rafmacalaba/data-use-mentions-extended huggingface dataset:
The United Nations Office for Disaster Risk Reduction defines exposure as “a situation of people, infrastructure, housing, production capacities and other tangible human assets located in hazard-prone areas”<sup>47</sup> . To understand infrastructure network exposure to floods, the most widely used method in the literature is spatial overlay—a geospatial approach first proposed by Ian McHarg in his landmark book _Design with Nature_<sup>48</sup> . Many studies in the past used this method to understand flood exposure for different asset types at national and regional scales<sup>49</sup> . For example, Narayanan et al. leveraged the flood hazard layer provided by the Federal Emergency Management Agency (FEMA) to evaluate flood exposure across five infrastructure sectors including transportation in the contiguous United States<sup>50</sup> . Jongman et al. used a similar approach to measure long-term trends and changes in hazard exposure to river and coastal flooding at the global scale<sup>23</sup> . To achieve optimal outcomes with minimum errors in measurement, explicit and highresolution spatial data sets for infrastructure assets and well-established flood hazard zones or flood inundation maps are often required. In the past decades, increased computing power and precision of remote sensing data sets have led to the development of multiple high-resolution global flood models<sup>37,51</sup> . In addition, open-access road transportation network mapping and transportation data surveys (e.g., dualcondition General Transit Specification Survey) enable new research efforts that aim to understand direct transportation network exposure to floods across multiple temporal and spatial scales. For example, He et al. leveraged high-resolution flood maps for both pluvial and fluvial floods from Fathom and spatially overlaid on multimodal transportation network assets to evaluate flood exposure<sup>2</sup> .

## **Understanding vulnerability and resilience**

Early research on evaluating the vulnerability and resilience of transportation networks focuses on network topology<sup>52</sup>
Union.<sup>5</sup> First, two-way trade between Turkey and the European Union is effectively duty free. Second, Turkey has sequentially adopted many of the other free trade agreements that the EU has negotiated with third countries, thus also extending preferential tariff access to these trading partners. Combined, nearly 60 percent of Turkey’s overall exports are sent to countries with which it either has an FTA or customs union, here referred to jointly as PTAs. This implies that the trade policy indicators that take into account Turkey’s tariff preferences and that trade weight these tariffs will reveal Turkey as being even more open than the indicators of its MFN policies in isolation, given that so much of its trade is with PTA partners.

> 3 The World Bank (2010) leads with “Turkey has one of the most liberal trade regimes, based on its 1.5 percent MFN Tariff Trade Restrictiveness Index (TTRI). It ranks as the 5<sup>th</sup> least restrictive tariff regime out of a 125 country sample.” Togan (2010) provides an assessment of the WTO’s 2007 Trade Policy Review of Turkey.

> 4 These measures not only take into account elasticities, but the OTRI also considers some non-tariff measures in addition to tariffs.  For a methodological presentation of the construction of these measures, see Kee, Nicita and Olarreaga (2009).

> 5 Turkey has been in accession negotiations with the European Union since 2005.

6
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE figures for 2009 show that the total number of seats grew by about 50 percent compared to 2007 levels. In particular, the international seats more than doubled between 2007 and 2009 (figure 8a). Connectivity is recovering, evident in the increase in city pairs being served within Africa. Between 2001 and 2007, the city pairs served dropped from 11 to 3. Preliminary numbers show that in 2009 city pairs went up to 6 (figure 8b).

**Figure 8. Evolution of seats and city pairs in the Central African Republic**

<!-- Start of picture text --> a. Seats b. City pairs<br>160,000 12<br>140,000 10<br>120,000<br>8<br>100,000<br>80,000 6<br>60,000<br>4<br>40,000<br>20,000 2<br>- 0<br>2,001 2,004 2,007 2009 (Est) 2,001 2,004 2,007 2009 (Est)<br>Total International Intercontinental excluding flights between NA and SSA Total International Intercontinental<br>City Pairs<br>Number of seats<br><!-- End of picture text --> _Source:_ Bofinger 2009. Derived from the AICD national database (http://www.infrastructureafrica.org/aicd/tools/data). _Note:_ As reported to international reservation systems Carriers in the CAR are using modernized air fleets. Air France and Libya’s Air Afriquia, the only air carriers in the CAR, have modernized 100 percent of their fleets, and as of today, the CAR is served by the newest fleet in the region (table 5).

**Table 5. Air transport indicators, benchmarked against select other countries**

|Country|Central<br
Benes, Elisa, and Kieran Walsh. 2018c. “Measuring Working time and Time-related Underemployment in Labour Force Surveys: Main findings from the ILO LFS pilot studies.” International Labour Organization.

Buehren, Niklas, Markus Goldstein, Ezequiel Molina and Julia Vaillant. 2017. “The Impact of Strengthening Agricultural Extension Services: Evidence from Ethiopia.” World Bank Policy Research Working Paper 8169.

Buvinic, Mayra, and Elizabeth M. King. 2018. “Invisible No More? A Methodology and Policy Review of How Time Use Surveys Measure Unpaid Work.” Data2X Report.

Buvinic, Mayra, and Rebecca Furst-Nichols. 2014. “Promoting Women's Economic Empowerment: What Works?” _World Bank Research Observer_ 31(1).

Calderon, Gabriela, Jesse M. Cunha, and Giacomo De Giorgi. 2013. “Business literacy and development: Evidence from a randomized controlled trial in rural Mexico.” Banco de Mexico Working Paper, No. 201324.

Campos, Francisco, Michael Frese, Markus Goldstein, Leonardo Iacovone, and Hillary C. Johnson. 2017. “Teaching personal initiative beats traditional training in boosting small business in West Africa.” _Science_ 357: 1287-1290.

Comblon, Virginie, and Anne-Sophie Robilliard. 2017. “Are female employment statistics more sensitive than male ones to survey design? Evidence from three West African countries.” DIAL Working Paper, DT/2015-22.

Data2X. 2017. _Women’s Work & Employment Partnership: Phase One Activities & Outcomes._ Technical Summary, October 2017.

Davis, Benjamin, Stefania Di Giuseppe, and Alberto Zezza. 2017. “Are African households (not) leaving agriculture? Patterns of households’ income sources in rural Sub-Saharan Africa.” _Food Policy_ 67: 153– 174.

Daum, Thomas. 2018. “Using a Smartphone App to Collect Time Use Data in Zambia.” Presentation made at the Workshop on Improving Data Collection on Women and Youth Employment, World Bank and IFAD, November 2018, Rome.

Deijl, Claudia, Agnes Andersson
as baseline characteristics and the 2018<sup>7</sup> data of WASSCE test outcomes as our main outcome variables in the analysis.

The third data source is a survey designed by the team, following the use of administrative data to build a control group. The survey collected data on students in grade 12 in both the program and control schools. The survey also gathered information on learning outcomes through written assessments in English and Math. The questions in the assessment were designed by teachers designated by MoBSE and based on the curriculum. We additionally gathered information on the socio-economic characteristics of students, teachers’ background, and school principal’s background. We also collected qualitative data to shed light on evaluation questions and topics that were not well-suited for quantitative measurement and analysis, using semi-structured interviews with program teachers, school headmasters and project beneficiaries. The survey allowed us to collect the unique identifier of students, assigned for the purpose of WASCEE 2018, and later used the identifiers to match the survey with students’ WASCEE performance. Additionally, we collected students' GABECE performance data to serve as the student level baseline performance. To allow for further matching at the student level, we oversampled two to three times as many students in the control groups compared to program students.

# 3.2. Identification

The identification and the causal interpretation of the findings rely on a combination of two main methods. First, the Ministry of Basic and Secondary Education used the following criteria to select the pilot schools: availability of electricity, availability of a sufficient number of science teachers, and the presence of a computer lab. As a result, a simple comparison of the program students and non-program students is ruled out. From Table 2, we see that PSI-PMI pilot schools are generally better off than the non-program schools but not across the board: the program pilot schools tend to be larger, with more enrolled students, but at a higher student-

> 7 The study team was able to obtain student-level WASSCE 2018 data for the 32
introducing the corresponding control

> 2 Unfortunately, data on other infrastructure types are too sparse for systematic empirical analysis.

> 3 The sample is reduced to 313 country-year observations, when controlling for disaster events.

> 4 The results are robust to using alternative dependent variables, including the LPI:Overall or LPI:Infrastructure indicators.

> 5 When introducing additional controls for disaster events, the overall fit as well as point and error estimates remain largely unaffected.

6
Extra3ijt refers to observations in which the origin is a NHI country inside and IPA but the origin is any country outside the IPA. Thus, β1

> 12 The gravity model has been widely employed to analyze many types of bilateral flows, including trade, investment, migration, and knowledge and innovation, among others. Recently, a small but growing literature has adapted the gravity equation to assess various determinants of bilateral patenting. See, for example, Picci (2010), Scherngell and Hu (2011) on bilateral patenting in international and intranational settings, or Montobbio and Sterzi (2013) for related work on a gravity model of international patent collaborations. Most closely related to our analysis is that of Coleman (2022), who conceptualizes a theoretical basis for a gravity equation of international patenting for the purpose of measuring the impacts on patenting of countries’ accession to international IP conventions, such as the Patent Cooperation Treaty (PCT) or the WTO TRIPS Agreement.

> 13 For purposes of identification in the gravity model, it is necessary to include this income-based breakdown to estimate impacts on both directional flows with non-members. See Beverelli et al. (2018) for discussion of this point in the trade-gravity context. We also estimate our specifications accounting for heterogeneity in countries’ R&D intensity (national rates of R&D spending over GDP) to divide countries into high-R&D and non-high-R&D categories. This approach yielded no qualitative differences from the baseline results. Because the R&D data are less complete than data for our other variables, we report the results only for the income-based regressions.

15
Policy Research Working Paper 9813

# **Abstract**

This paper investigates the relationship between the use of service inputs and integration in global value chains. Using macro and detailed firm-level data (for 1990–2017), the study documents the extent of India’s integration into global value chains. Older, larger, and more productive firms and firms with a higher leverage ratio are more likely to be deeply integrated into global value chains. Firms in the information technology services and electronics industry are more deeply integrated into global value chains, compared with textiles. Services are the engine for many global value chain industries as they help coordinate the different stages of production across geographical locations. The findings suggest that both the intensity of service usage as well as the composition or type of service used are important. Firms using service inputs, particularly complex services and information technology and information technology–enabling services intensively are typically more deeply integrated into global value chains.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice and the Macroeconomics, Trade and Investment 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 rmanghnani@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 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
to the results of the Built-Settlement Growth Model (BSGM) developed by Nieves et al. (2020). The WorldPop gridded population dataset was produced by disaggregating the projected subnational population totals into grid cells using machine learning techniques and a variety of geospatial layers derived from satellite imagery (Stevens et al., 2015).

## **3.1.2 Digitized Features Visible from the Ground**

The outline of enumeration areas (EAs) should be positioned in line with prominent visible ground features to facilitate ground-based data collection. Extensive digitized visible ground features are needed to ensure that the outline of the pre-EA meets the criteria during the automatic creation of the national sample frame in Armenia. Thus, extensive digital features, both natural and man-created, mostly from OpenStreetMap (OSM) are compiled. Panel (b) of **Figure 2** illustrates information obtained from the OSM data, including road networks, waterways, and railways. This data provides modifiable and updatable inputs to enable multiple iterations of EA generation. The figure depicts the input datasets where the two datasets have not been combined yet. The subsequent figures show how the input datasets have been used to divide the country with their estimated total population.

10
for policy research and analysis in developing countries. Since then more than twenty countries have administered nationally-representative household surveys based on the LSMS model of questionnaire design and quality control. The multi-country data set used in this analysis is cornposed 7In this paper, we reserve the term "access" to refer to a household's ability to obtain an infrastructure connection, should the household decide to do so. For example, a household has access to sewer service if there is a sewer network in the household's neighborhood.

3
# **2. Paradata from National Surveys in Cambodia, Ethiopia, and Tanzania**

# **2.1 Overview**

This paper uses the paradata from three nationally representative surveys supported by the LSMS+ program: the Cambodia Living Standards Measurement Study – Plus (LSMS+) Survey 2019/20, the Ethiopia Socioeconomic Survey (ESS) 2018/19, and the Tanzania National Panel Survey 2019/20. The country surveys were implemented by their respective NSOs. Each survey included a multi-topic household questionnaire, as well as a cross-country comparable individual questionnaire that aimed to collect self-reported data on adult household members’ work and employment, and ownership of and rights to physical and financial assets, among other topics.<sup>4</sup> The questionnaire structure, wording, and approach to implementing the individual-level survey modules was the same across countries. Table 1 reports general descriptions of each survey and the type of modules included. A more thorough list and short descriptions of topics covered in the household and individual questionnaires are available in the Appendix Table A1. Appendix Table A2 provides further information on the characteristics of respondents for each survey.

> 4 Each survey was supported by the World Bank Living Standards Measurement Study – Plus (LSMS+) project, which was established in 2016 to improve the availability and quality of individual-disaggregated survey data on key dimensions of men’s and women’s economic opportunities and welfare. For more information, please visit <u>www.worldbank.org/lsmsplus.</u>

6
<!-- Start of picture text --> Figure 1c: Tanzania<br>Firm Count & Employment by Firm Size<br>Number of Firms Share of Firms<br>2008 422 2008 0.62<br>10-49 Workers 2013 682 10-49 Workers 2013 0.68<br>50-199 Workers 20082013 163 212 50-199 Workers 20082013 0.210.24<br>200-499 Workers 20082013 5467 200-499 Workers 20082013 0.070.08<br>> 500 Workers 20082013 3841 > 500 Workers 20082013 0.040.06<br>Source: Tanzania Annual Survey of Industrial Production, 2008 & Census<br>Source: Tanzania Annual Survey of Industrial Production, 2008 of Industrial Establishments, 2013. Shares may not sum to one due to<br>& Census of Industrial Establishments, 2013. rounding errors.<br>Total Employment Employment Share<br>2008 8,477 2008 0.08<br>10-49 Workers 2013 13,863 10-49 Workers 2013 0.13<br>50-199 Workers 20082013 15,94621,110 50-199 Workers 20082013 0.150.20<br>200-499 Workers 20082013 17,32221,386 200-499 Workers 20082013 0.160.20<br>> 500 Workers 20082013 51,374 65,643 > 500 Workers 20082013 0.48 0.61<br>Source: Tanzania Annual Survey of Industrial Production, 2008 & Census<br>Source: Tanzania Annual Survey of Industrial Production, 2008 of Industrial Establishments, 2013. Shares may not sum to one due to<br>& Census of Industrial Establishments, 2013. rounding errors.<br><!-- End of picture text --> 37
between those who argue that teachers are paid too little and those who argue that teachers are paid too much (Das, 2017). In several countries in our sample, teachers earn less than comparable workers in terms of their monthly earnings, but they earn more than others per hour. In several countries, the earnings gap between teachers on fixed term and permanent contracts is large. Teachers are much more likely to take a second job than other

> 35 The macroeconomic indicators and labor market indicators are drawn from the World Development Indicators database (World Bank, 2019c). The level of teacher unionization is derived from the survey data used in this paper. A teacher is considered to be a union member if she answered yes to a survey question like “Do you belong to a union?”

> 36 Frandsen (2016) does find that increased unionization reduces teacher hours in the United States.

21
a) Working age population: 20-59 b) Working age populations: 15+ c) Working age population: 15-74 d) 1st quarter of 2003 Source: CEE countries and EU: Eurostat (Employment in Europe 2003) Bosnia and Herzegovina: World Bank (2002b) Croatia: Central Bureau of Statistics, Labour Force Survey 2002 (second half), Bank staff calculations. Serbia and Montenegro: Republic of Serbia, Republican Statistical Office, Labor Force Survey 2002 (October), Bank staff calculations Georgia, Kazakhstan and Kyrgyzstan: ILO LABORSTA Labour Statistics Database; Bank staff calculations. Russia: Goskomstat; Bank staff calculations.

Moldova: Department of Statistics and Sociology of the Republic of Moldova; Bank staff calculations Different labor force responses to the accelerated job destruction occurring in the wake of economic restructuring have given rise to different labor market patterns, which are summarized in Table 2.

**Table 2  Patterns of labor force adjustments**

|**Labor force participation**|**Unempl**<br>|**oyment**<br>|
|---|---|---|
||High|Low|
|High|_Limited job opportunities. But_<br>_workers look for jobs._<br>Georgia,<br>Lithuania<br>(late<br>1990s), Poland (late 1990s),<br>Serbia, Slovakia, Ukraine|_High employment. But are the_<br>_jobs sustainable?_<br>Czech R., Estonia, Lithuania<br>(2000s), Moldova, Romania,<br>Russia, Slovenia|
|Low|_Scarcity of jobs. Some workers_<br>_get discouraged while others_<br>_continue searching for jobs._<br>Bulgaria, BiH, Croatia, Latvia,<br>Macedonia,<br>Poland<br>(early<br>2000s)|_Limited_<br>_job_<br>_opportunities._<
referred to as a sub-area model. Finally, we could estimate poverty at the household level and then aggregate household-level poverty rates up to the municipality, using a household-level (or unitlevel) model.

These methods share some characteristics but also offer different advantages. One similarity across all three methods is that they combine survey data with auxiliary data. This is distinct from much of the recent literature on satellite estimation of poverty (see, for example, Blumenstock et al. (2015)), which tends to estimate poverty using a purely synthetic regression prediction. Small area 8
, extreme poverty has largely been increasing (Figure 2). We find large increases in extreme poverty for countries that already had high poverty rates. This could either be because these countries experienced larger shocks to average incomes, or because they have more people just above the international poverty line. India experienced the largest increases in the extreme poverty rate, which appears to be for both of these reasons. South Africa and Brazil, two countries that had substantial pandemic support (see World Bank 2022), report the largest declines in poverty.

15 This does not include the last group of countries which relies only on national accounts growth rates or the regional average. While the sectoral growth rates allow for some distributional effects, these are very limited.

16 Note that the change from 2019 to 2020 is equivalent to the net COVID-19 impact on within-country inequality because the country inequality is held fixed at the 2019 level for the counterfactual distribution. This does not apply to the global Gini which depends on both within- and between-country differences.

14
# Data Sources

We used district-level data for all 16 irrigated<sup>districts of Punjab, defined as those having at least</sup> 50% irrigated area. Together they account<sup>for over 90% of agricultural production in the Punjab.</sup> The district level data were then aggregated<sup>to the production system level for purposes of</sup> computing TFP. Production systems were<sup>defined based on: i) cropping intensity, and ii) the</sup> major summer (or kharif) crop during<sup>1980-81. Wheat is the dominant winter (or rabi) crop</sup> everywhere, accounting for over 80% of cropped<sup>area. Thus the districts of the province were</sup> divided into four commonly recognized systems<sup>based on the dominant summer (kharif) crop: 1)</sup> wheat-mixed summer crops (often maize<sup>or sugarcane), 2) wheat-cotton, 3) wheat-rice, and 4)</sup> wheat-mungbean (or wheat-fallow).<sup>9</sup> These systems represent quite different agronomic<sup>types</sup> from a cereal-legume or fallow system (more likely<sup>to be sustainable) to a continuous cereal</sup> cropping system (likely to be less sustainable-Hobbs and Morris, 1996). Livestock are important in all systems, especially cows and buffaloes<sup>for milk and meat.</sup> District level annual data were collected from<sup>secondary sources and the Statistical Division of</sup> the Federal Ministry of Food and Agriculture,<sup>at a high level of detail for the period 1966-94.</sup> The study includes 33 crops, 8 livestock products,<sup>six crop by-products, and 17 input categories.</sup> Inputs and outputs were valued at farmgate harvest<sup>prices, also collected