SME Finance Policy Guide

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86 GLOBAL PARTNERSHIP FOR FINANCIAL INCLUSION ANNEX II Abatement Curve Model A significant amount of research has gone into quantifying the SME credit gap and identifying barriers such as reliable data with which to assess the credit quality of borrowers, incomplete property registration, or banks’ cumbersome processes. Jointly with the IFC, Oliver Wyman developed the SME Finance Gap Abatement Curve methodology, aimed at establishing a quantitative link between credit market reform initiatives (and implied investment) and the benefits of reducing barriers to SMEs’ access to credit. It creates an objective framework to help policymakers decide where to focus limited resources to generate the greatest return on investment (e.g., employment, tax revenues). The methodology was applied to a sample of six countries: France, Kenya, Morocco, the Philippines, Turkey, and the United Kingdom. A key conclusion of relevance to policymakers is that the payback of any reform varies between countries: that is to say, each country has its own abatement curve. A common conclusion is that financial contribution to lending schemes (e.g., credit guarantees) is considerably less effective that addressing improvements in the underlying credit infrastructure (e.g., asset register, availability of financial information). i) Identifying the key drivers of the “addressable” 101 financing gap: The model’s basic premise is that policymakers should focus on understanding the risk/return dynamics of SME lending and use this to steer the focus of their interventions. To the extent that such interventions run counter to basic free market principles, they tend to be unsustainable. With this in mind, policymakers should target the “addressable” financing gap with interventions that will change the state variables in such a way to minimize the distribution costs within the system, so that lenders maximize credit supply under reasonable return objectives. Three main cost components are the key drivers of an SME financing gap: Probability of Default (PD): How much of the gap is a result of the fact that lenders are unable to differentiate between high and low risk credits, and therefore suffer from adverse selection? Even in markets with a structurally high default rate, sophisticated lenders will be able to identify – and finance – higher quality SMEs: this effect can be quantified via the PD of the SME loan portfolio “on the books” of lenders. Loss Given Default (LGD): How much is due to the fact that lenders suffer from high loss rates in the event of borrower default, because of their inability to recover on collaterals held against the loans? The ability to take – and recover on – collateral as security against the debt is a significant risk management lever: again, it can be quantified via the LGD suffered by lenders to the SME sector. Cost base: How much is due to inefficiencies in operating cost structures, or in higher than necessary capital and funding costs? The cost base of lenders to the SME market can be aggregated and its three major compo- 101 It should be noted, however, that this approach can only be applied to the “addressable” financing gap (i.e., the proportion of the total financing gap related to unnecessarily high distribution costs), and that neither this report nor the underlying model considers how best to tackle the “residual” financing gap.
G-20 SME FINANCE POLICY GUIDE 87 Figure 11 SME finance abatement curve 6,000 Probability of Default Loss Given Default Cost Base Financial institution capacity building Incremental tax revenues (USD $MM) 5,000 4,000 3,000 2,000 1,000 SME capacity building Financial institution capacity building Financial infrastructure: Institutional component Financial infrastructure: Related laws and regulations The steepness of the line indicates the size of the tax multiplier Financial infrastructure: Collateral registries Public support mechanisms Financial institution capacity building Financial infrastructure Public support mechanisms 0 2,000 4,000 6,000 8,000 10,000 12,000 SME finance gap (USD $ MM) nents can be compared against best-practice benchmarks to determine the extent of unnecessary cost. ii) Quantifying the benefits of closing the “addressable” financing gap The model next uses multiplier effects to calculate the second order effects that closing the addressable financing gap will have in the economy (part of which will flow back to government in the form of taxes). Given an acceptable rate of return for the taxpayer, this approach provides a framework for governing investment in intervention policies to reduce the SME finance gap. The Oliver Wyman model applied this methodology on the same six markets examined previously. Given the uncertainties, the investigators aggregated the results of the sensitivity analysis into three categories: • Low (0-15%) – least important driver • Medium (15% - 50%): driver with significant, but not primary, impact on the gap • High (>50%): driver with the greatest likely impact on the gap Furthermore, the model only presents the results for the second order effects of closing the gap (i.e., the multiplier) at an aggregate level, as given below: Impact of Drivers on Financing Gap UK France Morocco Turkey Kenya Philippines Tax Multiplier (average) “Addressable” gap 13% 32% 45% 115% 82% 229% Drivers of gap Probability of default Medium Medium Low High Medium Medium Loss Given Default high High High Medium High High Cost base Low Low Medium Low Medium Low ˜0.35 ˜0.17 ˜0.17 Tax Multiplier ˜0.13 ˜0.17 ˜0.27 ˜0.20 0.31 ˜0.45 ˜0.25
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