rafmacalaba/data-use-mentions-extended huggingface dataset:
- Alberto Abadie, Alexis Diamond, and Jens Hainmueller. Comparative politics and the synthetic control method. _American Journal of Political Science_ , 59, 06 2012. doi: 10.2139/ssrn.1950298.

- Accenture. 10 ways COVID-19 is impacting payments. `https://www.accenture.com/ _acnmedia/PDF-123/Accenture-10-Ways-COVID-19-Impacting-Payments.pdf` , 2020.

- Sumit Agarwal and Wenlan Qian. Consumption and debt response to unanticipated income shocks: Evidence from a natural experiment in singapore. _American Economic Review_ , 104(12):4205–30, December 2014. doi: 10.1257/aer.104.12.4205. URL `https://www.aeaweb.org/articles? id=10.1257/aer.104.12.4205` .

- Sumit Agarwal, Chunlin Liu, and Nicholas S. Souleles. The reaction of consumer spending and debt to tax rebates—evidence from consumer credit data. _Journal of Political Economy_ , 115(6): 986–1019, 2007. ISSN 00223808, 1537534X. URL `http://www.jstor.org/stable/10. 1086/528721` .

- Sumit Agarwal, Gene Amromin, Souphala Chomsisengphet, Tim Landvoigt, Tomasz Piskorski, Amit Seru, and Vincent Yao. Mortgage refinancing, consumer spending, and competition: Evidence from the home affordable refinancing program. Working Paper 21512, National Bureau of Economic Research, August 2015. URL `http://www.nber.org/papers/w21512` .

- Sumit Agarwal, Wenlan Qian, Bernard Y. Yeung, and Xin Zou. Mobile Wallet and Entrepreneurial Growth. _AEA Papers and Proceedings_ , 109:48–53, May 2019. URL `https://ideas.repec. org/a/aea/apandp/v109y2019p48-53.html` .

- Nader Alber and Mohamed Dabour. The Dynamic Relationship between FinTech and Social Distancing under COVID-19 Pandemic: Digital Payments Evidence. _International Journal of Economics and
%|48.5%|75.2%|83.4%|25.5%|
|<br>Share of workers registered with any social security institution related to|54.7%|58.9%|20.0%|57.5%|62.2%|37.1%|57.1%|62.2%|20.7%|
|your mainjob||||||||||

_Notes:_ Population estimates in 2014 are of the 15+ population. _Source_ : LFS 2009-2014. World Bank author’s calculations.

27
- (d) Temporary or permanent changes in relative prices of (i) consumption goods, (ii) wage rates, (iii) production goods (iv) asset prices, that arise through knock-on effects on the rest of the economy

- (e) Involuntary unemployment if the crisis leads to a generalized economic downturn.

Some relative price shifts and some changes in fiscal policy resulting from the crisis can also benefit some households. For instance, a program of debtor relief may be adopted which conveys considerable benefits to debtor households.

# _Impact on depositors_

Some indications of the distributional implications of the first channel can be calculated from surveys of financial assets holdings. This has been done for four Latin American countries by Halac and Schmukler (2004). For example, in their data the top decile of income earners own 36 percent of the total _number_ of household deposit accounts and the bottom half of the income distribution own only one of every six accounts. Unfortunately, we do not know from this evidence what is the distribution across income deciles of deposits by _value_ .<sup>5</sup> We do know that only about 24 percent of the adult population has a bank account in Mexico City. Even lower percentages are reported elsewhere.<sup>6</sup> It might, therefore seem that loss of deposits is likely to reduce inequality. If so, this might be taken as evidence that bailing-out depositors is a _regressive_ policy. However, in order to see just how regressive , the skewed distribution of bank deposit holdings needs to be compared with the skewed distribution of household incomes. After all, the top decile of income earners in the Mexico receives about 42 percent of household income whereas the bottom quintile receives little more than 3 percent of household income. Thus the departure from proportionality represents only a small fraction of the total sums disbursed in the bail-out.

Ironically, a policy which begins by financing liquidity problems of the banking system (and foreign exchange outflows) arising from depositor withdrawals but is
light density comes from the National Oceanic and Atmospheric Administration.

Table 2 presents descriptive statistics for all the main variables in our analysis. Our sample includes 1,080 observations that correspond to the 30 municipalities that represent cities of most in Colombia and 36 months.<sup>10</sup> We focus on capital departments capital cities that host about two-thirds of all Venezuelans in our sample (e.g., using the locations reported in the RAMV data) in order to reduce possible biases in crime reporting due to internal conflict that still persists in rural areas (Iba ̃nez et al., 2017). Yet, as a robustness test we also show that our main results hold for different levels of geographic aggregation B. A. (such as aggregating the data by department-month level) in Appendix Panel A of Table 2 illustrates the total number of crimes B the ones re- reported; panel ported _by Venezuelans_ in rates per 100,000 individuals. The rate of all crimes reported by Venezuelan migrants in our sample is 0.79 per 100,000 individuals (panel B). The most common crimes reported by Venezuelans is theft (0.44 per 100,000 individuals). These numbers are quite low, as expected—at the time, Venezuelans represented at most three percent of Colombia’s population, with heterogeneity in terms of locations, naturally. Yet, they have enough variation to exploit for our exercise. Indeed, given the high standard deviations relative to the mean, significant variation is evident across crime reports, cities, and months. Similar trends are observed for crimes perpetrated by Venezuelans (Panel C), where theft is also the most common crime.

In terms of the treatment variable, panel D of Table 2 shows that for the average city in our sample, there were 4,839 Venezuelans who received the PEP migratory status. That number, however, ranged between 13 and 35,729 across all cities in the sample. In proportion to the local
- Variables are not always available for the same population group, for example the most complete recent data on underweight/overweight (measured using BMI) in adults are for non-pregnant women 20-49, whereas hypertension and diabetes prevalence are available for all adults 18+;

- It is impossible to control for confounding variables;

- There may be differences in reporting methods for the same variables;

- Current prevalence of overweight in adults does not only depend on current diet, but on the diet trajectory over the life course to date;

- Risks of NCD similarly do not only depend on current diet, but on the diet trajectory over the life course to date; and

- Time trends (in per capita calorie supply and overweight/obesity for example) may mean that different age cohorts in the population have different health risks, such that analysis should be undertaken for narrower age cohorts and not for all adults combined.

Hence, the results from cross-country regressions presented here are only exploratory, pending more careful analysis using individual country data. Sample sizes for the regressions are modest (generally around 40 countries). A variety of specifications were tried and those presented here had the best fit (using AIC, Akaike Information Criterion to decide among different specifications using the same observations). Independent variables which were not significant and whose inclusion worsened the overall fit, were removed.

The initial results (summarized in tables 6, 7 and 8) are generally consistent with the literature on diet quality, although not all the independent variables are significant. Child stunting and women’s low BMI decrease as per capita calorie supply increases (significantly in both cases) and as diet diversity increases (significant for women but not children). Child wasting is not responsive to per capita calorie supply but decreases significantly as diet diversity increases. Wasting tends to reflect recent factors (e.g. seasonal hunger or epidemics) more sensitively than does stunting. The diet diversity variable may reflect circumstances at time of collection of anthropometric data, while calorie availability represents an
- Plus 50 percent of the GMMI for every child with disability;

- Plus 10 percent of the GMMI if the adult with disability is the only adult in the family.

When calculating the available global income for the applicant family, a number of sources of income are partly or entirely not accounted for, including:

- State financial support provided to the beneficiaries of social pensions or allocations in the amounts not exceeding MDL 1,500;

- MDL 200 from the wage declared by every employed person in the applicant family;

- MDL 200 from the child benefit for every child in the applicant family;

- Material support provided to the vulnerable persons from the applicant family from the local budgets or from the republican fund for social support of the population;

- For the families composed only of work-unable persons (children, adults with disabilities, persons aged above 75 years (62 for the aid for the cold period of the year)) – income derived from agricultural activities.

As of April 1, 2018, the GMMI has been established in the amount of MDL 1,025, up from MDL 961 in 2017. The increase follows the annual indexation based on the CPI index for the previous year (106.6 percent in 2017).

# **2.5. In-kind benefits: Health and education**<sup>**3**</sup>

CEQ’s analyses of the distribution of government expenditures on education and healthcare are based on the BOOST databases,<sup>4</sup> along with other official sources of public finance data (e.g. budget execution reports of the National Health Insurance Fund). The BOOST data represents a comprehensive source of information on the finances of local governments in Moldova. BOOST dataset is compiled based on the raw data extracted from the national Financial Management Information System (FMIS). A typical BOOST consolidates the raw data into one single, coherent database that can be analyzed along various budget dimensions – administrative levels, functions, costing
’s time-invariant employment share. For identification purposes, the key feature of these shares is that they are based on the graduates employed in a department independently of where they studied. Similarly, our relative employment variable measures the proportion of SCP graduates from a field who work in a department independently of where they studied. If these two variables were based on the SCP graduates who obtained their degree in the department rather than those who work there, they might be correlated with local SCP demand and supply and might not be exogenous. As a

> 24 fields listed in Table A.1 yet the field-category fixed effects, _αf_ , refer to the four _aggregate_ field categories from that table. We use field-category fixed effects for a more parsimonious model—particularly given the inclusion of year and department fixed effects—and because some of the 24 fields have very few observations.

16
observations  provide the most positive results for decreasing extreme poverty by 2030: median value of 4.6 percent or 371 million people in relative and absolute terms, respectively. In contrast, the simulations derived by combining data from the 1980–2014 base period with growth rates of per capita consumption provide the worst predictions for global poverty reduction by 2030: median value of 8.9 percent or 724 million people in relative and absolute terms, respectively.

Non-resource-output oriented economies, however, are predicted to achieve and even surpass the 3 percent poverty target by 2030. Although extreme poverty is expected to diminish in relative terms, in several simulation exercises, resource-output oriented economies could see an increase in absolute poverty over the period 2015–2030. Note that there is significant variability in the predicted future poverty outcomes for resource-output oriented countries, implying that given recent historical economic episodes, it is hardly possible to predict precise estimates of poverty rates in these economies. Moreover, poverty is expected to decline in the period 2015– 2030 in relative terms in low-output, middle-output, and high-output growth country categories. Nevertheless, poverty in low-output growth economies is expected to increase in absolute terms. The results show that the high dispersion—low precision—in the simulation outcomes denotes some inability to predict poverty outcomes in countries classified as low-output growth economies. The model simulations predict that high-output growth economies will quickly alleviate poverty below the 3 percent level before 2030. Noticeably, the simulations show a low degree of uncertainty in the expected poverty outcomes of countries with high-output growth rates.

The second main result indicates that changes in relative income inequality are predicted to be on the positive side, meaning that in general, the Gini coefficients across the studied country classifications are predicted to decrease on average over the period 2015–2030. Across this 2015– 2030 horizon, our estimates of the Gini coefficient at the global level are expected to decline between 0.7 and 1.9 Gini points (in the Gini scale of 0–100). The simulation
year or two years earlier ( is statistically significant but and are not). Based on the estimated coefficients, after the outbreak of terrorism, per capita GDP in the Basque Country declined about 10 μμ<sup>1</sup> μμ<sup>0</sup> μμ<sup>2</sup> percentage points relative to the synthetic control region.

Impacts on the formal sector are assessed by Blumenstock et al. (2018), who investigate how private sector firms in Afghanistan respond to changes in the local security situation. Whether the firms are active is assessed based on call detail records from one of the country’s largest mobile network operators between April 2013 and December 2016. Call detail records are from cell phone towers spanning two7
ipation is multifaceted and diverse across countries. This paper mainly focuses on backward GVC participation measures that have been recently developed in the empirical literature, drawing on newly available international input-output tables, including the EORA database, the Trade in Value Added (TIVA) database and the World Input-Output Database (WIOD).

A country’s backward GVC participation measures the import content of its exports relative to the country’s total gross exports. Those imported inputs are predominantly made up of foreign value added but can also contain domestic value added which has been previously exported. The import content of export measure was ...rst introduced by Hummels et al. (2001) using national input-output tables and was more recently computed by Borin and Mancini (2015) and Wang et al. (2013, 2016) based on international input-output tables. This measure takes into account indirect e¤ects (known as the Leontief inverse matrix) where imported inputs are embodied in domestic output, sometimes at several stages, before being used as inputs for exports.

As a robustness check, we also consider forward GVC participation. A country’s forward GVC participation measures the domestic value added in exports that is used by the country’s bilateral partner countries for export production as percent of the country’s total gross exports. In other words, it captures the portion of domestic value added that is not directly consumed by the bilateral partner (which is the ...nal stage in the value chain). For example, ...nal apparel exports from Bangladesh that are exported to and consumed in the United States (US) would not be accounted for in this measure. The measure was developed in Borin and Mancini (2019) based on a bilateral source-based decomposition of exports.<sup>6</sup> Both backward and forward GVC participation measures are such that their construction ensures that trade ‡ows cross at least two country borders –a key characteristic of a GVC.

The country-level backward and forward GVC participation measures
In equation (4), ∆BankDDi,t is the change in Merton distance-to-default variable for bank _i_ in week _t._ ∆SystemDDt is similarly the change in the value-weighted Merton distance-todefault variable for all banks in a given country. Mt−1are lagged state variables and include change in the term spread ( _TERM_ ), change in the default spread ( _DEF_ ), CBOE implied volatility index ( _VIX_ ), S&P 500 return ( _SPRET_ ) and the change in the 3 month t-bill rate ( _RATE_ ). The ΔCoVar variable is then computed as the change in the VaR of the system when the institution is at the _q_<sup>th</sup> percentile (or when the institution is in distress) minus the VaR of the system when the institution is at the 50% percentile: We compute the ΔCoVar measure for each bank in our sample for the pre-crisis and postcrisis time periods separately, and higher ΔCoVar indicates lower systemic risk.

# **2.3. Country Level Variables**

Country level variables are collected from a number of sources. We obtain a deposit insurance variable from Barth, Caprio, and Levine (2008). Specifically, deposit insurance is equal to 1 if a country has explicit deposit insurance and depositors were fully compensated the last time a bank failed and 0 otherwise. We obtain a second deposit insurance variable from Demirguc-Kunt, Kane, and Laeven (2008). Full coverage is equal to 1 if a country offers full coverage and 0 otherwise. Since deposit insurance schemes are often changed during times of crises and we are interested in the impact of deposit insurance on bank risk during the recent precrisis and crisis periods, we take the deposit insurance coverage in existence in 2003 as our independent variable and use bank-year observations from 2004 to 2009 in our study to mitigate the reserve causality problem. Although fixing the deposit insurance measure at year 2003 leads to measurement error in the deposit insurance variable and may bias us against finding any significant results, it alleviates the concern
R U DOM INIC AN E L HONDUR AS E C UADOR<br>R E P. SALVADOR<br>0.027<br>0.026***<br>0.024*<br>0.019<br>0.015** 0.015<br>0.014 0.014<br>0.011<br>0.009<br>0.008*** 0.008<br>0.005** 0.007** 0.007<br>0.004<br>0.003 0.003 0.003 0.003<br>0.002** 0.002<br>0.001** 0.001 0.001<br>-0.001<br>-0.002 -0.002<br>-0.003 -0.003<br>-0.004<br>-0.005**<br>-0.008 -0.006** -0.008<br>-0.009**<br>-0.010***<br>-0.011***<br>-0.013*** -0.013** -0.015<br><!-- End of picture text --> Note: *** p<0.01, ** p<0.05, * p<0.1. Each family color bar corresponds to a specification that includes one type of natural hazard controlled by exposure, coping mechanisms, and region. Source: authors' calculations based on ThinkHazard!, EM-DAT, and SEDLAC database.

14
)|(0.013)|(0.024)|(0.010)|
|Treatment effect for upper class|0.015|-0.030|-0.010|-0.016|
||(0.015)|(0.021)|(0.026)|(0.018)|
|Control mean|0.078|0.338|0.600|0.177|
|Mean for other poor|0.084|0.397|0.507|0.119|
|<br>Mean for middle class|0.065|0.268|0.721|0.182|
|Mean for uer class|0091|0329|0584|0373|
|pp<br>Observations|.<br>32594|.<br>32594|.<br>32594|.<br>32594|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (2), with the addition of indicators for wealth classes and their interactions with treatment and endline indicators, estimated with OLS. The sample includes observations from non-ultrapoor households. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. In Panel A, the dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1 if the respondent reported that her household received any transfers from any ultra-poor, other poor, middle class or upper class households (within her community) respectively. In Panel B, the dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1
- 3 -

On the supply side, limited connectivity complicates other factors as well. For instance, doctors and nurses are generally less available in rural health facilities. In the United States, only 9 percent of physicians practice in rural areas where about 20 percent of the total population lives (Rosenblatt and Hart 2000). As a result, rural physicians often have to see more patients than urban doctors do, therefore, potentially compromising the level of services they provide. In rural areas, fewer medical ancillary services are provided (Bronstein et al., 1997). Medical supplies may also be insufficient in rural and remote areas because of unreliable transport accessibility (Iimi and Rajoela, 2018). These supply-side constraints further reduce the rural residents’ demand for health services.

The current paper attempts to estimate the net impact of transport connectivity on people’s health care service access, by taking advantage of spatially disaggregated rich micro data from Mozambique. It will be focused on the people’s decision-making process on whether or not they visit a health facility when they have a fever. This is a critical decision in particular in many African countries. To prevent illness and death, it is essential to have good access to primary care services (e.g., Starfield et al., 2005). According to Laditka et al. (2009), rural residents are more likely to be hospitalized for ambulatory care sensitive conditions. This could have been prevented if people had better access to primary care, but because they do not, their condition tends to deteriorate.

Despite recent marked progress, malaria remains an important risk in many developing countries, including Mozambique. Children aged under 5 years are the most vulnerable group affected by malaria. Globally, it is estimated that 228 million cases of malaria occurred worldwide in 2018 and 405,000 people died from malaria (WHO 2019). Africa accounts for about 94 percent of global malaria deaths. Nigeria accounts for 24 percent, followed by 11 percent in the Democratic Republic of
.40|0.67|0.72|0.70|0.47|0.20|0.35|
|Brazil (BR)|0.85|0.85|0.47|0.97|0.85|0.75|0.67|0.28|0.42|
|Vietnam (VT)|0.60|0.72|0.39|0.64|0.70|0.70|0.44|0.11|0.37|
|Senegal (SN)|0.38|0.56|0.35|0.41|0.60|0.65|0.30|0.21|0.25|
|Gap: BR - SN|0.47|0.29|0.12|0.56|0.25|0.10|0.37|0.07|0.17|
|Relative Gap|12%|7%|3%|14%|6%|3%|9%|2%|4%|

Note: AGRI, MANF, and SVC represent agriculture, manufacturing, and services, respectively. Overall is the average of Brazil, Vietnam, and Senegal. For the columns on SSBF, the sample is restricted to the firms having sector-specific technologies. Technology measures are weighted by the sampling weights.

75
.0|27.0|35.9|
|Consumer Goods|33.5|102.6|132.0|234.9|62.8|30.7|38.6|26.1|34.5|
|of which textiles|12.1|23.5|47.5|74.2|57.5|11.1|8.8|9.4|10.9|
|clothing|2.3|4.2|7.5|14.9|59.9|2.1|1.6|1.5|2.2|
|Total(in millionofUS dollars)|108.9|265.7|505.9|680.3|58.1|100.0|100.0|100.0|100.0|
|Memo: Share in total exports to EU|3.8|8.5|15.4|15.9||||||

Source: Based on EU Data from UN COMTRADE Statistics.

Table 18 identifies top ten four-digit products that experienced the fastest growth in EUoriented exports over 2000-03. Except for sands (SITC 2733) and other wheat (0412), all of them are manufactures each with the value of exports exceeding US$ 1 million in 2003. None of these exports exceeded US$ 500,000 in 2003, and top three exported less than US$ 100,000. Except for blooms (SITC 6725), with exports worth US$ 12 million in 1997, and halogenated derivatives (5113), with exports of US$ 1.2 million in 1996 and US$ 1.8 million in 1997, not a single product exceeded the one-million dollar benchmark in 1996-2000. Last but not least, it is worth noting that except for air conditioners (7415) and photographic parts (8811) the shares of other products in EU external imports exceed