rafmacalaba/datause-encoder-data huggingface dataset:
proportional to the income and population of nearby coastal counties (Pielke and Landsea 1998; Pielke 2007; Pielke et al. 2008). Other studies assume that damages are simply a function of wind speed cubed (Hallegate 2007). The remaining studies assume that damages are proportional to US GDP (Nordhaus 2006; 2010). This study empirically tests the importance of local income and population. Income and population were based on the five coastal counties near where the eye of the storm strikes land. The assumption that damages largely depend on coastal counties is supported by empirical evidence suggesting this is where over 85 percent of aggregate storm damages occur (Pielke and Landsea 1988, Pielke 2007; Pielke et al 2008, Nordhaus 2010; NOAA 2009). Income and population for the historical storm sample was calculated using decennial census data by county (US Census of Population 1960, 1970, 1980, 1990, 2000). Values between Census years were inferred. Dollar values were updated to 2010 USD using the GDP deflator.

The results of the regressions of log damages on the log of the independent variables are shown in Table 1. The regressions reveal that the intensity of the storm is highly significant. Whether measured by maximum wind speed or minimum pressure at landfall, damages are a highly nonlinear function of intensity<sup>4</sup> . Damages increase inversely with the 86<sup>th</sup> power of minimum pressure. A 1% reduction in minimum pressure almost doubles storm damages. Damages increase with the fifth power of wind speed. A 20 percent increase in wind speed doubles storm damages. These results are much more nonlinear than commonly assumed (that damage is related to the cube of wind speed e.g. Emanuel 2005). However, these values are lower than comparable estimates by Nordhaus (2010) who finds that damages increase with the ninth power of wind speed. Comparing the results of the minimum pressure and wind speed regressions reveals that minimum pressure has a more significant coefficient. The minimum pressure mode also does a better job of explaining the variance in hurricane damages. This is probably due to the measurement error associated with maximum wind speed as measuring the maximum wind speed of a hurricane is very difficult.

> 4 We also examine a two stage model that first predicts wind speed on the basis of barometric pressure and then uses the predicted wind speed in the damage function. Damages increase with the sixth power of predicted wind speed. However, we do not present this model since it has almost identical effects as the barometric pressure model.

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presented for the entire time period covered by the aggregate indicators, from 1996 to 2007. As a new feature this year, this data is available both interactively on a countryby-country basis as it has been in the past, as well as in downloadable spreadsheets reporting the data from all countries for each data source.

As in past years, the WGI are based on subjective or perceptions-based data on governance reflecting the views of a diverse range of informed stakeholders including tens of thousands of household and firm survey respondents, as well as thousands of experts working for the private sector, NGOs, and public sector agencies. We rely on the reports of these stakeholders, which reflect their judgments and perceptions, for three main reasons.

First and most basic, perceptions matter because agents base their actions on their perceptions, impression, and views. If citizens believe that the courts are inefficient or the police are corrupt, they are unlikely to avail themselves of their services. Similarly, enterprises base their investment decisions - and citizens their voting decisions - on their perceived view of the investment climate and the government's performance. Second, in many areas of governance, there are few alternatives to relying on perceptions data. This is most particularly so for the case of corruption, which almost by definition leaves no 'paper trail' that can be captured by purely objective measures. Third, we note that even when objective or fact-based data are available, often such data may capture a _de jure_ notion of laws 'on the books' that differs substantially from the _de facto_ reality that exists 'on the ground'. In fact, in Kaufmann, Kraay and Mastruzzi (2005) we document sharp divergences between _de jure_ and _de facto_ measures of business entry regulation and find that corruption explains a good deal of the extent to which the former are subverted in practice.

And finally, we note, as discussed in more detail in Kaufmann and Kraay (2008), that virtually all measures of governance and the investment climate rely on judgment in some measure, so that the distinction between 'subjective' and 'objective' data is somewhat of a false dichotomy. Rather, a more useful distinction is between efforts to measure formal rules as distinct from their implementation in practice, recognizing that changes in formal rules (often associated with so-called 'actionable' indicators) need not lead to desired changes in outcomes.

3
Figure 27: Income growth by population of different income levels <!-- Start of picture text --> 6<br>5<br>5 4.5<br>4<br>4 Malaysia<br>3 Indonesia<br>3<br>2<br>Nigeria<br>1<br>0<br>-1<br>-1<br>-1.5<br>-2<br>Bottom 40% Middle 40% Top 20%<br>Source: World Bank.<br>Income Growth %<br><!-- End of picture text --> Elements of a strategy for mineral revenue management include the following:

- Promote transparency in accounting for revenues by adopting the EITI.

- Establish fiscal rules, including setting savings rules and maintaining fiscal discipline in decentralized fiscal systems.

- Strengthen public financial management and the Medium Term Expenditure Framework.

- Monitor and evaluate outcomes.

See also the papers by Devlin and Lewin (2008) and Budina and van Wijnbergen (2008) for further discussion on fiscal management of oil revenue and the case of Nigeria.

**_Increasing regional and global support._** Developing and implementing regional strategies for increasing connectivity to the world and within Africa are crucial. Crosscountry infrastructure projects are particularly important, but so too are institutional reforms, such as common customs procedures, that lower transactions costs. Complementary action by the global development community to reduce barriers to trade and to scale up aid to enhance the region’s capacity to trade is also required.

# **V. Sharing the Benefits of Growth**

Growth alone will not be enough to reach the Millennium Development Goals (MDGs) for Africa. At the same time that Africa’s governments are pursuing a new growth strategy—and with the same vigor—they will also need to focus on delivering more and better services for human development.

46
re-construct the 2005 PPP index following a similar methodology that was used for the 2011 PPP index and find that this methodological difference in the construction of the PPP factors explains a significant portion of the difference in relative price levels.

Findings from Ravallion (2014) lead to almost the opposite interpretation, suggesting that there are potentially significant concerns about the 2011 PPP data. He argues that the downward drift in prices observed for much of Asia (but not China) is in contrast to what would be expected given the observed rate of economic growth. A part of his interpretation of the data rests on the so-called “dynamic Penn Effect” (Ravallion, 2013), which suggests that the ratio of the PPP index to the market exchange rate rises with economic growth. Ravallion offers a hypothesis that over time the bundle of goods used for the PPP index has become more heavily weighted towards internationally traded goods (for which prices exist) and this has led to a downward shift in price levels relative to market exchange rates (conditioning on growth rates).

Bypassing the debate regarding the quality of the 2011 PPP data, others have carried out quick analyses on how the 2011 PPP data would affect global poverty counts. Immediately after the release of the 2011 PPP data, Dykstra, Kenny and Sandefur (2014) estimated that in 2010, the share of people in the developing world living below the $1.25 per day poverty line declined from 19.7 percent to 8.9 percent “overnight”. Their approach was to adjust the $1.25 poverty line, which is denominated in 2005 USD, to 2011 by using the U.S. inflation rate. This adjustment brings their poverty line to a value of $1.44 in 2011 USD. This methodology differs significantly from the approach followed by RCS and described in Jolliffe et al. (2014) for setting an international poverty line that reflect the poverty lines of some of the poorest countries in the world. Chandy and Kharas (2014) follow an approach similar in spirit (though different in the details) to the methodology used in RCS and re-estimate the poverty line accounting for changes in local price levels. They provide several candidate estimates of the count of the poor, but hone in on an estimate where the number of poor is about 300 million lower in 2010 as a result of using the 2011 PPP data (rather than the 2005 PPP data).

Jolliffe and Prydz (2015) note that all of the early estimates of global poverty based on the 2011 PPPs had been based on leaving in place an adjustment factor for presumed urban bias in India, China and Indonesia. The parameterization of the adjustment factor was specific to details of the fieldwork of the 2005 PPPs, and needed to be either dropped from the analysis or re-calibrated for the re-design of the 2011 PPP fieldwork. They show that the goal of reducing 9
**Inquiry about Progress.** Investment organizations provide evaluation templates to investors to facilitate their assessment of competence by assessing the growth potential of early-stage startups in the absence of a history of startup performance (Cohen et al. 2019b, Gompers et al. 2020). However, evaluating potential can disadvantage female candidates. For example, in a retail organization, evaluating “potential” for leadership did not result in promotions for equally-performing female candidates. If the organization had promoted based on current job performance, it would have reduced disparities (Benson et al. 2022). Whereas performance ratings are backward-looking and based on demonstrable achievements, potential ratings are based on an evaluator’s forecast of a worker's future performance and contribution. This makes rating “potential” fundamentally more subjective and uncertain, which could increase reliance on ascriptive characteristics such as gender (Ridgeway and Correll 2004, Correll and Benard 2006, Botelho and Abraham 2017, Snellman and Solal 2023).

How investment organizations might assess performance is complicated, given the lack of organizational performance data on startups (e.g., <mark>Stinchcombe 1965, Aldrich and Fiol 1994, Cohen et al. 2019a). However, some organizations assess short-term</mark> signals of performance during hiring processes. For example, the classic Goldin and Rouse study (2000) demonstrates that when orchestra hiring managers evaluated candidates’ performance through blind auditions, this resulted in hiring more female performers. This suggests that investors may be able to assess competence differently – inquiring about and assessing short-term signs of performance during the selection process.

In fact, some investors already do so. For example, program managers in Vilcap shared that progress made during the three-month program is important to investment decisions: “[We] invest in people that make the most progress during the program”. A VC investor, Mark Suster, (2010) blogged:

> “The first time I meet you, you are a single data point... Because I have no observation points from the past, I have no sense for where you will be in the future. Thus, it is very hard to make a commitment to fund you.”

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# **2.2 Case study area: Somalia**

Somalia is situated in the Horn of Africa with an official population estimated at 12.3 million in 2014, up from the 1975 estimate of 4.1 million with slightly more males (6.2 million) than females (6.1 million) (UNFPA, 2016). In 2012, the first nationwide Population Estimation Survey (PESS) took place. The Somalian government, United Nations Population Fund (UNFPA), and United Nations Development Programme (UNDP) collaborated, prepared, and carried out this survey, aiming to use the PESS as a basis for a census.

The PESS survey in 2014 estimated that 42% of the population was permanently settled in urban areas and 23% in rural areas, while 26% were nomadic people and 9% were internally displaced persons (IDPs) (UNFPA 2014). The Somali population is rapidly increasing with almost 3% population growth per year and a high fertility rate of 6.26 children per woman, which is the fourth highest in the world (Gure et al. 2015).

However, the results of the PESS alone were not suitable for creating a nationally representative sampling frame, as the PESS created EAs in urban areas only. The risks associated with fieldwork and the lack of funding were just two hurdles this approach faced; significantly displaced populations exist in parts of Somalia, without any official population information available. Therefore, the World Bank recognized that re-building Somalia’s statistical infrastructure and capacity was key in supporting resilience efforts and proposed a spatial analysis approach as an innovative way to create a new sampling frame, especially given the barriers in this context (e.g. security risks, lack of funding).

# **2.3 Data sources**

To conduct this work, several data sets have been compiled and combined from various resources (table 1).

Table 1. Data used for Somalia Enumeration Areas.

|**Source**|**Data description**|
|---|---|
|Road Data (OSM)|Lines|
|Waterway (OSM)|Lines|
|River (OSM)|Lines|
|Residential area (OSM)|Part points and part polygons|
|Building (OSM)|polygons|
|‘places’, ‘hamlet’, and ‘villages’ (OSM)|Points|
|DLR Global Urban Footprint (GUF)|Binary raster|
|BMGF/DigitalGlobe (DG) population estimates|scatter points|
|World Bank/Flowminder/WorldPop building<br>counts from Google Satellite imagery|polygons|
|UNFPA/PESS urban Enumeration Areas|Polygons|
|UNFPA/PESS urban Enumeration Areas (EAs)|# households per EA.|

6
**Table 1: AMR impact on  real GDP relative to a world without AMR**

|**Region by income level**|**Difference in**|**trillions of**|**2007 USD**|**Perce**|**nt deviatio**|**n**|
|---|---|---|---|---|---|---|
||2020|2030|2050|2020|2030|2050|
|**Low Income**|||||||
|Low case|-0.001|-0.021|-0.064|-0.1%|-1.0%|-1.2%|
|Middle case|-0.001|-0.044|-0.187|-0.1%|-2.2%|-3.6%|
|High case|-0.001|-0.071|-0.291|-0.1%|-3.6%|-5.6%|
|**Lower Middle Income**|||||||
|Low case|-0.003|-0.118|-0.364|-0.1%|-1.0%|-1.3%|
|Middle case|-0.004|-0.232|-0.750|-0.1%|-1.9%|-2.6%|
|High case|-0.004|-0.377|-1.268|-0.1%|-3.1%|-4.4%|
|**Upper Middle Income**|||||||
|Low case|-0.022|-0.312|-0.662|-0.1%|-1.0%|-1.4%|
|Middle case|-0.025|-0.577|-1.280|-0.1%|-1.9%|-2.7%|
|High case|-0.025|-0.933|-2.066|-0.1%|-3.1%|-4.4%|
|**Middle Income Total**|||||||
|Low case|-0.025|-0.431|-1.027|-0.1%|-1.0%|-1.4%|
|Middle case|-0.028|-0.809|-2.030|-0.1%|-1.9%|-2.7%|
|High case|-0.028|-1.309|-3.334|-0.1%|-3.1%|-4.4%|
|**High Income**|||||||
|Low case|0.001|-0.578|-0.664|0.0%|-0.9%|-0.8%|
|Middle case|-0.014|-1.191|-1.436|0.0%|-1.9%|-1.8%|
|High case|-0.014|-1.980|-2.481|0.0%|-3.2%|-3.1%|
|**World**|||||||
|Low case|-0.026|-1.030|-1.754|0.0%|-1.0%|-1.1%|
|Middle case|-0.043|-2.045|-3.653|-0.1%|-1.9%|-2.3%|
|High case|-0.043|-3.360|-6.107|-0.1%|-3.2%|-3.8%|

Source: Simulation results and authors' calculations

**Table 2: AMR impact on trade (real exports) relative to a world without AMR**

|**Region by income level**|**Difference in**|**trillions of 2**|**007 USD**|**Perce**|**nt deviatio**|**n**|
|---|---|---|---|---|---|---|
||2020|2030|2050|2020|2030|2050|
|**Low Income**|||||||
|Low case|0.000|-0.006|-0.018|-0.1%|-1.0%|-1.1%|
|Middle case|0.000|-0.013|-0.053|-0.1%|-2.0%|-3.3%|
|High case|0.000|-0.020|-0.083|-0.1%|-3.2%|-5.2%|
|**Lower Middle Income**|||||||
|Low case|-0.001|-0.029|-0.092|-0.1%|-0.9%|-1.2%|
|Middle case|-0.001|-0.058|-0.191|-0.1%|-1.8%|-2.5%|
|High case|-0.001|-0.095|-0.325|-0.1%|-2.9%|-4.2%|
|**Upper Middle Income**|||||||
|Low case|-0.006|-0.081|-0.173|-0.1%|-1.0%|-1.3%|
|Middle case|-0.007|-0.150|-0.338|-0.1%|-1.8%|-2.6%|
|High case|-0.007|-0.244|-0.550|-0.1%|-3.0%|-4.3%|
|**Middle Income Total**|||||||
|Low case|-0.007|-0.110|-0.265|-0.1%|-1.0%|-1.3%|
|Middle case|-0.008|-0.208|-0.530|-0.1%|-1.8%|-2.6%|
|High case|-0.008|-0.339|-0.875|-0.1%|-3.0%|-4.2%|
|**High Income**|||||||
|Low case|-0.001|-0.153|-0.187|0.0%|-0.9%|-0.8%|
|Middle case|-0.005|-0.314|-0.405|0.0%|-1.9%|-1.8%|
|High case|-0.005|-0.522|-0.698|0.0%|-3.1%|-3.1%|
|**World**|||||||
|Low case|-0.009|-0.269|-0.470|0.0%|-0.9%|-1.1%|
|Middle case|-0.013|-0.535|-0.987|-0.1%|-1.9%|-2.2%|
|High case|-0.013|-0.881|-1.655|-0.1%|-3.1%|-3.7%|

Source: Simulation results and authors' calculations 23
# **Figure A3.2: Impact of Changes in Combinations of Load Factors for Business Class on Carbon Footprint of HQ-Based Bank Staff Air Travel, 2009**

<!-- Start of picture text --> 350,000,000<br> 300,000,000<br> 250,000,000<br> 200,000,000<br>Business Class 30%<br> 150,000,000 Load<br>Business Class 90%<br> 100,000,000<br>Load<br> 50,000,000<br> -<br>0.3 0.4 0.5 0.6 0.7<br>First Class Load Factor<br>Overall Carbon Footprint (Kg)<br><!-- End of picture text --> Figure A3.3 charts the average passenger footprint per mile found across all distance classes and all aircraft in the sample used for the Phase III calculations, assuming a 60% load factor for both business and first classes. Figure 3 also shows the average fuel consumption per seat mile of those same flights, based on aircraft types found both in the ICAO base tables in their own methodology, which are again based on the modified CORINAIR tables in Version 3 of ICAO’s Carbon Emission Calculator.

The chart points out in both datasets that at first, as distances increase, the consumption per mile and resultant footprint goes down. As the distances increase, the consumption then increases again, because of the weight of the additional fuel to be used for the longer trip. A significant increase is seen between distance classes 7 (1,501 nm to 2,000 nm) and 8 (2001 nm to 2,500 nm), and 8 and 9 (2,501 nm to 3,000 nm). Between CY2009 and FY2012 a reduction of average distance per flight has occurred, making the average distance go from class 9 to class 8, reducing the per mile fuel burn.

In figure 3.3, the line representing fuel burn assumes an even distribution of fuel consumption among all seats, occupied or not, regardless of size. The average footprint per passenger mile however also reflects the distribution by class, combined with load factors (here, 60% for first and business class, with the remainder determined by regional load factors and passenger to freight distributions). The line representing the footprint would look identical to the per seat consumption if it were based just on the number of seats, with the values represented by the line being simply a multiple of the fuel burn.

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Table 5.1 Basic Earnings function by Years of Schooling in Ghana.

|Variables|1987|1991|
|---|---|---|
|Years of schooling|0.035<br>_(7.04)|0.061<br>(13.3)|
|Age|0.11<br>(6.8)|0.08<br>(4.5)|
|Age-squared|-0.001|-0.007|
||(-5.2)|(-3.1)|
|Log hoursworked<br>._________|-0.24<br>_________<br>(-8.25)|-0.34<br>(-7.6)|
|Female|0.1l<br>(1.76)|-0.06<br>-(1.04)|
|Other urban|0.09<br>(1.4)|-0.27<br>(-4.4)|
|Rural Coastal|0.12<br>(1.2)|-0.12<br>(-1.330|
|Rural forest|0.07<br>(0.8)|-0.22<br>(-3.03)-|
|Rural Savannah|0.16<br>(0.8)|-0.14<br>(-1.26)|
|Constant|2.75<br>(8.40)|4.9<br>(10.9)|
|R-squared|0.24|0.29|
|Sample size|765|1021|

_SoQ rce: Canagarajah and Thomas (1997)_ Table 5.2. Earnings function by levels of education in Ghana

|Variables|1987|1991|
|---|---|---|
|Below Primary|-0.07<br>-(0.58)|-0.07<br>(-0.57)|
|Primary|-0.2|-0.02|
||(-0.09)|(-O.1)|
|Middle|0.09|0.42|
||(0.8)|(5.2)|
|Secondary|0.27|0.77|
||.<br>(3.8)|(8.6)|
|Higher.|0.87|1.18|
||(7.4)|(10.8).|
|Age|0.11|0.09|
||(7.4)|(5.3)|
|Age-squared|-0.001|-0.008|
||(-5.7)|(-3.7)|
|Log hours worked|-0.29|-0.31|
||(-10.4)|(-7.25)|
|Other Urban|0.13|-0.27|
||(1.99)|(-4.3)|
|Rural Coastal|0.15|-0.08|
||(1.5)|(-0.9)|
|Rural forest|0.05|-0.19|
||(0.58)|(-2.5)|
|Rural Savannah|0.08|-0.18|
||(0.8)|(-1.7)|
|Constant|3.23|4.73|
||(10.3)|(10.9)|
|R-squared|0.24|0.27|
|Sarnple<br>size|919|1140|

_Sourc: Canagarajah and Thomas (2997)_ 30
consumption derived from these data frequently exclude large, once-in-a-lifetime, expenditures such as weddings and funerals as they tend to distort rankings of individuals, if not properly annuitized over the lifespan of the individual (Deaton and Zaidi, 2002).

A related concern with GDP is that despite significant efforts to establish international standards for the compilation of national accounts, guided by the UN Statistical Division’s System of National Accounts (SNA), there remain substantial heterogeneity in methods and standards across countries. This heterogeneity has recently been particularly pronounced in SubSaharan Africa, where several countries have revised their national accounts estimates to incorporate new methods and data sources in efforts to better capture more economic sectors and include emerging economic activities that were not previously captured. These methodological revisions to national accounts can produce significant breaks in comparability. For example, in 2017, Senegal’s GDP increased by 29 percent when it changed its base year from 1999 to 2014. In 2014, Nigeria’s GDP nearly doubled when it rebased from 1990 to 2010 (Angrist et al., forthcoming). Similar revisions of more than 20 percent occurred in Ghana, Kenya, Senegal, and Zimbabwe (see Kouame et al., 2019). In contrast to estimates from household surveys, the common practice is to revise the entire national accounts series, improving comparability over time within each country.<sup>19</sup> Of potentially more consequence to cross-country comparability of GDP is the shifting of profits from one country to another by multi-national corporations. Paul Krugman coined the term _leprechaun economics_ in 2015, when Ireland reported a 26 percent increase in GDP, which was mostly the result of an accounting maneuver by Apple to shift assets to Ireland on paper. This foreign direct investment in Ireland did not improve the well-being of the Irish in any meaningful way, nor did it increase economic activity, but was simply a tax-avoidance measure that resulted in distorting economic growth as measured by GDP. Damgaard et al. (2019) estimate that up to 40 percent of global foreign direct investment is phantom investment with no tangible links to the local economy.

> 19 Household surveys sometimes conduct survey-to-survey imputations when the methodology changes. This can yield a more comparable series over time, despite changes in methodology.

13