rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Figure 4: 10-year monthly bond yields for A- and B-rated issuers**

We present the end-of-month bond yield data. Although all bonds studied are investment grade, we can discern a clear co-movement in the yields of A-rated issuers, which is much less pronounced in B-rated issuers.

<!-- Start of picture text --> A-rated issuers (AAA to A-)<br>15.0<br>12.5<br>10.0<br>7.5<br>5.0<br>2.5<br>0.0<br>B-rated issuers (BBB+ to BB-)<br>2010 2012 2014 2016 2018 2020 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> explain the original sin. Instead, only the total size of the economy matters. Further, Claessens et al. (2007) find that the depth of the domestic financial system, exchange rate regimes and the composition of the investor base matter for how much a country decides to issue in foreign currencies. Regarding the sovereign credit risk, Amstad et al. (2020) do not find evidence for differences in credit ratings due to higher inflation risks. They also find that the gap in credit ratings between currencies has narrowed within the past two decades due to favorable global reserve conditions.

We use Bloomberg’s end-of-month generic government bond indices for the 10-year and 2-year tenors (Figure 4, Appendix). We treat the former as the long-term yields in this study and proxy the slope of the yield curve through the difference between the 10- and 2-year rates. Missing data points are imputed through cubic spline interpolation (Waggoner, 1997). See Figure 10, Appendix, for a visual example of this procedure.

# **2.3**

To control for effects due to short-term growth variables, we include monthly GDP growth and consumer price index (CPI) growth, to
"dominance" is generally defined as at least a 35% market share. In its initial years, the GAK classified thousands of firms as dominant at the federal and regional levels, and regulated these firms' prices, profits and output. The approach has been heavily criticized both inside Russia and by international experts because of its questionable economic rationale and unwieldy procedures, including its tenuous method for defining geographic and product market boundaries and justifiable costs. In 1997 the federal section of the GAK dominant firm registry included about 500 industrial enterprises, accounting for roughly 20% of all industrial output. At present, MAPSE oversight of prices and profits of dominant firms is less one of direct regulation and more of preventing anti-competitive pricing--both the charging of prices above competitively determined levels and of predatory and entry-deterring prices. However, the problems of a poorly clarified economic rationale and unwieldy procedures remain largely intact. With regard to mergers, prior approval of the MAPSE is required for acquisitions of more than 20% of the shares of a company, or acquisition of shares in any firm included in the register of dominant firms. Current rules specify two tiers of dominance: proposed mergers that result in 65% market share are _per se_ "undoubtedly dominant"; for proposed mergers that result in market share between 35% and 65%, the MAPSE bears the burden of proof that the merger would result in a "dominant" firm.

Surveys suggest that the MAPSE generally has been reluctant to enforce the law in clear cases of market abuse. In the WB-RAS survey, more than 30% of the respondent General Directors indicated that producers colluding to fix prices are not subject to sanctions under the anti-monopoly law. Political economy constraints and excessive use of discretionary--as opposed to rules-based--authority seem to be a major factor in preventing effective enforcement. The decline in industrial output during Russia's transition has made it difficult to take actions against important industrial enterprises. Case evidence reveals that regional MAPSE branches have had a tendency to protect
Policy Research Working Paper 6195

# **Abstract**

This paper examines the impact of bank ownership on credit growth in developing countries before and during the 2008-2009 crisis. Using bank-level data for countries in Eastern Europe and Latin America, it analyzes the growth of banks’ total gross loans as well as the growth of corporate, consumer, and residential mortgage loans. Although domestic private banks in Eastern Europe and Latin America contracted their loan growth rates during the crisis, there are differences in foreign and government-owned bank credit growth across regions. In Eastern Europe, foreign bank total lending fell by more than domestic private bank credit. These results are primarily driven by reductions in corporate loans. Furthermore, government-owned banks in Eastern Europe did not act counter-cyclically. The opposite was true in Latin America, where the growth of governmentowned banks’ corporate and consumer loans during the crisis exceeded that of domestic and foreign banks. Contrary to the case of foreign banks in Eastern Europe, those in Latin America did not fuel loan growth prior to the crisis and did not contract lending at a faster pace than domestic banks during the crisis.

This paper is a product of the Finance and Private Sector Development 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 mmartinezperia@worldbank.org and rcull@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
A related consideration is whether illness shocks affect the probability that a survey is successfully completed. Heath et al (2019) point out that attrition is very low and difficult to predict on the basis of observable worker and household characteristics: 91% of the weekly interviews scheduled were successfully conducted. Missed surveys very rarely correspond to complete disappearance from the survey; only two of the 949 respondents in the high frequency data were not available at endline. Moreover, note that even if missed surveys do correlate with illness in the household, this would only bias the estimated effect of illness on labor supply if this pattern differentially holds among respondents who were more or less likely to work during a given illness episode suffered by another family member. Finally, since missed surveys affect the probability that we can detect an illness – we only know an individual was ill if he or she responds to the survey – our control for the number of household member planning to work in a round also accounts for the fact that we have a greater chance of detecting sickness in rounds in which more household members were surveyed.

# **2.4 Econometric Strategy: Net effects of illness on households**

To estimate the net effect of a household member’s illness on the household, inclusive of the compensatory behavior of other workers, we also examine the effect of a household-level illness shock (Worker Ill��� – a worker missing a week of work due to unexpected illness in a given week – on household-level outcomes (Y��), namely, total labor supply and earnings. We include household (γ�� fixed effects and time fixed effects interacted with city (λ� �μ��: 17
was launched roughly a decade after displacement had begun from the Syrian Civil War, allowing ample time for construction of additional housing units and social integration.

The results also speak to the active ongoing policy debate on how best to support refugees and host states at the same time (Ash and Huang 2018; Baseler et al. 2023). When study participants were asked directly about what form of assistance they preferred (during the 1.5 year follow-up survey), respondents overwhelmingly stated that they preferred direct cash transfers to landlord subsidies (at 70%). Cash transfers to refugees in other contexts have been shown to provide only short-run benefits rather sustained gains (Hidrobo et al. 2014; Özler et al. 2021; Quattrochi et al. 2022; Moussa et al. 2022; Aygün et al. 2024), but the findings of this study suggest that delivering benefits more discreetly (such as via cash or mobile money) may at a minimum reduce the risk of host community backlash. An alternative approach, supported by Baseler et al. 2023, would explicitly pair refugee assistance with enhanced host community assistance, which in this case might have led to some housing investments among host community neighbors as well. There is promising evidence from Colombia that labor market reforms may be an effective way to improve refugee living standards without leading to crowd-out of host community jobs (Bahar et al. 2021; Rozo et al. 2023; Ibáñez et al. 2024). These remain critical areas for future research and policy innovation, and present important opportunities to improve program design and ultimately refugees’ lives.

# **References**

Agness, D. (2023). Housing and human capital: Condominiums in ethiopia. _3ie Registry_ .

- Anderson, M. L. (2012). Multiple inference and gender differences in the effects of early intervention: A reevaluation of the abecedarian, perry preschool, and early training projects. _Journal of American Statistical Association_ .

- Ash, N. and C. Huang (2018)
banking system to play its normal role in the transmission of the monetary policy stimulus would have prevented the economy from responding to sizable interest rate cuts.

According to the definition proposed by the Council of Economic Advisors (1991), a credit crunch is "a situation in which the supply of credit is restricted below the range usually identified with prevailing market interest rates and the profitability of - investment projects". Various reasons surveyed in Bernanke and Gertler (1995) and - Hubbard (1995) account for banks' desire to retrench their loan supply following a monetary tightening or some other negative shock to the economy. It is argued that the introduction of the Basel Committee capital standards, induced a credit crunch by making loans more costly to hold for banks than government securities.<sup>5</sup> Various authors, using different methodologies, have contributed to this interpretation. Some of the relevant papers have used cross-sectional bank-level data. Bernanke and Lown (1991) show that loan growth at individual banks between 1990:Q2 and 1991:Q1 was positively linked to initial capital ratios. Peek and Rosengren (1995) introduce an influential method to address the issue of identification of supply-induced effects. Namely, they argue that capital-unconstrained banks should react to negative shocks to capital by intensifying deposit taking, thus, when banks are not capitalconstrained, one should expect a negative relationship between shocks to capital and deposit taking. On the contrary, they find a positive link between shocks to capital and the dynamics of deposits in 1990. They conclude this evidence suggests that the capital constraints for banks were pervasive as the Basel Committee ratios were phased in and, indeed, show that this impact is larger for banks having lower initial capital ratios. Berger and Udell (1994) concur that the expansion of loans was lower in 1990-92 for lesscapitalized banks, but do not detect a sensitivity of loan expansion to capital ratios higher than the one observed during the recession of the early 1980s. All in all, most contributions published on the issue6 support
Our thesis in this paper is that demand-side empowerment matters for improving demand for a service, above and beyond the simple supply-side effects of feedback mechanisms. We posit that feedback tools can increase demand for a service not just by increasing monitoring, and therefore the quality of a service, but also by signaling to the users that the provider respects them. We set up a large field experiment to test our hypotheses, in which we formally document the impact of feedback tools on users’ demand in the context of agricultural services for 180 farmer groups in rural Rwanda. We randomly assign two types of feedback tools to farmer groups that are subscribed to an agricultural input and extension service. Our design allows us to capture two categories of treatment effects of interest. First, we capture the combined empowerment and monitoring effects of both feedback tools. Second, we separate out pure monitoring effects from user empowerment effects by announcing to extension workers that their work is being monitored, in both treatment groups (“true” announcement) and control groups (“false” announcement).

We track the impact of these different feedback and monitoring tools on a sample of 1,460 farmers. Given the nature of our interventions, we primarily rely on administrative data to establish changes in demand as a result of the different treatments and to track demand effect spillovers to farmers who were non-users at baseline. We also use survey data to document changes in farmers’ use of and satisfaction with the service, as well as empowerment to demand better service.

We find that feedback tools help sustain demand for the service among current clients and increase demand among non-users. Farmer groups offered the opportunity to provide feedback were half as likely to have members leave the service the following year as control groups. Perhaps surprisingly, this demand effect spills over to non-users in the vicinity of the treated groups, who are more likely to sign up in the following season. These effects are large and robust to multiple hypotheses testing, implying an increase in group size of up to 3
.018)_|
|Albanian|-0.081**|-0.042***|
||_(0.038)_|_(0.012)_|
|Roma|-0.073***|-0.099**|
||_(0.021)_|_(0.039)_|
|Other|-0.001|-0.013|
||_(0.014)_|_(0.016)_|
|N|33,454|33,454|
|N Control|12,426|12,426|
|N Treatment 1|10,995|10,995|
|N Treatment 2|10,033|10,033|
|_R2_|0.088|0.106|

* p < 0.05, ** p < 0.01, *** p < 0.001

_Notes:_ All regressions control for pre-treatment outcomes, demographic controls (including dummies for age, gender, academic year, and ethnicity), and municipality fixed effects. Robust standard errors are clustered at the school level in parentheses. All figures are rounded to three decimal places. _Source:_ Administrative data, school year 2016-2017, own calculations.

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**Fig. 1: GDP in OECD countries**

<!-- Start of picture text --> 105%<br>100%<br>95%<br>90%<br>85%<br>80%<br>75%<br>75% 80% 85% 90% 95% 100% 105%<br>Q2-2020 GDP as % of GDP Q4-2019<br>Q3-2020 Q4-2020<br>% of Q4-2019<br><!-- End of picture text --> Source: OECDstats.

20%. Along the vertical axis, the speed of the recovery in the third and the fourth quarter is measured by the distance between each point and the 45<sup>_◦_</sup> degree line (no recovery) and the 100% (full recovery) line. For example, the United States and New Zealand had a similar reduction of GDP in the second quarter of 2020, 10% relative to the pre-pandemic quarterly GDP, but the recovery was different. New Zealand’s output was 103% and 101% of the fourth quarter of 2019 GDP in the third and fourth quarters of 2020, respectively, while the corresponding numbers for the United States were 97% and 98%. Table 2 in Appendix A has the data underlying Figure 1.

In our model, we take the size of the shutdown as an unanticipated shock, which determines the fall in GDP during the lockdown. We are interested in characterizing the speed of the recovery.

We find the cases of the United States and New Zealand of particular interest. The US is our calibration benchmark and we assess the quantitative implications of our laboratory economy using the US data. Our interest in New Zealand, in turn, lies in the starkness of its containment policies. It implemented a very intense, but brief shutdown in the second quarter of 2020 and, except for some time intervals and locations, lifted mobility restrictions thereafter. In this
HRD appeal numbers are based on a bottom-up assessment process and agreed assumptions of need, as follows (Source: Food Economy Group 2015):

- (i) **_Pre-harvest assessment triggered_** : The pre-harvest needs assessment is triggered by the Disaster Risk Management Technical Working Group (DRM TWG<sup>29</sup> ) and conducted by _woreda_ - level staff. Assessments are conducted within two weeks before or after each rainy season, depending on rainfall conditions.

- (ii) **_Pre-harvest assessment:_** _Woreda_ -level staff conduct an initial assessment of the number of people in need of food assistance. Assessments are based on a range of information, including rainfall and crop production data. _Woreda_ -level staff submit needs estimates to the zones, which revise and compile needs estimates. Zonal-level beneficiary estimates are submitted to regional governments, who compile and revise beneficiary estimates again.

- (iii) **_Main seasonal needs assessments:_** The main seasonal needs assessments are conducted by multi-agency teams dispatched by the federal government to the regions. In particular, assessments are conducted in specific _woredas_ . Agreement is reached on which _woredas_ to visit through coordination between assessment teams and the regional-level governments, based on pre-harvest assessment results. In determining beneficiary numbers, checklists are used to guide teams through the assessment process. Beneficiary numbers were calculated initially based on the Household Economy Approach (HEA, see section 4.2 for further details), and were computed using the LIAS spreadsheet. The spreadsheet produces, as a final output, the number of people in need of food assistance. Two different thresholds are used: A Livelihood Protection Threshold<sup>30</sup> , below which households are unable to meet food needs whilst upholding their livelihood strategy, and a Survival Threshold<sup>31</sup> , below which households are unable to meet food needs. As of 2015, the DRM TWG decided to discontinue the use of LIAS data, as it was recognized
Data for this market are available from the beginning of 2011 and the annual average price (Figure 12) has fallen steadily from a peak reached in 2013 of 8 pesos/kwh to the 2017 value of 3.4 pesos/kWh (US$ 0.068). These gains are passed on to consumers through the pricing formula determined by the ERC. Some of these gains are due to the large fall in oil prices from 2013 to 2016, but the declining importance of oil in the power sector indicates that other forces were involved in the reduction of the WESM price.

**Figure 11: Annual average wholesale electricity spot market price (pesos/kwh)**

<!-- Start of picture text --> 9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> _Source_ : Philippines Electricity Market Corporation: WWW.wesm.ph At a utility level, efficiency measures related to distribution and collection losses for MERALCO are shown in **Figure** **_13_** and for BENECO in **Figure** **_14_** . In both cases the total percentage loss of revenue due to collection losses and T&D losses fell over the period.<sup>13</sup> MERALCO experienced collection losses only during a three-year period with other years seeing a collection rate of at least 100 percent. Distribution losses declined to about 8 percent of production (equivalent to 0.8 percent of revenue). BENECO experienced larger losses, with collection rates dipping to 97 percent in 2011-2012, and distribution losses at 10 percent of output. Together these losses were equivalent to 2 percent of BENECO’s revenue. An index of average tariffs (relative to the 1992 value) in local currency and
# 2) The purpose of UTAS

UTAS allows users to take full ownership of the tool and to develop it further to address varying country needs. It offers solutions to several challenges that typically complicate policy makers’ assessment of the impact of tariff reforms on production costs in a flexible and time‐efficient manner.

First, previous studies assessing the impact of tariffs on firms’ production costs and effective protection have generally relied on firm‐level survey or census data on manufacturing that allow disaggregating firms’ consumption of intermediate inputs. These data are then used to extract firms’ input cost shares and weigh input tariffs accordingly, and to subsequently attain firm‐level estimates of tariff‐related cost.<sup>4</sup> Most often, however, such highly disaggregated firm‐level data are not readily available, as they tend to include sensitive information and are expensive and time‐consuming to collect. The alternative pursued in UTAS is to build on economy‐wide input‐output (I/O) tables that provide a disaggregation of production costs by sector. Although more aggregated than firm‐level data, I/O tables are available in most countries and allow policy makers to attain a quick overview of varying input cost shares across sectors.<sup>5</sup> Yet the use of I/O tables is not without its challenges. Sector classifications of most I/O tables cannot be matched easily with available tariff‐schedules typically classified at a much finer product level on the basis of the Harmonized System (HS) tariff nomenclature of the WTO. UTAS overcomes this problem by drawing on pre‐defined concordance tables between HS codes and a range of commonly used I/O sector classifications (e.g. GTAP, ISIC or NAICS). A key function of UTAS is therefore to help users handling different sets of data to combine tariff schedules with I/O sectors’ input cost shares and to calculate tariff‐related input costs and effective protection rates for different sectors.
is not to say that its consumption-smoothing and risk-coping functions are not valuable in themselves. In fact, Collins et al. (2009) document that just to meet these needs, the poor juggle complex financial transactions on a daily basis. However, it is possible to achieve these benefits through other innovations like micro-savings (discussed later in the paper) and micro-insurance, which displace other pro-poor interventions to a much smaller degree.

The bigger puzzle here is that of existing businesses. Evidence has been inconclusive as to whether microcredit fosters investment in incumbent firms. This is surprising, given that limited access to finance is frequently stated as a stumbling block to business growth by entrepreneurs in developing countries; for example, 30.9 percent of firm owners in the World Bank Enterprise Survey list access to finance as a major constraint to growth.<sup>1</sup> Using regulatory changes in the Indian banking sector as a natural experiment, Banerjee and Duflo (2008) also find evidence that medium-sized enterprises are stymied by credit constraints. Both of these studies include large and medium sized firms, and this handicap is even more pronounced for small firms. In fact, Beck, Demirgüç-Kunt and Maksimovic (2007) find that financing constraints impede growth by 10 percent for small firms, as against 6 percent for large firms. Worse, Sabarwal and Terrell (2009) find evidence from Latin America consistent with the fact that when women own small or medium enterprises, they are less likely than men to receive formal credit when they apply for it.

Why, then, do small firms not borrow from MFIs? One possible factor driving low usage could be that returns to capital in microenterprises are low and therefore it does not make sense for their owners to borrow money to invest in them. However, multiple pieces of evidence allow us to negate this possibility.

> 1 The complete World Bank Enterprise Survey dataset is available at: http://www.enterprisesurveys.org.

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