rafmacalaba/data-use-mentions-extended huggingface dataset:
government.

The second type of multi-country survey collection consists of country-specific surveys where the questionnaires differ across countries but the data have been harmonized ex post by an agency, such as Eurostat, the Luxembourg Income Study (LIS) (Luxembourg Income Study (LIS) 2019), or the World Bank. Eurostat, for example, every five years takes the raw data from the HBS surveys of the EU member states and produces a harmonized data set for each country with the same constructed harmonized expenditure variables, including out-of-pocket health expenditures. The LIS also produces harmonized data sets based on HBS and HIES surveys for a wide range of countries; some of these have harmonized data on out-ofpocket health expenditures. In both cases, authorized users are able to use only the data set comprising the harmonized variables, not the original data. Several groups within the World Bank also construct harmonized data sets. In the regional World Bank units, the focus is on overall household consumption, the aim being to assist the Bank’s poverty-monitoring work; in this work, the methods used to construct consumption aggregates in the LSMS (Deaton and Zaidi 2002) typically guide the process. Other groups in the World Bank have goals other than facilitating povertymonitoring. One harmonization exercise (the Standardized Household Expenditure Survey (SHES)) is directed at assisting the process of constructing purchasing
in an urban or rural area, we provide the descriptive statistics for rural and urban schools separately.

> 22 The correspondence between those two sources of data was high but not perfect. For a few schools, we had no mean scores and for somewhat more schools the total number of students according to the two databases was not equal, though such deviations were typically very small. One possible explanation for these differences is that the database of the Ministry‟s EIT Center is updated, f.e. correcting mistakes or including the results of appeals. 23 The descriptive statistics for 2008 and 2009 give a qualitatively similar picture. Note that in this section we will only look at differences in means between groups, delaying the question of significance to the regression analysis.

14
in Default in Year 0)/ (Outstanding Loan Volume in Year I - Loan Volume in Default in Year 0).

b/ Excludes the Hu'u unit for w.lhich 1995 data were not available.

loans in arrears equal to 18 percent of total loans over the period. For Hu'u, loans in arrears reached 78 percent of total loans in 1993. The program did not have arrears data for the unit thereafter (Table 16). The program's arrears rate is significantly higher than that which would be considered sound by international microcredit standards.3<sup>4</sup> 33 The LKP system defines loans in default as being more than 90 days late in payment.

- 34 The Committee of Donor Agencies for Small Enterprise Development (1995) set as an acceptable standard for microenterprise lending, that 10 percent or less of total loans should have late payments of 30 days or more. BKK's arrears rate in 1995 includes loans that are one day late or more. so it is not directly comparable. However, it is twice as high as the standard, and did not include arrears information for the program's most troubled unit.

45
**Figure 13 – Land Cover Classification and Distance to City Center ‐ Kigali**

<!-- Start of picture text --> Kigali Land cover c. 2012 (Percent) Kigali Land cover c. 2012 (Total)<br>100% 120<br>80% 100<br>80<br>60%<br>60<br>40%<br>40<br>20%<br>20<br>0% 0<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Distance from CBD (kilometers) Distance from CBD (kilometers)<br> Residential (Regular) Residential (Irregular) Residential (Regular) Residential (Irregular)<br>Commercial/Industrial Commercial/Industrial<br>Total Land cover<br>Percent Land cover<br><!-- End of picture text -->

# **Addis Ababa, Ethiopia**

Addis Ababa is one of the oldest and largest cities in Africa, making it a central urban hub for many Ethiopians in search of better living and employment conditions. The city accounts for approximately 20% of the country’s GDP, although it is home to less than 5% of the country’s residents.<sup>46</sup> According to the 2007 Ethiopian census, roughly 2.7 million people live in Addis, a number that has doubled every decade since 1984. This rapid growth is expected to continue, with the UN Habitat estimating a population of 12 million by 2024.<sup>4748</sup> Triggered by this population growth, Addis is undergoing a construction boom. With new condominiums, office buildings and infrastructure projects breaking ground almost simultaneously throughout the city. Unfortunately this development has not always followed government regulations, resulting in roads cut off due to building construction, or major road
br>600<br>500<br>400<br>‐51.64 300<br>200<br>100<br>‐30.47 0<br>‐55.00 ‐45.00 ‐35.00 ‐25.00 ‐15.00 ‐5.00 5.00<br>Output per worker net of inter‐sectoral shifts Capital Labor Ratio TFP residual net of inter‐sectoral shifts<br>T h o u san d s o f CF A o f 2000<br>1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005<br><!-- End of picture text --> _Note_ : Capital stocks have been estimated following the perpetual inventory method, whereby the capital stocks values in a year “t” is obtained as the sum of capital stocks in year “t‐1” minus depreciation, plus the investment (gross capital formation) in “t”. In turn, the initial value of capital stocks is estimated under a steady state assumption on the growth rate of capital, as the value of initial gross capital formation divided by the sum of the depreciation rate and the growth rate of investment. _Source_ : Authors’ calculations based on WDI data, World Bank.

**Figure 6: Capital Formation in Togo and Benchmark Countries, averages 2000‐2005 (LHS) and Real Gross Capital Formation per capita (in logs) versus Real  per capita GDP growth, Averages 2000‐2008 (RHS).**

<!-- Start of picture text --> 45% 400 20%<br>40% 350<br>35% 300<br>30% 250 15%<br>25%<br>200<br>20%<br>15% 150 10%<br>10% 100<br>5% 50 5%<br>0% 0<br>0%<
**Figure 2. Bank Deposit Accounts per population against Financial Sector Depth and GDP per capita**

<!-- Start of picture text --> 4,000 4,000<br>3,000 3,000<br>2,000 2,000<br>UAE<br>Lebanon<br>1,0000 Yemen OmanAS lge yriariaTunisia E gyp tMoroccoJordan 1,0000 Y emen E gyp Syria Morocco t JordanTunisiaAlgeria Oman<br>0 50 100 150 0 5,000 10,000 15,000 20,000<br>Bank Deposits as % of GDP GDP per Capita<br>Deposits accounts per 1,000 Adults Deposits accounts per 1,000 Adults<br><!-- End of picture text --> Sources: CGAP and World Bank Financial Access 2010, International Financial Statistics, World Development Indicators. Iran data for 2008, all other data 2009.

# _Industry Structure_

# **Financial inclusion in the MENA region is characterized by NGO-dominated microcredit**

**sectors** , **postal networks and state banks.** A few countries have introduced legislation that allows for other legal forms of microfinance provider, such as finance companies or banks, but as yet there are few microfinance institutions that are legally allowed to offer (voluntary) deposit services in MENA.  Savings and loan cooperatives are not a major source of financial services in much of the MENA region.  Table A1.3 (Annex 1) sets out a typology of microfinance providers presented in table form. „Industry‟ is defined here as the set of providers of financial services to low income and microenterprise clients.

# _Banks and Financial Inclusion_

# **Direct commercial bank involvement in microfinance in the MENA region is very limited.**

Banks prefer to provide wholesale financing through specialized microcredit providers. Bankmicrocredit provider linkages build on the relative strengths of both types of entity – banks are able to raise funds from deposits and from the market and often have excess
calculating the size of area affected by fire for each fire event and subsequently taking the average thereof.

We use the Synthetic Control Method to estimate the FIP program’s impact on each of the four forest fire measures: forest fire occurrences, the share of a forest grid cells having been on fire at least once, the number of forest fires started, and the extent to which forest fires spread out.<sup>5</sup> As forest characteristics that predict fire incidences, we use the annual panel of 2006-2013 pre-treatment outcomes<sup>6</sup> , the average annual precipitation in the forest before the treatment, and the size of the forest (measured at baseline, in 2013) to construct the synthetic control forest for each of the treatment forests. Although the relationship between each of these two forest characteristics and the actual forest fire occurrences is ex-ante ambiguous<sup>7</sup> , including them in the weighting process will improve the fit, independent of the exact relationship. Regarding the composition of the synthetic control forests, 34 of the 65 non-intervention forests received a positive weight in at least one of the 12 synthetic control forests; on average, a project forest’s synthetic control

> 5While it would also be interesting to have information on the duration of forest fires, the MODIs data set does not contain the necessary information (Balboni et al., 2021). We can approximate it, however, by taking the ratio of the number of forest fire occurrences and the share of forest grid cells that were affected at least once during the month. This would provide an upper-bound estimate of the average duration of fires because it does not take into account whether grid cells were on fire on consecutive days or not. An analysis of this fifth (and imprecise) indicator would not yield any new insights because we already analyze the numerator and denominator separately.

> 6Despite having data on forest fires from 2003 onward, we do not include all pre-treatment periods as predictors.
# **Comprehensive Wealth, Intangible Capital, and Development**

# **1. Introduction**

It has been understood since at least the time of Irving Fisher (Fisher 1906) that income is the return on wealth. But if we scale this idea up to the level of the national economy, we arrive at a puzzle. If we measure wealth only as produced capital, we see from the national balance sheet accounts of countries such as Canada that wealth is only a small multiple of gross national income, implying unrealistically high rates of return on wealth.

Table 1 shows Canadian figures for 2009. The value of produced capital is less than three times GNI, while net worth (the sum of produced capital, commercial land and net financial assets) is a bit less than four times GNI – the implicit rates of return on wealth are correspondingly high, 35.9% and 25.4%, respectively. Canadians appear to be very productive.

The ‘solution’ to this puzzle, of course, is that the national balance sheets of the system of national accounts (SNA) exclude many intangible<sup>1</sup> asset values, such as human capital and the value of social / institutional capital. Moreover, the Canadian balance sheets highlighted in Table 1 exclude the value of commercial natural resources.<sup>2</sup> Since a ‘normal’ rate of return on assets should be on the order of 5%, a comprehensive measure of national wealth should be on the order of 20 times national income.

Hamilton and Hartwick (2005) show how to estimate a comprehensive measure of national wealth for a competitive economy with constant returns to scale. For production _F_  _F_ ( _K_ , _L_ , _R_ ) with production factors _K_ (produced capital), labor _L_ , natural resource flow _R_ , and interest rate _r_ (equal to the marginal product of capital), comprehensive wealth is given by That is, comprehensive wealth can be measured either by
balance sheet data directly and is scaled by total assets. Sales Growth is calculated as the annual change in sales over total assets. We follow Hsieh and Song (2015) and use average return to capital (measured as value added over fixed assets) as a proxy for the marginal product of capital. We also use information on firm size, firm age, and on whether a firm is state-owned or not.

We identify politically connected firms by merging Orbis with the PEP data set. As a first step we focus on firm names. Specifically, we identify politically exposed persons and their connections from the PEP data set and merge the PEP data set with Orbis by matching the firm names reported

> 2The original PEP data set has eight categories of politically exposed persons. These comprise individuals in International and Regional Organizations, National, Sub-national and Local Government, State-Owned Enterprises and State-Invested Enterprises, as well as Non-Governmental Organizations (Bussolo, Commander, and Poupakis (2018)). However, for the countries in our sample, after merging with Orbis data we find four afore-mentioned categories to be relevant. Please note that for our analysis we collapse State-Owned Enterprises and State-Invested Enterprises to a single category titled State Enterprises.

> 3The criteria for consistency check are based on a ”reasonable” move in total assets. We drop firms if we observe a sudden sharp spike in growth rate of total assets, with a lower threshold of -99% and an upper threshold 19900%.

4
# <u>Business environment</u>

**6. Improve insolvency and creditor rights regimes.** Weak regimes contribute to contracting and enforcement costs which boost bank operating costs and lower loan recovery rates (LGD) and thus higher lending rates and lower intermediation efficiency. Policy makers could consider strengthening legal and regulatory frameworks that inhibit viable businesses from corporate restructuring; improving commercial insolvency regimes to facilitate effective firm exit and collateral recovery; and promoting the efficiency and independence of the judiciary and insolvency practitioners.

**7. Improve information and collateral registries frameworks.** Strong frameworks lower agency and transaction costs and improve intermediation efficiency. Policy makers could consider strengthening the legal and regulatory underpinnings of these frameworks; updating local accounting principles and practices; promoting coverage and the quality of information held in credit and collateral registries; and support the exchange of information between eligible market players, such as through open Application Programming Interfaces (APIs)<sup>14</sup> which may also strengthen competition.

8. **Revisit direct policy interventions.** While these may prove useful to compensate for market failures and achieve fiscal or socio-economic objectives in the short term, they may carry unintended consequences and reduce intermediation, particularly if they are not well designed to ensure additionality. Policy makers could consider reviewing the impacts of (unremunerated) reserve requirements frameworks, interest rates restrictions, and directed credit and subsidy programs and carefully balance these impacts against stated objectives to justify the merit of their distortionary effects. In doing so, it is important not to compromise monetary and stability objectives. In this context, the literature has shown that financial liberalization should be pursued with care and institutional pre-conditions should be met first (e.g. Demirgüç-Kunt and Detragiache, 1998).

> 14 Subject to strong covenants for consumer data safeguards and cybersecurity.

23
of households captures their ability to smooth shocks to their income or consumption and recover quickly, and is more difficult to infer from survey data. As part of the World Bank’s _Unbreakable_ report (Hallegatte et al., 2017), Hallegatte _et al._ (2016) (H2016) developed a model that quantifies how disaster impact can result in income and consumption losses, which, consequently, lead to households experiencing welfare losses (using a utility function). This model, which was initially applied on an international scale across 117 countries, has been refined and used in additional detailed case studies, including in the Philippines, San Francisco, and Bangladesh (Walsh and Hallegatte, 2019; Markhvida et al., 2020; Verschuur et al., 2020).

The original H2016 model has several assumptions and limitations inherent to the modeling framework. First, the only disruptive channel resulting in households experiencing income losses is via asset losses, which assumes that households derive income from the assets in their vicinity. The vulnerability of a household to such asset losses is based on a proxy of household vulnerability (e.g. housing type), with households living in lower quality housing experiencing a disproportionately higher income shock given damages to assets, as it is assumed that their asset-base is more hazard-prone. While necessary to make the step from asset damages to income losses, it remains a coarse assumption ignoring which assets are affected, and how households may depend on these assets. Similarly, poor or non-poor households are determined based on their income, assuming that the only way wellbeing is affected is by experiencing a drop in household income. However, using income or consumption as a metric of poverty has its limitations (see next section), especially in low-income countries, and ignores the multidimensional aspects ( _e.g._ health, education, living conditions) that result in people living in a state of deprivation. Third, the H2016 operates using national household survey data, which is generally not available on a sufficiently
182 countries (including high-income countries). _Source:_ Authors’ calculation based on the variables and data sources described in Section III.

**Table A3** . Data on World Bank Group Engagement in DRM

|Country|Income<br>|Region|WB|G Engagement|<sup>1</sup>|Level of WBG<br>|
|---|---|---|---|---|---|---|
||Group||2009-2011|2012-2015|2016-2019|Engagement<sup>2</sup>|
|Afghanistan|LIC|SA|0.159|0.170|0.077|Low|
|Albania|UMIC|ECA|0.066|2.553||High|
|Algeria|LMIC|MENA||||None|
|Angola|LMIC|SSA||0.756||Medium|
|Armenia|UMIC|ECA|0.930|1.323||High|
|Azerbaijan|UMIC|ECA||||None|
|Bangladesh|LMIC|SA|0.060|0.028||Low|
|Belarus|UMIC|ECA|0.312|||Low|
|Belize|UMIC|LAC||||None|
|Benin|LMIC|SSA|0.527||0.389|Medium|
|Bhutan|LMIC|SA||0.738|0.121|Medium|
|Bolivia|LMIC|LAC||||None|
|Bosnia and Herzegovina|UMIC|ECA||||None|
|Botswana|UMIC|SSA||||None|

28
The main advantage of such approach is indeed related to its unique coverage, as it captures 28,000 firms, mainly Small and Medium Enterprises (SMEs), in selected emerging countries (more than 40 countries covered by the Enterprise Survey), which is exceptional among the ESG scores already available on the market. Indeed, ESG disclosure covers mainly large, listed firms, while the large majority of companies is neither subject to it nor is assessed regarding ESG aspects. EU regulation, which is the most advanced worldwide on ESG topics, will gradually enlarge the spectrum of companies covered, but SMEs’ disclosure will remain extremely limited. In the United States, any ESG disclosure remains voluntary, while the situation in emerging countries is by far worse (no visibility on ESG topics of non-listed firms).

The “Corporate ESG Responsibility” composite indicator suggests that firms in emerging economies lag significantly in terms of ESG practices. South EU and Central and Eastern Europe perform better, followed respectively by Central Asia, the Eastern Neighborhood, and the Western Balkans, while firms in Middle East and North Africa (MENA), Russia and Turkey are among the worst performers. Among emerging economies, our analysis focused on firms in the Middle East and North Africa as some of those countries are in urgent need to decarbonize their economies. They have seen their CO2 emissions rising in absolute terms and also in per capita terms during the last years, and are significantly exposed to climate change (especially to climate “chronic” risk, and heat and water scarcity in particular) and the social development (including gender issues) is lagging. MENA economies are the worst performers across the entire spectrum of the economies covered by the ES in terms of Environmental, Social and Governance practices. Egypt and the West Bank and Gaza appear to be the worst among the MENA economies, followed by Lebanon, but also the worst among the 40 economies covered by the ES. MENA economies’ poor performance is mainly driven by the social component (worst performer among
24 Jappelli, T. and M. Pagano (1995): “Saving, Growth, and Liquidity Constraints”, Quarterly Journal ofEconomics 109: 83-109.

Kiviet, J.F. (1995): “On Bias, Inconsistency, and Efficiency of various Estimators in Dynamic Panel Data Models”, Journal ofEconometrics 68: 53-78.

Levine, R., N. Loayza, and T. Beck (2000): “Financial Intermediation and Growth: Causality and Causes”, Journal ofMonetary Economics, forthcoming.

Loayza, N., H. Lopez, K. Schmidt-Hebbel, and L. Servén (1998a): “The World Saving Database,” World Bank manuscript, The World Bank, Washington, DC.

Loayza, N., H. Lopez, K. Schmidt-Hebbel, and L. Servén (1998b): “Saving in the World: Facts, Trends, Correlations,” manuscript presented at the World Bank Conference on Saving in The World: Puzzles and Policies, The World Bank, Washington, DC, Sep. 16-18.

Lopez, H., K. Schmidt-Hebbel and L. Servén (2000): “How Effective is Fiscal Policy in Raising National Saving?” Review ofEconomics and Statistics, this issue.

Maravall, A. and Planas, C. (1999: “Estimation Error and the Specification of Unobserved Component Models,” Journal ofEconometrics 92, p. 325-353.

Masson, P., T. Bayoumi and H. Samiei (1995): “Saving Behavior in Industrial and Developing Countries,” IMF manuscript.

Modigliani, F. “Saving in Developing Countries: Income, Growth and Other Factors”, Pacific Basin Capital Markets Research 3: 23-35.

Schmidt-Hebbel, K. and L. Servén (1997): “Saving across the World: Puzzles and Policies,” World Bank Discussion Paper No. 354.

Schmidt-Hebbel, K. and L. Servén (1999): The Economics of Saving and Growth: Theory, Evidence, and Implications for Policy.
on a reservation wage. This is analogous to the reservation productivity concept.

> 28There are two reasons why the productivity of a job may fall below the reservation value: idiosyncratic or aggregate shocks. Previous evidence for Colombia using plant-level data from the Annual Manufacturing survey estimates that the actual impact of reforms through factor adjustment on aggregate productivity was modest (Eslava, Haltiwanger, Kugler and Kugler, 2010), so introducing idiosyncratic shocks instead of aggregate shocks in the model seems more reasonable.

> 29Notice that I assume that productivity shocks affect both sectors symmetrically: workers receive shocks at the same rate, regardless of the sector in which they are in. Also, I assume that the idiosyncratic productivity that is drawn after the shock arrives is independent of the initial productivity _y_<sup>_′_</sup> and is irreversible (the firm must produce at the new productivity or shut down), where _x ∈_ [0 _, ∞_ ].

> 30 This means _x<RIU_ ( _y_ ) for the informal sector, and _x< RF U_ ( _y_ ) for the formal sector.

9