rafmacalaba/data-use-mentions-extended huggingface dataset:
> 14 The input-output matrix includes 66 manufacturing sectors and 16 services sectors. The manufacturing sectors were aggregated to 38 sectors at which sector-specific price deflators were available. The services sectors include:  construction, electricity, gas, water supply, railway transport services, other transport services, storage and warehousing, communication, trade, hotels and restaurants, banking, insurance, ownership of dwellings, education and research, medical and health, other services. Input shares are calculated relative to the total value of inputs sourced.  Banking services constitute on average 5% of all inputs, transport 4.4%, telecommunications 1.6% and insurance 1.4%. An alternative normalization, by gross output, leads to the same conclusions.

16
# _c. Measuring safety_

Safety focuses on the safety of the built environment at the public transit stops. It measures the level of “security” around the public transport stops. Security includes both sexual harassment and incidence of crimes. In practice, safety is measured using the built environment audits conducted in each city. Each public transit stop is assessed based on seven objective parameters (availability of lighting, walking path and security, the openness and visibility of the station, the crowdedness of the station and the presence of women – see section 2.1 for more details). Each of these parameters is assessed on a 4-item scale, and we compute a general index of safety for each station by averaging the score of these 7 parameters. The final indicator provides an index of safety with values from 0 (worse) to 100% (best). In order to pair each household with a measure of safety in their neighborhood, we assign to each household location the average index of safety of the three closest audited stations. This measure should be interpreted as a broader neighborhood level measure of safety around each household.

18
**The fall in foreign lending activity—consisting of cross border claims and claims of local affiliates—was supply driven during periods of extreme European financial stress.** Exhibit VI.4 provides an illustrative exercise which assesses the extent to which financial shocks in Europe were transmitted to EMDEs via European banks’ cross-border and local affiliates activity.<sup>34</sup> The exhibit is based on a panel regression model which disentangles supply from demand.<sup>35</sup> Supply factors are proxied by the 3-month EURIBOR-OIS spread which reflects interbank stress. Demand factors are captured by country-level quarterly GDP growth in EMDEs. The exhibit displays the portions of European foreign bank claims growth that can be explained by supply and demand factors for the _average_ EMDE country.<sup>36</sup> Claims growth is mostly driven by demand for credit in EMDEs, with the exception of a significant slowdown in 2009. Average EMDE demand explained 2 percentage points of pre-crisis claims growth and vanished in 2009. EMDE demand picked up in 2010 but has been falling and only explained about 1-1.5 percentage points. The exhibit clearly shows supply shocks propagated to EMDEs in 2007, 2008, and 2011 when European interbank stress conditions spiked.

**As European banks deleveraged in 2008 and their foreign claims contracted, the** **_domestic_ credit supply in recipient countries was directly negatively affected,** even after taking into account the impact of local demand effects.<sup>37</sup> A 1-percent reduction in foreign claims resulted in roughly a 0.5-0.7-percent decline in domestic credit. This suggests many economies have not been able to fill the gap European banks left behind with likely ramifications on domestic economic growth.

> 34 Note that foreign claims do not include cross-border flows between the parent bank and its affiliates. Moreover, the foreign claims data are not available in currency-adjusted form because the currency composition of cross-border claims in unknown.

> 35 The regression is based on country panel data
classifications, their extensive database is not only uses of market-determined or parallel exchange rates but also develops a _natural_ classification algorithm. Specifically, we use the fine classification of Reinhart-Rogoff that takes values between _1_ and _15_ where higher values indicate a higher level of flexibility in the exchange rate arrangements in place.

The data on _capital controls_ used in this paper is a binary variable collected from the IMF’s _Annual Report on Exchange Arrangements and Exchange Restrictions_ . It takes the value of _1_ in the years when restrictions on capital account transactions are in place and _0_ otherwise (Prasad, Rogoff, Wei and Kose, 2003). The typical problem of this type of data is that, although it captures the presence of controls, it fails to capture the intensity of the controls imposed.

As a result, countries with closed capital account may increase the stringency of those controls by imposing restrictions on current account transactions, multiple exchange rate practices or the surrender of export proceeds while countries with an open capital account may still restrict the flow of capital by imposing other restrictions on cross-border financial transactions (Chinn and Ito, 2007). To capture these aspects, we complement the measure mentioned above with the inverse of the _Chinn-Ito index of financial openness_ which incorporates the different types of restrictions on cross-border financial transactions stated above. We multiply the _Chinn-Ito index_ by -1 to capture the presence of different types of restrictions on cross-border financial transactions. Higher values of this new index would imply more strict restrictions on cross-border financial operations.

The data on intervention in the foreign exchange market is constructed following Levy-Yeyati and Sturzenegger (2007). We aim to show whether FOREX intervention has a lasting effect on the real exchange rate. Although it has traditionally been argued that nominal interventions are unlikely to have a real impact, we examine whether FOREX interventions help to sustain misalignments. According to LevyYeyati and Sturzenegger (2007) we construct a measure of intervention that is not affected by the growth-induced increases in money demand —which in turn may lead to either increases in domestic credit
About the Drivers and Consequences of this Phenomenon?‖ In Thorsten Beck, Stijn Claessens, and Sergio Schmukler (eds.), _Financial Globalization_ .

- Dages, Gerard, Linda Goldberg, and Daniel Kinney. 2000. "Foreign and Domestic Bank Participation in Emerging Markets: Lessons from Mexico and Argentina." _Federal Reserve Bank of New York Economic Policy Review,_ 6(3):17–36.

- de Haas, Ralph, Yevgeniya Korniyenko, Elena Loukoianova, and Alexander Pivovarsky, 2011. ―Foreign Bank During the Crisis: Sinners or Saints?‖ European Bank for Reconstruction and Development, mimeo.

- de Haas, Ralph and Neeltje Van Horen, 2011. "Running for the Exit: International Banks and Crisis Transmission," Working Paper 124, European Bank for Reconstruction and Development, Office of the Chief Economist.

- De Haas, Ralph and Iman van Lelyveld, 2006. ―Foreign Banks and Credit Stability in Central and Eastern Europe. A Panel Data Analysis.‖ _Journal of Banking and Finance_ , 30(7): 1927-1952.

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*Lower Poverty line (LPL)**|14.26|11.39|12.3|
|**Upper Poverty line (UPL)**|33.22|30.13|33.6|
||**_National average_**|**_of household specific poverty_**|**_lines - SYP per month_**|
|**Lower Poverty line (LPL)**|NA|1458|2183|
|||_($3.6 daily, 2011PPP)_<br>_($3.9 daily, 2017PPP)_|_($4.2 daily, 2011PPP)_<br>_($4.6 daily, 2017PPP)_|
|**Upper Poverty line (UPL)**|NA|2052<br>_($5.1 daily, 2011PPP)_|3037<br>_($5.8 daily, 2011PPP)_|
|||_($5.5 daily, 2017PPP)_|_($6.4 daily, 2017PPP)_|

_Source:_ Abu-Ismail et al. (2011); El Laithy, Abu-Ismail (2005). World Bank staff estimates for 2011 PPP conversion. Notes: SYP value of the poverty lines is expressed in current prices. Daily 2011 PPP value of the PL provided in parenthesis for reference _._ At present, the World Bank Poverty and Inequality Platform (PIP) only includes poverty estimates at international poverty lines for 2003, the only year for which microdata are available in the Global Poverty Monitoring Database. However, building on tabulations available in Abu-Ismail et al. (2011) and on the CBS website, it is possible to gather all relevant information to compute grouped-data poverty estimates at international poverty lines for 2007 and for 2009, hence to update the available pre-conflict baseline poverty series (Box 1).

> 6 Information from the HIES 1997 was collected between October
# **Table S18. Prevalence of food insecurity as it relates to health and financial concerns**

||Moderate or<br>Severe Food<br>Insecurity|Severe Food<br>Insecurity|
|---|---|---|
|Concerned about|0.042|0.010|
|household health|(0.030)|(0.021)|
|Concerned about|0.206***|0.062***|
|household finances|(0.031)|(0.019)|
|Ethiopia|-0.170***|-0.093***|
||(0.023)|(0.018)|
|Uganda|-0.311***|-0.173***|
||(0.022)|(0.015)|
|Ethiopia-Uganda|0.000|0.000|
|Observations|6,893|6,893|
|R-squared<br>_Note_: Each column reports|0.089<br>results from a sing|0.051<br>le regression of the|

_Note_ : Each column reports results from a single regression of the prevalence of either moderate or severe food insecurity or just severe food insecurity. Food insecurity is measured using FIES. The FIES variable is regressed on indicators whether or not the respondent is concerned about the health impacts of COVID-19 and the financial impacts of COVID-19. Also included are indicators for each country, with Malawi as the base case. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data come from the most recent round of phone survey data available. This is round 3 in Ethiopia, round 2 in Malawi and in Uganda. Nigeria is not included because questions about concerns were not asked in the
.1. Labor Market Monitoring and Analysis**

Online job-portal data can be used to monitor and analyze labor market trends in real time. Big data can complement official government statistics and other traditional forms of employment information-gathering, such as sample-based labor force and enterprise surveys, to provide richer and more granular data. Labor force surveys capture information on the personal characteristics, wages and experience levels of employees, as well as employer characteristics such as firm size and workforce composition. Enterprise surveys focus on formal-sector business and production activities and assess employee productivity. Both labor force and enterprise surveys tend to be conducted infrequently and focus on a small number of variables. A key advantage of online job-portal data is that it allows for a much larger sample to be monitored in real time, enabling analysts and policy makers to develop a more thorough and timely understanding of evolving conditions in the labor market.

8
low ability children), CCTs significantly outperform UCTs as we can reject the equality of the coefficients, while we cannot reject that equality non-marginal children.

> 27 Results (not shown) using the two alternative empirical specifications lead to similar conclusions. 28 We also collected parental self-reports on attendance for the two weeks prior to the survey and so the results are not directly comparable with the school-based data. Moreover, since some villages were surveyed after the end of the school year, this self-reported measure is potentially less-reliable. Nevertheless, the analysis using parental selfreports of attendance yield results similar and consistent with the Table 8 results.  .

> 29 Similar results using the other empirical specifications are presented in Appendix Table 4.

22
for the sales tax in the most recent period considered (solid lines). This is despite the fact that we consider payments made until April 2015, the payment deadline for scal year 2014. There are two potential explanations for this downward sloping prole of the average payment rate: a fall in payment compliance over time, and the presence of late-payers. If taxpayers service their tax obligations with substantial delays, then more recent periods will display lower payment compliance than earlier periods, for which a longer series of payment data is available.

To distinguish these two explanations, we add the average payment proles based on payments made until April 2013 and April 2011 respectively (dashed and dotted lines). These payment proles are similarly downward sloping and shifted to the left, suggesting that payment delays are indeed at play. For instance, while the income tax payment share for 2010 is about 88% when measured by April 2011, it is above 95% when measured in April 2015. A small share of taxpayers thus pay with large delays. This is consistant with anedotal evidence that cash-constrained rms make tax payments when they are liquid rather than when the payment is due, as nes and interest fees are small.<sup>30</sup> At the same time, the payment share, as measured by the period-specic payment deadline, is also decreasing over time, from 88% for the income tax in 2010 to 81% in 2014. This suggests that payment compliance has also fallen over time. Overall, however, payment compliance is relatively high, not only across rms, but especially in the aggregate. As non-payers and late-payers are disproportionately small, the aggregate payment rate, that is the sum of payments divided by the sum of nal liabilities, is close to 100% soon after the payment deadline and stable over time.<sup>31</sup> Although the enforcement of outstanding payments through administrative and judicial procedures is part of the regular activities of the tax authorities in Costa Rica
Further, it should be taken into account that there is no consensus among the centers about what is comprised in the marginal costs for providing the full suite of global products to an additional country. For some centers like MetService, it mainly includes the provision of products, while for others such as the Met Office, the provision of products always goes hand in hand with further support services such as training (Met Office 2015a; MetService 2015). In order to consider this variety in services and accounting, a range of different cost estimates were considered.

While Hallegatte (2012) is the most suitable source of benefit data for this research, we have to expect inaccuracy and bias inside the benefit categories measured by Hallegatte. There are many uncertainties associated with quantifying disaster risks, as they might be correlated to other factors such as climate change, and therefore could lack direct causation (World Bank 2013; IPCC 2012).

Strong assumptions had to be made to estimate benefits, which ultimately influences the results. This was necessary due to data limitations. Nevertheless, while it cannot be claimed that the data used to measure benefits are fully accurate, the direction of any bias is known. Net benefits and benefit-cost ratios resulting from this study are considered conservative estimates of the true potential benefits of providing the full suite of forecasting products on LICs. Lack of data is addressed by using ranges instead of point estimates and by conducting sensitivity analyses, with a bias towards conservative estimations (lower benefits).

# 3.6 Sensitivity analysis

To analyze sensitivity, three models were developed:

- Primary (previously described): assumes maximal values for both costs and benefits. As the benefit estimations are in any case conservative, this scenario is assumed to be the most realistic one.

- Conservative: assumes maximal values for costs and minimal values for benefits.

- Optimistic: assumes minimal values for costs and maximal values for benefits.

All models were analyzed over a period of 15 years, with no economic growth and no inflation. Alternative discount
affect wellbeing, measured using a _social welfare function_ ( _welfare_ is the metric used by economists to measure wellbeing). We first present the model, and the global data used to calibrate it. We then use the model to define and assess the socioeconomic resilience of 117 countries to natural disasters, identify policy priorities to reduce the impact of disasters on wellbeing, with a focus on adaptive social protection, and help design holistic risk management strategies tailored to each country. The model and the data are available online.<sup>1</sup> We propose a new quantifiable definition of _socioeconomic resilience_ , the ratio of _asset_ losses to _welfare_ losses<sup>2</sup> : Socioeconomic resilience �<sup>Asset losses</sup>

## Welfare losses

With this definition, socioeconomic resilience can be considered as a driver of the risk to wellbeing – measured through the expected welfare losses due to natural disasters – along with the three usual drivers, hazard (the probability an event occurs), exposure (the population and assets located in the affected area) and asset vulnerability (the fraction of asset value lost when affected by a flood): 1 The code and data set are available at github.com/adrivsh/resilience_indicator_public/

> 2 This definition combines the notions of macroeconomic and microeconomic resilience previously proposed by Hallegatte (2014).

2
combines these indicators according to the bureau’s judgment of what factors are important (using a decision rule not trained on data). Subscribers were matched to their financial histories based on an encrypted, anonymized identifier.

The mobile phone data include metadata for each call and SMS, with identifiers for the other party, time stamps, tower locations, and durations. It does not include top-ups, balances, data access, charges, handset models used, or mobile money transactions; thus performance is expected to improve with richer data. The data do not include any information on the content of any communication.

We aim to predict default based on the information available at the time credit was granted, and so include only mobile phone transactions that precede the date of plan switching. Descriptive statistics for the sample are presented in Table 1. Although 85% of our sample has a file at the credit bureau, many of these files are thin: 59% have at least one entity currently reporting an account, 31% have at least two, and only 16% have at least three. By construction, 100% of the sample has a prepaid mobile phone account. The median individual places 26 calls per week, speaking 32 minutes, and sends 24.4 SMS. The data includes the median individual’s phone usage for 16 weeks; an implementation that can obtain longer histories is likely to perform better.

# **3. METHOD**

The goal is to predict the likelihood of repayment using behavioral features derived from mobile phone usage. We consider a sample of completed plan transitions, and consider whether information that was available at the time the credit was extended could have predicted its repayment. Because this sample of individuals did obtain credit, risk is reported among those who received credit based on the selection criteria at the time, which was relatively permissive and spanned the distribution of credit histories (including no history at all).

The credit data provide an indicator for whether a particular borrower repaid their obligation (
(Schick, et al., 2018). The combustion process is an important source of noise. Fires generated from the combustion process dot the landscape with people living close to flare sites reported not able to distinguish daytime from night-time (Schick, et al., 2018). With combustion temperatures in excess of 1,000<sup>0</sup> C, the ambient temperature is raised in areas around the flares, and this negatively impacts local agricultural production (Dung, et al., 2008). It is especially the effect of flaring on air quality that may harm human health. Natural gas contains many different compounds, mainly methane, and a host of hydrocarbon gas liquids (ethane, propane, butane) and nonhydrocarbon gases such as carbon dioxide (U.S. Energy Information Administration, n.d.). Combustion of natural gas is a prominent source of pollutants such as carbon monoxide (CO), carbon dioxide (CO2), volatile organic compounds (VOCs), sulphur dioxide (SO2), polycyclic aromatic hydrocarbons (PAH), nitrogen oxides (NOX) and particulate matter (PMx) in the form of soot or black carbon which can travel over long distances due to the height at which flaring takes place (Fawole, et al., 2016, McEwen and Johnson, 2012).

Despite the importance of gas flaring in Nigeria, and the potential health impacts, to date there are only studies that compare health outcomes in a few communities with and without flaring sites (see Gobo et al., 2009 and the review in Ite et al., 2016). Moreover, these studies are not in the economics literature. This paucity of evidence is not surprising because identifying the health impacts of flaring requires detailed data on air quality, flaring volumes, and health records. To bridge this gap, we link geo-referenced child health data from Demographic Health Surveys with satellite-detected gas flaring locations in Nigeria (and estimates of gas flaring volumes). We examine the
and reported. This has been pioneered by the French government, as described in the box below: _Box 1 The Introduction of Procurement Cards by the French Government_ The French government introduced procurement cards to improve flexibility, and reduce commitment processing, and imprest accounts. The government partnered with a French bank to introduce procurement card services within the framework of a public market. Two types of cards were introduced:

- Level 1 cards: These cards are used for occasional purchases, such as a shop or online purchases, that can be made outside public markets. They can only be used with registered vendors.

- Level 3 cards: These are used for purchases made within the framework of registered public markets. They require software implementation on the vendor side, so as to transmit transaction information to the partner bank.

A limit of EUR 2,000 per transaction is applied that spending units can purchase up to. The cards may be regarded as “credit cards”, as the vendor is paid immediately by the Bank and the Bank gets reimbursement from treasury on a monthly basis. All card transactions are aggregated monthly by service, to generate a “record of bank transactions”. These records are sent electronically to the FMIS (Chorus), where they generate payment requests. All payment requests are checked and approved by services, to generate accounting records, and the refund payment to the bank.

A challenge in implementation is that there are limited numbers of points of sale. Vendors must be registered (levels 1 & 3) and must implement software to transmit public market references (level 3 only). Verification and approval tasks can be time consuming and may result in delays in processing the refund payment to the bank.

_Source:_ Ministère de l’Action et des Comptes Publics – Agence pour l’Informatique Financière <u>de l’Etat.</u> In many countries this type of flexibility is de facto already in place as they use manual or excel based systems, allowing them to capture and
>0<br>0<br>Energy Transport Telecoms Water and<br>2010 2011 2012 2013 2014 2015 2016<br>Sanitation<br><!-- End of picture text --> Sources: Bhattacharya (2012), China Economic and Industry Data Database (CEIC), Consensus Economics, General Statistics Office of Vietnam, Haver Analytics, Inderst (2016), Investment and Capital Stock database, International Monetary Fund, World Bank.

A. Share of countries in EAP region with investment growth below the long-term (1990-2008) average or negative investment growth (“contracting”).

B. Weighted averages of gross fixed capital formation growth rates in the public and private sectors, respectively, in constant 2005 U.S. dollars. The sample includes nine EAP economies.

C. Five-year ahead consensus forecasts made in the year denoted. Weighted average.

14