rafmacalaba/data-use-mentions-extended huggingface dataset:
geographical digital divide between the urban population – where 70 percent of the households own at least one mobile phone – and the rural population – where only 25 percent of the households own a mobile phone (Figure 5).

**Figure 3: Share of urban population and mobile 3G coverage**

<!-- Start of picture text --> 100% Egypt Morocco Tunisia<br>Lesotho<br>Yemen Ghana SyriaCôte d'Ivoire<br>80% Kenya Cameroon Djibouti<br>Nigeria<br>Sao Tomé and Cabo Verde<br>Swaziland<br>60% Principe Congo<br>Sudan<br>Zambia<br>40%<br>Mauritania<br>20%<br>0%<br>0% 20% 40% 60% 80% 100%<br>Urban population (% of total population)<br>3G population coverage (%)<br><!-- End of picture text --> Source: GSMA (2018), World Bank World Development Indicators)

**Figure 4: Digital device ownership gap between the richest and poorest households by quintile**

<!-- Start of picture text --> NATIONAL 10%<br>AVERAGE 75%<br>Richest quintile 27% 89%<br>4th quintile 9% 86%<br>3rd quintile 3% 81%<br>2nd quintile 2% 70%<br>Poorest quintile 0% 41%<br>Computer Cellphone<br><!-- End of picture text --> Source: Calculations based on EDAM4

**Figure 5: Digital device ownership gap between urban and rural households**

<!-- Start of picture text --> NATIONAL 10%<br>AVERAGE 75%<br>13%<br>Djibouti City<br>89%<br>Urban (except 7%<br>capital) 70%<br>
with higher gaps emerging in the main rainy and harvesting seasons relative to the short rainy season. However, we cannot reject the equality of reporting gaps across all three seasons. At the intensive margin of work, results are measured with less statistical precision as we do not find meaningful differential self/proxy survey design effects between girls and boys within and across seasons. A detailed power analysis shows that self-reporting appears to yield a good compromise between survey precision and costs for national surveys on child labor.

Our paper contributes to the measurement of agricultural data in some domains. First, we contribute to recent randomized studies in developing countries that analyze the effects of survey design on outcomes such as adult labor (Bardasi et al 2011), health (Das et al 2012), and education (Baird and Ozler 2012).<sup>2</sup> For child labor, Dillon et al. (2012) document the extent of variation in child labor statistics across respondent type and length of the child labor module in Tanzania. Dillon et al. (2012) reported that using a proxy or asking the child survey questions directly does not affect child labor statistics while using a short labor module generates lower child labor statistics by defining more precisely what work means, thus filtering out children that report labor market activities instead of domestic activities. While our experimental research design is motivated by and broadly similar to Dillon et al.’s (2012) study, we focused on agricultural activities and collected data in three different seasons of the agricultural calendar to account for the seasonality of self/proxy discrepancies.

Second, this study also contributes to the small but growing literature on seasonality and recall in agricultural data that, through its focus on adult labor, agricultural inputs and harvest measures, has cautioned that the degree of distortion in agricultural statistics depend, among other things, on the level of data aggregation and the seasonal timing of household surveys (e.g., Beegle et al 2012, Arthi et al. 2018,
|_6%_|_8%_|_9%_|_18%_|_3%_|
|_2_|_Top 1%_<br>_consumption_|_Not Monetary_<br>_Poor_|_49%_|_46%_|_21%_|_21%_|_16%_|_25%_|_5%_|
|_Wave_|_Top 5%_<br>_consumption_|_Not Monetary_<br>_Poor_|_58%_|_28%_|_11%_|_12%_|_8%_|_17%_|_13%_|
||_Top 10%_<br>_consumption_|_Not Monetary_<br>_Poor_|_58%_|_29%_|_11%_|_12%_|_8%_|_18%_|_13%_|
|_1_|_Top 1%_<br>_consumption_|_Not Monetary_<br>_Poor_|_49%_|_22%_|_6%_|_3%_|_7%_|_15%_|_28%_|
|_Wave_|_Top 5%_<br>_consumption_|_Not Monetary_<br>_Poor_|_44%_|_30%_|_7%_|_9%_|_6%_|_19%_|_25%_|
||_Top 10%_<br>_consumption_|_Not Monetary_<br>_Poor_|_40%_|_39%_|_11%_|_12%_|_11%_|_23%_|_21%_|

Source: Authors’ estimates based on GHS Wave 1-3.

Note: For each age group the respective maximum number of deprivations is considered. If a child is not deprived along any dimension, it is considered not deprived, if it is deprived along one or more dimensions it is considered deprived, irrespective of missing information on some other dimensions. However, if a child has missing information for one or more deprivations applicable in its age category
resolution could be used for a geospatial analysis to inform risk and design standards in many regions of the world. Data on water availability for cooling is also central to planning for electricity generation.

In each sector, and for all spending scenarios, we thus explore two extreme scenarios in terms of the knowledge on the spatial distribution of natural hazards and the ability to target strengthening to the places exposed to them.

- In the first scenario, it is assumed that the location and intensity of the hazard are perfectly known, now and in the future, and that different standards can be applied in different locations, depending on the level of risk.

- In the second scenario, it is assumed that the hazard is unknown, or too uncertain to be acted on, and that a uniform standard has to be applied to the full network.

Results for these two scenarios are compared here for three infrastructure systems: power, transport, and water and sanitation.

For the first set of scenarios, it is assumed that future infrastructure assets are exposed in a similar fashion to the existing infrastructure on average in each region (in other words, the sites available for future infrastructure construction are exposed to the same level of hazards, on average, as the sites already in use). We use the exposure to natural disasters calculated by Koks et al (2019) for transport and Nicolas et al (2019) for power. For water and sanitation, since we have no exposure data, we assume that the infrastructure has the average exposure of transport and power infrastructure in each region.

# Power

In the power sector, baseline investment needs range from $298 billion to $1 trillion a year in low‐ and middle‐income countries between 2015 and 2030 assuming current resilience levels. The position within this range depends on energy efficiency, and the timing of the transition towards carbon‐free power generation (which creates stranded assets, such as coal power plants that need to be decommissioned before the end of their lifetime). An
0139|0.102|0.118|-0.0207|0.0131|0.126|
|_(% of GDP, logs)_|(0.0965)|(0.0441)|(0.00883)|(0.104)|(0.114)|(0.0458)|(0.00993)|(0.122)|
|Financial Openness|0.00865|-0.0141*|-0.000974|0.0192|0.0151|-0.00961|-0.000260|0.0243|
|_(Chinn‐Ito Index)_|(0.0193)|(0.00766)|(0.00155)|(0.0208)|(0.0267)|(0.0100)|(0.00215)|(0.0284)|
|_External Factors_|||||||||
|Foreign Growth|0.0178**|0.00145|-0.00209***|0.0198**|0.0164**|0.00105|-0.00237***|0.0187**|
||(0.00715)|(0.00267)|(0.000548)|(0.00769)|(0.00759)|(0.00301)|(0.000660)|(0.00806)|
|VIX Index|-0.141***|0.0182|-0.0145***|-0.134***|-0.159*|-0.00341|-0.0196**|-0.136|
||(0.0471)|(0.0235)|(0.00445)|(0.0507)|(0.0853)|(0
<mark>al., 2013) for an ensemble of five Global Climate Models (GCMs) from the CMIP5 project (Taylor et al.,</mark> 2012).<sup>3</sup> <mark>Because Peru’s most important highway is along the coast, we also overlay the network with coastal flood data.</mark><sup>4</sup> <mark>But since there are no probabilistic data we simply identify the links exposed to coastal</mark> floods and do not calculate disruption losses linked to coastal floods.

<mark>Finally, some parts of Peru are susceptible to landslides triggered by high rainfall. Thus, we use landslide susceptibility maps provided by Ingemmet to associate landslides to flood events.</mark><sup>5</sup> <mark>In places where landslide susceptibility is high, we add landslide impacts to flood impacts for high return period floods.</mark>

# 2.3. Vulnerability of critical links

Here we define the vulnerability of a link as the economic consequences of a hazard event. It is therefore broader than structural damages to the infrastructure and includes impacts on both road users and the government or concessionaries.

Assessing the vulnerability of exposed roads to floods was the most difficult part of this exercise because of a lack of local data on critical parameters such as flood duration, structural damages, and traffic rerouting. Instead of working with best guesses we built several scenarios for each unknown parameter and relationship.

## _Flood duration_

Data on flood duration and on the relationship between flood duration and depth were not available. This relationship generally depends on many factors other than flood depth, such as water velocity and topography. Thus, we constructed simple curves based on information about past floods on the Carretera Central, the only highway for which data were available (Figure 2). We validated those curves with expert consultations.

> 3 The GCMs used are GFDL ‐ ESM2M, HadGEM2 ‐
program initiation. Virtually all barangays had received the program by Round 3, with one exception (N = 32) which we also excluded from the analysis.

Based on information on the start of the program provided by the project implementation office that controls procurement and the release of funds, the duration of exposure apparently was dependent on administrative lags in central procurement rules and centralized actions rather than on preferences in the program areas. Data from this office indicate that due to lags in disbursements, in 70 percent of program barangays, the program did not get started until after the first round of data was collected, and in another 29 percent, not until after the second round. In the second round, lack of funds or delays in releasing funds were a problem reported by 28 of the 33 municipality-level ECD project teams with available data, and was the most common implementation problem they named (OPS 2005).

**Table 7. Percentage Distribution of Children across Exposure Categories by Age, Philippines ECD Study**

|Age in years<br>(Round 3)|<4 months|4-12 months|13-16 months|17+ months|Total|
|---|---|---|---|---|---|
|2|4.42 (24)|33.2 (180)|32.6 (177)|29.8 (162)|100 (543)|
|3|6.13 (53)|33.7 (291)|28.4 (245)|31.8 (275)|100 (864)|
|4|6.51 (50)|34.4 (264)|28.4 (218)|30.7 (236)|100 (768)|
|5|5.67 (49)|39.2 (339)|24.1 (209)|31.1 (269)|
Republic, Estonia, Latvia, Lithuania, FYR Macedonia, Hungary, Poland, Romania, Slovakia and Slovenia. The FSU country group irtcludes Armenia, Azerbaijan, Belarus, Georgia, Kazakstan, Kyrgyz Republic, Moldova, Russia, Tajikistan, Turkmenistan and Ukraine.

- 4 We had to be careful for the exchange rate used in calculating dollar GDP given the large changes in real exchange rate for this period. Some smoothing was necessary, which was done using the World Bank Atlas _$-_ GDP figures.

- _5_ Sobol, 1996, also highlights the rapid surge in private capital flows to Central and Eastem Europe.

4
models provide a large gain in efficiency compared with traditional ELL methods that do not incorporate Empirical Best methods.

We compare the estimates generated by a household EBP model with direct estimates obtained solely from the survey, as well as the well-known Fay-Herriot area-level model.<sup>7</sup> The latter sacrifices precision by discarding the variation in the geospatial indicators across subareas within target areas. Once the area estimates are obtained from the household EBP and Fay-Herriot models, we compare them to nonmonetary poverty rates calculated directly from the full census. The availability of census data provides a credible benchmark for assessing how different methods and their variants perform, both in terms of the accuracy of both the small area point estimates and their confidence intervals. We compare the predictions from different methods in terms of their precision, their accuracy, and the extent to which the estimated confidence intervals contain the true value, which is known as the coverage rate.

The main result is that incorporating remote sensing data in an EBP framework substantially improves the accuracy and precision of small area estimates of non-monetary poverty, largely by incorporating information from non-sampled subareas.<sup>8</sup> This comes at no cost to coverage rates in Sri Lanka and a moderate cost in Tanzania, compared with standard direct estimates. The corresponding efficiency improvement is comparable to roughly tripling the size of the survey in Sri Lanka and quintupling it in Tanzania.<sup>9</sup> The small area estimates moderately underestimate mean squared error by, first, failing to account for uncertainty in estimated variance parameters from the model, second, by incorrectly assuming that sample observations are independent within areas, and third, by failing to incorporate sample weights in model estimation. Estimated coverage rates remain respectable, however, at 75 percent in Tanzania and 84 percent in Sri Lanka when benchmarked. This is comparable to the 76 percent coverage rate in both countries when using standard direct estimates. In Tanzania, the estimates from the unit level model are roughly as accurate and moderately more
|<br>1|
|<br>Tenure perceived very insecure|0.246|0.225|0.248|0.327|0.270|0.393|0.067|0.045|0|0|1|
|<br>No. of agric. parcels|3,670|299|3,371|208|428|719|450|460|2,032|690|948|
|Population size|1 201 399|108 825|1 092 575|<br>50 829|130 802|302 875|132 356|195 313|<br>683 002|<br>223 345|295 052|
|Total no. ofparcel samples|7 457|1 341|6 116|625|789|1328|856|877|4263|1312|1882|

_Source_ : Own computation from 2017/18 Quarterly Labor Force Survey conducted by Zambia’s Central Statistical Office. Sampling weights adjusted with non-response probabilities are used to estimate the mean values.

_Note_ : Questions on area and gender-differentiated transfer rights administered only in quarters 3 and 4 (for 1,828 residential and 2087 agricultural parcels).

24
electricity use in the Yemen is remarkably high compared to other middle income countries. 50

# **4.2.2 Policy implications**

The key policy implications of this discussion are that:

- subsidised universal service is often a poor policy objective. Where governments do not have such an objective, there is no need to restrict entry on this account;

- in many places, attempts to provide universal service through crosssubsidies supported by exclusivity have failed to achieve their

- 49 Solo and Snell (1997).

- _50_ ESMAP (1991).

_46_
Linkages

This section reviews countries’ exposure to macro‐financial and climate related risks. We start with a discussion of physical and transition risks and then seek to identify countries that are subject to “double jeopardy” due to the simultaneous presence of both risks. The data and analysis presented in this section are subject to a range of caveats and should thus be considered as an initial illustration of macro‐financial – climate risk linkages. The chosen climate risk indicators provide a broad indication of countries’ vulnerability. However, there is ongoing work on some of these risks (e.g., transition risks) to develop better indicators. Second, country coverage is limited for some of the indicators. The listing of countries that are subject to both elevated macro‐financial and climate related risks can thus not claim to capture all countries that may face high twin risks. Another caveat is that the classification of countries according to climate related risks is very simple, dividing countries for each indicator into two groups, depending on whether they are above or below the median of the indicator. Additional work will be required to identify more appropriate thresholds for risk categories. Further analysis will be required to deepen the understanding of macro‐financial – climate risk linkages and to address some of these caveats.

### _Measuring climate‐related and macro‐financial risks_

This section presents information on four sets of indicators used to measure climate change‐related physical (linked to extreme weather events and gradual global warming) and transition risks (linked to high carbon intensity or high fossil fuel exports). For the measurement of macro‐financial risks, we use macro‐financial risk indicators generated by the Economist Intelligence Unit (EIU).

### _Extreme weather events_

We use data from the Emergency Events Database (EM‐DAT) compiled by the Centre for Research on the Epidemiology of Disasters (CRED) as the measure of countries’ vulnerability to extreme weather events.

23
FIGURE 4. Yearly Estimated Coefficients of Diaspora on R&D Offshoring, 1991–2010 <!-- Start of picture text --> Year<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>.8<br>.6<br>.4<br>ln (Diaspora) * time dummies .2<br>0<br><!-- End of picture text --> Source: Authors’ analysis based on data described in the text.

Furthermore, these specifications (columns [1] and [2]) are used to include the focal variable interacted with time dummies, in order to explore the effects of skilled diasporas over time. Figures 3 and 4 present the estimated coefficients of the interaction variables, and show quite interesting, although expected, results. As can be seen, diaspora effects over time on co-inventorship and R&D offshoring present a marked decreasing trend. Quite likely, the use of information and communication technologies (ICT) has made the utilization of international ethnic ties less relevant now than 20 years ago. Similarly, the economic and institutional development of emerging economies has also contributed to mitigate the role of skilled diasporas in reducing the costs of asymmetric information—for example, think of the role of intellectual property in these emerging countries in the aftermath of their subscription to TRIPs, the Trade Related Intellectual Property Agreements that comes with membership of the World Trade Organization (WTO).

Columns (3) and (4) of table 2 mimic estimations (1) and (2) but control for time-variant multilateral resistance. While country FE controls for average multilateral resistance to collaborating over time (Feenstra 2004), some elements of this multilateral 25
.01, ** p<0.05, * p<0.1 The table contains the results from a single, exactly-identified OLS instrumental variable regression for 2009, 2014, 2019, and 2020 Cambodia Socioeconomic Survey (CSES) data.

In sum, the increase in Cambodian exports arising from trade policies in the last few decades correlates with improving local labor market outcomes. First, the decrease in informality rate has proven to be related with increases in trade exposure. Moreover, in those districts where trade exposure is higher, workers (especially women) tend to be working more, which might help mitigate gender disparities (at least in terms of participation). These findings are all supported by the literature and classical theories that increases in trade are beneficial for local labor market outcomes.

# **5. Conclusions**

In Cambodia, the trade liberalization policies of recent decades have coincided with one of the strongest growth rates among developing countries. Moreover, structural transformation from agriculture to other industries—largely provoked by increases in trade—boosted job creation, triggered productivity gains, and helped reduce poverty and inequality.

This paper expands on the Cambodian trade liberalization literature by using the most recent data available to examine the effects of trade policies on trade flows, and how increasing 26 | P a g e
# **Figure A3: Fiscal data**

A. Government debt held by external creditors <!-- Start of picture text --> Percent Median Interquartile range<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>C. Short-term debt to total government debt<br>Percent Median Interquartile range<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br><!-- End of picture text -->

# C. Short-term debt to total government debt

# B. Foreign-currency debt to total government debt

<!-- Start of picture text --> Percent Median Interquartile range<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> _Source:_ Haver Analytics _Note:_ Shaded area reflects Federal Reserve hiking cycles. A. Sample includes a maximum of 25 countries from 2008Q1 to 2021Q2.

B. Sample includes a maximum of 20 countries from 2008Q1 to 2021Q2.

C. Sample includes a maximum of 43 countries from 2000Q1 to 2021Q2.

38
coming out of poverty. In short, the core results reported in the introduction are not highly sensitive to the omission of these admittedly important countries.

# **Table 9. Alternative Estimates of Global Self-Reported Food Insecurity Trends after Allowing for Omitted Countries (Millions of People)**

||**Estimated change in global food insecurity,**|
|---|---|
|**Estimation scenarios**|**2005–06 to 2007–08**|
|Raw results, 69 countries (excluding China),_covering 57%_<br>_of developing world population_|−132|
|As above, plus pessimistic assumptions for 16 omissions,||
|_covering 67% of developing_|−60|
|_world population_||
|As above, plus a 3-percentage-point reduction in China,<br>_covering 87% of world population_|−100|

_Source_ : Author’s calculations from GWP data (Gallup 2011), FAO Global Information and Early Warning System data (2010), and ILO food inflation data (2011). _Note_ : See text in this section for more details regarding the assumptions and data as well as table A3.

Another objection may be that the 2005–06 GWP results are less reliable than subsequent rounds because the first round of the GWP may be regarded as a trial run for Gallup. We have the option of using the second round of the GWP (2007) as a base year instead of the 2005–06 round, but the 2007 round contains fewer countries and does not include China. Nevertheless, the 2007 GWP round includes India and other large countries and therefore covers approximately 43 percent of the population in the developing world. A second potential problem with using the 2007 round as a base year is that maize and wheat prices were already increasing in 2007, so it is difficult to regard 2007 as a pure precrisis period. Thus, we might underestimate the food insecurity impacts of the crisis if the 2005