rafmacalaba/data-use-mentions-extended huggingface dataset:
# _Ratio of loans to deposits_

The loans to deposits ratio is a measure of the liquidity of financial institutions as well as the ability of the banking sector to use available resources. These ratios were highest in South Africa for most of the period (figure 5). They were very low and declining in Egypt. The ratio rose consistently in Turkey over the period, increasing from 37.1 in 2002 to about 130.4 in 2015.

**Figure 5 Ratio of loans to deposits in six middle‐income countries, 2000–16**

<!-- Start of picture text --> 160%<br>140%<br>120%<br>Egypts<br>100%<br>South Africa<br>80%<br>Turkey<br>60%<br>Morocco<br>40%<br>Malaysia<br>20%<br>Thailand<br>0%<br><!-- End of picture text --> _Source_ : Data from the World Bank’s Financial Structure Database (2017).

# _Ratio of liquid liabilities to GDP_

The ratio of liquid liabilities to GDP declined in Egypt between 2008 and 2012 (figure 6). It was higher than the ratio in Turkey and South Africa. Malaysia had the highest ratio. Morocco experienced a continuous increase in the ratio between 2000 and 2015.

13
fixed effects.

all specification, whereas NO2 eventually drops off. This is likely due to the fact that GDP and lights trend increase across most countries over time, whereas NO2 is trend decreasing in many countries during the sample (Table 1, Panel B). Thus, lights are a valuable indicator of economic growth, while NO2 is perhaps preferable when considering cyclical fluctuations.

Table S4 does the same without country time trends by-grade, comparable with the withtime trends Table 3 in the text. Column 1 is equivalent to Column 6 in Table S3. From a trend growth perspective, NO2 is primarily useful in higher-graded countries, whereas lights are primarily useful for lower-graded countries. The basic difference when including time trends in Table 3 is that NO2 also has explanatory power for cyclical fluctuations in nations with lower-graded statistical systems.

Table S5 repeats the analysis pertaining to data manipulation summarized in Table 4, now considering the effects of omitting country-specific time trends. Column 1 of Table S5 replicates Column 3 of Table 4, which showed no evidence of data manipulation with respect to cyclical fluctuations. This is indicated by the counterintuitive negative interaction between lights and the FiW index of autocracy. However, when in Column 2 we omit the country time trend, the positive interaction reappears. We also find that there is a difference between crisis and non-crisis years in this case. Thus, the issues we uncover with respect to lights’ unreliability concerns off-trend fluctuations, specifically.

S12
level surveys conducted in 1999 and 2000 for over 10,000 firms in 81 countries.<sup>20</sup> This database has several advantages over other firm-level databases. First, the survey includes a broad variety of firms of different ownership structures, sectors, legal forms, and – most importantly – different sizes; 80% of the surveyed firms are small or medium-sized, with fewer than 500 employees. Second, firm managers were asked about the obstacles they face in their operation and growth, including several questions related to the financial system.

Managers of the surveyed firms were asked to rate how problematic general financing obstacles are for the operation and growth of their firm. Responses varied between a rating of one (no obstacle), two (minor obstacle), three (moderate obstacle) and four (major obstacle). 36% of all firms rate financing as a major obstacle, 27% as moderate, 18% as minor and 19% as no obstacle. In addition to growth obstacles and firm size, the survey also provides general information on firms such as size, sector and ownership.

Self-reported financing obstacles might be subject to biases if slow-growing firms or firms with low efficiency and productivity report higher obstacles. Using the WBES database, Beck, Demirguc-Kunt and Maksimovic (2005) show that firms reporting higher financing obstacles indeed grow more slowly, but that this relationship is not due to reverse causation. Further, as reported in Beck, Demirguc-Kunt and Levine (2004), firm-reported financing obstacles are negatively and significantly correlated with the efficiency of investment, as measured by Wurgler (2000).<sup>21</sup> While our outreach indicators are available for up to 99 countries and the WBES dataset covers 81 countries, there is no perfect overlap, so that our outreach indicator regression sample contains data for at most 7,000 firms in 71 countries.

> 20 For a detailed discussion of the survey see Batra, Kaufmann, and Stone (2002).

> 21 This is
numbers are in different years, there are some irregular instances where the adult population exceeds the total population by approximately 15,000 people, for example, in rural areas of Ararat.

**Table 1: Population and number of voters across strata**

|Marz name|Settlement<br>type|Strata ID|Population<br>(2022 estimate<br>based on 2020<br>WorldPopdata)|Number of voters<br>(18 or older, 2023<br>election data)|Difference<br>between total and<br>adult population|
|---|---|---|---|---|---|
||||(1)|(2)|(3)=(1)-(2)|
|Aragatsotn|Urban|1|26,738|27,847|-1,109|
|Aragatsotn|Rural|2|98,949|82,622|16,327|
|Ararat|Urban|3|72,294|56,730|15,564|
|Ararat|Rural|4|136,288|150,805|-14,517|
|Armavir|Urban|5|82,953|76,205|6,748|
|Armavir|Rural|6|183,009|137,823|45,186|
|Gegharkunik|Urban|7|65,902|61,823|4,079|
|Gegharkunik|Rural|8|161,812|109,803|52,009|
|Kotayk|Urban|9|137,493|126,680|10,813|
|Kotayk|Rural|10|108,771|94,876|13,895|
|Lori|Urban|11|124,050|134,962|-10,912|
|Lori|Rural|12|87,532
). This paper uses a gravity model to infer bilateral trade frictions from observable trade data by comparing actual trade flows and predicted trade flows in the absence of non-tariff measures. They then show that NTMs such as product standards are highly correlated with these inferred trade frictions.

Finally, the most common approach is to estimate directly the impact of non-tariff

> 14 Computable General Equilibrium (CGE) models, such as the Global Trade Analysis Project (GTAP), can in principle be used to assess the impact of non-tariff measures. However, the various treatments of NTMs in CGE models present a series of problems that have limited their use in the literature. See Fugazza and Maur (2008) for an overview.
at the time they export. The only way to solve this issue would be to use higher frequency data which would allow us to match transactions with daily or monthly exchange rates. Indeed, Fosse (2012) shows using Danish data that moving from annual to monthly data increases the elasticity of unit values to the exchange rate (from 0.14 to 0.19 in his case – see also Mallick and Marques, 2010). This might explain why the literature using yearly trade customs data typically finds lower estimates of pass-through than those found in papers using direct price data at a higher frequency (e.g. Gopinath and Itskhoki, 2010).

> 23We use estimations in levels rather than first-difference for three main reasons. First, exchange rates appear to be stationary in our data (this result might be caused by the low power of unit root tests, as the time series

12
the economic sector from the workers. To overcome this problem, we combine the I2D2 with the WTS database by calculating the aggregated salaries and employment levels for every region and multiplying them by the proportion of wages across the three economic industries calculated at the national level. Additionally, some of the regions in the Western Balkans lack information on the salaries for specific sectors. Therefore, to circumvent this second problem, we input them using a regression model based on the industry, education, and age variables.

Finally, we use data from the GRIP to calculate the travel times across the Western Balkans region and the EU countries. This dataset gathers and harmonizes information on all the road infrastructure for over 222 countries worldwide.

> 1Table 12 lists all the sub-national regions of the Western Balkans included in the analysis

3
14 Logistics. Some key logistical clcmcnis of implementing a household energy survey are legal clearance, lodging/per dicm, transport, availability of cquipment and supplics, supervision and quality control. Depending on how thc survey team is organized, thcsc may be the responsibility of thc director, an administrativc assistant, a supervisor or a commiltec of supervisors. These responsibilitics can bc illustratcd by the main tasks assigned to the supervisor for tl;e CotC d'lvoire Living Standards Survey (1986) which wcre:

- Contacting authorities in villagcs before thc survcy to advise them of the dates of intcrviews;

- Preparing the household questionnaires for a pre-selectcd list of dwellings;

- Helping interviewers to locate households, reviewing all non-contacts and refusals, and replacing sclccted households when necessary; - Verifying that all parts of the questionnaire are properly compltetd before returning to the regional office;

- Coding items that are not pre-coded on the questionnaire;

- Conducting re-interviews of _25_ percent of the households; and

- Reviewing the printouts of both rounds of the questionnaire to detect interviewer and data entry errors, and supervising correction of all errors in the field and in the office.

Thus, in this case, the supervisor had several logistical responsibilities, the most important of which involved quality control.

Selection and Training of Interviewers. Interviewers should be selected from the local population for their knowledge of the language, culture, transport system, etc. Often, female surveyors are preferred in household energy work because of their better rapport with women who are usually the most important users (and gatherers) of household fuels. Generally, the interviewers should not be in any position of social or political authority which would induce a strong bias in answers. Some practitioners go so far as to recommend that no official institution, especially a governmental one, should be in direct contact with the survey sample as this may cause a distortion of responses.

The quality of the interviews also depends on the education of the surveyors. Their training should be planned carefully. This can be done with
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continued working afterwards. Then the analysis focuses on labor market entries and job transitions.

# **6.1 Income Reductions**

Our interest now is to assess whether for workers who continued to work during the pandemic, income reductions were associated to the possibility of working from home and whether there were asymmetries by gender.<sup>20</sup> Unfortunately, we only have a discrete measure of individual income going up, down or staying the same, and we cannot construct a household income measure to assess the relative income falls across genders. However, knowing what happened to women’s earnings from work is still useful to assess possible changes in their economic autonomy as a result of the pandemic.

We estimate a variant of model 2 where the dependent variable is an indicator that takes the value 1 if individual _i_ from country _c_ suffered a fall in her income in wave _w_ relative to the pre-pandemic period. We estimate this model for the sample of individuals who were working both before and during the pandemic. Table 9 reports the results.<sup>21</sup> Once again, the estimated coefficient for the WFH variable is negative and significant, indicating that the probability of suffering an income fall was lower among workers whose pre-pandemic jobs were more compatible with working from home. As expected, the possibility of working from home seems to have played a much more important role in avoiding job losses than income reductions for those who did not lose their jobs —notice that the coefficient of the variable WFH in the first column of Table 4 is more than 50 times larger than the corresponding one in Table 9.

Regarding gender differences, the coefficient of the variable _Women_ is positive and significant, meaning that the probability of suffering income reductions for workers in jobs incompatible with remote work was higher for women than for men. On the other hand, the estimated coefficient of the interaction between gender and WFH is negative but very small and not statistically significant, implying that the possibility of working from home
survey conducted on behalf of the Equality and Human Rights Commission (EHRC) finds that employers in the UK are hesitant when it comes to recruiting pregnant women and young mothers. Sixty percent of the 1,106 employers interviewed for the survey said that a woman should disclose a pregnancy during the recruiting process; 46 percent thought it was reasonable to ask women if they have young children; and a third agreed it was reasonable to ask women about their plans to have children in the future, during recruitment.<sup>2</sup> In India too, married women with children may be seen as having low commitment to the labor market. Newly married women may not be given certain responsibilities in the implicit ‘fear’ that they may get pregnant. Using anecdotal evidence, Johari (2014) notes how Indian employers prefer to hire women without care duties. On their part, female workers with children or other care responsibilities are under considerable pressure to show that they are both good wives and mothers as well as good workers. They also need to live up to societal notions of propriety in their behavior and dress, especially outside the home. Younger, unmarried women too face tremendous obstacles, especially in cultures where female seclusion and older notions of modesty prevail. In India for example, families exert tight control over women’s mobility, including deciding whether a job is safe or appropriate. Marriage, and the social pressure to protect young women’s ‘virtue’, trumps the prospect of getting a good job.

While the empirical evidence on attitudes towards women workers in India is evolving, several studies show the pervasiveness of such discrimination. In a phone survey of 3,360 respondents across three states and one city in India, Coffey et al (2018) asked men and women whether married women should work outside the home, if their husbands earn a good living. There was little variation across the sample in disapproval for married women’s work: in most places, about half of the adults
is about 1% of GDP, which is consistent with collected revenues in Armenia.<sup>14</sup> Given that Armenia participates in preferential trade areas with the other CIS member states, it was necessary to make further adjustments. That is, since, in principle, tariff rates should be zero within these preferential trade areas, we set tariff collections on imports from CIS countries at zero. We then increased the tariff rates for the other regions in our model so that the overall weighted average collected tariff rate is unchanged at the tariff line level. We used the trade flow data, disaggregated by regions and sectors of our model to weight the tariff rates. This

> 13 http://www.armstat.am/file/article/ft_2nish_07_14.pdf 14

> For the year 2008, aggregate data from Armenia show that tariff collections are 1% of GDP.

17
are considered, 8 percent of people in the region that are not PI or IRI are vulnerable to poverty. In addition, bi-maps enable the identification of provinces with a higher incidence of vulnerability to poverty, along with the districts with high natural hazards. They provide evidence for policy makers to start designing appropriate policies and strategies to mitigate the impact of</mark> climate <mark>hazards on the most vulnerable populations. Finally, the study highlights the need to address the indirect effects of climate risk in the region, which might be more closely associated with household resilience.</mark>

# **References**

Aguilar, A. & Vicarelli, M. (2022). El Niño and children: Medium-term effects of early-life weather shocks on cognitive and health outcomes. World Development, Elsevier, vol. 150(C).

Baquie S. & Fuje H. (2020). Vulnerability to Poverty following Extreme Weather Events in Malawi. _Policy Research Working Papers_ N° 35. The World Bank. https://doi.org/10.1596/1813-9450-9435.

Birkmann, J. (2006). Measuring vulnerability to promote disaster-resilient societies: conceptual frameworks and definitions. In: _Measuring Vulnerability to Natural Hazards: Towards Disaster Resilient Societies_ [Birkmann, J. (ed.)]. United Nations University Press, Tokyo, Japan, pp. 9-54.

Cardona, O.D. (1990). _Terminología de Uso Común en Manejo de Riesgos_ . AGID Reporte No. 13, Escuela de Administración, Finanzas, y Tecnología, Medellín,Colombia.

Cardona, O.D., M.K. van Aalst, J. Birkmann, M. Fordham, G. McGregor, R. Perez, R.S. Pulwarty, E.L.F. Schipper, and B.T. Sinh (2012). Determinants of risk: exposure and vulnerability. In: _Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation_ [Field, C
Banking_ 3: 100071.

- Passari, E., and Rey, H. (2015). Financial flows and the international monetary system. _Economic Journal_ 125(584): 675-698.

- Pennings, S., Ramayandi, A., and Tang, H.C. (2015). The impact of monetary policy on financial markets in small open economies: more or less effective during the global financial crisis? _Journal of Macroeconomics_ 44: 60–70.

- Pesaran, M.H. and Smith, R. (1995). Estimating long-run relationship from dynamic heterogeneous panels. _Journal of Econometrics_ 68: 79-113.

- Rabanal, P. (2007). Does inflation increase after a monetary policy tightening? answers based on an estimated DSGE model. _Journal of Economic Dynamics and Control_ 31: 906-937.

- Ramey, V.A. (2016). Macroeconomic shocks and their propagation. In Handbook of Macroeconomics, Vol 2A, ed. J.B. Taylor and H. Uhlig: 71-162.

- Ravenna, F., and Walsh, C.E. (2006). Optimal monetary policy with the cost channel. _Journal of Monetary Economics_ 53(2): 199-216.

- Rey, H. (2015). Dilemma not trilemma: the global financial cycle and monetary policy independence. National Bureau of Economic Research Working Paper 21162.

- Rey, H. (2016). International channels of transmission of monetary policy and the Mundellian trilemma. _IMF Economic Review_ 64(1): 6-35.

- <mark>Romer, C.D. and Romer, D.H. (2004). A new measure of monetary shocks: derivation and implications.</mark> _<mark>American economic review 94</mark>_ <mark>(4): 1055-1084.</mark>

- <mark>Rusnák, M., Havranek, T., and Horváth, R. (2013). How to solve the
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# **2. Data**

The data we use are from the Demographic and Health Surveys (DHS). The DHS are nationally representative household surveys that have been conducted in more than 70 lowand middle-income countries since 1985. Most countries have multiple survey rounds, with an average spacing of about 5 years between surveys. Calculating the maximum absolute child mortality reduction achievable with investments in water and sanitation infrastructure requires recent estimates of water and sanitation coverage and child mortality rates. Since it does not appear plausible to assess current infrastructure gaps and investment needs based on data from the 1980s and 1990s, we exclude surveys prior to the year 2000. In countries where two surveys are available after 2000, we use the most recent survey. As summarized in Table 1, this leaves us with 38 surveys collected between 2000 and 2006, a sample more up-to-date but also smaller than the full DHS sample analyzed in the preceding paper in this series (Günther and Fink, 2010).

**Table 1: Survey List**

|**#**|**Country**|**Year**|**#**|**Country**|**Year**|**#**|**Country**|**Year**|
|---|---|---|---|---|---|---|---|---|
|1|Azerbaijan|2006|14|Haiti|2005|27|Nicaragua|2001|
|2|Bangladesh|2004|15|India|2005|28|Niger|2006|
|3|Benin|2006|16|Indonesia|2002|29|Nigeria|2003|
|4|Bolivia|2003|17|Kenya|2003|30|Pakistan|2006|
|5|Burkina|2003|18|Lesotho|2004|31|Peru|2003|
|6|
account accurately, thus making flood exposure difficult to compare over time<sup>27,28,29</sup> . Another study based on satellite data from 2000 to 2018 documents increasing flood exposure, but omits at-risk populations who were unaffected during the study period and events that were not detected by satellite observations<sup>29</sup> .

Due to data limitations, past studies also focus on certain types of flood, rather than assessing the combined risks from all flood types (that is, fluvial, pluvial and coastal flooding)<sup>30,31,32,33,34</sup> . Others assess risks for a subset of countries only, falling short of full global coverage<sup>35,36</sup> . Similarly, studies that use relatively coarse spatial resolution flood hazard data tend to inaccurately represent major fluvial floodplains. They do not capture pluvial flood hazards and flooding along secondary rivers, drastically underestimating exposure<sup>35,37,38</sup> . One study using coarse flood data projected that the global number of flood-exposed people will reach 1.3 billion by 2050<sup>3937</sup> but a more recent high-resolution study showed that this threshold has already been exceeded by at least 39% in 2020<sup>39</sup> . This illustrates the importance of using high-resolution data to capture the highly localized nature of flood hazards and people’s tendency to avoid settling in the most hazardous locations<sup>355</sup> .

This study contributes to the literature in several ways: First, by considering flood exposure trends with annual frequency, we demonstrate the value of annual monitoring of evolving flood exposure. The study documents significant variation in year-on-year growth rates, thus highlighting the need for spatial planners to be highly responsive. Second, it explicitly distinguishes the growth dynamics of safe versus flood-exposed spatial development. This allows us to document a divergence in flood exposure – regions that are either increasing or