rafmacalaba/datause-encoder-data huggingface dataset:
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Policy Research Working Paper 9895

# **Abstract**

Partial credit guarantees have been a central tool in countries’ strategies to support firms through the COVID-19 pandemic. This paper presents the results of a set of surveys covering 19 partial credit guarantees in 12 countries in the Latin America region, which were repeated at different stages of the pandemic crisis. The paper finds that most partial credit guarantees in the region are not large enough to foster change in the way the local financial sectors conduct business, how they engage with small and medium-size enterprises, and how they assimilate climate and gender considerations. Moreover, the response to the pandemic shows that most partial credit guarantees in the region have made limited use of the toolkit of parametric changes for boosting participation among financial institutions. Finally, the paper stresses the need for fair pricing methodologies and risk-based capital adequacy that may ensure the longterm sustainability of partial credit guarantee schemes.

This paper is a product of the Finance, Competitiveness and Innovation 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 hrudolph@worldbank.org, fdiazkalan@worldbank.org, and fmiguelliriano@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
**Table 3: Impact on Outcomes for the Migrant**

||Currently<br>Employed|Changed<br>Occupation<br>in last 5 years|Change<br>in occupation <br>score|New<br> occupation <br>score|Old<br> occupation<br>score|Has<br>Tertiary <br>Education|Currently<br> Studying|Subjective<br>Welfare<br>Ladder|Social<br>Respect<br>Ladder|Mental<br>Health<br>MHI‐5|Health<br>disruption<br>in 6 months|Currently<br>Smokes|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Panel A: ITT Im**|**pact**||||||||||||
|Ballot Winner|0.217***|0.093*|17.100***|11.935**|‐5.164|0.146**|0.078**|‐0.011|‐0.035|0.687***|‐0.005|0.027|
||(0.070)|(0.052)|(5.486)|(4.604)|(3.541)|(0.066)|(0.031)|(0.084)|(0.115)|(0.259)|(0.031)|(0.064)|
|**Panel B: LATE I**|**mpact**||||||||||||
|Migrate|0.299***|0.128*|21.908***|15.292***|‐6.616|0.201**|0.103***|‐0.016|‐0.048|0.871**|‐0.007|0.037|
||(0.085)|(0.066)|(6.655)|(5.293)|(4.687)|(0.091)|(0.038)|(0.115)|(0.159)|(0.371)|(0.042)|(0.088)|
|Sample Size|385|385|70|70|70|385|282|385|384|279|384|385|
|Control Mean|0.659|0.121|‐16.952|23.143|40.095|0.181|0.000|5.852|6.352|17.475|0.082|0.440|

Notes: Robust standard errors in parentheses, *, **, *** indicate significance at the 10, 5, and 1 percent levels respectively. All regressions include controls for age, gender, island of birth, ballot years entered, and survey form (long or short). Regressions weighted for population proportions of compliers and non‐compliers. New and old occupation scores are only available for those who changed occupation in the last 5 years.
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Industry Survey ( _Encuesta Industrial Annual_ —EIA) surveys maintained by INEGI to create the non-experimental panel dataset. The ENESTYC periodically surveys manufacturing firms with sample sizes of between 5,000 and 8,000 establishments, fielded in 1995, 1999, 2001 and 2005. In 2001 and 2005, the ENESTYC included a module of questions on participation in major government SME support programs. In 2005, INEGI also fielded a Micro-ENESTYC survey with greatly expanded coverage of micro and small enterprises. The EIA is the annual manufacturing survey fielded by INEGI, and a linked panel of establishments can be created over the 1992-2006 period from the annual surveys. Our strategy was to link the 2001 and 2005 ENESTYC to the 1994-2005 EIA to exploit the availability of annual panel data in that survey.

The ENESTYC uses the same sampling frame as the annual EIA, that is a stratified random sample by 54 sectors and four size categories—micro with 1-15 employees, small with 16100, medium with 101-250 and large with over 250 employees. The sample size of each ENESTYC survey is fairly large, varying from 5,000 establishments in 1995 to 7,500 establishments in the 2005 ENESTYC. However, because ENESTYC was never designed to be a panel survey and smaller firms in each survey were randomly sampled, only a small proportion of SMEs can be tracked over time. Few micro enterprises can be linked over time in the ENESTYC, but sample sizes for the panel of small and medium enterprises should be adequate for the proposed analyses (Table 14).

**Table 14 Number of Panel Firms by Size and ENESTYC Years**

|Si|1995 and|1999 and|2001 and|
|---|---|---|---|
|ze|2001|2001|2005|
|Micro (1 – 15)|2|54|10|
|Small (16 – 100)|161|335|192|
|Medium (101 – 250)|723|1,273|1,192|
|Large (251 - +)|954|1,404|1,244|
|Total|1,840|3,066|2,638|

_Source_ : Research team estimates from ENESTYC surveys.

The 2001 ENESTYC surveys included, for the first time, questions about familiarity with and participation in a list of major government-sponsored SME programs, including date of participation, duration, and type of services used. This module was repeated in the 2005 ENESTYC survey. In both surveys, firms could indicate participation in one or more programs. The 2005 ENESTYC dropped several SME programs that had since ceased operation, and included a number of other SME initiatives introduced since 2001.

The authors worked with INEGI on linking establishments from the 2001 and 2005 ENESTYC with earlier ENESTYC surveys, and with the panel EIA covering the 1992-2006 period. INEGI linked the EIA and the 2001 and 2005 ENESTYC that generated a panel EIA dataset for the sample of enterprises that report information on program participation in the 17
|**Table A29: Reporters Without B**<br> <br>|**orders Pr**|**ess F**|**reedo**|**m Ind**|**ex (R**|**SF)**||||
|---|---|---|---|---|---|---|---|---|---|
|**Data Provider**<br>Reporters Without Borders<br> <br>||||||||||
|**Description**<br>International nongovernmental<br>|organizatio<br>|n head<br>|quarte<br>|red in|Paris,|Franc|e, dev|oted t|o the|
|protection of reporters and resp<br>**Website**<br>www.rsf.org|ect of press|freedo|m.|||||||
|**Data Source**<br>Worldwide Press Freedom Inde|x|||||||||
|**Type**<br>Expert assessment||||||||||
|**Respondents**<br>Reporters, researchers, legal ex|perts and pr|ess fre|edom a|dvoca|tes in|assesse|d cou|ntries||
|**Frequency**<br>Annual since 2002||||||||||
|**Coverage**<br>Global sample of countries||||||||||
|**Public Access**<br>Yes||||||||||
|**Description**<br>The press freedom index is base<br>restrictions on reporters and the|d on a 50-q<br>media|uestio|n chec|klist o|n the i|nciden|ce and|severi|ty of|
||2007|2006|2005|2004|2003|2002|2000|1998|1996|
|**Voice and Accountability**||||||||||
|Press Freedom Index|X|X|X|X|X|X|..|..|..|
|**Political Stability and Absence of Violence**<br>NA|..|..|..|..|..|..|..|..|..|
|**Government Effectiveness**<br>NA|..|..|..|..|..|..|..|..|..|
|**Regulatory Quality**<br>NA|..|..|..|..|..|..|..|..|..|
|**Rule of Law**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Control of Corruption**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Country Coverage**|**166**|**166**|**165**|**165**|**164**|**138**|**..**|**..**|**..**|
|**Year of Publication**|**2007**|**2006**|**2005**|**2004**|**2003**|**2002**|**..**|**..**|**..**|

67
Bliss, M.A., F.A. Gul. 2012. “Political Connection and Cost of Debt: Some Malaysian Evidence.” _Journal of Banking and Finance_ 36(5), 1520-1527.

- Bond, S., Elston, J. A., Mairesse, J. and Mulkay, B. 2003. “Financial Factors and Investment in Belgium, France, Germany, and the United Kingdom: A Comparison Using Company Panel Data,” _Review of Economics and Statistics_ , 85(1), 153-165.

- Boubakri, N., JC Cossett, W Saffar. 2008. “Political Connection of Newly privatized Firms,” _Journal of Corporate Finance_ 14(5), 654-673.

- Boubakri, N., O. Guedhami, D. Mishra, W. Saffar. 2012. “Political Connections and the Cost of  Equity Capital,” _Journal of Corporate Finance_ 18(3), 541-549.

- Boyreau-Debray, Genevieve, 2003. “Financial Intermediation and Growth: Chinese Style,” World Bank Policy Research Working Paper 3027.

- Boyreau-Debray, Genevieve, and Sheng-Jin Wei, 2005. “Pitfalls of a State-dominated Financial System: The Case of China,” NBER working paper 11214.

- Bruhn, Miriam, Dean Karlan, and Antoinette Schoar. 2010. “What Capital is Missing in Developing Countries?” _American Economic Review: Papers & Proceedings,_ 100(2): 629–633.

- Cai, Hongbin, Hanming Fang, Lixin Colin Xu. 2011. “Eat, Drink, Firms, Government: An Investigation of Corruption from Entertainment and Travel Costs of Chinese Firms.” _Journal of Law and Economics_ 54, 55-78.

- Calomiris, C.W., R. Fisman, Y.X. Wang. 2010. “Profiting from Government Stakes in a Command Economy: Evidence from Chinese Asset Sales.” _Journal of Financial Economics_ 96(3), 399-412.

- Chan, K.S., V.Q.T. Dang, and I.K.M. Yan. 2012. “Chinese Firms’ Political Connection, Ownership, and Financing Constraints,” _Economics Letters_ 115(2), 164-167.

- Chaney, Paul K., Mara Faccio, and David C. Parsley. 2011. "The quality of accounting information in politically connected firms", Journal of Accounting and Economics, February 2011, vol. 51(1-2), 58-76.

- Chen M. 2008. “Financial Constraints to the Investment of Chinese Listed Firms Across Firm Characteristics,” _The University of Nottingham Discussion Paper._

- Chow, C. K. and Fung, M. K. Y. 1998. “Ownership Structure, Lending Bias, and Liquidity Constraints: Evidence from Shanghai’s Manufacturing Sector,” _Journal of Comparative Economics_ , 26: 301316.

- Chow, C.K. and Fung, M. K. Y. 2000. “Small businesses and liquidity constraints in financing business investment: evidence from Shanghai’s manufacturing sector.” _Journal of Business Venturing_ , 15, 363-383.

- Claessens, S., E. Feijen, L. Laeven. 2008. “Political Connections and Preferential Access to Finance: The Role of Campaign Contribution,” _Journal of Financial Economics_ 88(3), 554-580.

- Cleary, S. 1999. “The Relationship between Firm Investment and Financial Status,” _Journal of Finance,_ 54, 673-692

- Cooper, M.J., H. Gulen, A.V. Ovtchinnikov. 2010. “Corporate Political Contributions and Stock Returns.” _Journal of Finance_ 65(2), 687-724.

38
Finally, two additional attribute variables of individual countries are used. In particular, the number of PCT patents per country, for five-year annually repeated timewindows. This variable controls for the size of country innovation systems, which clearly determines a country’s capacity to collaborate with foreigners, as well as its capacity to attract inventors from abroad or send them to other locations. In addition, it includes GDP per capita, from the World Development Indicators (World Bank), expressed in US$ 2005 at PPP, in order to capture the market potential of countries as well as their capacity to innovate. Table 1 contains summary statistics of the variables included in the models, and appendix S6 the correlation matrix.

TABLE 1. Summary Statistics

||Observations|Mean|St. Dev|Min.|Max.|
|---|---|---|---|---|---|
|Collab. inv_i- inv_j|26,160|1.26|10.55|0|678|
|Collab. app_i- inv_j|26,160|1.73|14.87|0|708|
|Diaspora size|26,160|22.29|447.18|0|26,661|
|Distance|26,160|6,889.27|4,539.38|59.62|19,629.50|
|Contiguity|26,160|0.01|0.10|0|1|
|Common language|26,160|0.09|0.29|0|1|
|Lang. similarity|26,160|0.16|0.18|0|0.89|
|Colonial links|26,160|0.04|0.18|0|1|
|Religion similarity|26,160|0.13|0.19|0|0.90|
|EXP+IMP|26,160|0.01|0.03|0|0.41|
|Tech.distance|26,160|0.48|0.27|0.02|1|
|# patents_i|26,160|816.86|3,501.12|0|64,990|
|# patents_j|26,160|43,314.25|99,659.75|52|692,364|
|GDP p.c._i|26,160|9,258.23|9,566.77|432.05|74,113.90|
|GDPp.c._j|26,160|29,423.23|6,171.54|11,382.60|48,799.70|

Source: Authors’ analysis based on data described in the text. Notes: “_i” and “_j” stand for, respectively, migrant inventor’s sending country and migrant inventor’s receiving country. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia, and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia, and Slovenia only from 1992; and data for the Czech Republic, Slovakia and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800.

Note that all time-variant explanatory variables are lagged one period in order to lessen potential biases caused by system feedbacks. Notwithstanding this common practice, other sources of endogeneity and biased estimates are likely to arise. Hence, I discuss alternative solutions in the results section.

20
|Lao PDR MobilizingEthnic Communities for Improved Livelihoods anSatoshi Ishihara|
|---|
|Lao PDR MobilizingEthnic Communities for Improved Livelihoods anSatoshi Ishihara|
|Southeast Europe and Caucasus Catastrophe Risk Insurance FacilityEugene N. Gurenko|
|Southeast Europe and Caucasus Catastrophe Risk Insurance FacilityEugene N. Gurenko|
|Southeast Europe and Caucasus Catastrophe Risk Insurance FacilityEugene N. Gurenko|
|Southeast Europe and Caucasus Catastrophe Risk Insurance FacilityEugene N. Gurenko|
|Southeast Europe and Caucasus Catastrophe Risk Insurance FacilityEugene N. Gurenko|
|GFDRR-GHI Partnershipfor School and Hospital Earthquake SafetyinDechen Tshering|
|GFDRR-GHI Partnershipfor School and Hospital Earthquake SafetyinDechen Tshering|
|GFDRR-GHI Partnershipfor School and Hospital Earthquake SafetyinDechen Tshering|
|Productive Social SafetyNet Project<br>Claudia Zambra Taibo|
|Productive Social SafetyNet Project<br>Claudia Zambra Taibo|
|Productive Social SafetyNet Project<br>Claudia Zambra Taibo|
|Productive Social SafetyNet Project<br>Claudia Zambra Taibo|
|Productive Social SafetyNet Project<br>Claudia Zambra Taibo|
|Productive Social SafetyNet Project<br>Claudia Zambra Taibo|
|Productive Social SafetyNet Project<br>Claudia Zambra Taibo|
|Productive Social SafetyNet Project<br>Claudia Zambra Taibo|
|Productive Social SafetyNet Project<br>Claudia Zambra Taibo|
|Productive Social SafetyNet Project<br>Claudia Zambra Taibo|
|Productive Social SafetyNet Project<br>Claudia Zambra Taibo|
|Productive Social SafetyNet Project<br>Claudia Zambra Taibo|
|Productive Social SafetyNet Project<br>Claudia Zambra Taibo|
|Productive Social SafetyNet Project<br>Claudia Zambra Taibo|
|Productive Social SafetyNet Project<br>Claudia Zambra Taibo|
|Integrated Disaster and Land Management Project<br>Koffi Hounkpe|
|Integrated Disaster and Land Management Project<br>Koffi Hounkpe|
|Integrated Disaster and Land Management Project<br>Koffi Hounkpe|
|Integrated Disaster and Land Management Project<br>Koffi Hounkpe|
|Integrated Disaster and Land Management Project<br>Koffi Hounkpe|
|Integrated Disaster and Land Management Project<br>Koffi Hounkpe|
|Integrated Disaster and Land Management Project<br>Koffi Hounkpe|
|Integrated Disaster and Land Management Project<br>Koffi Hounkpe|
|Integrated Disaster and Land Management Project<br>Koffi Hounkpe|
|Integrated Disaster and Land Management Project<br>Koffi Hounkpe|
|RUSSIA FOREST FIRE RESPONSE PROJECT<br>Gayane Minasyan|
|RUSSIA FOREST FIRE RESPONSE PROJECT<br>Gayane Minasyan|
|RUSSIA FOREST FIRE RESPONSE PROJECT<br>Gayane Minasyan|
|RUSSIA FOREST FIRE RESPONSE PROJECT<br>Gayane Minasyan|
|RUSSIA FOREST FIRE RESPONSE PROJECT<br>Gayane Minasyan|
|RUSSIA FOREST FIRE RESPONSE PROJECT<br>Gayane Minasyan|
|RUSSIA FOREST FIRE RESPONSE PROJECT<br>Gayane Minasyan|
|RUSSIA FOREST FIRE RESPONSE PROJECT<br>Gayane Minasyan|
|RUSSIA FOREST FIRE RESPONSE PROJECT<br>Gayane Minasyan|
|RUSSIA FOREST FIRE RESPONSE PROJECT<br>Gayane Minasyan|
|RUSSIA FOREST FIRE RESPONSE PROJECT<br>Gayane Minasyan|
|RUSSIA FOREST FIRE RESPONSE PROJECT<br>Gayane Minasyan|
ages ranging from 19 to 61. Education levels were average for the low-income population, ranging from primary to undergraduate education. Credit auditors made 153 visits (total 153 auditor-branch pairs since no multiple visits were needed to complete a loan application process). Savings auditors carried out a total of 156 visits.

Credit and savings visits took place between the months of March and July 2015. In a given location, a branch was always visited by more than one auditor. After each visit the auditor was required to complete a questionnaire.

Immediately after recruitment, auditors were randomized into savings and credit scripts, stratifying by gender. Auditors memorized the script and used a uniform language when asking about the products. Appendix Table 1 documents the contract terms about which auditors had to ask if the staff did not disclose the information voluntarily. During the training, the auditors and field manager were never told about the purpose of the study nor the specific hypotheses we wanted to test.

The scripts, also described in Giné and Mazer (2017) varied along three dimensions: financial sophistication or literacy, the degree of perceived competition and the dress code used in the visit.

Appendix Table 2 shows that for the 5 auditor characteristics we collected, there is balance in 9 out of 10 comparisons. In particular, credit auditors assigned high experience profiles are marginally more likely to be older. We conclude therefore that the profiles are assigned randomly and that the samples of auditors in each script are balanced.

# **5. Data**

We use data from 4 different sources. First, the questionnaire filled by auditors after each visit which covers various aspects of the visit, including its length, the products offered and their features. Second, the printed materials that the staff handed to the auditor, consisting of either brochures about the product or, less frequently, personalized amortization tables when credit was requested. The third data source comes from a survey of a sample of credit officers that serviced the auditors. The survey was conducted after the audit study was completed and asked about basic socio-economic characteristics, financial literacy, the marketing of financial products and bonus schemes offered by their employers. The fourth and final data source is what we refer to as the market products dataset containing the terms of all the savings and credit products offered by the institutions in the study at the time of the visits. This dataset was assembled using various sources including the web, market surveys and conversations with the regulatory authorities and staff of the financial institutions. The dataset only includes credit products with loan sizes in the range requested by the auditors, and savings products that can be contracted with the initial deposit amount mentioned. The construction of this dataset proved challenging for two reasons. First, the authorities in charge of collecting market information only require the average of certain terms for the institutions they supervise. As a result, they did not have detailed data on all the terms for all credit 9