rafmacalaba/data-use-mentions-extended huggingface dataset:
While comparisons at the annual 10km level show a relatively high correlation between ground-based PM2.5 and AOD with an _R_<sup>2</sup> of 0.61 (Gendron-Carrier _et al._ , 2022), others find a weak correlation between AOD and PM2.5 at the daily 1km x 1km level (Handschuh _et al._ , 2023). In general, it is necessary to model the relationship between AOD and ground-based PM2.5 including other covariates to obtain good predictions.

In addition to AOD, other instruments on satellites can measure the levels of specific gases and aerosols. For example, the TROPOMI (TROPOspheric Monitoring Instrument) instrument on the Sentinel-5P satellite is capable of measuring levels of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide, formaldehyde and methane (Zeng _et al._ , 2020). These raw data from different satellites and instruments have also been processed and integrated to generate assimilations, where data is integrated into atmospheric models. Examples of assimilations include the Copernicus Atmosphere Monitoring Service (CAMS) and NASA’s MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, version 2), which provide usable atmospheric information in one system (Buchard _et al._ , 2017; Flemming _et al._ , 2017).<sup>10</sup> In preliminary comparisons, AOD captured from MODIS does not correlate well with the data from regulatory-grade monitors in Dakar at the daily level, which aligns with findings in the literature for other contexts (Handschuh _et al._ , 2023). This is likely due to several important limitations of the AOD measure. AOD has trouble distinguishing water vapor from other particles, while the ground-based monitors capture only dry particles. In a context like Dakar, which is surrounded by water, this can have important consequences (Gendron-Carrier _et al._ , 2022). Additionally, particles that are present in the atmosphere, such as smoke or dust from long-range transport, can lead to no (or poor) correlation between
0.0|
|Cash Crops|-25.4|-4.5|-2.9|-1.8|-1.1|-0.6|
|Male Owned|-23.0|-4.5|-2.9|-1.9|-1.2|-0.8|
|Female Owned|-30.4|-4.5|-2.8|-1.7|-0.9|-0.2|
|Livestock, Fish, Forestry|5.1|2.2|1.1|0.4|0.1|0.0|
|Male Owned|0.3|0.3|-0.3|-0.3|-0.3|0.0|
|Female Owned|13.6|5.6|3.4|1.7|0.8|0.1|

Source: CGE model simulation results The drop in labor force participation level (i.e., the level difference in FLFP from baseline) is highest among farmworkers as most women are employed in this category (Figure 3). A disproportionate reduction in FLFP (in percentage from baseline) occurs among salaried and informal non-farm (i.e., services and manufacturing) female labor (Figure 4). Inevitably, more women will lose their jobs compared to men, notably in service sectors, where most women are employed in urban areas. Preliminary evidence from a World Bank Enterprise Survey (ES) in Chad demonstrates that the proportion of women as a fraction of all permanent full-time workers decreased by 6.6 percent, driven by a steep change in medium-sized enterprises. The follow-up COVID-19 Enterprise Survey was conducted during June and July 2020 to interview respondents of 377 firms which were initially surveyed between January and May 2019 as part of the standard ES.

10
7 incomes. The selection of explanatory variables is restricted to those variables that can also be found in the census (or some other large dataset) or in a tertiary dataset that can be linked to both the census and survey. The parameter estimates are then applied to the census data, expenditures are predicted, and poverty (and other welfare) statistics are derived. The key assumption is that the models estimated from the survey data apply to census observations.

Let _W_ be a welfare indicator based on the distribution of a household level variable of interest, _yh_ . Using a detailed household survey sample, we estimate the joint distribution of _yh_ and observed correlates _xh_ . By restricting the explanatory variables to those that also occur at the household level in the population census, parameter estimates from this “first stage” model can be used to generate the distribution of _yh_ for any target population in the census conditional on its observed characteristics and, in turn, the conditional distribution of _W_ . Elbers et al (2002, 2003) study the precision of the resulting estimates of _W_ and demonstrate that prediction errors will fall (or at least not rise) with the number of households in the target population, and will also be affected by the properties of the first stage models, in particular the precision of parameter estimates. A general rule of thumb is that welfare estimates obtained on this basis will be estimated fairly precisely as long as the target population comprises at least 1,000-5,000 households.

The first-stage estimation is carried out using household survey data.<sup>7</sup> The empirical models of household consumption allow for an intra-cluster correlation in the disturbances (see Elbers, Lanjouw and Lanjouw, 2002, 2003, Elbers, Lanjouw and Leite, 2008, and Demombynes et al, 2007, for more details). Failing to take account of spatial correlation in the disturbances would result in underestimated standard errors in the final poverty estimates (Tarozzi and Deaton, 2009). Different models are estimated for each region
A third way in which labor market could be impacted is through the impact of COVID-19 related measures on commuters traveling to work. Not only could labor mobility be limited during the outbreak, but there is also a higher risk to contract the infection while using public transport due to contact with fellow commuters. About 43 percent of those employed in Tunisia rely on public transport to get to work (Figure 5). The more well-off individuals are more likely they are to use public transport. This is because a high share of the poor walk to work. Looking at regions, as expected, about two-thirds of those employed in Grand Tunis use public transport.

**Figure 5: Use of public transport to travel to work**

<!-- Start of picture text --> a. By consumption decile b. By region<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Grand Nord Nord CentreCentre Sud Sud<br>Tunis Est Ouest Est Ouest Est Ouest<br><!-- End of picture text --> Source: Author’s calculations based on EBCNV 2015 Note: Public transport is defined as the use of any of the following to go to work: metro, train, bus, taxi, driver car or passenger car A fourth way through which labor market could be impacted is by the viability of certain jobs to be performed from home. Dingel and Neiman (2020) compute a score measure of teleworking or homebased work (HBW) which illustrates the possibility of working from home. Hence, workers with the lowest HBW scores may be most likely to immediately lose their job due to COVID-19. Analysis using this approach shows that most jobs in sectors such as education, finance and
A Measurement Based on Questionnaire Surveys. Copenhagen: The Rockwool Foundation Research Unit, Study no. 10.

Perry, G., W. Maloney, O. Arias, P. Fajnzylber, A. Mason and J. Saavedra-Chanduvi (2007).

_Informality: Exit and Exclusion_ . Washington D.C.: The World Bank Pfau-Effinger, B _._ (2009). Varieties of Undeclared Work in European Societies. _British Journal of Industrial Relations_ 47 (1): 79–99 Pissarides, C. (2000). Equilibrium unemployment theory. MIT Press.

Riedmann, A. and G. Fischer (2008). _Undeclared work in the European Union_ . Special Eurobarometer 284: Report.

Schneider, F. (2005). Shadow Economies around the World: What Do We Really Know. _European Journal of Political Economy_ 21, 598–642.

Schneider F., Buehn A., Montenegro C.E. (2010). Shadow Economies All over the World: New Estimates for 162 Countries from 1999 to 2007 (Revised Version). _World Bank Policy Research Working Paper_ No. 5356.

Say, F. (2011), Does employment protection help immigrants? Evidence from European labour markets. _Labour Economics_ (forthcoming, doi:10.1016/j.labeco.2011.01.006 ) Slonimczyk, F. (2011). The Effect of Taxation on Informal Employment: Evidence from the Russian Flat Tax Reform. Moscow: Higher School of Economics, mimeo http://www.iza.org/conference_files/InfoETE2011/slonimczyk_f6969.pdf Torgler, B. (2007). Tax Compliance and _Tax Morale: A Theoretical and Empirical Analysis_ , Cheltenham, UK: Edward Elgar Torgler, B. (2010). Tax Morale, Eastern Europe and European Enlargement. Background paper for _In from the Shadow: Integrating Europe’s Informal Labor_ , a World Bank regional report on the informal sector in Central, Southern Europe and the Baltic countries (Task number P112988)
one, when combined, are suggestive that where entry costs for females are high, women entrepreneurs have a strong tendency to shift away from FDI industries to non-FDI industries.

When women’s access to finance, as measured by adult women with financial institutions accounts, is better, _Female Entrep_ is higher, as indicated by the total effect of this variable evaluated at the means of the FDI interaction variables. Increasing this variable by one standard deviation (i.e., 25.1) is associated with higher _Female Entrep_ by 5 percentage points, almost half of the mean level, indicating a huge effect. Interestingly, the FDI variables do not interact with women’s access to finance in influencing _Female_

> 8 The original data sources are Doing Business for “time required to start a business for female entrepreneurs,” and Global Findex of World Bank, both available from World Development Indicators.

> 9 This is based on evaluating the effect of the “barriers indicator” at the mean of the interaction terms.

15
expenditure and number of beneficiaries for Direct Transfers:  Abono Salarial, Bolsa Família, Benefício de Prestação Continuada, Salário Família, and Unemployment benefits. 17F<sup>18</sup>

**Education expenditure.** We use the estimates of direct investment per student in nominal values estimated by the National Institute of Educational Studies and Research Anísio Teixeira ( _Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira,_ INEP).  These estimates are used as a valuation of the public education price to add on the final income of families that have members attending public education.

**Health expenditure.** The figures for health by type were obtained from Brazil’s National Health Accounts 2015-2019 (Brazil, 2022a). This report includes disaggregated figures for current government health expenditure in five categories: (i) curative attention, (ii) dental care, (iii) rehabilitation, (iv) medicines, exams, and ambulance and (v) others. Such estimates are used to produce a monetary valuation for the public health services' price and add to the final income of families that may have benefited from these services.

> 15 These taxes are levied on revenues under cumulative and non-cumulative systems; some items have exemptions. 16 It is a tax on imports and manufactured products; for some items, it works like a value-added tax; for others, it works like excise.

> 17 This is a value-added tax on the circulation of goods and provision of interstate, inter-municipal transportation and communication services.

> 18 Available at https://portaldatransparencia.gov.br/beneficios.

11
_ESTIMATING FOOD PRICE INFLATION_ _5_ local currency quotes. As a result, the full data set is highly heterogeneous and challenging to work with. For example, while it contains common staples like rice, sorghum, and maize, that are monitored in many countries, there are also items specific only to one country.

The focus of the paper is purely on monitoring food price inflation, in a reasonably cross-comparable manner.<sup>8</sup> The strategy has been to extract from the very large set of price data set a stable set of commodity prices that are defined as homogeneously as possible across countries, and that are as widely available as possible across markets, while having the best coverage over time. The period of analysis starts in January 2007, or the next first date at which data was available. After carefully examining the prevalence of price data across commodities, markets, and time for each country, 43 foods were identified for which price data are reasonably abundant across multiple markets in at least one country. Tables A2 and A3 list the selected food items by country. The foods are either staples, agricultural produce, or dairy products.<sup>9</sup> Aside from non-foods, the selection excludes only fish and meat products due to the very high dietary heterogeneity in these foods. The resulting country-specific baskets thus consists mostly of staples, often similar in nature as the type of foods traded at global markets. For example, maize, sorghum, millet, wheat, vegetable oil, to name a few common food items, are also tracked in the World Bank Commodities Price Data (The Pink Sheet) that is used to construct the World Bank Food Price Index used to track international food price developments. In most cases, the different food items can substitute one another to certain degrees, or may act as complements, so that the prices of most foods will be strongly associated with the price developments in others. For example, in Afghanistan there are only four food items: bread
surprising; the impact of growth on poverty reduction has been well-known for decades (Kraay, 2006; Ferreira and Ravallion, 2009). On the other hand, ample evidence has found large inconsistencies between consumption measured in household surveys and national accounts within and across countries (Ravallion, 2003; Deaton, 2005; Ferreira _et al._ , 2010; Pinkovskiy and Sala-i Martin, 2016; Prydz _et al._ , 2021; Deaton and Schreyer, forthcoming) and noted the difficulty of measuring GDP in developing countries (Angrist _et al._ , 2022).

Several factors can explain why we nonetheless find GDP to be such an important predictor of poverty. First, discrepancies between levels of GDP and levels of welfare do not directly affect the accuracy of the method, which is based on growth rates from the two data sources. Second, the average discrepancy between growth rates in the two sources is accounted for by only allowing a fraction of in real GDP to to welfare as measured growth per capita ’pass through’ in household surveys. The preferred method has a passthrough rate of about 0.7 for consumption-based poverty estimates, and a passthrough rate of 1 for income-based poverty estimates. Third, GDP is probably the statistic in which the global community has invested the most amount of financing and capacitybuilding to improve quality and ensure cross-country comparability (see Angrist _et al._ (2022) for a discussion of this). Despite measurement issues and shortcomings, we would expect GDP to have more signaling properties for cross-country measures of average well-being than any other statistic. Fourth, although using growth in real GDP per capita is the best performing method, no method is able to explain more than a quarter of the variation of growth in mean welfare. Hence, a large part of the variation in poverty rates no model is able to predict, and may reflect a substantial amount of random noise in measured welfare, resulting from To our knowledge, this is the first paper comparing different ways of nowcasting poverty at a global
picture text --> Source: Own computations. Data: OECD TiE and TiVA data, covering 48 countries (see Appendix 2) and 45 sectors for the period 1995-2018. Note: Trade-employment (trade-income, resp.) elasticities at the country-sector level have been estimated using employment (labor income, resp.) (in natural logs) as dependent variable and the instrumented trade variables (in levels and natural logs) as independent variable. For more information on the instruments, see Appendix 1. The panel data regressions control for country-sector, country-time, and sector-time fixed effects. Only results significant at the 10% level and above the critical value for the Kleibergen-Paap test statistic are reported.

The average intermediate import-employment elasticity is 0.42 percent, while final imports show an elasticity of 0.18 percent – less than half that size (Figure 1, left panel). Intermediate goods and services can be used in domestic production destined either for final consumption or exports and in both cases support job growth. The lower correlation between final imports of goods and services and employment in a sector implies that they also compete with domestic production and therefore employment.

Comparing trade-employment with trade-income elasticities suggests that the income channel is stronger, especially for imports. A 10 percent increase in exports is associated with a 3.9 percent increase in labor incomes over the full period 1995-2018. This implies that while exports have the potential to expand the number of jobs within a sector, they raise incomes more strongly. One explanation could be the productivity gains associated with exports which increase the average wage rate and thus total labor value added. More strikingly, a 10 percent increase in intermediate imports is on average correlated with

> 11 We use the Kleibergen-Paap test statistic and reject the hypothesis of weak instruments if the test statistic exceeds 10, following the rule of thumb by Staiger and Stock (1997). 12 Importantly, this only takes into account direct employment effects within sectors but does not consider indirect effects through job creation in supplying sectors, implying
. A., Tatem, A. J., Lessler, J. (2015). Reduced vaccination and the risk of measles and other childhood infections postEbola. _Science_ , _347_ (6227), 1240–1242.

-

- UNICEF. 2022. Guinea Country Profile. <u>Guinea (GIN) Demographics, Health & Infant Mortality - UNICEF DATA</u> (accessed February 7, 2022).

- Wilhelm, JA,  and Helleringer, S. 2019. Utilization of non-Ebola health care services during Ebola outbreaks: a systematic review and meta-analysis. Journal of Global Health. 9: 1.

18
China will also have to continue to invest in its human capital, as the quality of its labor force catches up to the OECD countries (Figure 25). Over 30 years, China has made primary education universal and expanded access to education at all levels. To obtain greater benefits from technology diffusion from abroad and to nurture frontier discovery, China now has to focus on investing in the skills of its labor force (Eaton and Kortum, 1996; Seck, 2012).

**Figure 24 China’s TFP relative to the Figure 25 Human capital index, 2017 global technology frontier, 2017**

(x-axis in log scale) (both axes in log scale) <!-- Start of picture text --> CHN<br>CHN<br>PPP GDP per capita in 2017 (thousand USD)20 40 60 80 100 PPP GDP per capita in 2017 (thousand USD)20 40 60 80 100<br>1.5 4<br>1<br>3<br>.5<br>2<br>Human capital index<br>TFP relative to technology frontier (US)<br>1<br><!-- End of picture text --> Source: PWT 9.1; authors’ calculations. Note: OECD countries in orange.

Source: PWT 9.1; authors’ calculations. Note: OECD countries in orange.

Over the past four decades, China has made such great strides in physical capital deepening that its government capital stock per worker has already reached OECD levels (Figure 26).<sup>20</sup> This suggests that there may be limited economic gains from future high growth in government investment. But there is scope for rapid growth of private capital. Starting from a very low base in the 1970s, private sector capital increased at a remarkable pace but remains considerably below advanced country levels (Figure 27).

> 20 Government investment in the IMF data set accounts for capital
Figure C2: Impacts on Individual Components of Operational Productivity and Commercial Practices Indices <!-- Start of picture text --> Operational Productivity Practices<br>Developed improvement programs of KPIs<br>Measured KPIs on operations<br>Operationalized preventive measures<br>Standardized work sequence<br>Developed continuous improvement methods<br>Developed 5S Methods<br>Used VSM for production processes<br>Identified and managed bottlenecks<br>Evaluated plant lead according to goals<br>Communicated Strategic Goals<br>-.2 -.1 0 .1 .2 .3 .4<br>ITT Treatment Impact<br>Commercial Practices<br>Measured KPIs for Commercial<br>Created incentive schemes for salesmen<br>Sales training courses<br>Defined role of salesmen<br>Advertising and brand improvements<br>Feedback meetings with customers<br>Uses a CRM system<br>Market research on distribution<br>Market research on customers<br>Market segmentation<br>Marketing and sales plan<br>-.2 -.1 0 .1 .2 .3 .4<br>ITT Treatment Impact<br><!-- End of picture text -->

**Notes** : Estimated ITT treatment impacts along with 95 percent confidence intervals are shown.

fair. In technical terms, we want to know your beliefs about the intention-to-treat effect (ITT). This is the effect of BEING OFFERED the full program, regardless of whether or not a firm takes up the program. It is simply the DIFFERENCE IN MEAN OUTCOMES for the 100 firms that get the full intervention compared to the 100 firms that just get the diagnostic and trade fair. For example, suppose we look at the percentage growth in exports. We might imagine the 100 firms in the full intervention group can be broken down into:

- 10 firms that decide not to pay for the program, and see no benefit: 0% export growth
# 3.2 Comparison with previous study

Carrington and Detragiache’s study clearly initiated new debates on the size and the distribution of the brain drain. Our purpose is to re...ne their method by incorporating additional statistical sources. By collecting Census, Register and Survey data from all OECD countries, we eliminate two sources of bias:

- 2 Relying on OECD statistics on immigration brings on several problems. First, in 1990, these data only provided information on the country of origin for the top-10 or top-5 sending countries. Hence, small sending countries are usually not identi...ed, at least in the majority of receiving countries. Second, immigration in EU countries is based on the concept of citizenship rather than on country of birth. Third, immigration data are missing for a few OECD countries (Greece, Iceland, Mexico, Slovakia, Turkey). Finally, the OECD provides data on the total immigration stock rather than on the adult immigration stock (which can be compared with the labor force in sending countries). Compared to national Censuses, we estimate that relying on OECD statistics implies an average underestimation in skilled workers migration rates by 11.3% in 1990 and 10.6% in 2000. This is the major source of bias, especially for small sized countries which are usually not identi...ed as important sending countries;

- 2 Transposing the US educational structure on other OECD countries induces an average overestimation in skilled workers migration rates by 7.25% in 1990 and 6.22% in 2000. The bias is obviously strong in countries sending a minor part of their emigrants to the US.

On average, Carrington and Detragiache’s method underestimates the emigration rates of skilled workers by 4% in 1990 and 4.4% in 2000. This appears in ...gure 3 (...g 3.A and 3.B) which gives skilled migration rates evaluated under three measurement methods: (i) a method fully based