The impact of financial crisis on MFIs performance in Zimbabwe

Hlupo, Patience; Mukute, Tafadzwa; Chinoda, Tough; Chagwedera, Edson Article The impact of financial crisis on MFIs performance in Zimbabwe The Journal of Entrepreneurial Finance (JEF) Provided in Cooperation with: The Academy of Entrepreneurial Finance (AEF), Los Angeles, CA, USA Suggested Citation: Hlupo, Patience; Mukute, Tafadzwa; Chinoda, Tough; Chagwedera, Edson (2022) : The impact of financial crisis on MFIs performance in Zimbabwe, The Journal of Entrepreneurial Finance (JEF), ISSN 2373-1761, Pepperdine University, Graziadio School of Business and Management and The Academy of Entrepreneurial Finance (AEF), Malibu, CA and Los Angeles, CA, Vol. 24, Iss. 2, pp. 1-25, https://doi.org/10.57229/2373-1761.1415 , https://digitalcommons.pepperdine.edu/jef/vol24/iss2/4 This Version is available at: https://hdl.handle.net/10419/264429 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/4.0/ The Journal of Entrepreneurial Finance The Journal of Entrepreneurial Finance Volume 24 Issue 2 Winter 2022, Issue 2 Article 4 4-2022 The Impact of Financial Crisis on MFIs Performance in Zimbabwe The Impact of Financial Crisis on MFIs Performance in Zimbabwe Patience Hlupo Ms Bindura University of Science Education Tafadzwa Mukute Mr Bindura University of Science Education Tough Chinoda Ph.D. Women's University in Africa Edson Chagwedera Mr Ezekiel Guti University Follow this and additional works at: https://digitalcommons.pepperdine.edu/jef Part of the Entrepreneurial and Small Business Operations Commons, and the Finance and Financial Management Commons Recommended Citation Recommended Citation Hlupo, Patience Ms; Mukute, Tafadzwa Mr; Chinoda, Tough Ph.D.; and Chagwedera, Edson Mr (2022) "The Impact of Financial Crisis on MFIs Performance in Zimbabwe," The Journal of Entrepreneurial Finance : Vol. 24: Iss. 2, pp. 77-102. Available at: https://digitalcommons.pepperdine.edu/jef/vol24/iss2/4 This Article is brought to you for free and open access by the Graziadio School of Business and Management at Pepperdine Digital Commons. It has been accepted for inclusion in The Journal of Entrepreneurial Finance by an authorized editor of Pepperdine Digital Commons. For more information, please contact bailey.berr[email protected]. 1. Introduction The philosophies of microfinance originated in Europe with the establishment of the pawn shops in the 15th century as another possibility to usury money-lending. The financial cooperatives were established in Germany in the 1800s by Friedrich Wilhelm Raiffeisen and his followers. These cooperatives had a co-business of improving the well-being of the urban and rural people. In the early 1900s, Latin America and elsewhere witnessed the appearance of savings and credit activities (Helms, 2006). Private Banks and government agencies developed new banks for the poor to promote investments through mobilisation of ‘idle’ savings. Microfinance in Zimbabwe originated in the 1960s when people formed savings or funds clubs through joining gatherings with the casual acquiring from family and companions (Mago, 2013). In the 2000s, microfinance in Zimbabwe, grew exponentially due to several elements that lead to the solemnisation of the microfinance sector. Zimbabwe has a population of approximately 13 million, of which almost 70% reside in rural areas and no less than 72% reside in poverty, with about 80% rate of unemployment, (Mago, 2013). Currently, the microfinance sector is the biggest employer in Zimbabwe. Financial crisis significantly affects performance of MFIs especially when there is assets and liabilities mismatch in currency which poses severe economic threat. Scholars have researched on MFIs and social performance neglecting the sustainability of such institutions (Hossain & Khan, 2016; Bhanot & Bapat, 2015; Nurmakhanova, Kretzschmar & Fedhila, (2015). The study therefore fills the gap by exploring the effects of financial crisis on MFI performance using the Vector Autoregression (VAR) Model opposed to previous studies which used simple linear regressions for data analysis. The study is structured as follows: Relevant empirical evidence on MFIs performance and financial crisis and the methodology are covered under section 2 and 3 respectively. Section 4 contains interpretation of the study findings. Lastly, conclusions and policy implications are discussed under Section 5. 2. LITERATURE REVIEW 2.1 Financial performance theories 2.1.1 The Theory of Market Power The theory of market power advocates that a product’s price is determined by forces of supply and demand, (Ito & Reguant, 2016). Firms operating under perfect competition are presumed to have no market power. Hence, each corporate has to admit the existing market 1 Hlupo et al.: Financial Crisis and MFI Performance in ZimbabwePublished by Pepperdine Digital Commons, 2022 price without trying to control it. This theory also posits that outside market forces enhances profitability and financial operations, (Ito & Reguant, 2016). In addition, it ascertains that firms with well differentiated product portfolios and large market share outdo their competitors and earn monopolistic retains. The theory subdivides into the relative-market power and the structure-conduct performance hypotheses. The relative-market power hypothesis entails that large financial institutions containing brand identification only influences pricing and increase profits compared to the structure-conduct performance proposition which states that concentrated markets results in lower deposit rates and higher loan rates due to reduced competition. 2.1.1.2 Efficiency Structure Theory Efficiency structure hypothesis opines that greater managerial scale of efficiency cause more profitability through higher concentration. Nzongang & Atemnkeng (2006) stressed that the balanced portfolio theory put forward a dissimilar dimension to the study of financial performance. The theory advocates that of the microfinance bank portfolio composition, its shareholders return and retained earnings are a result of the management’s decisions and firm’s policy decisions. The theory concludes that internal and external factors impacts on financial performance. The efficiency structure theory has two hypothesisscale efficiency and the X-efficiency hypothesis. The scale-efficiency proposition states that microfinance banks attain reduced costs due to better scale of operation. Firms grow fast as a result of reduced costs which lead to more profit. The X-efficiency hypothesis states that microfinance banks with improved practices and management regulate costs and raise profits. 2.1.2 Monetarist View of the financial crisis The Monetarists view financial crisis as a form of appearance of the banking crisis where the stability of the financial system is at risk if the central bank chose not to intervene. Friedman and Schwartz (1963) opine that the state of panic results in banks failures. Bank failure results in a contraction of money supply and reduced public confidence, and this leads to the evolvement of the crisis. So banking crisis usually occurs when financial systems become insolvent or illiquid resulting in acquisitions, fusions and need for government assistance on a large-scale. To solve the crisis, the monetarists advocate for increasing money supply which results in re-inflation of the economy so as to counter the monetary reduction. Therefore, the inflation has been recognized as a monetary phenomenon which cause of financial crisis. The 2The Journal of Entrepreneurial Finance, Vol. 24, Iss. 2 [2022], Art. 4 https://digitalcommons.pepperdine.edu/jef/vol24/iss2/4 inflation is related to money supply and interest rates since a growth in inflation culminates in a hike in interest rates. 2.1.3 The hypothesis of financial fragility Minsky (1992) pioneered the hypothesis of financial fragility. The concept sought to elucidate the problem of indebtedness during a revival period. Kindleberg (1978) opines that there exist shocks in the financial system which greatly impact on profitability in already existing or new sectors. The shocks which expose untapped profit opportunities include events such as the evolvement or the end of a war, a popular new technology, or changes in the monetary policy. The borrower transfers their finances to new areas of profit, financing the economic boom and backing the increase in the money supply. The investors’ euphoria appears and thus the financial system begins to become fragile. Bubbles defined as excessive price increases in several areas are created by the irrational behaviour. Minsky advanced Irving Fisher’s approach by introducing the fragility concept in an endeavor to elucidate the magnitude of indebtedness in an economic upswing. Minsky (1992) divided crisis into stages, -replacement, euphoria, climax and panic. 2.1.4 Marxist theories Global recurrent major depressions at the pace of 20 and 50 years have been the catchphrase since Sismondi’s (1773–1842) time. Grossman, (2018) critiqued the assumption of the supply and demand equilibrium of the classical political economy. The mature work of Karl Max was centred on coming up with a theory to explain economic crisis. The propensity for the profit rate to fall as outlined in Marx's law borrowed several features of Mill (1848) argument of the propensity of profits to decline. This theory is a consequence of the affinity towards the profits centralization. Capitalist businesses pay lower wages and salaries to their employees as compared to the price at which the products trade on the market. The profit made is initially appropriated towards recoupment of the business initial outlay. Ultimately, for the whole business sector, workers earn less while more is reinvested into the business in the longer term. The extent to which this theory stands depends on the government corporate tax rate, the rate at which the general public benefits from such taxes and the proportion of the employed versus the employers and investors. 3 Hlupo et al.: Financial Crisis and MFI Performance in ZimbabwePublished by Pepperdine Digital Commons, 2022 2.1.5 Coordination games Financial crisis models anticipate positive feedback from all market actors. This happens when a slight change in economic fundamentals attracts a disproportionate shift in asset values as market participants react similarly and promptly. More specifically, currency crisis models imply that long-term stability in the exchange rate due to a fixed exchange rate system suddenly ends when government funding declines or when economic conditions change. Some theorists suppose that this phenomenon postulates the existence of more than one Nash equilibrium point in an economy. One such point is set when anticipated rise in asset values cause market participants to increase their holding of such assets. Diamond and Dybvig's model of bank runs in which savers who receive bad news, enter panic mode and withdraw their assets from the bank causing others to panic too and trigger a bank run, resonates with financial crisis models, (Diamond, 2007). 2.1.6 Minsky Theory Minsky’s (1992) post Keynesian financial fragility theory best explains a capitalist economy. He said that a closed economy is more prone to a financial crisis. Minsky posited that firms can choose among three financing options in line with its risk tolerance. The first approach is the hedge finance where expected income flows are matched with expected liabilities (both advances and related finance costs) periodically. The second option called speculative finance allows a firm to roll over debt because expected income flows are only sufficient to meet interest costs without paying off the principal amount The third approach is Ponzi finance where expected income flows are not enough to cover interest costs, such that the firm has to supplement its income with more debt or selling off some assets to meet its obligations every period. The income or market value of assets rises until they match periodic obligations. Financial fragility imitates business cycles. Following a recession financial institutions chooses hedging which is the safest. When profits rise due to economic growth, firms engage in speculative financing knowing that proceeds cannot cover all interest at any time. 2.1.7 Performance of Microfinance companies All organisational activities related to a particular period can be summed up by the term performance, (Kothari, 2003). Microfinance is frequently assessed by outreach performance which is the degree to which MFIs provide financial services to those previously financially excluded (Brown et al., 2005; Rahman and Luo 2010). Breadth of outreach in terms of total number of clients provided with access to financial services, is thus a critical indicator of MFI 4The Journal of Entrepreneurial Finance, Vol. 24, Iss. 2 [2022], Art. 4 https://digitalcommons.pepperdine.edu/jef/vol24/iss2/4 performance. This is an advancement in the measurement of MFI performance given that original performance measures were mainly aimed at assessing the achievement of social not financial goals, (Pankaj & Sinha, 2015). MFIs are supposed to ensure equitable distribution of financial resources and poverty reduction. However, the new thrust on measuring financial aspects of MFI performance has little appeal in development finance because it compares MFIs to banking institutions yet they are not supposed to compete with mainstream financial institutions but to stand in the gap left by them. It is criticised for rendering MFIs technically insolvent despite the fact that they meet their social goal. Appreciating that MFIs are social institutions will not require them to be treated as financially viable or sustainable like banks but that they meet their social goals as non-governmental organisations. A study conducted by Zeller and Meyer (2002) led to the “critical triangle of microfinance” concept-the need for MFIs to simultaneously manage the outreach problems (reaching the poor in terms of poverty depth and numbers), financial sustainability (meeting financial and operating costs over the long term) and the impact (having distinct effect upon client’s standard of living). 2.2 Empirical literature review Bela (2011) examined the impact the global financial crisis has on micro-finance in Asia and Central America. They found an inverse relationship between financial crisis and microfinance institutions (MFIs) performance since scarcer borrowing opportunities constrain lending growth, whilst asset quality and profitability are negatively affected by economic slowdown. The study also reveals that MFIs charge comparatively high interest rates to their customers who earn low incomes. In addition, it discloses that MFI performance is also correlated to shifts in global stock market performance. It also contains an empirical study of interest rates with the intention of informing policy decisions. Boyd, Levine & Smith (2001) investigated the impact of inflation on financial performance. The results show a significant nonlinear negative effect of inflation on MFI performance. As the inflation rate increases, the marginal impact on MFI lending activity and performance decreases. The study reveals that economies with rate of inflation above 15% experienced a discrete drop in financial performance of MFIs. Loppata and Tchikov (2017) examined the causal relationship between MFIs and economic development using transnational data in Germany for the period 1995-2012. In their study 5 Hlupo et al.: Financial Crisis and MFI Performance in ZimbabwePublished by Pepperdine Digital Commons, 2022 they investigated the causality relationship between MFIs and economic development using the Vector Autoregressive (VAR) model and the Granger causality test. They obtained data for 952 MFIs from 101 countries from MIX database and annual data used. They found a bidirectional relationship between economic growth and MFIs and performance. They suggested that future empirical research accounts for the geometric causality between microfinance and economic growth. They recommended policy makers to engage progressive and decisive action that considers the causality directions between microfinance and economic growth to alleviate poverty and promote economic growth. Wagner (2013) explored the link between real credit growth and crisis in microfinance using a baseline panel of 74 countries centered on yearly data from 2000-2009 for 722 MFIs. The researcher used the basic panel regression model in the methodology to analyse the growth trends in real credit of MFIs registered on Mix Market. In the study, credit growth depended on financial crisis, a time dummy. Results indicate that microcredit remain a main driver of credit booms that were dominant in traditional banking. Foreign capital inflows in turn exacerbated the credit boom. In conclusion, the study noted that MFIs have become less resilient to financial crisis by competing with traditional banks in international financial markets. Wagner and Winkler (2012) examined the exposure of MFIs to financial fragility in times of the global financial crisis using panel regression analysis. The independent variable used is financial crisis a time dummy variable and the dependent variable real credit growth which shows performance of MFIs. The researcher used secondary data obtained from Mix Market (2011) expressed in US dollars. The study provided strong evidence of a significant effect of large-scale financial crisis on the growth of MFI real credit. Dokulilova, Janda and Zetek (2009) evaluated the exposure of microfinance institutions in financial crisis in Czech Republic. The study was to elucidate the problems of microfinance and the micro-finance institutions (MFI) sustainability in financial crisis. The study reveals that MFIs are often regarded as one of the most flexible and effective strategies in the fight against global poverty. 6The Journal of Entrepreneurial Finance, Vol. 24, Iss. 2 [2022], Art. 4 https://digitalcommons.pepperdine.edu/jef/vol24/iss2/4 2.3 Conceptual framework We proposed a model that outlines various variables which explain the impact of financial crisis on performance of MFIs as shown in Figure 1. We expected the relationship between inflation and performance to be nonlinear. Boyd, Levine and Smith (2001) indicate that there is an adverse linkage between inflation and MFI performance. Exchange rates should positively relate to financial performance, (Lagat & Nyadema, 2016). Financial crisis should negatively affect performance of MFIs. Economic growth is expected to positively affect performance of MFIs. The study expects a positive linear relationship between interest rates and performance since an increase in interest rates leads to higher profitability (Ngure, 2014). Figure 1: Conceptual framework 3. Methodology and Data 3.1. Diagnostic tests and Model specification 3.1.1. Stationarity tests It is vital to conduct stationarity tests before conducting VAR model, in order to decide on its appropriateness. Otherwise, the VECM model will be employed. We conducted Augmented Dickey Fuller test to determine the stationarity of study variables. A VECM is ideal if cointegrating equations can be estimated. If the level VAR model is used instead, consistent but inefficient estimates are obtained, (Sims, 1980). This study mainly intends to ascertain causal relationships and to obtain unbiased IRFs and VDs, as opposed to determining long- Financial crisis Financial Performance of MFIs Exchange Rates Lending Rates Inflation Money supply GDP 7 Hlupo et al.: Financial Crisis and MFI Performance in ZimbabwePublished by Pepperdine Digital Commons, 2022 Source: Authors’ computation, 2019 Figure 1. 1 Impulse Response Ananlysis The result s of the study shows a bidirectional causality between inflation rates and exchange rates as shown by strong oscillatory response of inflation rates to a shock in exchange rates and vice versa. Lastly, the response of exchange rates to a unit shock in GDP creates strong oscillations, implying how GDP impacts exchange rates and not the opposite. 4.4: Vector Autoregression Results Table 8: VAR Results D_pfmc D_ER D_FC D_GDP D_LR D_MS D_INFLN D_pfmc(-1) D_pfmc(-2) -.55225 (.1513) [-3.6490] -017815 (.14068) [-.12664] .00571 (.0066) [.86854] .006425 (.00611) [1.05086] .00135 (.0020) [.67651] .001767 (.00187) [.94666] -.02558 (.0900) [-.28422] -.008006 (.08365) [-.09570] .03218 (.0470) [.68428] .053623 (.04372) [.99782] -.02697 (.1232) [-.21889] .002366 (.11454) [.02065] -.06302 (.0816) [-.77228] -.044632 (.07585) [-.58842] -40 0 40 2 4 6 8 10 Response of D_PERFORMANCE_ to D_MONEY_SUPPLY_ -40 0 40 2 4 6 8 10 Response of D_PERFORMANCE_ to D_FINANCIAL_CRISIS_ -40 0 40 2 4 6 8 10 Response of D_PERFORMANCE_ to D_LENDING_RATES_ -40 0 40 2 4 6 8 10 Response of D_PERFORMANCE_ to D_GDP_ -40 0 40 2 4 6 8 10 Response of D_PERFORMANCE_ to INFLATION -2 -1 0 1 2 2 4 6 8 10 Response of D_EXCHANGE_RATES_ to D_GDP_ -20 0 20 2 4 6 8 10 Response of INFLATION to D_EXCHANGE_RATES_ 14The Journal of Entrepreneurial Finance, Vol. 24, Iss. 2 [2022], Art. 4 https://digitalcommons.pepperdine.edu/jef/vol24/iss2/4 D_ER (-1) D_ER (-2) 21.71690 (4.9837) [4.3576] -2.74604 (4.6826) [-.58643] .280005 (.21660) [1.29271] 0.241087 (.20352) [1.18460] -.095905 (.06612) [-1.45042] -.096194 (.06213) [-1.54832] -1.820302 (2.96359) [-.61422] -2.466127 (2.78455) [-.88565] .102462 (1.54881) [.06615] -3.33453 (1.45525) [-2.29139] 5.570567 (4.05769) [1.37284] 9.444419 (3.81255) [2.47719] 6.801122 (2.68713) [2.53100] -7.184858 (2.52480) [-2.84571] D_FC (-1) D_FC (-2) 60.75358 (20.979) [2.8959] -9.79885 (34.076) [-.28756] 1.084652 (.91180) [1.18957] 2.512578 (1.48103) [1.69650] .058820 (.27834) [.21132] .071178 (.45211) [.15743] 1.377408 (12.4753) [.11041] -1.012393 (20.2636) [-.04996] 52.75462 (6.51979) [8.09146] -79.88447 (10.5900) [-7.54335] -1.379827 (17.0810) [-.08078] -42.10255 (27.7445) [-1.51751] 22.10489 (11.3116) [1.95418] 31.76388 (18.3734) [1.72880] D_GDP (- 1) D_GDP(-2) 1.596498 (.58708) [2.7194] -.607443 (.83741) [-.72539] -.004978 (.02552) [.19496] .017406 (.03640) [.47824] -.002238 (.00779) [-.28732] -.018638 (.01111) [-1.67751] -.528600 (.34911) [-1.51414] -.714891 (.49797) [-1.43562] .012921 (.18245) .07082] -.416430 (.26024) [-1.60015] 1.116399 (.47799) [2.33560] .200042 (.68181) [.29340] .460777 (.31654) [1.45566] .003354 (.45152) [.00743] 15 Hlupo et al.: Financial Crisis and MFI Performance in ZimbabwePublished by Pepperdine Digital Commons, 2022 D_LR(-1) D_LR(-2) -.259939 (.43837) [-.59297] .121747 (.38991) [.31225] -.061458 (.01905) [-3.22572] .044058 (.01695) [2.59986] -.004971 (.00582) [-.85476] -.008362 (.00517) [-1.61650] -.135775 (.26068) [-.52086] -.228267 (.23186) [-.98451] .444766 (.13623) [3.26475] -.321964 (.12117) [-2.65706] .488268 (.35691) [1.36804] .049332 (.31746) [.15540] -.024284 (.23636) [-.10274] .276820 (.21023) [1.31674] D_MS(-1) D_MS(-2) .081073 (.34656) [.23394] .821473 (.22044) [3.7265] -.019711 (.01506) [-1.30964] -.013288 (.00958) [-1.38694] .005609 (.00460) [1.21979] .002959 (.00292) [1.01156] .227090 (.20608) [1.10193] .059781 (.13108) [.45605] .104948 (.10770) [.97443] .124696 (.06851) [1.82021] -.445834 (.28217) [-1.58004] -.428633 (.17948) [-2.38822] .079712 (.18686) [.42659] .241909 (.11886) [2.03530] INFLN(-1) INFLN(-2) 2.158947 (.39789) [5.4259] -2.49043 (.52437) [-4.7494] .027672 (.01729) [1.60018] -.029573 (.02279) [-1.29763] .004887 (.00528) [.92579] -.007005 (.00696) [-1.00690] .219713 (.23661) [.92859] -.389298 (.31182) [-1.24847] .137349 (.12365) [1.11074] -.219249 (.16296) [-1.34540] .908887 (.32396) [2.80556] -.376197 (.42694) [-.88115] .772460 (.21454) [3.60060] -.064897 (.28273) [-.22954] C -2.77519 (8.3452) [-.33255] .117236 (.36270) [.32323] .171075 (.11072) [1.54508] 5.203858 (4.96254) [1.04863] 3.683799 (2.59349) [1.42040] -13.20780 (6.79462) [-1.94386] 1.802761 (4.49962) [0.40065] 16The Journal of Entrepreneurial Finance, Vol. 24, Iss. 2 [2022], Art. 4 https://digitalcommons.pepperdine.edu/jef/vol24/iss2/4 The interpretation below is for significant variables which have t-statistics >2 Performance: Findings show that the lag 1 MFI performance strongly influences itself significantly as indicated by a t-statistic of 3.649011 > 2. This implies that a 1% increase in performance in the previous year cause a 0.552% decrease in performance. The findings shows an inverse relationship between lag 2 inflation and performance with a t-statistic of 4.749361 which is significant entailing that a 1% increase in inflation reduces performance by 2.49% . At lag 1, exchange rates positively influences MFI performance with a t-statistic of 4.357567 suggesting that a 1% rise in exchange rates results in a 21.717% rise in performance. Equally, financial crisis positively influence MFI performance at lag 1 with a t-statistic of 2.895899 implying a 1% increase in financial crisis accounts for 60.754% increase in performance. One lag of GDP positively affects MFI performance having a t-statistic of 2.719395. This implies that a 1% increase in GDP causes a 1.596% increment in performance. The lag 2 of money supply significantly affects performance with a t-statistic of 3.726539 which suggests that money supply predicts MFI performance. A percentage increment in money supply accounts for 0.821% increase in MFI performance. Money supply has a positive correlation with performance at lag 2 showing that if money supply increases, performance also increases. Inflation equation: Results indicates a significant strong influence of inflation lag 1 (3.600595>2) entailing that 1% increase in inflation in the lagged period resulted in a 0.772% increase in inflation. A 1% increase in exchange rates lag 1 lead to a 6.8% increase in inflation rates whilst a 1% increase in exchange rates lag 2 resulted in a 7.18% decrease in inflation rates. A 1% increase in money supply lag 2 resulted in a 0.24% increase in inflation rates. Exchange rates: Results shows that a percentage increase in lending rates caused a 0.061% significant decrease in exchange rates and lending rates lag 2 significantly influences exchange rates ( 2.599862>2) which is significant and entails that a percentage increase in lending rates causes a 0.044% increase in exchange rates. Lending interest rates: Findings shows that a percentage increase in exchange rates accounts for 3.334% decrease in lending rates and the result is significant (2.291389 >2). Also findings reveal a significant positive effect of financial crisis lag 1 on lending rates (8.091464 > 2). A percentage increase in financial crisis accounts for 52.75% increase in 17 Hlupo et al.: Financial Crisis and MFI Performance in ZimbabwePublished by Pepperdine Digital Commons, 2022 lending rates. Financial crisis lag 2 influences lending rates and the effect is significant (7.543354 > 2).A 1% increase in financial crisis caused a 79.88% decrease in lending rates. Lending rates lag 1 significantly influences itself (3.264751 > 2). A percentage increase in lending rates resulted in a 0.44% increase in lending rates. Money supply: The findings indicate that inflation lag 1 influences money supply and has 2.805556 > 2 which is significant. A 1 percentage increase in inflation caused 0.909% increase in money supply whilst a 1% rise in exchange rates caused a 1.38% fall in money supply. A 1% increase in GDP influenced a 1.116% increase in the money supply. Money supply lag 2 impacted itself and 2.388216 > 2 which is significant and suggests that a percentage increase in money supply caused a 0.429% decrease in the money supply. 4.5: Forecast Error Variance Decomposition Analysis Table 9: Variance decomposition of D_Performance Variance period S.E D_pfmc D_GDP D_FC INFLN D_ER D_MS D_LIR 1 18.3588 100.000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 2 24.9306 56.8895 2.93802 15.1790 13.3560 11.4111 0.08471 0.14170 3 30.0587 42.2755 2.91913 13.1640 14.4876 10.4431 12.5722 4.13854 4 35.0587 34.1439 3.52087 20.2852 14.1486 8.09122 16.1116 3.69871 5 45.7318 26.5313 4.39437 38.7256 12.1517 5.08408 10.7621 2.35100 6 52.4841 20.1472 4.62113 29.5842 18.4643 12.8830 11.0548 3.24543 7 67.3201 14.4737 7.00861 45.6970 14.3284 8.90918 7.35277 2.23386 8 72.3076 14.7289 10.2064 43.3466 14.2586 8.59440 6.93227 1.93784 9 75.3122 15.3645 11.1012 44.2009 13.1462 7.96704 6.40019 1.82000 10 77.3726 14.9816 14.6706 42.2276 12.5576 7.61714 6.22061 1.72488 Source: Authors’ computation, 2019 18The Journal of Entrepreneurial Finance, Vol. 24, Iss. 2 [2022], Art. 4 https://digitalcommons.pepperdine.edu/jef/vol24/iss2/4 Presented in Table 9 are the variance decomposition outcomes. We employed the analysis as further proof presenting additional detailed information relating the variance amongst performance and selected macroeconomic variables. We employed year 1 and year 2 to denote the short run period while year 10 represented the long-run. To comprehend the results in Table 9 above, we broke the analysis into short-run and longrun dynamics. In the short-run, 100% forecast error variance in the performance is accounted for by performance itself showing that the other variables in the model have no effect on performance. This means that, they have a strong exogenous impact. 56.89% of forecast error variance in performance in year 2 is significantly predicted by performance itself. The influence of other variables is increasing gradually but exogenous reveals a weak influence predicting performance in the future. 14.98% of forecast error variance of performance in the long run is influenced by performance and also by financial crisis which influences by 42.23%. So performance and financial crisis shows strong influence in the short-run and long run but as performance decreases other variables are increasing and dropping gradually but overall the influence is weak and insignificant in the long term. Hence, GDP is significant in the long run since it is increasing gradually, whilst money supply, performance, lending interest rates, financial crisis, exchange rates, and inflation are insignificant since they show an opposite trend. Table 10: Variance decomposition of D_FC Variance period S.E D_pfmc D_GDP D_FC INFLN D_ER D_MS D_LIR 1 0.24357 6.70639 10.6406 82.653 0.0000 0.0000 0.0000 0.0000 2 0.28168 5.70232 14.9470 62.830 8.2997 4.2035 3.6113 0.4059 3 0.28589 5.87504 14.8879 61.485 8.6717 4.5802 4.0135 0.4860 4 0.29384 5.61618 16.5005 58.826 9.2819 5.0040 4.2172 0.5540 5 0.29636 5.54638 16.8175 58.775 9.2263 4.9363 4.1503 0.5473 6 0.29769 5.52834 16.9870 58.481 9.4142 4.9131 4.1185 0.5571 7 0.29806 5.54377 17.0348 58.344 9.5034 4.9028 4.1146 0.5558 19 Hlupo et al.: Financial Crisis and MFI Performance in ZimbabwePublished by Pepperdine Digital Commons, 2022 8 0.29954 5.50068 17.0820 58.2758 9.62763 4.87132 4.07965 0.56286 9 0.30029 5.48921 17.1340 58.1219 9.72774 4.85210 4.11441 0.56063 10 0.30063 5.47735 17.0960 58.0531 9.78563 4.88351 4.12398 0.58040 Source: Authors’ computation, 2019 82.65% of forecast error variance in financial crisis in period 1 is explained by financial crisis itself and is strongly endogenous signifying a strong influence from its own variation. The other variables are exogenous and strong implying weak influence on financial crisis. In period 2, financial crisis is also forecasting itself into the future with 62.83% of forecast error variance. Other variable’s influence is still significantly weak in the period 2, implying that they contribute less in the future. In period 10, 58.05% of forecast error variance of financial crisis is strongly influenced by financial crisis. This entails that in the long run financial crisis continues to have influence on itself and other variables have an insignificant influence in this variable. 4.6: Long-run analysis Table 11: Johansen cointegration Trace Maximum Eigenvalue Hypothesized No. of CE(s) Eigen value Critical Value 0.05 Trace Statistic Prob** Max - Eigen statistic Critical value 0.05 Prob** None* 0.99758 125.615 326.218 0.0000 156.584 46.2314 0.0000 At most 1* 0.93877 95.7537 169.634 0.0000 72.6201 40.0775 0.0000 At most 2* 0.87163 69.81889 97.01398 0.0001 53.37283 33.7869 0.0001 Source: Authors computation 2019 Since the trace and max statistics exceeds 0.05, we reject the null hypothesis and conclude that there is cointegration among the variables as shown in the Table 11 above. This shows the presence of a long-run relationship amongst the study variables. 20The Journal of Entrepreneurial Finance, Vol. 24, Iss. 2 [2022], Art. 4 https://digitalcommons.pepperdine.edu/jef/vol24/iss2/4 Table 12: Normalised Cointegration Coefficients Performance MS LIR INFLN GDP FC ER 1.000000 1.129862 11.90746 -7.861845 1.07816 277.6148 -22.79190 (0.26413) (0.30087) (0.28052) (0.32802) (14.0899) (1.49281) Source: Authors’ computation 2019 The normalised cointegration coefficients results show that GDP, Financial crisis, interest rates negatively affects MFIs performance in the long run. Inflation and exchange rates positively influences MFIs performance in the long run. 4.7: Discussion of Findings Our study found an inverse relationship between lag 2 inflation and performance signifying that if inflation increases performance decreases and vice versa. These results are consistent with Boyd, Levine and Smith (2001) who also found a nonlinear relationship between the two variables. We found a complementary relationship between the first lags of exchange rate and performance in line with Lagat and Nyadema (2016). The positive relationship between exchange rates and performance reflects how the fluctuations and volatile exchange rates have contributed to the profitability of microfinance banks. The relationship between lag 1 financial crisis and performance was positive implying that an increase in financial crisis enhances performance. These findings contradict (Bela, 2011) who found a `negative linkage between the variables. This implies that financial crisis impacted adversely on MFI lending which suffered from scant borrowing opportunities, while financial crisis adversely affected asset quality and profitability. We found a positive relationship between lag 1 economic growth and performance which implies an increase in GDP increases MFI performance and vice versa in line with Sultan and Masih (2017). This reignforce results from a study by Loppata and Tchikov (2017) which also confirmed causal linkages running in both directions between economic growth and performance. According to their findings, progressive and purposeful action that considers the directions of causality between MFIs and economic growth verified in their study is taken to alleviate poverty and promote economic growth. The second lag of money supply positively impacted on performance signifying that as money supply is increased, performance increases and/or as money supply decreases, 21 Hlupo et al.: Financial Crisis and MFI Performance in ZimbabwePublished by Pepperdine Digital Commons, 2022 performance decreases. These findings approve findings of Meshak and Nyamute (2016) who concluded a positive relationship between money supply and performance. We also observed that interest rates have a direct relationship with performance and the results affirm the findings of Ngure (2014) who found a linear positive relationship between interest rates and performance. However in this study the positive relationship is insignificant negative at lag 1 since the t-statistic is less than 2, so this relationship is not considered. 5.0: Conclusions and policy recommendations Several conclusions can be drawn out of our study. The findings implies that in the short-run performance influences itself because other variable’s influence is strong exogenously which shows a weak influence on our dependent variable performance. In the second year of shortrun forecast error variance of financial crisis is 15.18% and in the long-run, the influence of financial crisis is 42.2% on performance. Findings from VAR entail an inverse relationship between lagged financial crisis and MFIs performance implying that an increase in financial crisis reduces microfinance performance and vice versa. Lagged exchange rates, money supply and GDP relate positively with performance showing that a rise in these variables causes MFI performance to increase and vice-versa. We found an inverse relationship between lag 2 of inflation and performance implying that a 1% increase in inflation reduces microfinance performance and vice versa. We recommended policy makers to enforce comprehensibility in MFIs so as to expose any form of earnings manipulation in their financial statements. This help to avoid a crisis especially given that performance influences itself to a greater extent. Tightening regulation of MFIs will also go a long way in ensuring their success. 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