rafmacalaba/data-use-mentions-extended huggingface dataset:
debt literature, but unlike previous authors, we do not rely on the off-the-shelf country-level EMBI series but exploit the rich microdata on individual bonds that underlie the aggregate index. We focus on bonds that appear in the broadest of their indices, the EMBI Global, which are USD instruments from low- and middle-income countries with a minimum issue size of US$500 million and “easily accessible and verifiable daily prices either from an inter-dealer broker or a certified JP Morgan source” (see JP Morgan [1999] for details).

A main advantage of using the bond-by-bond EMBI data compared to the standard country-level indices is that we get a cleaner, more homogenous sample that is consistent with our historical time series, facilitating long-run comparisons. Scrutinizing and winnowing the sample is important for our purposes because the EMBI includes many _non_ -sovereign, _non-_ USD (or UK pound) instruments, such as bonds issued by large public banks, as well as local-currency bonds.

Specifically, we drop all EMBI bonds issued by public companies and other sub-sovereign bonds guaranteed by the government. Furthermore, we exclude local-currency bonds, as well as a few dozen bonds in international currencies other than the USD or GBP, such as the French or Swiss franc. The resulting dataset includes more than 600 external sovereign bonds from the EMBI. Some bonds have prices as early as 1990, but the sample becomes representative only from 1995 onward when more than 40 Brady bonds were actively traded. We therefore show results for the modern period starting in 1995.

Our merged (historical plus modern) bond pricing sample thus covers 1,552 foreign-currency sovereign bonds issued by 91 countries.<sup>32</sup> The coverage and granularity of this global dataset close to that compiled for individual advanced countries.<sup>33</sup> For the United States, the Chicago-based CRSP Database provides monthly instrument-level data on US Treasuries back to 1925. For England, Ellison and Scott (2020) gathered granular data on prices and issuance patterns of UK government debt
crisis countries tended to allow for less stringent definitions of capital, had more discretion in how banks calculated capital requirements, and exhibited lower actual capital ratios than the rest of the world. Both the 2007 and 2011–12 BRSSs show that crisis countries were more likely to allow the initial disbursement or subsequent injections of capital to be done with assets other than cash or government securities. At the same time, while 80 (100) percent of crisis countries allowed Tier 3 (Tier 2) in regulatory capital, only 28 (85) percent among non-crisis countries did so. Moreover, crisis countries were also more likely to allow hybrid debt instruments to be part of Tier 1. Also, the share of crisis countries that allowed banks to calculate their capital requirement for credit risk based on banks’ internal ratings models was 95 percent, about twice as a large as in the rest of the world. Finally, while the average level of the actual risk based capital ratio (Tier 1 capital to assets) of the banking system prior to the crisis was 16 (14) percent among non-crisis countries, this figure was 12 (9) percent among crisis countries. The same pattern is observed if we look at the statistics gathered in the 2011–12 Bank Regulation and Supervision Survey.

Second, banks in crisis countries faced fewer restrictions to engage in non-bank activities such as insurance, investment banking, real estate, as well as in non-financial activities. Using an index of 1 to 4 to measure restrictions imposed on bank activities, where higher values denote greater restrictions, we find that non-crisis countries were less likely to allow for a full range of non-bank activities to be conducted directly in banks.

Third, crisis countries were less likely to have in place non-performing loans and provisioning requirements and were more lax in the treatment of bad loans and loan losses. Close to 90 percent of non-crisis countries had an asset classification system under which banks had to report the quality of their loans using a common regulatory scale, while 57 and 65
**Table 16**

Developed and Developing Economies: Anti-Dumping Investigations by WTO Reporting Members 1989-1997 (in number of cases and %)

||**Developed**<br>**Members**|**Developinp**<br>**Members**|**Developed As**<br>**% of Total**|
|---|---|---|---|
|1989|_66|30|_<br>69|
|1990|147|**18**|.<br>89|
|1991|184|44|81|
|1992|._261|65|80|
|1993|141|158|47|
|1994|115|113|50|
|1995|73|83|47|
|1996|73|148|33|
|1997|118|115|51|

Explanation:

* Includes Poland (25 investigations) and Israel **_(I5_** investigations).

**_Source:_** _YflO, Antidumping database._

Most of developed country -antidumping investigations and definitive measures against developing countries have been directed to higher and middle income developing countries - frequently the same countries which have been making an increasing use of antidumping measures themselves - and non WTO Members (see below). Bangladesh is the only least-developed country to have been 3ubjected to antidumping investigations and definitive measures on three occasions, in 1992 (see Miranda et al., 1998).

The major product groups which are the object of anti-dumping investigations are muchf the same for developed as for developing countries: Table 17 shows that Basic Metals, Chemicals, Plastics and Machinery and Electrical Equipment are the four sectors which account for the bulk of ant-dumping investigations in both developed and developing countries - although the emphasis naturally tends to differ from country to country. The four sectors together accounted for more than two-thirds of all the antidumping investigations world-wide over the
# 1. Introduction

Even before the start of its civil war in 2014, the Republic of Yemen had one of the lowest living standards in the world. Since 2014, its population has suffered from one of the world’s severest humanitarian crises with a serious further deterioration of living standards, manifested in increased poverty rates, severe malnutrition, food insecurity, and physical insecurity. Basic public health and education services have suffered, infrastructure (including road networks) has been destroyed, and government employees have been left without pay for extended periods. In the background, like many other low-income countries, Yemen is vulnerable to accelerating climate change. Given these circumstances, the country was ill-prepared to deal with the Covid-19 pandemic and international price shocks for food and energy.

Given this, it is more urgent than ever to put an end to the war and identify policies that will help to overcome the humanitarian crisis and put the country on a path toward a better future. With this aim, this paper explores potential scenarios for the economy of the country up to 2030, focusing on alternatives under which the conflict subsides, and the country manages to start addressing the urgent needs of the population and the constraints that stand in the way of resumed sustainable growth and improved living standards.

The analysis is based on simulations with the Sustainable Development Goal Simulation model (SDGSIM), a dynamic computable general equilibrium (CGE) model designed for country-level policy analysis.<sup>1</sup> For this analysis, the model was adapted to the Yemeni context and calibrated to a new dataset, including a social accounting matrix (SAM) for 2020, the most recent year with sufficient information. The analysis, which covers the period 2022-2030, presents a base scenario without significant improvements in the economy or the state of security and six alternative scenarios which, compared to the base, reflect varying degrees of improvement. The scenarios are underpinned by different trajectories along seven dimensions: health and education; infrastructure; social transfers; remittances from abroad;
s GEP, so the findings are not driven by differences in the sample composition. These results are confirmed by more rigorous econometric estimations in Sections IV and V.

**Figure 1. January Same-Year GDP Growth Forecast Errors by Region and Institution (2010–2020)**

<!-- Start of picture text --> Panel (A) Panel (B)<br>Optimism: Forecast Error Inaccuracy: Absolute Forecast Error<br>4.0 4.0<br>3.5 3.5<br>3.0 3.0<br>2.5 2.5<br>2.0 2.0<br>1.5 1.5<br>1.0 1.0<br>0.5 0.5<br>0.0 0.0<br>MENA LAC SSA SA EAP ECA MENA LAC SSA SA EAP ECA<br>IMF-WEO WB-GEP IMF-WEO WB-GEP<br>World average (IMF-WEO) World average (WB-GEP) World average (IMF-WEO) World average (WB-GEP)<br>Percentage Points Percentage Points<br><!-- End of picture text --> Source: Authors’ calculations based on the International Monetary Fund’s January _World Economic Outlook_ (WEO) and the World Bank’s January _Global Economic Prospects_ (GEP).

Note: The figure displays the same-year January GDP growth forecast errors (Panel A) and same-year absolute GDP growth forecast errors (Panel B) of the IMF’s _World Economic Outlook_ and the World Bank’s _Global Economic Prospects_ . Forecast errors are calculated as the forecasted GDP growth rates minus realized GDP growth rates. Absolute forecast errors are calculated as the absolute value of the forecast errors. The figure is constructed based on a common sample of 141 countries (largely developing economies) collected in January for each year between 2010 and 2020. The MENA region includes both GCC and non-GCC countries.

MENA also stands out as the
http://madb.europa.eu/madb/barriers_crossTables.htm, https://investmentpolicyhub.unctad.org/</u> and <u>http://www.oecd.org/investment/fdiindex.htm. See also Golub (2009); and Reisman and Vu (2012).</u>

> 14 <u>https://www.worldbank.org/en/research/brief/services-trade-restrictions-database</u>

> 15 - <u>http://www.oecd.org/trade/topics/services trade/</u>

> 16 In 2013, the World Bank and the WTO signed a Memorandum of Understanding concerning the establishment a unique database on services policies, covering commitments taken by WTO Members in various agreements, applied regimes and services statistics. This cooperation between the two organizations materialized in I-TIP Services, the services component of the Integrated Trade Intelligence Portal (I-TIP), which was released in 2013. The main objective of the integrated database is to make it easier for WTO Members and other stakeholders to access the various types of information relevant for services trade policymaking, see http://i-tip.wto.org/services/default.aspx.

> 17 I-TIP Services offers information on trade in services policies for a large sample of economies spanning all income groups. In its four modules (GATS, RTA Commitments, Services Trade Policy Database (STPD), and Statistics), the integrated database permits searches by economy (mainly WTO Members), subsector, agreement, or source of information. Search results are presented in summary form, as well as in more detail. Search results can be filtered using a number of additional criteria. Further, users can easily switch from one module to another, for example from looking at a particular economy's WTO commitments in a given subsector, to relevant commitments in RTAs, and then to applied policies and related services statistics.

8
from the Gallup World Poll (GWP), due to its extensive spatial and temporal coverage, and we

> 2See Verme and Schuettler (2019) for a review on the minimal evidence on economic effects on host communities.

> 3See Clemens et al. (2018) for a discussion of the literature on the economic effects of policies for both refugees and hosts, and Bahar et al. (2021) for an analysis in Colombia of a recent regularization program.

1
in 2017 US$ to adjust for inflation. An overview of data compiled for the case studies, the years covered, and the data sources is provided in Table 2.

2. **Pre-reform cost-recovery level of electricity tariffs (1980s-1990s).** Data on tariff levels during the 1980s and 1990s were collected for all 17 jurisdictions and compared to LRMC estimates, which reflect the long-run marginal cost of supply that would need to be covered to expand the system, taking into account shadow prices for inputs such as fuels, labor and capital. Comparable LRMC estimates are available for most countries for the 1980s and 1990s, when the World Bank financed numerous LRMC studies across the world. For 12 jurisdictions, the tariff and cost data are for 1987 and based on The World Bank (1990) and based on a strictly comparable methodology. For two further jurisdictions, the data is based on the same LRMC methodology but for different years, 1991 (Tanzania) and 1993 (Vietnam) (The World Bank, 1993c, 1995). For the remaining three cases, the assessment compares tariffs and current cost-of-service estimates (as opposed to LRMC) for 1991 (Senegal), 1994 (Ukraine), and 2003 (Tajikistan) (The World Bank, 1994, 1998, 2004). The results of this second analytical step provide a baseline for the assessment of reform impacts and allowed to assess if countries were able to raise tariffs or reduce costs compared to the pre-reform period; which types of sector reforms and other factors were

> 8 The cases are Colombia, Dominican Republic, Egypt, Arab Rep., India (Rajasthan, Andhra Pradesh and Odisha), Kenya, Morocco, Pakistan, Peru, Philippines, Senegal, Tajikistan, Tanzania, Uganda, Ukraine, and Vietnam. 9 Externalities were not assessed as part of C3 because of data availability issues.

8
-32-

It is usually not possible to make these quantitative comparisons for all products of an industry, because: a) one cannot always match each product with a corresponding one in the US census; b) several products in an industry are only specified by value and not by quantity. Therefore we cannot arrive directly at the formulae given above for output comparison of country X with country U, but only at a comparison of the covered part of output. The components of (3.1a) and (3.1b) which refer to the quantity at a country's own prices, i.e. I (QyX * pX) and I (QU * PU) y y y y are taken directly from the production censuses. The problem is how to estimate the quantity at prices of the other country, i.e. I (QU * pX) and I (Qy * ), when we only have a figure for I (Q)y * c and I (Qy * Py)c where **"c"** indicates the "covered" (or matched) part of output.

Two alternative solutions are available. One may assume that the quantity relationship between matched output in country X and country U applies to the industry as a whole, according to the following equations: and If, for example, country X's matched output at unit values of country U came to one half of matched output in country U, then country X's total output at US unit values is assumed to be one half of total output in country U. It follows from the equations (3.2a) and (3.2b) that on the basis of this assumption the output value of one country in prices of the other country c__ be obtained by blowing up the value of its covered output by the ratio of total to covered output in the other country:
>Informality and Employment in Zimbabwe</u>_

Zimbabwe’s statistical office (ZimStat) reports some figures on the informal sector in its 2014 labour force survey (LFCLS). According to the LFCLS, about 14% of employment is provided by the informal sector (informal employment, by contrast, nears 90%, implying substantial off-thebooks employment elsewhere). ZimStat places unemployment in Zimbabwe at 11%, a contested figure, as its definition of the labor force excludes informal operators and vendors not actively seeking employment.<sup>7</sup> Some accounts at the time of the LFCLS claimed that once this informal employment was included, nearly 9 out of 10 Zimbabweans were unemployed.

> 6 They use data from surveys using what is known as the 1-2-3 method, which finds informal sector businesses by first interviewing a representative sample of households.

> 7 BBC, “Reality Check: Are 90% of Zimbabweans unemployed?” <u>https://www.bbc.com/news/business-42116932</u>

(7)
Data merging. A key aspect of our research methodology is the merging of the Orbis and BERKEARTH datasets using latitude and longitude information. This common element allows us to associate firm-level financial performance data from Orbis with the corresponding weather data from BERKEARTH, both mapped to the same geographic region.

Given that the weather data is gridded with one degree of latitude and longitude, we first assign a unique index to each grid. For example, Index 1 is assigned to the upper leftmost grid (longitude −180 to −179 and latitude −90 to −89). Subsequently, we assign the same index to the location of each firm. For instance, a firm located at longitude −179 _._ 3 and latitude −89 _._ 3 would also be assigned Index 1.

These indices are then used to merge the two datasets. As a result, the final firm-level data includes annual observations of the firm, its location, sector, and fixed assets, as well as the mean, variance and maximum of the temperature for the gridded location of the firm.

# 3.2 Data pre-processing

We carry out several cleaning procedures to enhance the quality and consistency of the data. First, we exclude firms with non-positive assets from our dataset. This exclusion is based on the accounting principle that fixed assets should always be non-negative. While it is technically possible to have zero fixed assets, these cases are excluded to enable the calculation of the growth rate of fixed assets. Second, to avoid potential double counting, our analysis is limited to unconsolidated data. We select U1 and U2 as the consolidation codes in Orbis data.<sup>11</sup> This choice ensures that the data reflects each firm’s independent operations, preventing distortion from inter-company transactions or accounts. This is crucial for examining the impact of location-specific factors, such as weather, on firms’ behavior, which would be limited with consolidated data. Finally, We also focus on countries with more than 500 observations and more than
and literacy rates (Cambodia, Lao PDR, Papua New Guinea, TimorLeste). Extended school closures during the pandemic led to substantial further learning losses, especially for the poor.

Environmental challenges. Many countries in the region face environmental problems that threaten to undermine not only economic growth and stability but living standards, lives, and livelihoods. The main challenges include water management, deforestation and land degradation, air pollution, and climate change. According to the Verisk Maplecroft Global Risk Analytics Dataset, which ranks the world’s 576 largest urban centers on their exposure to a range of environmental and climate-related threats, 99 of the world’s 100 riskiest cities are in Asia, including 37 in China, where air and water pollution presents a growing health risk. The worst-performing city in the ranking, Jakarta, also suffers from severe air pollution, but added to this are perennial threats from seismic activity and flooding. These have prompted the government of Indonesia to initiate relocating the capital.

# 5.4 Regional policy priorities
focus on gender-specific outcomes as well as responsiveness to women farmers’ demands are essential to building strong programs that reach women farmers.

# **_Credit_**

Female-headed households are significantly less likely than are male-headed households to use credit in a majority of the countries for which we have data (figure 11).

**Figure 11**

<!-- Start of picture text --> Household access to credit in rural areas,<br>by headship type<br>FHHH no male FHHH with male MHHH<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Source: RIGA data, most recent year available.<br>Note: FHHH with male defined as FHHH with at least one working-age (15-59) male.<br>percent receving credit in last year<br><!-- End of picture text --> In six of eight countries, the gap between male- and female-headed households is significant (not indicated in the figure) when there is no male present. When a male is present, however, the gap (which is then much smaller) is only significantly different in three of eight countries. One significant counterpoint to this pattern (and others) comes from China, where de Brauw et al. (2008) find that female-headed households have equal credit utilization as male-headed households, and they have equal access to land and labor.

30
to determine this geographic area. Using GIS, we overlaid flood maps with population maps from the Gridded Population of the World v3 (CIESIN-CIAT 2005) to obtain estimates of the population exposed to each event. Population grid maps are available in 5- year intervals since 1990. We calibrated an exponential curve for the remaining years to complete the panel. We used the resulting variable in the flood fatality and magnitude models (equations (2)-(4)) instead of national population, which was used in the flood frequency model (equation (1)) as observations in the latter model were defined as countryyears.

The resulting estimates of population exposure to a flood are more accurate than statistics based on country-level population statistics, which previous studies on natural disasters have used. In our sample, mean population density in areas affected by floods is 394.46 persons/km<sup>2</sup> , while mean population density at the country level is 154.53 persons/km<sup>2</sup> . This difference reflects the fact that people have tended to concentrate in flood plains for millenia. Increased populations in flood plains is one explanation for the observed growth in number of floods (Figure 1; Freeman et al. 2003; IPCC 2007a, Chapter 3).

# _3.3 Vulnerability: Socioeconomic and governance indicators_

The indicator of income is GDP per capita converted to constant 2005 international dollars using purchasing power parity rates. It comes from the World Development Indicators (WDI 2010). Although GNI per capita more accurately measures the income of a country‘s population, it was available for slightly few countries and years than GDP per capita. The choice between GDP per capita and GNI per capita is inconsequential in our models: we estimated all the models using both measures, and results were virtually indistinguishable.

11
# **Section 13: Banking Sector Characteristics**

Section 13 of the 2011–12 survey asked about quantifiable characteristics of the banking sector such as size, structure of the banking sector, performance, and asset and liabilities composition. It has 27 quantitative questions where respondents were requested to enter a value for any given question (Table A13). Although, there is no equivalent section in the previous round of the survey in 2007, some of the questions in section 13 of the 2011–12 survey could be located in other parts of the 2007 survey. Two immediate observations and comparisons can be made. First, bank concentration is quite high both in advanced economies and EMDEs alike. The median share of assets held by the largest five banks is about 75 percent across all countries. Such a high level of concentration, as we saw with financial crisis, may lead to “too big to fail” scenarios and, therefore, may distort the incentives of the senior management of these large institutions. Second, the median fraction of banking system's assets in foreign-controlled banks increased notably from 39 percent to 49 percent. Finally, some of the quantitative questions were excluded from the published version of the survey due to issues of comparability. Those questions tend to cover topics where consistent cross-country data are already available (such as total banking system assets) are indicated by an asterisk in the questionnaire accompanying the dataset at http://www.worldbank.org/financialdevelopment.

**Table A13. Banking Sector Characteristics: Selected Responses**

||**ALL**|**ADV**|**EMDEs**|
|---|---|---|---|
|**A. Overview**|**Median**|**Median**|**Median**|
|How many commercial banks were there at the end of:<br>2010?<br>2005?|24<