Analysis Of The Research On Commercialization And The Decline Of Joint Liability Microcredit
From the model, the authors find the “welfare, interest rates and market scale results for the modelled non-profit, for-profit monopolist and competition cases, while varying the level of social capital (S) as key independent variable”. The key main findings of the paper are as follows. In regards to borrower welfare, the authors interpreted that borrower utility is higher under a non-profit lender compared to a monopolist for both IL and JL, which reflects the notion that non-profit lenders transfer their profit onto their lenders. For interest rates, the monopolist charges substantially higher interest rates for IL compared to non-profit interest rates for JL, which reflects the increased risk involved in IL. Market scale is larger under JL compared to IL. Furthermore, the level of social capital of a borrower affects whether they are offered IL or JL by the lender. Intuitively this is sound as the lender assesses the borrowers ability to repay the loan before offering it. If the borrower is financially better off they will offer them a high risk loan compared to them being poorer and hence being offered IL. In regards to the differing market structures the authors found that “non-profit and competition achieve similar performance despite the externality under competition”. However, they also determine that constraining the market power of the monopolist improves borrower welfare.
To further reinforce the reliability of their results, the authors conduct a sensitivity analysis and regional analysis. They found that welfare under a monopolist lender is not sensitive to any of the parameters. This makes intuitive sense because the non-profit passes profits onto lenders and the monopolist keeps profits for itself. Furthermore, in the regional analysis they applied the results they found to the different regions around the world. The key findings from this analysis accurately reflect the nature of the markets around the world. For example in Eastern Africa the model predicts only IL lending under all market structures. This reflects the poverty of the region and thus the borrowers would have lower social capital, meaning they are higher risk of not paying their loans back and are thus offered IL.
The results found in this paper agree with the hypothesis from the beginning of the paper, that is, the monopolist exploits the borrowers social capital. Furthermore, the results put a positive light on competition in the microfinance industry as it alleviates the market power of the monopolist. The authors correctly interpreted the results in this case. All of the findings give insight into the functioning of the microfinance industry, which is helpful when analysing the different structures of the industry around the world. This paper could be useful for governments when assessing the structure of their microfinance industry within their own economy. Additionally the findings in this paper could be used by organisations such as the world bank when looking at developing nations around the world and how to best financially provide for their needs. Differences if writing paper nowThe microfinance industry has experienced recent changes to its structure. This includes the increasing number of microfinance institutions profiting from poor borrowers resulting in a transition from non-profit organisations to commercialisation.
This paper was written in response to these changes evident by the analysis of market structure when looking at borrower welfare. Being a mere four months old, this paper should accurately reflect the current state of the microfinance industry, however the authors duly noted how the model could progress to more accurately reflect the nature of the microfinance industry. That is by modelling “coercive loan collection methods by lenders”. XXX conducts an analysis into the microfinance industry in their paper XXX.
A key issue to note in this paper is the use of old data. Despite the paper being written in 2018, data from 2009 MIXMarket. org was used, when estimating the key parameters. Despite that data being old, as outlined previously, it is still accurate and validated. If rewriting this paper alternative data sources should be investigated in an attempt to find a more updated source. However it is important to note that de Quidt el al (2016) also use this data set, yet de Quidt et al (2018) use a MIX Market set which covers the years 2008-2014, which would be more current for this paper. Additionally, Scilab was used instead of Matlab to compute the data. The Scilab software is not as industrially recognised as Matlab. Thus, the results in this paper could be improved with a more up to date data set in a more sophisticated program. If re-writing this paper its important to consider whether we would keep the same assumptions. The key assumptions in the model are XXX, which are all justified. However the authors make notes of an assumption that isn’t right, which would be reconsidered if rewriting the paper.
Furthermore, the authors refer to the extending the model to include heterogeneity, which accounts for the differences in borrowers. This is an important extension of the model as it makes it more applicable to the real microfinance industry. Thus this paper could be improved by further investigating the effects of heterogeneity on the model and explaining the differing results from including it in the model which will make it more relevant today.
