rafmacalaba/data-use-mentions-extended huggingface dataset:
The population is extracted from the WorldPop 2020 global raster layer (Tatem, 2017; WorldPop, 2019), the cell phone penetration is treated as the GSMA unique number of mobile subscribers (GSMA, 2020), and the smartphone adoption rate is taken from the Research ICT Africa After Access Survey (Research ICT Africa, 2018). Metrics representing the future number of unique individuals with cell phone subscriptions and smartphones are used to ensure that demand represents the number of data-intensive users simultaneously accessing the network at once (capturing both currently unconnected _and_ underserved users). The market share ( Market_Sharei ) is exogenously stated depending on the number of MNOs present, which is treated as 3 in all countries, except Mali (where there are currently only 2 major MNOs) (Observatoire des Marches des Telecommunications, 2019). As not all users access the network simultaneously, an active user rate ( ActiveUsers ) of one-intwenty is used (Holma and Toskala, 2011; Souza et al., 2021). Both the number of unique mobile subscribers and the smartphone adoption rate are forecast forward as a set of exogenous inputs for the simulation model based on their historical trajectory, as illustrated with the scenario trends in Figure 3. Indeed, the penetration rate growth is forecast at 2%, 3% and 4% for the low, baseline and high scenarios, respectively. The high scenario broadly aligns with SDG Target 9c, as defined by the ITU, whereby ~90% of adults adopt broadband by 2030 (International Telecommunication Union, 2020). In contrast, the baseline scenario is an optimistic continuation of the current trajectory (akin to a standard technology diffusion curve), whereas the low scenario is conservative with only modest adoption (representing an untypical technology diffusion curve).

13
# 6.2 Case Example TWO

The second case example is a potential approach for economic evaluation of an intervention to utilize deep learning neural networks to interpret chest radiography (CXR) to screen and triage patients with pulmonary tuberculosis (TB). The example draws on an existing assessment of the diagnostic accuracy of three systems “CAD4TB”, “qXR” and “Lunit” for a hypothetical decision for scale up of the interventions in the Cameroon country context, funded by domestic health resources. The existing retrospective assessment investigates the diagnostic accuracy of the systems using CXR images collected from Nepal and Cameroon as part of different studies. Included populations were adults (aged 15 years or older) with symptoms suggestive of TB (cough more than 2 weeks, fever, night sweats, weight loss). While the existing evaluation provides useful insights into the accuracy of the different systems compared to existing methods of radiologist interpretation of CXR, an economic evaluation would increase the understanding and representation of the different components of value for the interventions. In this example, it is assumed the government seeks information to guide allocatively efficient use of the limited health budget.

**Table10  Framework Example 2 – AI-enabled diagnostic:**

|**FRAMEWORK**<br>**COMPONENT**|**SPECIFICATION**|**CONSIDERATIONS**|
|---|---|---|
|**STEP 1: IDENTIFY T**|**HE DECISION MAKER AND**<br>|**CONTEXT**<br>|
|**STEP 2. DETERMIN**|Country government,<br>Cameroon<br>**E THE INTERVENTION TYP**<br>|Existing evidence is from Nepal and<br>Cameroon, analysis would isolate<br>Cameroon data only and/or consider the<br>transferability of data from Nepal to<br>Cameroon setting<br>**E**<br>|
||
**Consumption and trade shares (** p p **):** The National Accounts Main Aggregates database of the United Nations Statistics Division (UNSD) (UN, 2019) contains long-term data on final CC<sup>�</sup> , NN<sup>�</sup> consumption, exports, and imports at the economy-wide level. The sectoral splits of any of the three variables are, however, not available. On the other hand, the UNSD database does report the sectoral composition of value-added. Hence, given the values of other variables in equation (5), it is possible to infer the sectoral composition of final consumption p . To execute this strategy, I first aggregate the value-added series to the three sector level using the CC<sup>�</sup>

To execute this strategy, I first aggregate the value-added series to the three sector level using the earlier taxonomy. Like raw employment data, the value-added sectoral shares computed using the UNSD data also feature spurious jumps. Therefore, I refine the raw series before using it in the analysis (see data appendix for details). Next, I source data on the sectoral exports and imports shares from a different source, which I use to estimate the net export shares p . I exploit the Atlas of Economic Complexity (Center for International Development, 2018) database that reports a country’s exports and imports across 11 NN<sup>�</sup> sectors. Agriculture and services trade data in the database is presented at the aggregate level and is consistent with ISIC Rev. 3.1. The database reports the industrial data disaggregated into nine subsectors, and I sum across them to construct sectoral exports and imports. Once I have the estimates of p and p (and the estimates of and ) from the above steps, I employ equation (5) to recover final consumption shares VV<sup>�</sup> NN<sup>�</sup> p
.50)|(0.45)|(0.39)|
|Number of food transfer recipients|1.00|1.27|1.70|1.91|
||(1.24)|(1.30)|(1.21)|(1.13)|
|Fraction of transfer recipients within village|0.95|0.94|0.92|0.87|
||(0.18)|(0.19)|(0.21)|(0.26)|
|_D. Reciprocity:_|||||
|<br>Reciprocity of transfers within-village|0.42|0.50|0.67|0.80|
||(0.44)|(0.44)|(0.42)|(0.35)|
|Received transfers from an employer|0.10|0.08|0.03|0.00|
||(0.30)|(0.28)|(0.16)|(0.00)|
|Observations|6,732|7,340|6,742|2,215|

**Source:** Author’s analysis based on original survey data.

**Notes:** The sample includes observations from the baseline survey. The sample is restricted to ultra-poor households in column 1, other poor households in column 2, middle class households in column 3 and upper class households in column 4. For variable definitions, see section 8. All monetary values are in Bangladeshi TAKAs. In 2007, 1USD=69BDT nominal and 1USD=18.5BDT at PPP. Appendix Table S1.2 reports p-values for tests of equality of means across the four wealth classes.

36
A, the marginal contribution of direct taxes to poverty reduction is negative when considering a poverty line of 5.5 dollars per day PPP 2011. The results obtained suggest that there is a possibility for improvement in the Colombian tax system that allows increasing its progressivity and its redistributive effect.

<!-- Start of picture text --> FIGURE V-5: PROGRESSIVITY AND REDISTRIBUTIVE EFFECT OF TAXES IN LATIN AMERICA<br>Panel A: Direct Taxes<br>0.5 0.021<br>0.018<br>0.4<br>0.015<br>0.3 0.012<br>0.2 0.009<br>0.006<br>0.1<br>0.003<br>0 0<br>Kakwani<br>Coefficient<br>Marginal<br>contribution<br>Panel B: Indirect Taxes<br>0.09 0.015<br>0.06 0.012<br>0.03 0.009<br>0 0.006<br>-0.03 0.003<br>-0.06 0<br>-0.09 -0.003<br>-0.12 -0.006<br>-0.15 -0.009<br>Kakwani Coefficient<br>Marginal contribution<br>Source: The Impact of Taxes and Social Spending on Income Distribution and Poverty in Latin America. An Application of the<br>Commitment to Equity Methodology (CEQ), 2017. Colombia World Bank Group staff estimates based on the ENPH 2017<br>Note: The results for all countries other than Colombia were obtained by applying the old methodology of the CEQ, which<br>among other things did not include indirect effects of indirect taxes, accounted for income in kind differently, while the<br>results for Colombia were obtained by applying the new methodology of
. They congregated in the <mark>provincial capital Qala-e-Naw to receive emergency humanitarian assistance.</mark> <mark>To identify patterns of displacement, the team combined displacement data from the IOM Displacement Tracking Matrix (DTM) with Nighttime Lights (NTL) data obtained from NOAA’s VIIRS satellite.</mark><sup>3</sup> <mark>This exploratory study was regionally focused on capturing displacement in and around Badghis. The results suggest</mark> that cumulated displacement movement over 2018-2020 can be proxied by trends in NTL imagery. Settlements with higher net inflows of displaced persons between 2018 and 2020 have larger NTL growth than others, and the results hold with additional robustness checks. Allowing for non-linearity suggests diminishing returns to displacement flows; settlements with the largest level of NTL expansion are those with the lowest level of displacement inflows.

The model uses NTL data to predict whether a settlement was a net receiver of displacement flows in 20182020 and finds promising results. The paper uses a fixed effect model to predict the total inflow of displaced persons and estimate whether a settlement has a positive or negative net inflow, i.e., whether it was an inbound area or served as an outbound area that people fled. We find a significant and concave relationship between the average NTL and the cumulated net inflow of displaced population over 2018-2020. The reverse model correctly classifies 63.2 percent of these settlements, a promising baseline of accuracy that could be improved upon with more training data.

This study provides a proof of concept to test whether population displacements can be proxied by variations in geospatial data. It contributes to the literature on geospatial data and displacement by combining remote sensing data and IOM administrative data in Afghanistan. This country has experienced large-scale displacement and where security and accessibility constraints make ground-truth data sparse. Geospatial data offers a unique opportunity to track the evolution of indicators through time-series satellite images in hard-to-reach areas. With this work, we aim to complement the literature on displacement by providing an
weeks was 14 in urban and 18<br>in rural areas.|

Source: World Bank staff analysis of household surveys _Note_ : All percentages are percentages of households connected to the grid who replied to the questions on blackouts.

# Gender of Head of Household

Is there evidence that female‐headed households tend to be poorer? If they are, examining the relationship between the gender of the head of household and access could be misleading, confusing poverty with gender. Are female‐headed households less likely to have access to electricity, or buy less electricity, than their male‐counterparts, after accounting for per capita and household expenditures?
# 7. Unit Costs

Our results on the determinants of earnings in Peru have corroborated a finding reported for some other countries, that the returns to investment in VTE are comparable to the returns to investment in general education. In most of the studies done elsewhere, however, the costs of VTE have been shown to exceed the costs of general education. In Peru, no such cost differential is clearly evident. Table 11 presents the average central government recurrent expenditure per stulent per year in secondary and post-secondary education in all years between 1960 and 1981 for which data were available.13 For post-secondary education, although some year-to-year variation exists, the Ministry of Education (MoE) data show quite consistently that less was spent per year of study in nondegree granting institutions than Ln universities. The average ratio of VTE costs to general education costs was 0.8 at the tertiary level between 1970 and 1981. Since most of the individuals in our 1985/86 sample would have attended post-secondary education before 1982, the period covered by the data series is qut-de relevant for the purpose at hand.

The situation with regard to unit costs at the secondary level is much less clear. According to the MoE figures, VTE costs were between two and three times more than general education costs in the period prior to 1970. For 1978 and 1979, the last two years in the available series, VTE again outstripped general education in terms of unit costs. For the intervening years, howeveo., the costs of the two educational streams appear to have been nearly equivalent. VTE's costs exceeded general education's on average by about 10 percent only.

Thers is little in the history of Peruvian education and the reforms announced in 1972 and 1982 that helps to explain the wild swings in the relative costs of secondary VTE and general education. Nearly all of the shifts in the relative positi ns of the two are accounted for by shifts in the former rather than the latter. Although there was an overall downward trend over the period, the
At first glance, this suggests support for the first explanation - the labor intensity of household tasks is too high to engage in economic activities. However, the survey questions used to identify participation specifically ask that respondents exclude “housework” when reporting their activities. Following the literature cited above, the significant proportion of women who report being too busy with housework could also suggest that, in fact, these tasks in rural Honduras include activities that fall under the definition of employment.

A closer look at the data illustrates the extent to which agricultural work is male dominated in Honduras even in farm-owning despite its critical economic importance for rural households. To better understand 10 The definition, aligned with ICLS 13, is “all people ages 10 or over who in the week prior to the survey worked at least one hour in a job, own business, or as unpaid family workers... <also those who> were absent during the previous week due to the following reasons: health, leave, vacation, strike, or other factor beyond one’s control.” (Translated from INE’s 2015 EPHPM methodology document <u>https://www.ine.gob.hn/index.php/component/content/article?id=87.) Included as unpaid workers are those who</u> work in product cooperatives, the self-employed, and family members who work in family enterprises including farms.

> 11 Paulson’s (2003) ethnographic study in the central Andes suggests that gender identity and gender roles in indigenous communities in this region could play a role in explaining higher female labor participation. When residents were asked to describe women’s resources and responsibilities, several asked, “What woman? A young woman? Or a mature woman with family?” Others differentiated the rights and responsibilities of widows from those of married women, suggesting that the essentialist category of women does not coincide with the multiple gender identities and resource-management roles at play in the community. When asked whether
# **Figure 4. Government debt in small states and other EMDEs**

_Following a gradual rise since 2011, the average government debt-to-GDP ratio in small states increased sharply at the onset of the COVID-19 pandemic. While it has since declined, it remains higher than before the pandemic and in other EMDEs. Increases in debt among small states have been widespread. Small states rely more heavily on official borrowing than other EMDEs and have lower debt-carrying capacity, with debt risk ratings triggered at lower average debt levels._

## **A. Government debt**

## **B. Shares of small states and other EMDEs with high public debt**

<!-- Start of picture text --> Percent of GDP Percent<br>Small states Small states<br>120 EMDEs excl. small states 70 EMDEs excl. small states<br>60<br>100<br>50<br>80<br>40<br>60<br>30<br>40<br>20<br>20<br>10<br>0<br>0<br>2011 2019 2020 2023<br>2011 2019 2023<br>C. Composition of external public debt by D. Debt level by debt sustainability rating<br>creditor<br>Percent Private Multilateral Percent Small states<br>Paris Club Non-Paris Club 120 EMDEs excl. small states<br>100<br>100<br>80<br>80<br>60<br>40 60<br>20<br>40<br>0<br>20<br>2011 2019 2020 2022 2011 2019 2020 2022<br>Small states EMDEs excl. small 0<br>states Moderate High In distress<br><!-- End of picture text --> _Sources_ : International Debt Statistics (GDDS database); International Monetary Fund; WEO (database); World Bank.

_Note_ : EMDEs =
Harmonized data from the EU Statistics on Income and Living Conditions (EU-SILC) show that in Q2 of 2020 across all EU countries for which data is available, the risk of temporary layoffs or reduced hours declines with household income. Moreover, in several countries in Southern Europe and in Ireland, the probability of employment loss is considerably higher for low-income households (Figure 3). Chetty et al (2020) similarly find that employment loss between January and April 2020 decreased monotonically with income, from 37 percent in the bottom wage quartile to 14 percent in the top wage quartile.

The within-country socio-economic disparities in access to remote work is mirrored by patterns across countries, with richer countries more likely to have a higher share of urban jobs that can be performed from home (Hatayama _et al.,_ 2020). This is largely because the share of self-employed workers, whose occupations are less conducive to working from home, tends to be large in poor countries (Gottlieb _et al.,_ 2020). Jobs in poor countries tend to be more intensive in physical/manual tasks, less intensive in ICT use, and workers do not always have as good internet connectivity at home (Hatayama _et al.,_ 2020). This means that urban areas in poorer countries were likely to be harder hit by lockdown policies and aversion behavior.

The impact of the shock is likely to have amplified some of the pre-existing inequalities within countries. The differences in losses across socio-economic groups are driven at least to some extent by structural inequalities in the labor market – in terms of how jobs before the pandemic were distributed by type among individuals with different circumstances. Crises tend to have a larger impact on households that have lower access to markets, capital, and basic services.<sup>3</sup> This implies that all other things being equal, crises would have larger distributional impacts in economies with greater pre-existing inequality of opportunities – in access to basic services, and to jobs, markets, and capital – that are
# **1 Introduction**

Banks and stock markets operate in fundamentally different ways, with stock markets providing a public platform that directly connects investors with firms while banks serve as an intermediary between savers and borrowers. Such structural differences have inspired an extensive literature on the distinct roles of banks and stock markets in the real economy (e.g., Demirg ̈u ̧c–Kunt and Maksimovic 1998, 2002; Levine and Zervos 1998; Beck and Levine 2002; Levine 2002; Beck and Levine 2004; Shen and Lee 2006; Song and Thakor 2010; Demirg ̈u ̧c–Kunt, Feyen, and Levine 2013; Langfield and Pagano 2016). However, the evidence arising from this research is often inconclusive, especially when it comes to the stock markets (see, e.g., Zingales 2015).

This paper examines how domestic banks and stock markets help exporters survive in foreign product markets, moving beyond the traditional focus of existing literature on domestic output. Financial constraints are even more important in international trade than in domestic production due to additional costs like cross-border transport, customs clearance, conforming with foreign product market regulations, etc. The bilateral character of the data in international trade also allows us to exploit the variation in the strength of domestic banks and stock markets across exporting countries while at the same time holding constant the product market structure as well as the legal, regulatory and institutional environment by focusing on a single destination market (the United States). Last but not least, the highly disaggregated product-level data on international trade are available and directly comparable across many different countries at all stages of banking and stock market development.

The focus of our analysis is the different financing needs of exported products that can be addressed by well-developed financial markets and institutions. Certain products rely on extensive investment in physical capital (Rajan and Zingales 1998), high levels of working capital (Raddatz 2006), or a significant use of intangible assets that are less suitable to serve as collateral (Braun
# I. Introduction

Tropical cyclones (hurricanes, typhoons) have become the icon of climate change with pictures from space parading the front covers of many climate change books and even a new journal on the Economics of Climate Change. The Intergovernmental Panel on Climate Change (IPCC) argues that tropical cyclones and other extreme events are an important reason to support greenhouse gas mitigation efforts. Several scientists report an increase in tropical cyclone intensity over the last 30 years (Emanuel 2005, IPCC 2007a). The IPCC (2001) and Swiss Re (2006) report dramatic increases in tropical cyclone damages over time.

And yet despite these findings, the link between climate change and tropical cyclone damage remains controversial. Partly this is due to the fact that tropical cyclones are rare events and appear to be subject to long term cycles so it is difficult to detect changes in underlying frequencies and severity (Landsea et al. 1999; 2006). There are approximately 50 tropical cyclones a year globally but some years can have very few and others over a hundred. Many things changing over time can influence the damages from storms, most noticeably population and income (Pielke et al. 1998; 2008). Accounting for changes in the vulnerable population and capital in the path of storms, it is not clear that there is any trend in tropical cyclone damages (Pielke et al 1998; 2008). The historic record may simply not be long enough and clear enough to detect how climate may be affecting tropical cyclones.

The average current global damage from tropical cyclones is currently $26 billion/year (EMDAT 2009). Several authors have relied on the general result by Emanuel et al. (2005) that tropical cyclone intensity (wind speed cubed) would increase for the US with warming. Assuming damages increase with the cube of wind speed, US damages would increase by 56 percent (Hallegate 2007). However, most authors find that damages are proportional to a higher order power of wind speed and they predict that tropical cyclone damages in
Authors’ calculations Table A2.2 shows the total population (rural and urban) and the equivalent number of households at a national level based on the household weights.

Table A2.2: Population estimates based on returns from cleaned data sample in survey year

||**Total**<br>**national**|**Total po**|**pulation, cle**|**aned data**|**Total ho**|**useholds, cle**|**aned data**|**Samp**<br>**cl**|**le house**<br>**eaned da**|**holds,**<br>**ta**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**population**|**Rural**|**Urban**|**Total**|**Rural**|**Urban**|**Total**|**Rural**|**Urban**|**Total**|
|Guatemala|1.30E+07|6.73E+06|6.24E+06|1.30E+07|1.22E+06|1.42E+06|2.64E+06|7,857|5,799|13,656|
|India|1.09E+09|7.10E+08|2.39E+08|9.49E+08|1.45E+08|5.49E+07|2.00E+08|76,783|43,644|120,427|
|Indonesia|2