rafmacalaba/data-use-mentions-extended huggingface dataset:
Rep.|34.3|2.9%|
|10|Malaysia|35.9|2.7%|Canada|33.1|2.8%|
|11|Thailand|34.0|2.5%|Mexico|30.8|2.6%|
|12|United Kingdom|22.1|1.6%|United Arab Emirates|30.0|2.6%|
|13|Singapore|21.5|1.6%|Italy|26.4|2.3%|
|14|France|21.3|1.6%|Singapore|24.4|2.1%|
|15|Ireland|19.1|1.4%|India|24.3|2.1%|
||Top15|1,207.03|89.3%|Top15|971.26|82.9%|
||Total Top30|1,352.01|100.0%|Total Top30|1,171.79|100.0%|

* While Singapore does import intermediate ICT goods, a significant portion is re-exported. Hong Kong SAR, China, and the Netherlands also have extremely high re-exporters.

Source: World Bank MC-GVC Database using a consistent 175 country panel that accounts for 95-98% of world trade, see: http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator 7
(0.152)||(0.143)||(0.147)|
|T x Owns productive land||0.250*||0.281**||0.144|
|||(0.134)||(0.123)||(0.149)|
|T x Num children 8-17||-0.042||-0.051||0.011|
|||(0.039)||(0.045)||(0.048)|
|Constant|1.408***|1.329***|1.268***|0.976***|-0.101|-0.286|
||(0.150)|(0.223)|(0.139)|(0.240)|(0.173)|(0.270)|
|T x interactions (p-value)||0.678||0.331||0.075|
|Observations|12,163|12,163|9,635|9,635|6,905|6,905|
|Mean|0.641|0.641|0.541|0.541|0.607|0.607|

This table shows the relationship between attrition, based on the capability to find the original households from the 1997 ENCASEH sample, and household and locality characteristics measured at baseline with 1997 ENCASEH data (columns 1 to 4). Columns 3 and 4 only include households in localities sampled in 2017 ENCEL. In columns 5 and 6 attrition is defined based on the capability to find young members who formed their own household. Standard errors clustered at the locality level.<sup>_†_</sup> indicates a dummy variable. Asterisks indicate significance at the ***1%, *
(ATUS) for the years 2003-

> 11Data for PJM, ERCOT and SPP are taken from their websites. Data for ISO-NE, CAISO, MISO and NYISO are taken from energyonline.com.

> 12Unfortunately, load data for the remaining RTOs is not readily available. In particular, the data does not cover most of the southeast, and parts of the northwest and the southwest of the United States.

> 13Specifically, I construct percent deviations from the weekly average as _et_ = <u>�</u> _tt′_<sup>_′_</sup> == _<u>Ett</u>_ +3 _−t_ 3<sup>_Et′_,</sup>

where _Et_ denotes the electricity load data.

14
, _D_ is the level of debt held by household _j_ , and _RE_ is the value of real estate; it is assumed that _D_ > _RE_ and that _RE_ = 0.8 _Dj_ . Some caveats are needed, however. On the positive side, a loan-to-value ratio of 100 percent is implicitly assumed. In all likelihood this is too high, thus facilitating banks’ recoveries on bad loans. On the negative side, losses given default in non real estate-related loans might be higher and, in addition, housing prices could enter a vicious declining path as NPLs rise—in particular if banks vigorously pursue foreclosure procedures.<sup>15</sup>

> 15 A few caveats. The decline in housing prices shows a marked difference between price developments in the Adriatic coast and those that have taken place elsewhere within the country. Indeed, central bank data suggests that much of the decline since the 2008 peak has occurred in Zagreb. Also, as noted by Kraft (2007), ever-growing competition in the banking sector in the one or two years just before the global financial crisis points to, for example, increases in loan-to-value ratios. But these factors were short-lived and brought to an abrupt end by the crisis; i.e., they are likely to affect a small share of the outstanding household debt stock. Of greater concern might be the situation in commercial real estate, but this is not a subject examined in this paper.

13
# **4. Evidence on World War 2 and the COVID-19 pandemic in Europe**

In the following analysis we test for an effect of exposure to WW2 on outcomes under the COVID-19 pandemic. In addition to the potential impact of WW2 experience, we take it that mortality from COVID-19 depends on a country’s demographic composition, total population, population density, the average level of education in the country, and the measures of government effectiveness or democracy (Bosancianu et al., 2020). In terms of our model in Section 2, these variables can be thought of as determinants of the probability of a big shock occurring. The variable of interest in our analysis is the country’s death rate during WW2, as the indicator of prior experience with this big shock.

We use multiple data sources. Data on COVID-19 come from the European Center for Disease Prevention and Control (2020). We use COVID-19 related-deaths-per-million of the population as our main dependent variable. Data on war military casualties is derived from UCDP/PRIO Battle Death Data (Bethany et al. 2006) and Wikipedia; civilian casualties are based on multiple sources of mostly country-specific data (Wikipedia 2020). Civilian casualties in the post-Soviet countries come from Erlikman (2004). We use death as a proportion of the population as our control variable. The total deaths as a proportion of the population come directly from the Wikipedia dataset. Military deaths as a proportion of the population are calculated as a ratio of military (battle) deaths and the population of that country in 1939. Both total and military deaths for several countries that did not exist during WW2 are imputed based on the corresponding losses of the “parent” countries.<sup>4</sup> The list of countries with WW2 casualties and COVID-19 statistics are shown in the Appendix (Table A1).

Recall from Section 2 that our hypothesis about the response to past big shocks is conditional on the probability of such shocks occurring ( ), which one would expect to
WPS3443

**Improving Credit Information, Bank Regulation and Supervision:**

**On the Role and Design of Public Credit Registries**

Andrew Powell, Nataliya Mylenko, Margaret Miller, and Giovanni Majnoni

# **Abstract**

This paper analyzes how data in public credit registries can be used both to strengthen bank supervision and to improve the quality of credit analysis by financial institutions. Empirical tests using public credit registry data were performed in collaboration with the central banks in Argentina, Brazil and Mexico. The results of the empirical tests confirm the value of the data for credit risk evaluation and provide insights regarding its use in supervision, including in calculations of credit risk for capital and provisioning requirements or as a check on a bank’s internal ratings for the Basel II’s internal rating-based approach. We also define a set of critical design parameters and use the results to comment on appropriate public registry design. Finally, the paper includes a discussion of the relationship between the different objectives of a public credit registry and how they influence the registry’s design.

# World Bank Policy Research Working Paper 3443, November 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

**_______________**

> * The authors are from the World Bank (Giovanni Majnoni, Margaret Miller and Nataliya Mylenko) and from the Universidad Torcuato Di Tella (Andrew Powell). This paper has been completed as part of a project on “Credit Information,
Figure A.6: Ricardian and Interest Rate Channels: Fiscal Multipliers <!-- Start of picture text --> A. On Impact B. 2 Years<br>2 3<br>Strong position Weak position 2<br>1<br>1<br>0 0<br>-1<br>-1<br>-2<br>Strong position Weak position<br>-2 -3<br>Baseline (Figure 3) Advanced economies 2009-14 Three lags Five lags Debt gap (group) Debt gap (country) Baseline (Figure 3) Advanced economies 2009-14 Three lags Five lags Debt gap (group) Debt gap (country)<br><!-- End of picture text --> Note: The graphs show the fiscal multipliers on impact and after two years (cumulative change in output relative to cumulative change in government consumption after a fiscal shock) by government debt, for the 10<sup>th</sup> to the 90<sup>th</sup> percentile of government debt in the sample. These are based on estimates from the IPVAR model, where model coefficients are conditioned only on fiscal position. (The exception are the last two sets of multipliers which condition on the deviation of government debt from the group-specific mean, “Debt gap (group-mean)”, and country-specific mean, “Debt gap (country-mean)”.) Government debt as a percentage of GDP is the measure of fiscal position. The strong fiscal position corresponds to the 10<sup>th</sup> percentile of debt-to-GDP ratio from the sample, while the weak fiscal position corresponds to the 90<sup>th</sup> percentile. Vertical error bars represent the 16-84 percent confidence bands. The upper row uses a model that allows for both Ricardian and interest rate channels. The bottom row uses a model that allows only for Ricardian channels, to preserve degrees of freedom.

33
As expected, the average gains from these protectionist trade reforms are now negative in the vast majority of countries. While there are 45 winners under liberalization, we find between 43 and 48 losers with increased protection. The estimated average income gains ( _G_ (0)) across countries in the three scenarios are, respectively –0.2%, –1.3% and –5.7% (Panel A of Table 10). More importantly, the prevalence of trade-offs is widespread (Panel B). There are only 8-10 countries without trade-offs and, of these, between 6 and 9 prefer the status quo to more protection. There are between 44 and 46 countries with trade-offs. The resolution of these trade-offs is fairly stable in favor of the status quo for plausible levels of _ε_ (1, 1.5 or 2). That is, in all three protectionist scenarios the vast majority of countries attain higher levels of welfare with the pre-existing structure of trade protection rather than with more protection. Most countries would be hurt by protectionist trade reforms, even after inequality impacts are taken into consideration.

# **6 Conclusion**

Using household survey data for 54 low and middle income countries harmonized with trade and tariff data, this paper offers a quantitative assessment of the income gains and inequality costs of trade liberalization and the potential trade-off between them.

A stylized yet comprehensive model that allows for a rich range of first-order effects on household consumption and income is used to quantify welfare gains or losses for households in different parts of the expenditure distribution. These welfare impacts are subsequently explored by deploying the Atkinson social welfare function that allows us to decompose inequality adjusted gains into aggregate gains and equality (distributional) gains.

Liberalization is estimated to lead to income gains in 45 countries in our study, and to income losses in 9 countries. The developing world as a whole would enjoy gains of about 1.9 percent of real household expenditures, on average. These income gains are negatively correlated with equality gains,
of outcome|0.65|1.13|0.76|0.90|0.56|0.14|

Notes: Each panel represents treatment impacts from a separate regression. 70 of the 159 firms were given the WMS survey in 2016, of which 50 had also received this survey in 2013. Panels A and C regress outcomes on treatment dummies only. Panels B and D add controls for dummies for the Cundinamarca and Valle regions, a dummy for having 10 to 50 workers at baseline, the number of employees in 2013, labor productivity in 2013, and the 2013 Anexo K management practice score. Panel E also controls for the baseline value of the outcome measure.

Robust standard errors in parentheses. *, **, and *** indicate significance at the 10, 5, and 1 percent levels.

68
# **1. Introduction**

Household consumption survey data that underlie monetary poverty estimates in low- and middle-income countries are often unavailable, unreliable or incomparable. To address these challenges, imputation-based methods have become increasingly more common not only to fill poverty data gaps in data-scarce and resource-constrained contexts, but also to identify project/program beneficiaries and evaluate development project/program impacts on poverty at low cost (World Bank, 2021; Smythe and Blumenstock, 2022; Dang and Lanjouw, 2023).<sup>1</sup> Building on the seminal technique that obtains small area estimates of monetary poverty by imputing from a household consumption survey into a census (Elbers _et al.,_ 2003), survey-tosurvey imputation builds an imputation model using appropriate predictor variables from an existing older consumption survey (base survey), which can be subsequently applied to the same variables in another non-consumption survey (target survey) to provide poverty estimates for the latter survey. The target survey can be either an existing, non-consumption survey, such as a Demographic and Health Survey (DHS) or a labor force survey (Stifel and Christiaensen, 2007; Douidich _et al._ , 2016), or a purposefully commissioned survey that only collects the requisite predictors. Recent applications also include sourcing the data for the requisite predictors from administrative records to impute poverty for hard-to-reach refugee populations <mark>(Altindag</mark> _<mark>et al.</mark>_ <mark>, 2021; D</mark> ang and Verme, 2023), or phone call detail records to target the ultra-poor (Aiken _et al._ , 2023).

Three key conceptual, but understudied, issues motivate our work. First, the literature on survey-to-survey imputation has long emphasized the requirement of having identical questions

> 1 Imputation techniques are widely used by international organizations and national statistical agencies to fill in missing data gaps such as education statistics (UOE, 2020) and income data (US Census Bureau, 2017)
for crop rotation, table 2 combines remotely sensed with statistical information for the four main crops in 2017 and 2018. In 2017, some 20% of cereals, 10% of sunflower, 24% of maize, and 17% of soybean was mono cropped (i.e., grown on the same field where the same crop had also been cultivated in 2016) while figures for 2018 are 17%, 10%, 28%, and 15%, respectively. Availability of data on the last two crops grown in 2018 suggest that 12% and 4.6% of cereal area were cropped in two or three consecutive seasons with analogous figures of 7.4% and 2.5% for sunflower, 19% and 9% for maize, and 11% and 4% for soybean, respectively.

Based on the 2018 figures, cereals were most often preceded by sunflower (33%), others (26%), cereals (17%), maize (14%) or soybean (10%). Most of the area planted to sunflower was previously occupied by cereals (39%) or maize (29%), other crops (15%), sunflower (10%) or soybean (7%). For maize, mono-cropping (28%; 19% with maize grown consecutively for two years and 9% for three years) is the most frequent sequence, followed by rotations where maize was preceded by sunflower (24%), cereals (20%), others (19%) or soybean (9%). Soybean was most often preceded by maize (28%), cereals and others (21% each), sunflower (15%) or itself (15%). In most cases, cultivated area as determined by remote sensing exceeds that reported by state statistics data, with a mean difference 9
is the emissions of CO2 per capita. Emissions data come from the _Emissions Database for Global Atmospheric Research_ (EDGAR). The EDGAR dataset uses a bottom-up methodology to calculate CO2 emissions in the power, industry, buildings, transport, and agriculture sectors. The methodology is applied consistently to all countries to facilitate comparison across economies.<sup>6</sup> We also use the EDGAR data to calculate

> 6 Further information and downloads are available at https://edgar.jrc.ec.europa.eu/report_2022.

9
variety of data on management practices—not documented here—as well as administrative records of teacher attributes, including age, gender, and qualifications. The data cover all teachers in the school, regardless of their eligibility for the intervention. Teacher surveys were administered to all teachers responsible for at least one upper-primary, core-curricular subject and included questions about demographics, training, qualifications and experience, earnings, and other characteristics.

The ‘lab-in-the-field’ instruments were administered to the same set of teachers, and were intended to measure the two characteristics introduced in the theory: intrinsic motivation and ability. In the model, more intrinsically motivated teachers derive a higher benefit (or lower cost) from their efforts to promote learning. To capture this idea of other-regarding preferences towards students, taking inspiration from the work of Ashraf et al. (2014), we used a framed version of the _Dictator Game_ (Eckel and Grossman, 1996).<sup>20</sup> Teachers were given 2,000 Rwandan francs (RWF) and asked how much of this money they wished to allocate to the provision of school

> 20Previous work shows the reliability of the DG as a measure of other-regarding preferences related to intrinsic motivation (Banuri and Keefer, 2016; Brock et al., 2016; Deserranno, 2019).

15
World Development Indicators of the World Bank. We also take trading partners’ GDP from World Development Indicators.

To test our hypothesis, we collect additional information at sector and product level. First, we use Rauch’s (1999) classification of export products into differentiated and homogenous products. Second, we use data on outstanding loans by sector, for the period 2015-2017 from the two largest export finance schemes run on a concessional basis by the SBP: Export Finance Scheme (EFS) and Long-Term Finance Facility (LTFF), to proxy sectoral access to finance. Because these schemes target specific sectors and are by far the country’s largest export financing schemes, the outstanding loans by sector is a good indicator of the sectoral access to finance.<sup>10</sup> Third, we use sectoral measures of finance dependence as presented in Rajan and Zingales (1998). The indicators signal the reliance on external funds to cover long-term investments and dependence from banks, as the fraction of capital expenditures not funded by internal funds. Fourth, we use data on labor intensity estimated for 63 sectors of the economy, based on a Social Accounting Matrix for 201314, constructed by the International Food Policy Research Institute (IFPRI) for Pakistan. Fifth, we proxy dollar export prices with unit values, by calculating the ratio of export values and quantities at a six-digit disaggregation of the HS classification, from UN Comtrade.

# _Empirical Strategy_

We start analyzing the link between exports and the RER at aggregate levels. We model exports as a function of the RER and global income and use an error correction model (ECM) to analyze

> 10 The EFS was put in motion in 1973 and provides short-term financing to exporters. The LTFF started in 2008 and provides long-term financing for investments in plant and machinery for export purposes.

15
, ISIC or NAICS). A key function of UTAS is therefore to help users handling different sets of data to combine tariff schedules with I/O sectors’ input cost shares and to calculate tariff‐related input costs and effective protection rates for different sectors.

Second, a common problem to both firm‐level data and I/O tables is that it involves assigning weights to each input tariff according to a firm’s/industry’s _current_ reliance on that particular input. To date, most academic research has followed this approach (e.g. Amiti and Konings, 2007; Topalova and Khandelwal, 2011; Yu, 2014). The drawback of pursuing such a weighting approach is that it will understate the true cost impact of an upstream tariff reduction, as it does not consider substitution effects that could kick‐in, if tariffs were to be removed. Moreover, _new_ intermediates, which are used only after an input tariff is removed, will not be taken into account, as they will carry an effective weight of zero. Diakatoni and Escaith (2012) underline that the failure of taking into account substitution effects also puts the calculation of effective protection rate on weak theoretical grounding. In practice, a strategy to assess the magnitude of this bias could for example involve the use of a proxy I/O cost structure pertaining to a different country that is deemed less distorted by tariffs and is assumed to represent the sectors’ I/O structure in the absence of input tariffs. This implies assuming technological convergence across countries or across groups of countries with similar characteristics. By allowing for an easy switch between different I/O tables, UTAS enhances policy makers’ ability to carry out such sensitivity analyses and allows benchmarking results against the use of different I/O tables in a straightforward manner.

Third, studies that have calculated upstream tariff costs and effective protection rates have generally done so on the basis of direct input requirements alone. This omits the possibility of input tariffs affecting production costs indirectly through
bank independence for 11 Latin American countries: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, Uruguay, and Venezuela. The left y axis presents the reserves-to-GDP ratio using data from the IMF and the right y axis reports a _de jure_ central bank independence index based on the Cukierman, Webb, and Neyapti index.

> 1Rodrik (2006) estimates that emerging economies incur an annual average GDP loss of 1% by maintaining high levels of reserves and debt.

2The assumption of a consolidated entity choosing reserves and debt implies perfect coordination between policymakers. Subsection 2.3 presents evidence on the lack of perfect coordination between the central bank and government.

1
# **Section1. Introduction**

Measurement of food access typically relies on more than a single indicator, particularly when making decisions about how and where to target emergency food assistance (e.g., Barrett 2010; IPC 2021; etc.). In part, this is because food access metrics may capture different aspects of poor food access and a wider range of metrics maximizes the chances that the various manifestations of poor food access be captured (e.g., Maxwell et al. 2014). But what exactly is captured by each indicator and which specific indicators should be included in a suite of indicators to best target food assistance and measure progress towards reducing food insecurity is far from settled (e.g., Headey and Ecker 2013; ICRC 2021; etc.). This is an urgent issue given rising levels of food insecurity in recent years and the need to distribute increasingly scarce resources to populations with the worst food access (e.g., UN 2020; FAO 2021; World Bank 2022; etc.).

Despite the clear need to track a number of different food access indicators, national statistical systems typically only collect data on a small number of food access indicators. In part, this is because the multipurpose household surveys in which food access indicators are often included are already long and survey fatigue can threaten overall data quality.<sup>1</sup> Within the small number of food security modules included in these surveys, there is a growing trend of including the FIES as one of the few food access measures. Nearly 100 countries have either incorporated the FIES into their national statistical systems or are in the process of doing so as of 2019.<sup>2</sup> Furthermore, in tracking the impacts of the COVID-19 pandemic, there were a very large number of surveys that included the FIES as the _only_ food access measure (e.g., FAO 2020; United Nations 2020; World Bank 2020; Adjognon et al.