rafmacalaba/data-use-mentions-extended huggingface dataset:
to poverty statistics using estimation models.” (Yoshida et al. (2015)). It employs Computer Assisted Personal Interviewing technology to collect the data and carefully builds and estimates the model that is used for prediction. Both nested and non-nested double sampling approaches are considered.<sup>5</sup> While SWIFT is still relatively young, at the time of writing, it has already been applied 34 times in 27 countries. Assessments of the cost-precision trade-offs however are still limited. Ahmed et al. (2014) denotes an exception which provides a SWIFT-like application of double sampling to poverty estimation in Bangladesh, where a variety of different data collection scenarios are being considered. Their simulation results indicate that substantial cost savings could be achieved with a moderate loss in precision. It is unclear however how much of this may be attributed to the use of double sampling as, in addition to relying on predicted data, they also reduce the number of primary sampling units.<sup>6</sup> Pape and Mistiaen (2015) apply a double sampling approach to poverty measurement in Mogadishu. They extend the set of predictors by including a sub-set of consumption items. Ligon and Sohnesen (2016) explore a similar strategy and include an empicical illustration using data from Uganda, Tanzania and Rwanda. Both these studies build on the idea that was put forward by Lanjouw and Lanjouw (2001). This approach strengthens the correlation with total household consumption but also adds to the costs, although Pape and Mistiaen (2015) in their application to Mogadishu are able to keep the face-to-face interview time below 60 minutes.<sup>7</sup> Other popular examples of prediction-based poverty estimation include the “Simple Poverty Scorecard” (SPS) project and the “Poverty Assessment Tool” (PAT) developed by the IRIS Center for USAID, see Schreiner (2014a) for a comparison.<sup>8</sup> These approaches predict a household’s poverty status on the basis of a small number
**_<u>________________________________________________________</u>_**

# **2.3 Customs and Border Crossing Times**

The performance of customs is one of the focal points of international trading. Longer clearance times is normally associated with lower efficiency in port operations management, complex cumbersome procedures and higher transport costs dues to high costs of storage etc. Reducing clearance requirements and guaranteeing fast and predictable release of goods is an important function of customs administrations. The effectiveness of a Customs administration can be measured by the average time taken between the arrival of the goods and their release. This helps Customs identify both the trouble areas and counteractive actions to increase their efficiency. It can also help Customs respond to tight production schedules and just-in-time inventory systems that need forward planning. Computerization of customs procedures at the border crossing stations and electronic filing of customs declarations can increase efficiency, improve compliance, facilitation and reduce long delays in customs clearance.

# _<u>Doing Business: Trading Across Borders -- 2006</u>_

The Doing Business Database provides objective measures of business regulations and their enforcement. The Doing Business indicators are comparable across 145 economies. They indicate the regulatory costs of business and can be used to analyze specific regulations that enhance or constrain investment, productivity and growth. It provides indicators of the cost of doing business by identifying specific regulations that enhance or constrain business investment, productivity, and growth. The indicators were developed by Bank in cooperation with the Lex Mundi Association of law firms and the International Bar Association.

The principal data collection methods for the indicators are the study of the existing laws and regulations in each economy; targeted interviews with regulators or private sector professionals in each topic; and cooperative arrangements with other departments of the World Bank, other donor agencies, private consulting firms, business and law associations. The project team prepared a set of templates or questionnaires for use by staff of the World Bank Group, or other agencies, in their work on business environment issues.

Trading
# Figure 2: U.S. MFN and Column 2 tariffs

## (A) A comparative snapshot of U.S. tariffs (B) U.S. Column 2 tariffs

<!-- Start of picture text --> Percent Rate increase Frequency Cumulative %<br>40 Column 2 rate 3500 100%<br>35 MFN tariffs 3000 90%<br>30 80%<br>25 2500 70%<br>20 60%<br>2000<br>15 50%<br>10 1500<br>40%<br>5<br>1000 30%<br>0<br>20%<br>500<br>10%<br>0 0%<br>Tariff rate<br>Steel<br>Autos<br>Washing machines Solar panels Aluminum and parts All products<br>Number of products<br>0 5 10 15 20 25 30 35 40 45 50 55 60 >60<br><!-- End of picture text --> Source: USITC. A. Trade weighted averages.

B. Based on the 2018 8-digit HTSUS harmonized tariff schedule of the United States and 11,796 product lines. Specific tariffs are not represented. Tariffs on washing machines and solar panels: above-quota safeguard tariffs to be applied in the first year. Tariffs on autos and parts are not yet official.

imports from countries of the Soviet Union and others under communist control, except Yugoslavia. The Jackson–Vanik amendment to the Trade Act of 1974 withheld NTR status from communist, non-market economies that restricted the freedom of emigration and other human rights.

China, the biggest in the group of non-market economies, was first granted non-discriminatory MFN status in 1980. This was, however, subject to annual renewal and even in the absence of actual changes in U.S. tariffs, introduced a great deal of uncertainty and political controversy in US-China trade relations. Every year between 1990
exert upward pressure on local food prices (Durevall et al. 2013). However, the transmission of global food prices to domestic prices remains unclear. According to UNCTAD trade statistics, Ethiopia is a net exporter of basic foods, while it is a net importer of cereals and cereal preparations. This issue deserves further empirical investigation.

Another channel of external pressure on domestic prices is imports of petroleum and related products. Petroleum and related products represent 10-20 percent of Ethiopia’s total imports. The country is therefore exposed to the vagaries of international energy prices which filter through the domestic cost of production of goods and services, ultimately fueling inflation.

The exchange rate also plays an important role in any economy that is import-dependent, as is the case for Ethiopia. In the event of a devaluation, the weakening of the domestic currency raises the cost of imports, which in turn increases the domestic price level. In an economy facing foreign exchange constraints, a devaluation can set off an inflationary spiral whereby devaluation causes domestic prices to rise, causing an appreciation of the real exchange rate which discourages exports. Further devaluation aimed at preventing the deepening of the trade deficit can ignite other rounds of domestic price increases while raising expectations of further exchange rate devaluation. Under tight foreign exchange reserve constraints and dependence on imports, inflation can indeed offset some of the intended benefits of devaluation. The World 5
pillars from the World Economic Forum's (WEF) Global Competitiveness Indices (GCI) and export diversification from the United Nations Conference on Trade and Development (UNCTAD).<sup>5</sup> The mapping process was conducted using keyword searches in the titles of extracted laws and decrees.<sup>6</sup> To the best of our knowledge, this presents the first comprehensive data set covering all GCC reforms passed from 1980 to 2019 by policy pillar (see Appendix A1 for a detailed explanation).

The number of passed laws and decrees has increased rapidly across the GCC countries in the last few years (see Figure II.1), reflecting intensifying reform efforts. Among the policy pillars, “Diversification” contributes most significantly to the increase in many countries (see Figure A1.2 in Appendix A1).

**Figure II.1: Total Number of Laws and Decrees in the GCC countries – 1980-2019**

<!-- Start of picture text --> 700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>Year<br>Bahrain Kuwait Oman<br>Qatar Saudi Arabia UAE<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br>Number of Laws and Decrees<br><!-- End of picture text -->

# II.2 Do Economic Reforms in the GCC Focus on Lagging Policy Areas?

We examine whether the GCC countries implement reforms in lagging policy areas, as measured by the World Economic Forum's (WEF) Global Competitiveness Indices (GCI) and the United Nations Conference on Trade and Development (UNCTAD)’s Export Diversification. We construct a "Distance to Policy Frontier” measure for each GCC country and policy pillar. A higher score implies that the country is further away from the policy frontier, based
derived from an extract of the 2018 household census: (1) PMT scores calculated in the 2017 UBR administrative data, (2) the Meta Relative Wealth Index,<sup>4</sup> (3) predictions derived from a village-level model estimated using a 2016 household survey and publicly available geospatial indicators, and (4) a two-step procedure that utilizes a hypothetical partial registry of 450 randomly selected villages – 10 percent of the villages in the population -- in addition to the 2016 household survey and publicly available geospatial indicators. This hypothetical partial registry would collect selected proxy welfare indicators such as asset and demographic information from _all_ households in the selected villages. The first step entails predicting per capita consumption from the household survey into the partial registry data, which we simulate by sampling from the census extract. The second step uses the partial registry predictions to train a model using publicly available geospatial data to generate estimates for the remaining 90 percent of non-registry villages.

> 1 Serajuddin et al. 2015, Yeh et al., 2020.

> 2 Van Der Weide et al. (2022). 3 Burke, 2021, World Bank, 2021. 4 The Meta relative wealth index is described in Chi et al (2021).

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EGS and EGSS from other countries and estimate emissions based on the industry and input-output data. In the first case, the analyst must assume that the technology of production of goods and services is similar in both countries and will need a detailed mapping from EGS to the industrial classifications, both in the country used as reference and in the country of its application. Such mappings are not usually available in all countries, which means that further assumptions are needed regarding the shares of green and non-green outputs for each industry code, and across country-specific industrial classifications.<sup>19</sup> The output approach is useful for understanding the implications of structural transformation on sectoral employment, as well as the linkages between green industrial policy,<sup>20</sup> green stimulus packages, and employment growth. For example, Bontadini and Vona (2021) are interested in understanding the comparative advantage of green production as a way to reconcile economic growth with environmental preservation and the mitigation of climate change-related risks. The authors find that green production is highly concentrated in a set of high-tech industries producing capital goods, while polluting and green 13 Eurostat (2009).

14 For example, the UK uses multiple data sources including satellite images. For more information consult the UK Office for National Statistics at - <u>https://www.ons.gov.uk/economy/environmentalaccounts/bulletins/ukenvironmentalaccounts/2023#measuring the-data.</u>

> 15 BLS (2013a).

> 16 Eurostat data collection handbook (2009) proposes various ways to adjust the estimate.

> 17 Winkler et al. (2022) forthcoming.

18 Countries report their emissions through what is known as a ‘bottom up’ approach, where national emissions are estimated by combining data on types of activity with the emissions typically produced by those activities.

> 19 The implications of these assumptions are further explored for the task approach.

> 20 Including policies related to trade on green outputs and services.

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.... 19**|
|Table 4.4: Breakdown by sector and asset class ....................................................... 21|
|**V.** **Impact on Emerging Markets**................................................................................ 23|
|Table 5.1 MSCI Emerging Market index .................................................................. 23|
|Table 5.2 Dollar index (broad) .................................................................................. 24|
|Table 5.3 MSCI Gulf States Index ..............................
# **MACROPRUDENTIAL STRESS TESTING OF CREDIT RISK: A PRACTICAL APPROACH FOR POLICY MAKERS**<sup>_∗_</sup>

DANIEL BUNCIC<sup>¶</sup> MARTIN MELECKY<sup>§</sup>

**Abstract**

Drawing on the lessons from the global financial crisis and especially from its impact on the banking systems of Eastern Europe, the paper proposes a new practical approach to macroprudential stress testing. The proposed approach incorporates: (i) macroeconomic stress scenarios generated from both a country specific statistical model and historical cross-country crises experience; (ii) indirect credit risk due to foreign currency exposures of unhedged borrowers; (iii) varying underwriting practices across banks and their asset classes based on their relative aggressiveness of lending; (iv) higher correlations between the probability of default and the loss given default during stress periods; (v) a negative effect of lending concentration and residual loan maturity on unexpected losses; and (vi) the use of an economic risk weighted capital adequacy ratio as the relevant outcome indicator to measure the resilience of banks to materializing credit risk. The authors apply the proposed approach to a set of Eastern European banks and discuss the results.

**Keywords:** Macroprudential Supervision, Stress Test, Individual Bank Data, Eastern Europe.

**JEL Classification:** G28, E58, G21.

**Sectoral Board:** Financial Sector (FSE).

> _∗_ We thank Martin ˇCih ́ak, Joaquin Gutierrez and Mar ́ıa Soledad Mart ́ınez Per ́ıa for comments on an earlier draft of this paper. The views and opinions expressed in the paper are those of the authors and do not reflect those of the World Bank or its Executive Directors.

> _¶_ Institute of Mathematics and Statistics, University of St. Gallen, Bodanstrasse 6, 9000 St. Gallen, Switzerland. Tel: +41 (71) 224 2604. Email: daniel.buncic@unisg.ch. Web
To examine whether actual levels of insecurity changed as a function of refugee presence, we use ACLED data, which geocodes violent events. For each parish-year, we construct a binary variable, _Any Violent Event_ , which equals 1 if any of the following events occurred: violence against civilians, riot, attack, mob violence, or violent event.

# **4.4 Control Variables**

We (flexibly) control for the following covariates in our parish-year analyses. From the 2002 census, we include measures of each parish’s population, average age, proportion male, literacy rate, unemployment rate, agriculture share, share of the parish population that is coethnic with the president, and average household wealth (based on a composite index of household items). We also include a binary indicator from ACLED for any violent events from 2002, as well as each parish’s distance to the nearest oil well, distance to the nearest border, distance to a major road, and distance to Kampala, Uganda’s capital. To prevent post-treatment bias, we only use the 2002 measures of these variables. We interact these time-invariant variables with year to allow their effects to change over time.

For the individual-level analyses—the _Health Utilization_ outcome based on the DHS and Afrobarometer survey items—we control for respondents’ gender, age, urban or rural residency, household wealth, and education attainment, and for location-based controls (distance to the nearest border, to the nearest major road, and to the capital). We further interact these demographic and location-based controls by year.

# **4.5 Empirical Strategy**

Our main analysis uses a time-varying DiD research design. We run the following OLS model interacting refugee presence (time varying) with year, interacting time-invariant controls with year, and including parish, year, and region fixed effects, with standard errors clustered at the parish level: _yit_ = _ηi_ + _ηt_ + _ηr_ + _β_ 1exposure _it_ + _β_ 2exposure _it ×_ **1*
# **1. Introduction**

In most low- and middle-income countries, household surveys continue to be the main data source for measuring and monitoring poverty and inequality. At the center of poverty and inequality measurements are income and consumption expenditure approaches (Deaton and Zaidi 2002; Mancini and Vecchi 2022). Given the challenges of accurately measuring income in household surveys in low-income settings with a high proportion of the population working in informal and seasonal jobs, the consumption expenditure approach has dominated poverty and inequality measurements over the years and continues to garner attention in the development economics literature. A critical component of household consumption expenditure is food, which is estimated to contribute about 40% of total household spending in Sub-Saharan Africa (SSA) and 17% in advanced economies (Bogmans et al. 2022). There is growing interest in improving the accuracy and quality of food consumption expenditure measurement in household surveys (Beegle et al. 2012; Christiaensen et al. 2022; Battistin et al. 2023; Brzozowski et al. 2017), including understanding the sources of measurement errors in these variables (Friedman et al. 2017).

Two methods are globally used to generate household food consumption data – diary and recall – though the duration and reference periods vary by the countries adopting each approach. Generally, the method used by a country is based on their own discretion and is a function of what they have done in the past and have the human capital to continue with such. While this may pose no problem with national poverty and inequality analysis in respective countries across time, international comparison becomes difficult, both cross-sectional and temporal. Although crosscountry differences in these indicators could be attributed to _actual_ changes in the standard of living of the countries or variations in methodology, or both, international comparisons generally do not consider methodological disparities.

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approach and relying on predicted data in the hope of reducing financial costs while preserving a reasonable degree of statistical precision. Consider for example the recent initiative by the World Bank under the name of SWIFT (Survey of Well-being via Instant and Frequent Tracking) which “does not collect direct income or consumption data which can be both time-consuming and vulnerable to error without the right know-how and resources; instead, it collects poverty correlates, such as household size, ownership of assets or education levels, and then converts them

> 3It is also known as “two-phase sampling”.

> 4The literature on double sampling dates back to Neyman (1938) and Bose (1943). Many of the existing studies, including Hidiroglou (2001), Kim et al. (2006), Palmgren (1987), Rao and Sitter (1995), and Sitter (1997), provide analytical or simulation results on the properties of the estimators based on double sampling. While there are some explicit empirical applications of double sampling such as Tamhane (1978), Hansen and Tepping (1990), and Armstrong et al. (1993), the use of double sampling appears to be comparatively limited. Even fewer studies take into account the costs of data collection (Cochran (1977), Davidov and Haitovsky (2000), S ̈arndal et al. (2003), and Fujii and van der Weide (2013)), despite the fact that one major attraction of double sampling is the reduction in data collection cost.

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# **<<A>>** _I. POLICY INDICATORS AND STYLIZED FACTS_

We employ two different indicators of agricultural and food policies: the nominal rate of assistance ( _NRA_ ) to agriculture and the relative rate of assistance ( _RRA_ ), both from the World Bank’s agricultural distortions database (see Anderson and Valenzuela 2008, for calculation details). This database reports the most consistent and comparable estimates of agricultural protection across countries and over time. In our econometric analysis, we use a sample of 74 countries, comprising yearly data from 1955 to 2005 (see table S.1 in the supplemental appendix, available at http://wber.oxfordjournals.org/). The average number of observations per country is 35. We work with an unbalanced panel with more than 2,600 observations.

The _NRA_ measures total transfers to agriculture as a percentage of the undistorted unit value. The _NRA_ for agriculture is obtained as a weighted average of the _NRA_ at the product level, using the undistorted value of production as a weight. The _NRA_ is positive when agriculture is subsidized, negative when it is taxed, and zero when net transfers are zero. The _NRA_ includes both the assistance provided by all tariff and nontariff trade measures applied to agricultural products and any domestic price-distorting measures.<sup>1</sup> The price equivalent of any direct intervention regarding inputs or outputs is also included.<sup>2</sup> To account for the protection of manufacturing sectors, which is an important source of indirect taxation on agriculture, especially in developing countries, we use both the _NRA_ and the _RRA_ , which is calculated as the ratio of agricultural _NRA_ to nonagricultural _NRA_ .<sup>3</sup> The _RRA_ is a useful indicator for international comparisons of antior proagricultural policy regimes. There are fewer observations for the _RRA_ because the country and time-series coverage is smaller than for the _NRA_ . Specifically, the _RRA_ data contain five fewer countries (69 instead of 74)
Heavy vegetation cover may make certain boundaries invisible, making it necessary to at least supplement the aerial photography with a ground survey. In general, the use of aerial survey is most advantageous in open country with small, irregular fields having physical boundaries. Aerial survey is most feasible in rural areas with large, regular fields having physical boundaries (Hanstad, 1996).

- 16 Such as land owner(s), forms of land rights, land use types, actual area of each plot belonging to the land owner, lease/mutation/encroachment details, and other relevant details. This is primarily a fiscal record to show from whom the assessment of each holding is to be realised, and the amount.

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those pertaining to acquisition and rental of land and real estate by foreigners, notification and screening of foreign investment, restrictions on payments and transfers, conditions on visas, restrictions on cross-border data flows, etc. The <u>common questions cover measures that are</u> potentially pertinent for all sectors of the economy, but that are dealt with in _sector-specific legislation_ (and not in generally-applicable legislation, as in the case of general questions), such as the existence of a regulatory authority, forms of establishment and ownership conditions, licensing,

> 10 We are very grateful to the OECD Secretariat for its long-standing cooperation in our effort to put together the Services Trade Policy Database, which involved sharing information from the OECD regulatory database for the year 2016 and clarifying diverse aspects regarding the collection and comparability of data. For further information on the OECD data collection process, including the validation of information, see <u>https://www.oecd.org/trade/topics/services-trade/.</u>

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