A methodological framework for exploring SME finance with SAFE data

Marie Finnegan; Lucía Morales

doi:10.1371/journal.pone.0307361

Peer Review History

Original SubmissionMarch 15, 2024
15 Apr 2024 Decision Letter - Daphne Nicolitsas, Editor PONE-D-24-10511A methodological framework for exploring SME finance access with SAFE dataPLOS ONE Dear Dr. Finnegan, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by May 30 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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If your research concerns only data provided within your submission, please write "All data are in the manuscript and/or supporting information files" as your Data Availability Statement. 4. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: No ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: No ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Dear Authors, The study titled "A methodological framework for exploring SME finance access with SAFE data." is an overall successful study. However, there are some shortcomings. I strongly recommend you to make the following revisions. Please write your own hypotheses and refer to the literature. Also express the hypotheses verbally. Write clearly how this study differs from the literature. Write the table names formally. You can add hypothesis tests as notes under the table. Write the limitations of the study and suggestions for future studies. There are frequent references to Finnegan and Kapoor in the study. Is this an extension study? If so, state this in the study, if not, state this clearly in the literature section and state that you have followed this study in other literature. This is a blind review, so it is seen as an extension since the identity of the authors is known and this study is also the authors. All the best, Reviewer #2: In my opinion, the paper offers a good contribution. So, I recommend accepting this paper, but after making the following modifications: 1- I think that the abstract needs improvement. 2- Some equations need correction. Moreover, in some equations, there are some symbols that are not defined. 3- Authors should test multicollinearity and outliers in the data before estimating the model. This point is very important for this study. Furthermore, adding references addressed the two problems in the LPM, logit, and probit models. 4- The authors should put the dataset used of this study in the “Supporting Information File". 5- At the conclusion of this work, limitations of this research should be mentioned. ========================================= ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. 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Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0307361.r001
Revision 1
3 Jun 2024 Author Response Response to Reviewers Dear Reviewers, Thank you very much for your insightful and valuable comments regarding this research paper. We have carefully addressed each comment and include a detailed response to each one below. Reviewer#1 Dear Reviewer #1, thank you very much for your insightful comments. The research paper has been subject to a comprehensive review to ensure that all your comments are addressed. Your suggestions and recommendations makes the paper more readable and stronger. We respond to each comment in detail below: Please write your own hypotheses and refer to the literature. Also express the hypotheses verbally. We have updated section 4.1 Empirical model to write the hypothesis verbally and refer to the literature as follows: For H1, γ^' is an interaction term and is the main coefficient of interest that captures if firms with increased debt-to-assets decreased their probability of being credit-constrained during UMP. It is expected that UMP for leveraged firms should make accessing bank finance easier via their improved balance sheets and collateral, and this should translate into a reduction in credit constraints (80, 81). A negative relationship is, therefore, expected between the probability that a firm is credit-constrained and the interaction between increased debt-to-assets ratio and UMP. For H2, the interaction between monetary policy and firm risk variables is the main coefficient of interest - γ^' - as it captures the probability of a risky firm being credit-constrained during periods of UMP. 〖FirmRisk〗_(i,c,t) is modelled categorically, and the measures for firm risk include a future predictor of risk - profit decreased in the previous six months, as well as a selection of subjective measures of risk - the firm's own view if there has been deterioration in credit history, own economic outlook and own capital in the previous six months and finally, an activity-based measure of risk - innovative activity given that such activity is more uncertain and therefore riskier. Given the low-interest rate environment generated by UMP, banks are expected to chase higher yields. This will manifest in lending to riskier firms via the monetary policy risk-taking channel (76, 82). A negative relationship is, therefore, expected between the probability that a firm is credit-constrained and the interaction between increased firm risk and UMP. Write clearly how this study differs from the literature. We added the following to the introduction This paper contributes to existing literature by introducing a methodological framework aimed at aiding researchers in determining the preferable categorical dependent model when utilising SAFE data. Write the table names formally. You can add hypothesis tests as notes under the table. We have rewritten all the table names formally. We have added a hypothesis test note under each table. For example: Table 5. The hypothesis being tested is that H1: MPt-2 x Debt to assets increased is significant and negative Write the limitations of the study and suggestions for future studies. We have added this section to the conclusion: Some limitations to this study require further research to keep building a solid framework on using SAFE data for policy making. This study employs robust standard errors but does not investigate the rationale for this standard error treatment. Other standard error treatments, such as clustering at the country level, may be more appropriate as these regressions combine the aggregate effect of monetary policy on micro units by merging aggregate data with micro-observations from SAFE (93). Further, this study does not employ firm fixed effects - in line with the majority of literature which uses SAFE - given the cross-sectional nature of the SAFE firm data. Using firm fixed effects would further isolate the impact of UMP on SME bank liquidity limitations by absorbing any firm-specific credit demand shocks. The appropriate use of standard errors and firm fixed effects is worthy of further investigation. There are frequent references to Finnegan and Kapoor in the study. Is this an extension study? If so, state this in the study, if not, state this clearly in the literature section and state that you have followed this study in other literature. This is a blind review, so it is seen as an extension since the identity of the authors is known and this study is also the authors. Yes, it is an extension study of Finnegan and Kapoor (2023) and this is now stated clearly in the introduction and methodology sections. The research study builds on the mentioned research paper as it provides a good framework for data analysis and testing of outlined hypotheses. Introduction: It is an extension study on the recent work by Finnegan and Kapoor (1), which compares their probit model to an LPM and logit model. Methodology: . In particular, it in an extension of the study by Finnegan and Kapoor (1) - which studies the impact of unconventional monetary policy on SMEs' access to bank finance - in terms of assessing the methodological framework when using binary dependent variables and SAFE Reviewer #2: In my opinion, the paper offers a good contribution. So, I recommend accepting this paper, but after making the following modifications: Dear Reviewer #2, thank you very much for your valuable comments which we have taken on board to strengthen the paper. We address each one in detail below. 1- I think that the abstract needs improvement. We have expanded the abstract to describe the main objective of the study and summarise the most important results and their significance. The revised abstract is as follows: Abstract Research on small and medium-sized enterprises (SMEs) access to bank finance is vital for the euro area economy. SMEs heavily represent the European business sector, employing around 100 million people and accounting for more than half of Gross Domestic Product. Research studies in the field often rely on the ECB/EC Survey on the Access to Finance of Enterprises (SAFE). Many studies employ probit or logit models with categorical dependent variables derived from SAFE. The research findings show that hardly any study employs the simpler linear probability model (LPM), with a dominant lack of research providing evidence that justifies the model selection process and suitability. However, it is well known that different econometrics models can lack consistency and frequently yield different results. Yet, the literature has no consensus on the best econometric approach. In addition, there is a lack of robustness tests in the literature to ensure model validity, underlining the need for a comprehensive review of the methodological framework that dominates SAFE data use. This paper addresses the identified research gap by introducing a robust methodological framework that helps researchers identify and choose an appropriate categorical model when using SAFE data. The study adds significant value to the extant literature by identifying four criteria that need to be considered when selecting the appropriate model among three common binary dependent models: LPM, probit and logit models. The findings show that the probit model was appropriate is all cases but that the LPM should not be disregarded, as it can be used in two cases: when considering the interaction between monetary policy and debt to assets and monetary policy and innovation. The use of the LPM is justified as a less complex econometric model, allowing for clearer communication of the results. This innovative, robust approach to choosing the appropriate econometric categorical dependent model when employing SAFE data contributes to support policy effectively. 2- Some equations need correction. Moreover, in some equations, there are some symbols that are not defined. Hypothesis 1 (H1): UMP decreases the probability of firms with increased debt-to-assets being credit constrained. To test H1, they model Equation 1 as follows: P(〖CreditConstrained〗_(i,c,t)=1│x)=α_i+β^' 〖MP〗_(c,t-2)+γ^' 〖DebttoAssets〗_(i,c,t)〖MP〗_(c,t-2)+δ^' X_(i,c,t)+θ^' 〖Macro〗_(c,t-2)+φ^' 〖BankCh〗_(c,t-2)+τ_(c,s)+ε_(i,s,c,t) Hypothesis 2 (H2): UMP reduces the probability of risky firms being credit constrained To test H2, they model Equation 2 as follows: P(〖CreditConstrained〗_(i,c,t)=1│x)=α_i+β^' 〖MP〗_(c,t-2)+γ^' 〖FrimRisk〗_(i,c,t)〖MP〗_(c,t-2)+δ^' X_(i,c,t)+θ^' 〖Macro〗_(c,t-2)+φ^' 〖BankCh〗_(c,t-2)+τ_(c,s)+ε_(i,s,c,t), We include table S1 to ensure that all symbols in the equation are defined. Variable Data Source Definition Dependent variable P(〖CreditConstrained〗_(i,c,t)=1│x) ECB/EC Survey on the access to finance of enterprises (SAFE) Q7a, Q7b Binary variable = 1 if the firm reported (i) to have applied for bank loans in the previous six months but was rejected (Credit Denied) or (ii) to have applied but received less than 75% of its demand (Rationed) or (iii) to have refused credit because it was offered at a too high cost (Refused due to high cost) or (iv) not to have applied because of possible rejection (Discouraged). 0 = if the firm reported (i) to have applied for bank loans in the previous six months and they received everything or (ii) they received 75 percent or more of their demand. Independent variable H1 H1 γ^' 〖DebttoAssets〗_(i,c,t)〖MP〗_(c,t-2) Debt to Assets increased ECB/EC SAFE Q2 = 1 if the firm’s debt to assets increased in the past 6 months, 0 if it remained unchanged or decreased MPc,t-2 = ECB BS Assets (million) ECB Statistical Data Warehouse Continuous variable, monthly, total ECB assets (after subtracting the autonomous factors that are beyond the direct control of the ECB including banknotes in circulation and government balances) from individual central bank balance sheet for stressed countries, following Peydro et al. (2021). Monthly data averaged over half years ending in March and September. Independent variable H2 H2 〖MP〗_(c,t-2)+γ^' 〖FirmRisk〗_(i,c,t) Where 〖FirmRisk〗_(i,c,t)= Profit decreased ECB/EC SAFE Q2 = 1 if the firm's profit decreased in the past 6 months, 0 if it remained unchanged or increased Credit history deteriorated ECB/EC SAFE Q11 = 1 if the firm's credit history deteriorated in the previous 6 months, 0 if it remained the unchanged or improved Own capital deteriorated ECB/EC SAFE Q11 = 1 if the firm's own capital deteriorated in the previous 6 months, 0 if it remained unchanged or improved Own outlook deteriorated ECB/EC SAFE Q11 = 1 if the firm's own outlook deteriorated in the previous 6 months, 0 if it remained unchanged or improved Innovation ECB/EC SAFE Q1 = 1 if the innovated (in terms of new or improved product, new or improved production process, new organisation of management, new way of selling goods or services) in the previous 6 months, 0 if did not X_(i,c,t) = Firm controls Micro ECB/EC SAFE QD1 = 1 if the firm has between 1 and 9 employees, 0 otherwise Micro ECB/EC SAFE QD1 = 1 if the firm has between 1 and 9 employees, 0 otherwise Small ECB/EC SAFE QD1 = 1 if the firm has between 10 and 49 employees, 0 otherwise Medium ECB/EC SAFE QD1 = 1 if the firm has between 50 and 249 employees, 0 otherwise More than 10 years ECB/EC SAFE QD5 = 1 if the firm is 10+ years old, 0 otherwise Between 5 and 10 yrs ECB/EC SAFE QD5 = 1 if the firm is between 5 and 10 years old, 0 otherwise Between 2 and 5 yrs ECB/EC SAFE QD5 = 1 if the firm is between 2 and 5 years old, 0 otherwise Less than 2 yrs ECB/EC SAFE QD5 = 1 if the firm is less than 2 years old, 0 otherwise Stand-alone firm ECB/EC SAFE QD2 = 1 if the firm is an autonomous profit-oriented enterprise, 0 otherwise Individual or family owned ECB/EC SAFE QD2 = 1 if the firm's owner is an individual or a family, 0 otherwise Turnover up to 2mn ECB/EC SAFE QD4 = 1 if the firm's annual turnover is less than €2 mln, 0 otherwise Turnover between 2 and 10mn ECB/EC SAFE QD4 = 1 if the firm's annual turnover is between €2 mln. and €10 mln, 0 otherwise Turnover between 10 and 50mn ECB/EC SAFE QD4 = 1 if the firm's annual turnover is between €10 mn. and €50 mn, 0 otherwise Industry ECB/EC SAFE QD3 = 1 if the firm's main activity is in industry, 0 otherwise Construction ECB/EC SAFE QD3 = 1 if the firm's main activity is in construction, 0 otherwise Wholesale or Retail Trade ECB/EC SAFE QD3 = 1 if the firm's main activity is in wholesale or retail trade, 0 otherwise Services ECB/EC SAFE QD3 = 1 if the firm's main activity is services, 0 otherwise 〖Macro〗_(c,t-2) = Macroeconomic controls Unemployment Eurostat Continuous variable, unemployment rate (share of active population), seasonally adjusted. The unemployment rate is the number of people unemployed expressed as a share of the labour force. The labour force is the total number of people employed and unemployed. Quarterly data averaged over half years ending in March and September and expressed as decimals. Inflation Eurostat Continuous variable, inflation rate measured by HICP monthly data (annual rate of change). Average of monthly inflation rate data over half years ending in March and September and expressed as decimals. 〖BankCh〗_(c,t-2)= Bank controls Non-performing loans IMF Financial Soundness Indicators Continuous variable, quarterly non-performing loans as a share of total gross loans averaged over half years ending in March and September and expressed as decimals. Regulatory capital to risk-weighted assets ratio IMF Financial Soundness Indicators Continuous variable, quarterly regulatory tier 1 capital as a share of risk weighted assets averaged over half years ending in March and September and expressed as decimals. OR We also clarify these definitions in the text of section 4.1 empirical model to define each symbol in the definition. See changes below: This dependent variable ‘credit constrained’ equals 1 if the firm reported to have (i) applied for bank loans in the previous six months but was rejected (Credit Denied) or (ii) applied but received less than 75% of its demand (Rationed) or (iii) refused credit because it was offered at too high a cost (Refused due to high cost) or (iv) not applied because of possible rejection (Discouraged). Alternatively, the variable equals 0 if the firm reported to have applied for bank loans in the previous six months and received everything or received 75% and above. Given that the indicator is equal to 1 if the firm is credit constrained, a negative coefficient indicates that SMEs are less likely to be credit constrained. MPt−2 is the one-year lag (equivalent to two survey waves in SAFE) of the logarithm the assets of individual central bank balance sheets - minus autonomous factors - for stressed countries. H1. Debt-to-assets increased is a categorical variable which equals 1 if the firm’s debt-to-assets increased, and 0 if it remained the same or decreased in the previous six months. H2. 〖FirmRisk〗_(i,c,t) is modelled categorically and the measures for firm risk include a future predictor of risk - profit decreased in the previous six months, as well as a selection of subjective measures of risk - firms own view if there has been deterioration in credit history, own economic outlook and own capital in the previous six months and finally, an activity-based measure of risk - innovative activity given that such activity is more uncertain and therefore riskier. 3- Authors should test multicollinearity and outliers in the data before estimating the model. This point is very important for this study. Furthermore, adding references addressed the two problems in the LPM, logit, and probit models. Thank you very much for this comment. We agree that this is very important for validity of results. We have therefore added a new section 5.3 on ‘multicollinearity and outliers’ to address this concern. We have also updated Tables 5-10 with measures of multicollinearity and results if outliers are excluded. New section below: 5.3 Multicollinearity and outliers In the SAFE literature, there is barely any discussion on checks for multicollinearity and no discussion of outliers. In terms of multicollinearity, Mac an Bhaird (25), Calabrese et al. (12) a Attachments Attachment Submitted filename: Response to reviewers.docx* https://doi.org/10.1371/journal.pone.0307361.r002
4 Jul 2024 Decision Letter - Daphne Nicolitsas, Editor A methodological framework for exploring SME finance with SAFE data PONE-D-24-10511R1 Dear Dr. Finnegan, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Daphne Nicolitsas Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The authors have revised the points I had highlighted as important in the previous review round. All comments are addressed. Thank you. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No ******** https://doi.org/10.1371/journal.pone.0307361.r003
Formally Accepted
10 Jul 2024 Acceptance Letter - Daphne Nicolitsas, Editor PONE-D-24-10511R1 PLOS ONE Dear Dr. Finnegan, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Daphne Nicolitsas Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0307361.r004

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