rafmacalaba/data-use-mentions-extended huggingface dataset:
gov/Weather/best_practices/ArterialImpactPaper.pdf

- Grogan, L., & Sadanand, A. (2013). Rural Electrification and Employment in Poor Countries: Evidence from Nicaragua. _World Development_ , _43_ , 252–265. https://doi.org/10.1016/j.worlddev.2012.09.002

Hensher, D., Shore, N., & Train, K. (2005). Households’ willingness to pay for water service attributes. _Environmental and Resource Economics_ . https://doi.org/10.1007/s10640-005-7686-7

- Holmgren, J., & Merkel, A. (2017). Much ado about nothing? --A meta-analysis of the relationship between infrastructure and economic growth. _Research in Transportation Economics_ , _63_ , 13–26. https://doi.org/10.1016/j.retrec.2017.05.001

- Hoxie, N. J., Davis, J. P., Vergeront, J. M., Nashold, R. D., & Blair, K. A. (1997). Cryptosporidiosisassociated mortality following a massive waterborne outbreak in Milwaukee, Wisconsin. _American Journal of Public Health_ , _87_ (12), 2032–2035. https://doi.org/10.2105/AJPH.87.12.2032

- Huang, L., Wang, Y., Liu, C., Wu, T., Chou, C., & Sung, F. (2011). Water outage increases the risk of gastroenteritis and eyes and skin diseases.

- INRIX Research. (2018). INRIX Global Traffic Scorecard. _Inrix Global Traffic Scorecard_ , (February), 44. Retrieved from https://media.bizj.us/view/img/10360454/inrix2016trafficscorecarden.pdf

- Jalan, J., & Ravallion, M. (2003). Does piped water reduce diarrhea for children in rural India? _Journal
compilation may open doors to GDP manipulation. Since local leaders are rewarded for economic growth, they have incentives to exaggerate local growth. First, because the local government has appointment authority over major personnel of the local Bureaus, the local leader’s “requests” for a higher GDP growth figure, albeit in violation of the Statistics Law, are unlikely to be rejected by the subordinate statistics bureau. In addition, it is stipulated that key data compiled by the local Bureaus, such as GDP data, should be approved by local government leaders before being submitted to the upper statistical bureau. Thus, the GDP calculation by local Bureaus is inevitably subject to administrative interference under the existing oversight structure. Second, as mentioned above, the NBS survey teams conduct sampling surveys only for enterprises _below_ the cutoff level in industry, wholesale and retail, and hotels and catering sectors, i.e., relatively small firms. Based on the available data from 11 provinces in 2010, the below-scale industrial firms only accounted

> 15 In this paper, we mainly focus on the provincial and prefectural governments and their statistical bureaus. 16 The cutoff was 5 million yuan for the revenue from principal business until 2010, and since then, it was changed to 20 million yuan.

8
to trace demand in the ICT sector in the pre- and COVID-19 periods. To measure demand for delivery services, we use the term "delivery," which can effectively capture demand for takeout and delivery of food, groceries, and other items.

# **2.3 Temporal Evolution of Demand for Services**

The Google search intensity data are expected to exhibit some form of seasonality. For instance, demand for transport and tourism services have seasonal patterns, with peaks during the summer and holiday months. To disentangle the effects of seasonality, we use three years of data (20182020) and compare search trends in the COVID-19 period (January–April 2020) with the same time window in the previous two years (2018 and 2019). Once seasonality is removed, structural breaks in Google search trends during the COVID-19 period would reflect the impacts of the pandemic.

Figures 1 and 2 present the global temporal evolution of demand for various services in the last five years, as captured by global Google search data. The search trends demonstrate an interesting mix of secular trend and seasonality in the pre-COVID-19 period. Figure 1 shows that demand for hotel services has remained stable, but with significant seasonal variation, whereas demand for delivery services has been steadily rising with little seasonality. The other services exhibit varying degrees of the two dimensions.

Since the first confirmed COVID-19 cases in China in late December 2019, demand for some services has dipped while demand for others has gone up. Demand for hotels and restaurants, transport and tourism has sharply dived to their lowest levels. On the other hand, demand for ICT and delivery services have peaked following the outbreak. The scale of the fall or rise in demand for these services is much greater during the pandemic than seasonal variations in demand. For instance, while demand for hotels and restaurants peaks in the summer months (July-August), air travel peaks around December-January. In the five pre-COVID-19 years, Google search queries for hotel and restaurant, transport, and tourism key terms dropped
4 MDL contained 4,539 datasets at the time of this research (December 2022), some of which were also cross-listed in the UNHCR MDL.

The UNHCR MDL contains micro-level datasets that are of concern to UNHCR’s mission and mandates. These datasets often – if not always – include a sample of refugees, asylum seekers, IDPs, or stateless people. The datasets come from censuses, registration and administrative exercises, and surveys. This MDL contained 546 datasets at the time of our mapping exercise.

One of the key advantages of using these databases is that they offer detailed metadata which help us understand the main characteristics of each dataset. The metadata contains a rich set of attributes pertaining to each micro-level dataset, including the country of data collection, the producer(s) of the dataset, brief description or abstract and thematic scope of a dataset, dates of data collection, unit of analysis (e.g., households, individuals), geographic coverage (e.g., national, regions, camps), data type (e.g., sample survey data, census, administrative), questionnaire modules, as well as sampling strategy. A complete list and explanation of each attribute in the metadata can be found in Annex B: Metadata.

In terms of geographical coverage, this study covers all low-income and middle-income countries with a significant presence of refugees, other Venezuelan refugees and migrants<sup>11</sup> in need of international protection, and/or IDPs. We apply 100,000 (in terms of the number of FDP as of 2021) as an inclusion threshold for this analysis. This results in the final list of 53 low-income and middle-income countries, which altogether account for 23 million refugees and 58 million IDPs.<sup>12</sup>

# 3. Methodology

The methodology we apply to analyze micro-level datasets in the UNHCR and WB MDLs follows a procedure that is systematic, replicable, and scalable (Figure 1). First, we
|ULC|OECD|Unit labor cost index (2005=100)|labor markets|2001-2011|AUS, AUT, BEL, CAN, CZE, DEU, DNK, ESP,<br>EST, FIN, FRA, GBR, GRC, HUN, IRL, ISR, ITA,<br>JPN, KOR, LTU, LUX, MEX, NLD, NOR, NZL,<br>POL, PRT, SVK, SVN, SWE & USA|
|---|---|---|---|---|---|
|Female labor|WDI|Share of females in the workforce|labor markets|1995-2011|Full except TWN|
|Education|Barro and Lee<br>(2010)|Expected Years of Schooling<br>(intrapolated)|education and<br>skills|1995-2010|Full|
|Secondary<br>degree|WDI|Share of workers with secondary<br>degree or higher in the workforce|education and<br>skills|1995-2011|Full except TWN, BRN, VNM & CHN|
|Educational<br>Quality|WEF|Survey-based index of educational<br>quality,1-7(best)|education and<br>skills|2006-2011|Full|
|On-the-job<br>training|WEF|Survey-based index of availability<br>and quality of on-the-job training,<br>1-7(best)|education and<br>skills|2006-2011|Full|
|Quality ISOs|ISO|Stock of reported ISO certifications<br>related to quality management per<br>capita|<br>quality and<br>innovation|1995-2011|Full|
|R&D|WDI|Research and development<br>expenditure(% of GDP)|quality and<br>innovation|1995-2011|Full except BRN, KHM & VNM|
|Technology<br>adoption|WEF|Index on technology adoption, 1-7<br>
However, when we include quality, we find that this share goes up to 20–44 percent. These results suggest that measuring human capital with quality substantially increases the role of human capital in explaining cross-country output per worker differences.

#$(&$%)(#$ (&"%) In Table III, we show results using the following decomposition: . We compare #$("$%)(#$ (""%) results to the recent literature, which varies from nearly all (Jones 2014) to potentially none (Caselli and Ciccone 2018). We find that when including our measure of quality, the share of human capital varies between 46 to 58 percent. Together with Table II, our results suggest human capital accounts for between a fifth to around half of cross-country income differences – a middle ground in a literature which ranges from zero to nearly all. These results are consistent with models of human capital capturing the role of educated entrepreneurs and more comprehensive measures of human capital including schooling, learning, and health (Gennaioli, La Porta, Lopez-de-Silanes, and Shleifer 2013; Campbell and Üngör 2020). In this development accounting exercise, our central contribution is not to provide a conclusive result, but rather to motivate the use of a direct measure of schooling quality and thus a better measure of human capital.

In Table IV we further find the average relationship between learning and income masks significant heterogeneity across countries. First, we find human capital explains between a tenth and a fifth of cross-country income differences among low-income countries but up to two-thirds among high-income countries. This suggests human capital plays a more central role as economies develop. Second, we find the income gradient is often as steep or steeper as the quantity to quality gradient, more than tripling the contribution of human capital. We find even steeper gradients by regions. For example, when measured by schooling, human capital accounts for 31 percent of cross-country income differences in Europe and just 5 percent in sub-Saharan Africa.
commuting costs considering only trips from work to home or viceversa between 6am to 10am, and between 5pm to 9pm. The second column reports the results to estimate trade costs using trips to retail shops, outlets, and restaurants. I restrict the sample to trips after 1pm.

# **C Data and Quantification Appendix**

## **C.1 Calibration of Speeds**

This section describes the calibration of speeds across different transportation modes. I use different sources of information. For the transportation network in Mexico City, I use data from the Government of the city.<sup>55</sup> For the network of roads, I use information from the New York University digital archive in which they report different types of roads for each census tract in the commuting zone of Mexico City. The different roads include: autopistas, calles, viaductos, etc. I calibrate an average speed for each one of the roads. With this information I compute commuting times across census tracts in Mexico City using the Network analysis toolkit from Arcmap

> 55 The data can be found here.

12
Identifying Urban Areas by Combining Human Judgment and Machine Learning: An Application to India Virgilio Galdo, Yue Li and Martin Rama<sup>1</sup>

**Key words:** urban area; urbanization rate; human judgment; Google images; crowd sourcing; population census; satellite imagery; machine learning

**JEL Classification:** O1, O18, R1

1 Virgilio Galdo and Yue Li are with the Office of the Chief Economist for the South Asia Region and Martin Rama is with the Office of the Chief Economist for the Latin America and Caribbean Region at the World Bank. The corresponding author is Yue Li. Her contact information is yli7@worldbank.org.

This research was funded by the World Bank and by the Department for International Development of U.K. as part of the Sustainable Urban Development Multi-Donor Trust Fund. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Skillful research analysis was provided by Maria Florencia Pinto, Sutirtha Sinha Roy, and Wenqing Zhang. The authors thank Gilles Duranton, Hans Timmer, two anonymous reviewers and participants at the 8th European Meeting of the Urban Economics Association for very helpful comments and suggestions. The authors appreciate operational support provided by Ryan Engstrom and Richard Hinton from the Center for Urban and Environmental Research at George Washington University, and Charles Fox and Keith Garrett from the Geospatial Operational Support Team at the World Bank.
Special Supplement|
|Sierra Leone|Dec 85|3516 F|Bank projection from census,<br>adjusted for 5% urdercount|Based on 1974 census|U.N. 1988revision<br>(pretim.)|
|Singapore|Jun80|2414 F|UNPVSR<br>7/86(official<br>est.)|Based on U.N. 1984<br>assessment|Based on UNPVSR<br>4/87|
|SolomanIslands|Feb 76|197 F|USBOC<br>1985|Based on USBOC<br>1985|USBOC<br>1985|
|Somalia|Feb 75|3253 F|Bank projection from census,<br>adjusted for 20%undercount|Bank est.|Bank est.|
|South Africa|May 80|25017 F|<br>U.N. 1988 revision (prelim.)|U.N. 1988 revision (prelim.)|U.N. 1988 revision (preLim.)|
|Spein|Mar 81|37746 F|Eurostat 1987|Eurostat 1987|Eurostat 1987|
|Sri Lanka|Mar 81|14847 F|UNPVSR<br>7/86 (official<br>est.)|1987DHS|Based on UNPVSR<br>4/87|
|St. Helena|Feb 87|<br>6 F|UNPVSR<br>1/88 (official<br>est.)|--|-|
|St. Kitts<br>and Nevis|may 80|<br>44 F|Official<br>est., Statistics<br>Office,<br>Plarting<br>Unit|Based on official<br>data,<br>Statistics<br>office,<br>Planning<br>Unit|Based on official<br>data,<br>Statistics<br>Office,<br>Planning<br>Unit|
|St.
4.77|

Coefficient is positive if it has the expected sign. T-statistic reported here are for a statistical test of the hypothesis that mean abnormal growth expectations is zero.

# **5.4 Using capital controls to improve GDP nowcast**

In this section, I use a conventional econometric model to nowcast Brazilian quarterly real GDP between 2006 and 2013. The GDP-nowcasts are my preferred measure of the expectation of economic fundamentals and the data series used to produce the nowcasts are my preferred measure of the forecaster’s information set. To my knowledge, this methodology is new to the empirical literature of capital controls. I add the dummy variable of policy stance and show that it improves the precision of the GDP-nowcast.

31
Table D2: Large-firm vessels underreport percentage juvenile more

|Vessel type|Reported %|juvenile<br>Corrected % juvenile|∆% juvenile|
|---|---|---|---|
|Large-frm vessels|11.6|20.4|-8.8|
|Medium-frm vessels|11.1|16.6|-5.5|
|Singleton vessels|7.6|12.4|-4.8|

Notes: All values are weighted averages of North-Central zone data for all vessels between 2017 and 2019, where the weights are the individuals caught by each set. Reported % juvenile is the percentage juvenile that vessels report to the regulator after each set. I describe how I calculate corrected percentage juvenile values in Section 3 and Appendix C. ∆% juvenile is the reported % juvenile minus the corrected % juvenile. Large-firm vessels are vessels that belong to one of the seven largest firms, which each own at least 19 vessels. Medium-firm vessels belong to firms that own 2 to 10 vessels. Singleton vessels belong to a firm that owns only one vessel.

they belong to larger firms. 96% of large-firm vessels are above-median length, but among medium firms it is possible to examine heterogeneity by vessel length because 75% are belowmedian length and 25% are above-median length. I re-estimate Equation 3 using only juvenile catch among medium firms to calculate the outcome variable. In contrast to the result using all vessels, I find that the increase in juvenile catch because of the closures policy is smaller for above-median-length vessels (24%) than for below-median-length vessels (45%). This difference is statistically significant and could indicate that firm size is the more relevant dimension of heterogeneity. Larger firms may be more able to aggregate information from closures and dispatch vessels accordingly.

44
was obtained from Fitch Connect. The privatization events were individually examined to exclude noncommercial bank privatizations, and also to incorporate events announced in financial media outlets such as the Financial Times or The Economist. Non-commercial bank sales excluded from our database include development banks (agricultural banks, industrial banks, export-import banks), financial companies associated to other industries (steel, real estate development), securities depositories and asset sales by central banks. Using the information from Igan et al. (2019), we excluded from our data set institutions that had received public financial support during the GFC and later proceeded to offer equity totally or partially to private investors.

Our data set allows us to detect important trends and developments in SOCB privatization, which are presented below as 10 key stylized facts:

- **Privatizations increased sharply post-2000.** The number of transactions doubled from 11 per year in the second half of the 1990s to an average of 20 per year in the pre-GFC period (200007). This trend accelerated after the GFC when the average number of transactions increased further to 27. Growth was even stronger in terms of proceeds, rising from an annual average of USD 6.8 billion (this and all further references in 2017 constant US dollars) in the late 1990s to USD 16.6 billion in the pre-GFC years and surging to USD 26.8 billion in the post-GFC years. These numbers count any sale of public banks’ equity as a transaction regardless of its size, though we consolidate these by year and entity. Only considering sales of more than 30

> 9 See section 3 for more details on the definition of privatization events.

5
<mark>five developing countries during 2020 –Brazil, Sri Lanka, the Philippines, South Africa, and</mark> Türkiye.

<mark>The objectives are threefold. The first is to understand the extent to which employment projections based on GDP elasticities may be biased during a crisis. Changes in employment levels are a key input into these macro-micro models. When employment data is not available, projections of job gains or losses are needed, and these are often based on employment-to-GDP elasticities. The second objective is to better understand the projected distributional impacts of the economic shock during the first year of the pandemic in five countries from different regions of the world. Finally, the third objective is to better understand in Brazil, where post-crisis data on shock magnitudes exist, the extent to which the proposed methodology generates accurate estimates of the</mark> distributional impacts of the shock.

<mark>The combination of pre-COVID-19 microdata with National Accounts data from 2020 to estimate 2020 poverty rates is similar to other studies (ECLAC, 2020; Brum and De Rosa, 2021; World Bank, 2021). The methodology used here differs from these studies in two respects. First, we use a different procedure than in ECLAC (2020) and Brum and De Rosa (2021) to simulate changes in individual and household labor incomes. In particular, similar to World Bank (2021), we predict an individual probability of becoming unemployed, as a function of both sector of employment and formality status.</mark><sup>3</sup> <mark>Second, we differ in the consideration of government transfers and work from home possibilities. Unlike several other studies, our study does not include changes in government transfers in the simulation of household income changes, due to lack of information across all the countries under study here and thus to maintain comparability.</mark><sup>4</sup> <mark>As such, it predicts total income changes, poverty rates,
thus holding within-country inequality fixed.<sup>9</sup> Seventh, for the 3% of the global population with no micro data in PIP, in line with what the World Bank does for global poverty calculation, we assign the average regional distributions.<sup>10</sup> Given that one of our main purposes is to study how inequality within countries changed in 2020, we prefer methods that can speak to this over projections using per capita GDP growth rates for all households. For the purpose of studying how poverty changed in 2020, we also prefer methods that model distributional changes. Though projections using per capita growth rates may work well to predict poverty in normal times, there are strong reasons to believe that it is less appropriate in 2020. In many countries, the pandemic impacted households differentially based on their occupation, location, and age, and resulted in a large government response to the crisis (e.g. Bundervoet et al., 2022; Kugler et al., 2021). These events cast doubt on whether a GDPbased projection works well in 2020.

Table 1 reports the share of the population (panel a) and the number of countries (panel b) covered by each data source used in 2020. The data sources are ordered from the most preferred on the left column (survey data) to the least preferred on the right (regional average). We have either survey data or tabulated data for 28 countries (covering 42% of the world’s population). Our preferred methods include the first four columns – household surveys, tabulations, phone surveys, and estimates from the literature. This includes 101 countries that cover 82% of the global population.

In addition to the observed 2020 distribution, we also construct a counterfactual 2020 distribution. The income and consumption distributions would have certainly changed in 2020 even in the absence of the COVID-19 pandemic. Hence, the change from 2019 to 2020 captures both the changes due to the pandemic and those changes
not address the validation of the gridded population estimates, it is important to provide some clarity to understand the limitations of the data. The automatic creation of a national sampling frame requires granular information about the population to ensure that the output sampling units are manageable. This level of granularity does not exist in the existing census data in Armenia. Instead, gridded population data is leveraged. In developing countries, several gridded population datasets with various spatial resolutions are accessible, including Gridded Population of the World (GPWv4) (CIESIN, 2016), WorldPop (Bondarenko et al., 2020), High-Resolution Settlement Layer (HRSL) (Facebook and CIESIN, 2016), Demobase Population datasets (Azar et al., 2013), Global Human Settlement Population Grid (GHS-POP) (JRC, 2015), Global Rural-Urban Mapping Project (GRUMP) (CIESIN, 2011) and LandScan (Dobson et al., 2000). The quality of the input data model, which includes census data, satellitederived covariates, and statistical model selection, is primarily responsible for the accuracy and quality of the gridded population data. The WorldPop-constrained gridded population data for Armenia in 2020 has been used to create the national sampling frame. It was WorldPop’s most recent gridded population available. It implies that there may be notable differences between the population’s size and distribution in 2020 and today. Since the user can update the national sampling frame’s population based on their desired population such as population registry, this should not be a major concern.

This paper established an accessible and usable urban and rural classification in Armenia for the first time, and it fits within the concept of a national sampling frame. The output might not reflect the reality, despite a significant correlation between the aggregated population estimates from the census and gridded population estimates for urban and rural areas, as discussed in Section 5. It is primarily because the GHSL SMOD divides the world into urban and rural classes using gridded populations and built-up areas developed using various data sources.