rafmacalaba/data-use-mentions-extended huggingface dataset:
but then began to rise and in 2013 accounted for about 1.2 percent of GDP.

**Households Contribute the Largest Share of National Income to Education in Low-income Countries** _Household Education Spending as a Percentage of GDP, 2006_ – _13_

<!-- Start of picture text --> 3.0<br>2006–09 2010–13<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Income group Region<br>LIC LMIC UMIC HIC ECA (24) MNA (5) EAP (7) LCR (13) AFR (13)<br><!-- End of picture text --> _Source:_ World Bank calculations using UIS and IMF online databases. _Note:_ Income groups are defined by World Bank country income group classification in 2017. Figures in parentheses record the average number of countries in each region for which data are available. LIC = low-income country, LMIC = lower-middle-income country, UMIC = upper-middle-income country, and HIC = high-income country. AFR = Africa, ECA = Europe and Central Asia, LCR = Latin America and Caribbean, EAP = East Asia and the Pacific, MNA = Middle East and North Africa. There was no information for <u>South Asia.</u> 6
.15 and 3,560 million at $6.85 when overlaying 2019 subnational poverty rates with 2020 gridded population data.

Exposure by region and income classification is presented in Table 7. South Asia (SAR) is the region whose population is most exposed to shocks, with 87 percent exposed (driven by very high rates of exposure to heatwaves). East Asia and the Pacific (EAP) follows. Exposure is lowest in Europe and Central Asia (ECA) and Latin America and the Caribbean (LAC), but even in these regions a third of the population is exposed. Important differences in exposure are present across regions. Drought is the most predominant shock in ECA and in Sub-Saharan Africa (SSA). In SAR and the Middle East and North Africa (MENA), exposure to heatwaves is the highest. Exposure varies less across type of shocks in LAC and EAP.

**Table 7: Number of people exposed, by region and income category** **<u>(millions)</u>**

||Total<br>population|Flood|Drought|Heat|Cyclone|Any shock|Any shock (%<br>ofpopulation)|
|---|---|---|---|---|---|---|---|
|_Region_||||||||
|East Asia & Pacific|2,073|407|322|810|247|1,308|63|
|Europe & Central Asia|494|36|117|18|0|155|31|
|Latin America & Caribbean|628|56|96|48|35|205|33|
|Middle East & North Africa|402|47|43|93|0|156|39|
|Other High Income|1,029|104|159|101|182|452|44|
|South Asia|1,
state governments.

The ‘Unlock’ phase of containment policies commenced from June 1, 2020. Several restrictions were eased, and primary rule-making authority devolved to state governments. Thus, our analysis compares the severe restrictions which were uniform across the country in March and April 2020, to the time after May 4 when restrictions varied by district, until July 2020.

# **3 Data**

We combine multiple sources of district-level information on nighttime light intensity, household consumption and income, mobility, and district-specific characteristics. This information is merged with the government’s district level zonal classification and COVID-19 infection data. We use 2020 district boundary classifications to match how zonal containment policies and infections are reported.

We extract district level nighttime light data from the VIIRS-DNB Cloud Free Monthly Composites (version 1) provided by the Earth Observation Group at Colorado School of Mines. Due to a wider radiometric detection range and onboard calibration correcting for saturation and blooming effects, these data are more comparable over time than previous nighttime light products. However, the monthly composite still includes some temporary lights like fires and gas flaring and filtering out background noise strengthens the relationship of nighttime lights and economic activity. Following Beyer et al. (2020), we hence consider only lights outside a background noise mask. For the latter, we identify different clusters by removing outlier observations, averaging cells over time, and clustering areas based on their nighttime light intensity.<sup>4</sup> In practice, this approach amounts to setting to zero cells that are distant from homogeneous bright cores. The adjusted monthly data are aggregated to the district level and standardized by area.

We access mobility information from Facebook Data for Good,<sup>5</sup> which is based on individuals who use Facebook on a mobile device, provide their precise location, and are observed for a meaningful period of the day. Facebook quantifies how much people move around by counting the number of level-16 Bing tiles (approximately 600 meters by 600 meters area)
, the average tenure for workers in this age group is about 13 years. As in EU countries, less-educated workers enjoy longer tenure than more-educated workers, except in Kosovo and Russia, where the average tenure is higher for workers with more education. Workers on permanent contracts keep the same job longer than temporary workers do. The patterns of job tenure by employer size vary across countries. In Albania, Serbia, and <mark>Türkiye</mark> , tenure is longest among people who work in small firms (firms with ten or fewer employees); in the remaining countries, the longest tenure is among people who work in large firms (firms with more than 50 employees). In terms of occupational groups, people who work in routine jobs have shorter tenure than people who work in non-routine jobs in five of the seven countries.

> 1 Whenever possible, we maintain the codification presented in each labor force survey. When this variable is not reported, we consider a worker to hold a part-time job if he or she works fewer than 30 hours per week.

> 2 We distinguish three occupational groups based on their task content: routine occupations, non-routine cognitive occupations, and non-routine manual occupations. See the appendix in Bussolo, Torre, and Winkler (2018).

7
conflicting effects of public goods provision and distortionary taxation at various levels of government size. Moreover, public goods will tend to be subject to excess demand if no mechanism -- such as a production tax -- exists to offset such congestion V The public goods model of government provides a useful benchmark for predicting the consequences of types of government activity. This is clearly useful given that governments characteristically do not restrict themselves to providing public goods. Yet, empirical tests for the effects of government have proven far from conclusive. Earlier work with cross country data sets, for example, has found not even marginal and average tax rates to be important explanatory factors behind comparative growth experiences, in part perhaps because of covariation with income, measurement problems 2 and the lagged effects of taxation . The evidence suggests that public spending on infrastructure3 and the provision of a stable and predictable environment for transactions act positively on growth while government consumption tends to act negatively 4. The latter result is generally explained by the fact that govermnent consumption will not directly affect productivity nor enter private agents' production functions. Even if it affects productivity, its benefits at the margin fall short of the costs of distortionary taxation needed to finance it. However, this association may not be that robust, given I See Barro and Sala-i-Martin (1995).

2 Easterly and Rebelo (1993).

> 3For example, Aschauer (1989) and Hulten (1996). See also, Pritchett (1996) for an alternative view and Devarajan,Swaroop and Zou (1996) for contrary evidence.

See, Landau (1986), Barro (1991) and (1996b), and Barro and Sala-i-Martin (1995).

4
international payments. Athey et al (2016) also document that bitcoin ownership is highly concentrated. Indeed, even as the popularity of Bitcoin has continued to grow in the past few years, Marakov and Schoar (2021) more recently confirmed that bitcoin ownership and mining capacity are still very concentrated. Auer and Claessens (2020) find that in light of significant regulatory uncertainty, regulatory news regarding crypto-assets has had a significant impact on crypto-assets market prices and trading volumes.

# **<mark>Box 1: Women and Crypto-assets Adoption</mark>**

<mark>In their</mark> _<mark>Global Report on Women and Cryptocurrencies</mark>_ <mark>, Spindler and Rodriguez (2021) conducted a small survey of crypto-asset ownership and adoption factors among 60 women from 31 countries, with a focus on residents of Latin America. 36% of participants in the survey highlighted their interest in the underlying technology, while 14% saw crypto-assets as a long-term investment vehicle. Participants cited a number of factors that increased their interest in crypto-assets, including value fluctuations of their local fiat currency, domestic and regional financial crises, lack of economic empowerment and control over finances within their household, and perceived inefficiency of legacy financial systems. Importantly, 95% of the women respondents had some form of higher education, signaling that educational</mark> background could play a role in crypto-asset adoption.

# _Measuring crypto-assets activity_

To understand the various motivations behind crypto-assets activity, it is important to properly measure it. However, while open distributed ledgers allow anyone to observe the complete historical on-chain transaction data, their pseudonymous nature makes it challenging in practice to match individual persons or businesses to on-chain addresses, unless they, for example, underwent Know-Your-Customer checks to obtain accounts at centralized intermediaries such as cryptoexchanges.

Several studies have taken different approaches to measure crypto-assets activity. To estimate the number of bitcoin and ether owners, Wang (2020, 2021) utilizes on-chain data to count the total number of on-chain
the degree of destitution high. What is more likely is that countries with severe restrictions on refugees’ ability to work and earn a living create aid-dependency. Indeed, as Figure 7 shows, comparisons within countries reveal that refugees in camps are less self-reliant and receive more aid compared to those in cities who are more self-reliant and receive less assistance.

**Figure 6: Aid and self-reliance of refugees**

<!-- Start of picture text --> 80 73<br>62<br>60<br>48<br>41<br>40 28 32 31 29<br>20 14 15 15 18 18 17<br>9<br>6<br>2 2<br>0<br>Uganda Ethiopia Chad Niger Kenya Jordan Colombia West Bank Costa Rica<br>& Gaza<br>2.15 3.65 6.85<br>IPL<br>Aid Self-reliance<br>Figure 7: Aid and self-reliance by location within countries<br>100 40<br>80<br>30<br>60<br>20<br>40<br>10<br>20<br>0 0<br>Ethiopia Uganda Kenya Jordan<br>Aid Self-reliance<br>Percent<br>Aid<br>Self-reliance<br>Camps Addis Ababa Outside Kampala Kampala Kalobeiye Kakuma Dabaab Nairobi Kenya other urban Azraq Zaatari Outside Amman Amman<br><!-- End of picture text -->

**Figure 7: Aid and self-reliance by location within countries**

**Self-reliance is far more likely to reduce refugee poverty than aid.** Figure 8 uses data from Table 2 to calculate how much it would cost to bring refugee income in line with the poverty line. The figure illustrates

14
>(0.730)|5.345<sup>***</sup><br>(0.592)|
|Sub-county fixed effects|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|
|Adjusted R-squared|0.121|0.206|0.200|0.215|
|Observations|3026|3026|3026|3026|

Notes: The dependent variable is poverty rate (in percent). Conflict debt is the discounted history of conflict years where a conflict year is defined as by more than 0.08 fatalities per 1000 population. We use average population to avoid time variation in the denominator of the indicator. County and district conflict debts are computed in the same manner at higher administrative units, respectively. Regressions are on the balanced panel dataset in 2012 and 2016. Robust standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01 Figure 6 shows a strong correlation between conflict debt and poverty within Uganda. In particularly, the formerly violent North is also poorer. Second, as conflict debt is reduced over time, so is poverty. This raises the question of whether regions that were most heavily affected by poverty are catching up with the rest of the country. We test this more specifically in Table 5. Here we first look at the conflict debt at the sub-county level. We control for regional fixed effects and a time dummy for 2016 to capture general trends in poverty. There is a statistically and economically significant relationship between conflict history and the poverty rate at the regional level. Importantly, this relationship holds controlling for sub-county fixed effect which rules out long-term fixed characteristics driving the 17
Figure 6: Have sales and employment increased after joining Chaldal?

<!-- Start of picture text --> 85<br>39<br>34<br>Total sales revenue No. of full-time employees<br>No. of skilled employees<br>80<br>60<br>40<br>Percent of firms (%)<br>20<br>0<br><!-- End of picture text --> Another potential consequence of platforms such as Chaldal is the facilitating of business formalization. This could be a side-effect of the growth experienced by platform sellers, since many business licensing and tax registration requirements start to apply after firms cross certain minimum size thresholds. Moreover, selling on a platform may entail becoming more visible to tax and regulatory authorities, increasing the risk of being penalized for regulatory non-compliance. More than 85 percent of the Chaldal survey respondents strongly agree or agree with the statements that joining a platform such as Chaldal increases the incentive to formally register the business, and that it increases the likelihood of being visible to tax authorities (figure 7). Note that Chaldal requires its suppliers to comply with requisite business licenses and tax registration, and also offers advisory assistance to promote compliance among its sellers. Some 26 percent of Chaldal sellers report having changed their legal structure or registration since joining Chaldal.

12
<!-- Start of picture text --> Figure 6: Relative Size of New Products’ Exports for Incumbent Exporters<br>Panel A. Averages in 2005-2007 Panel B. Averages in 2008-2009<br>.11<br>.17 .09<br>.06 .06 .06 .06 .06 .05<br>.05<br>.08 .04<br>.07 .04<br>.06 .05 .03 .06 .05 .05 .03 .04 .03 .02 .03<br>.02 .01<br>A.Natural Resource B.Simple Processing C.Manuf.D.Other A.Natural Resource B.Simple Processing C.Manuf. D.Other<br>LAC countries Other countries LAC countries Other countries<br>.2<br>.1<br>.15 .08<br>.06<br>.1<br>.04<br>.05<br>.02<br>Size of New Products Rel. to Incumbent Products 0 Size of New Products Rel. to Incumbent Products 0<br>Ecuador South Africa Costa Rica Peru Chile Bangladesh Cambodia Guatemala Nicaragua El Salvador Dominican Rep Mexico LAC countries Colombia Ecuador South Africa Costa Rica Peru Chile Bangladesh El Salvador Nicaragua Guatemala Cambodia Dominican Rep Mexico LAC countries<br><!-- End of picture text --> Notes: in Panel A the average for Ecuador covers 2006-2007. Colombia does not enter in Panel A due to lack of data. Brazil does not enter in the figure due to lack of access to the exporter-level customs data.

Finally, we examine the contribution of new products to short-run export growth of incumbent exporters in LAC and comparator countries. Figure 7 shows the average annual growth rate in total exports of incumbent exporters for each country during 2005-2007 in Panel A and 2008-2009 in Panel B. The average contribution of the three terms in the incumbent exporters
Bank, 2018). But there is significant variation among the countries we study, with private school teachers ranging from 5% (in Namibia) to 38% (in The Gambia) of the teaching force. Depending on the nature of a private school (e.g., elite or low-cost) and the contract type, teachers’ salary and benefits could vary substantially, which we discuss in section 3.3.

Another important group in the African teaching force is contract teachers. Due to the increasing demand for education, hiring contract teachers is common in Sub-Saharan African countries—as it is in other parts of the world (Chudgar, 2015).<sup>25</sup> Although their job responsibilities are similar to those of civil-service teachers, contract teachers’ salaries are typically substantially lower and these teachers lack job security. In the survey data we use for most countries, all employed respondents—including teachers—are asked whether they have a permanent or a fixedterm/temporary contract. The share of teachers on fixed term or temporary contracts ranges from 5% (Tanzania) to 42% (Côte d'Ivoire) (Table 2). On average, the share of teachers on a fixed term or temporary contract is slightly higher for secondary teachers (25%) than for primary teachers (23%). In Uganda, the share of teachers on a fixed term or temporary contract at the secondary level (74%) is substantially higher than that at the primary level (33%).<sup>26</sup> Table 3 presents the educational attainment of teachers. The majority of teachers have at least secondary school education in all countries in our sample. The only countries where more than 7 percent of teachers have only a primary degree are Côte d’Ivoire (13.5%) and Zambia (15.8%). Overall, 53% of teachers have post-secondary education. Relative to other wage workers, teachers are much more likely to
time a mosquito becomes infected until it is infectious. The parameter _ri_ is the recovery rate of humans. I make four assumptions arising from the epidemiology literature based on modeling the system in a low malaria setting:

1. The total parasite incubation time is one month based on the malaria cycle.

2. The mosquito population is at the steady state since mosquito populations have a relatively rapid turnover<sup>30</sup> 3. All malaria cases in month t are treated immediately and recover in month t.<sup>31</sup> 4. Based on D. L. Smith and McKenzie (2004), when the proportion of infected humans is small, the number of infectious bites received per day by a human (known as the entomological inoculation rate, taking the form _EIRit_ =<sup>_mit_</sup> _μ_<sup>_a_</sup> _<u>it</u>_<sup>2</sup> _it_<sup>_c_</sup> +<sup>_it_</sup> _a_<sup>_e_</sup> _it_<sup>_−_</sup> _c_<sup>_μitτit_</sup> _itxit_<sup>_xit_</sup> ) can be approximated by _citCitxit_ , where _Cit_ is the expected number of humans infected per infected human per day, assuming perfect transmission eciency ( _bit_ = _cit_ = 1 ), known as the vectorial capacity. The assumption applies because the analysis is conducted

> 30According to the CDC, adult female mosquitoes, which spread malaria, do not live more than 1-2 weeks in nature (Center for Disease Control 2015).

> 31The incidence data in the analysis is based on diagnosed cases, which are provided with free antimalarial treatment upon diagnosis. The literature shows that within a few days of treatment the majority of parasites are eliminated (Nosten and Nicholas J White 2007, N. White 1997).
<!-- Start of picture text --> Men Women<br>100 | 100<br>2007 census<br>8060 | 80 2007 census<br>| 60<br>40 40<br>20 |<br>a | 20<br>2011 2012 2013 2014 2015 2016 0 2011 2012 2013 2014 2015 2016<br>HN OHS) )«- ML SS-SA<br>EE irs<br><!-- End of picture text -->

<!-- Start of picture text --> Men Women<br>80al 2008 census | 80 -<br>2008 census<br>——<br>60- 60-<br>40- 40-<br>|<br>|<br>20 20<br>oO 2010 2011 2012 2013 2014 2015 2016 0 2010 2011 2012 2013 2015 2016<br>ME DHS ME iFs<br>ML sus-sa<br><!-- End of picture text -->
unstable over time. Demand generally remains below US$ 15 million. Conversely, goat imports have been declining since the start of the decade, going from US$ 15 million in 2000 to US$ 1 million in 2017.

<!-- Start of picture text --> Figure 7: Trend of livestock import of Nigeria (in Figure 8: Nigerian imports of meat and edible<br>thousands of USD) offal (in thousands of US dollars)<br> 180 000 120000<br> 160 000 100000<br> 140 000<br>80000<br> 120 000<br>60000<br> 100 000<br> 80 000 40000<br> 60 000 20000<br> 40 000<br>0<br> 20 000<br> -<br>Total l'Afrique Aggregation<br>Source: FAOStat, estimates Source: Trademap<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> For the import of meat and edible meat, unlike ECCAS, Nigeria's imports of meat and edible offal have experienced a checkered pattern since 2001, with a peak in 2010. The protectionist policies explain this. Indeed, Nigeria imposed drastic measures prohibiting imports of beef meat. Since 2003, the bulk of global imports have come from Africa (Figure 8).<sup>6</sup> While the Nigerian government and trading community's concerns leading to recourse to imports bans or restrictions could be legitimate, this is by far the best approach. A preferred way forward is to promote exports and the use of market-friendly import regulations that foster a universal trade rule-of-law environment. The list of potential measures includes (a) a competitive foreign exchange regime, (b) use of the flexibilities
cell were obtained by averaging results of 21 different OLS specifications. In particular, for each independent variable, for calculating the magnitude of the coefficient rating (left hand side), we computed the product of the average coefficient magnitude across all specifications times twice the standard deviation of the variable. Then we assigned magnitude values according to the following criteria: for positive coefficients, we applied a “++++” for any value above 1.2; “+++” for any value between 0.7 and 1.2; “++” for any value between 0.4 and 0.7 ; “+” for any value between 0.1 and 0.4; and finally a “0”’for any value below 0.1. Similarly, minus signs were applied to negative coefficients. For the rating on the significance of the coefficient in each cell (right hand side), we averaged the various significance levels across the 21 specifications, where *** indicates significance at 1%, **” indicates significance at 5%, * indicates significance at 10% and ‘ indicates significance at 15%. Values for number of cities and R-square were computed as simple averages across all specifications.

Sources: All dependent variables drawn from UN 1998 and EOS 2003; globalization index downloaded from Foreign Policy Magazine, 2002 (Kearney); country population and urbanization drawn from WDI 2002; city population downloaded from website: www.citypopulation.de ; port, capital, budget and business dummies drawn from KLM 2003; Control of Corruption drawn from KK02; Global city (3) drawn from GAWC (Taylor, 2001); income per capita drawn from Herston-Summers and CIA World Factbook (2001); bribery in Utility drawn from EOS 2003.

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