rafmacalaba/data-use-mentions-extended huggingface dataset:
quantitative information is recorded up to year 2010 in BRSS 4 and up to year 2016 in BRSS 5.

20 The capital instruments that might be allowed into the definition of Tier 1 capital are: a) hybrid debt capital instruments, b) asset revaluation gains, and c) subordinated debt. The instruments that might be deducted from Tier 1 capital are: a) goodwill, b) deferred tax assets, c) intangibles, d) unrealized losses in fair valued exposures, and e) investment in the capital of certain financial intermediaries. The selection of these capital instruments reflects availability of information over the two most recent BRSS surveys.

25
*[0.213]**|
||ln (rice price)||**2.046*****<br>**[0.338]**|
|Other Dummy|Rural|**-0.565*****|**-0.373*****|
|||**[0.134]**|**[0.123]**|
||Observations|665|665|

_Source_ : 2010 Yemen RIC Survey; _Notes:_ Sampling weights are applied; standard errors in parentheses; *,**, and *** denote coefficients significant levels at the 10, 5, and 1% levels respectively 17
Policy Research Working Paper 8735

# **Abstract**

Somalia is highly data-deprived, leaving policy makers to operate in a statistical vacuum. To overcome this challenge, the World Bank implemented wave 2 of the Somali High Frequency Survey to better understand livelihoods and vulnerabilities and, especially, to estimate national poverty indicators. The specific context of insecurity and lack of statistical infrastructure in Somalia posed several challenges for implementing a household survey and measuring poverty. This paper outlines how these challenges were overcome in wave 2 of the Somali High Frequency Survey through methodological and technological adaptations in four areas. First, in the absence of a recent census, no exhaustive lists of census enumeration areas along with population estimates existed, creating challenges to derive a probability-based representative sample. Therefore, geospatial techniques and high-resolution imagery were used to model the spatial population distribution, build a probability-based population sampling frame, and generate enumeration areas to overcome the lack of a recent population census. Second, although some areas remained completely inaccessible due to insecurity, even most accessible areas held potential risks to the safety of field staff and survey respondents, so that time spent in these areas had to be minimized. To address security concerns, the survey adapted logistical arrangements, sampling strategy using micro-listing, and questionnaire design to limit time on the ground based on the Rapid Consumption Methodology. Third, poverty in completely inaccessible areas had to be estimated by other means. Therefore, the Somali High Frequency Survey relies on correlates derived from satellite imagery and other geo-spatial data to estimate poverty in such areas. Finally, the nonstationary nature of the nomadic population required special sampling strategies.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at upape@worldbank.org.

_The
>

> 24 Robinson 2013, p. 36.

> 25 Kishtainy and others 2012, p. 52.

> 26 See Plumer, 2012.

> 27 Ibid; see also Kabanda 2016b, p. 5.

> 28 See Kaul 2009, p. 6.

> 29 Trask 2000. See also Chris Gibson and Connell, 2005; Kaul 2009, 6; and Harris and Moreno.

> 30 “A Satellite Account is a term developed by the United Nations to measure the size of economic sectors that are not defined as industries in national accounts. Tourism, for example, is an amalgam of industries such as transportation, accommodation, food and beverage services, recreation and entertainment and travel agencies.” UNWTO, p. 1; see also Kabanda 2014, pp. 51-52. 31 NEA 2012. See also BEA 2016.

> 32 Coyle 2014, p. 138. Regarding the efficacy of such accounts, climate change is a perfect example.  “Some countries have been publishing satellite accounts on the environment for a number of years.”  Yet “it is hard to identify any direct influence they have had on economic policy debates.” — Coyle 2014, p. 138.
: Tensions and Trade-Offs of Land Registration and Titling in Sub-Saharan Africa." _Journal of Development Studies_ , **_55_** (3), 384-400.

Bruce, J. W. and S. E. Migot-Adholla. 1994. _Searching for Land Tenure Security in Africa._ Kendall/Hunt Publishers, Dubuque, IA. Carletto, C. and S. Gourlay. 2019. "A Thing of the Past? Household Surveys in a Rapidly Evolving (Agricultural) Data Landscape: Insights from the Lsms-Isa." _Agricultural Economics_ , **_50_** , 51-62. Casey, K., R. Glennerster, E. Miguel and M. Voors. 2019. "Skill Versus Voice in Local Development." Stanford University, Graduate School of Business, Research Papers. Chen, C. 2017. "Untitled Land, Occupational Choice, and Agricultural Productivity." _American Economic Journal: Macroeconomics_ , **_9_** (4), 91-121. Cherchye, L., B. De Rock, S. T. Walther and F. Vermeulen. 2016. "Where Did It Go Wrong? Marriage and Divorce in Malawi." University of Oxford, Department of Economics, Economics Series Working Papers: 786. Christiaensen, L. 2017. "Agriculture in Africa--Telling Myths from Facts: A Synthesis." _Food Policy_ , **_67_** , 1-11.

de Janvry, A., K. Emerick, M. Gonzalez-Navarro and E. Sadoulet. 2015. "Delinking Land Rights from Land Use: Certification and Migration in Mexico." _American Economic Review_ , **_105_** (10), 3125-3149.

de Kadt, D. and H. A. Larreguy. 2018. "Agents of the Regime? Traditional Leaders and Electoral Politics in South Africa." _The Journal of Politics_ , **_80_** (2), 382-399. Deininger, K. and R. Castagnini. 2006. "Incidence and Impact of Land Conflict in Uganda.
value of equipment); and agricultural investments (surface cultivated, quantity produced and yields, input spending, type of crops). These outcomes cover a wide range of potential investments. We also consider the participation in _tontines_ (number of _tontines_ , amount received, and usage).

> 13 Several food security variables were collected in 2010 only for the subsample of households in Tahoua. These variables are analyzed as a robustness check.

11
same move-in & move-out|0|8.3|50.0|19.4|
|No move-in & move-out|16.7|8.3|16.7|13.9|

_Source:_ 2010 Yemen RIC Survey

# **4.3.1** **_Migration Model_**

The migration model is used to analyze which household characteristics and community level RIC components may affect migration workers to flow into the community. In other words, the hypothesis of the migration model is to find out which household characteristics and community level RIC components affect migration. The Probit model is also applied for the dependent variable “more move-in” with household characteristics and community-level RIC variables.

_Mi= 0+ 1 (_ **_H_** _i) + 2 (_ **_C_** _j) + 3 (_ **_D_** _j) + εi_ (6) where _M_ denotes a dummy that equals 1 when more people moved into the communityduring the past three years.

The regression results are shown in Table 4.4.

16
footwear and leather.

- Note: The breakdown between food products and beverages for the US on a national accounts basis was assumed to be proportionately the same as on a US Census basis (1975 figures derived from Annual Survey of Manufactures).

Source: Brazil value added in national currencies from Censo Industrial (see table 2.1 which does not exclude bank costs). US value added in national currencies from National Income and Products Accounts of the United States: 1929-76 Statistica. Tables (1981c) after adjustment for inventories indirect taxes and subsidies **an** net intrest (see table 2.6). PPPs from table 3.14.
physical and human capital by almost one third. In Weil (2007) accounting for health differences reduces the variance of log GDP per worker by 9.9 percent.

> 9 Hanushek and Kimko (2000) combine the results of cognitive achievement tests in mathematics and science into two educational quality indexes.  We take an average of the two indexes and, as Woessman (2003), normalize the educational quality index for each country relative to the US. Woessman argues that the US is a good reference country as the returns to schooling should be relatively undistorted in the competitive US labor market. Data are available for 80 countries in our sample.

> 10 From both data sets we take the years of schooling in the population aged 15 and over because this age group corresponds better to the labor force for most developing countries (the majority in our sample) than the population aged 25 and over (Woessman 2003, p.256). The Barro and Lee dataset contains the average years of schooling in 5- year intervals from 1960 to 1995 and projections for 2000. Values for 2005 were predicted by using a linear trend fitted from the observations between 1960 and 2000. When a country-specific growth rate in the years of schooling could not be calculated, the regional average growth rate was used. A visual inspection of the data suggests that a linear trend is a good approximation. A caveat, however, is that educational attainment is asymptotically a constant, and by fitting a linear trend we may overstate the educational attainment for the countries that have reached the asymptote. This would affect mainly OECD countries.  Our second source for schooling data (Cohen and Soto, 2007) suggests that this may not be a problem. Virtually all the developed countries with the exception of Norway and Sweden exhibit more years of schooling in this second dataset for all the years considered, including 2005. For Norway and Sweden, the largest difference between the two datasets is 0.52 years of schooling. Cohen and Soto (2007) report educational attainment
# using non-grain feedstocks.

<!-- Start of picture text --> 880.00%<br>2005 Value/2010 Target Value 2010 Value/2010 Target Value<br>320.00%<br>250.00%<br>200.00%<br>112.11% 121.82% 112.00%<br>95.07%<br>61.58% 25.20% 36.36% 28.00% 53.33% 46.67% 25.00% 18.50%<br>0.00% 0.00%<br><!-- End of picture text --> Fig. 1. Achievement of 2010 targets for various renewable energies in China Source: the 2005 data are from _Medium and Long-term Development Plan for Renewable Energy in China_ ; the 2010 data of solar PV power generation is from Shi et al. (2011), non-grain fuel ethanol is from Liu et al.(2011), and biogas consumption is from DSERE(2011); other data of 2010 are from Zhao et al.(2011).

There are numerous challenges facing the development of biofuels in China, including the potential impact of biofuels production on the grain security and agriculture development (Huang, et al., 2009; Huang, et al., 2010; Fu, et al., 2011), uncertainty of available land potential for biofuels development (Tian, et al., 2009), economic supply of feedstock (Zhang, et al., 2009) and potential ecological environment problems caused by large-scale cultivation of energy crops (Yan and Zhao, 2009). Much of the debate has now been focused on what the impact of meeting the 2020 targets. Some existing studies have analyzed this issue. For example, Shi, et al.(2010) projected that to meet the target of 15% non-fossil energy by 2020, set by the State Council
# I- Introduction

global competitiveness (Brynjolfsson and Unger, 2023), making its adoption a critical priority for nations worldwide.<sup>2</sup> The most prominent accounting and consulting firms claim that AI is projected to contribute significantly to the global economy, with estimates ranging from US$2.6 trillion to US$4.4 trillion annually (Chui et al., 2023), to US$15.7 trillion by 2030, driven by productivity and consumption gains (PwC, 2017). Some recent NBER publications<sup>3</sup> highlight that AI has the potential to significantly boost productivity and economic growth by automating tasks and driving innovation. However, the realization of these gains depends on complementary policies, institutions, and societal adaptation to ensure equitable and sustainable outcomes (Aghion, Jones, and Jones, 2019; Trammell and Korinek, 2023; Acemoglu, 2024). To summarize, AI, and particularly generative AI, has the potential to boost economic growth and productivity but poses risks of labor market disruptions, including premature de-professionalization—where skilled jobs are replaced or diminished before workers can fully capitalize on their expertise—and challenges for developing economies, such as reduced opportunities for high-skill job creation, increased inequality, and difficulties in integrating AI technologies into less-developed industrial and educational systems (Liu, 2024).

Such substantial socio-economic impacts underscore the critical importance for countries to adopt and integrate AI technologies to remain competitive and keep a sovereign public policy.<sup>4</sup> However, the benefits of AI are not uniformly distributed. The 2023 Government AI Preparedness Index (AIPI) from the International Monetary Fund (IMF) reveals significant disparities in AI preparedness and readiness among nations, highlighting a growing divide between those equipped to leverage AI and those at risk of falling behind (Cazzaniga et al., 2024).<sup>5</sup> Related to this, Liu and Wang (2024) highlight that middle-income countries exhibit unexpectedly high
TTC Profits can be computed as a long term averages based on, for example, historical Return on Assets (ROA) data.<sup>32</sup> In order to compute PIT and Stress scenario Profits, a forecasting model is required, as forecasts need to be conditioned on the state of the economy. If no forecasting model is available, one can set the Profit term in (13) to zero for the PIT and Stress scenarios.<sup>33</sup> This assumption is commonly adopted by stress testers and can be seen as a rather conservative estimate of profits.

We follow the guidelines in Basel II and compute Risk Weighted Assets (RWA) as: where _Ki_ , _j_ is the capital charge (or capital requirement) equation of Basel II (see page 64).<sup>34</sup> The capital charge is a function of each bank’s PD, LGD, asset performance Correlation<sup>35</sup> and maturity adjustment for the individual asset classes, conditional on the selected cutoff point of the loss distribution, ie., the risk preference parameter _c_ ̄ in (16). It is computed as: where _bi_ , _j_ is the maturity adjustment term of Basel II, _Mi_ , _j_ is the effective residual maturity of an asset class computed as the average of the residual maturity of loans within a given

> 32 This calculation is thus the same as for the TTC macroeconomic scenarios in Section 3.1.

> 33 We leave the estimation of a Profit regression using cross-country panel data for future research, and set the Profit term in the empirical application to zero for the PIT and Stress scenarios.

> 34These are the guidelines in the ”Credit Risk - Internal Ratings Based Approach” section, with the relevant formulas being on pages 63 _−_ 64 and 76 _−_ 78 for the different asset classes that are considered. 35In Basel II this is just referred to as Correlation (R).

43
in payment usage to that point. For these reasons, the coefficients for the remaining

> 14 For additional details on the construction of these indexes, see World Bank (2021, Chapter 6).

> 15 Note that the Findex data were not collected in 2018 or 2019, so 2017 is the last date prior to the pandemic for which indicators of DFS usage are available for a wide sample of countries via Findex.

14
residence-workplace<br>location, Average travel<br>times and distances by<br>mode of travel and<br>commuting arrangement|Calibration and<br>Simulation|Increasing bus services in<br>the city center would<br>reduce overall CO2<br>emissions, and that<br>expanding suburban roads<br>would increase them.|
|Lalive et al.<br>(2013)|Germany|1994-<br>2004|Frequency of service<br>defined as the number of<br>passenger trains per year on<br>a line, county-level data on<br>road traffic accidents, air<br>pollution, infant mortality<br>and travel modes|Difference-in-<br>differences|Increasing rail services<br>reduce emissions|

45
- 11 -

# 1.2.1 Country set

The estimation of an econometric equation using the data for a set of countries assumes that the relationship studied does not differ significantly between the countries of the sample. In other words, as far as we are concerned here, it assumes that the differences between the observed growth rates are explained by the different values assumed by the independent variables for the various countries.

This implicit hypothesis of structural homogeneity of economic relationships would call for systematic tests. But the econometric studies using cross-sectional data are generally accompanied by a much more limited number of diagnostic tests than the studies done using time data. Most of the customary software produces, for example, the Durbin and Watson statistic, which has no clear significance in cross-sectionul analysis. However, the pertinent tests for this type of research, such as the coefficient stability test between two subsamples due to Chow (1960), or the homos- 'sticity tests that bring out the variability of the residual variances between the different observation points -- and therefore that of the coefficients, according to Breush and Pagan (1979) -- generally call for supplemental work by the investigators. They would therefore be very helpful for justifying econometric analysis using cross-sectional e3ta by means of a given model or, on the contrary, for excluding .ertain countries from the sample.

Whereas including a country in a sample implies the hypothesis of homogeneity, the fact of excluding a country does not necessarily imply a hypothesis of structural heterogeneity. It may simply reflect the investigator's tastes or limits on the availability of data. To be tested, the homogeneity hypothesis requires that the model be estimated with and without the countries in question.

Howe;er, the econometrician is tempted to include the largest possible number of observations in the sample in order to have sufficient degrees of freedom to apply statistical tests under satisfactory conditions. The temptation to do 3o can sometimes conflict with the homogeneity requirement stressed earlier. It can also be pointed out that the exclusion of a
focuses on the _new_ FDI projects and provides cleaner information on the FDI sector level patterns relative to the balance of payments (BOP) based FDI data. The latter contains sector level information on FDI flows only for a limited number of countries, does not distinguish between existing (reinvested earnings) and new FDI activities and may also be distorted by “round-tripping” behavior of firms. A potential disadvantage of the _fDi Markets_ database is that the reported values of FDI projects are estimates which may differ from the actual recorded values.<sup>16</sup> Moreover, while the data set coverage is extensive after 2010, in earlier years the coverage may not be fully comprehensive. We circumvent these disadvantages by focusing the analysis on the last decade (2010-19 period) and using the available information to construct binary FDI success variable – which takes value 1 if the country/sector pair received FDI (from any country) over the specified period and zero otherwise. The use of a binary indicator is imperfect as it does not distinguish by size and number of investments, and thus the relative share/frequency of inward FDI that a country may command relative to the rest of the world and to the size of its economy. However, a discrete indicator is possibly less inadequate than potential alternatives. The project value in _fDi Markets_ database is a rough

> 16 The fDi Markets database uses public information sources such as media sources to inform its database. See: https://www.fdimarkets.com/faqs/

9
our haircut estimates can be regarded as a valid measure of sovereign LGDs in the vein of existing corporate LGD databases.

# 3.1. Sovereign debt restructurings, 1815–2016

To estimate haircuts requires moving well beyond identifying the incidence of debt crises, as in Reinhart and Rogoff (2009) and others. We conduct a census of all distressed sovereign debt restructurings with foreign commercial creditors from 1815 to 1980. We then combine this historical sample with the updated restructuring and haircut dataset of Cruces and Trebesch (2013), which covers 1978–2013, and with Fang, Schumacher, and Trebesch (2020), to add events until 2016. The result is a full sample of sovereign debt restructurings with foreign banks and bondholders for 1815–2016. To select cases, we apply the same criteria as in Cruces and Trebesch (2013) and focus on:

- (i) Distressed restructurings, defined as exchanges of debt at a loss (as in Moody’s 2012).

- (ii) Restructurings of external sovereign debt, meaning bonds or loans by the central government and owed to private, foreign creditors, that is, international banks or bondholders. We do not include sub-sovereign bonds (as these are a separate asset class), private-to-private debt

> 23 One can use the bond prices in our database to compute any price-based recovery rates of choice. Figure C.3 in the Appendix does so, using sovereign bond prices at the _start_ of default (90 days post-default, as in Moody’s 2011a), and compares them to our set of haircut estimates at _exit_ from default (we compute discounted payment streams at the restructuring date). The two measures are highly correlated, suggesting that, at the entry into default, the market does well at predicting recovery rates at the exit from default.

9
comparisons, they found that urban counties saw especially large reductions in NO2 after March 13, 2020, compared to historical data. At the global level, Venter et al. (2020) connected mobility data with environmental indicators to argue that a strong link exists between global vehicle transportation declines and the reduction in ambient NO2 exposure, though they did not assess how differences in lockdown policy measures differentially affected NO2.

Liu _et al._ (2021) correct for temporal trends and accounted for lockdown timing in the Californian context, finding that sharp decreases in air pollutants occurred shortly after the lockdown policies were implemented. This illustrates the important influence of lockdown timing. Another study focusing on Seattle, WA, Cui (2020) find through sophisticated time series analyses that local traffic decreased considerably, while air quality improved during lockdown. He emphasizes the importance of adjusting these analyses to take into account meteorological conditions. Zhang _et al._ (2021) report that, compared with the base period from 2018 to 2019, the COVID-19 lockdown measures significantly reduced air pollutants in China. Winchester _et al._ (2021) find that when cities’ most stringent social distancing policies in the United States were implemented, a significant reduction in average daily congestion and decrease in average daily mobility and average daily NO2 compared to unrestricted days.

In this context, real-time information can play a most valuable role for policy makers in developing countries, particularly to address the questions highlighted in what follows. How has the massive blow in public transportation been affected by the evolution of the pandemic, sector-specific restrictions and the stringency of lockdowns? What were the relative impacts of the disease versus the sectoral restrictions and health policy remedy of lockdown? Has the use of private vehicles increased? What is the evidence that a silver lining story in terms of reduction in congestion can be drawn after the relaxation of lockdown measures?

The focus of these studies has been only on a selected number of countries, mostly the United States and developed countries with the exception of few emerging countries