rafmacalaba/data-use-mentions-extended huggingface dataset:
-0.002|-0.032|0.002|0.000|-0.059|-0.008|0.005|-0.011|-0.044|-0.009|0.002|
|60th|0.600 0.513|-0.088|-0.040|-0.005|-0.043|0.001<br>|-0.013|-0.071|-0.009|0.005|-0.017|-0.062|-0.012|0.002|
|80th|0.800 0.711|-0.089|-0.025|-0.021|-0.034|-0.009<br>|-0.027|-0.057|-0.005|0.001|-0.027|-0.051|-0.010|0.000|

Source: Own computations using DHS 1995, 2005

**Table 8. Asset-poverty and vulnerability to asset-poverty**

|%|Number of<br>individuals<br>('00,000)|Number of<br>households<br>('00,000)|Fraction<br>poor|Mean<br>vulnerability<br>to poverty|Fraction<br>vulnerable<br>to poverty|Fraction<br>highly<br>vulnerable<br>to poverty|Fraction<br>extremely<br>poor|Mean<br>vulnerability<br>to extreme<br>poverty|<br>Fraction<br>vulnerable<br>to extreme<br>poverty|Fraction<br>highly<br>vulnerable<br>to extreme<br>poverty|
|---|---|---|---|---|
|30.8|46.0|50.8|24.6|30.2|
|Public Sector|4.0|3.6|8.4|8.8|8.9|9.0|
|Area (percent of employed population)|||||||
|Rural|78.6|71.6|36.5|34.6|68.4|66.8|
|Urban<br>Source: Own estimations based on Peru's ENAHO 2004 -2010, Thailand's SES 20|21.4<br>00 -2009, and Ban|28.4<br>gladesh's HIES|63.5<br>2000 -2010.|65.4|31.6|33.2|

29
applied a different methodology – as explained above.

Because of the problems associated with those discrepancies, the Study focused on years 2008 and 2009, which were also central years of the PEN life. Table 4 below shows HIV/AIDS expenditures for 2008 and 2009 disaggregated by expenditure categories:

> 42 MEGAS (Medición del gasto nacional relativo al SIDA) is a tool designed by UNAIDS that provides indicators of the financial country response to AIDS, and supports the monitoring of resource mobilization. Please see UNAIDS: http://www.unaids.org/es/dataanalysis/tools/nasapublications/ 43 MPS (2011) Informe UNGASS – 2010. Seguimiento de la Declaración de Compromiso sobre el VIH/SIDA, Bogotá: MPS.

40
||Description|Resp|onse wa|s justifed|N|
|---|---|---|---|---|---|
|||_L_|_H_|_H_-_L_||
|V0|Dinesh robs Mahesh’s house, after which Mahesh beats<br>Dinesh, reports him to the police, and Dinesh receives a<br>jail sentence.|0.90|0.93|0.03|61|
|V1|Dinesh digs a canal through Mahesh’s feld without<br>permission. Mahesh later argues with Dinesh in the<br>village and beats him up.|0.03|0.38|0.35*|59|
|V2|Dinesh lets his cattle graze on Mahesh’s felds without<br>permission. Mahesh later argues with Dinesh in the<br>village and beats him up.|0.20|0.43|0.23*|60|
|V3|Dinesh calls Mahesh names and roughs him up. They<br>later meet in public, argue, and Mahesh beats up<br>Dinesh.|0.10|0.33|0.23***|59|
|V4|Dinesh’s daughter marries Mahesh, even though Mahesh<br>is lower caste than the daughter. Dinesh later beats up<br>Mahesh.|0.22|0.70|0.48***|203|

- ** ***

- _p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001

Table 4: Proportion of respondents who agree that the response was justified in the punishment survey. Standard errors have been clustered by village. Of the 241 respondents, 38 were not able to answer for vignette V4, one was not able to answer for V1, and
0.046***<br>(0.005)|0.082***<br>(0.005)|0.057***<br>(0.003)|
|Teen pregnancy [14-19]|0.128|0.111|0.016**<br>(0.007)|0.021***<br>(0.007)|0.008*<br>(0.005)|

_Source_ : REDATAM INDEC Census Argentina 2010. _Notes_ : SE clustered at department level in parenthesis. Column 4 includes gender and age fixed effects and column 5 also includes department and urban fixed effects. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01.

are greater than the differences between Afro-Argentines and the rest of the population on average. While Afro-Argentines obtain, on average, statistically significant worse outcomes with respect to home ownership, being legally married and number of disabilities, they obtain statistically significant better outcomes regarding years of education, use of computer and being economically active.

Another benchmark for comparison is the differences between Natives and non-Natives in the US, see Tables A2 and A3 in the Appendix. These tables are constructed using the five-year sample (2010-2014) of the American Community Survey (see Ruggles et al., 2021). We chose outcomes that are somewhat comparable with the outcomes in Argentina, including home ownership, years of education, being economically active, unemployment, precarious housing, health insurance, number of disabilities and teen motherhood. Natives face a disadvantage in both countries for all outcomes except health insurance. For health insurance, Natives have a small advantage in the US thanks to the Indian Health Services, while Natives experience a disadvantage in Argentina. For the other seven outcomes in which Natives face a disadvantage in both countries, the disadvantage is larger in Argentina than in the US for three
# **6.5 Loan Repayment Rates**

A typical metric of loan non-performance used by MFIs is the “Portfolio at Risk 90 Days (PAR-90)” rate, i.e. the percentage of the portfolio that is at least 90 days overdue on scheduled repayment. Prior to the onset of COVID-19, the psychometric-appraised, uncollateralized WEDP-X loans showed a PAR-90 of around 2%. This figure is healthily low, and was similar to that achieved by regular, collateralized WEDP loans offered by the MFI. However, the psychometric loans disbursed in early 2020 performed worse than the psychometric loans disbursed prior to 2020, since loans disbursed in early 2020 were almost immediately hit by the consequences of the COVID-19 pandemic. The psychometric loans disbursed in early 2020 also performed worse than regular, collateralized loans disbursed early in 2020, although very few regular collateralized loans were being disbursed at that time. Most of the psychometric loans that fell into arrears during the COVID-19 pandemic had already been partially repaid. By September 2022, all WEDP-X loans had matured and Wasasa was no longer collecting on the initial WEDP-X portfolio. Total defaults amounted to just under 9% of total funds disbursed under WEDP-X. Out of the 62 borrowers, 54 fully repaid the WEDP-X loan, and 8 partially defaulted.

Appendix Table A8 shows that the psychometric score positively predicts repayment of WEDP-X loans within the treatment group as observed from Wasasa administrative data. — — Thus psychometric scoring has predictive power for its intended outcome repayment even among a sample that already passed a cut-off score as well as due diligence checks. We note that by design we cannot observe whether a score below versus above the cut-off predicts repayment of uncollateralized lending, since those scoring below the cut-off were not eligible for an uncollateralized loan. However, in Columns 6 and 8 of Appendix Table A1 we show that women who scored below 370 went on to have lower annual profits and monthly sales by endline compared to the impact evaluation control group who scored above 370 (and passed due diligence) but were randomly
Figure 39. EACR Decomposition of Debit Card Ownership between countries <!-- Start of picture text --> FINDEX 2011 Household (LITS)<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>-10 -10<br>-20 -20<br>Belarus United Arab Korea, Rep. Mongolia Belarus United Arab Korea, Rep. Mongolia<br>Emirates Emirates<br>Composition Equalization Scale<br><!-- End of picture text --> _Notes:._ FINDEX is an individual level data base. In FINDEX, 141 countries are sorted on Coverage in 2011 and 143 countries are sorted in 2014, and only nearest neighbors in coverage rates are shown. _Sources_ : FINDEX Figure 40. EACR Decomposition of Debit Card Ownership between countries <!-- Start of picture text --> Household (LITS)<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>Latvia Czech France Croatia<br>Republic<br>Composition Equalization Scale<br><!-- End of picture text --> _Notes:._ LITS is a household level data set. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. See Appendix for all countries.

_Sources_ : LITS 43
**Figure 2:  Domestic general government health expenditure (% of general government expenditure)**

<!-- Start of picture text --> %<br> 7.0<br>5.92<br> 6.0 5.54<br>5.43<br>5.15 5.12 5.09<br>4.85<br> 5.0 4.47<br> 4.0<br> 3.0<br> 2.0<br> 1.0<br> -<br>2016 2017 2018 2019 2020 2021* 2022* 2023*<br><!-- End of picture text --> Notes: The World Bank Development Indicators (WDI) and Ministry of Finance (MoF) (data from 2021, 2022, and 2022 are extracted from the MoF website) are used to draw the figure.

12
32 Together, these results combine to provide cautious optimism that poverty can be tracked in the absence of (comparable) consumption data by tracking poverty predictors. However, the results also clearly call for further validation of the parameter stability assumption in more settings, for shorter and longer time periods, and especially in settings of rapid poverty deterioration. In such endeavours, the additional explanatory power of more time variant variables, such as rainfall data in agricultural dependent settings, but also health variables and subjective poverty indicators deserves particular attention.
urban residential reference buildings using clustering analysis of satellite images." Energy and Buildings 169 (2018): 417-429.

Li, X., & Zhou, Y. (2017). A stepwise calibration of global DMSP/OLS stable nighttime light data (1992– 2013). Remote Sensing, 9(6), 637.

Ma, T., Zhou, C., Pei, T., Haynie, S., & Fan, J. (2012). Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities. Remote Sensing of Environment, 124, 99–107. http://dx.doi.org/10.1016/j.rse.2012.04.018 <mark>Mendoza Jr, C. B., Cayonte, D. D. D., Leabres, M. S., & Manaligod, L. R. A. (2019). Understanding multidimensional energy poverty in the Philippines. Energy Policy, 133, 110886.</mark> <mark>Meyer, S., Laurence, H., Bart, D., Middlemiss, L., & Maréchal, K. (2018). Capturing the multifaceted nature of energy poverty: Lessons from Belgium. Energy research & social science, 40, 273-283.</mark> <mark>Moore, R. (2009). A New Approach to Assessing Fuel Poverty. Energy Action, 108.</mark> <mark>Nguyen (2022). Energy poverty in Belgium: are you affected? Link:</mark> <u><mark>https://www.energyprice.be/blog/energy-poverty-in-belgium/</mark></u> Nussbaumer, P., Bazilian, M., & Modi, V. (2012). Measuring energy poverty: Focusing on what matters. Renewable and Sustainable Energy Reviews, 16
WPS3862

# **Bank Ownership Type and Banking Relationships**

# **Allen N. Berger**

Board of Governors of the Federal Reserve System, Washington, DC 20551 U.S.A. Wharton Financial Institutions Center, Philadelphia, PA 19104 U.S.A. aberger@frb.gov

# **Leora F. Klapper**

Development Research Group, World Bank, Washington, DC 20433 U.S.A. lklapper@worldbank.org **Maria Soledad Martinez Peria** Development Research Group, World Bank, Washington, DC 20433 U.S.A. mmartinezperia@worldbank.org **Rida Zaidi** Wolfson College, University of Cambridge, Cambridge CB2 1TN, UK. rz213@hermes.cam.ac.uk

# **Abstract**

We formulate and test hypotheses about the role of bank ownership types – foreign, state-owned, and private domestic banks – in banking relationships, using data from India. The empirical results are consistent with all of our hypotheses with regard to foreign banks. These banks tend to serve as the main bank for transparent firms, and firms with foreign main banks are most likely to have multiple banking relationships, have the most relationships, and diversify relationships across bank ownership types. The data are also consistent with the hypothesis that firms with state-owned main banks are relatively unlikely to diversify across bank ownership types. However, state-owned banks often do not provide the main relationship for firms they are mandated to serve (e.g., small, opaque firms), and the predictions of negative effects on multiple banking and number of relationships hold for only one type of state-owned bank.

JEL Classification Numbers: G21, G28, G34, F33 Keywords: Banks, Government Policies, International.

World Bank Policy Research Working Paper 3862, March 2006 _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
positive for a turnaround in 2010–11, as reflected in the study of aggregate state budgets by the RBI. It also appears that fiscal discipline at the state level may have acted as a source of comfort for the market. This is corroborated by the fact that after witnessing some stress during the initial period of the global financial crisis, most states reverted to the path of fiscal consolidation, with lower deficit ratios during 2010–11.

In sum, even though the immediate impact of the global financial crisis on state finances was somewhat subdued, this has implications going forward. The challenges are likely to emerge through the reduced impact of central transfers, given that the center‘s deficit has not shown signs of abatement. The overall current transfers to states are budgeted to decline by 0.4

> 47 Thirteenth Finance Commission, Chapter 9, p. 136.

30
inter-city prices differences are on average, 43 percent. These differences within South Sudan exceed those reported in the literature for inter-countries price gaps in Central and Western Africa (Brenton et al., 2015).

Second, price volatility has increased substantially since the imposition of the trade restrictions with Sudan. Groundnuts and sorghum, for example, show the most geographically widespread increase in price volatility when comparing the pre- and the post-border closure periods. These increases in price volatility tend to hurt disproportionately the poor, who cannot smooth purchasing decisions over time due to liquidity constraints.

Third, trade restrictions associated with the Sudan border closure in mid-2011 have increased market segmentation, particularly for cities in northern states. The border closure increased price differences in northern states by about 6 percent, on average. And this segmentation does not include that induced by other restrictions to trade within the country, chiefly the formal and informal tolls charged in checkpoints throughout the country to users of the road network.

Fourth, transportation costs explain up to 33 percent of the variation of food price differences across South Sudan. In particular increasing the portion of primary roads by 10 percentage points

> 3 See World Bank (2014) “Trade, Poverty, and Economic Diversification in South Sudan”, PREM Africa Region, World Bank, mimeo.

> 4 The ‘greenbelt’ comprises the states of Western Equatoria, Central Equatoria and portions of Eastern Equatoria, and it is so named because of the usual rainfall, beginning in March and ending in October. The soils there are generally productive, ranging from coarse-textured sands to loamy clays. See World Bank (2012) “Agricultural Potential, Rural Roads and Farm Competitiveness in South Sudan, Report No. 68399-SS.

> 5 The major towns with available data considered are Aweil, Bentiu, Bor, Kajok, Juba, Malakal, Rumbek, and Wau. 6 For Sorghum, for example, data for Malakal and Wau are available since 2006 and 2007 respectively. For Sesame, most data are available since 2010. For diesel
_on procurement market integration?_ Answering this question is challenging because we face an identification problem. As mentioned above, low levels of integration in government procurement could also be driven by bilateral natural frictions unrelated to the government’s home bias. In particular, factors like geography, information frictions, or lack of historical ties between regions and countries could prevent non-local firms from being competitive relative to local firms. Therefore, unless we account for the effects of these bilateral frictions, comparing procurement flows across regions and countries would be inconclusive about the government’s role in impeding procurement market integration.

**Novel strategy.** To overcome this challenge, we propose a new identification strategy that exploits variation in the geographical scope of the home bias. In particular, we hypothesize that the government’s home bias will coincide with the geographical scope at which a particular government operates. Under this hypothesis, regional governments will care about the regional border and will discriminate against all firms from other regions. In contrast, national governments will care about the national border and will discriminate against firms from other countries but will be indifferent relative to all national

To account for the above-mentioned natural frictions (as well as for firm-specific factors), our strategy relies on observing how subnational and national governments in a particular region buy a specific good from local and non-local firms. Intuitively, our approach exploits a double difference. First, we compare how much a specific firm sells a given product to national vs. subnational governments. Second, we look at how these relative purchases change for local vs. non-local<sup>5</sup> To implement this strategy we need very rich data on procurement contracts with information about the winning firm’s location as well as about the buying government agency. We leverage a detailed dataset of 1.2 million procurement contracts awarded in France and Spain between 2009 and 2019. In our dataset, we observe the winning establishment and the buying government agency, as well as their geographic location and contract characteristics
Figure 2: Secondary school density and teenage birthrate across Brazil, 2002 <!-- Start of picture text --> 14<br>13 Acre Roraima<br>12 Amapa<br>11 Para Amazonas Tocantins<br>Maranhao<br>10<br>Rondonia<br>9 Alagoas MatoGrosso<br>Piaui<br>8 Pernambuco MatoGrosso do Sul<br>7 Bahia Paraiba Parana Goias<br>Rio de Janeiro<br>6<br>Distrito Federal<br>Minas Gerais Santa Catarina<br>5 Sao Paulo<br>.1 .2 .3 .4<br>Number of secondary schools per 100 females 15-19<br>State weighted Best fit unweighted Best fit weighted<br>Births per 100 females 15-19<br><!-- End of picture text --> Notes: Data for 2002 cross-section. Marker size weighted by population of females aged 15–19. Broken line shows linear fit weighted by population size, solid line shows unweighted linear fit. Births are for mothers aged 15–19.

Source: Authors’ analysis based on school data from the 2002 wave of the Brazilian School Census; official population estimates from the Brazilian Census Bureau; births by age from Brazilian Vital Statistics.

27
Contributions to pension funds and premiums for direct insurance enjoy limited tax advantages through the application of a flat 20 percent tax instead of the higher corporate tax rate. The preferential tax, however, will only be applied up to a limit. Employers' contributions represent taxable income for the employee. Pension benefits of pension funds and direct insurance are taxed only on the portion that represents interest earned; lump-sum distributions from direct insurance are tax-free. Benefits from book reserve schemes and support funds, however, represent fully taxable income for the beneficiary.

Unfortunately, there is no reliable comprehensive information on the benefits provided by the different occupational pension plans. In a survey for the state of Bavaria, almost all companies with pension schemes offered defined benefit plans, some with fixed benefits and some earnings-related, while defined contribution plans were offered by only 2 percent of the companies. Among the companies with more than 1,000 employees about 12 percent had defined contribution plans.

German companies continue to prefer book reserves due to the tax advantages of this option and, more generally, the easy access to financing without having to provide collateral and savings in transaction costs compared to external financing. A further reason why book reserves are popular is the fact that there are no limits imposed on the level of benefit promises and the formation of corresponding book reserves. The combination of internal financing and pension commitments is seen as the major reason why supplementary pension provision has expanded in Germany despite the voluntary nature of second pillar.The experience with the book reserve schemes has so far been favorable and the PSV has proven to be an effective mechanism of protecting members' interests. For employers using book reserves, however, the risk of being able to finance the pension promises remains if reserve planning and investment is not conducted adequately .

# Public supplementary pensions

All public sector employees are insured in the supplementary pension institution of the federal and the state governments; in addition there are 22 municipal and 5 church institutions providing supplementary pensions. Participation is quasi-mandatory as it is based on employment
.

- **Setting up time‐limited programs** . It is important that the incentives are sustainable and reflect market conditions. Applying sunset provisions creates the need to regularly evaluate whether the incentive should be continued or not. Making incentives time‐limited also mitigates the risk that they will become entitlements for firms.

# **Country Example 4. Malaysia: Tax incentives aim to train women and encourage them to return to the workforce**<sup>5657</sup>

**Program:** In 2013, the Malaysian government began offering tax incentives to encourage employers to train women who have re‐entered the workforce after a career break (e.g. for childcare purposes). The incentive was available when a company hires a woman for a managerial role who has been out of the workforce for at least two years and provided her with a career development program designed by TalentCorp, a government partner responsible for addressing workforce talent needs in Malaysia. In conjunction, TalentCorp developed a database of female professionals intending to return to work,

> 52 “The Role of Incentives on Job Training.”

> 53 Duanjie, “Framework for Assessing Tax Incentives: A Cost Benefit Analysis Approach.”

> 54 “Options for Low Income Countries’ Effective and Efficient Use of Tax Incentives for Investment.”

> 55 M Andersen, B Kett, and E von Uexkull, “Corporate Tax Incentives and FDI in Developing Countries,” World Bank: Global Investment Competitiveness Report 2017/18 (Washington DC, 2017).

> 56 “PM Announces New Measures To Optimise On Malaysian Graduate And Women Talent,” May 29, 2012, https://www.talentcorp.com.my/media‐centre/press‐office/press‐releases‐and‐statement/pm‐announces‐new‐measures‐to‐ optimise‐on‐malaysian‐graduate‐and‐women‐talent.

> 57 Jamny Rosli, “Minister: One‐Year Tax Break for Women Rejoining