rafmacalaba/data-use-mentions-extended huggingface dataset:
each country before the pandemic. As part of the LSMS-ISA surveys, phone contact numbers of all household members (where available) as well as from a reference contact such as a neighbor were collected (Gourlay et al. 2021). The list of households with a phone contact, or a random subset of it, constituted the sample to be contacted for the phone surveys and covered between 73% (Malawi) and 99% (Nigeria) of households included in the in-person LSMS-ISA survey. This approach also led to response rates that compare favorably to other phone surveys, especially those employing random digit dialing (Dillon et al. 2021; Gourlay et al. 2021; Henderson and Rosenbaum 2020). It also allowed to draw on a rich set of household characteristics to attenuate coverage biases through reweighting techniques (Ambel et al. 2021; Himelein et al. 2020; Brubaker et al. 2021). The respondent for each phone survey interview was purposively selected as an adult (15+) household member that is knowledgeable of the affairs of the household, typically the household head.

The data we use in this study comes from a harmonized survey module on COVID-19 vaccination that was first fielded in August 2020 and then periodically repeated. The content of the survey module on vaccines varied over time in response to changing data demands as vaccination campaigns progressed. The focus of this paper is a question on whether the respondent had been vaccinated for COVID-19 which was included in the survey module after COVID-19 vaccines 5
The Size, Origins, and Character of Mongolia's Informal Sector During the Transition James H. Anderson janderson2@worldbank.org

* I am grateful to Peter Murrell for advice throughout the research and to Natasha Beschorner and Bert Hofman for comments on earlier drafts. I am also grateful to Berta Heybey, Gunpihnaa, Bayanchimeg, Tuul, Tsogt, Burmaa, and Gantumur for excellent research assistance; to the Ulaanbaatar City Administration, and especially to capable staff of the State Statistical Office for cooperation on the informal sector surveys, and kind assistance throughout this research; and to many other Mongolia officials, economists, statisticians, and informals for providing valuable information and advice. The financial support of the World Bank is gratefully acknowledged.
# **Figure 7**

Decomposition of the Fall in Monetary Poverty Rate in LAC during the Golden Decade <!-- Start of picture text --> 100%<br>18.0<br>90%<br>16.0<br>7.7 41.2 80%<br>Cyclical income<br>14.0<br>70%<br>12.0<br>60%<br>10.0<br>50%<br>Permanent income<br>4.5 23.8<br>8.0<br>40%<br>6.0<br>30%<br>Redistribution<br>4.0 20%<br>6.6 35.0<br>2.0 10%<br>0.0 0%<br>In absolute terms As percentage<br>Fall in Poverty Rate (in percentage points)<br>Fall in Poverty Rate (as percentage of total fall in poverty)<br><!-- End of picture text --> Notes: Poverty line of 5.50 dollars per person a day (2011 PPP). LAC aggregate based on 15 countries in the region, weighted by population, for which microdata are available at the national level: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, El Salvador, Ecuador, Honduras, Mexico, Panama, Paraguay, Peru, and Uruguay. See Appendix B for details.

Sources: Authors’ estimations based on SEDLAC (CEDLAS and World Bank) and WEO (October 2018).

23
household.<sup>15</sup> There is also evidence of significant changes in the labor market: the share of nonagricultural wage/salaried workers (out of all working adults) increased substantially for both men and women. This occurred with some decline in the share of agricultural wage workers and a much sharper decline in the share of farmers (agricultural self-employed). Similarly, the rising share of nonagricultural wage workers along with declining share for agricultural employment is consistent with the pattern of structural transformation that other studies have found for India.<sup>16</sup> A few differences between rural and urban areas are worth highlighting. Dependency rates are much higher in rural areas, but the share of adults who are working is much higher in rural areas. Unsurprisingly, the share of workers in non-agricultural activities is higher in urban areas in comparison to rural ones. However, rural areas have experienced significant changes in occupational structure that are in the same direction (and larger in magnitude) than what is seen for the country as a whole—in contrast to much smaller changes in urban areas. The transformation that is changing the nature of employment in India away from agriculture thus seems to have occurred much more visibly in rural than in urban areas, which is not surprising given the high dependence of households on agriculture in rural India to start with.

Differences between men and women are worth highlighting as well. While the share of workers among total number of males is much higher than that among women, women are more likely to be working in agriculture than men. The share of non-agricultural wage workers is rising at a lower rate among female workers than male workers, while that of agricultural self-employed is declining at a faster rate among female workers than among male workers.

> 15 Trends in the IHDS panel are similar to the NSS with one prominent exception. In the IHDS panel, female workforce participation rates increase from 66% to 69% between 2005 and 2012.  This is in marked contrast to the
934 and 999 ha for areas controlled by Ukraine (ACUs) and areas affected by conflict (AACs), respectively. In Form 29, the corresponding figures for farms greater than 500 ha are 2,834, 2,255, and 2,137 ha, respectively, for the SAR and Form 29 in ACUs and AACs. 18 As area cultivated in 2022 is likely to have been affected by the conflict and to avoid bias that might arise from the fact that 2021 was an exceptional year with very favorable growing conditions, we use both satellite imagery and Form 29 data from 2020. Area cultivated excludes forest, bare land, wetlands, water, and areas with artificial (built up) cover.

17
20e-05|

Source: Authors’ calculations based on World Economic Outlook database 2019.

Notes: Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1 Underidentification tests. Ho: matrix of reduced form coefficients has rank=K1-1 -underidentified. Ha: matrix has rank=K1-identified. Weak identification test. Ho: equation is weakly identified. Weak-instrument-robust inference. Tests of joint significance of endogenous regressors B1 in main equation. Ho: B1=0 and overidentifying restrictions are valid.

Table 2 presents the results using a fixed effects estimator (Columns 1 and 2) and an instrumental variable estimator (Columns 3 and 4). During periods of expansion (when approaches zero and E approaches one), changes in output are associated with a more than proportional increase RRr in public consumption in developing economies. In contrast, the same output change leads to a EE less than proportional effect in advanced economies. The semi-procyclical behavior is visible from
6 each region villages were randomly selected among those with lowland for rice cultivation. Proximity to the rice mill was retained as a criterion for the selection of the project pilot villages to ensure sufficient market access, for buying inputs and selling their products. In total, 21, 20, and 19 villages were selected in Poro, Tchologo and Tonkpi, respectively.

Households were selected through a stratified random sampling, with each village a stratum. Prior to the survey, a census of all households within each selected village was conducted with the help of the village leaders. Subsequently, a list of rice growing households was compiled, among which only rice growing households with suitable lowland for rice cultivation were retained. From this final list, 15 rice growing households were each time randomly selected for the survey, resulting in a total of 1,446 rice growing households interviewed in all three regions. Sixty households that did not grow rice during the 2019 harvest season were removed. The final sample consists of 1,386 rice growing households.

The survey collected information on farmers’ socio-economic and demographic characteristics (e.g., age, sex, contact with extension agents, group membership, etc.), psychometric variables such as farmers’ information seeking attitudes and risk attitudes,<sup>5</sup> production related variables including the quantity of labor, fertilizers, pesticides, and equipment used as well as the total harvest and sales (including units and price). Special attention was given to the collection of detailed agricultural labor input data. This remains challenging and relatively uncommon in household surveys. Labor inputs were collected at the plot level in person-days<sup>6</sup> for

> 5 We follow Läpple & Rensburg (2011) to derive the information attitude and risk aversion measures. These variables are obtained from Principal Component Analysis of a set of items that describes the interest of the producer in information gathering, and the level of risk aversion. The individual items were measured using a five points Likert
2.20|2.24|0.05|0.01|-0.05|
||(0.13)|(0.11)|(0.09)|(0.17)|(0.16)|(0.14)|
|Grows main crop (1=yes)|0.95|0.96|0.97|-0.01|-0.02|-0.01|
||(0.01)|(0.01)|(0.01)|(0.02)|(0.01)|(0.01)|
|N|416|387|803|803|1,219|1,190|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Total no. of observations N = 1,606. FREG stands for ‘Farmer research and extension group’. For variable definitions see notes of Table 1. Robust standard errors in parentheses, clustered at the mws level. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.

12
0.287|-0.154|0.400|-3.042|0|13,475|
|Log no loan application (%)|-1.254|-1.151|0.850|-3.836|0|11,203|
|Log bribes (% annual sales)|0.502|0.187|0.699|0|3.857|13,148|
|Log licensing obstacle (%)|-2.079|-1.976|1.031|-5.105|0|10,835|

Notes: Authors’ calculations based on WBES, CEPII, Jeffrey Bergtrand’s website, WITS, Doing Business and LPI. Descriptive statistics computed from the sample of Column 1 of Table 3. Variables from WBES calculated over the entire population of firms (Panel A) or the sample of exporting firms only (Panel B). The description and source of variables are provided in Table A3 in the Appendix.

13
despite large reductions over recent years.<sup>9</sup> Based on budget data from the IHS, health care accounted for 12.5 percent of the cash consumption expenditure by Georgian households in 2018 (Geostat 2019).

Some previous work has attempted to estimate the health and economic costs specifically linked to tobacco in Georgia, as well as the potential outcomes of different policy options for tobacco-control. According to the Tobacco Atlas, the economic costs of smoking in Georgia add to GEL 619 million, including direct health care costs and the indirect costs in productivity associated with tobacco-attributable mortality and morbidity (The Tobacco Atlas 2019).

A study conducted in partnership between the UNDP, the WHO FCTC Secretariat and the NCDCPH (National Center for Disease Control and Public Health of Georgia) analyzed the effect of implementing four FCTC priority policies for tobacco-control (UNDP et al. 2018).<sup>10</sup> The study calculated the total economic costs of tobacco in Georgia at GEL 825 million annually, or 2.43 percent of GDP in 2016. Those estimated costs included direct health care expenses and the indirect economic costs related to productivity losses (related to premature mortality, increased morbidity, and time allocation to smoking breaks). A back of the envelop estimation suggests that tobacco-related medical expenses amounted to GEL 327 million in 2016.<sup>11</sup> The indirect losses in productivity accounted for the remaining 60 percent of the total burden of tobacco. Full implementation and enforcement of the four interventions could potentially advert GEL 3.6 billion in health care and economic losses over a 15-year period (including 1.4 billion in direct health care expenses and GEL 2.2 billion in productivity losses). It would also save 53,110 lives over the 15-year period, at a financial cost

> 9 Calculations for 2016, based on the WDI database. This share has dropped significantly from 72.7 percent in 2010. 10 The policies considered are: (1) Increasing taxes on
Retail trade**|14.77|
|**Computer programming**|3.87|
|**Manufacturing of wearing apparel**|3.21|
|**Real estate activities**|3.14|
|**Security and investigation activities**|2.68|

_Source:_ World Bank staff calculations based on SBS data set.

_Note:_ The table shows only sectors for which the probability of a firm being in the top decile of markup distribution is higher than the sector’s overall frequency ratio.

- 4.2. HOW HAVE FIRM DYNAMICS INFLUENCED AGGREGATE MARKUP PERFORMANCE? ARE THERE SIGNS OF MARKET POWER CONSOLIDATION AMONG INCUMBENT FIRMS?

**To indirectly assess whether the rise in markups is associated with weakening competition, the analysis decomposes the evolution of aggregate markups into that of incumbents and of net entry of firms.** To do so, the Melitz and Polanec (2015) decomposition technique is applied to the aggregate markup changes between 2008 and 2017. This approach follows two steps. First, the aggregate (ln) markup ( _μ_ ) at year _t_ is defined as

> 21 Under the NACE 2-digit wholesale trade sector, the group of top decile firms are overrepresented in the following NACE 4-digit sectors: sector 4690–Non-specialized wholesale trade; sector 4673–Wholesale of wood, construction materials, and sanitary equipment; sector 4621–Wholesale of grain, unmanufactured tobacco, seeds, and animal feeds; sector 4646–Wholesale of pharmaceutical goods; sector 4675–Wholesale of chemical products; and sector 4631–Wholesale of fruit and vegetables. Under the NACE 2-digit retail services sector, the sample of top decile firms are overrepresented in the following NACE 4-digit sectors: sector 4711–Retail sale in nonspecialized stores with food, beverages or tobacco predominating; and sector 4730—Retail sale of automotive fuel in specialized stores.

> 22 The same analysis is applied for ownership structure, again controlling for overrepresentation. Results show that the sample of top decile firms
<mark>Source: Meyer-Sahling et al. (2021)</mark>

# **<mark>Methodological Approach of the Global Survey of Public Servants</mark>**

<mark>Surveying public servants around the world poses an immediate comparability challenge: how to ensure that survey responses can be compared across countries? We take several duties of care, while also being explicit about the limitations of our data. Our intention is also that over time, the initiative will improve the core comparability of the measures as well as learn more about the validity of comparing measures over time, space, and setting.</mark> <mark>First, in terms of duties of care, our surveys are conducted with comparable survey population frames. All surveys included in the Global Survey of Public Servants target public servants in a broad range of central government institutions – or, in short, central government bureaucrats. Our data does not contain responses from frontline officials such as teachers or doctors, in part as other efforts – such as the OECD Talis survey (OECD 2018) – focus on them.</mark> <mark>Second, we invite a census or representative sample of bureaucrats in all or a subset of central government institutions in each country to respond to the survey (rather than, e.g., a convenience sample). In a number of cases where comparable sub-national surveys of public administrators were conducted, we include this data in the initiative’s data release. However, the core rationale for inclusion is that the survey has a central government component.</mark> <mark>Third, we conducted over 150 cognitive interviews with public servants across countries in which we fielded surveys to develop our measures and safeguard a comparable understanding of measures across countries.</mark> 9
# Table 4.  Panel fixed-effects regression of government spending on commodity prices

|||**Dependent**|**variable: Real**|**Government**<br>|**Spending**<br>||
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|**Explanatory variables**|**EMDE**|**EMDE**|**EMDE**|**Full**|**Full**|**Full**|
|**Commodity Export Price Index (EPI)**|**0.061 ***|**0.080 ***|**0.061 ***|**-0.123 *****|**-0.072**|**-0.121 *****|
|**Terms of Trade**||**-0.060**|||**-0.072**||
|**GDP (-1)**|||**0.688 *****|||**0.625 *****|
|**Commodity Export Price Index x EMDE**||||**0.184 *****|**0.158 *****|**0.183 *****|
|**Observations**|**276**|**269**|**276*
- It is flexible and open, as we plan to release updates to include new ICIO database, as soon as the data become available, as well as other measures to assess the participation and position of countries and sectors in GVCs and trade policy analysis.

It is worth noting that the measures computed with `icio` , as any other measure obtained from ICIO tables, suffer from some limitations (Antr`as, 2019). In fact, ICIO tables are built under the strong proportionality assumptions, i.e. all output within each country-industry is built with the same input mix (de Gortari, 2019). However, input-output datasets will soon start exploiting customs data to allow for more heterogeneity in production and trade (United Nations, 2018). Once ICIO tables become more detailed, value-added trade measures obtained with the different perspectives featured in `icio` will diverge more and more, making it even more important to have available the best suited accounting framework to answer each specific empirical question.

33
**Figure 1 Emigration is highest for middle-income countries**

_Sources_ : Original estimation based on migration data from UN DESA 2020 and GDP data from the Penn World Tables (PWT) 10.0 (Feenstra, Inklaar, and Timmer 2015).

_Note_ : The figure plots the relationship between total migrant stock (as a percentage of origin country population) in 2020 against the per capita GDP (in constant 2017 US dollars in purchasing power parity terms, 2017 PPP$) in 2020. The solid line presents a locally linear fit. GDP for 2020 is calculated by applying the GDP growth rate applied to the 2019 GDP from PWT 10.0. Emigration is capped at 30 percent for countries with higher emigration rates for the scatterplot. Data labels use International Organization for Standardization (ISO) country codes.

However, early empirical research on this relationship was limited by lack of data. Early results relied on a cross-sectional relationship or used only a subset of countries for which reliable historic data were available (such as the member countries of the Organisation for Economic Co-operation and Development, OECD). This changed with a comprehensive compilation of historical migration data from 1960 to 2000 by Özden et al. (2011), which collated information from various censuses and surveys in origin and destination countries and using parsimonious modeling to fill in the gaps in data. For recent years, the United Nations, using a similar methodology, has been providing regular updates to data on the bilateral migrant stock (UN DESA 2020). Combining the data on migrant stock from 1960 to 2018 from these sources, Clemens (2020) produced one of the first studies to thoroughly examine how emigration changes as countries develop.

This paper extends the non-parametric analysis in Clemens (2020) to provide a nuanced picture of the relationship between development and emigration. It divides countries into three groups based on their baseline levels of income (for 1960 or the first year in which data are available) and analyzes the migration response separately by country income group. The analysis
0.49|
|El Salvador|0.63|0.54|0.33|1.14|1.17|0.92|0.51|0.63|0.59|
|Uriguay|0.18|0.13|0.12|0.77|0.89|0.72|0.60|0.76|0.59|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng). _Note_ : Wages are defined as real hourly income (using 2005 purchasing power parity conversion rates) in the main occupation. The log of real hourly wages in the main occupation (labor income in 2005 purchasing power parity) was regressed on dummies of level of education (five levels), five-year intervals of experience, and all possible interactions. All education categories (college, high school, and primary education) follow country-specific classifications for university degrees, secondary education, and primary education, as defined in each household survey. The college educated labor force comprises workers who completed a university degree or higher. “High school” includes complete secondary education and incomplete college educaton. “Primary or less” includes no formal education, incomplete primary, complete primary, and incomplete secondary education. The sample was restricted to individuals between ages 18 and 65 years who were employees or self-employed and between the 1st and 99th percentiles of the wage distribution.

54
|District|District|District|District|District|
|Region x Year of Birth<br>dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|3823|3055|3823|3055|3823|3055|3823|3055|
|No. of Clusters|75|75|75|75|75|75|75|75|
|R-Squared|0.1106|0.0370|0.1118|0.0368|0.1105|0.0415|0.1130|0.0382|

All specifications are estimated using the panel fixed-effects estimator. School destructions and abductions by Maoists are expressed per 1000 inhabitants. Sample only includes individuals surveyed in the Nepal DHS 2006 and aged 5-9 (treatment group) or 16-19 years old (control group) at the start of conflict in 1996. Definition of district controlled by Maoist based either on matches between People’s Army and government classifications as of 2003 (Definition 1) or based on government classification (Definition 2). Standard errors clustered at the district level are in parentheses. *p<0.10, **p<0.05, ***p<0.01.

41
|0.9%|0.6%|1.5%|
|South (Top 20)|South (Top 20)|1.0%|1.2%|0.2%|0.5%|0.2%|0.5%|

Note: This table shows the year-end value of international investment positions scaled by world gross domestic product (GDP) for alternative groupings of countries. Bilateral data are aggregated for all countries within a source region to all countries within a destination region. For each investment type and year, world GDP includes the GDP of destination countries for which we have data for that year (all source countries with data for a given year also have data as destination countries). Within each investment type and year, world GDP is the same across blocks. Panel A separates China from the rest of the South. For portfolio investment, there are no data available for China as a source of investments for 2001. Panel B separates the top-20 richest South countries from the rest of the South. The top-20 richest South countries are classified based on the International Monetary Fund's current GDP per capita in 2018 and include the following economies: Australia; Bahrain; Brunei Darussalam; Czech Republic; Estonia; Hong Kong SAR, China; Israel; Korea, Rep.; Kuwait; Lithuania; Malta; New Zealand; Oman; Qatar; Saudi Arabia; Singapore; Slovak Republic; Slovenia; Taiwan, China; and United Arab Emirates.