rafmacalaba/data-use-mentions-extended huggingface dataset:
disaster). We include non-linear combinations of these variables to enhance model fit. We then impute the value corresponding to the category with the highest probability.<sup>8</sup>

# _3.3 Other variables_

We use data from the World Development Indicators (WDI) for country-level indicators such as GDP per capita and population size (World Bank 2024). We derive the distance to catastrophe variable based on the centroid of the event (locations of the plants or the epicenter of the earthquake). We construct the distance to nuclear plants from the respondent’s primary sampling unit (PSU) indicator was constructed based on the coordinates of each nuclear plant in operation compiled by the <u>International Atomic Energy Agency (2023).</u><sup>9</sup> <u>Table 1 provides descriptive</u> statistics of all the variables in the analysis.

# **4. Empirical strategy**

We examine the effects of factors determining the respondent’s evaluation of the number of deaths by estimating the following equation for each disaster _d_ : i i i i i , (2) dd dd dd dd dd ii where, i is a set of individual characteristics such as the respondent’s age, gender, highest level OO = �RRFF −AAFF �= αα XX + γγ GG + θθ + εε , jj= 1, ... ,4 of education, marital status, religion, mental health, and main source of information; i is a vector XX of regional characteristics, such as distance to the disaster site, the distance to a nuclear station GG index, and the type of location (urban or rural); is the time-invariant country _c_ fixed effect, and i is an innovation term. ii θθ εε

> 8 In each estimation, we use 80 percent of the observations with no missing responses for a particular catastrophe question to train the model and evaluate the model’s performance on the remaining 20 percent
a road of each particular class, which we denote _rkit_<sup>_n_1.Mirroringwhatwedidforbuilt-uparea,we</sup> construct a similar proportion of roads in the _n_ 2 neighborhood of _i_ , which we denote _rkit_<sup>_n_2.</sup>

# **3 Conceptual framework**

The objective of this paper is to make predictions about the evolution of buildup patterns over time at the high level of geographical disaggregation allowed by our data. While we do not attempt to pinpoint where any construction will occur in the future, we wish to uncover patterns that are relevant for regional planning. Indeed, the population of Africa continues to grow at a rapid pace and this will undoubtedly translate into more buildings. Where these buildings locate will have large consequences for public service delivery as well as for economic growth – two issues that we

> 6There is also a class 5 which is labeled ’unknown road’. Our of an abundance of caution, we ignored these roads in our analysis.

> 7We also have data on roads in 2008, divided into primary, secondary, and tertiary roads. Unfortunately, these categories reflect administrative definitions, and they do not overlap with those used in 1976 and 1986, which focus on the physical qualities of the road. Since we are interested in predicting settlement patterns starting in 1975, this data is less relevant and is ignored in most of the analysis.

8
Madagascar|480,369|||**5,691**|1,483|1,092|**2,575**|3,116|55%|
|Gambia, The|131,368|340|1,126|**1,466**|181|442|**623**|843|58%|
|Congo, Rep.|205,000|||**2,279**|155|689|**844**|1,435|63%|
|Ethiopia|1,936,244|||**11,839**|885|3,392|**4,277**|7,562|64%|
|Malawi|274,005|592|1,881|**2,473**|232|623|**855**|1,618|65%|
|South Africa|5,477,602|||**41,787**|4,648|9,596|**14,244**|27,543|66%|
|Zimbabwe|601,609|1,593|4,477|**6,070**|629|1,367|**1,996**|4,074|67%|
|Zambia|662,526|||**6,771**|432|1,506|**1,937**|4,834|71%|

_Source_ : World Bank staff calculations based on utility annual reports and financial statements. _Note_ : Data for Ghana are available for T&D only.

48
>3% 1%<br>SAR IDA Blend<br>21% Upper 16% 16%<br>EAP middle<br>MNA 34% incom<br>9% e Lower<br>46%<br>middle<br>ECA incom IBRD<br>34% e 68%<br>50%<br><!-- End of picture text --> Source: WIND database.

The WIND database is the most comprehensive and up-to-date database on Chinese BRI-related outside investment. The American Enterprise Institute (AEI) compiles the China Global Investment Tracker (CGIT),<sup>8</sup> which tracks both overseas investment and construction contracts and identifies BRI-related projects (See Annex II for key statistics comparing the CGIT and the World Bank database). However, the AEI database includes fewer countries (38 vs 44 BRI countries with identified investment), it does not include data for

> 8 <u>http://www.aei.org/china-global-investment-tracker</u>

6
Table 14: Changes in health care and mortality in villages with new all-weather roads

|Question|1|2|3|4|5|6|
|---|---|---|---|---|---|---|
|Village|||||||
|1|yes|yes|yes|yes|yes|yes|
|2|yes|yes|yes|yes|yes|yes|
|3|yes|yes|yes|yes|yes|no|
|11|yes|yes|yes|d.k.|yes|d.k.|
|18|yes|yes|yes|no|yes|yes|
|19|yes|yes|yes|yes|yes|yes|
|26|yes|yes|yes|d.k.|yes|d.k.|
|29|yes|yes|yes|no|yes|no|
|30|yes|yes|yes|yes|yes|no|

Q1: Are children taken more regularly to health facilities for check-ups and immunisations? Q2: Do the chronically sick receive better and more regular treatment? Q3: Do villagers receive more timely treatment and thereby suffer fewer long-term problems? Q4: Do doctors come to the village more readily to treat those who are too sick to be transported? Q5: Has mortality fallen as a result of better access to health facilities? Q6: In emergencies, is the poor quality of treatment or absenteeism among doctors still the main problem? d.k.: don’t know.

be substantial relative to the ‘commercial’ ones. It should be remarked at the outset that whereas the great majority of the respondents in the household survey were male, both sexes were strongly represented in the focus groups.

Beginning with the individual households, the respondents in those nine villages that had received an all-weather road
duty exemptions are applied to some food products to help stabilize prices. In times of crisis, emergency food or cash transfer programs to households have been rolled out. In recent years, a few small, localized nutrition schemes have targeted malnourished children, pregnant women, persons infected with HIV and TB patients. Several school-feeding programs, often targeted to girls, have been implemented in selected poor areas. Recent food-for-work schemes set up by the World Food Program sometimes also target women though the programs have limited coverage. Finally, the government has a program of health fee waivers for the elderly and indigent, and for women without resources.<sup>9</sup> The few, short-term, small and ad hoc targeted policies that exist often try to reach elderly women or school-aged girls, and to be gender sensitive. However, there is no sign that existing social protection schemes or policies in Mali have any focus on widows (whatever their age) or their dependents.<sup>10</sup> Neither the government nor donors appear to be particularly concerned about this group. In the next two sections, I turn to the data to examine whether they should be more concerned.

# **3. Are female-headed households poorer?**

This question is harder to answer than one might imagine, and the attempts to answer it given in the literature to date could well be wrong. A data problem lies at the heart of the issue. Examining whether and how the gender differentiation detailed above affects interpersonal differences in well-being is difficult empirically due to a lack of appropriate data. Individual level data detailing the intra-household allocation of resources, consumption, health, work and time allocation are typically not available. Thus, the poverty status or vulnerability of individual household members cannot be directly calculated from most household surveys. One common approach is to compare female- and male-headed households to infer effects of an individual‘s gender on welfare. However, female-headed households (FHH) can be extremely heterogeneous. Some have remitting
also observed in synthetic panels constructed from three rounds of NSS data over the period 2005‐2012 (Dang & Lanjouw, 2015a).<sup>7</sup> Their analysis classifies the population into the poor, the vulnerable and the middle‐class (those who are neither poor nor vulnerable), where the vulnerable population are defined as those with consumption above the poverty line and below a “vulnerability line” (VL) that corresponds to a 20 percent probability of falling into poverty.<sup>8</sup> This yields a VL that is about 72 percent higher than the national poverty line – slightly lower than an alternative VL equivalent to twice the poverty line, which has been used in past work by the World Bank in India as a threshold for vulnerability.<sup>9</sup> Figure 5a shows that 52 percent of the population changed status from 2005 to 2012, using the lower VL. As in the IHDS panel, upward mobility (35 percent of the population) is much more common 7 See Dang & Lanjouw (2013, 2014) for a detailed discussion of the methodology of measuring poverty dynamics with synthetic panels and its application to define vulnerability lines in different countries.

8 The vulnerability line (VL) corresponds to a vulnerability index (VI) of 0.2, which is the probability of an individual being poor in 2012 given s/he is non‐poor in 2005. VL is thus a welfare threshold such that an individual with consumption below this level in 2005 has a greater than 20 percent probability of being poor in 2012.

9 The Country Partnership Strategy (2013‐17) for India (World Bank, 2013), uses twice the poverty line as the vulnerability threshold. It is associated with a vulnerability index of around 0.18, which implies that an individual with consumption below this threshold in 2005 has 18 percent or higher probability of being poor in 2012. Thus the seemingly arbitrary standard of twice the poverty line is
of decentralized mechanisms of data collection. The Uganda case implies that some of the concerns that have traditionally been raised to caution against broader use of diaries, in particular the notion that these might be too expensive or unsuitable for illiterate people, are not supported by the facts. The more participatory nature of diaries could imply that, if properly designed, diaries could not only enhance our understanding of the facts but also be combined with other data collection tools. The usefulness of production diaries may be further enhanced if they could be used to also collect other types of data e.g. on inputs such as labor, that are spread out over the year and thus very difficult to measure based on recall.

Third, while diaries are not a magic solution, they appear to be better suited than traditional recall-based surveys to obtaining estimates for high frequency events with some seasonal component. These include continuously harvested crops but possibly also use of agricultural inputs, especially labor, and output from livestock production. More systematic analysis of the extent to which diaries could help to improve the quality of information in these and related areas, and scaling up of its application, could potentially have high payoffs. This is reinforced by our finding of low attrition and negligible non-response for nearsubsistence producers close to the tail-end of the distribution for whom the errors associated with traditional surveys are often particularly large. Doing so may also have large benefits in terms better understanding the behavioral dynamics underlying agricultural production (e.g. with respect to gender) for a particularly vulnerable group with possibly far-reaching implications for poverty reduction. Identifying the niche, in terms of data and respondent characteristics, for which diaries could be suitably used will require more work but given the potential, this appears to be a worthwhile line of analysis.

15
- End of picture text --> Source: World Bank.

A. LICs = low-income countries. LICs in 2001 had per capita GNI (US$, current) at $755 or below, while LICs in 2020 have per capita GNI at or below $1025 in 2018. B. Other EMDEs exclude the 2001 LICs.

Several factors have contributed to the rapid economic growth of the 2001 LICs. Twelve 2001 LICs were transition economies, of which nine rebounded sharply during the 2000s from their deep recessions in the 1990s. Among non-transition 2001 LICs, rapid investment growth boosted overall economic growth, on average contributing more than one-third to output growth during 2001-18. In six of these countries (Cameroon, the Democratic Republic of Congo, Guinea, Indonesia, Mauritania, Mozambique), investment booms followed new resource discoveries. Debt relief in the early 2000s, and the fiscal space created by it, supported government spending on non-

> 1 LICs in 2020 reflect the country classification of the 2019/20 World Bank fiscal year and are defined as countries with GNI per capita (World Bank Atlas method) of $1,025 or less in 2018; 2001 LICs reflect the country classification of the 2000/01 fiscal year and had GNI per capita of $755 or less in 1999. New thresholds are determined at the start of each World Bank fiscal year in July. Of the 64 2001 LICs, 35 moved to middle-income country status while 29 remained classified as LICs in 2019. Two countries are classified as LICs today that were not part of the 2001 LICs (the Syrian Arab Republic, South Sudan).

> 2 A fourth country, Senegal, temporarily relapsed into LIC status during this period before returning to MIC status in the 2019/20 country classification.

2
- Olley, G. S. and A. Pakes (1996). The Dynamics of Productivity in the Telecommunications Equipment Industry. _Econometrica 64_ (6), 1263–97.

- Pavcnik, N. (2002). Trade Liberalization, Exit, and Productivity Improvements: Evidence from Chilean Plants. _Review of Economic Studies 69_ (1), 245–276.

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informal sector are agriculture, hunting and forestry, wholesale and retail trade, hotels and restaurants and “other community, social and personal activities” (see Table 1).

**Table 1: Informal economy output at current prices by industry and absolute values of population** **<u>projections</u>**

|**Industry**|**2011**|**2012**|**2013**|**2014**|
|---|---|---|---|---|
|Agriculture, hunting and forestry|1436.6|1441.0|1446.0|1451.5|
|Manufacturing|826.1|828.7|831.6|834.7|
|Construction|520.1|521.7|523.6|525.5|
|Wholesale and retail trade|1910.3|1916.2|1922.9|1930.2|
|Hotels and restaurants|1220.8|1224.5|1228.8|1233.5|
|Transport, storage and communications|919.6|922.4|925.6|929.2|
|Education|389.1|390.3|391.6|393.1|
|Health and social work|276.5|277.3|278.3|279.3|
|Other community, social and personal activities|1508.1|1512.7|1518.0|1523.8|
|**TOTAL OUTPUT (million Maloti)**|**9007.1**|**9035.0**|**9066.5**|**9100.8**|
|**Population projections**|1,896,833|1,902,707|1,909,321|1,916,
7|82.9|6.88|0.94|

* All latest year data is from 1997. The only exceptions are Singapore for which data is for 1998 and Taiwan for which it refers to 1996

_Source:_ For all countries, except Vietnam: export data is from COMTRADE. Manufactures refer to categories 5-8 less 68 of the Standard International Trade Classification (SITC); Education and population data are from Barro and Lee (1996) and land 13
dependency ratio have a positive impact on life insurance demand, while social security expenditures have a negative impact.

More recent empirical studies include Browne and Kim (1993), Outreville (1996), Beck and Webb (2003), and Li et al (2007). These studies rely on larger cross-country datasets, especially the latter two. Browne and Kim (1993) use data from 1987 for a cross-section of 45 developed and developing countries, while Outreville (1996) bases his analysis on a crosssection of 48 developing countries for the year 1986. Beck and Webb (2003) use a large dataset of 68 developed and developing countries over the 1961-2000 period, while Li et al (1997) rely on a panel of 30 OECD countries over the 1993-2000 period. These studies use life premiums as the dependent variable (usually expressed as ratios to GDP or the population). Browne and Kim (2003) and Li et al (2007) define the estimated equation as the demand for life insurance. Outreville (1996) specifies briefly the demand and supply for life insurance and defines the estimated equation as a reduced form. Beck and Webb (2003) also indicate that the life premium reflects both demand and supply factors, while stressing the difficulties of distinguishing between the demand and supply sides.

Table 1 summarizes the main variables that should drive the development of the life insurance sector. Most of these variables have been explored in empirical research. The variables are clustered into four major groups: (1) economic variables (income level, income inequality, inflation, and interest rates); (2) demographic variables (population, population density, age dependency, and life expectancy); (3) social/cultural variables (education, religion); and (4) institutional/market structure variables (size/scope of the social security system, the level of financial development, the share of state and foreign insurers, market concentration., and the quality of the legal/regulatory framework). The second
This increase is solely driven by jobs that can be done at home _and_ do not require internet whose share increases from 3.1 to 3.6 percent in high-income countries, and from 1.7 to 2.0 percent in low-income ones (Figure 2a).

Next, we use PIAAC (Programme for the International Assessment of Adult Competencies) surveys rather than O*NET data to identify the types and extent of occupations requiring internet access. The main shortcoming of O*NET data is that they are based on the task content of occupations as performed in the United States. The PIAAC surveys, in contrast, include rich information on jobs’ characteristics for 35 countries. We restrict the sample to 29 high-income countries where internet coverage is near universal to avoid our measures of ICT usage being downward biased by limited internet availability. Following Hatayama, Viollaz and Winkler (2020), we use several questions related to internet use at work such as frequency of computer and email use, frequency of ICT usage, programming, and participating in video calls. We construct a continuous index of ICT usage and we calculate the share of jobs within each ISCO 2-digit occupation that are above the 50<sup>th</sup> percentile of this index. Occupations above the median in ICT usage are determined to require internet access. We then combine this occupation-level measure of ICT requirements with the DN2020 index to identify the share of jobs that are telecommutable and do not require the internet versus the shares of jobs that are telecommutable conditional on internet access. We identify the share of jobs that are telecommutable and require the internet as the minimum of the share of jobs that can be performed from home according to Dingel and Neiman and the share of jobs requiring internet in each ISCO 2-digit occupation. Telecommutable jobs that do not require internet are obtained by subtracting telecommutable jobs that require internet from all telecommutable jobs. This alternative index does not change the total share of jobs that can be done from home with respect to our baseline results significantly
For such a synergy to occur, however, the country must be well endowed with human capital.

The aggregate data, as well as our review of the Scandinavian experience since the late nineteenth century, provide supporting evidence for our model. In addition, we have shown that abundance of natural resources leads to higher income, so that one cannot infer from the growth effects alone what the welfare implications of being rich in natural resources might ultimately be. Indeed, from the perspective of our model, increased natural resources imply higher current and future income, so that welfare increases when natural resources become more abundant. A country would not benefit from giving away its natural resource endowment, as one might mistakenly conclude from models that emphasize only the growth effect.

As this paper has shown, a country that is rich in natural resources can start with a high level of income, accumulate human capital, and see its growth accelerate. In this sense, natural resources need not be a curse. However, extremely low levels of human capital may cause such an economy to stagnate, because it then tends to specialize in natural resources extraction.

# **References**

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Barro, Robert and Xavier Sala i Martin, _Economic Growth_ , Mc Graw Hill, 1995.

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- Benavot, Aaron and Phyllis Riddle, “The Expansion of Primary Education, 18701940: Trends and Issues,” _Sociology of Education_ , July 1988, pp. 191–210.

Blomstrom, Magnus and Patricio Meller, eds, _Trayectorias Divergentes. Comparaci ́on de un siglo de desarrollo econ ́omico Latinoamericano y Escandinavo_ , CieplanHachette, 1990. Santiago.

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