Are microfinance markets monopolistic?

ABSTRACT

Do microfinance institutions (MFIs) operate in a monopoly, monopolistic competition environment or are their revenues derived under perfect competition markets? We employ the Panzar–Rosse revenue test on a global panel data to assess the competitive environment in which MFIs of five selected countries operate: Ecuador, India, Indonesia, Peru and Philippines, over the period 2005–2009. We estimate the static and the dynamic revenue tests, with analyses of the interest rate and the return on assets. We control for microfinance-specific variables such as capital-assets-ratio, loans-assets and the size of the MFI. The analyses also account for the endogeneity problem by employing the fixed-effects two-stage least squares and the fixed-effects system generalized method of moments. Our results suggest that MFIs in Peru and India operate in a monopolistic environment. We also find weak evidence that the microfinance industry in Ecuador, Indonesia and Philippines may operate under perfect competition.

KEYWORDS:

• Microfinance
• competition
• market structure
• dynamic panel estimation
• GMM estimation
• Panzar–Rosse revenue tests

JEL CLASSIFICATION:

• D4
• G21
• L11
• N20
• O16

Acknowledgements

The first author Kar gratefully acknowledges research funding from the Academy of Finland (grant number 260894) and Bali Swain gratefully acknowledge research grant from the Swedish Research Council VR/Uforsk.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ Operationally, there are non-profit- and social service-oriented MFIs (for example, the Grameen Bank and BRAC in Bangladesh) as well as commercially oriented MFIs (for instance, the Compartamos Banco in Mexico). Socially motivated MFIs put emphasis on providing subsidized credit to help overcome poverty, whereas the financially motivated MFIs emphasize financial sustainability of microfinance operations.

² MFIs need to fulfil their social objectives of reaching the very poor (the first bottom line) while attaining financial self-sufficiency (the second bottom line).

³ ‘Dynamic incentives’ link clients’ future access to credit with proper repayments of earlier loans to discipline them and ensure repayments on time.

⁴ For a detailed literature review on the assessment of competitive behaviour in banking see, for example, Turk-Ariss (2009).

⁵ It is also known as the new empirical industrial organization (NEIO) models.

⁶ The formal derivation of the H-statistic can be found in Panzar and Rosse (1987).

⁷ For further details, see: https://www.themix.org/resource/glossary/glossary

⁸ For a detailed discussion on the endogeneity between the capital-assets-ratio and total revenue see Delis, Staikouras and Panagiotis (2008).

⁹ The original Arellano–Bond ‘difference GMM’ model transforms the regressors by differencing and uses the generalized method of moments (Hansen 1982). A potential weakness of this estimator was revealed in later works by Arellano and Bover (1995) and Blundell and Bond (1998). The lagged levels are often rather poor instruments for first differenced variables, especially if the variables are close to a random walk. Their modification of the estimator includes lagged levels as well as lagged differences.

¹⁰ Individual MFI data are maintained in their publicly available information platform: www.mixmarket.org.

¹¹ The level of disclosure for each MFI is indicated through a ‘diamond’ system: The higher the number of diamonds, the higher the level of disclosure.

¹² Another country with significant history and vibrant presence of microfinance activities, Bangladesh, is excluded from the sample mainly due to non-availability of sufficient number of observations on selected MFIs that can handle statistical tests and dynamic panel data estimations as applied in this exercise.

¹³ The HHI and the GDP figures are not presented in the summary statistics, but they are available from the authors on request.

¹⁴ The results are available on request from the authors.

Additional information

Funding

The first author Kar gratefully acknowledges research funding from the Academy of Finland (grant number 260894) and Bali Swain gratefully acknowledge research grant from the Swedish Research Council VR/Uforsk.