rafmacalaba/data-use-mentions-extended huggingface dataset:
) excludes establishments of fossil fuel firms, defined those with NAICS codes categorized as upstream or midstream oil and gas or upstream coal industries in Greenspon and Raimi (2024); Column (3) includes a control for fossil fuel subsidies (% of GDP) from the OECD Inventory of Support Measures for Fossil Fuels; Column (4) includes a control for green industrial policies from the Global Trade Alert database; and Column (5) repeats the baseline analysis but with a dependent variable equal to one if an establishment had any green technology-related job postings that month. Levels of significance denoted by (*) _<_ 0.10, (**) _<_ 0.05, and (***) _<_ 0.01.

# Table 1: Baseline results and robustness

We show these results are robust to a range of alternative specifications in Columns 2 through 5 of Table 1.<sup>17</sup> In Column (2) we exclude 2,948 establishments of fossil fuel producing-firms,

# which may have been positively impacted by the energy shock, and find virtually unchanged

17In addition, placebo test results provided in Supplementary Appendix Table A4 show that establishments more exposed to the 2022 energy price shock did not differentially increase job openings unrelated to green technologies after February 2022.
Policy Research Working Paper 11034

# **Abstract**

This paper develops a method to predict comparable income and consumption distributions for all countries in the world from a simple regression with a handful of country-level variables. To fit the model, the analysis uses more than 2,000 distributions from household surveys covering 168 countries from the World Bank’s Poverty and Inequality Platform. More than 1,000 economic, demographic, and remote sensing predictors from multiple databases are used to test the models. A model is selected that balances out-ofsample accuracy, simplicity, and the share of countries for which the method can be applied. The paper finds that a simple model relying on gross domestic product per capita, under-5 mortality rate, life expectancy, and rural population share gives almost the same accuracy as a complex machine learning model using 1,000 indicators jointly. The method allows for easy distributional analysis in countries with extreme data deprivation where survey data are unavailable or severely outdated, several of which are likely among the poorest countries in the world.

This paper is a product of the Development Data Group and the Poverty and Equity Global Department. 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.

_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
settlers. This theory emphasizes the role of geography (latitude and natural endowments) and disease environment (which affected the settler mortality) in shaping property rights.

Others have proposed that differences in culture, defined as a system of beliefs, can help explain the differences in investor protection and financial development. Stulz and Williamson (2003) highlight the role of religion while Guiso, Sapienza, and Zingales (2004) highlight the role of social capital in shaping financial systems. Rajan and Zingales (2003), Haber (2004), Haber, Maurer, and Razo (2003), Pagano and Volpin (2001), and Roe (1994) focus on how political economy and political stability underlie the development of financial markets. Finally, Easterly and Levine (1997) and Alesina et al. (2003) show that ethnic fractionalization in a country is an important determinant of both rent seeking and the social polarization that affects property rights and social institutions.

The broad spectrum of studies above suggests that several factors - legal, cultural, political, ethnic, and geographic - influence the financial system and some studies have run a horse race between the various theories to understand which matters more. Beck, DemirgucKunt, and Levine (2003) empirically compare the law and finance theory with the endowment theory to conclude that both are equally important determinants of financial development. Ayyagari, Demirguc-Kunt, and Maksimovic (2008) run an empirical horse-race between the various theories using data from the World Business Environment Survey on firms‟ perceptions of how well protected their property rights are in practice. Using a regression based, variance decomposition approach, they compare the contribution to adjusted R-sq of different institutional theories in a regression of firms‟ perception of property rights on each of the institutional variables and firm level variables.

They conclude that all the institutional theories address first-order factors accounting for about 50% of the explainable cross-country variation in property rights (country dummies alone explain 17.82% and all the institutional theories together explain 8.88% of the variation
calculations for electricity, and 2014-2015 for the case of water. In the case of EEU, the utility company also provided information on capital cost shares of electricity generation, transmission, and distribution. In the case of AAWSA, the provided information helped us to separate the costs for providing water from those for providing sewerage services. Table 4 summarizes the total average unit costs calculated for water and electricity.<sup>21</sup>

**Table 4: Summary of Average Unit Costs for Water And Electricity** **<u>(Addis Ababa, 2015/2016)</u>**

|**Electricity**<br>**( USD / KWh)**|**Water**<br>**(USD / m3)**|
|---|---|
|**Cost component by type of cost**||
|**Operation and Maintenance**<br>0.004|0.19|
|**Capital cost**<br>0.088|0.70|
|**Total Cost**<br>0.092|0.89|
|**Cost component by production step**||
|**Distribution**<br>0.023||
|**Transmission**<br>0.014||
|**Generation**<br>0.055||
|**Total Cost**<br>0.092||

Authors calculation based on primary data collected from the utility company (1 US Dollar = 20.68 Bir)

# **5. Results**

# **_Description of sample households_**

Our subsample of households with private water and electricity connections for the exclusive use of household members represent about 39% of households in Addis Ababa. Households in this subsample are wealthier than those with shared water or electricity connections (or both
# **1. Introduction**

In 2001, the Government of Thailand launched the Thailand Village and Urban Revolving Fund (VF) program. This was an important component of the electoral platform of newly-elected Prime Minister Thaksin Shinawatra, and envisaged providing a million baht (about $22,500 at the exchange rate of the time) to every village and urban community in Thailand as working capital for locally-run rotating credit associations.<sup>1</sup> The stated purpose of the fund was to “stimulate the rural economy,” with the stated intention of targeting “new activities such as processing and packaging” (Thaksinomics 2006). An element of the platform of the government elected in July 2011 is to add another million baht per village to the Village Funds.

The program was put in place rapidly; by the end of May 2005 the VF committees had lent a total of 259 billion baht ($6.9 billion at an exchange rate of Baht 37.6/$) to 17.8 million borrowers, some of whom had by then borrowed more than once (Arevart 2005). At the time, the Thailand Village Fund was the largest single microfinance scheme in the world.

The first purpose of this paper is to explain the performance of the Village Funds themselves. In this context we are interested in the extent to which they have maintained and/or expanded their lending capacity, their loan recovery and profit rates, and the extent to which their loans go to a wide swath of society (“outreach”). These are important if one is to assess the sustainability of the VF model of microcredit. The second goal of the paper is to weigh the costs and benefits of the Village Fund program, which may potentially serve as a guide to the desirability of expanding it further.

In the following sections, we first summarize the main features of Village Fund lending since 2004, using data from the Socio-Economic Surveys of 2004, 2009, and 2010, and show that
For Cambodia, Lin et al. (2021) discuss the longer-term damage wrought by unexploded ordnance and farmers’ adjustment to the associated dangers. In Ukraine, the oblasts included are <mark>Kyiv, Chernihiv, Sum, Kharkiv, Luhansk, Donetsk, Zaporizja, Kherson, and Mykolaiv.</mark>

> 5 While the measure used here is based on manual classification, Kussul et al. (2023) discuss the methodology and use of the data as an input into a machine learning model in more detail.

> 6 Open-source data (https://novy.tv/ru/news/2022/06/02/karta-bojovyh-dij-v-ukrayini/ and https://liveuamap.com/ru/time).

6
The paper is organized in the following way: Section 2 gives a literature review; Section 3 presents the data; Section 4 describes our identification strategy in greater details; Section 5 examines the main results; Section 6 presents two extensions; and Section 7 concludes.

# **2 Literature Review**

Our paper is related to current literature on the severe decline in demand during the Great Recession. Most prominently, Mian, Sufi, and other co-authors have examined the effects of the decline in housing net-worth on consumption and employment during the crisis. Mian, Rao, and Sufi (2013) find that ZIP codes with higher pre-crisis household leverage (and steeper subsequent declines in housing net-worth in the Great Recession) cut their consumption more sharply during this period. Similarly, Mian and Sufi (2014) find that housing net worth declines were driving consumption cuts and job losses during the Recession. Nguyen (2015) examines job loss spillovers driven by the demand channel. In particular, he argues that job losses in the tradable sector caused further job losses in local retail and restaurant industries, via the aggregate demand channel.

There are surprisingly few studies on cross-sector spillovers based on production linkages. Bodrin et al. (2013), using U.S. national data, examine job loss propagation from the construction sector to other sectors. They argue that this channel has important effects on aggregate employment. Li and Martin (2015), also using national data at the sector level, find the financial sector to be the “epicenter” during the Great Recession, from which emanates job loss spillovers into other sectors. Unlike those two papers, we utilize _sectoral_ data at the _county_ level, and for that reason, we are less concerned about the endogeneity issue.

In a different context, using national employment data at the sectoral level, Acemoglu et al. (2016) study the transmission of trade shocks across U.S. manufacturing industries, via input5
## **Management and Organizational Practices Survey (MOPS)**

Our final measure of management practices comes from a 16-question survey given to firm owners in 2017, derived from the Management and Organizational Practices Survey (MOPS). This survey was created by the U.S. Census Bureau, and was designed to enable basic management practices to be measured in a self-administered survey format. The survey asks questions related to monitoring, targeting, and incentives, and is intended to measure similar concepts to the WMS (Bloom et al, 2018). It was carried out by Innovations for Poverty Action during in-person visits to the firms, and firms were also asked to recall what these practices were five years earlier (in 2012). Table A6.1 shows that these data were collected for 95 firms.

# **Associations between different measures of management and over time**

The WMS and MOPs are collected in a much less in-depth way than the Anexo K, and measure different aspects of management. Table A7.1 looks at the baseline correlations between different measures. At baseline, the Anexo K management score has a correlation of 0.26 with the WMS management score, and 0.23 with the MOPS score. By way of comparison, the 38 management practices in Bloom et al. (2013) had a 0.40 correlation with the WMS score. The Anexo K is most highly correlated with the monitoring component of the WMS (correlation of 0.44). When we examine the five areas of the Anexo K, the finance, logistics and production scores are more highly correlated with the WMS than the HR and marketing scores. Recall the WMS does not measure marketing practices, and there is a difference in emphasis in how the two focus on human resource practices. The WMS is more focused on how good and bad performers are hired and rewarded, whereas the Anexo K has more of an emphasis on organizational culture and links to overall business strategy. Notably, while the MOPS and
# **IV. Concluding remarks**

Reweighting procedures do not fully correct the bias in poverty estimation, and the selection of reweighting procedures listed above does not make much difference in correcting the bias. However, our findings show that a combination of reweighting and the SWIFT-based poverty projection approach effectively eliminates the bias in poverty estimation in all four case studies. The literature proposes several weight adjustment procedures to reduce sampling bias in phone or web surveys. However, as far as we know, there has been no previous attempt that examined the role of reweighting on poverty projections.

To monitor the impacts of the COVID-19 pandemic in a frequent and timely manner, the World Bank launched the COVID-19 HFPS surveys in around 80 countries. The COVID-19 HFPS surveys offer valuable and timely information on the living conditions, livelihoods, coping mechanisms, and social assistance during the pandemic, but they also have serious limitations. First, these surveys do not have direct poverty or inequality measures. Second, the data collected often suffers from sampling and nonresponse bias due to limited phone ownership in many developing countries and nonresponses associated with phone interviews. To address the first limitation, SWIFT methodology was adopted. SWIFT utilizes a machine-learning-based technique to impute poverty and inequality statistics using 10 to 15 simple questions included in the HFPS surveys (Yoshida et al., 2022a). However, the second limitation, namely, bias arising from sampling and nonresponses, can lead to bias in the poverty statistics estimated by SWIFT. To address this issue, this paper explores some widely used reweighting techniques and examines if they could alleviate these bias in poverty projections.

Specifically, this paper identifies a few variants of propensity score weightings (PSWs), including 1) PSWs and 2) a combination of PSW and non-PSW techniques. To assess the performance of reweighting procedures and the SWIFT-based poverty projections, we conduct experiments by drawing subsamples of phone owners from reference surveys in Rwanda, St Lucia, Uganda (refugees), and Ethiopia. In the experiment in Ethiopia, we excluded households
The data shown in the following table for imports of cabuya sacks per country of origin draws attention to the fact that in 1995 Nicaragua increased its exports to Costa Rica more than twofold compared to the previous year. This situation, together with the large increase of imports from other countries (including Bangladesh), meant an aggregate increase of 290% in the total imports of cabuya sacks to Costa Rica in that same year.

**Table 4.7   Costa Rica: Origin of cabuya sacks imports 1992-1995**

|||(In –units)<br>||
|---|---|---|---|
|**Year**|**Total imported**<br>**sacks**|**Sacks imported**<br>**from Nicaragua**|**Sacks imported from**<br>**Bangladesh and other**<br>**countries**|
|1992|371,490|332,409|39,081|
|1993|286,582|253,596|32,986|
|1994|209,218|209,218|0|
|1995|816,683|489,609|330,074|

Note: The periods correspond to January-August of each year. Source: MEIC’s resolution published in the Official Gazette No. 91, May 13, 1998.

These figures confirm the substantial increase in imports that would result in the eventual injury to the domestic production of jute sacks. Unfortunately, the case’s file and the respective MEIC’s resolution fail to provide any information or analysis of the impact (if any) of said increased imports on the domestic production of jute sacks.

Likewise, there are no data available on the sector’s evolution or its share in production and employment. However, given that one of the main uses of jute sacks is to package coffee for export, the
43 Pesaresi, Martino; Ehrilch, Daniele; Florczyk, Aneta J.; Freire, Sergio; Julea, Andreea; Kemper, Thomas; Soille, Pierre; Syrris, Vasileios (2015): GHS built-up grid, derived from Landsat, multitemporal (1975, 1990, 2000, 2014). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-ghsl-ghs_built_ldsmt_globe_r2015b Pesaresi, M; Ehrlich, D; Ferri, S; Florczyk, A.J; Freire, S; Halkia, M; Syrris, V (2016). Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014; JRC Technical Report EUR 27741 EN. Ispra (VA), Italy. https://doi.org/10.2788/253582 Prosperi, D., Moudon, A. V., & Claessens, F. (2009). The Question of Metropolitan Form: Introduction. _Footprint_ , _3_ (2), 1-4.

Rosenthal, SS; Strange, WC (2004). Evidence on the nature and sources of agglomeration economies, in J.V. Henderson and J.-F. Thisse, eds., Handbook of Urban and Regional Economics, Volume 4, New York: North Holland, 2119-2171.

Schwarz, N (2010). Urban form revisited—Selecting indicators for characterising European cities. Landscape and Urban Planning, 96:29–47 Sorensen, A., 2000. Land readjustment and metropolitan growth: an examination of suburban land development and urban sprawl in the Tokyo metropolitan area. Prog. Plan. 53 (4), 217–330.

Squires, G.D. (2002). Sprawl: Causes and Consequences and Policy Responses. The Urban Institute Press, Washington, D.C.

Sutton, P. C., Cova, T. J., & Elvidge, C. D.
National Center for Energy Economics, 2022). In each of these baselines, emissions are expected to rise to just above 40 GtCO2 yr<sup>_−_1</sup> before declining. It has been argued that higher emissions scenarios are unlikely (Hausfather and Peters, 2020). We therefore assume that, without abatement, emissions will maintain their peak levels in each of these emissions scenarios, i.e., at _∼_ 40 GtCO2 yr<sup>_−_1</sup> . This describes our “business-as-usual” scenario<sup>9</sup> ; we probe the impact of assuming an increasing emissions baseline in Section 2 of the _Supplementary Materials_ . Thus, in this calibration, once all emissions are abated, society is entirely decarbonized. We set the time horizon for decarbonization to 2100; this translates to solving the model for 80 years.

# **2.3.2 Marginal abatement and investment costs**

Throughout the main text, we use the low cost, linear marginal cost calibration shown in Figure 1; we explore the impact of the other two alternative calibrations given in Figure 1 in Section 3 of the _Supplementary Materials_ . In this calibration, we assume that our cost functions _ci_ ( _xi,t_ ) and _γi_ ( _ai,t_ ) are quadratics, such that We calibrate these cost functions using IPCC marginal cost data (Intergovernmental Panel on Climate Change, 2022). Marginal abatement costs are given by, which implies that marginal abatement costs are linear in abatement potential. We translate the marginal abatement cost into the marginal investment cost by requiring that between the two approaches (MAC and INV) the relative cost between each sector are equal, such that for each unique pair ( _i, j_ ). This defines marginal costs up to one remaining parameter. The final cost coefficient is calibrated by demanding that the total discounted costs between both approaches are equal for a given target (Grubb, 1997) under the assumption of certainty about the
∗</sup>|0.003|0.036|0.001|
||(0.333)|(0.352)|(0.379)|(0.505)|(0.365)|(0.414)|(0.007)|(0.028)|(0.083)|(0.169)|
|Controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|Province FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|Observations|985|974|796|786|857|850|968|952|690|683|
|_R_<sup>2</sup>|0.392|0.446|0.460|0.496|0.417|0.458|0.483|0.516|0.387|0.432|
|F-test (η1 =−β1) _p_-value|.15|.42|.45|.42|.72|.87|.3|.3|.87|.87|
|F-test (η2 =−β2) _p_-value|.45|.42|.3|.24|.54|.51|.15|.18|.24|.21|
|F-test(η3 =−β3) _p_-value|.78|.96|.48|.84|.18|.42|.54|.27|.57|.51|

_Notes:_ Authors’ analysis based on data described in the text. The table reports OLS estimates. The unit of observation is a household. This table relies on the second definition of demobilized ex-combatants ( _D_<sup>_CNDD_</sup> _i_<sup>+2</sup> and _Di_<sup>
**Table 8. Regional Trends in Self-Reported Food Insecurity (Percent Prevalence)**

|**Developing region**|**No. of**<br>**obs.**|**2005–06 surveys**<br>**(precrisis)**|**2008 surveys**<br>**(food crisis)**|**2009 surveys**<br>**(financial crisis)**|
|---|---|---|---|---|
|Eight most populous developing<br>countries*|8|32.7|28.0|30.6|
|sub-Saharan Africa|14|55.8|54.6|57.2|
|West Africa, coastal|4|48.5|51.3|58.0|
|West Africa, Sahel|5|59.6|49.2|55.2|
|Eastern & Southern Africa|5|57.8|62.8|58.6|
|Latin America & Caribbean|15|33.2|36.4|35.7|
|Central America, Caribbean|7|38.4|41.4|40.3|
|South America|8|28.6|32.0|31.6|
|Middle East (including Turkey)|3|19.7|26.0|21.3|
|Transition countries|13|31.9|30.2|34.6|
|Eastern Europe|6|21.8|19.7|25.8|
|Central Asia|7|40.6|39.1|42.1|
|Asia|12|28.8|29.0|30.8|
|East Asia|7|30.1|30.
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for agriculture, manufacturing, and mining sub-sectors shown.

As an alternative to Eq. (1) we use detailed data on the tariffs that the US applied to all HS 6-digit products and all countries over the period 1997-2017, to estimate the trade effects of tariff preferences granted by the US under different schemes, including AGOA, using the following specification: MFN Pref MFN Pref − τ − τ ln(Impcpt) = γ(τcpt cpt ) × 1[c∈AFR] + γ(τcpt cpt ) × 1[c∈NonAFR] + δcp + δct + δpt + εcpt MFN Pref where τcpt − τcpt is the difference between the MFN tariff rate and the best available US preferential tariff rate. As in Equation (2) we control for a stringent set of fixed effects: country-product, country-year, and product-year. Since Eq. (D1) relies on a continuous tariff preference variable (which captures the magnitude of the trade preference) rather than discrete country-product treatment groups, we do not control for treated group-specific time trends. The coefficient of interest γ captures the trade effects of the tariff preferences given to a country-product pair relative to the average export of that country-product pair. To capture the trade effect of the tariff cuts provided to African countries, we include the tariff preference 8