{"type":"script","doc_desc":{"producers":[{"name":"Reproducibility WBG","abbr":"DECDI","affiliation":"World Bank - Development Impact Department","role":"Verification and preparation of metadata"}],"prod_date":"2026-09-04","version":"1"},"project_desc":{"authoring_entity":[{"name":"Sandeep Singh","affiliation":"World Bank ","email":"ssingh13@ifc.org"},{"name":"Patrick Zeitinger","affiliation":"School of Social Sciences and Technology, Technical University of Munich","email":"patrick.zeitinger@tum.de"},{"name":"Robert Schulz","affiliation":"ProCredit Holding AG","email":"Robert.Schulz@procredit-group.com"},{"name":"Florian Egli","affiliation":"School of Management, Technical University of Munich","email":"florian.egli@tum.de"}],"title_statement":{"title":"Reproducibility package for Do Banks Overestimate Green Credit Risk? Evidence From SME Lending","idno":"RR_WLD_2026_716"},"data_statement":"All data is restricted and has not been included in the reproducibility package. For more details, please refer to the README file. ","software":[{"name":"Stata","version":"19.5 MP"}],"scripts":[{"title":"Reproducibility package for Do Banks Overestimate Green Credit Risk? Evidence From Sme Lending","date":"2026-09","notes":"Computational reproducibility verified by Development Impact (DECDI) Analytics team, World Bank.","instructions":"See README in reproducibility package.","file_name":"RR_WLD_2026_716","zip_package":"RR_WLD_2026_716.zip","dependencies":"Stata dependencies are listed in the ado folder."}],"repository_uri":[{"name":"Reproducible Research Repository (World Bank)","uri":"https:\/\/reproducibility.worldbank.org"}],"production_date":"2026-09-04","abstract":"Using a novel ten-year panel of over 43,000 small and medium-sized enterprises (SMEs) rated under the same risk model across 11 countries, we find that banks systematically overestimate the default risk of green SME borrowers. This credit risk misperception arises because credit models assign identical expected default probabilities to green and non-green borrowers, yet green SMEs exhibit a 13.7% lower default hazard than non-green peers at the same risk rating. Exploiting granular use-of-proceeds data absent from prior work, we show this effect is driven by energy-related investments: renewable energy (52%) and energy efficiency (16%) are associated with substantially lower default hazard, while other green investment categories show no significant effect. This pattern is consistent with a hedging mechanism against energy cost volatility and survives within-firm identification, matching on observables, and country-level subsamples. Incorporating green investment types into bank credit risk models would improve market efficiency, reduce the cost of capital for energy-related green SME borrowers, and thereby accelerate the decarbonization of the SME segment.","geographic_units":[{"name":"World","code":"WLD"}],"keywords":[{"name":"Green Loans"},{"name":"Credit Risk"},{"name":"Esg Risk"},{"name":"Smes"}],"topics":[{"id":"G21","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"Banks \u2022 Depository Institutions \u2022 Micro Finance Institutions \u2022 Mortgages","parent_id":"G2"},{"id":" Q56","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"Environment and Development \u2022 Environment and Trade \u2022 Sustainability \u2022 Environmental Accounts and Accounting \u2022 Environmental Equity \u2022 Population Growth","parent_id":"Q5"},{"id":" O16","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"Financial Markets \u2022 Saving and Capital Investment \u2022 Corporate Finance and Governance","parent_id":"O1"}],"output":[{"type":"Working Paper","description":"Policy Research Working Papers (PRWP)","title":"Do Banks Overestimate Green Credit Risk? Evidence From SME Lending"}],"language":[{"name":"English","code":"EN"}],"technology_requirements":"Runtime ~ 1 hour and 5 minutes","disclaimer":"The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development\/The World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.","license":[{"name":"MIT License","uri":"https:\/\/opensource.org\/license\/mit"},{"name":"World Bank IGO Rider","uri":"https:\/\/github.com\/worldbank\/metadata-editor\/blob\/main\/WB-IGO-RIDER.md"}],"contacts":[{"name":"Sandeep Singh","affiliation":"World Bank","email":"ssingh13@ifc.org"},{"name":"Reproducibility WBG","affiliation":"World Bank","email":"reproducibility@worldbank.org"}],"datasets":[{"name":"ProCredit Group SME Loan Portfolio Panel 2015-2024","note":"Data accessed continuously by the authors 2023-2026 under a data use agreement between Patrick Zeitinger and ProCredit Group. Proprietary internal credit database covering 783,188 contract-year observations; 155,357 unique SME clients; 12 countries (11 after Germany drop); 2015-2024. Format: Stata binary (.dta), approximately 1.8 GB. Key variables include: contract identifier, client identifier, calendar year, country (Bank variable), sector (ISIC), internal risk classification (RC_EOY, RC_initial), binary default indicators (default_nextyear, default_thisyear), green loan type indicators (EEtype, REtype, GRtype), outstanding principal (EUR), eco-classifier codes (ProCredit green taxonomy sub-subcategories), and financial statement ratios (CurrentRatio, RoAA, DebtEBITDA). Variable labels are embedded in the Stata file; a codebook is available from the authors upon request. Researchers interested in access should contact ProCredit Holding AG: Robert Schulz (robert.schulz@procredit-group.com) or the legal department (PCH.Legal@procredit-group.com). Access requires execution of a Data Use Agreement\/NDA and may take several weeks to arrange. Steps 1-4 of the data pipeline (how raw ProCredit database extracts were cleaned, merged, and aggregated to produce Loans_merged_2015.dta) are documented in Code\/00_data_documentation.do, which cannot be executed without direct access to the ProCredit database. Loans_merged_2015.dta serves as the reproducible starting point in the sense of the WB FAQ on starting points for reproducibility packages. File location: Data\/Loans_merged_2015.dta.","access_type":"Data access was granted directly to the study authors by the data owners\/managers. It was obtained with a custom data license that does not allow for redistribution and it is not included in the reproducibility package.","license":"Custom License","citation":"ProCredit Group. 2024. \"SME Loan Portfolio Panel 2015-2024\" [Proprietary dataset]. ProCredit Holding AG, Frankfurt am Main, Germany. Accessed by the authors under a data use agreement between Patrick Zeitinger and ProCredit Group, 2023-2026."}],"reproduction_instructions":"To reproduce the findings in this paper, a replicator must:\n1. Secure Access to Data: Access the dataset \"Loans_merged_2015.dta\" not included in the package. See the Datasets section for more details\n2. Open the do file `main`, update the directory on line 23, install the required directories and run the script\n\nSince all data is restricted, the package includes the outputs produced by the replicators, which can be used to review the results presented in the paper","technology_environment":"Paper exhibits were reproduced on a computer with the following specifications:\n\u2022 OS: Windows 11 Enterprise\n\u2022 Processor: Intel(R) Xeon(R) Gold 5218 CPU @ 2.30GHz (2.30 GHz) (2 processors)\n\u2022 Memory available: 16 GB"},"datacite":{"creators":[{"givenName":"Sandeep","familyName":"Singh","nameType":"Personal","affiliation":[{"name":"World Bank Group","affiliationIdentifier":"https:\/\/ror.org\/02md09461","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Patrick","familyName":"Zeitinger","nameType":"Personal","affiliation":[{"name":"School of Social Sciences and Technology, Technical University of Munich, Munich, Germany","affiliationIdentifier":"https:\/\/ror.org\/02kkvpp62","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Robert","familyName":"Schulz","nameType":"Personal","affiliation":[{"name":"ProCredit Holding AG","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Florian","familyName":"Egli","nameType":"Personal","affiliation":[{"name":"School of Management, Technical University of Munich, Munich, Germany","affiliationIdentifier":"https:\/\/ror.org\/02kkvpp62","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]}],"titles":[{"lang":"en","title":"Reproducibility package for Do Banks Overestimate Green Credit Risk? Evidence From Sme Lending"},{"title":"RR_WLD_2026_716","titleType":"Other"}],"publisher":"World Bank","publicationYear":"2026","types":{"resourceType":"Reproducibility package","resourceTypeGeneral":"Other"},"url":"https:\/\/reproducibility.worldbank.org\/index.php\/catalog\/study\/RR_WLD_2026_716","language":"en"},"tags":[{"tag":"DOI"},{"tag":"Open Code"},{"tag":"Restricted Data"}],"schematype":"script"}