rafmacalaba/datause-encoder-data huggingface dataset:
we do not have data on the banks that have relied on the new instruments and non-cash options to date, this too is an issue worth monitoring.

# _2.2 Market discipline_

The latest BRSS survey responses suggest that market discipline may have deteriorated in many countries because of weaker incentives to monitor bank risk-taking. Deposit insurance coverage has become more generous and government interventions in the banking sector to rescue ailing banks have probably undermined the incentives of market participants to monitor their banks’ risk-taking behavior. This has likely encouraged banks, especially large banks, to take on more risk. Moreover, the information set available to assess the risk profile of banks has not improved substantially according to the round 5 BRSS responses compared to what was recorded in round 4.

The presence of an explicit deposit insurance scheme can reduce the incentives of depositors to monitor banks (Anginer et al., 2014). Many more countries now have explicit deposit insurance than before the GFC (104 in round 5 versus 86 in round 4 among those countries that responded to both rounds).<sup>8</sup> Moreover, we observe an increase in the generosity of deposit insurance schemes in all regions, except MENA, as reflected in the share of countries that report that coverage has been broadened, though the change over time is significant only for high-income OECD countries (Figure 6, Panel B). The share of countries that report an increase in the amount covered also increased in high-income OECD countries, ECA and EAP (Figure 6, Panel C). The reach of deposit insurance agencies has also been expanded as a higher share of countries now indicates that the deposit insurance fund can be used for purposes other than depositor protection, such as recapitalization of distressed banks. Those increases are statistically significant

> 8 Considering round 5 submissions only, 115 countries had an explicit deposit insurance scheme at the end of 2016. In addition, the People’s Republic of China is not included among the countries that adopted a formal deposit insurance scheme in round 5 because section 8 – Deposit (Savings) protection schemes – of their survey was not completed. Nonetheless, since May 2015, a formal deposit insurance system is in place in this country for all deposit taking institutions. Furthermore, for round 4, data for Tonga and Lesotho have been adjusted, as country officials confirmed that there was not a formal financial safety net operating in the country as per the end of year 2010.

12
How do Women Fare in Rural Non-Farm Economy?

Habtamu Fuje<sup>_∗†_</sup> The World Bank Poverty and Equity Global Practice

> _∗_ Correspondence: Habtamu.Fuje@columbia.edu or Habtamu_Fuje@post.harvard.edu.

> _†_ I would like to extend my heartfelt appreciation to Chris Barrett for his guidance and mentorship during the completion of this work. He has contributed substantially in the development of the research idea and its implementation. Chris was kind enough to agree to mentor me through Agriculture in AfricaTelling Facts from Myths  project, a multi-institution research project that had a component for mentoring young African development professionals.
<!-- Start of picture text --> a H ' ' H<br>:<br>6 | | |<br>3<br>5 | |<br>| |<br>|<br>|<br>3 i<br>500 1000 2500 5000 10000 20000 500(<br>GDPicapita (2011 USD PPP, logarithmic scale)<br><!-- End of picture text -->

<!-- Start of picture text --> : H ! ! H<br>: | | :<br>5 | |<br>fe) : :<br>g 6 |<br>& | |<br>|<br>| |<br>A ' H<br>500 1000 2500 5000 10000 20000 500(<br>GDP/capita (2011 USD PPP, logarithmic scale)<br><!-- End of picture text -->
O.A.M.D.G.

with and without a public order problem (guerrilla or paramilitary presence) for asset definition (1).

||Participation in mobile assets|Total|
|---|---|---|
|Participation in fixed assets|no<br>yes||
|no|2%<br>3%|5%|
|yes|4%<br>92%|95%|
|Total|5%<br>95%|100%|

Table 5: Fixed and mobile asset holdings without guerrilla/paramilitary presence

||Participation in mobile assets|Total|
|---|---|---|
|Participation in fixed assets|no<br>yes||
|no|3%<br>3%|6%|
|yes|4%<br>90%|94%|
|Total|7%<br>93%|100%|

Table 6: Fixed and mobile asset holdings with guerrilla/paramilitary presence We observe that very few people do not hold either asset at all. An analysis of participation only detects the movement of investment at the very poorest fringe of the population. Note that holdings of both assets go down in the presence of guerrillas/paras. However, the single participations in fixed or mobile assets respectively do not seem to change much. A multivariate analysis is needed to shed light on the relationship.

# **Sample selection issues**

There may be issues of sample selection in the database we use for our analysis. As explained earlier, we use the 2001 _Familias_ survey as the backbone for our analysis and merge it at the municipality level with violence and municipal variables, also from 2001. The households in the _Familias_ survey have been identified from the government’s 20
# Table 7: Neighborhood effects: Comparable neighbors

||Within p<br>|rovince<br>|A<br>|cross provinces<br>|<br>|
|---|---|---|---|---|---|
||(1)<br>Comparable|(2)<br>Non-<br>comparable|(3)<br>Comparable|(4)<br>Comparable<br>W/I 400km|(5)<br>Non-comp.<br>W/I 400km|
|Ave. neighbors’ over-reporting|0.751<sup>∗∗∗</sup>|0.189<sup>∗∗∗</sup>|0.130|0.072<sup>∗</sup>|0.035|
||(0.129)|(0.052)|(0.104)|(0.039)|(0.030)|
|PS YearsInOffice|0.094<sup>∗∗∗</sup>|0.106<sup>∗∗∗</sup>|0.104<sup>∗∗∗</sup>|0.101<sup>∗∗∗</sup>|0.100<sup>∗∗∗</sup>|
||(0.027)|(0.028)|(0.028)|(0.028)|(0.028)|
|PS YearsInOffice<sup>2</sup>|−0.009<sup>∗∗</sup>|−0.010<sup>∗∗</sup>|−0.010<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|Lag own over-reporting|0.211<sup>∗∗∗</sup>|0.220<sup>∗∗∗</sup>|0.223<sup>∗∗∗</sup>|0.223<sup>∗∗∗</sup>|0.223<sup>∗∗∗</sup>|
||(0.032)|(0.032)|(0.033)|(0.033)|(0.033)|
|City Controls|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|
|City FE|Yes|Yes|Yes|Yes|Yes|
|Province-Year FE|Yes|Yes|Yes|Yes|Yes|
|# of Observations|3264|3264|3264|3264|3264|
|# of Cities|272|272|272|272|272|

_Notes_ : This table presents the results of a dynamic, spatially autoregressive Durbin model estimated by MLE following Yu, De Jong, and Lee (2008). Each column corresponds to a different spatial weighting matrix in terms of economic distance defined in the header of each column. Two cities are defined as comparable neighbors if the difference in their per capita GDP in 2001 is within one standard deviation across cities. City controls include citylevel time-varying socioeconomic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights) of their own and their neighbors, and leader controls are the same as the ones displayed in the table of baseline results. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

45
- 5 -

and laws and regulations on the books may carry little weight.<sup>5</sup> It might be also argued that assessments are not comparable across countries, despite the best efforts of expert supervisors and internal reviewing teams at the IMF and the World Bank to ensure a uniform methodology and uniform standards. If our negative results arise because compliance assessments do not reflect reality or are not comparable across countries, then – at a minimum – they should lead us to question the value of these assessments in ensuring that countries’ supervision measures up to global standards.

The paper is organized as follows: The next section contains a review of related literature. Section 3 presents the methodology and data. Section 4 contains the results, and Section 5 concludes.

# **II.   LITERATURE REVIEW**

Defining good regulatory and supervisory practices is a difficult and complicated task. Barth, Caprio, and Levine (2001, 2004, 2006) were the first to compile and analyze an extensive database on banking sector laws and regulations using various surveys of regulators around the world, and to study the relationship between alternative regulatory strategies and outcomes. This research finds that regulatory approaches that facilitate private sector monitoring of banks (such as disclosure of reliable, comprehensive and timely information) and strengthen incentives for greater market monitoring (for example by limiting deposit insurance) improve bank performance and stability. In contrast, boosting official supervisory oversight and disciplinary powers and tightening capital standards does not lead to banking sector development, nor does it improve bank efficiency, reduce corruption in lending, or lower banking system fragility.<sup>6</sup> They interpret their findings as a challenge to the Basel Committee’s influential approach to bank regulation which heavily emphasizes capital and official supervision.

> 5 Measurement error, if random, would not bias coefficients, but would increase standard errors, thereby resulting in in insignificant results.

> 6 Laeven and Levine (2008) extend this analysis to show that the impact of regulations on bank risk taking also varies with the comparative power of shareholders within the corporate governance structure of each bank.
26 Table 9: The "Coal" ECSC Sub-Group: Exports to the EC and Pre-rTAs Market Access

||Expor|ts, 1991|Share i<br>**Expo**|n Industrial<br>**ns,**1991|NTB Coverag<br>Ratio,|e Simple Averag<br>Tariff Ratc|e Range of<br>tariffs<br>Max-Min|
|---|---|---|---|---|---|---|---|
||(a)?|(bj)|(a)|(b)||||
|Bulgaria|(in US<br>1|S million<br>0|)<br>0.1|0|(in percent)<br>0|4.2|8.3-0.0|
|<br>FCSK|1|140|neg.|3.0|0|5.9|8.3-0.0|
|Hungazy|3|1|<br>0.1|neg.|0|2.0|2.0-0.0|
|<br>Poland|268|285|4.3|<br>4.6|0|4.3|8.3-0.0|
|Romania|0|0|0|0|0|0.0|0-0.0|

_'J_ This subgroup includes SITC.Rev.3. 27862+281+321+(322-3223). 2/ (a) The EC excluding Gernmany and Spain. (b) Germany and Spain only.

# Sources: See Table 2.

Exports of coal products are not likely to increase significantly in the near future. Increaszs in efficiency of energy use will reduce domestic consumption, however, coal production is not likely to expand and may even contract as many coal mims in the region face bankruptcy. In contrast to steel products, there was litle (if any) redirection of sales from tbeCMEA. The FCSK and Poland were established suppliers Of coal to EC markets well before the collapse of the CMEA. They both sought to minimize coal shipments to their former CMEA parmers, simply because coal was more marketable in hard currency markets than low quality man red goods. Thus, the contraction in FSU import demand affected raw materials less than tmanufacures and there was no explosion in exports to OECD markets as in many industrial products.

# E. The "Residual" Free Trade Gro.

The residual group is subject to free trade upon entry into force of the ITAs. This group consists of products falling into CN 25-97 minus products identified in the five groups discussed above includig imports below ceiling or falling within tariff quota. It icludes 5,078 eight digit CN items of which 4,362 (68 percent _of_ "fre-trade" itms) have been subject to GSP rates. As a resut, average triffs for this group are below ite-average for all industrial products. CEE-5 exports of this group were less affected by NTBs than any otha group of industial products specified in the iTAs-the average NTB coverage ratio of between
# 1 Introduction

The climate-aware version of the World Bank’s macrostructural model (MFMod) used in this paper is based on Burns et al. (2019) with an application to Pakistan [named PAKMod, see Burns et al. (2021)]. PAKMod describes several climate features related to mitigation of the energy sector through a carbon tax, as well as physical risk modeling and adaptation/resilience to floods and how they are linked to an otherwise standard macroeconomic model.

Several enhancements were made to the methodology used in PAKMod during the first phase of the _Country Climate Development Report_ (CCDR) process and rolled out across different countries. The paper describes changes to the modeling of power sector decarbonization; first by extending the PAKMod framework to further endogenize energy dynamics into the macroeconomic system, then by proposing an alternative approach based on soft-linking a techno-model<sup>1</sup> to the macro framework.

For the CCDRs, the modeling of the power sector has been enriched in comparison to previous exercises. In PAKMod energy demand and supply were determined from a macroeconomic setup and solely dependent on the elasticity of substitution between fossil fuels and renewables, providing a simple and tractable technological landscape of the power sector that responds endogenously to the macroeconomic environment. During the CCDR process, MFMod was coupled with the World Bank Electricity Planning Model [EPM] (Chattopadhyay et al., 2018). This soft-linking approach reduced the reliance on the macroeconomic system to derive electricity supply (and thus the power mix with capital vintages as well as more granular technological representation) and demand. Instead, aggregate energy supply from renewables and fossil fuels was based on per-unit electricity production within a country, while accounting for distribution losses and the investments required to meet new demand.

The soft-linking approach takes the concept of stranded assets into account as well. Stranded assets are assets that are no longer economically viable and are therefore written off balance sheets. In the context of decarbonization, stranded assets can include fossil fuelbased power plants, oil and gas reserves, and other infrastructure that is no longer needed as the world transitions to a low-carbon economy. By using technoeconomic modeling, we keep track of capital vintages with path-dependency. This means that the model accounts for the fact that past investments in fossil fuel infrastructure can still have an impact on the future, even if those assets are no longer used. For example, a coal-fired power plant

> 1Applications are based on the World Bank’s electricity planning model in this paper. However, this method can be extended to any techno-economic model.

1
This study also finds that large-scale expansion of biofuels leads to a slight increase in the number of poor people if the poverty line used is $1.25 PPP per day. The increase largely comes from South Asia (India) and Sub-Saharan Africa. A large number of countries in SubSaharan Africa would realize an increase in poverty due to the large-scale expansion of biofuels. If the poverty line is drawn at $2.50 PPP per day, the number of people below the poverty line would increase significantly. The GINI coefficient in Sub-Saharan Africa and Latin America would drop, whereas it would increase in the rest of the regions. At the global level, the GINI coefficient would drop, although the magnitude is small.

While the effects of higher biofuel mandates on rural unskilled workers are positive in terms of higher relative wages and labor movement from urban to rural, the increase in food prices especially in developing countries is a major concern. Thus, since the increase in food prices is largely due to dependence of the current biofuel technology on food crops as raw materials in biofuel production, from the perspective of global poverty the development of a biofuel technology that uses less food crops is critical.

# **References**

- Al-Riffai, P., B. Dimaranan, and D. Laborde, 2010. "Global Trade and Environmental Impact Study of the EU Biofuels Mandate", Report for the European Commission, DG TRADE, ATLASS Consortium.

- Ackah, C., Bussolo, R. de Hoyos, and D. Medvedev. 2008. “A New Dataset on Global Income Distribution”. Washington, DC: World Bank.

- Arndt, C., R. Benfica, F. Tarp, J. Thurlow, and R. Uaiene. 2008. “Biofuels, Poverty and Growth: A Computable General Equilibrium Analysis of Mozambique”. IFPRI Discussion Paper 803. Washington, D.C. International Food Policy Research Institute.

- Beghin, J., Timilsina, G, van der Mensbrugghe, D. and Mevel, S. 2010. “The Impacts of Biofuels Targets on Land-Use Change and Food Supply: A Global CGE Assessment” Unpublished World Bank manuscript.

- Birur, D, Hertel, T., and Tyner, W. 2008. “Impact of Biofuel Production on World Agricultural Markets: A Computable General Equilibrium Analysis”. GTAP Working Paper No. 53.

- Boeters, S. Veenendaal,P. van Leeven, N. Rojas-Ramagoza, H. 2008. “The Potential for Biofuels alongside the EU-ETS”. Paper for Presentation at the Eleventh Annual GTAP Conference „Future of Global Economy‟, Helsinki, June 12-14.

Bussolo, M., R. de Hoyos, and D. Medvedev. 2008. “Economic Growth and Income 31
|H||||||||
|---|---|---|---|---|---|---|---|
||Savi|ngs stock (in|log)|Has|savings (du|mmy)|Share in|
||Total<br>(1)|Institutions<br>(2)|At home<br>(3)|Total<br>(4)|Institutions<br>(5)|At home<br>(6)|institutions<br>(7)|
|Panel A: equation (3), incl. tim|e, t and t?,|union FE"||||||
|Estimated risk of polygamy, F,|12.078"|13.839°°|2.167|1.474"*|1.650°**|0.437|0.310|
||(6.188)|(6.008)|(3.389)|(0.595)|(0.577)|(0.379)|(0.248)|
|Number of observations|1548|1548|1548|1548|1548|1548|1522|
|Number of unions|774|774|774|774|774|774|761|
|Within R2|0.011|0.011|0.029|0.017|0.015|0.034|0.84|
|Panel B: equation (4), incl. tim|e-varying c|ontrols X_, t a|nd t?, uni|on FE*||||
|Estimated risk of polygamy, FE,|14.417**|17.172°*|0.801|14g99°*|1.776"**|0.317|0.415*|
||(7.179)|(6.847)|(3.658)|(0.686)|(0.654)|(0.401)|(0.240)|
|Numberofobservations|1482|1482|1482|1482|1482|1482|1462|
|<br>Number of unions|741|741|741|741|7Al|741|731|
|Within R2|0.021|0.024|0.052|0.026|0.027|0.058|0.84|
|Panel C: equation (5), incl. 1st-|step contro|ls x time X,|= t, X;, t,|and #, un|ion FE*|||
|Estimated risk ofpolygamy,FE|14.649*|15.756°*|-2.399|1.665"*|1.767"*|-0.070|0.403|
||(8.070)|(7.894)|(4.349)|(0.778)|(0.754)|(0.459)|(0.345)|
|Numberofobservations|1482|1482|1482|1482|1482|1482|1462|
|<br>Number of unions|741|741|741|741|741|741|731|
|Within R2|0.063|0.060|0.091|0.071|0.068|0.097|0.84|
|Unconditionalmean(wave1)|3.94|3.76|0.47|0.38|0.36|0.05|0.35|

Panel union fixed effect model estimates. Standard errors in parentheses are bootstrapped (300 replications); * p<0.12 * p<0.10, ** p<0.05, *** p<0.01.

Dependent variables: col (a): stock of savings of the wife (in log); col. (b): a dummy equal to one if wife has savings, col. (2) and (5) correspond to savings entrusted to informal or formal institutions, col. (3) and (6) to savings kept at home. In col. (7): Share of the total stock of savings entrusted to informal or formal institutions Sample: monogamous unions in the two waves. F;: predicted failure probability estimated by a Cox survival model stratified by the polygamy status of the husband’s father (see Table 1).

All regressions include union fixed-effects ? Controls not shown: time (in months) between the two survey waves and its square ? Controls not shown: time, time squared, dummies for the deceased father of each spouse, household size, relative cell size, share of dependents

* Controls not shown: time, time squared, dummies for the deceased father of each spouse, household size, relative cell size, share of dependents, all step-1 controls (see Table 1) interacted with time

In col. 7: we additionally control for the total level of savings in log Data source: PSF Survey, waves 1 and 2 (2006-2007 and 2010-2012)
Mendelsohn, R., Kurukulasuriya, P., Basist A., Kogan F., and Williams C., 2007. Climate Analysis with Satellite versus Weather Station Data. Climatic Change, 81:71-83.

- Mitchell, D. and M. Ingco, 1993. _The World Food Outlook._ International Economics Department. Washington DC: World Bank.

- (OECF) Oversees Economic Cooperation Fund, 1995. _Prospects for Grain Demand-Supply Balance and Agricultural Development Policy_ .  September, Tokyo, Japan, Research Institute of Development Assistance.

Paarlberg, R. 1997. Feeding China, L A Cobfident View. _Food Policy_ , 22(3): 269-279.

- Parry, M.L., C. Rosenzweig, A. Iglesias, M. Livermore, G. Fischer, 2004. Effects of Climate Change on Global Food Production under SRES Emissions and Socio-economic Scenarios. _Global Environmental Change_ , 14, 53–67.

- Quiggin, J. and J. Horowitz. 1999.  The Impact of Global Warming on Agriculture: A Ricardian Analysis: A Comment. _American Economic Review,_ 89(4): 1044-1045.

- Reilly, J., et al. 1996. “Agriculture in a Changing Climate: Impacts and Adaptations” in IPCC (Intergovernmental Panel on Climate Change), Watson, R., M. Zinyowera, R. Moss, and D. Dokken (eds.) Climate Change 1995: Impacts, Adaptations, and Mitigation of Climate Change: Scientific-Technical Analyses, Cambridge University Press: Cambridge p427468.

- Ren, Guoyu, 2007(eds). _Climate Change and Water Resources in China_ , Meteorological Publishing House, Beijing, China (Forthcoming).

- Robin, M. and R. Wassman, 2003.  Modelling the Impacts of Climate Change and Methane Emissions Reduction on Rice Production: A Review. _European Journal of Agronomy_ , 19:573-598.

- Rosegrant, M. Agcaoili-Sombilla, M., and N. Perez, 1995. _Global Food Projections to 2020: Implications for Investments_ .  2020 Vision Discussion Paper 5.  Washington DC: International Food Policy Research Institute.

- Rozelle, S. and Rosegrant, M.W., 1997. China's Past, Present and Future Food Economy: Can China Continue to Meet the Challenges? _Food Policy_ , 22 (3), 191-200.

20
Policy Research Working Paper 10003

# **Abstract**

Based on survey data for more than 5,000 Kenyan households, this study shows that, despite government efforts to introduce remote learning options, access to education declined markedly during a nine-month-long period of school closures. Remote learning was adopted by only a small minority of students, and disadvantaged children fell further behind. During the first semester of 2021, reports of alterations in children’s externalizing and internalizing behavior more than tripled, with one in five children being affected by June 2021. After schools reopened, children learning remotely or through alternative means were more likely to suffer from these disruptions in emotional well-being than those who returned to school. While the medium- and long-term effects on learning outcomes and human capital remain unknown, the findings suggest that girls and children from poorer and less educated households have been disproportionately affected.

This paper is a product of 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 ecameron1@worldbank.org; adelius@worldbank.org; adevercelli@worldbank.org; upape@worldbank.org; and samuel. siewers@uni-goettingen.de.

_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