WTP for electricity services. A considerable share of households to be provided with grid electricity had already had a Solar Home System. The data in all countries provide detailed information about households’ socio‐economic situation and especially details on their electricity and energy sources, as well as expenditures, household appliances, and lighting patterns.

> 7 For more information on the program, see www.peracod.sn.

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# Dynamic, High-Resolution Wealth Measurement in Data-Scarce Environments

Zhuo Zheng,<sup>_a_</sup> Timothy Wu,<sup>_a_</sup> Richard Lee,<sup>_b,c_</sup> David Newhouse,<sup>_d_</sup> Talip Kilic,<sup>_d_</sup> Marshall Burke,<sup>_c,e_</sup> Stefano Ermon,<sup>_a_</sup> and David B. Lobell<sup>_b,c_</sup> _aDepartment of Computer Science, Stanford University, Stanford, 94305, CA, USA bDepartment of Earth System Science, Stanford University, Stanford, 94305, CA, USA_ _cCenter on Food Security and the Environment, Stanford University, Stanford, 94305, CA, USA dDevelopment Economics Data Group, World Bank Group, Washington DC, 20433, DC, USA eDepartment of Environmental Social Science, Stanford University, Stanford, 94305, CA, USA_

## A R T I C L E I N F O

_Keywords_ : Economic well-being High-resolution Poverty mapping Satellite image Deep learning _JEL codes: C45, I32_ Accurate, up-to-date, and highly resolved measurements of economic well-being are essential for monitoring and achieving international goals of poverty alleviation. These goals include the United Nations’ Sustainable Development Goal 1 of “No Poverty," which is nearing its original 2030 deadline, as well as countless other international and regional poverty targets. Granular estimates of household poverty and wealth are critical for understanding whether these goals are being met, as well as for targeting and evaluating anti-poverty interventions in regions where progress is lagging [6].

Official poverty measurement in low- and middle-income countries has long relied on household surveys, an indispensable but time-consuming tool for livelihood measurement. Given the technical capacity needed for reliable survey measurement and the substantial logistical difficulties in carrying out nationally or sub- nationally representative livelihood surveys, such surveys are often infrequently completed in much of the world, rendering com- prehensive and timely measures of
the estimate of β and σ� (see Rao, 2003 for more details).

When calculating the term φ� needed for the Fay-Herriot approach, there are several potential methods for considering the stratified and clustered two stage sample designs of the surveys used in this application. Perhaps the simplest approach is to use the Horvitz-Thompson (HT) estimator of P�, which can be expressed as: The term s� denotes the set of sampled units in domain _d_ . However, the HT estimator is often not preferred in SAE applications (National Research Council, 2013; Eurostat, 2013). It is commonly unstable, especially for small domains with few survey observations (a common challenge in this context). To address this, there are various alternative direct estimators that may out-perform the HT estimator.

Common practice in the World Bank has been to obtain sampling variances associated with the district-level poverty rates by taking the variance estimate from the survey data source and dividing it by the sample size for each domain to obtain a set of “smoothed” sampling variance estimates. This ignores components of the clustered sample design; however, these smoothed sampling variances are commonly less volatile than simple weighted direct HT estimates.

A final approach is to compute variance and the associated root mean square error of the mean using the linearized variance estimator approach—based on a first-order Taylor series (Wolter 2007). In sensitivity analyses this was the most stable variance measure of the three described here, and the preferred approach for this application. Annex G reports comparisons and analysis of sensitivity to the choice of domain variance measure. Final results are quite similar when comparing the second (“smoothed”) and third (“linearized”) options described, though as expected, the HT approach yields results that are more distant from those of the remaining two options. For more detail on the tradeoff between approaches for domain variance estimation, see Heeringa et. al., (2017); Molina and Rao (2010); Wolter (2007);
systematic evidence on these issues.

We first uncovered new stylized facts on the emergence and dissemination of green technologies over the last two decades using textual analysis of patents. We identified a set of business and technical-relevant keywords for technologies aimed at protecting the environment and making a positive impact to climate change mentioned in WIPO green patents data set over the period 2000-2021. The textual analysis of these data suggests that green innovation was primarily driven by new technologies from the construction, energy and transportation sectors, including technologies such as biofuels, geothermal energy, LED technology, drones and hybrid/electric vehicles.

We have then provided evidence on patterns of adoption and diffusion of these green technologies across firms and countries drawing on the textual analysis of ECTs. We followed a growing literature seeking to infer firm-level technology adoption using general-text classification methods for identifying business-relevant keywords associated with specific technologies; and by counting the number of times these words are mentioned in conference calls that public listed firms host with financial analysts. Using unique firm identifiers, we were able to merge these firm-level textual based indicators with information on inter-firm relationships and firm-level information for the period 2012-2021. These multi-country firm-level data cover buyer-supplier relationships, thereby allowing us to inspect for evidence of dissemination of green technologies across global value chains. They also contain information on innovation partnerships between firms.

The textual analysis of earnings conference calls transcripts reveals a growing importance of green technologies since 2019, as revealed by a rapid increase in the share of ECTs mentioning keywords associated with specific green technologies, as well as in the set of firms that have ECTs mentioning these green technologies. This increase is primarily driven by a few green technologies, including construction material, carbon capture technology, hybrid/electric vehicles, and wind turbines, which tend to be mentioned together in the same ECT. It is concentrated in firms operating in a few industries— especially manufacturing and utilities and among larger firms within industries. We also find evidence of an increase in the spatial spread of these green firms, with a growing presence in large
Box 1. Wasted Potential—Indonesian women have higher levels of human capital, but work less, earn less, and are promoted less Indonesia is currently failing to support the very segment of the population that has higher levels of human capital. While Indonesia’s male labor force participation rate is 83.2 percent, for females the figure is at a dismal 51 percent.<sup>22</sup> With only roughly half the female working population currently employed or seeking employment, this represents a significant loss of human capital potential for the country. Reasons for the low female participation rate in Indonesia include, among others, lack of childcare options and social norms. A World Bank study in 2017 found that Indonesian women in urban areas who live with elderly family members take up to two years after childbirth to return to work; for women without elderly family members, this period can be twice as long due to a lack of childcare options.<sup>23</sup> Once women leave the workforce for an extended period of time, they may also experience more difficulties in returning.

Even when women in Indonesia do work, they are paid less than their male counterparts. In Indonesia, the gender wage gap exists with male wages being 20 to 25 percent higher than female wages (World Bank, forthcoming). Additionally, data from Sakernas 2017 show that there is a lack of gender parity in wages in six of the top nine job industries in Indonesia. Estimation shows that Indonesia could add US$135 billion a year to annual GDP by 2025 in a best-in-region scenario with three drivers: i) higher female labor-force participation, ii) a higher share of women working full-time rather than part-time, and iii) more women working in higher productivity sectors such as manufacturing instead of agriculture (MGI, 2018).

**Figure 5. Gender Parity Index (GPI) for wages/salaries by main industries**

|Trade<br>Transportation<br>Finance<br>Services
1. With data on wages and employment, we can recover the relative productivity _δsg_ for each group of workers from the CES production function. In particular, we can solve the following system of equations up to scale for each location and sector:

2. With data on wages and the number of workers, we can recover the amenity/productivity distribution of the sectoral choices. In particular, we can recover the vector _Bisg_ up to scale by matching the sectoral shares in each location that we observe in the data with the model: 3. After recovering _δisg_ and _Bisg_ , with data on wages, employment, and capital, we can construct the labor supply and recover the productivity distribution _Ais_ up to scale by matching the labor supply with the labor demand. We can recover the productivity vector by matching the following system of equations: 4. Finally, with the employment data, wages, sectoral amenities, aggregate price indices, migration costs, and the return to capital, we can recover the amenity distribution of each location and group _Big_ by matching the population share we observe in the data with the ones implied by the model. We solve the following system of equations: After we recover these four vectors, we match the initial equilibrium from the model with the employment shares and wages that we observe in the data, and we can simulate the policy counterfactuals by comparing the equilibrium after the shock with the baseline equilibrium.

12
# **1. Background and Introduction**

Integrated water resources management (IWRM) and organizing it primarily at the river basin level are two of the most common and widely repeated recommendations in the water resources literature of the last decade if not longer (Allee 1988; Galloway 1997; McDonald and Kay 1988; World Bank 1993). Basin management is often associated with the concept of decentralization, of managing water resources at the “lowest appropriate level.” (See, e.g., International Conference on Water and the Environment 1992; Mody 2001.) Several conceptual arguments have been presented in favor of decentralization in water resource management, and basin-level management in particular, including that the whole array of resources and use patterns in the basin will be taken into account, management decisions will be based on better knowledge of local conditions, and incentives for stakeholders to actively participate in management will be stronger.

Empirical studies of river basin management systems provide opportunities to examine the claims made for basin-level integrated resources management, and to explore factors that appear to influence its implementation and outcomes. In this research project the project team has searched for those factors and their relationships to river basin management in two ways: with a survey of river basin organizations throughout the world, and with case studies of eight river basins analyzed in greater detail. Some of those eight cases have long histories of basin-scale institutions for water resource management, such as the Guadalquivir river basin in Spain and the Murray-Darling river basin in Australia. Others have emerged recently, as in the Warta river basin in Poland, where the Polish government established regional water management authorities over the past 15 years.

This case has been extremely valuable because the formation of the river basin authorities in Poland was a central government initiative that occurred in the fairly recent memory of many individuals who are still actively involved in water and government, and whose perspectives on the origin, growth, and recent difficulties of the basin management effort are both fresh and rich. The Warta basin therefore provided
food security crises, allocate resources efficiently, and implement timely interventions that address the compounded risks of climatic and economic factors. This ongoing monitoring is essential for managing the dynamic nature of food insecurity and improving resilience in vulnerable regions.

# 6.3 Limitations and Future Research

While this study provides insights into the impacts of climatic and economic shocks on food security, several limitations exist related to data availability, model specifications, and variable interactions.

The analysis relies on available datasets, limiting the granularity of certain indicators. Gaps in data, such as from FEWS NET, highlight the need for higher-frequency monitoring and underscore challenges in model validation when target data are missing. This introduces potential uncertainties about precision during critical periods and may bias training. For example, data gaps during conflicts or restricted access to regions may lead to underrepresentation of actual food insecurity, resulting in lower risk estimates.

Although the models capture joint effects of climatic and economic shocks, disentangling causal pathways remains challenging. The interactions between environmental conditions, market dynamics, and food security are complex, even with linear models. Lagged effects in climatic and 29
Policy Research Working Paper 10259

# **Abstract**

The shock of the COVID-19 pandemic affected the human capital formation of children and youths. As a consequence of this disruption, the pandemic is likely to imply permanent lower levels of human capital. This paper provides new evidence on the impact of COVID-19 and school closures on education in Latin America by exploiting harmonized microdata from a large set of national household surveys carried out in 2020, during the pandemic. In addition, the paper uses microsimulations to assess the potential effect of changes in human capital due to the COVID-19 crisis on future income distributions. The findings show that the pandemic is likely to have significant long-run consequences in terms of incomes and poverty if strong compensatory measures are not taken soon.

This paper is a product of the Poverty and Equity 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 gasparinilc@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# _Sample selection issues_

Although we have established that the country ranking that can be derived from Table 1 is ordinally equivalent to the pre-standardization ranking, the issue of PISA sample selection remains a potential problem. As noted in Section 2, coverage rates range from a low of 0.47 in Turkey, to 1.02 in Switzerland.<sup>11</sup> Selection would not be a problem if one were interested _exclusively_ in the performance of 15 year-olds that are in school, and within a reasonable range of their expected grade of attendance. But this is likely to be an excessively narrow prism through which to assess a country’s educational system and – even more so – to make international comparisons. Consider the example of two hypothetical “educational strategies”, illustrated by countries A and B, which have identical distributions of school and family characteristics, as well as of underlying ability in the population of 15 year-olds. Country A seeks to be inclusive, and allocates resources towards retaining as many students as possible in school, and towards promoting learning by those with the lowest demonstrated achievement. Country B, on the other hand, actively discourages enrollment by those with lower ability, and seeks to retain only the top half of performers in school by age 15. Looking only at the test scores for the samples of enrolled fifteen year-olds will naturally suggest that Country B has both a higher mean and a lower variance than country A, and thus a superior educational system altogether.

This is not to suggest, of course, that Brazil, Indonesia, Mexico, Turkey, or any of the other countries with low coverage rates in Table 1 actively pursue an exclusionary strategy like that of hypothetical country B. But dropping out and lagging behind are, nevertheless, extremely likely to be selective processes, in the sense that they are correlated with family and student characteristics that also affect test scores. If one is interested in comparing the educational achievement of the _population_ of fifteen year-olds across countries, therefore, the PISA samples