Determinants of Access to Bank Financing in SMEs in Mexico
Abstract
:1. Introduction
2. Literature Review
2.1. Theoretical Framework of Business Financing
2.1.1. Pecking-Order Theory
2.1.2. Financial Life-Cycle Theory
2.2. Variables of Bank Financing of SMEs
2.2.1. Economic Sector
2.2.2. Size of the Enterprise
2.2.3. Age of the Enterprise
2.2.4. Foreign Participation and Export Capacity
2.2.5. Legal Status
2.2.6. Entrepreneur Attributes
2.3. Empirical Studies on the Determinants of Bank Financing
2.4. Research Hypothesis
- H1. Economic sector. Enterprises in the manufacturing sector are more likely to access bank loans than those in other sectors. In contrast, those in the service sectors are less likely to have access to bank financing than those in other sectors.
- H2. Enterprise size. The larger the enterprise, the greater the access to bank financing. Small enterprises are less likely to access bank loans.
- H3. Enterprise age. The older the enterprises, the greater the access to bank financing. Older enterprises are more likely to access bank loans.
- H4. Foreign participation. Foreign-owned enterprises are less likely to use bank credit.
- H5. Legal status. Enterprises legally incorporated as societies or associations are more likely to access bank loans than enterprises that have a sole owner.
- H6. Exporter. Exporting enterprises are more likely to use bank loans.
- H7. Checking/saving account. Enterprises that have a checking or savings account are more likely to access bank financing.
- H8. Annual sales. The higher the annual sales of the enterprises, the greater the access to bank credits.
- H9. Permanent employees. The more full-time permanent employees an enterprise has, the greater the probability of accessing bank financing.
- H10. Manager female. Enterprises in which the general manager is a woman are less likely to access bank loans.
- H11. Manager experience. Enterprises in which the general manager has more experience are more likely to use bank financing.
3. Data and Methodology
3.1. Sample and Data
3.2. Variables
3.3. Research Design
4. Empirical Results
4.1. Characterization of the Sample and Descriptive Statistics
4.2. Correlations
4.3. Multivariate Analysis: Probit Regression Model
4.4. Profiles Analysis
5. Discussion and Conclusions
5.1. Contributions of the Research
- H1. Economic sector. It is confirmed that enterprises in the manufacturing sector are more likely to access bank loans than enterprises in other economic sectors, while those in the service sector are the least likely to have access to bank financing. In the same way, it is confirmed that the commerce variable does not have a significant relationship with the dependent variable, as had been proposed in the hypothesis.
- H2. Enterprise size. It was confirmed that small enterprises are less likely to access bank loans. Regarding the large variable, the hypothesis that the larger the companies, the greater the access to bank financing is rejected. On the other hand, it is confirmed that the medium enterprise variable does not have a significant relationship with the dependent variable.
- H3. Enterprise age. It is confirmed that older enterprises are more likely to access bank financing. The age of the enterprise is the most robust predictor of bank credit; according to the profile analysis, as the age of the enterprise increases, the probability of acquiring a bank loan increases progressively.
- H4. Foreign participation. It confirms that foreign-owned enterprises are less likely to obtain bank financing because foreign parent enterprises are the main source of financing for their subsidiaries.
- H5. Legal status. This hypothesis is partially confirmed. On the one hand, it is rejected that enterprises legally constituted as societies or associations are more likely to use bank financing than enterprises that have a sole owner, because it did not result significantly in any of the regression models in which this variable was included. On the other hand, it is confirmed that the single-owner variable does not have a significant influence on access to bank credit.
- H6. Exporter. The hypothesis that there is a positive relationship between exporting enterprises and the probability of using bank loans is rejected, as it was only 90% significant in models M2 and M8; however, in all other models, it was not significant.
- H7. Checking/saving account. It is confirmed that enterprises that have a checking or savings account are more likely to acquire bank loans than enterprises that do not have such an account.
- H8. Annual sales. It is confirmed that enterprises with higher annual sales are more likely to use bank financing.
- Hypothesis H9, H10, and H11. The hypotheses H9, H10, and H11 are rejected; the regression models indicate that there is no significant relationship between the independent variables (permanent employees, female manager, and the experience of the general manager) with the dependent variable, so they are not considered determinants of the access to bank financing.
5.2. Research Recommendations and Limitations
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Author, Year | Sample | Variables of Interest Related to the Study | Methodology | Main Results | ||
---|---|---|---|---|---|---|
Country | Size | Period | ||||
(Michaelas et al. 1999) | United Kingdom | 3500 SMEs | 1986–1995 |
| Panel data analysis |
|
(Beck et al. 2005) | 54 countries | 4000 small, medium, and large enterprises | 1995–1999 |
| Estimated regression |
|
(Gregory et al. 2005) | USA | 4637 SMEs | 1994–1995 |
| Multinomial logit model |
|
(Beck et al. 2006) | 80 countries | 10,000 small, medium, and large enterprises | 1995–1999 |
| Probit model |
|
(López-Gracia and Sogorb-Mira 2008) | Spain | 3569 SMEs | 1995–2004 |
| Panel data analysis |
|
(Gómez et al. 2009) | Mexico | 128 SMEs | 2007–2008 |
| Logistic regression by the Wald method |
|
(Pasquini and De Giovanni 2010) | Argentina | 5536 SMEs | 2009 |
| Heckmann’s correction |
|
(Cowling et al. 2012) | United Kingdom | 9362 SMEs | 2007–2008 |
| Multivariate regression |
|
(Botello 2015) | Colombia | 85,000 SMEs | 2006–2010 |
| Logit modelProbit model |
|
(Xiang et al. 2015) | Australia | 2732 SMEs | 2005–2007 |
| Panel data analysis |
|
(Briozzo et al. 2016a) | Argentina | 222 SMEs | 2006 and 2010 |
| Multinomial logit model |
|
(Cowling et al. 2016) | United Kingdom | More than 30,000 SMEs | 2011–2013 |
| Probit model |
|
(Yazdanfar and Öhman 2016) | Sweden | 15,952 SMEs | 2009–2012 |
| ANOVA multivariate regressions |
|
(Andrieu et al. 2018) | 12 European countries | 72,849 SMEs | 2009–2014 |
| Binary probit model |
|
(Rao et al. 2018) | India | 174 SMEs | 2006–2013 |
| Generalized method of moments |
|
(Nizaeva and Coskun 2019) | 6 countries of Southeast Europe | 1520 SMEs | 2012–2016 |
| Ordered probit Feasible generalized least squares |
|
(Chaudhuri et al. 2020) | India | 1,155,877 MSMEs | 2006–2007 |
| Bivariate probit model |
|
Item | Variables | Concept Definition | Operational Definition |
---|---|---|---|
Dependent variable | |||
k8 | Bank credit | Indicates whether the enterprise has a bank loan from any financial institution. | Binary variable, where 1 indicates whether the enterprise has a bank loan and 0 otherwise. |
Economic sector | |||
a4a | Manufacturing | Includes enterprises that carry out their main activity in the manufacturing sector according to the classification of item a4a. | It was recoded to a dummy variable, where 1 indicates that the enterprise DOES belong to this economic sector and 0 that it does NOT belong to this sector. |
a4a | Commerce | Includes enterprises that carry out their main activity in the commerce sector according to the classification of item a4a. | It was recoded to a dummy variable, where 1 indicates that the enterprise DOES belong to this economic sector and 0 that it does NOT belong to this sector. |
a4a | Services | Includes enterprises that carry out their main activity in the service sector according to the classification of item a4a. | It was recoded to a dummy variable, where 1 indicates that the enterprise DOES belong to this economic sector and 0 that it does NOT belong to this sector. |
Enterprise size | |||
a6a | Small | A small enterprise is considered if it has between 5 and 19 employees. | It was recoded to a dummy variable, where 1 indicates that the enterprise DOES correspond to this size and 0 that it does NOT correspond. |
a6a | Medium | A medium enterprise is considered if it has between 20 and 99 employees. | It was recoded to a dummy variable, where 1 indicates that the enterprise DOES correspond to this size and 0 that it does NOT correspond. |
a6a | Large | A large enterprise is considered if it has 100 or more employees. | It was recoded to a dummy variable, where 1 indicates that the enterprise DOES correspond to this size and 0 that it does NOT correspond. |
Characteristics inherent to the enterprise | |||
b5 | Age | It represents the number of years between the start of operations of the enterprise and the year in which the survey was applied. | Quantitative variable. Logarithm of the enterprise age in number of years. |
b2b | Foreign participation | It means that private foreign persons, enterprises, or organizations have an ownership interest in the enterprise. | It was recoded to a dummy variable, where 1 indicates that the enterprise has a % of foreign ownership and 0 otherwise. |
Legal status of the enterprise | |||
b1 | Single owner | Represents the legal status of the enterprise when it has a sole owner. | It was recoded to a dummy variable, where 1 indicates that the enterprise has a sole owner and 0 otherwise. |
b1 | Society or association | It represents the legal status of the enterprise when it is legally constituted as a society or association of any type. | It was recoded to a dummy variable, where 1 indicates that the enterprise is legally constituted as a society or association of any type and 0 otherwise. |
Linked to enterprise performance | |||
d3c | Exporter | It means that the enterprise makes direct exports of a percentage or the total of its sales. | It was recoded to a dummy variable, where 1 indicates that the enterprise directly exports part or all its sales and 0 otherwise. |
k6 | Checking/saving account | Indicates whether the enterprise has a checking or savings bank account at the time of the survey. | Binary variable, where 1 indicates whether the enterprise has a checking or savings bank account and 0 otherwise. |
d2 | Annual sales | Total annual sales in pesos of the last fiscal year of the enterprise. | Quantitative variable. Logarithm of the total annual sales in pesos of the last fiscal year of the enterprise. |
l1 | Permanent employees | Full-time permanent employees of the enterprise at the end last fiscal year. | Quantitative variable. Logarithm of the total number of permanent full-time employees in the last fiscal year of the enterprise. |
Entrepreneur attributes | |||
b7a | Manager is female | Indicates when the general manager of the enterprise is a woman. | Binary variable, where 1 indicates if the general manager of the enterprise is a woman and 0 otherwise. |
b7 | Manager experience | It represents the number of years of experience of the general manager working in the sector. | Quantitative variable. Logarithm of the number of years of experience of the general manager working in the sector. |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1. Manufacturing | 1.000 | |||||||||||||||
2. Commerce | −0.620 ** | 1.000 | ||||||||||||||
3. Services | −0.702 ** | −0.124 ** | 1.000 | |||||||||||||
4. Small | 0.008 | 0.012 | −0.022 | 1.000 | ||||||||||||
5. Medium | 0.045 | −0.041 | −0.020 | −0.317 ** | 1.000 | |||||||||||
6. Large | −0.046 | 0.024 | 0.036 | −0.590 ** | −0.578 ** | 1.000 | ||||||||||
7. Single owner | 0.034 | −0.027 | −0.019 | 0.444 ** | 0.040 | −0.415 ** | 1.000 | |||||||||
8. Society or association | −0.034 | 0.027 | 0.019 | −0.444 ** | −0.040 | 0.415 ** | −1.00 ** | 1.000 | ||||||||
9. Foreign participation | 0.017 | 0.021 | −0.041 | −0.151 ** | −0.119 ** | 0.231 ** | −0.158 ** | 0.158 ** | 1.000 | |||||||
10. Manager is female | −0.007 | 0.009 | 0.001 | 0.104 ** | 0.055 * | −0.136 ** | 0.109 ** | −0.109 ** | −0.050 | 1.000 | ||||||
11. Exporter | 0.177 ** | −0.121 ** | −0.113 ** | −0.229 ** | −0.153 ** | 0.327 ** | −0.206 ** | 0.206 ** | 0.291 ** | −0.064 * | 1.000 | |||||
12. Checking/saving account | −0.011 | 0.016 | −0.001 | −0.115 ** | −0.051 | 0.143 ** | −0.142 ** | 0.142 ** | 0.067 * | 0.015 | 0.091 ** | 1.000 | ||||
13. Enterprise age | 0.121 ** | −0.038 | −0.118 ** | −0.198 ** | −0.072 ** | 0.231 ** | −0.146 ** | 0.146 ** | 0.048 | −0.036 | 0.159 ** | 0.091 ** | 1.000 | |||
14. Manager experience | 0.109 ** | −0.075 ** | −0.069 ** | −0.056 * | −0.060 * | 0.099 ** | −0.057 * | 0.057 * | −0.043 | −0.082 ** | 0.040 | 0.031 | 0.396 ** | 1.000 | ||
15. Annual sales | −0.053 * | 0.084 ** | −0.009 | −0.616 ** | −0.210 ** | 0.709 ** | −0.498 ** | 0.498 ** | 0.300 ** | −0.184 ** | 0.386 ** | 0.205 ** | 0.308 ** | 0.111 ** | 1.000 | |
16. Permanent employees | −0.012 | 0.034 | −0.016 | −0.649 ** | −0.226 ** | 0.750 ** | −0.485 ** | 0.485 ** | 0.264 ** | −0.174 ** | 0.387 ** | 0.180 ** | 0.323 ** | 0.125 ** | 0.877 ** | 1.000 |
Variable | M1 | M2 | M3 | M4 | M5 | M6 | M7 | M8 | M9 | M10 |
---|---|---|---|---|---|---|---|---|---|---|
Constant | −1.484 *** | −1.474 *** | −1.758 *** | −1.460 *** | −1.330 *** | −1.158 ** | −1.630 *** | −1.333 *** | −1.250 *** | −1.648 *** |
(0.4757) | (0.3570) | (0.3809) | (0.5154) | (0.4491) | (0.5129) | (0.4653) | (0.3740) | (0.3923) | (0.3898) | |
Economic sector | ||||||||||
Manufacturing | 0.2342 *** | 0.2595 *** | 0.3019 *** | 0.2254 ** | 0.2341 *** | |||||
(0.08990) | (0.08794) | (0.1109) | (0.08961) | (0.08971) | ||||||
Commerce | 0.1574 | −0.1445 | −0.1563 | |||||||
(0.1586) | (0.1302) | (0.1295) | ||||||||
Services | −0.2626 ** | −0.2898 *** | −0.3019 *** | −0.2682 ** | −0.3001 *** | |||||
(0.1092) | (0.1087) | (0.1109) | (0.1093) | (0.1106) | ||||||
Enterprise size | ||||||||||
Small | −0.3008 *** | −0.3988 *** | −0.3332 *** | −0.4051 *** | −0.3334 *** | −0.4051 *** | −0.3537 *** | −0.3670 *** | −0.3167 *** | −0.3381 *** |
(0.1097) | (0.1020) | (0.1070) | (0.1343) | (0.1061) | (0.1343) | (0.1048) | (0.1040) | (0.1090) | (0.1069) | |
Medium | −0.08779 | −0.08779 | ||||||||
(0.1059) | (0.1059) | |||||||||
Large | 0.09026 | 0.1343 | 0.1186 | 0.1207 | ||||||
(0.1056) | (0.09902) | (0.09962) | (0.09941) | |||||||
Characteristics inherent to the enterprise | ||||||||||
Age | 0.08218 * | 0.09239 ** | 0.08904 ** | 0.06748 | 0.08906 ** | 0.06748 | 0.08293 * | 0.09085 ** | 0.08532 ** | 0.08756 ** |
(0.04336) | (0.04268) | (0.04255) | (0.04609) | (0.04311) | (0.04609) | (0.04297) | (0.04300) | (0.04301) | (0.04262) | |
Foreign participation | −0.5240 *** | −0.5337 *** | −0.5004 *** | −0.5147 *** | −0.4928 *** | −0.5147 *** | −0.5210 *** | −0.5346 *** | −0.5349 *** | −0.5308 *** |
(0.1296) | (0.1302) | (0.1269) | (0.1308) | (0.1249) | (0.1308) | (0.1300) | (0.1297) | (0.1296) | (0.1303) | |
Legal status of the enterprise | ||||||||||
Single owner | −0.1108 | −0.09194 | −0.1231 | −0.09194 | −0.1244 | −0.1170 | ||||
(0.09938) | (0.09991) | (0.09887) | (0.09991) | (0.09876) | (0.09918) | |||||
Society or association | ||||||||||
Linked to enterprise performance | ||||||||||
Exporter | 0.1191 | 0.1681 * | 0.1240 | 0.1240 | 0.1291 | 0.1646 * | 0.1309 | 0.1301 | ||
(0.09663) | (0.09452) | (0.09695) | (0.09695) | (0.09613) | (0.09454) | (0.09646) | (0.09641) | |||
Checking/saving account | 0.1418 * | 0.1471 * | 0.1476 * | 0.1397 * | 0.1397 * | 0.1468 * | 0.1415 * | 0.1424 * | 0.1475 * | |
(0.07558) | (0.07531) | (0.07544) | (0.07597) | (0.07597) | (0.07542) | (0.07544) | (0.07557) | (0.07546) | ||
Annual sales | 0.05445 | 0.07233 *** | 0.07256 *** | 0.04485 | 0.06348 * | 0.04485 | 0.06184 * | 0.06568 *** | 0.05864 ** | 0.06647 *** |
(0.03511) | (0.02145) | (0.02357) | (0.03563) | (0.03360) | (0.03563) | (0.03473) | (0.02222) | (0.02468) | (0.02399) | |
Permanent employees | 0.02387 | 0.03470 | 0.04105 | 0.03470 | 0.03869 | |||||
(0.05339) | (0.05347) | (0.04890) | (0.05347) | (0.05014) | ||||||
Entrepreneur attributes | ||||||||||
Manager is female | −0.06397 | −0.06397 | ||||||||
(0.1130) | (0.1130) | |||||||||
Manager experience | 0.05697 | 0.05697 | ||||||||
(0.05922) | (0.05922) | |||||||||
Observations | 1374 | 1376 | 1376 | 1365 | 1376 | 1365 | 1375 | 1375 | 1375 | 1376 |
McFadden’s R2 | 0.0639 | 0.0620 | 0.0623 | 0.0650 | 0.0604 | 0.0650 | 0.0632 | 0.0624 | 0.0640 | 0.0632 |
Log-likelihood | −890.9 | −894.0 | −893.7 | −884.2 | −895.5 | −884.2 | −892.2 | −892.9 | −891.4 | −892.8 |
Akaike criterion | 1803.9 | 1803.9 | 1803.4 | 1796.3 | 1807.0 | 1796.3 | 1802.5 | 1803.9 | 1804.8 | 1803.6 |
Schwarz criterion | 1861.3 | 1845.7 | 1845.2 | 1869.4 | 1848.8 | 1869.4 | 1849.5 | 1850.9 | 1862.3 | 1850.6 |
Hannan–Quinn criterion | 1825.4 | 1819.6 | 1819.1 | 1823.7 | 1822.7 | 1823.7 | 1820.1 | 1821.5 | 1826.3 | 1821.2 |
Correctly predicted cases | 63.3% | 62.6% | 63.8% | 63.2% | 63.7% | 63.2% | 63.3% | 61.7% | 63.1% | 63.2% |
References
- Andrieu, Guillaume, Raffaele Staglianò, and Peter Van Der Zwan. 2018. Bank debt and trade credit for SMEs in Europe: Firm, industry, and country-level determinants. Small Business Economics 51: 245–64. [Google Scholar] [CrossRef]
- Aterido, Reyes, Thorsten Beck, and Leonardo Iacovone. 2013. Access to finance in sub-Saharan Africa: Is there a gender gap? World Development 47: 102–20. [Google Scholar] [CrossRef]
- Baker, H. Kent, Satish Kumar, and Purnima Rao. 2020. Financing preferences and practices of Indian SMEs. Global Finance Journal 43: 100388. [Google Scholar] [CrossRef]
- Bank of Mexico (Banco de México). 2022a. Evolución del Financiamiento a las Empresas durante el Trimestre octubre—Diciembre de 2021. Available online: https://www.banxico.org.mx/publicaciones-y-prensa/evolucion-trimestral-del-financiamiento-a-las-empr/%7B7F3AFEC9-A775-BC0B-1640-5950FAAE46E3%7D.pdf (accessed on 15 January 2023).
- Bank of Mexico (Banco de México). 2022b. Resultados de la encuesta de evaluación coyuntural del mercado crediticio, octubre—Diciembre 2021. Available online: https://www.banxico.org.mx/SieInternet/consultarDirectorioInternetAction.do?accion=consultarCuadro&idCuadro=CF471§or=19&locale=es (accessed on 28 January 2023).
- Bardasi, Elena, Shwetlena Sabarwal, and Katherine Terrell. 2011. How do female entrepreneurs perform? Evidence from three developing regions. Small Business Economics 37: 417–41. [Google Scholar] [CrossRef]
- Beck, Thorsten, and Asli Demirgüc-Kunt. 2006. Small and medium-size enterprises: Access to finance as a growth constraint. Journal of Banking & Finance 30: 2931–43. [Google Scholar] [CrossRef]
- Beck, Thorsten, Aslı Demirgüç-Kunt, and Vojislav Maksimovic. 2005. Financial and legal constraints to growth: Does firm size matter? The Journal of Finance 60: 137–77. [Google Scholar] [CrossRef]
- Beck, Thorsten, Aslı Demirgüç-Kunt, Luc Laeven, and Vojislav Maksimovic. 2006. The determinants of financing obstacles. Journal of International Money and Finance 25: 932–52. [Google Scholar] [CrossRef]
- Berger, Allen N., and Gregory F. Udell. 1998. The economics of small business finance: The roles of private equity and debt markets in the financial growth cycle. Journal of Banking & Finance 22: 613–73. [Google Scholar] [CrossRef]
- Botello, H. 2015. Determinantes del acceso al crédito de las PYMES en Colombia. Ensayos de Economía 25: 135–56. Available online: https://revistas.unal.edu.co/index.php/ede/article/view/53631/53078 (accessed on 30 December 2022).
- Bougheas, Spiros, Paul Mizen, and Cihan Yalcin. 2006. Access to external finance: Theory and evidence on the impact of monetary policy and firm-specific characteristics. Journal of Banking & Finance 30: 199–227. [Google Scholar] [CrossRef]
- Briozzo, Anahi, and Hernán Vigier. 2014. The role of personal loans in the financing of SMEs. Academia Revista Latinoamericana de Administración 27: 209–25. [Google Scholar] [CrossRef]
- Briozzo, Anahí, Hernán Vigier, and Lisana B. Martinez. 2016a. Firm-level determinants of the financing decisions of small and medium enterprises: Evidence from Argentina. Latin American Business Review 17: 245–68. [Google Scholar] [CrossRef]
- Briozzo, Anahí, Hernán Vigier, Natalia Castillo, Gabriela Pesce, and Carolina M. Speroni. 2016b. Decisiones de financiamiento en pymes: ¿existen diferencias en función del tamaño y la forma legal? Estudios Gerenciales 32: 71–81. [Google Scholar] [CrossRef]
- Chaudhuri, Kausik, Subash Sasidharan, and Rajesh Seethamma Natarajan Raj. 2020. Gender, small firm ownership, and credit access: Some insights from India. Small Business Economics 54: 1165–81. [Google Scholar] [CrossRef]
- Chittenden, Francis, Graham Hall, and Patrick Hutchinson. 1996. Small firm growth, access to capital markets and financial structure: Review of issues and an empirical investigation. Small Business Economics 8: 59–67. [Google Scholar] [CrossRef]
- Cowling, Marc, Weixi Liu, and Andrew Ledger. 2012. Small business financing in the UK before and during the current financial crisis. International Small Business Journal 30: 778–800. [Google Scholar] [CrossRef]
- Cowling, Marc, Weixi Liu, and Ning Zhang. 2016. Access to bank finance for UK SMEs in the wake of the recent financial crisis. International Journal of Entrepreneurial Behavior & Research 22: 903–32. [Google Scholar] [CrossRef]
- Cressy, Robert, and Christer Olofsson. 1997. The financial conditions for Swedish SMEs: Survey and research agenda. Small Business Economics 9: 179–92. [Google Scholar] [CrossRef]
- Demirgüç-Kunt, Asli, Inessa Love, and Vojislav Maksimovic. 2006. Business environment and the incorporation decision. Journal of Banking & Finance 30: 2967–93. [Google Scholar] [CrossRef]
- Donaldson, Gordon. 1961. Corporate Debt Capacity: A Study of Corporate Debt Policy and the Determination of Corporate Debt Capacity. Boston: Graduate School of Business Administration, Harvard University. [Google Scholar]
- Ferrer, María Alejandra, and Alvaro Tresierra. 2009. Las PYMEs y las teorías modernas sobre estructura de capital. Compendium: Revista de investigación científica, 65–84. Available online: https://www.researchgate.net/publication/44897242 (accessed on 2 February 2023).
- Forte, Denis, Lucas A. Barros, and Wilson T. Nakamura. 2013. Determinants of the capital structure of small and medium sized Brazilian enterprises. BAR-Brazilian Administration Review 10: 347–69. [Google Scholar] [CrossRef]
- Gómez, Alicia, Domingo García Pérez de Lema, and Salvador Marín Hernández. 2009. Restricciones a la financiación de las PYME en México: Una aproximación empírica. Análisis Económico 24: 217–38. [Google Scholar]
- Gregory, Brian T., Matthew W. Rutherford, Sharon Oswald, and Lorraine Gardiner. 2005. An empirical investigation of the growth cycle theory of small firm financing. Journal of Small Business Management 43: 382–92. [Google Scholar] [CrossRef]
- Guercio, M. Belén, Lisana B. Martinez, and Hernán Vigier. 2017. Las limitaciones al financiamiento bancario de las Pymes de alta tecnología. Estudios Gerenciales 33: 3–12. [Google Scholar] [CrossRef]
- Guercio, María Belén, Anahí Eugenia Briozzo, Hernán Pedro Vigier, and Lisana Belén Martinez. 2020. The financial structure of Technology-Based Firms. Revista Contabilidade & Finanças 31: 444–57. [Google Scholar] [CrossRef]
- Gujarati, Damodar N., and Dawn Porter. 2010. Econometría. Ciudad de México: McGraw-Hill Education. [Google Scholar]
- Hall, Graham C., Patrick J. Hutchinson, and Nicos Michaelas. 2004. Determinants of the capital structures of European SMEs. Journal of Business Finance & Accounting 31: 711–28. [Google Scholar]
- Hernández-Sampieri, Roberto, and Christian Mendoza. 2018. Metodología de la investigación. Las rutas cuantitativa, cualitativa y mixta. Ciudad de México: McGraw-Hill Education. [Google Scholar]
- Hutchinson, Patrick. 2004. How much does growth determine SMEs’ capital structure? Small Enterprise Research 12: 81–92. [Google Scholar] [CrossRef]
- Kumar, Satish, and Purnima Rao. 2016. Financing patterns of SMEs in India during 2006 to 2013—An empirical analysis. Journal of Small Business & Entrepreneurship 28: 97–131. [Google Scholar] [CrossRef]
- Kumar, Satish, Riya Sureka, and Sisira Colombage. 2020. Capital structure of SMEs: A systematic literature review and bibliometric analysis. Management Review Quarterly 70: 535–65. [Google Scholar] [CrossRef]
- La Rocca, Maurizio, Tiziana La Rocca, and Alfio Cariola. 2011. Capital Structure Decisions During a Firm’s Life Cycle. Small Business Economics 37: 107–30. [Google Scholar] [CrossRef]
- López-Gracia, José, and Francisco Sogorb-Mira. 2008. Testing trade-off and pecking order theories financing SMEs. Small Business Economics 31: 117–36. [Google Scholar] [CrossRef]
- Mac an Bhaird, Ciarán, and Brian Lucey. 2010. Determinants of capital structure in Irish SMEs. Small Business Economics 35: 357–75. [Google Scholar] [CrossRef]
- Mac an Bhaird, Ciarán, and Brian Lucey. 2014. Culture’s influences: An investigation of inter-country differences in capital structure. Borsa Istanbul Review 14: 1–9. [Google Scholar] [CrossRef]
- Martinez, Lisana B., Valeria Scherger, and M. Belén Guercio. 2019. SMEs capital structure: Trade-off or pecking order theory: A systematic review. Journal of Small Business and Enterprise Development 26: 105–32. [Google Scholar] [CrossRef]
- Michaelas, Nicos, Francis Chittenden, and Panikkos Poutziouris. 1999. Financial policy and capital structure choice in UK SMEs: Empirical evidence from company panel data. Small Business Economics 12: 113–30. [Google Scholar] [CrossRef]
- Muravyev, Alexander, Oleksandr Talavera, and Dorothea Schäfer. 2009. Entrepreneurs’ gender and financial constraints: Evidence from international data. Journal of Comparative Economics 37: 270–86. [Google Scholar] [CrossRef]
- Myers, Stewart C. 1984. The capital structure puzzle. The Journal of Finance 39: 575–92. [Google Scholar] [CrossRef]
- Myers, Stewart C., and Nicholas S. Majluf. 1984. Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics 13: 187–221. [Google Scholar] [CrossRef]
- National Institute of Statistic and Geography (Instituto Nacional de Estadística y Geografía). 2020. Resultados definitivos. Censos Económicos 2019. Available online: https://www.inegi.org.mx/programas/ce/2019/default.html#Documentacion (accessed on 10 December 2022).
- Nizaeva, Mirgul, and Ali Coskun. 2019. Investigating the relationship between financial constraint and growth of SMEs in South Eastern Europe. SAGE Open 9: 1–15. [Google Scholar] [CrossRef]
- Organization for Economic Cooperation and Development [OECD]. 2018. Financing SMEs and Entrepreneurs 2018: An OECD Scoreboard. Available online: https://www.oecd-ilibrary.org/industry-and-services/financing-smes-and-entrepreneurs-2018_fin_sme_ent-2018-en (accessed on 6 February 2023).
- Pasquini, Ricardo, and Martín De Giovanni. 2010. Access to financing of SMEs in Argentina. Working Papers No. 2010/08. CAF Development Bank of Latinamerica. pp. 1–51. Available online: https://www.researchgate.net/profile/Ricardo-Pasquini-2/publication/252326224_2010_Credit_Requests_and_Access_to_Financing_of_SMEs_in_ArgentinaCAF_Working_Paper_Series_WP_201008/links/0046351f366c5a073a000000/2010-Credit-Requests-and-Access-to-Financing-of-SMEs-in-ArgentinaCAF-Working-Paper-Series-WP-2010-08.pdf (accessed on 27 February 2023).
- Petersen, M. A., and G. R. Rajan. 1994. The benefits of lending relationships: Evidence from small business data. The Journal of Finance 49: 3–37. [Google Scholar] [CrossRef]
- Presbitero, Andrea F., Roberta Rabellotti, and Claudia Piras. 2014. Barking up the wrong tree? Measuring gender gaps in firm’s access to finance. Journal of Development Studies 50: 1430–44. [Google Scholar] [CrossRef]
- Rao, Purnima, Satish Kumar, and V. Madhavan. 2018. Reflection of owner’s attributes in financing decisions of SMEs. Small Enterprise Research 25: 52–68. [Google Scholar] [CrossRef]
- Serrasqueiro, Zélia, and Paulo M. Nunes. 2012. Is age a determinant of SMEs’ financing decisions? Empirical evidence using panel data models. Entrepreneurship Theory and Practice 36: 627–54. [Google Scholar] [CrossRef]
- Wellalage, Nirosha Hewa, and Stuart Locke. 2015. Impact of ownership structure on capital structure of New Zealand unlisted firms. Journal of Small Business and Enterprise Development 22: 127–42. [Google Scholar] [CrossRef]
- Wooldridge, Jeffrey M. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge and London: The MIT Press. [Google Scholar]
- World Bank. 2010. Enterprise Surveys 2010, Documentation, Core Questionnaire, Mexico. Available online: https://microdata.worldbank.org/index.php/catalog/870/download/19386 (accessed on 8 February 2023).
- World Bank. 2011. Enterprise Surveys 2010, Documentation, Description of Mexico ES 2010 Implementation. Available online: https://microdata.worldbank.org/index.php/catalog/870/download/19390 (accessed on 8 February 2023).
- World Bank. 2017. Enterprise Surveys, Finance. Available online: https://www.enterprisesurveys.org/en/data/exploretopics/finance (accessed on 8 February 2023).
- Xiang, Dong, Andrew C. Worthington, and Helen Higgs. 2015. Discouraged finance seekers: An analysis of Australian small and medium-sized enterprises. International Small Business Journal 33: 689–707. [Google Scholar] [CrossRef]
- Yazdanfar, Darush, and Peter Öhman. 2016. Capital structure dynamics among SMEs: Swedish empirical evidence. The Journal of Risk Finance 17: 245–60. [Google Scholar] [CrossRef]
Sample | Bank Credit | |||||
---|---|---|---|---|---|---|
Independent Variable | Total | % | No | % | Yes | % |
Manufacturing | 1145 | 77.8% | 569 | 49.7% | 576 | 50.3% |
Commerce | 144 | 9.8% | 83 | 57.6% | 61 | 42.4% |
Services | 182 | 12.4% | 109 | 59.9% | 73 | 40.1% |
1471 | 100% | 761 | 51.7% | 710 | 48.3% | |
Small | 358 | 24.3% | 251 | 70.1% | 107 | 29.9% |
Medium | 349 | 23.7% | 182 | 52.1% | 167 | 47.9% |
Large | 764 | 51.9% | 328 | 42.9% | 436 | 57.1% |
1471 | 100% | 761 | 51.7% | 710 | 48.3% | |
Single owner | 319 | 21.7% | 209 | 65.5% | 110 | 34.5% |
Society or Association | 1151 | 78.3% | 551 | 47.9% | 600 | 52.1% |
1470 | 100% | 760 | 51.7% | 710 | 48.3% | |
No foreign participation | 1324 | 90.1% | 676 | 51.1% | 648 | 48.9% |
With foreign participation | 145 | 9.9% | 84 | 57.9% | 61 | 42.1% |
1469 | 100% | 760 | 51.7% | 709 | 48.3% | |
General manager is not a woman | 1310 | 89.1% | 665 | 50.8% | 645 | 49.2% |
General manager is female | 160 | 10.9% | 96 | 60.0% | 64 | 40.0% |
1470 | 100% | 761 | 51.8% | 709 | 48.2% | |
Nonexporter | 1160 | 78.9% | 636 | 54.8% | 524 | 45.2% |
Exporter | 311 | 21.1% | 125 | 40.2% | 186 | 59.8% |
1471 | 100% | 761 | 51.7% | 710 | 48.3% | |
No checking/savings account | 506 | 34.5% | 299 | 59.1% | 207 | 40.9% |
With checking/savings account | 961 | 65.5% | 460 | 47.9% | 501 | 52.1% |
1467 | 100% | 759 | 51.7% | 708 | 48.3% |
Bank Credit | |||
---|---|---|---|
Independent Variable | Sample Average | No | Yes |
Enterprise age (average years) | 24.0 | 22.2 | 25.9 |
Manager experience (average years) | 23.0 | 22.3 | 23.8 |
Permanent employees (average number) | 216.2 | 170.0 | 267.8 |
Annual sales Quantile 1 (%) | 72.4% | 27.6% | |
Quantile 2 (%) | 49.4% | 50.6% | |
Quantile 3 (%) | 43.2% | 56.8% | |
Quantile 4 (%) | 41.3% | 58.7% |
Variable | Obs. | Minimum | Maximum | Mean | Standard Deviation |
---|---|---|---|---|---|
Manufacturing | 1480 | 0 | 1 | 0.7784 | 0.4155 |
Commerce | 1480 | 0 | 1 | 0.0986 | 0.2983 |
Services | 1480 | 0 | 1 | 0.1230 | 0.3285 |
Small | 1480 | 0 | 1 | 0.2446 | 0.4300 |
Medium | 1480 | 0 | 1 | 0.2372 | 0.4255 |
Larger | 1480 | 0 | 1 | 0.5182 | 0.4998 |
Single owner | 1478 | 0 | 1 | 0.2185 | 0.4134 |
Society or association | 1478 | 0 | 1 | 0.7815 | 0.4134 |
Foreign participation | 1478 | 0 | 1 | 0.0981 | 0.2976 |
Manager is female | 1479 | 0 | 1 | 0.1089 | 0.3116 |
Exporter | 1480 | 0 | 1 | 0.2115 | 0.4085 |
Checking/saving account | 1475 | 0 | 1 | 0.6549 | 0.4756 |
Enterprise age (Log) * | 1470 | 0.0000 | 5.3083 | 2.8686 | 0.8593 |
Manager experience (Log) * | 1467 | 0.0000 | 4.1744 | 2.9697 | 0.6430 |
Annual sales (Log) * | 1396 | 10.5970 | 27.6310 | 16.7550 | 2.3187 |
Permanent employees (Log) * | 1479 | 0.6932 | 9.9968 | 3.8116 | 1.5840 |
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Jiménez-Rico, A.; Gómez-López, C.S.; Zamilpa, J. Determinants of Access to Bank Financing in SMEs in Mexico. J. Risk Financial Manag. 2023, 16, 477. https://doi.org/10.3390/jrfm16110477
Jiménez-Rico A, Gómez-López CS, Zamilpa J. Determinants of Access to Bank Financing in SMEs in Mexico. Journal of Risk and Financial Management. 2023; 16(11):477. https://doi.org/10.3390/jrfm16110477
Chicago/Turabian StyleJiménez-Rico, Artemio, Claudia Susana Gómez-López, and Johanan Zamilpa. 2023. "Determinants of Access to Bank Financing in SMEs in Mexico" Journal of Risk and Financial Management 16, no. 11: 477. https://doi.org/10.3390/jrfm16110477
APA StyleJiménez-Rico, A., Gómez-López, C. S., & Zamilpa, J. (2023). Determinants of Access to Bank Financing in SMEs in Mexico. Journal of Risk and Financial Management, 16(11), 477. https://doi.org/10.3390/jrfm16110477