rafmacalaba/data-use-mentions-extended huggingface dataset:
Note: The sample includes households from Indonesia, Mongolia, Myanmar, Philippines, and PNG that are not missing coping and income data. The reference period for food insecurity is the past 30 days for all countries except Indonesia, for which it is the past 7 days.

The final dimension that we analyze that may have long term implications relates to children’s access to education. School closures may have a real and detrimental effect on learning outcomes for children, particularly if prolonged over an extended period of time. In Indonesia, an estimated 62.5 million students from pre-primary to higher education have been affected by school closures that started in March 2020 and continued late into the year.<sup>28</sup> In the absence of online learning platforms or other tools that enable effective home learning, school closures have the potential to make a lasting impact on human capital accumulation for school-age children. Estimates suggest that students in the EAP stand to lose an average of about two-thirds of a year of learning-adjusted years of schooling, with loses twice as high in countries such as Cambodia, Lao PDR and Myanmar (World Bank 2021c). These losses may have the longer-term effect of negatively impacting the future earning potential of individuals, reducing educational and income mobility (Narayan et al. 2018, World Bank 2018).

Across most countries analyzed, children in poorer households were at greater risk of being left behind: Among households with children attending school pre-pandemic, those in the bottom quintile were on average 16 percentage points less likely than those in the top quintile to engage in face-to-face learning or utilize online, mobile, or video platforms that allow more interactive learning (Figure 11). In the Philippines, for instance, students from the richest quintiles were twice as likely to engage in these more interactive educational activities than those at in the bottom quintile (38 vis-à-vis 16 percent, respectively). Most of these mediums require access to technology, particularly the internet or a computer or mobile phone, presenting a greater challenge
as “ _PEP_ ”; (3) by foreign shareholders—either foreign government or private shareholders—the bank is classified as _foreign-_ owned; and (4) by the Kazakhstan government, the bank is classified as “ _state local_ .” Table 2 reports the total number of observations in the sample and the breakdown over time of the number of banks by ownership type.

A sufficiently important bank can be rescued because of the large ramifications within the banking system (Freixas and Rochet, 2013). Systemic importance is a multidimensional concept (Financial Stability Board et al., 2009) that is often proxied using bank size (Drehmann and Tarashev, 2011), computed as the natural logarithm of bank total assets ( _size_ ). We follow this practice in the literature. We also include a measure of severity of banking system troubles computed as the total number of banks in distress in each year to control for the potential presence of a Too-Many-To-Fail effect that affects the regulatory response (Acharya and Yorulmazer, 2007; Brown and Dinç, 2011).

We also include additional controls that may explain the likelihood and severity (cost) of resolution. To that effect, we employ the traditional _CAMEL_ framework.<sup>t</sup> Through _C_ , we account for _capital_ adequacy using the variable total regulatory capital to risk-weighted assets. Through _A_ , we control for _asset_ quality measured by the ratio of loan loss provisions to total gross loans. Through _M,_ we control for _management_ quality using the ratio of overhead costs to total income. Through _E_ , we control for _earning_ s (profitability) using interest margin divided by total assets. Through _L_ , we control for _liquidity_ which is captured through the ratio of liquid assets to deposits. Due to the lag structure of the empirical model, data for CAMEL variables are drawn from 2008 through 2020. Furthermore, we compute annual averages for the indicators for each bank using the monthly information. Finally, we include macroeconomic variables to account for the overall economic conditions of
information about job vacancies to registered job seekers and/or information about job seekers to firms, as well as organizing job fairs.

_Capital (Assets and finance)._ Using firm-level data from 107 countries, Aterido et al. (2007) show that improved access to finance has a significant and positive effect on firm growth, especially for small-size firms. Restricted access to capital markets limits productivity growth, and for several reasons these constraints are relatively high for smaller firms in developing countries (Kersten et al., 2017). _First_ , banks are relatively less willing to lend to small firms because of risks of default, as well as the costs of appraisal, monitoring and liquidizing assets in case of default. _Second_ , developing countries suffer from larger market failures relative to advanced economies, given their weak financial institutions. They may lack information about credit risks and/or operate within a weak legal framework for assets collateralization. Firm financial support includes direct interventions, such as grants, matching grants, subsidized loans, equity financing, vouchers and public procurement, as well as indirect instruments, such as fiscal incentives and loan guarantees. All these market-based instruments induce firms to undertake activities they would otherwise not do, such as technological upgrading, management improvement, market extension (Grover, Medvedev and Olafsen, 2019).

_Intermediate inputs._ A country’s competitive advantage in a given industry is also determined by the presence of networks of suppliers, buyers and competitors (Porter, 1990). For example, improved access to foreign intermediate inputs enhanced firm performance in China and India (Imbruno, 2015; Goldberg et al., 2010). Services policy reforms in India during the period 1993-2005 had a positive effect on manufacturing firms’ productivity (Arnold et al., 2014).<sup>5</sup> In developing countries, large firms, including multinationals, find that local suppliers have poor capabilities in producing high-quality intermediate inputs, due to lack of information, experience, technology, and human and
cially, these data provide dates associated with major decisions, including the date of offense and date of arrest. Combining these data with detailed daily temperature data allows us to measure the causal effect of heat on the probability of arrest for different types of crime.

Our approach offers several advances on the existing literature. Previous work on the impacts of heat on crime has been restricted by data limitations to focusing either on a wide geographic region with little detail on individual defendants or temporal resolution (for instance, Ranson (2014)), or to using detailed information for a smaller geography, generally a single city (for instance, Heilmann and Kahn (2019); Harries et al. (1984)).<sup>3</sup> Our data have the advantages of both of these approaches. We are able to cover the geographic scope of the second largest state in the United States, with 28 million residents living across an area that is larger than any country on the European continent aside from Russia. Despite this large geographic scope, we are not constrained to analyzing monthly or geographic aggregates of crime. Rather, we have individual-level crime data, including the address at which the defendant resided, on a daily scale. These two factors allow us to examine the impact of heat on crime across a variety of climates and where the entire study area is not subject to the same temperature shocks (as might be the case with data from an individual city).

The individual scale of our data also allows us to conduct richer analyses of adaptation than existing work. Much of existing work has relied on reported crime data (for instance, Ranson (2014); Heilmann and Kahn (2019)) and so uses information about the location of the crime (or report), but not the residence of the defendant.<sup>4</sup> This makes it difficult to link defendants to a specific built environment in order to examine how this built environment might mediate the impact of heat.
holds without phones are ineligible for benefits. After presenting these main results, we compile data from several existing targeting programs to give an indication of the substantial reduction in marginal costs associated with CDR-based targeting.

These results connect two distinct strands of prior work. The first is the literature on program targeting, which studies the effectiveness of different mechanisms for identifying program beneficiaries. In LMICs, research has focused on the performance of proxy means tests (PMTs) (Grosh & Baker, 1995; Filmer & Pritchett, 2001; Brown et al., 2018), community-based targeting strategies (CBTs) (Alatas et al., 2012; Fortin et al., 2018), and related approaches (Banerjee et al., 2007; Karlan & Thuysbaert, 2019; Premand & Schnitzer, 2020). A meta-analysis by Coady et al. (2004), which includes 8 PMTs and 14 communitybased programs, finds little difference in targeting accuracy between the two methods — but notes that targeting is regressive in a quarter of programs reviewed. In addition to issues with targeting accuracy, the current methods available for poverty targeting in LMICs are time- and resource-intensive, and may be infeasible in fragile or conflict-affected areas or in contexts where social interaction is limited, such as during a pandemic.

The second body of work explores the extent to which non-traditional sources of data, in conjunction with machine learning, might help address data gaps in LMICs (e.g., Blumenstock, 2016; Burke et al., 2021). Much of this work focuses on estimating the geographic distribution of poverty at fine spatial granularity, using data from satellites (Jean et al., 2016; Engstrom et al., 2017), mobile phones (Blumenstock et al., 2015; Hernandez et al., 2017), social media (Fatehkia et al., 2020; Sheehan et al., 2019), or some combination of these data sources (Steele et al., 2017; Pokhriyal & Jacques, 2017; Chi
# **1. Introduction**

Based on 2007 data, only 76 percent of primary school-aged children were enrolled in SubSaharan Africa, which is the lowest net enrollment rate of all developing country regions (United Nations 2010).<sup>1</sup> The estimated 31 million not enrolled African children represent over 45 percent of the world‟s total of out-of-school children. Over half of the countries in Sub-Saharan Africa do not provide legal guarantees for free primary school education. In settings without universal free primary education and where most children engage in child labor activities, as is true in many African countries,<sup>2</sup> understanding how parents decide how to allocate limited resources across siblings is important as these decisions have long-term impacts on each child‟s future earnings, health, and overall welfare. Schooling decisions depend on parent perceptions about the returns to school for a child, the opportunity cost of sending that child to school, the child‟s ability, and the presence and characteristics of the child‟s siblings. To raise levels of school enrollment, it is critical to understand these links between child ability, school enrollment, and child labor.

An extensive literature examines the source of inequalities for children‟s educational investments within a household building on seminal work by Becker and Tomes (1976) that highlights the tradeoff between child quantity and their „quality.‟ In making decisions about schooling and child labor, parents have information about their children‟s abilities that is often not available to researchers, a fact that partly explains why much of the empirical research on the

> 1 This compares with an average net enrollment rate of 89 percent in developing regions and 96 percent in developed regions. These enrollment rates in Africa still represent an enormous improvement since 1998 when African net enrollment rates were only 58 percent. In Burkina Faso, the focus of this paper, the net enrollment rate was 35 percent in 2000 and improved to 55 percent by 2007 (UN Statistics Division).
questions around time spent on domestic and care responsibilities were also included.

The target population of the Future of Business Survey are Facebook Page administrators that are identified as business owners and managers. There are over 80 million businesses estimated to be represented in this sampling frame (Facebook, 2018). A Facebook Page serves as a key interface for business activity on the Facebook platform and is most commonly used by small businesses to engage in advertising or generating content for Facebook audiences. The probability of the same business being resampled in any given wave has been calculated to be less than 0.06% among this population.

Facebook implements a modified simple random sample for survey invitations within each country. Surveys at Facebook are randomly sampled and once a respondent has been invited to a survey, they are ineligible for another survey for six months. This is to reduce response burden and maintain response quality. Sampling proceeds until country-level start quotas are met (quota for the monthly surveys were set to approximately 400 business owners and managers replying to the survey in most of the countries). Lower targets were set for countries where the population is not large enough to sustain 400 business owners and managers responding to the survey. Sampling weights are determined for survey non-response patterns using characteristics most closely associated with non-response to Facebook surveys.<sup>3</sup> Non-response weights are constructed using an inverse-probability weighting approach (IPW) and a logistic regression is used to predict the probability of response given eligibility for the survey (i.e. being in the target population). Initial weights are constructed using the inverse of the predicted probability of responding to the survey for every respondent and final weights are then created by trimming the initial weights the 1st and 99th percentiles for each country. The data are intended to be representative of the Facebook Page

> 1 The country level aggregated dataset from the Future of Business Survey is provided for free through a Creative Commons license and is available on the World Bank Data Catalog as well as on Humanitarian Data Exchange.
# **Outline of the Paper**

In this paper, I report on the efforts of my co-authors and I to incorporate foreign direct investment in business services with endogenous productivity effects. I show that our approach results in estimated gains from liberalization that are several orders of magnitude larger than in models without services and endogenous productivity effects. In order to understand the key features, I begin in section 2 with a stylized model where it is relatively straightforward to describe the model mathematically, understand clearly the FDI and endogenous productivity mechanisms that are key to the results and important differences we may obtain incorporating FDI in services with endogenous productivity effects in the model. In section 3, I describe the first full economy application of this model: the case of Russian accession to the World Trade Organization, where our model of Russia contains a single representative consumer. To capture the productivity effects of a more open trade regime, I also employ the Dixit-Stiglitz-Ethier mechanism in goods sectors subject to increasing returns to scale, but do not incorporate FDI in goods sectors. In section 4, I extend the model to a ―real household‖ model by incorporating all 50,000 plus households of the Russian Household Budget Survey as agents in the model, which retaining the modeling features introduced in the representative household model described in section 3. I conclude in section 5 with some extensions and other applications of these models and a discussion of how these models impacted policy discussions in Russia, Ukraine and Kazakhstan.

10
employed/individual businesses, and professionals is €2.36 billion, with an additional €1.36 billion for the social insurance payments.<sup>10</sup> Additional funds were allocated to benefits for unemployed workers. On March 20, a measure was introduced to extend payments of the regular unemployment benefit, the long-term unemployment benefit, and the unemployment allowance for the selfemployed by 2 months for those whose entitlement ended on March 31. The measure was then extended to cover those whose entitlement ended at the end of April and at the end of May. In addition, a lump sum stipend of €400 was introduced for 155,000 long-term unemployed individuals, registered with the public employment agency (Hellenic Manpower Employment Organization (OAED)) from April 1, 2019 who were maintaining their status until April 16, 2020 and were not receiving any other benefit from the state. The budget allocation for these measures related to unemployment benefits is about €300 million.

In terms of numbers affected and financial commitment, the government’s mitigation measures have emphasized the preservation of employment in enterprises where operations were suspended. A key condition of the benefits provided to affected businesses was that they were obliged to maintain the same headcount. In fact, layoffs in designated industries were temporarily prohibited from March 18 until the restriction was lifted on June 16.

# **4. Data and methods**

This section briefly presents the data sources, relevant indicators, and the methods that are used to assess labor market adjustments in Greece due to the COVID-19 pandemic and the relevant mitigation policies.

## **4.1. Labor market indicators**

We report monthly estimates from the Labor Force Survey (from ELSTAT) on the labor force participation rate, employment to population ratio, and unemployment rate for the periods January-April 2019 and January-April 2020 to see differences in trends before and during the lockdown.<sup>11</sup> We also use the LFS data to identify how other aspects of the
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Source: All statistics were compiled from United Nations Series D Commodity Trade Tapes. Appendix Table I provides a tabulation of prooucts in tne labor intensive group in terms of the SITC classification system. The "other lmports" group shown in this table Includes all other SITC products with the exception of items falling In SITC 3.
# 6. References

Acemoglu, D. and D. Autor, 2011. “Skills, Task and Technologies: Implications for Employment and Earning”. Handbook of Labor Economics. Elsevier, pp: 1043-1171 Apella, I. and G. Zunino, 2017. “Technological Change and The Labor Market in Argentina and Uruguay. A Task Content Analysis”. Policy Research Working Paper 8215, World Bank, Washington D.C.

Apella, I. 2016. “Employment-GDP Elasticity in Argentina During 2004-2014”. International Journal of Social Science Studies 4(3): 74-84 Brews, P and C. Tucci, 2004. “Exploring the structural effects of internetworking”, Strategic Management Journal 25(5): 429–451.

Hardy, W., R. Keister and P. Lewandowski, 2015. “Do Entrants Take It All? The Evolution of Task Content of Job in Poland”. IBS Working Paper 10, Poland.

International Labour Organization, 2016. Non-standard employment around the world: understanding challenges, shaping prospects. International Labour Office, Geneva.

International Labour Organization, 2015. Non-standard forms of employment. Report for discussion at the Meeting of Experts on Non-Standard Forms of Employment. International Labour Office, Geneva. February, 2015.

Kalleberg, A. L., 2009. “Precarious Work, Insecure Workers – Employment Relations in Transition”, American Sociological Review, 74 (1): 1-22.

Keister, R. and P. Lewandowski, 2016. “A Routine Transition? Causes and Consequences of the Changing Content of Jobs in Central and Eastern Europe”. _IBS Policy Paper 5/2016, Poland._ Kirkpatrick, I. and K. Hoque, 2006. “A Retreat from Personal Employment? Accounting for the Rise of Professional Agency Work in the UK Public Services”, Work, Employment and Society 20(4): 649-666 Kunda, G., S. Barley and J. Evans, 2002. “Why do Contractors Contracts? The Experience of Highly Skilled
compute the energy intensity in physical terms, the total energy consumed is divided by the sales of the firm if the total energy consumed is non-zero. The units of energy intensity in physical terms is BTU per US$.

# **A1.6 Treatment of Outliers**

We winsorize observations in the main sample by excluding the top and bottom 1 percentile on energy intensity (physical), energy intensity (expenditure), and output measures. We retain 19,521 observations, although not every firm reports energy expenditure, physical energy use, and output. The management and green economy survey modules were administered to a subset of the randomly-selected firms in the full sample. Constructing our data set thus required trading off its size against a requirement that all major variables be observed. We do not systematically drop firms with missing observations of key variables, but instead remain mindful that our regression samples differ in composition. For instance, in the winsorized sample, we observe energy intensity (physical) for 8,272 firms and energy intensity (value) for 14,713 firms. We observe at least six out of eight general management practices for 8,476 firms, and energy management practices for 6,178 firms. Firms that have non-missing values for all covariates are included in each regression. If we limit the sample to only firms that observe both measures of energy intensity plus output, the sign of the results remains mostly unchanged (not shown). However, firms that report both physical energy use and expenditures may differ systematically from those that report only one or the other. For instance, these firms may have better developed general management or energy management practices that support monitoring of input use. The composition of firms across countries and two-digit sectors before and after winsorization is similar.

# **A1.7 Descriptive Statistics**

The composition of firms across countries and two-digit sectors before and after winsorization is similar. Observations of services firms are generally missing capital stock and share of high-skilled labor. Using the World Bank