Plotting your course in the unchartered seas of blended finance: three navigational tips
The business model for development finance institutions targeting the private sector (DFIs) is relatively straightforward, with the following assumptions (which is not to say those postulates are easy to implement). First, investments are good for development [1]. Second, DFIs extend financing on a commercial basis [2] and are financially self-sustainable [3]. Third, DFIs are additional – their contribution goes beyond what is available, or that is otherwise absent from the market [4]. Under these premises, interventions where economic and social returns exceed private returns are justifiable from a development economics standpoint.
Things get trickier when you operate on a concessional basis (a fancy word for subsidy), the uncharted seas of blended finance. Granted, to the extent public money is involved, the boundaries between concessional and commercial are flimsy at best, see Figure 1. The distinction between “implicit” and “explicit” subsidy [5], or “soft” and “hard” blended finance [6], is to a large extent an artificial, yet practical construct, as there is a difference in magnitude that should not be ignored. For the sake of this article, let’s assume a clear dividing line between commercial and concessional finance.
This article provides three decision making precepts that are implementable by any financial institution familiar with development issues (see Figure 2). I dubbed them “navigational tips”, as I have been longing for a nautical metaphor.
Blending has raised some eyebrows in the development finance community. From the criticism that it was diverting Official Development Assistance (ODA) resources away of recipient countries governments, to the argument that DFIs were using concessional finance to outbid and crowd-out competition (whether public or private), you name it [7]. Spoiler alert, those are warranted risks.
Notwithstanding, blended finance, when done right, can be a powerful instrument to unlock markets and spur economic development [8]. Be that as it may, it’s hardly the “silver bullet” that can tackle all problems at once [9], and trust the practitioner, it’s darn hard. Alas, praises or invectives aside, there is little guidance that would provide practical directions on what success ought to look like [10]. IFC recently published a paper that proposes a qualitative framework to guide investment decisions in blended finance [11]. The relative novelty in this article is to argue for a complementary set of quantitative and logical tools to improve decision making. None of them come out of the blue and are referenced accordingly. While featuring their own imperfections, they have the benefit of translating complex qualitative considerations into comparable quantitative metrics, providing a complementary sanity check. Finally, I am mindful that the format of a LinkedIn article is naturally constrained, to the detriment of important nuances, detailed assumptions, relevant exceptions, etc., but so be it. I hope I will be writing more on the topic; this is meant to test the waters so to speak.
First navigational tip: Marginal social returns net of subsidies should exceed private returns.
It is traditional wisdom that classical economics is unkind to public interventionism, let alone subsidies. In a 2015 article on blended finance, economist Paddy Carter provided useful groundwork challenging that oversimplistic statement [12]. Take positive externalities. An externality is the byproduct cost or benefit of an economic activity imposed to a bystander. In plain English, when I light a cigar in my backyard and my wife complains about the smoke, I am imposing a negative externality on her. Think of fossil fuel electricity generation and its impact on climate change and air quality, same idea. The inverse of a negative externality is, as I am sure you have guessed correctly, a positive externality. Those can be in turn subdivided into positive consumption and production externalities. Renewable energy – when uncompensated for the GHG emission it is abating – generates positive production externalities (as does infrastructure in general), see Figure 3 (right). Merit goods, such as education or healthcare are example of positive consumptions externalities, see Figure 3 (left). Going back to the opening paragraph, we can infer that DFIs operate in the positive externalities space (whether the subsidy they are extending is implicit or explicit). If a transaction generates economic and social returns exceeding private returns, then some portion of the social benefit is not being captured by the market (the reverse but less common argument can be made for negative externalities as well). Typical examples include job creation (hence the financing of small and medium enterprises) or infrastructure (in and itself, see Table 6 further in the article). Note that a transaction may still occur without any sort of public support, even if it generates a positive externality.
Externalities are one category of market failures, defined as an interference that cause the market economy to deliver an outcome that does not maximize efficiency. Inter alia, solving the issue (i.e. internalizing the price of externality in economists’ jargon) would improve market’s efficiency. Private actors could then set social costs equal to social benefits, thereby maximizing welfare surplus [13]. Economics theory provides several solutions to do so. First, private bargaining [14]. My wife co-owns our backyard, and therefore has a “right” to decide what’s going on out there. As such, she might be willing to leverage her co-ownership, say by having me walking our dog Ipanema any time it’s raining outside in exchange of letting me enjoy my robusto once a week [15]. Payments for environmental services work similarly to the extent rights and ownership can be established (which is precisely a challenge in many instances). Second, corrective taxes or subsidies [16]. We will focus on the subsidy side, since this is what concessional finance is all about. Figure 4 shows the workings of a corrective subsidy. Consider the consumption of bed nets in a malaria prone area [17]. Bed nets limit malaria risk, not only for its users, but also reducing transmissions, in turn decreasing child mortality rate and increasing labor productivity of an otherwise weakened labor force. Thus, bed nets generate positive consumption externalities. In Figure 4 (left), the current market equilibrium is Em, where marginal private cost (MPC, i.e. supply) intersects with marginal private benefit (MPB, i.e. demand), at price of Pm and quantity of Qm. The marginal social benefit (MSB) curve captures the positive externality of bed nets, where the desired quantity of bed nets consumed should increase from Qm to Qs (and the price from Pm to Ps). In other words, the current market setting prevents the consumption of a socially optimal quantity of bed nets. Suppose that we want to increase the consumption of bed nets by making them more affordable. We can reach Qs by subsidizing supply (Figure 4, right), thereby shifting our supply curve to SubMPC (for subsidized marginal cost). In doing so, we are providing a subsidy S, equal to Ps minus Psub (the latter being the equilibrium price where my new supply curve meets demand).
So what? Well, not only this provides a theoretical justification for subsidizing markets, but it also caps the amount of such subsidy to the externality it is trying to correct.?Plugging this to our initial assertion that “any intervention where social returns exceed private returns is justifiable from a development economics standpoint”, we can derive our first navigational tip that marginal social returns net of subsidies should exceed private returns [18]. The concept of marginal – a synonym for incremental – warrants additional explanation. Within the perimeter of concessional finance, we tend to think of additionality as a binary outcome, where a project would not occur without concessional financing, ergo the entirety of social returns should be considered. In some instances, however, a subsidy may be exclusively targeting incremental impact. For example, incentivizing the training of women technicians in an energy generation project that otherwise does not require an explicit subsidy to be financed. Using the concept of marginal allow us to consider the two types of projects.
Plotting our first navigational tip is relatively easy. It is not uncommon for development practitioners to determine economics rate of returns (ERR) for their interventions. In its simplest form, ERR is the project’s private rate of return (IRR), to which we would add up its social benefits not captured by the private transaction, as illustrated in Table 1. In Step 1, imagine a US$ 100, 5-year project, yielding US$ 30 of net cash flow per year. IRR is 15%. The project is mitigating 0.1 ton of C02 eq. per year and requires DFI financing on a commercial basis (no explicit subsidy). We will value our ton of CO2 eq. US$ 70 [19]. ERR is accordingly 25%, and ERR > IRR. Step 2, that same project could yield an additional 0.1 ton of C02 eq. per year for an additional capital expenditure of US$ 10 (not yielding any private returns). Let’s assume the extreme case that we are subsidizing upfront 100% of these costs. IRR is unchanged since the cost of the additional investment is paid fully by the subsidy. Conversely, the subsidy is considered a negative cashflow when calculating the ERR from the incremental intervention, which stands at 20%. ERR > IRR, therefore the transaction passes. Step 3 – still in Table 1 – shows a simpler way, where we calculate the rate of return of the subsidy, relative to the incremental impact it is generating. In that case, as long as the rate is above zero, we are good to go.
Alternatively, we can calculate the subsidy amount per unit of impact (say one ton of CO2eq abated) and compare it to the unit social cost of said impact. In our example we are getting 0.5 ton for US$ 10 (nominal), i.e. US$ 20 per ton, which is more attractive than the social cost of US$ 70 per ton. By that token, the US$ 1.68 subsidy per ton of CO2eq paid by the World Bank’s Climate Auctions Program (WB Climate Auction) [20] looks quite attractive when compared to a US$ 70 social cost of carbon.
Second navigational tip: Subsidy amount should be lower than its opportunity cost times its additionality probability (with impact held constant)
Ever heard of the infamous Sustainable Development Goals (SDGs) financing gap? First tip sounded quite all right in the utopian world of infinite subsidies. Thing is, whereas all estimates point towards a yearly US$ 1 trillion-ish gap [21], Official Development Assistance (ODA) barely exceed US$ 150 billion a year [22]. Further, we should hardly expect domestic resource mobilization (i.e. taxes and related) to make up for the difference in the short run (although it is certainly a laudable objective to strive for). As Charles Kenny – from the Center for Global Development – reminds us, “ODA diverted to subsidize private firms is usually at the cost of aid for other uses”. Back to our previous example, if our project were to occur at the exact same moment than the latest WB Climate Auction, then our US$ 20/ton would have compared poorly to the auction’s US$ 1.68/ton, even as it still delivers a robust ERR. In theory, we would always want to ensure that the subsidy price tag of our intervention is more competitive than its opportunity cost. In practice, determining the marginal subsidy cost of the next readily available aid project is like randomly throwing an 1866 Whitehead torpedo at a full-speed modern submarine located somewhere within a 10 nautical miles radius at an unknown depth. You’ll be very lucky to get a hit. Nevertheless, you can rely on proxies, or determine a minimum subsidy rate of return threshold (which in turn could be based off relevant benchmarks) [23]. In our previous example, we could set a minimum 700% social return rate on subsidy or a subsidy cap of US$ 2 per ton of CO2eq. In that case our project would no longer pass with a subsidy of US$ 10 but would work with a subsidy of US$ 1, as shown in Table 2. In IDB Invest’s blended finance team, we use the latest WB Climate Auction as a comparison point for climate mitigation blended finance projects. The auction may not be a perfect benchmark, but it is worldwide, repeated periodically, and the price is competitive relative to other carbon markets, both compliance and voluntary.
Finally, we can go for the subsidy auctioning route, although that may raise some additional operational headaches [24]. Further, I would still argue in favor of a minimum threshold or proxy even when tendering subsidies.?
We could leave it at that, but then we need to factor in additionality. As a reminder, additionality, in the context of private sector operations, is defined as a contribution that is beyond what is available, or that is otherwise absent from the market, and should not crowd out the private sector [25].
Additionality is like an inverse Schr?dinger box. Once you opened it, you no longer know if it was there. The only way to know if a contribution is additional is to do nothing and wait for the counterfactual to occur, which could be tomorrow, ten years from now, or never. That is obviously a problem – even more so in areas like climate change, where timing does matter, and today versus ten years may possibly determine humanity’s fate. That additionality is “unobservable” doesn’t imply it should be ignored all together [26]. Once again, let’s revert to our project, to which I am now allocating US$ 1 of subsidy. Imagine that we could determine for sure that the additional investment would happen without subsidy, but still decide to subsidize it. That means that we have “wasted” US$ 1 which we could have used elsewhere (and in the process contravene with the blended finance principle of minimum concessionality). Additionality has been around long enough that a number of DFIs have developed qualitative and quantitative tools to come up with an ex ante proxy of the degree of additionality. To the extent those tools are based on a numerical scale, the later could be translated into a probability and added to our formula [27]. Thus, subsidy amount should be lower than its opportunity cost times its additionality probability. If this sounds too complex, a simpler approach would be coming up with say five levels of additionality (certain, probable, possible, unlikely, no way) and translate it to a ratio (1, 0.75, 0.5, 0.25, 0).
Alternatively, you could do the reverse exercise, by determining the additionality breakeven. This consists of determining what should be the level of additionality to meet a determined social return threshold. Sticking to our project, where our desired return is 700%, then the additionality breakeven is 100%, implying that we had better have a good case on the project’s additionality before we proceed with the subsidy.
If you are still reading this, now is the time you probably think, “but hang on a second, shouldn’t I also factor-in spillovers that my financing will generate on the market”? For instance, I am subsidizing this first project for US$ 10 per ton of CO2eq but I know for sure that it will result in another four quasi-identical projects for no subsidy at all. Shouldn’t I also factor these subsequent projects? You can. The formula still works. In that case the average subsidy is US$ 2 per ton of CO2eq and you pass the opportunity cost test (assuming a 100% additionality). The elephant in the porcelain shop is how do you know for sure that this will lead to four other projects, and that the sole intervention needed was your initial subsidy (the causality conundrum). Once again, a breakeven analysis might be the way to go. The larger the required spillover for the formula to work, the higher the scrutiny level. Conversely, if a project is competitive on a standalone basis, at least you have that level of comfort and should run with it (not that it should prevent you from going after the catalytic impact). From a calculation standpoint, it may work the same way than the additionality breakeven.?Taking that initial US$ 10 per ton of CO2eq subsidy project, I know that I need to generate 500% of impact to breakeven, which the four subsequent unsubsidized quasi-identical projects will provide. For the avoidance of doubt, this should not discard investments that support long term transformation, even if they don’t deliver the best value for the money on a standalone basis. Evidence indeed suggests that such interventions may provide the better value for the buck in the long run [28]. Rather, the breakeven analysis provides an order of magnitude of the required spillover which can then be compared with market-level estimates to assess if expectations are realistic.
Finally, running the equation ex-post – where additional data points become available – may be helpful in an evaluation context (and informing future interventions). As a summary, Table 3 compares several examples in climate mitigation, where applying our second navigational tip shows that all projects are equally competitive and within our threshold. A byproduct of the table is that the amount of private capital in the transaction is irrelevant for the purpose of determining subsidy efficiency.
Third navigation tip: Blended Finance is unsustainable without a shift in the market equilibrium beyond the point directly induced by its subsidy
The claim that “I need to subsidize this first project for US$ 10 per ton of CO2eq but I know for sure that will result in another four quasi-identical projects for no subsidy at all” is somewhat of a challenge in economics terms (among other things). Let’s revert to our good old supply and demand curves in our bed nets example. We determined that with a subsidy S, I could increase my output from Qm to Qs. All else been equal, if I want to go from Qm to Qs’ where Qs’ > Qs , the required subsidy S’ will be bigger than S, as shown in Figure 5 (as long as my supply curve is ascending and my demand curve is descending).
Hence the expectation that subsidy is time-bound is untenable, and so is our second navigational tip when we consider a succession of projects. The exception? A rightward shift in either the demand or the supply curve that would move the market equilibrium beyond the point directly induced by our subsidy. A quick look at your dusty economics textbook reminds us that these shifts can be narrowed down to two categories: changes in the variables that are held fixed when building the curves, or solving a distortion that contradicts the assumption of efficient markets (i.e. a market failure). A summary is provided in Table 4 [29].?
Hence our final navigational tip, blended Finance is unsustainable without a shift in the market equilibrium beyond the point directly induced by its subsidy. This makes up for interesting combinations, such as supporting low carbon technology adoption in a country. The starting point is that renewable energy generates positive externalities. The technology itself is established. On the paper, it is more competitive than its substitute good (e.g. fossil fuel generation) [30], once reaching a minimum efficiency scale. The countries’ market ecosystem (commercial banks, corporate lawyers, project developers, etc.) and regulatory environment (untested regulation) may generate market failures (e.g., unwillingness of local commercial institution to provide adequate financing). This could motivate the use of concessional finance not only to establish the minimum scale for the technology to compete against the substitute good [31] but also supporting earlier entrants to generate knowledge spillovers available to subsequent market players (legal documentation are recycled, bankability is established by earlier financiers, fixed set-up costs are amortized, etc.) [32]. Blended finance thus becomes an exercise in identifying and financing positive externalities where spillovers or increased returns can be established and maximized.
Needless to say, this narrative comes with its own set of issues. It may be very easy to overpromise multiple future benefits to compensate for weak projects’ fundamentals. That the former may only be established ex post – in practice several years later, makes it even more pervasive (this is an issue that is not unique to blended finance, but development in general). In that context, it is paramount to (i) clearly establish a market-level investment thesis but also (ii) set-up adequate check and balances within the investment decision process, as established by the multilateral development banks and European DFIs blended finance principles [33]. However, these are not market-wide standards, and there are many other players accessing taxpayers’ money for blending in addition to DFIs subscribing to the principles.
Conclusion
I am mindful the above may raise more questions than it answers. Here’s a couple, as a “food for thought” conclusion.
Interested in the topic? More articles on Blended Finance?here.
Acknowledgement
My thanks to Paddy Carter, Daniel Hincapie, Elee Muslin and Vanessa Ruperez for their review and feedback, much appreciated. This post also draws on the work and lessons learned of IDB Invest’s Blended Finance team (Elee Muslin,?Joan Miquel Carrillo,?Pilar Carvajo,?Stephanie Eder, Eduardo Gutierrez and Ana Carolina Aquino).
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Erratum
Table 3 was updated on September 9, 2021 to correct some figures.?
Disclaimer
I am writing those articles from the practitioner’s point of view, recognizing that I am primarily relying on empirical (at best) or anecdotical (at worst) evidence. In blunter terms, I may very well end up being wrong. Blended finance is still at a nascent stage and any honest debate on the efficient use of concessional finance for development will contribute to strengthening the practice. Views are my own, and English is not my native language, so pardon my mistakes. Legalese version click here:?link.
Notes
[1]: Paddy Carter, 2015, Why subsidize the private sector? See §1.3. Retrievable here.
[2]: Sonny Kapoor, 2019, Billions to Trillions – A Reality Check. See the concept of “soft blending”. Retrievable here. Carter, Van de Sijpe and Calel, 2018 provide a similar explanation in §2 (see [4]).
[3]: DFIs are capitalized by their countries’ shareholders, some leverage their capital and AAA-ish rating to borrow on the markets (MDBs in particular). DFIs are generally financially self-sustainable and profitable, with the latter profits either re-invested in the organization or returned to shareholders. To what extent this relates to ODA consumptions is a bit of a headache, due to the complexity of ODA accounting rules (Kenny, 2019 makes the linkage, see [6]).
[4]: Paddy Carter, Nicolas Van de Sijpe and Raphael Calel, 2018, The Elusive Quest for Additionality. See §2. Retrievable here.
[5]: Kapoor 2019, see [3].
[6]: Charles Kenny, 2019, Five Principles for the Use of Aid in Subsidies to the Private Sector. Retrievable here.
[7]: Kenny 2019, see [6].
[8]: BloombergNEF, 2019, The Clean Technology Fund and Concessional Finance. Retrievable here.
[9]: Kapoor 2019, see [3].
[10]: In all fairness, there are already several papers that provide useful insight, some being referenced in this article.
[11]: IFC, 2021, Using Blended Concessional Finance to Invest in Challenging Markets—Economic Considerations, Transparency, Governance, and Lessons of Experience. Retrievable here.
[12]: Carter, 2015, see [1].
[13]: I am setting aside equity consideration for the sake of simplicity, see conclusion as well as Carter, 2015, see [1].
[14]: Look for Coase Theorem for more details.
[15]: To which she commented, when she reviewed the article, “that’s actually a good idea”. Ouch.
[16]: Otherwise known as Pigouvian taxes or subsidies, after British Economist Arthur Cecil Pigou.
[17]: The choice of bed nets is a nod to the controversy within development circles on whether these should be subsidized or not, see Poor Economics (Banerjee and Duflo, 2012) for a stint of it (also used as source for this paragraph).
[18]: Render therefore unto Caesar the things which are Caesar's, I did not come up with that on my own. For instance, Carter, 2015 and Kenny, 2019 toy with a similar concept, see [1] and [6] respectively.
[19] United States Government, 2014, Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis. Retrievable here. The US$ 70 per ton of C02 equivalent is a simplification,?approximately the high end of the Obama administration’s range assuming a 3% discount rate.?
[20]: Retrievable here. I am deducting the premium from the auction’s clearing price to derive the subsidy.
[21]: Plenty of sources, here are three in inverse chronological order: Homi Kharas and John McArthur, 2019, Building the SDG economy (retrievable here), Vitor Gaspar, David Amaglobeli, Mercedes Garcia-Escribano, Delphine Prady, and Mauricio Soto, 2019, Fiscal Policy and Development: Human, Social, and Physical Investment for the SDGs (retrievable here) and UNCTAD, 2014, World Investment Report 2014 (retrievable here).
[22]: OECD.
[23]: As similarly proposed by Carter, 2015, see [1].
[24]: Nancy Lee, 2017, Billions to Trillions? Issues on the Role of Development Banks in Mobilizing Private Finance. Retrievable here. Also see Kenny, 2019 in [6]. I am admittedly elusive when referring to the operational constraints. Not trying to be dismissive but this is somewhat beyond the scope of the article.
[25]: EBRD et al, 2012, Multilateral Development Bank Principles to Support Sustainable Private Sector Operations. Retrievable here.
[26]: Carter, Van de Sijpe and Calel, 2018, see [4].
[27]: A similar idea is provided in Carter, Van de Sijpe and Calel, 2018, see [4].
[28]: World Bank Group, 2018, Strategic Use of Climate Finance to Maximize Climate Action. See §1.5.3. Retrievable here.
[29]: On shifts in the demand and supply curve, most economics textbook should do the trick. I used Daron Acemoglu, David Laibson and John List, 2015, Economics. On market failure, this is based on Dani Rodrik, 2007, One Economics, many recipes: globalization, institutions, and economic growth.
[30]: BloombergNEF, 2019, see [8].
[31]: World Bank Group, 2018, in box 1-1. See [28].
[32]: Drawn from Rodrik, 2007, see [27].
[33]: ADB et al, 2020, DFI Working Group on Blended Concessional Finance for Private Sector Projects - Joint Report December 2020 Update. Retrievable here.
[34]: ADB et al., 2020, see [33].
[35]: EBRD et al., 2013, DFI Guidance for Using Investment Concessional Finance in Private Sector Operations. Retrievable here.
Finance Canada - International Branch
3 年Une très bonne analyse, merci Matthieu.?