rafmacalaba/datause-encoder-data huggingface dataset:
In addition to concerns about market monitoring, specific concerns have been raised for a long time now with regards to large, complex financial institutions ⎯ LCFIs in short ⎯ (e.g., going back to the so-called Ferguson G-10 report of 2001). Besides the difficulty markets and supervisors may have to assess conglomerates, LCFIs may be too big to ignore or too complex to fail by supervisors. As such, they may get preferential treatments during periods of financial stress. And while the safety net is surely not the main reason for the emergence of large conglomerates, financial institutions may have an incentive to grow and become more complex to maximize the benefits from a public financial safety net. While LCFIs have been an issue for regulators globally for the last decade, no easy solutions have been found to limit any benefits from a public safety net. ⎯ ⎯ Of course, the reasons to treat banks and financial conglomerates by extension special may itself be subject to debate (see box 1).

# **Box 1: The changing special nature of banks**

Why banks are “special” and what it means for regulation (and supervision) has been analyzed many times and is clear in principle (see, for example, Goodhart, 2000, or Padoa-Schippoa, 2004 for a review). But the dramatic recent changes in financial services provision calls for continuous inspection of this aspect.

In some respects, banks are getting less special, reflected in part in a decreased role of traditional banking products (payments, deposits and credit) in overall financial intermediation. The share of banking credit in total financial intermediation, for example, has fallen by half in many developed countries. Corporations can now get liquidity at very short-notice from capital markets and non-bank financial institutions. Households can have access to mutual funds to meet their liquidity needs. As traditional liquidity providers, an important reason to treat banks special, banks may thus have become less important.

The changing, and perhaps less special, role of banks is arguably partly recognized in financial markets. There are signs, for example, of increased applications of principles traditionally used for non-bank financial institutions to financial institutions. This includes bringing financial reporting closer to corporate sector reporting, applying standard corporate governance, market discipline and the like principles to banks, and moving away from compliance supervision towards more risk management based supervision. Increased application of corporate finance tools can also be found in banking research. But how government policy, especially with regard to the public safety net, needs to adjust to these changing roles of banks has been less clear. So far, the traditional model has largely been maintained.

10
PRE Working Paper Series

||_TA6_|Auhor||Contact<br>for paper|
|---|---|---|---|---|
|WPS469|Modeling Economic Behavior in<br>Peru's Informal Urban Retail Sector|J. Barry Smith<br>Morton Stelcner|August 1990|M. Abundo<br>36820|
|WPS470|What Do Alternative Measures of<br>Comparative Advantage Reveal<br>About the Composition of Developing<br>Countries' Exports?|Alexander J. Yeats<br>|August 1990|J. Epps<br>33710|
|WPS471|The Determinants<br>of Farm<br>Investment and Residential<br>Construction in Post-Reform China|Gershon Feder<br>Lawrence J. Lau<br>Justin Lin<br>Xiaopeng Luo|August 1990|C. Spooner<br>30464|
|WPS472|Gains in the Education of Peruvian<br>Women, 1940 to 1980|Elizabeth M. King<br>Rosemary Bellew|August 1990|C. Cristobal<br>33640|
|WPS473|Adjustment, Investment, and the<br>Real Exchange Rate in Developing<br>Countries|Riccardo Faini<br>Jaime de Melo|August 1990|R. Sugui<br>37951|
|WPS474|Methods for Measuring the Effect<br>of Adjustment Policies on Income<br>Distribution|Anne Maasland|August 1990|P. Dixon<br>39175|
|WPS475|Does Divestiture Matter? A<br>Framework for Learning from Experie|Ahmed Galal<br>nce|August1990|G. Orraca-Tette<br>37646|
|WPS476|Health Insurance in Sub-Saharan<br>Africa: A Survey and Analysis|Ronald J. Vogel|August 1990|K. Brown<br>35073|
|WPS477|Private Participation in the Delivery<br>of Guinea's Water Supply Services|Thelma A. Triche|August 1990|M. Dhokai<br>33970|
|WPS478|Interrelations Among Child Mortality,<br>Breastfeeding, and Fertility in<br>Egypt, 1975-80|John Marcotte<br>John B. Casterline|August 1990|S. Cochrane<br>33222|
|WPS479|Conversion Factors: A Discussion<br>of Alternate Rates and Correspondin<br>Weights|Michael Hee<br>g|August 1990|E. Zamora<br>33706|
|WPS480|An Evaluatior of Neutral Trade Policy<br>Incentives Under Increasing Returns<br>to Scale|Jaime de Melo<br>David Roland-Holst|August 1990|R. Sugui<br>37951|
|WPS481|The Effects of Trade Reforms on<br>Scale and Technical Efficiency:<br>New Evidence from Chile|James Tybout<br>Jaime de Melo<br>Vittorio Corbo|August 1990|R. Sugui<br>37951|
|WPS482|Membership in the CFA Zone:<br>Odyssean Journey or Trojan Horse?|Shantayanan Devarajan<br>Jaime de Melo|August 1990|R. Sugui<br>37951|
|Bihar|0.121|18|Bihar|0.304|18|Bihar|0.395|18|
|---|---|---|---|---|---|---|---|---|
|C.V.|47.1|||41.8|||32.7||

Source: Author’s Calculations. C.V- Coefficient of Variations

**_Financial Infrastructure:_** Table 4 compares the performances of different states in terms of the financial infrastructure index, where we find large inequality among the states. Though the variations across states in financial infrastructure over time have narrowed down, the CV is still high. The CV is higher for financial infrastructure than physical and social infrastructure for all three-time points.  Additionally, the relative positions of states have not changed much from 2005 to 2019. Exceptions include Haryana and Chhattisgarh experienced significant improvement in their relative positions, whereas Andhra Pradesh witnessed deterioration in its position and is among one of the worst-performing states along with Uttar Pradesh and Bihar.

**<u>Table 4: Relative Performances of States for Financial Infrastructure Index</u>**

||**20**<br>|**05**<br>||**2010**<br>|||**2019**<br>||
|---|---|---|---|---|---|---|---|---|
|**_States_**|**_Score_**|**_Rank_**|**_State_**|**_States_**|**_Score_**|**_Rank_**|**_State_**|**_States_**|
|HP|1.505|1|HP|1.624|1|HP|2.277|1|
|Punjab|0.780|2|Kerala|0.950|2|Punjab|1.789|2|
|Kerala|0.777|3|Punjab|0.945|3|Kerala|1.733|3|
|Karnataka|0.677|4|Karnataka|0.841|4|Karnataka|1.500|4|
|Tamil Nadu|0.625|5|Tamil Nadu|0.788|5|Haryana|1.458|5|
|Andhra|0.609|6|Andhra|0.680|6|Tamil Nadu|1.372|6|
|Gujrat|0.538|7|Maharashtra|0.671|7|Maharashtra|1.290|7|
|Odisha|0.519|8|Gujrat|0.626|8|Odisha|1.128|8|
|Maharashtra|0.471|9|Haryana|0.617|9|Gujrat|0.932|9|
|Haryana|0.443|10|Odisha|0.601|10|Rajasthan|0.847|10|
|Rajasthan|0.438|11|Rajasthan|0.472|11|WB|0.809|11|
|MP|0.305|12|WB|0.367|12|Chhattisgarh|0.754|12|
|WB|0.291|13|MP|0.349|13|MP|0.722|13|
|Assam|0.282|14|Jharkhand|0.335|14|Jharkhand|0.718|14|
|Jharkhand|0.281|15|Chhattisgarh|0.326|15|Assam|0.672|15|
|Chhattisgarh|0.278|16|Assam|0.324|16|UP|0.671|16|
|UP|0.249|17|UP|0.311|17|Andhra|0.640|17|
|Bihar|0.143|18|Bihar|0.143|18|Bihar|0.440|18|
|C.V.|61|||56.8|||46||

_Source: Author’s Calculations. C.V- Coefficient of Variations_ Overall, we find inter-state variation in all three infrastructure indices, but there has been a gradual reduction in interstate disparities measured by the coefficient of variation from 2005 to 2019 (Figure 1). While some states like Kerela, Himachal Pradesh, Tamil Nadu, and Karnataka have performed well across all three indices, States like Bihar, Uttar Pradesh, and Jharkhand have underperformed and lagged behind the remaining states. Figure 2 reports interstate variation (Coefficient of variation) in per capita income growth, investment rate, and 18
# References

Barro, Robert J., and Xavier Sala-i-Martin (1995), _Economic Growth._ New York: McGraw-Hill. Ferrantino, Michael J., Ian Gillson, and Gabriela Schmidt (2015), “Russia, the WTO, and the ECU: Tariff and Non-Tariff Policy Changes,” World Bank Policy Note. Washington, DC: The World Bank.

Gill, Indermit, Ivailo Izvorski, Willem van Eeghen, and Donato de Rosa (2014), _Diversified Development: Making the Most of Natural Resources in Eurasia._ Washington, DC: The World Bank.

Guriev, Sergei, and Elena Vakulenko (2012), “Convergence between Russian Regions,” CEFIR / NES Working Paper No. 180, October.

Guriev, Sergei, and Elena Vakulenko (2015), “Breaking Out of Poverty Traps: Internal Migration and Interregional Convergence in Russia,” processed.

Hausmann, Ricardo, Hwang, and Dani Rodrik (2007), “What You Export Matters,” _Journal of Economic Growth_ Vol. 12 no. 1, 1-25.

Helpman, E., and P. Krugman. 1985. Market Structure and Foreign Trade. Cambridge, MA: Massachusetts Institute of Technology Press.

Lederman, Daniel, and William F. Maloney (2012), _Does What You Export Matter?_ In _Search of Empirical Guidance for Industrial Policies._ Washington, DC: The World Bank.

Lee, Xiaoying, and Liu, Xiaming (2005), “Foreign Direct Investment and Economic Growth: An Increasingly Endogenous Relationship,” _World Development_ Vol. 33 no. 3, 393-407.

Kuznetsov, Alexey (2010), “Inward FDI in Russia and its Policy Context,” Columbia FDI Profiles, New York: Vale Columbia Center on Sustainable International Investment.

Mankiw, N. Gregory, David Romer, and David N. Weil (2012), “A Contribution to the Empirics of Economic Growth,” _Quarterly Journal of Economics_ Vol. 107 no. 2, 407-437.

Melitz, M.J. 2003. The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity. _Econometrica_ 71(6): 1,695–725 Sáez, Sebastian, and Erik van der Marel (2015), “Russia Case Study: How Services Contribute to Competitiveness.” World Bank Policy Note. Washington, DC: The World Bank.

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19
through proxy interviews and two question interviews with refusals to ascertain whether individuals who could not be contacted or had refused were currently operating a business, and if so, the number of employees they had.

The first follow-up survey took place between November 2012 and May 2013. The survey took place approximately one year after individuals had first applied to the program, 8 months or more after the winners had been announced, and an average of 5 to 6 months since the winners received their first tranche payment. Table 1 shows the median firm had received 4,000,000 Naira ($US25,000) by the time of this first survey. Overall 2,323 interviews were completed, representing an overall response rate of 74 percent, with data on whether the business was in operation available for 2,420 individuals. Appendix Table 3 summarizes the response rates by group, breaking these down also by new and existing firms. Response rates were highest for the YouWiN! winners, with 84.6 percent of the experimental treatment group and 83.4% of the national and zonal winners providing at least information on whether the business was operating or not. The response rate was 75 percent for the experimental control group, and only 69 percent for the regression discontinuity booster sample, reflecting that individuals who had not benefited as much from the YouWiN! program were more reluctant to participate. We use Lee (2009) bounds to show robustness to this differential attrition by treatment status.

The second follow-up survey took place between October 2013 and February 2014, approximately two years after application and just as firms had received their last tranche payments. This was an even more intensive effort in data collection, and succeeded in completing 2,712 interviews, and getting information on business ownership and employees for 2,777 individuals (88.5%). In particular, in this round we were able to greatly close the gap between the treatment and control firms, with data on enterprise ownership available for 93.0 percent of the experimental winners and 90.6 percent of the experimental control group. Moreover, some of those not interviewed in round 2 were interviewed in round 1, so there are only 4 percent of the experimental sample for which we have no follow-up data in either of the first two rounds.

The third and final follow-up survey took place between September 2014 and February 2015. This corresponds to three years after application, and between 12 and 18 months after firms had received their last tranche payment from the program. 2,618 interviews were completed (83.4%) with 14
over the last four decades. The seven energy policy packages are (1) Legal framework (LF); (2) Planning for expansion (PE); (3) Incentives and regulatory support (IR); (4) Attributes of financial and regulatory incentives (AI); (5) Network connection and use (NC); (6) Counterparty risk (CR); and (7) Carbon pricing and monitoring (CP).

# 1.1 CONTEXT AND MOTIVATION

Our study is closely related to two large empirical literature streams. First, we position our analysis within the broad social science literature that analyzes the effect of individual renewable energy policies and their outcomes.

Peñasco et al., (2021) conducted a systematic review of 211 studies that evaluate "the effect of a specific policy instrument [related to the clean energy transition] into a specific outcome" across all the social sciences. Overall, their review includes studies of 50 countries. A relevant finding is that the existing literature is heavily biased toward the OECD countries. So, instead, we look only at non-OECD and cover more than 100 developing countries.

The 211 studies on developed countries suggest a significant variation across the effects of energy policy instruments. They help support the evidence that material endowment and “integrated policy and economy-wide approaches” coupled with “enabling conditions (governance, institutions, behavior, innovation, policy, and finance)” are key to decarbonization (Bang et al., 2015; Bednar-Friedl et al., 2022; Boasson et al., 2020; Lamb & Minx, 2020).

Second, this study also engages with and contributes to a large body of economics literature on the reform of energy sector governance (see Foster & Rana (2020) and Jamasb et al. (2005, 2015) for excellent surveys of this literature) that began in the 1980s. In the spirit of this literature, our work subjects the supposed benefits of energy sector reform to 3
Policy Research Working Paper 10106

# **Abstract**

The information set from which individuals make their decision on vaccination includes signals from trusted agents, such as governments, community leaders, and the media. By implementing restrictions, or by relaxing them, governments can provide a signal about the underlying risk of the pandemic and indirectly affect vaccination take-up. Rather than focusing on measures specifically designed to increase vaccine acceptance, this paper studies how governments’ nonpharmaceutical policy responses to the pandemic can modify the degree of preventive health behavior, including vaccination. To do so, the paper uses repeated waves of a global survey on COVID-19 beliefs, behaviors, and norms covering 67 countries from August 2020 to February 2021. Controlling for the usual determinants, the analysis explores how individuals’ willingness to get vaccinated is affected by changes in government restriction measures (as measured by the Oxford Stringency Index). This relationship is mediated by individual characteristics, social norms (social pressure to conform with what most people do), and trust in government institutions. The results point to a complex picture as the implementation of restrictions is associated with increased acceptance in some contexts and decreased acceptance in others. The stringency of government restrictions has significant positive correlations with vaccine acceptance in contexts of weak social norms of vaccine acceptance and lower trust in government. In countries or communities where social norms are tighter and trust in government health authorities is high, vaccine acceptance is high but less sensitive to changes in policies. These results suggest that the indirect effect of government policy stringency is stronger among individuals who report lower trust and weaker social norms of vaccine acceptance.

This paper is a product of the Office of the Chief Economist, South Asia Region and the Office of the Chief Economist, Europe and Central Asia Region. 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 mbussolo@worldbank.org, nsarma@worldbank.org, and itorre@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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# **1. Introduction**

The trade performance of South Asian countries over the past two decades has been poor relative to other regions. Exports from South Asia have only doubled over the past 20 years to approximately $100 billion. In contrast, exports in East Asia grew ten times (Pireola and Newfarmer 2006). South Asia’s share of the total exports from developing countries has declined, due in part to its relatively slow export growth (Figure 1). This reflects both South Asia’s limited trade integration with the rest of the world and the limited intraregional trade. In particular, Pierola and Newfarmer(2006) note that South Asia’s intraregional trade as a share of its total trade volume has remained around 2 percent since 1980. This is very low compared to approximately 15 percent for East Asia (excluding Japan), for example (Figure 2). Overall intraregional trade in South Asia constitutes about 33 percent of gross domestic product (GDP), while it contributes to 71 percent of the GDP in East Asia.

Figure 1: Exports from Developing Countries, 1980-2002 <!-- Start of picture text --> 4,000<br>3,500<br>3,000<br>2,500<br>All Developing<br>2,000 East Asia Pacific<br>South Asia<br>1,500<br>1,000<br>500<br>-<br>1980 1984 1988 1992 1996 2000<br>$billion<br><!-- End of picture text --> Source: Calculated by the authors based on United Nations COMTRADE data Note: South Asia includes : Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. East Asia Pacific includes : American Samoa, Cambodia, China, Fiji, Indonesia, Kiribati, Korea, Lao PDR, Malaysia, Marshall Islands, Micronesia, Mongolia, Myanmar, Palau, Papua New Guinea, Philippines, Samoa, Solomon Island, Thailand, Tonga, Vanuatu, and Vietnam.

2
# **Figure 4g East Asia: Agricultural extent impacted**

<!-- Start of picture text --> 4 meter<br><!-- End of picture text --> <!-- Start of picture text --> Vietnam Myanmar<br><!-- End of picture text --> <!-- Start of picture text --> Indonesia<br><!-- End of picture text --> <!-- Start of picture text --> Cambodia Taiwan China<br><!-- End of picture text --> <!-- Start of picture text --> Thailand<br><!-- End of picture text --> <!-- Start of picture text --> Malaysia<br><!-- End of picture text --> <!-- Start of picture text --> D.P.R. Korea Philippines<br><!-- End of picture text --> <!-- Start of picture text --> Rep. of Korea<br><!-- End of picture text --> <!-- Start of picture text --> Brunei<br>Guinea<br>Papua New<br><!-- End of picture text -->

# **Figure 4h East Asia: Wetlands impacted**

<!-- Start of picture text --> 1 meter 2 meter 3 meter 4 meter 5 meter<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>% Impact (Wetland)<br>Vietnam Myanmar Thailand Taiwan China Philippines Rep. of Korea D.P.R. Korea Cambodia Indonesia Malaysia China Brunei Papua New Guinea<br><!-- End of picture text --> 33