rafmacalaba/data-use-mentions-extended huggingface dataset:
firms and workers, and it uses mobility data to document the evolution of the lockdown. In section 4, we describe the different data sources and methods used for our labor market analysis. That analysis is presented in section 5. It tracks employment and unemployment trends during the lockdown, with an emphasis on how the overall picture has been shaped by the dynamics of hirings and separations. Finally, in section 6, conclusions are presented as well as some key research questions moving forward to understand the impact of the pandemic on the Greek labor market.

# **2. Literature Review**

## **2.1 Challenges related to data and measurement**

The COVID-19 pandemic has affected entire economies and labor markets at a very fast pace, requiring policy makers to have access to up-to-date information to design suitable and timely policy responses. However, the disruptions caused by the pandemic and the speed with which the crisis has unfolded have shown that the methods and sources normally used to track labor market outcomes may have significant shortcomings in this context. For example, even gold standard surveys designed to provide up-to-date information, such as the Current Population Survey (CPS) in the United States, may not be sufficient to keep up with the fast spread of the virus and its disruptive impacts. Administrative data released with shorter time lags, as in the case of UI claims in the United States,<sup>4</sup> have also shown to have shortcomings that limit their effectiveness in timely and comprehensively informing policy responses during the pandemic (Cajner et al, 2020).

Furthermore, social distancing and other transmission prevention measures have affected several data-related activities around the world with potential impacts on data quality and reliability. ILO (2020b) shows that data collection, supervision, cleaning and analysis have been affected in several countries. Adjustments in survey instruments, data collection methods and weighting schemes have become necessary to address issues related to low response rates and non-random patterns in nonresponses. Even with these adjustments, response rates
Policy Research Working Paper 9860

# **Abstract**

This paper evaluates different methods for nowcasting country-level poverty rates, including methods that apply statistical learning to large-scale country-level data obtained from the World Development Indicators and Google Earth Engine. The methods are evaluated by withholding measured poverty rates and determining how accurately the methods predict the held-out data. A simple approach that scales the last observed welfare distribution by a fraction of real GDP per capita growth—a method that departs slightly from current World Bank practice—performs nearly as well as models using statistical learning on 1,000+ variables. This GDP-based approach outperforms all models that predict poverty rates directly, even when the last survey is up to five years old. The results indicate that in this context, the additional complexity introduced by applying statistical learning techniques to a large set of variables yields only marginal improvements in accuracy.

This paper is a product of the Development Data Group, Development Economics and 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/prwp. The authors may be contacted at dmahler@worldbank.org, acastanedaa@worldbank.org, and dnewhouse@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the
**marginalized Roma households in six countries in the Western Balkans.**<sup>**12**</sup> The 2017 RRS is a multitopic household survey representative of communities in which the share of the Roma population is larger than the Roma share in the national population. The survey encompasses both the Roma population and neighboring non-Roma. The RRS also has a time usage module, in which a randomly selected individual reported time spent in several activities during the previous day; the section was administered to only one randomly selected respondent. The survey, implemented by the United Nations Development Programme (UNDP) with the support of the World Bank, was commissioned by the European Commission (EC) Directorate-General for Neighborhood and Enlargement Negotiations (DG NEAR) to explore changes in core development outcomes among Roma and non-Roma who lived nearby. A full inclusion of gender dimensions across all the modules of the survey, including decision making, gender gaps in outcomes (labor, health, education, and other), time use, and gender norms, were included in the survey with the objective of capturing gender dynamics and gender gaps among the Roma population. The World Bank also conducted parallel qualitative research in Serbia to help to understand the mechanisms behind gaps in education and labor markets, with special attention to gender.

**In the 2017 RRS, Roma refers to those who self-identified as Roma,**<sup>**13**</sup> **Ashkali, or Egyptians, and the sample targeted marginalized Roma and their non-Roma neighbors** . The Roma are not a homogeneous population, but rather encompass diverse groups. The Roma sample was constructed based on the implicit endorsement of external identification (or implicit self-identification).<sup>14</sup> The sample focused on settlements where the Roma population’s share equals or is higher than the national share of Roma population, also known as “marginalized Roma.”<sup>15</sup> “Non-Roma”
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data where B and are the stock values of and the depreciation rate for buildings; and are the stock values of and the depreciation rate for machinery, equipment and vehicles; r is the return rate (or rental price) of capital.<sup>15</sup> The term in equation (5) is not observed without firm-level information on total labor productivity and prices. Price and productivity dispersion have been well documented in the literature and therefore will vary across firms (Bartelsman and Doms 2000). Without direct measures of total factor productivity and prices, firm characteristics such as the location and industry of firms, firm size, ownership structure, and the investment climate faced by firms (e.g. corruption and regulation burden) are used to approximate the ratio . If we denote these firm-level observable proxy variables by the vector , equation (5) can be written as: where a and b are parameters and u is the disturbance capturing either measurement errors or productivity shocks or other unobserved effects that are not captured by X1. Equation (7) is a structural equation for unobserved (‘true’) sales (latent variable) and can be viewed as a short-run supply function.

> 15 Hours worked per capital are a good proxy for capital service flow, but we do not have such data.

14
* _<mark>European Commission (2011) ''Taxes in Europe - Tax reforms'' database, http://ec.europa.eu/taxation_customs/tedb/taxSearch.html</mark>_

The pension reforms reduced pension expenditures by the state pension system and, together with strong economic growth in the 2000s, which boosted contributions from wage earners, improved the financial performance of the state pension system (table 4). The pension system even managed to achieve a small surplus of revenues over expenditures in 2006 and 2007. However, several changes were implemented in 2007 which increased pension benefits, such as restoring the indexation of net benefits to gross wages, and the introduction of a mandatory gross replacement ratio of 45 percent.<sup>10</sup> There were further changes in 2009 and 2010, including the guarantee of a minimum pension for low income pensioners and increases in contribution rates. Another “pension reform” was introduced for agriculture pensioners, in 2010, which implies significant additional costs for the state. As a result, pension expenditures have risen steeply, from 5.4 percent of GDP in 2006 to 8.3 percent of GDP in 2010. The increase in expenditures

> 10 The replacement ratio is the ratio of net average pensions to gross average earnings. The average replacement ratio in Romania was 0.56 in 2009, compared to the average for the EU of 0.51.

19
Table 7: Alternative Measures of Stock Mar<br>ard rate for an export spell of product _k_ from country _c_ to the world mar<br>Cox, 1972) and control for exporting-country and time fxed efects. Estima<br>le. In columns (1)-(2), the stock market liberalization measure is a dummy<br>three dates: the year of ofcial liberalization, the year of issuing the frst A<br>asure is a continuous variable capturing the liberalization intensity at the <br> index and IFC Global index. In columns (3) and (4), we use IFC indices<br>rket liberalization. In columns (5) and (6), we use the average value for a <br> column (4) of Table 3. We report coefcients and not hazard ratios. Ro<br> *** denote statistical signifcance at the 10%, 5%, and 1% levels, respect<br>(1)<br>All<br> Distance to comparative advantage<br>0.126***<br>(0.017)<br>-1.063***<br>(0.040)<br>closest year) x Distance to comparative advantage<br>closest year)<br>average) x Distance to comparative advantage<br>average)<br>antage<br>0.078***<br>(0.011)|Yes<br>Yes<br>Yes<br>Yes<br>85,675|
|ndent variable is the haz<br>ortional Hazard Model (<br>product _k_ as strata variab<br>est among the following <br> market liberalization me<br>uted from IFC Investable<br>cial year of the stock ma<br>of controls corresponds to<br> in parentheses. *, **, and
1991 and 1993. They were accompanied by the use of consolidation bonds to cover obligations arising from the non-servicing of government-guaranteed loans. The bulk of conversion and consolidation bonds was issued with a 7-year maturity and, in some cases, a 3- year grace period. They earned interest at a variable rate linked to the 12-month Treasury bill rate plus a spread that was set for each issue.

The level of central government debt increased further from 73 percent of GDP in 1985 to 119 percent in 1995 (Table 3.5). Non-marketable debt, which accounted for 61 percent of total debt in 1980, declined to 22 percent in 1995. The share of treasury bills initially rose from 39 percent in 1980 to 46 percent in 1990 but then fell to 26 percent of the total. The main increase took place in variable cost bonds, which include both variable-rate and foreign-currency-indexed bonds. They reached 47 percent of total debt in 1995. Foreign debt rose between 1980 and 1985 but then declined and accounted for one fifth of total debt. The total debt data for 1995 refer to central government debt, before deducting net holdings of government debt by other public 30
, the metadata of the datasets was extracted in December 2022. This means that our data gap map may already be missing datasets that have been added since then, calling for periodic updates in the analysis of data gaps. Second, our datasets only capture publicly available microdata from the UNHCR and World Bank MDLs. While we believe these MDLs contain the most datasets pertinent to microdata on FDPs, there may be other datasets not contained in either of these databases. Finally, as with all automated processes, it is possible that some datasets are miscoded due to the fact that the underlying metadata we use for this study is sometimes incomplete. We attempt to mitigate this concern by manual verification and making corrections in the metadata itself but there still remains more work to be done to ensure that the metadata is thoroughly populated in the UNHCR and WB MDLs.

Despite the limitations, this paper contributes to advancing our knowledge about existing data gaps on FDP and by developing a systematic process to identify them based on the information from the UNHCR and WB MDLs. Future data collection efforts may build upon the findings from this report and strategically target those geographical or thematic areas where data gaps still abound. Furthermore, the methodology developed in this study can be adopted, modified, and applied in other contexts, thereby making it easier to undertake this sort of a data gap mapping exercise.
20 Following the standard household model, it can be assumed that a rural household maximizes utility by allocating the land, labor and capital of its family members to various agricultural and non-agricultural tasks. From the first-order optimum conditions, land, labor time and capital service allocation functions can be derived to various household tasks that commonly depend on a set of factor prices, technology, personal characteristics of household members, and ownership of land and nonland resources. Factor prices (including land rent and residual return to land) depend on technology and personal household characteristics (such as management ability) that cannot be assumed to be exogenous. For this reason, it is desirable to estimate the factor price and factor allocation functions simultaneously. Unfortunately, this procedure cannot be used here because the quantities and prices of household-supplied factors for most household activities cannot be accurately estimated, either in this or most other household-level surveys. Therefore, in this section the reduced form income determination functions are estimated without distinguishing factor prices and quantities, which depend on technology, ownership of resources, and other household characteristics.

Specifically, in order to identify the determinants of income, each of the five sources of income in rural Egypt - nonfarm, agricultural, transfer, livestock and rental - are regressed on three types of household-level inputs which are thought to cause income: land (i.e. size of landowned, size of land rented in); labor (i.e. household size, mean age of all household members, number of males over age 15); and capital (i.e. mean education of males over age 15, value of farm equipment owned, value of enterprises owned). In addition, since the Egyptian data come from widely scattered rural areas, differences in land, water and other inputs may affect the determination of income. For
<!-- Start of picture text --> n 1,691 1,612 1,469 799 n 1,747 1,589 1,469 763<br>p-value 0.6 0.32 0.8 0.63 p-value 0.03** 0.18 0.35 0.85<br>Correlation of Orig. Commitments w/ Correlation of Disbursements w/ Quarterly<br>Quarterly Real GDP (2000-2019) - Pre and Post Real GDP (2000-2019) - Pre and Post Global<br>Global Financial Crisis Financial Crisis<br>0.09<br>0.05 0.06<br>0.01 0.00 0.03 0.03<br>0.02 0.01 0.01<br>0.01<br>-0.02<br>-0.03 -0.04 -0.03 -0.04<br>MAS FIG INR CDF MAS FIG INR CDF<br>Pre Crisis Post Crisis Pre Crisis Post Crisis<br><!-- End of picture text -->

# **5 Implications of Investment Procyclicality for IFC Operations**

## **5.1 Potential Drivers of Procyclicality**

Both demand- and supply-side factors could explain the observed procyclicality of IFC investment. The key demand-side factor is the typical impact of the business cycle on capital inflows and demand for credit described in the literature review: during downturns, worsening macroeconomic indicators lead to higher risk premiums and fewer profitable opportunities, reducing demand for investment across the economy. Meanwhile, at the firm level, unfavorable market conditions may prompt IFC clients with approved loans to delay disbursement until an improving macroeconomic environment bolsters their ability to service debt.

While investment demand is driven by the business cycle, supply-side factors are directly influenced by IFC policy. For most investment projects, the key additionality of IFC financing is its relatively long tenor. By investing in illiquid assets
# 1. Introduction

Private property price levels and their movement over time have critical implications for the economy of most countries, but they also play a fundamental role in household (HH) wealth. Buying property is undoubtedly the most significant investment for many families around the world and reliable information on private property prices and their determinants are essential decision-making factors for HHs. The relationship between property prices and HHs’ disposable incomes determines what type of property HHs can afford. Therefore, from a policy standpoint, reliable property price data are key to understanding the scale of affordability issues and identifying market failures leading to supply-demand mismatches in the housing sector.

House price data also bear key insights on countries’ financial and macroeconomic stability. Disproportionally rising private property prices, or high price-to-income ratios, can be an early indication of imbalances and risks in the financial system (e.g., Anundsen et al. 2016; Drehmann & Juselius 2014). Decreasing house prices, in turn, may be associated with a decrease in HH wealth and a decline in consumption (e.g., Campbell & Coco 2007; Mian et al. 2017). If monitored over time, property prices can function as an early warning system of systemic banking stress or economic downturn. In addition, urban planners, private developers, economists, and policy makers depend on reliable property price data to update zoning regulations, formulate housing policies, and decide how to efficiently allocate scarce resources to support housing solutions where they are needed most.

Despite the importance of property price data for various stakeholders, comprehensive house price data are scarce in developed economies and virtually non-existent for emerging markets and developing economies (EMDEs). If available at all, residential property prices are mostly presented and published in indexed format, allowing to track changes over time; however, indices are not informative to understanding distributional aspects of prices, affordability issues, or the degree of price segments underserved in a market. When housing markets mature, properties' characteristics and attributes may be more stable over
RESEARCHT WORKING PAPER t170p4 [POLICY

# Summary findings

China's environmental regulators rt spond to more han 100,000 citizen complaints a year, 'I he complainit process undoubtedly provides uie.ul infe-rmaltion hel-ps encourage corrmuniry participation in enivironmental pol,cy. 13ut **it** also dir cts a bug sliate **(.of** inspecTion resources _tO_ areas wh7ere people rend to complai1n.

treranyzig provincial data for 1987-93, Dasgupca an 7heeler fir.d the subsequent allocation of remoources biased, in terms of socil welfare. 'T he incidenict (of comrplainis refiects potential abat!mncnf henefits and the u,ntunsltv ow exposure to lilghly vIslb piollutants. Howevet, citi7en compkaints seem.. **nor** to be .ilfecrtd by huarrnfI pollutants ,hat are iess L And al, education seems to _have_ a stror7g ; **n** d-perdent etlteC o _0._ propensity to coimipl1ain. R iving on comnplaints a,one wvould lead -o inappropriately lc,w aliocatiion of Inspecton resources to less-educated, relatively "silent" rcgions.

Citizens' incomplete iriformatino creates the biggest problem for regulators who retly on cornpiaints for guidance. To compensate for this probtlem, say Dasgupta and Wheele-, agencies should invest in **public** environmental educationi targeted especially to comrlmuni;ties witih less schooling. 'Tlhey might also explore targeted Outreach programis, Since poorly ed_ticattd people may ailso be more timid about comr,phlining.

More important, Dasgupta **and(** 'WXheeler recornmend giving ptrioritt-, tO rechnical **r1sk-** assessments in deter -ining resoirce allocation. () L.r tine, citizen Crsp ixntF sh.ot: d decline if regnilators establish strategic _ri nou_ ocs and pursue ihem systematically, while inainr ir:ing close contact with the communities affected.

' his paper -a producr ot ,lc Eu **i** ye!Otent, 1 otrastructuic, and Ag-iculture Division, Policv Ree arc.h Department-is par-tof a
As shown in Teti (2020), the standard sources for tariff data, i.e., the World Integrated Trade Solution (WITS) that draws on data from the UN and the WTO, suffer from massive measurement error. Existing data include many missing observations because countries do not report tariffs every year. The issue is particularly bad for preferential tariffs and for low-income countries. WITS deals with the missing tariffs by imputing them with MFN tariffs of the respective importing country. For time series data this procedure leads to large irregularities: In years in which the country does not report preferential tariffs, the tariffs jump up from the relatively low preferential tariffs of the previous year to the MFN tariff which is often substantially higher. This type of time variation creates substantial measurement error, in particular in models which identify on time variation as specified in Equation (4).

Figure 1 from Teti (2020) illustrates this point for the tariff that can be found through WITS for exports from the United States to Mexico. Panel (a) shows the simple average of the raw tariffs, MFN and preferential, which can be downloaded through WITS. The average comprises over 5,000 products. The blue dots equal Mexico’s MFN tariff, the red dots are the preferential tariffs under NAFTA that entered into force in 1994. The figure shows that neither MFN nor preferential tariffs are available each year. However, the preferential tariffs (red dots) are less likely to be available indicating that Mexico is not reporting the preferential tariffs on imports from the United States every year. The solid line displayed in Panel (b) equals the tariff that WITS reports when one downloads the so-called “effectively applied tariff”. Recall, WITS imputes the missing preferential tariffs with MFN tariffs, resulting in the spikes as the effectively applied tariff shoots up to the level of the MFN tariff every time when the data on preferential tariffs are missing but MFN tariffs are available.

**Figure 1:** Missing Data and Resulting Measurement Error