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Impacto financ= iero del COVID 19 en las instituciones de economía popular y solidaria del Ecuad= or, ańo 2021

Monetary impac= t of COVID 19 on the popular and solidarity economy institutions of Ecuador, year 2021

Gema Viviana Paula Alarcón

https://orcid.org/0000-0001= -8968-0740

Uni= versidad Estatal Península de Santa Elena, Facultad de Ciencias Administrativas, S= anta Elena, Ecuador

gema.paulaalarcon0602@u= pse.edu.ec

Juan Carlos Olives Maldonad= o https://orcid.org/0000-0001-871= 0-1995

Uni= versidad Estatal Península de Santa Elena, Facultad de Ciencias Administrativas, S= anta Elena, Ecuador

jolives@upse.edu.ec

&nb= sp;	Art= ículo de Investigación Científica y Tecnológica Enviado: 08/04/2022 Revisado: 21/05/2022 Aceptado: 02/06/2022 Publicado:05/07/2022 DOI= : https://doi= .org/10.33262/visionariodigital.v6i3.2197

Cít= ese:		Paula Alarcón, G. V., & Olives Maldonado,= J. C. (2022). Impacto financiero del COVID 19 en las instituciones de econom= ía popular y solidaria del Ecuador, ańo 2021. Visionario Digital, 6(3), 97-1= 22. https://doi= .org/10.33262/visionariodigital.v6i3.2197
		*VIS= IONARIO DIGITAL,* es u= na revista científica trimestral, que se publicará en soporte electrónico tiene como misión contribu= ir a la formación de profesionales competentes con visión humaníst= ica y crítica que sean capaces de exponer sus resultados investigativos y científicos en la misma medida que se promueva mediante su intervención cambios positivos en la sociedad. https://visionariodigital.org La revista es editada por la Editorial Ciencia Digital (Editorial de prestig= io registrada en la Cámara Ecuatoriana de Libro con No de Afiliación 663) www.celibro.o= rg.ec
		Esta revista está protegida bajo una licencia Creative Commons Attribution Non Commercial No Derivatives 4.0 International. Copia de la licencia: http://creativecommons.org/licenses/by-nc-nd/4.0/
Palabras claves:= Impacto financiero, COVID 19, captaciones, colocaciones, indicadores financieros, estrategias financieras.		Resumen Introducción: Tras dos ańos de débil crecimiento económico, el 2021 cerró con una contracción del Producto Interno Bruto (PIB) de -8,10%, reflejando el impacto de la pandemia en el Ecuador. El Sistema Financiero Nacional obtuvo una variaci= ón positiva de 10 % en captaciones y un 15 % en colocaciones. A partir de ma= yo del 2021, se observó una reducción sostenida en las tasas de interés en t= odos los segmentos crediticios. El Sector Financiero Popular y Solidario del Ecuador (SFPS) representa aproximadamente un tercio de la actividad de intermediación financiera del sistema financiero nacional privado, al concentrar el 29,6% con respecto a las captaciones y el 30,9% de las colocaciones. El análisis del proyecto de investigación desarrollado en el SFPS con el tema: “Impacto Financiero del COVID 19 en las Instituciones de Economía Popular y Solidaria del Ecuador, ańo 2021”. Objetivos: Determinar los efectos del COVID-19 en el ámbito financiero del Sector Financiero Popular y Solidario durante el período 2= 021, mediante análisis econométrico de cambio estructural, para la determinaci= ón de estrategias financieras que fortalezca la gestión de captación, coloca= ción e indicadores financieros y que los directivos tomen acertadas decisiones frente a la crisis sanitaria mundial. Metodología: El método utilizado fue deductivo, con enfoque cualitativo y cuantitativo= , se aplica la investigación correlacional de tipo no experimental, con una investigación bibliográfica, documental y descriptiva; a través de la utilización de libros, folletos, revistas, manuales de las Instituciones Financieras de Economía Popular y Solidaria. El nivel de investigación co= rresponde al descriptivo. Se tomó como base la población a las 522 instituciones que integran el SFPS de diferentes ciudades y provincias del Ecuador para el análisis de los recursos en captaciones y colocaciones presentados por las Instituciones Financieras de la Economía Popular y Solidaria. Resultados: La investigación re= sponde a la pregunta: żCuál ha sido el impacto financiero causado por la crisis sanitaria del COVID-19, en el Sector Financiero Popular y Solidario del Ecuador, ańo 2021? Es así como la crisis sanitaria incide significativame= nte en el aspecto financiero de las instituciones del SFPS. Conclusiones: Concluyendo; los niveles de intermediación financiera del SFPS a inicios de la pandemia (marzo 2020) partió con un v= alor de 97,52% y a diciembre del 2021 alcanzó un 88,69%, con un decrecimiento = del 2% en relación con el 2020; el indicador tendió a la baja de 8,83%. En relación con el indicador de morosidad a partir de septiembre del 2021 tu= vo una tendencia ligera decreciente sin embargo en períodos anteriores exist= en despuntes de la morosidad. El indicador de liquidez que refleja la disposición de recursos para realizar sus pagos de manera inmediata a diciembre del 2020 fue del 28,93% con relación al 2021 del 27,62% con poca volatilidad. Sin embargo, la solvencia del SFPS como la capacidad para ha= cer frente a sus deudas y obligaciones fue a diciembre del 2020 del 18,97% y = que para finales del 2021 alcanzó el 17,48%.
Key= words: Financial Impact, COVID 19, deposits, placements, financial indicators.		Abstract Introduction: After two years of weak economic growth, 2021 closed with a contraction of the Gross Domestic Product (GDP) of -8.10%, reflecting the impact of the pandemic in Ecuador. The National Financial System obtained a positive variation of 10% in deposits and 15% in placements. As of May 2021, a sustained reduction in interest rates was observed in all credit segments. The Popular and Solidarity Financial Sector of Ecuador (SFPS) represents a third of the financial intermediation activity of the private national financial system, concentrating 29.6% with respect to deposits and 30.9% = of placements. The analysis of the research project developed in the SFPS wi= th the theme: "Financial Impact of COVID 19 on the Institutions of Popu= lar and Solidarity Economy of Ecuador, year 2021". Objectives: To determ= ine the effects of COVID-19 in the financial field of the Popular and Solidar= ity Financial Sector during the period 2021, through an econometric analysis = of structural change, for the determination of financial strategies that strengthen the management of deposits, placement and financial indicators= and those managers make the right decisions in the face of the global health crisis. Methodology: The method used was deductive, with a qualitative an= d quantitative approach, non-experimental correlational research is applied, with bibliographic, documentary, and descriptive research; using books, brochu= res, magazines, manuals of the Financial Institutions of Popular and Solidarity Economy. The level of research corresponds to the descriptive. The popula= tion of the 522 institutions that make up the SFPS of different cities and provinces of Ecuador was taken as a base for the analysis of the resource= s in deposits and placements presented by the Financial Institutions of the Popular and Solidarity Economy. Results: The research answers the question: What has been the monetary impact caused by the COVID-19 health crisis, in the Popular and Solidarity Financial Secto= r of Ecuador, year 2021? Thus, the health crisis has a significant impact on t= he financial aspect of the SFPS institutions. Conclusions: Concluding; the financial intermediation levels of the SFPS at the beginning of the pande= mic (March 2020) started with a value of 97.52% and as of December 2021 reach= ed 88.69%, with a decrease of 2% in relation to 2020; the indicator tended to fall by 8.83%. In relation to the delinquency indicator as of September 2= 021, it had a slight downward trend, however, in previous periods there were spikes in delinquency. The liquidity indicator that reflects the availabi= lity of resources to make payments immediately as of December 2020 was 28.93% compared to 2021 of 27.62% with little volatility. However, the solvency = of the SFPS as the ability to meet its debts and obligations was 18.97% as of December 2020 and by the end of 2021 it reached 17.48%.=

Introducción

El COVID‑19 es una enfermedad infecciosa causada por el coronavirus, tan= to este nuevo virus como la enfermedad que provoca eran desconocidas hasta el = 31 de diciembre de 2019 donde las Organización Mundial de la Salud (OMS), dio a conocer sobre un brote de neumonía el cual se originó en la ciudad Wuhan perteneciente a provincia de Hubei China. El 7 de enero del 2020 la OMS identificó un nuevo coronavirus denominado SARS-Cov-2 conocido como COVID-1= 9, el 29 de febrero el Ministerio de Salud Pública (MSP) confirmo el primer ca= so positivo en Ecuador e inicia la elaboración de informes para el tratamiento médico de los casos, vigilancia epidemiológica, políticas de protección y comunicación de riesgos a la ciudadanía ecuatoriana. El 17 de marzo del 2020 inició el plan de emergencia sanitaria donde se estableció la suspensión de actividades económicas y laborales. La epidemia del coronavirus (COVID-19), afectó a la economía mundial de tres maneras principales: afectando directamente a la producción, creando trastornos en la cadena de suministro= en el mercado, y por ende su impacto financiero en las empresas y mercados financieros.

La epidemia del coronavirus (COVID-19) represento una amenaza significativa pa= ra el SFPS ya que representa una participación y uno de los pilares importante= s de la economía solidaria del Ecuador. Además, la Superintendencia de Economía Popular y Solidaria (SEPS) también ha brindado a los ciudadanos mecanismos = de apoyo para mitigar el impacto financiero por la epidemia del coronavirus (C= OVID-19) en el SFPS.

La presente investigación hace referencia al “Impacto financiero del COVID 19 = en el sector financiero popular y solidario del ecuador, ańo 2021”. Esta investigación surge a partir de los ańos 2019, 2020 y 2021; considerando los primeros meses del 2020 que se confirmó la llegada de la epidemia del coronavirus (COVID-19) a Ecuador, ante esta situación marzo 14 del 2020 el = presidente de la República decreta el estado de excepción en todo el territorio nacion= al con la suspensión de diferentes actividades económicas y el trabajo presencial. Esto provocó una crisis económica a gran escala afectando a personas, famil= ias, pequeńas-medianas-grandes empresas, compańías, así como también a las Instituciones Financieras de la Economía Popular y Solidaria. La investigac= ión tiene la finalidad, dar a conocer los efectos financieros provocados por el COVID-19 en relación con las captaciones y colocaciones de las Instituciones Financieras de la Economía Popular y Solidaria. Ante estos contextos, es primordial articular una respuesta a la crisis a través de estrategias financieras eficaces que permitan aliviar el impacto, tanto en la fase inic= ial, durante y en las posteriores fases de reactivación y recuperación.

Una vez revisado las distintas fuentes bibliográficas de los trabajos de investigación relacionadas se puede detallar los siguientes:

Según Torres et al. (2020), en el trabajo titulado “Efectos financi= eros y tributarios del COVID-19 en los contribuyentes personas naturales de Villavicencio, Comuna 7”, cuyo objetivo es analizar los efectos del COVID-1= 9 en la información tributaria de las personas naturales de la ciudad de Villavicencio en la comuna 7 concluye seńalando que el impacto de la crisis sanitaria COVID-19 generó efec= tos financieros como: la disminución de sus ingresos, cierres temporales de los negocios y endeudamiento financieros siendo estos los efectos más repetitiv= os según la encuesta aplicada para este sector.

Según Mendieta & Zambrano (2021), = en el trabajo de titulado “Impacto financiero de la Pandemia COVID 19 en la industria hotelera de la ciudad de Guayaquil”, cuyo objetivo es analizar el impacto financiero post pandemia Covid-19 en la industria hotelera en la ci= udad de Guayaquil concluye seńalando que por la aparición del virus Covid-19 a principio del ańo 2020 el sector hotelero se vio enfrentado a una paralizac= ión en sus actividades económicas por la cuarentena y medidas estipuladas por el gobierno, por lo que, a pesar de ser un sector altamente sostenible en térm= inos financieros se vio afectado agresivamente en sus ventas. =

Según Cortés et al. (2020), en el trab= ajo titulado “Trabajo remoto en tiempos de COVID-19 y su impacto en el trabajad= or”, cuyo objetivo es entender cómo los trabajadores interpretan y enfrentan la interacción de la vida laboral y la vida familiar, en el trabajo remoto, concluye seńalando debido a la situación del COVID-19 los participantes decidieron retirar la figura de la empleada doméstica para evitar una mayor posibilidad de contagio y preservar la salud de su familia.

Por lo tanto, el objetivo de la investigación es determinar los efectos del COVID-19 en el ámbito financiero en las Instituciones Financieras de la Economía Popular y Solidaria, mediante análisis econométrico de cambio estructural, para la determinación de estrategias financieras que fortalezc= a la gestión de captación, colocación e indicadores financieros.

A continuación, se detallan algunos conceptos que forman parte de la investigación:

Conceptualiz= ación variable independiente: COVID-19

Pandemia

Se utiliza el término pandemia a la propagación mundial de una nueva enfermedad. Se produce una pandemia de gripe cuando un nuevo virus gripal surge y se propaga por el mu= ndo en donde la mayoría de las personas no tienen inmunidad contra él. Por lo c= omún, los virus que han causado pandemias con anterioridad han provenido de virus gripales que infectan a los animales (Mendieta & Zambrano, 2021). =

Coronavirus

Los coronavirus son u= na extensa familia de virus que causan diversas afecciones, que van desde el resfriado común hasta enfermedades más graves, como ocurre con el coronavir= us causante del síndrome respiratorio de Oriente Medio (MERS-CoV) y el que ocasiona el síndrome respiratorio agudo severo (SRAS-CoV). La nueva cepa de= 17 coronavirus no se había encontrado antes en el ser humano. Los coronavirus = se pueden transferir de los animales a las personas (transmisión zoonótica). Acorde con estudios exhaustivos al respecto, sabemos que el SRAS-CoV se transmitió de la civeta al ser humano y que se ha producido transmisión del MERS-CoV del dromedario al ser humano. Así mismo, se conoce que hay otros coronavirus circulando entre animales, que todavía no han infectado al ser humano (Mendieta & Zambrano, 2021, p.16).

Definición Covid-19

La enfermedad por cor= onavirus (COVID 19) es una ‎enfermedad infecciosa causada por un ‎coronavirus recientemente descubierto, un nuevo virus que ha propaga= do por todo el mundo puede causar desde síntomas leves hasta casos de enfermed= ad grave (CDC, 2020).

Como se propaga el COVID-19

Puede infectarse al entrar en contacto cercano (alrededor de 2 metros) con una persona que tenga COVID-19 generalmente se propaga principalmente de persona a persona, tambi= én puede infectarse por medio de gotitas respiratorias si una persona infectada tose estornuda o habla, también podría contraer la enfermedad al tocar una superficie o un objeto en los que este el virus y luego tocarse la boca, la nariz o los ojos (CDC, 2020).

Conceptualización variable dependiente: Impacto Financiero

Financiero

Financiero es todo aquello relativo a la gestión y obtención de capitales. Esto, tanto a peque= ńa como a gran escala, Asimismo, finanzas es una entidad dedicada a la intermediación financiera. Es decir, la empresa obtiene recursos de los depositantes y otorga préstamos a los agentes solicitantes es decir una ent= idad financiera. Cabe seńalar que el campo financiero es diferente al contable, = no se limita a reflejar la situación actual, por ejemplo, las cuentas de la empresa, sino una previsión de futuro. Esto constituye diferentes escenario= s, desde los más conservadores hasta los más optimistas (Westreicher, 2020). <= o:p>

Análisis de la Cartera de crédito de las instituciones Financieras

Se centra su atención= en el comportamiento de las operaciones de crédito, otorgando información vali= osa sobre las posibles pérdidas ocasionadas por el incumplimiento de las obligaciones financieras contraídas por los clientes a través de reportes de Análisis de Operaciones, Análisis de Morosidad de Cartera y Análisis de Segmento de Crédito (Villacis, 2022).

Análisis del Desempeńo Financiero de las Instituciones Financieras durante la Pandemia =

En el Portalfindev (2021), seńala que el desempeńo financi= ero: “Es uno de los indicadores que se utilizan para medir el éxito de una institución de microfinanzas en términos de su rentabilidad”. Por lo tanto,= se puede decir que el desempeńo financiero, son los resultados cuantitativos q= ue presentan, los indicadores de éxito que puede tener una empresa necesariame= nte deben enlazar con los objetivos propuestos de la empresa y así mostrar el camino que debe seguir.

Indicador financiero

Los indicadores financieros pueden determinar la capacidad de liquidez, el nivel de endeudamiento, la rentabilidad y la eficiencia de la empresa a través del balance general establecido. Esto permite que los indicadores financieros se utilicen como parte esencial del mejor desempeńo contable, económico y financiero de una entidad u organización. Es necesario, principalmente para= su correcta interpretación y comprensión, si se trata de un estudio de la prop= ia empresa o de una empresa externa (Nava, 2009).

Tipos de indicadores Financieros

Los indicadores de rentabilidad, de liquidez, endeudamiento y eficiencia hacen posible determi= nar cuándo es adecuado conveniente aumentar el endeudamiento de la empresa para ampliar su inversión o generar una mayor productividad y producir una mayor rentabilidad de esta (Mendieta & Zambrano, 2021).

Metodología

En la investigación se empleó el método deductivo según Gutiérrez afirma que el método deductivo “= es el método que permite pasar de afirmaciones de carácter general a hechos particulares” (Gutiérrez, 2016, p.24). Se aplicó este método partiendo = de lo general a lo particular mediante el análisis de información obtenida del Sector Financiero Popular y Solidario, determinando el impacto causado por = la crisis sanitaria de la epidemia del coronavirus (COVID-19) en las áreas financieras.

La investigación es de tipo documental porque “es el método investigativo basa= do en la revisión de textos, artículos, bibliografías, tesis, ya existentes so= bre un tema” (Gonzáles, 2017, p.26). Se utilizó la investigación documental porque se pretende analizar los documen= tos necesarios que contengan datos relacionados del SFPS.

El diseńo no experimental es el que se realiza sin manipular en forma delibera= da ninguna variable. El investigador no sustituye intencionalmente las variabl= es independientes. Se observan los hechos tal y como se presentan en su contex= to real y en un tiempo determinado o no, para luego analizarlos (Sampieri, 201= 7, p.34).

Se orienta al diseńo no experimental, tipo de investigación documental y descriptiva; con la utilización de libros, folletos, revistas, manuales tan= to de carácter científico como documentación de las Instituciones Financieras para resolver el problema que atraviesan las mismas. Para la obtención de result= ados se tomó como base información documental como: estados financieros, registr= os de captaciones y colocaciones durante los períodos 2019, 2020 y 2021. El di= seńo de la investigación es Correlacional.

De acuerdo con lo manifestado por Rodríguez & Alipio (2017), el enfoque cuantitativo: “utiliza la recolección y el análisis de datos para contestar preguntas de investigación y probar las Hipótesis y confía en la medición numérica, el conteo y el uso de la estadística para establecer con exactitud patrones en una población” (p.106).

Según Ocańa (2015), manifiesta que el enfoque cualitativo: “consiste que en más d= e un conjunto de técnicas para recoger datos; es un modo de encarar el mundo de = la interioridad de los sujetos sociales y de las relaciones que establecen con= los contextos y con otros actores sociales” (p.15).

El enfoque utilizado en la presente investigación fue cualitativo y cuantitati= vo, cualitativo porque se describe y analiza las características de la población objeto estudio y la forma como está con la variable de la investigación, pa= ra establecer un análisis cuantitativo de con información numérica o de calificación para convertir en estadísticas para tomar decisiones empresari= ales

El nivel de investigación corresponde al descriptivo porque “este permite seleccionar las características fundamentales del objeto de estudio y su descripción detallada de las partes, categorías o clases” (Bernal, 2016, p.= 10). Esto se debe a que la investigación se describe situaciones y hechos con relación a las captaciones y colocaciones del SFPS.

Para López (2015), la población “es un conjunto de medidas o el recuento de todos los elementos q= ue presentan una característica en común. El término población se usa para den= otar el conjunto de elementos del cual se extrae la muestra” (p.69).<= /span>

La población consider= ada para el estudio de la investigación corresponde a las 522 instituciones que integran = el sector financiero popular y solidario (ver tabla 1).

Tabla 1

Insti= tuciones financieras de la economía popular y solidaria

Segmento

N.ş de Entidades

SEGMENTO 1

Activos mayores a USD 80 M

39&n= bsp;

SEGMENTO 2

Activos mayores a USD 20 M hasta USD 80 M

46

SEGMENTO 3

Activos mayores a USD 5 M hasta USD 20 M

84

SEGMENTO 4

Activos mayores a USD 1 M hasta USD 5 M

163

SEGMENTO 5

Activos hasta USD 1 M

190

TOTAL

522

Nota: = ;Elaboración propia referencia

= Fuent= e: Superintendencia de Economía Popular y Solidaria (SEPS, 2011)

Muestra

Cobo et al. (2021), e= xisten dos tipos de muestra probabilística y no probabilística, la selección de la muestra requiere la utilización de técnicas y procedimientos para obtener la muestra, que es un subconjunto que permite conocer las características en u= na parte de la población relacionadas a la investigación.

Se aplicó el muestreo probabilístico a través de la fórmula de cálculo para poblaciones finitas, determinando el tamańo de la muestra aleatoria simple.

Cálculo del tamańo de= la muestra

n: Tamańo de la muestra.

N: 522

Z: =3D 1,65 (Nivel de confianza; para el 90%)=

p: =3D 0,5 (Posibilidad de ocurrencia de un e= vento)

q: =3D 0,5 (Posibilidad de no ocurrencia de un evento)

E: =3D 0,05 (Error de la estimación, se considera el 5%)

Resultados

Institucionalidad de la Economía Popular y Solidaria

La Constitución de la República del Ecuador, vigente desde el ańo 2008, define al sistema económi= co es social y solidario como las formas de organización económica pública, privada, mixta, popular y solidaria.

En mayo del 2011, ent= ra en vigor la Ley Orgánica de la Economía Popular y Solidaria, donde se defin= e a la economía popular y solidaria como organizaciones económicas en la que sus integrantes realizan actividades de producción, intercambio, comercializaci= ón, financiamiento y consumo de bienes y servicios; es así como gracias a esta = Ley y su Reglamento se crea la Superintendencia de Economía Popular y Solidaria (SEPS) como el organismo de supervisión y control.=

El Sector Financiero Popular y Solidario está conformado por:

a)&n= bsp;     cooperativas de ahorro y cré= dito

b)&n= bsp;     cajas centrales

c)&n= bsp;     entidades asociativas o solidarias, cajas y bancos comunales y cajas de ahorro

d)&n= bsp;     entidades de servicios auxil= iares; y,

e)&n= bsp;     asociaciones mutualistas de = ahorro y crédito para la vivienda.

Estas entidades, a excepción de las entidades de servicios auxiliares, se identifican, en principio, por su lógica asociativa y realizan actividades de intermediación financiera y de responsabilidad social con sus socios, situando a las finan= zas al servicio de las necesidades de las personas.

En cuanto a las entid= ades asociativas o solidarias, cajas y bancos comunales y cajas de ahorro; cabe referir que, estas entidades en observancia del ordenamiento jurídico vigen= te realizan sus actividades únicamente con sus socios y son sujetas de autocon= trol por parte de sus órganos internos.

Por otro lado, las organizaciones de la Economía Popular y Solidaria están conformada por:

a)&n= bsp;     cooperativas de producción; =

b)&n= bsp;     cooperativas de consumo;

c)&n= bsp;     cooperativas de vivienda;

d)&n= bsp;     cooperativas de servicios.

Figura 1

Instituciones Financieras y Organ= izaciones de la Economía Popular y Solidaria

Nota: Contexto de la Economía Popular y Solidaria

Fuente: SEPS (2011)

El sector de la Econo= mía Popular y Solidaria está compuesto por 16.577 organizaciones y 9´233.469 certificados de aportación, cuotas de admisión y aportes. Las cooperativas = de ahorro y crédito son entidades predominantes en el Sector Financiero Popula= r y Solidario con un total de 488 instituciones; mientras que en el Sector Económico Popular y Solidaria las asociaciones son las más representativas = con un total de 13.312 organizaciones.

Figura 2

Composición de la economía popula= r y solidaria a diciembre del 2021

Nota: Característica del sector de la Economía Popular y Solidaria

Fuente: SEPS (2021)

Instituciones del Sector Financiero Popular y Solidario (SFPS)

Se encuentra integrado por 516 cooperativas de ahorro y crédito, 4 asociaciones mutualistas de aho= rro y crédito para la vivienda, una caja central y una corporación; sin conside= rar estas dos últimas, las 520 entidades que conforman el sector registran 8,7 millones de certificados de aportación, con una suma de USD 21.533 millones= en activos, USD 18.737 millones en pasivos y USD 2.798 millones en patrimonio.=

Figura 3

Composición del SFPS por segmento= s, a diciembre del 2021

Nota: Característica del sector Financi= ero Popular y Solidario

Fuente: SEPS (2021= )

En cuanto al dinamismo de una determin= ada serie temporal, existen instantes de tiempo, en el cual pueden ocurrir cier= tos shocks o alteraciones que afectan de manera positiva o negativa la evolución normal de la misma, esto se lo conoce econométricamente como cambio de pendiente o quiebre estructural. Al existir un cambio de pendiente en un determinado punto temporal, este provoca que existan dos ecuaciones que mod= elen a la serie, una antes y después del shock. Si no se detecta ningún quiebre estructural, se puede modelar la serie con una sola ecuación para todo el horizonte de datos (Gujarati, 2010).

La prueba que ayuda a determinar si una serie posee o no quiebre estructural es el de CHOW. Lo primero que se debe realizar, es encontrar la ecuación que explique la evolución de una determinada serie. Para ello, se debe aplicar modelos ARMA, cuyos componentes muestran el constructor de series autorregresivas y de me= dias móviles. A continuación, los modelos estimados aplicando la metodología de = lo general a lo particular:

Morosidad por regiones:

Morosidad Costa (MC):

                         (0.00)           (0.00)                   (0.00)                     (0.00)

R² =3D 0.70

Morosidad Sierra (MS):

                         (0.00)           (0.00)                   (0.00)                     (0.00)

R² =3D 0.56

Morosidad Amazonia (MA):

                                       (0.00)           (0.00)                   (0.00)

R² =3D 0.49

Morosidad Insula (MI):

                         (0.00)           (0.00)                   (0.00)                     (0.00)

R² =3D 0.60

Una vez estimado lo modelos para cada= una se las series de la morosidad por región, se aplica la prueba de Chow. Para ello se deben formular las siguientes hipótesis:
Ho: No existe quiebre estructural en el punto temporal evaluado; significancia (p) > 0.05.

Ha: Existe quiebre estructural en el pu= nto temporal evaluado; significancia (p) < 0.05.

El valor de la significancia (p) es el resultado de la aplicación de la prueba de Chow, si este número resulta mayor a 0.05, se acepta la hipótesis nula (Ho), es deci= r no existe la evidencia estadística que, en el punto temporal evaluado, exista quiebre estructural. Pero, si resulta menor a 0.05, se rechaza la Ho a favo= r de la hipótesis alternativa (Ha), es decir existe cambio de pendiente. A continuación, los resultados:

Tabla 2

= Aplicación de la prueba de quiebre estructural (Test de Chow)=

Región

Test de Quiebre de Chow

Periodo 2019:06<= /span>

Cos= ta

Est= adístico-t

0.0= 4

Sig= nificancia

0.9= 9

Log likelihood ratio

0.2= 2

Sie= rra

Est= adístico-t

1.7= 7

Sig= nificancia

0.1= 6

Log likelihood ratio

8.1= 6

Ama= zonía

Est= adístico-t

0.6= 1

Sig= nificancia

0.7= 8

Log likelihood ratio

104= .85

Ins= ular

Est= adístico-t

2.5= 4

Sig= nificancia

0.0= 4

Log likelihood ratio

11.= 19

Nota: Elaboración propia a partir del procesamiento econométrico de los datos de Morosidad a nivel nacional en Ev= iews 10.

De acuerdo con los resultados, solo l= os datos de morosidad de la región Insular rechazaron la hipótesis nula a favo= r de la hipótesis alternativa, es decir, existe un quiebre estructural en el mes= de junio de 2019.

Tabla 3

= Aplicación de la prueba de quiebre estructural (Test de Chow)=

Región

Test de Quiebre de Chow

Periodo 2019:12<= /span>

Cos= ta

Est= adístico-t

5.2= 8

Sig= nificancia

0.0= 0

Log likelihood ratio

20.= 23

Sie= rra

Est= adístico-t

1.8= 9

Sig= nificancia

0.1= 4

Log likelihood ratio

8.6= 6

Ama= zonía

Est= adístico-t

0.7= 0

Sig= nificancia

0.5= 5

Log likelihood ratio

2.4= 5

Ins= ular

Est= adístico-t

3.2= 0

Sig= nificancia

0.0= 2

Log likelihood ratio

13.= 60

Nota: Elaboración propia a partir del procesamiento econométrico de los datos de Morosidad a nivel nacional en Ev= iews 10.

Con base a los resultados, solo los datos de morosidad de la región Costa e Insular rechaz= aron la hipótesis nula a favor de la hipótesis alternativa, es decir, existen quiebres estructurales en las series de dichas regiones en el mes de diciem= bre de 2019.

Tabla 4

= Aplicación de la prueba de quiebre estructural (Test de Chow)=

Reg= ión

Tes= t de Quiebre de Chow

Per= iodo 2020:03

Cos= ta

Est= adístico-t

12.= 15

Sig= nificancia

0.0= 0

Log likelihood ratio

36.= 04

Sie= rra

Est= adístico-t

5.2= 3

Sig= nificancia

0.0= 0

Log likelihood ratio

20.= 08

Ama= zonía

Est= adístico-t

2.3= 2

Sig= nificancia

0.0= 4

Log likelihood ratio

7.5= 3

Ins= ular

Est= adístico-t

3.4= 0

Sig= nificancia

0.0= 2

Log likelihood ratio

14.= 28

Fuente: Elaboración propia a partir del procesamiento econométrico de los datos de Morosidad a nivel nacional en Ev= iews 10.

Según los resultados, en todas las se= ries de morosidad de las regiones del país se rechazó la hipótesis nula a favor = de la hipótesis alternativa, es decir fueron afectadas por un quiebre estructu= ral en el mes de marzo del 2020.

Tabla 5<= /o:p>

= Aplicación de la prueba de quiebre estructural (Test de Chow)=

Reg= ión

Tes= t de Quiebre de Chow

Per= iodo 2021:04

Cos= ta

Est= adístico-t

0.7= 6

Sig= nificancia

0.5= 5

Log likelihood ratio

3.7= 3

Sie= rra

Est= adístico-t

0.0= 3

Sig= nificancia

0.9= 9

Log= likelihood ratio

0.1= 5

Ama= zonía

Est= adístico-t

3.2= 6

Sig= nificancia

0.0= 3

Log likelihood ratio

10.= 18

Ins= ular

Est= adístico-t

25.= 61

Sig= nificancia

0.0= 0

Log likelihood ratio

54.= 86

Fuente: Elaboración propia a partir del procesamiento econométrico de los datos de Morosidad a nivel nacional en Ev= iews 10.

De acuerdo con estas cifras, solo en los datos de morosidad de las regiones Amazónica e Insular = se rechaza la hipótesis nula a favor de la hipótesis alternativa, es decir exi= sten quiebres estructurales en dichas series para el mes de abril del 2021.=

Tabla 6

= Aplicación de la prueba de quiebre estructural (Test de Chow)=

Reg= ión

Tes= t de Quiebre de Chow

Per= iodo 2021:09

Cos= ta

Est= adístico-t

1.3= 6

Sig= nificancia

0.2= 7

Log likelihood ratio

6.4= 4

Sie= rra

Est= adístico-t

1.5= 2

Sig= nificancia

0.3= 6

Log likelihood ratio

7.3= 6

Ama= zonía

Est= adístico-t

1.4= 6

Sig= nificancia

0.2= 4

Log likelihood ratio

4.9= 3

Ins= ular

Est= adístico-t

0.3= 0

Sig= nificancia

0.8= 6

Log likelihood ratio

1.5= 6

Nota: Elaboración propia a partir del procesamiento econométrico de los datos de Morosidad a nivel nacional en Ev= iews 10.

Con estos resultados, en todas las series de morosidad se acepta la hipótesis n= ula, es decir no existe evidencia estadística de la presencia de un quiebre estructural en septiembre de 2021.

Colocaciones a nivel nacional:

Colocaciones comerciales (CC):

                         (0.00)           (0.00)                   (0.02)                     (0.00)

R² =3D 0.95

Colocaciones consumo (CCO):

                                        (0.00)           (0.00)                   (0.02)

R² =3D 0.96

Colocaciones educativo (CE):

                          (0.00)                 (0.00)                   (0.00)                     (0.00)

R² =3D 0.83

Colocaciones microcrédito (CM):

                                        (0.00)            (0.00)                   (0.00)

R² =3D 0.98

Colocaciones vivienda (CV):

                                                =       (0.00)              (0.00)

R² =3D 0.79

Una vez estimados los modelos econométricos para las colocaciones a nivel nacional, se aplica la prueba de quiebre estructural (Chow) con las mismas hipótesis planteadas en= la morosidad. Los resultados:

Tabla 7

= Aplicación de la prueba de quiebre estructural (Test de Chow)=

Col= ocaciones

Tes= t de Quiebre de Chow

Per= iodo 2019:06

Com= ercial

Est= adístico-t

0.8= 21

Sig= nificancia

0.5= 31

Log likelihood ratio

4.5= 54

Con= sumo

Est= adístico-t

0.1= 67

Sig= nificancia

0.9= 16

Log likelihood ratio

0.6= 71

Edu= cativo

Est= adístico-t

0.6= 5

Sig= nificancia

0.0= 4

Log likelihood ratio

2.6= 5

Mic= rocrédito

Est= adístico-t

0.2= 83

Sig= nificancia

0.8= 36

Log likelihood ratio

1.1= 23

Viv= ienda

Est= adístico-t

5.1= 4

Sig= nificancia

0.0= 15

Log likelihood ratio

9.9= 6

Nota: Elaboración propia a partir del procesamiento econométrico de los datos de Morosidad a nivel nacional en Ev= iews 10.

De acuerdo con los resultados, so= lo las series de colocaciones a nivel nacional, tanto del sector educativo com= o de vivienda, rechazaron la hipótesis nula a favor de la hipótesis alternativa,= es decir existió un quiebre estructural en julio de 2019.

Tabla 8

Aplicación de la prueba de quiebre estructural (Test de Chow)

Colocaciones<= /p>

Test de Quiebre de Chow

Periodo 2019:12

Comercial

Estadístico-t=

2.981

Significancia=

0.045

Log likelihood ratio=

13.454

Consumo

Estadístico-t=

21.844

Significancia=

0.00

Log likelihood ratio=

36.340

Educativo

Estadístico-t=

2.047

Significancia=

0.151

Log likelihood ratio=

10.407

Tabla 8=

Aplicación de la prueba de quiebre estructural (Test de Chow) (continuación)

Colocaciones<= /p>

Test de Quiebre de Chow

Periodo 2019:12

Microcrédito<= /p>

Estadístico-t=

2.837

Significancia=

0.05

Log likelihood ratio=

9.337

Vivienda

Estadístico-t=

29.23

Significancia=

0.00

Log likelihood ratio=

32.80

Fuente: Elaboración propia a partir del procesamiento econométrico de los datos de Morosidad a nivel nacional en Eviews 10.

Con base a los resultados, todas las series de las colocaciones nivel nacional, excepto la= del sector educativo, rechazaron la hipótesis nula a favor de la alternativa, es decir existió quiebre estructural para el mes de diciembre de 2019.

Tabla 9

= Aplicación de la prueba de quiebre estructural (Test de Chow)=

Col= ocaciones

Tes= t de Quiebre de Chow

Per= iodo 2020:03

Com= ercial

Est= adístico-t

10.= 05

Sig= nificancia

0.0= 0

Log likelihood ratio

29.= 988

Con= sumo

Est= adístico-t

2.3= 06

Sig= nificancia

0.1= 12

Log likelihood ratio

7.8= 52

Edu= cativo

Est= adístico-t

1.1= 75

Sig= nificancia

0.3= 70

Log likelihood ratio

6.6= 10

Mic= rocrédito

Est= adístico-t

0.4= 54

Sig= nificancia

0.7= 17

Log likelihood ratio

1.7= 73

Viv= ienda

Est= adístico-t

3.2= 11

Sig= nificancia

0.0= 4

Log likelihood ratio

6.6= 84

Fuente: Elaboración propia a partir del pro= cesamiento econométrico de los datos de Morosidad a nivel nacional en Eviews 10.<= /o:p>

Con base a los resultados, solo las series de colocaciones de los sectores comerciales y de vivienda rechazaron la hipótesis nula a favor de la hipótesis alternativa, = es decir hubo quiebre estructural en marzo de 2020.
Captaciones a nivel nacional:

Depósito a la vista (DV):

                                                =                                                                     .                 (0.00)                 (0.00)                       (0.02)                     (0.00)

R² =3D 0.97

Clientes - Depósito a la vista (CDV):

                                                =                                                                             (0.02)                     (0.00)

R² =3D 0.90

Captaciones a plazos (CP):

                         (0.00)                 (0.00)                   (0.02)                     (0.00)

R² =3D 0.98

Clientes- Captaciones a plazos (CAP):

                         (0.00)                 (0.00)                   (0.02)                     (0.00)

R² =3D 0.91

Depósitos restringidos (DR):

                                                =                                                                             (0.02)                     (= 0.02)

R² =3D 0.93

Clientes – Depósito restringidos (CDR):

                     (0.00)      (0.00)     (0.00)      (0.00)      (0.00)       (0.00)     (0.00)

R² =3D 0.96

Estimados los modelos econométric= os para las captaciones a nivel nacional, se aplica la prueba de quiebre estructural (Chow):

Tabla 10

= Aplicación de la prueba de quiebre estructural (Test de Chow)=

Cap= taciones

Tes= t de Quiebre de Chow

Per= iodo 2019:06

Dep= ósito a la vista

Est= adístico-t

2.3= 54

Sig= nificancia

0.9= 85

Log likelihood ratio

0.3= 2

Cli= entes

Est= adístico-t

0.3= 13

Sig= nificancia

0.7= 33

Log likelihood ratio

0.6= 99

Tabla 10

= Aplicación de la prueba de quiebre estructural (Test de Chow) (continuación)

Cap= taciones

Tes= t de Quiebre de Chow

Per= iodo 2019:06

A plazos

Est= adístico-t

2.3= 14

Sig= nificancia

0.9= 96

Log likelihood ratio

3.1= 9

Cli= entes

Est= adístico-t

2.3= 4

Sig= nificancia

2.3= 6

Log likelihood ratio

6.4= 7

Dep= ósitos restringidos

Est= adístico-t

0.0= 75

Sig= nificancia

0.9= 27

Log likelihood ratio

0.1= 71

Cli= entes

Est= adístico-t

0.6= 74

Sig= nificancia

0.8= 72

Log likelihood ratio

0.3= 42

Fuente: Elaboración propia a partir del procesamiento econométrico de los datos de Morosidad a nivel nacional en Ev= iews 10.

Con base a los resultados, todas las series de captaciones y de clientes aceptaron la hipótesis nula, es decir no poseen evidencia de la ocurrencia de un quiebre estructural en el mes de junio de 2019.

Tabla 11

= Aplicación de la prueba de quiebre estructural (Test de Chow)=

Cap= taciones

Tes= t de Quiebre de Chow

Per= iodo 2019:12

Dep= ósito a la vista

Est= adístico-t

0.6= 29

Sig= nificancia

0.7= 82

Log likelihood ratio

58.= 614

Cli= entes

Est= adístico-t

11.= 548

Sig= nificancia

0.0= 00

Log likelihood ratio

126= .4655

A plazos

Est= adístico-t

5.2= 05

Sig= nificancia

0.0= 99

Log likelihood ratio

121= .36

Cli= entes

Est= adístico-t

386= .163

Sig= nificancia

0.0= 02

Log likelihood ratio

562= .948

Dep= ósitos restringidos

Est= adístico-t

7.3= 46

Sig= nificancia

0.0= 02

Log likelihood ratio

13.= 578

Cli= entes

Est= adístico-t

33.= 934

Sig= nificancia

0.0= 06

Log likelihood ratio

192= .03

Fuente: Elaboración propia a partir del procesamiento econométrico de los datos de Morosidad a nivel nacional en Ev= iews 10.

De acuerdo con los resultados, solo las series de captaciones de depósitos restringidos rechaz= an la hipótesis nula a favor de la alternativa, es decir existió un quiebre estructural en diciembre de 2019.

Por otro lado, en cua= nto las series de clientes, todas rechazaron la hipótesis nula a favor de la alternativa, por lo que hubo quiebres estructurales para diciembre de 2019.=

Tabla 12

= Aplicación de la prueba de quiebre estructural (Test de Chow)=

Cap= taciones

Tes= t de Quiebre de Chow

Per= iodo 2020:03

Dep= ósito a la vista

Est= adístico-t

20.= 345

Sig= nificancia

0.0= 01

Log likelihood ratio

108= .001

Cli= entes

Est= adístico-t

10.= 438

Sig= nificancia

0.0= 00

Log likelihood ratio

107= .990

A plazos

Est= adístico-t

2.3= 64

Sig= nificancia

0.1= 44

Log likelihood ratio

72.= 605

Cli= entes

Est= adístico-t

90.= 293

Sig= nificancia

0.0= 00

Log likelihood ratio

185= .602

Dep= ósitos restringidos

Est= adístico-t

14.= 372

Sig= nificancia

0.0= 00

Log likelihood ratio

118= .744

Cli= entes

Est= adístico-t

0.0= 925

Sig= nificancia

0.0= 03

Log likelihood ratio

0.9= 38

Fuente: Elaboración propia a partir del procesamiento econométrico de los datos de Morosidad a nivel nacional en Ev= iews 10.

Con base a los resultados, todas las series de captaciones (excepto a plazos) y de = clientes rechazaron la hipótesis nula a favor de la hipótesis alternativa, es decir existe evidencia de la ocurrencia de un quiebre estructural en el mes de ma= rzo de 2020.

Tabla 13

= Aplicación de la prueba de quiebre estructural (Test de Chow)=

Cap= taciones

Tes= t de Quiebre de Chow

Per= iodo 2021:04

Dep= ósito a la vista

Est= adístico-t

1.3= 46

Sig= nificancia

0.2= 81

Log likelihood ratio

6.4= 80

Cli= entes

Est= adístico-t

0.0= 48

Sig= nificancia

0.9= 99

Log likelihood ratio

0.6= 30

A plazos

Est= adístico-t

0.2= 42

Sig= nificancia

0.9= 11

Log likelihood ratio

1.2= 67

Cli= entes

Est= adístico-t

0.3= 42

Sig= nificancia

0.6= 32

Log likelihood ratio

2.4= 8

<= /o:p>

<= /o:p>

Tabla 13

= Aplicación de la prueba de quiebre estructural (Test de Chow) (continuación)

Cap= taciones

Tes= t de Quiebre de Chow

Per= iodo 2021:04

Dep= ósitos restringidos

Est= adístico-t

0.8= 70

Sig= nificancia

0.5= 63

Log likelihood ratio

9.8= 90

Cli= entes

Est= adístico-t

0.1= 49

Sig= nificancia

0.9= 96

Log likelihood ratio

2.3= 22

Fuente: Elaboración propia a partir del procesamiento econométrico de los datos de Morosidad a nivel nacional en Ev= iews 10.

Los resultados muestran que, todas las series de captaciones y de clientes aceptaron la hipótesis nula, es dec= ir no poseen evidencia de la ocurrencia de un cambio de pendiente en el mes de abril de 2021

Tabla 14

= Aplicación de la prueba de quiebre estructural (Test de Chow)=

Cap= taciones

Tes= t de Quiebre de Chow

Per= iodo 2021:09

Dep= ósito a la vista

Est= adístico-t

1.3= 45

Sig= nificancia

0.3= 64

Log likelihood ratio

6.3= 14

Cli= entes

Est= adístico-t

0.0= 68

Sig= nificancia

0.9= 90

Log likelihood ratio

0.3= 31

A plazos

Est= adístico-t

0.3= 24

Sig= nificancia

0.6= 53

Log likelihood ratio

1.6= 75

Cli= entes

Est= adístico-t

0.4= 23

Sig= nificancia

0.7= 85

Log likelihood ratio

1.6= 28

Dep= ósitos restringidos

Est= adístico-t

0.8= 85

Sig= nificancia

0.4= 84

Log likelihood ratio

4.0= 33

Cli= entes

Est= adístico-t

0.2= 74

Sig= nificancia

0.3= 52

Log likelihood ratio

2.6= 94

Fuente: Elaboración propia a partir del procesamiento econométrico de los datos de Morosidad a nivel nacional en Ev= iews 10.

Y finalmente, también como el caso anterior, todas las series de captaciones y de clientes acepta= ron la hipótesis nula, es decir no poseen evidencia de la ocurrencia de un quie= bre estructural en el mes de septiembre de 2021.

Estrategias financieras

Ř Ofrecer servicios financieros los cuales = sean más atractivos en tasa, plazos y accesible para cualquier persona es decir = no muy costoso, los requisitos no debe ser una traba si no los adecuados y establecer más medios para dar a conocer sobre los servicios y productos financieros ofrecidos a los clientes/socios.

Ř Hacer uso de sus propios recursos, mitiga= r los gastos, llevar un control apropiado de los mismo y tomar las consecuencias = de esta crisis causada por la epidemia de covid-19 como una oportunidad y no c= omo una desventaja debido a que las cooperativas son factor clave para la recuperación después de la pandemia y realizar análisis exhaustivo al momen= to de otorgar crédito, llegar a un ac= uerdo con los socios sobre el pago de los créditos atrasados debido a la situación económica que enfrenta las familias por COVID-19 ofreciendo facilidades de pago.

Conclusiones

·         La afectación financiera ocasionada por el COVID – 19 en las Instituciones Financieras del Sector de la Economía Popular y Solidaria tiene que ver con= el retorno de las colocaciones debido a que sus socios se vieron imposibilitad= os a generar ingresos económicos ya que a diciembre del 2021 la distribución de cartera por segmento fue: segmento 1 el 81%, segmento 2 el 11%, segmento 3 = el 6%, segmento 4 el 2%, segmento 5 el 0% y la distribución de la cartera por = tipo el 49% consumo, 39% microcrédito, 10% vivienda y el 2% productivo. Esto conllevo a la generación de procesos como concesiones o refinanciamientos.<= o:p>

·         La intermediación financiera en el SFPS que mide la relación entre captaciones (depósitos) y colocaciones (créditos) considerando la tasa que define el ni= vel de eficiencia con el que se están gestionando los recursos de una entidad financiera. En este sentido, a inicios de la pandemia (marzo 2020) partió c= on un valor de 97,52% y a diciembre del 2021 alcanzó un 88,69%, con un decrecimiento del 2% en relación con el 2020; el indicador tendió a la baja= de 8,83%.

·         La situación financiera de las Instituciones Financieras de la Economía Popula= r y Solidaria refleja el indicador de liquidez que muestra la disposición de recursos para realizar sus pagos de manera inmediata a diciembre del 2020 f= ue del 28,93% con relación al 2021 del 27,62% con poca volatilidad. Sin embarg= o, la solvencia del SFPS como la capacidad para hacer frente a sus deudas y obligaciones fue a diciembre del 2020 del 18,97% y que para finales del 2021 alcanzó el 17,48%.

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<= ![endif]>

El artículo que se publica es de exclusiva responsabilidad de los autores y no necesariamente reflejan el pensamiento de la Revista Visionar= io Digital.

&nb= sp;

El artíc= ulo queda en propiedad de la revista y, por tanto, su publicación parcial y/o t= otal en otro medio tiene que ser autorizado por el director de la Revista Visionario Digital.<= /o:p>

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Economía Mundial Página 971

Segmento	N.ş de Entidades
SEGMENTO 1 Activos mayores a USD 80 M	39&n= bsp;
SEGMENTO 2 Activos mayores a USD 20 M hasta USD 80 M	46
SEGMENTO 3 Activos mayores a USD 5 M hasta USD 20 M	84
SEGMENTO 4 Activos mayores a USD 1 M hasta USD 5 M	163
SEGMENTO 5 Activos hasta USD 1 M	190
TOTAL	522

Región	Test de Quiebre de Chow
Periodo 2019:06<= /span>
Cos= ta	Est= adístico-t	0.0= 4	Sig= nificancia	0.9= 9
Log likelihood ratio	0.2= 2
Sie= rra	Est= adístico-t	1.7= 7	Sig= nificancia	0.1= 6
Log likelihood ratio	8.1= 6
Ama= zonía	Est= adístico-t	0.6= 1	Sig= nificancia	0.7= 8
Log likelihood ratio	104= .85
Ins= ular	Est= adístico-t	2.5= 4	Sig= nificancia	0.0= 4
Log likelihood ratio	11.= 19

Región	Test de Quiebre de Chow
Periodo 2019:12<= /span>
Cos= ta	Est= adístico-t	5.2= 8	Sig= nificancia	0.0= 0
Log likelihood ratio	20.= 23
Sie= rra	Est= adístico-t	1.8= 9	Sig= nificancia	0.1= 4
Log likelihood ratio	8.6= 6
Ama= zonía	Est= adístico-t	0.7= 0	Sig= nificancia	0.5= 5
Log likelihood ratio	2.4= 5
Ins= ular	Est= adístico-t	3.2= 0	Sig= nificancia	0.0= 2
Log likelihood ratio	13.= 60

Reg= ión	Tes= t de Quiebre de Chow
Per= iodo 2020:03
Cos= ta	Est= adístico-t	12.= 15	Sig= nificancia	0.0= 0
Log likelihood ratio	36.= 04
Sie= rra	Est= adístico-t	5.2= 3	Sig= nificancia	0.0= 0
Log likelihood ratio	20.= 08
Ama= zonía	Est= adístico-t	2.3= 2	Sig= nificancia	0.0= 4
Log likelihood ratio	7.5= 3
Ins= ular	Est= adístico-t	3.4= 0	Sig= nificancia	0.0= 2
Log likelihood ratio	14.= 28

Reg= ión	Tes= t de Quiebre de Chow
Per= iodo 2021:04
Cos= ta	Est= adístico-t	0.7= 6	Sig= nificancia	0.5= 5
Log likelihood ratio	3.7= 3
Sie= rra	Est= adístico-t	0.0= 3	Sig= nificancia	0.9= 9
Log= likelihood ratio	0.1= 5
Ama= zonía	Est= adístico-t	3.2= 6	Sig= nificancia	0.0= 3
Log likelihood ratio	10.= 18
Ins= ular	Est= adístico-t	25.= 61	Sig= nificancia	0.0= 0
Log likelihood ratio	54.= 86

Reg= ión	Tes= t de Quiebre de Chow
Per= iodo 2021:09
Cos= ta	Est= adístico-t	1.3= 6	Sig= nificancia	0.2= 7
Log likelihood ratio	6.4= 4
Sie= rra	Est= adístico-t	1.5= 2	Sig= nificancia	0.3= 6
Log likelihood ratio	7.3= 6
Ama= zonía	Est= adístico-t	1.4= 6	Sig= nificancia	0.2= 4
Log likelihood ratio	4.9= 3
Ins= ular	Est= adístico-t	0.3= 0	Sig= nificancia	0.8= 6
Log likelihood ratio	1.5= 6

Col= ocaciones	Tes= t de Quiebre de Chow
Per= iodo 2019:06
Com= ercial	Est= adístico-t	0.8= 21	Sig= nificancia	0.5= 31
Log likelihood ratio	4.5= 54
Con= sumo	Est= adístico-t	0.1= 67	Sig= nificancia	0.9= 16
Log likelihood ratio	0.6= 71
Edu= cativo	Est= adístico-t	0.6= 5	Sig= nificancia	0.0= 4
Log likelihood ratio	2.6= 5
Mic= rocrédito	Est= adístico-t	0.2= 83	Sig= nificancia	0.8= 36
Log likelihood ratio	1.1= 23
Viv= ienda	Est= adístico-t	5.1= 4	Sig= nificancia	0.0= 15
Log likelihood ratio	9.9= 6

Colocaciones<= /p>	Test de Quiebre de Chow
Periodo 2019:12
Comercial	Estadístico-t=	2.981	Significancia=	0.045
Log likelihood ratio=	13.454
Consumo	Estadístico-t=	21.844	Significancia=	0.00
Log likelihood ratio=	36.340
Educativo	Estadístico-t=	2.047	Significancia=	0.151
Log likelihood ratio=	10.407

Cap= taciones	Tes= t de Quiebre de Chow
Per= iodo 2019:06
Dep= ósito a la vista	Est= adístico-t	2.3= 54	Sig= nificancia	0.9= 85
Log likelihood ratio	0.3= 2
Cli= entes	Est= adístico-t	0.3= 13	Sig= nificancia	0.7= 33
Log likelihood ratio	0.6= 99

Cap= taciones	Tes= t de Quiebre de Chow
Per= iodo 2019:12
Dep= ósito a la vista	Est= adístico-t	0.6= 29	Sig= nificancia	0.7= 82
Log likelihood ratio	58.= 614
Cli= entes	Est= adístico-t	11.= 548	Sig= nificancia	0.0= 00
Log likelihood ratio	126= .4655

A plazos	Est= adístico-t	5.2= 05	Sig= nificancia	0.0= 99
Log likelihood ratio	121= .36
Cli= entes	Est= adístico-t	386= .163	Sig= nificancia	0.0= 02
Log likelihood ratio	562= .948

Dep= ósitos restringidos	Est= adístico-t	7.3= 46	Sig= nificancia	0.0= 02
Log likelihood ratio	13.= 578
Cli= entes	Est= adístico-t	33.= 934	Sig= nificancia	0.0= 06
Log likelihood ratio	192= .03