rafmacalaba/data-use-mentions-extended huggingface dataset:
survey are limited to those business owners/managers<sup>9</sup> with internet access and Facebook accounts with a business page. Therefore, the authors caution that the sample should not be considered representative of all business owners but a select group of global micro, small and medium enterprises. Nonetheless, the unprecedented level of geographic coverage and sample size on these variables offered by the survey allow us a unique chance to examine these questions on a global scale. Since internet bandwidth and Facebook coverage have increased dramatically in recent years, with an approximate 2.3 billion monthly active users on Facebook<sup>10</sup> at the time of writing, there is considerable scope to learn from this survey. The geographic coverage of the survey has also increased with 11 Sub-Saharan African countries included in this current round that were not present in previous rounds.<sup>11</sup>

> 5 Map presented in Appendix A displays the geographic coverage of the December 2018 wave of the _Future of Business_ Survey and the table in Appendix B provides information for all the countries included in the analysis plus the percentage of female respondents per each country.

> 6 Facebook did not share any platform data that were not collected through this particular survey round. In the analysis for this paper, we use only anonymized survey responses.

> 7 Note some categories of businesses were excluded (governments etc.) from the sampling frame.

> 8 World Bank Open Data portal: https://datacatalog.worldbank.org/dataset/future-business-survey-aggregated-data

> 9 While the _Future of Business_ survey targets administrators of business pages which could include business owners, managers, and employees; this paper analyzes the responses from owners and managers only.

> 10 Total number of individuals on Facebook are estimated to be equal to the number of monthly active users worldwide as of 4th quarter 2018 that were using Facebook Source: https://www.statista.com/statistics/264810/number-of-monthly-active-facebook- <u>users-worldwide/<
**Figure 3 – Trends in Correlations Among Different Types of Governance Indicators**

Notes: This figure shows average pairwise correlations among selected expert assessments (WGI data source codes Economist Intelligence Unit Country Viewswire, Political Risk Services International Country Risk Guide, Crisis24 Country Security Assessment Rating, and S&PGlobal Country Risk Service) as solid lines, and average pairwise correlations between these four expert assessments and two surveys (World Economic Forum and Institute for Management and Development Executive Opinion Surveys) as dashed lines (see Table 1 for WGI data source codes). The six panels correspond to the six aggregate WGI indicators.

35
<br>system; ranges from 0 to 6.|International Country Risk<br>Guide (ICRG)|
|Government size|General government total expenditure (percent of<br>GDP)|WEO|
|Investment price|Price level of investment|Penn World Table|
|Capital account<br>openness|The Chinn-Ito index (KAOPEN) measuring a country’s<br>degree of capital account openness; available from<br>1970-2019 for 182 countries.|Chinn and Ito (2006)|
|Openness|Sum of exports and imports (percent of GDP)|World Integrated Trade<br>Solution (World Bank)|
|Resources rents|Sum of oil rents, natural gas rents, coal rents (hard<br>and soft), mineral rents, and forest rents. The<br>estimates of natural resource rents are calculated as<br>the difference between the price of a commodity<br>and the average cost of producing it.|World Development<br>Indicators (World Bank)|
|Fiscal rules|<br>Covers four types of rules: budget balance rules,<br>debt rules, expenditure rules, and revenue rules,<br>applying to the central or general government or<br>the public sector.|Davoodi et al. (2002)|
|SWF (sovereign<br>wealth fund)|Dummy variable: 0 if a country does not have an<br>SWF and 1 if it has an SWF.|https://www.swfinstitute.<br>org/fund-<br>rankings/sovereign-|

46
||Constantbasket|Sato-Vartia|Feenstra|CES Unified Price<br>Index|
|---|---|---|---|---|
|Price index|0.013**|0.020***|0.019**|0.021***|
|TFPQ (Foster et al.)|-0.010|-0.017**|-0.017**|-0.018**|
|Observations|60.971|54,584|34.584|34.584|

See Table 2. See Eslava and Haltiwanger (2021) for all price indices. Specifically, see Sato (1976) and Vartia (1979) for SatoWartia approach to aggregating firm product price index under the assumption of invariant firm appeal shocks and constant baskets of goods. Feenstra (2004) derives an empirical adjustment of the Sato-Vartia approach to account for changing baskets of goods, keeping the assumption of a constant firm appeal distribution for continuing products. Redding and Weinstein (2020) estimate the CES Unified Price Index, which accounts for the fact that Sato-Vartia approach likely overstates price inflation for the common goods produced by plants such that it understates the quantity growth and therefore TFPQ.
# **Methodology**

Due to resource limitations, this study is necessarily a rapid exploratory assessment. Six countries form a sample representative of different stages of housing sector and policy development (Armenia, Lithuania, Poland, Romania, Russia, and Serbia).<sup>5</sup> For this group the study is the first in-depth tenure structure analysis with disaggregate household data using the Living Standards Measurement Surveys (LSMS) and annual Household Budget Surveys (HBS). These data are enhanced by other evidence from national statistics, analysts’ reports and findings,<sup>6</sup> and interviews with experts and market participants in all six countries. The review of policies and best practice relies on interviews and selective analysis of legislative and regulatory texts. More in-depth analysis is both desirable and possible, but the form of analysis used in the study provides sufficient insight to support, or reject, the key hypothesis.

# **Organization of the Report**

Chapter 2 discusses conceptual considerations about what determines rental sector size in a market economy and the basic drivers of rental demand and supply found in the literature. It also briefly addresses the implications of the existence of rental sectors for economic efficiency, and rationales for policy interventions designed to support or create efficiency.<sup>7</sup> Chapter 3 provides a basis for supporting or rejecting the key hypothesis. It identifies supply and demand structures that correspond with the conceptual framework and main issues identified in Chapter 2.

Chapter 4 deals with actual tenure choice policies in the studied countries in three ways:

- _First_ , by analyzing existing investor types and the extent to which they pursue formal or informal market practices, and how their basic business models are structured.

- _Second_ , by identifying gaps in the legal and regulatory frameworks governing rental contracts and tenant-landlord relations, then analyzing the tax environment.

- _Third_ , by discussing the broader housing policy context of tenure choice policies in the studied countries, giving specific attention to the privatization, “
# **Small and Medium Enterprises in Emerging Economies: The Achilles’ Heel of Corporate ESG Responsibility Practices?**

**Matteo Ferrazzi, Annamaria Tueske**<sup>1</sup>

JEL codes: C43, M14, Q56, R11 Keywords: ESG, environmental, social, governance, sustainability, corporate responsibility, SME, Middle East and North Africa, index aggregation 1 <u>m.ferrazzi@eib.org, Senior Economist, European Investment Bank, Economics Department; a.tueske@eib.org, Economist,</u> European Investment Bank, Economics Department. This paper was commissioned by the World Bank Middle East and North Africa Chief Economist office as part of the activity “Assessing the MENA formal Unlocking Sustainable Private Sector Growth in the Middle East and North Africa: Evidence from the Enterprise Survey." The opinions expressed in this paper do not represent the views of the World Bank Group, its Board of Directors or the Governments they represent. The opinions also do not reflect the views of the European Bank for Reconstruction and Development (EBRD) and the European Investment Bank (EIB). All errors and omissions are the authors’ responsibility. We are thankful to Francesca Maritati (EIB Corporate Responsibility) and other colleagues from EIB, EBRD and World Bank for comments. We would also like to thank the participants of two workshops hosted by the World Bank, EBRD, and EIB for their feedback.

1
group over the same period of time, i.e.,

# _Data_

To conduct the analysis, we assemble a large dataset, compiled from multiple sources and linked at the district level. These are regional electoral information from the government (Ministry of Home Affairs, MoHA), and regional budget data from SIKD for the period 2001 to 2006.

The district-level electoral information from the government has been compiled by the Jakarta World Bank team with the collaboration of the Ministry of Home Affairs and a number of

> 12 The specification of (1) follows closely what is suggested by the literature on the use of DID method for impact evaluation. See, for example, Imbens and Wooldridge (Imbens and Wooldridge, 2009) (2009), pp. 67-70.

14
Policy Research Working Paper 9767

# **Abstract**

This paper investigates how the sector-specific source or the changing sectoral composition of labor productivity has contributed to aggregate beta convergence, using a newly constructed eight-sector database. The main findings are twofold. First, both within and sectoral reallocation have become important drivers of aggregate convergence in labor productivity. Second, agricultural productivity growth has been a significant contributor to aggregate convergence, whereas catch-up in other sectors has only contributed a small amount to convergence. The strong growth of the agriculture sector has been the most important driver of aggregate productivity convergence even though agricultural productivity itself in low-income countries is weakly converging to that in advanced economies.

This paper is a product of the Prospects Group. 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 alistair.dieppe@ecb.europa.eu and hideaki.matsuoka@mof.go.jp.

_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 Research Support Team
|0.148|0.130|0.112|
|Number of observations|26,574|8,600|3,079|6,596|7,592|
|Number of countries|95|37|10|30|15|

_Note:_ Standard errors (in parentheses) are robust to heteroskedasticity and clustered on countries. Regressions for the Middle East and North Africa and South Asia are excluded because of insufficient data. Model: _EG_ = _b_ 0 + _b_ 1All 15 Obstacles + _b_ 2Firm Characteristics + Region + Country Fixed Effects + _e._ * Significant at the 10 percent level. ** Significant at the 5 percent level. *** Significant at the 1 percent level. _Source:_ Authors' estimations based on World Bank Enterprise Surveys (2006-10).

25
fnancing in LICs and MICs has created a scarcity of literature in this area. Yet data on fnancing for refugee situations has improved dramatically in recent years. Most notably, the Organisation for Economic Cooperation and Development (OECD) surveyed Development Assistance Committee members, participants, observers and partners on their fnancing for refugee situations in 2018, 2020 and 2023 (OECD, 2021, 2023). The second, and fnal, of these surveys asked donors to disaggregate their fnancing for refugee situations by sector, representing the frst time that data on sector-level ODA for refugee situations had been collected at scale, allowing the OECD to produce the frst sector-specifc estimates of ODA for refugee education in LICs and MICs</mark> (Hesemann et al., 2023). Hesemann et al. (2023) <mark>found education to be the third largest sector recipient of ODA for refugee situations in 2021, receiving US$ 2.3 billion, or 8% of total ODA for refugee situations in LICs and MICs. While this estimate is imperfect, and is improved upon in this paper’s analysis,</mark> Hesemann et al.’s work (2023) <mark>contributed signifcantly to improving transparency and the quality of data and analysis on fnancing for refugee situations.</mark> situations in LICs and MICs is scarce. Financing data on refugee education is neither publicly, nor internally, available for many LIC and MIC governments, with <mark>budget allocations seldom</mark> 3
Policy Research Working Paper 5778

# **Abstract**

Between 2000 and 2002, the authors followed 1621 individuals in Delhi, India using a combination of weekly and monthly-recall health questionnaires. In 2008, they augmented these data with another 8 weeks of surveys during which households were experimentally allocated to surveys with different recall periods in the second half of the survey. This paper shows that the length of the recall period had a large impact on reported morbidity, doctor visits, time spent sick, whether at least one day of work/school was lost due to sickness, and the reported use of self-medication. The effects are more pronounced among the poor than the rich. In one example, differential recall effects across income groups reverse the sign of the gradient between doctor visits and per-capita expenditures such that the poor use health care providers more than the rich in the weekly recall surveys but less in monthly recall surveys. The authors hypothesize that illnesses—especially among the poor—are no longer perceived as “extraordinary events” but have become part of “normal” life. They discuss the implications of these results for health survey methodology, and the economic interpretation of sickness in poor populations.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at jdas1@worldbank.org, jhammer@princeton.edu, and csanchezparamo@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. The papers carry the names of the
hampered by missing household consumption data.<sup>2</sup> <mark>There is an established literature in statistics that has developed methods to impute missing data (see Little and Rubin (2002) for a review of these methods). Survey-to-survey imputation methods have been widely employed in economics to estimate household welfare trends across time periods in the context of repeated cross-section or panel data (e.g., Dang and Lanjouw 2018), across geographical areas in the context of poverty mapping exercises (e.g., Elbers et al. 2003), or across</mark>

> 2 <mark>Missing data issues are not a problem limited to displaced populations, but can emerge because of lack of survey data on a particular topic of interest, population group, or time period. These issues can also be caused by sampling errors, incomplete data due to unit or item nonresponse, data input errors, or post-survey data manipulations such as top-coding or censoring.</mark>

2
expert assessments of the quality of policies and institutions produced by the African Development Bank (Country Policy and Institutional Assessment), Asian Development Bank (Country Performance Assessment), World Bank (Country Policy and Institutional Assessment), and the International Fund for Agriculture and Development (Rural Sector Performance Assessment). These assessments all cover a similar set of topics relating to policy and institutional performance and are scored by staff of these organizations, often located in the countries they assess, using a structured methodology and clear scoring benchmarks. All four organizations use

> 6 See Kraay and Murrell (2016) for a discussion of respondent reticence and ways to measure it.

> 7 For example, we do not use data from the Transparency International Corruption Perceptions Index because it simply is a compilation of existing data sources, in the same way that the WGI are.  In addition, we have over time dropped specific questions and specific data sources from the WGI when their methodologies have changed to rely explicitly on other data sources.  This includes selected dimensions of the Heritage Foundation’s Index of Economic Freedom, as well as the European Bank for Reconstruction and Development Transition Index.

7
**Table 2.B Variables for analysis of fiscal policy volatility**

|**Variable**|**Description**|**Source**|
|---|---|---|
|General government<br>revenue|Consists of taxes, social contributions, grants<br>receivable, and other revenue.|IMF_World Economic_<br>_Outlook_(WEO)|
|Primary balance|Primary net lending/borrowing is net lending<br>/borrowing plus net interest payable/paid (interest<br>expense minus interest revenue; percent of GDP)|WEO|
|Primary expenditure|Obtained by subtracting interest payments from<br>general government total expenditures. Interest<br>payments are calculated as the difference between<br>overall fiscal balance and the primary balance (all in<br>percent of GDP).|WEO and authors’<br>calculations|
|Government<br>consumption|General government final consumption expenditure<br>includes all government current expenditures for<br>purchases of goods and services (including<br>compensation of employees) and most expenditures<br>on national defense and security; excludes<br>government military expenditures that are part of<br>government capital formation.|World Development<br>Indicators (World Bank)|
|Real GDP; real GDP<br>per capita||WEO|
|Political constraints|The Political Constraints Index (POLCON) measures<br>the extent to which policy changes are constrained<br>by institutional and political factors.|POLCON data set (Henisz<br>2000)|
|Control of corruption|An index measuring corruption within the political<br>system; ranges from 0 to 6.|International Country Risk<br>Guide (ICRG)|
|Government size|General government total expenditure (percent of<br>GDP)|WEO|
|Investment price|Price level of investment
# **How do the poor cope with shocks in Bangladesh? Evidence from survey data**

Indhira Santos* Iffath Sharif Hossain Zillur Rahman Hassan Zaman JEL Classification: I32, O12 Key words: Shocks, poor, safety nets, microfinance, Bangladesh

* Indhira Santos is an Economist in the Eastern Europe and Central Asia region of the World Bank. Iffath Sharif is a Senior Economist in the South Asia region in the same institution. Hassan Zaman is a Lead Economist in the Poverty Reduction and Equity Group of the World Bank.   Hossain Zillur Rahman is the Executive Chairman of Power and Participation Research Centre. The authors are grateful to Aniqah Khan for excellent research assistance. The views and opinions in this paper are those of the authors and do not reflect those of The World Bank and/or its Executive Directors. Corresponding author: Iffath Sharif (isharif@worldbank.org), The World Bank, 1818 H St., N.W., MSN 3-300, Washington, D.C. 20433.
.1%|
|||||<br>673 Iron and steel bars,rods,angles,sha|51.1%|6.1%|1.6%|
|||||677 Iron/steel wire,wheth/not coated,bu<br>721 Agricultural machineryandparts|51.9%<br>741%|7.3%<br>62%|0.1%<br>03%|
|||||<br>783Roadmotor vehicles,n.e.s.|.<br>59.9%|.<br>7.7%|.<br>0.6%|
|||||899 Other miscellaneousmanufactured ar|15.4%|8.4%|0.5%|
||||||**To**|**tal ------>**|**19.8%**|

_Source_ : PRMED staff calculations based on UN/COMTRADE data.

**Table A4. Mincerian regression of income on education and a set of controls**

|**Mincerian regressions of**|**income per individual, Togo 2006**|||
|---|---|---|---|
||**Number obs.**||**1507**|
||**Prob F>0**||**0**|
|**Dependent variable**|**: Individual income, in logs**<br>**R‐squared**||**0.37**|
||**Coefficient**|**t‐statistic**||
||Years of Education<br>0.05|5.24|*******|
||Age<
Libor due to concerns about the credit worthiness of banks, so the Libor-CD spread is viewed by some as a measure of pure liquidity scarcity. We view the premise that purchasers of CDs are not liquidity constrained during a crisis as questionable, but Libor-CD is nonetheless a clear candidate for a robustness check of our main liquidity variable. When Libor-CD is included in lieu of Libor-OIS (column _R1_ ), the coefficient is close to zero and statistically insignificant. When both Libor-CD and Libor-OIS are included (column _R2_ ), Libor-OIS retains its sign, magnitude, and significance.

Our preferred measure of balance sheet problems is an index of CDS spreads, which has the advantages of being forward-looking, and of being based on rates faced by a wide sample of banks in both the U.S. and Europe. However, more direct measures of solvency issues can be found in bank financial data. As a robustness check, loss allowances as a fraction of outstanding loans, as reported by U.S. banks to the FDIC, are included in place of the CDS index (column _R3_ ). The coefficient is insignificant, as is that on CDS in the benchmark estimates that do not include lagged channel variables. Although not reported, similar results are found for other variables reported to the FDIC: measures of noncurrent loans and net charge-offs as fractions of outstanding loans, and the fraction of lenders which are unprofitable. These results are broadly supportive of the idea that balance sheet problems have little, if any, contemporaneous impact on cross-border bank lending.

In the benchmark estimates, we rely on a GARCH (1,1) conditional volatility index as a measure of uncertainty, for reasons given above. Here, we consider the robustness of this measure by instead using an unconditional measure of volatility: an index of the rolling standard deviations of prices in the same 7 asset markets, along with VIX, used to compute the conditional volatility measure.<sup>17</sup> Results with this alternative measure of volatility are reported