August 2013

Green Growth Diagnostics

A do-it-yourself guide for green economy practitioners

A. H. Faure

2    

Introduction   This “Green Growth Diagnostics Manual” provides an introduction to the growth diagnostics method and how it can be applied to analyze constraints to green growth in a particular country context. “Growth diagnostics” is a practical economic exercise in deriving policy priorities developed by professors Dani Rodrik, Ricardo Hausmann, and Andres Velasco in 2004. Since its inception, the growth diagnostics approach has become widely used to analyze low-growth economies, especially within multilateral organizations and government agencies. In a world where policymakers often develop interventions in an ad hoc manner, growth diagnostics provides a systematic—though not foolproof—methodology for rationally prioritizing alternative policies. Growth diagnostics helps practitioners map out the full list of potential constraints to achieving growth (or some other policy goal) and then identify evidence to pinpoint which constraint is the most significant bottleneck. Easing the constraints in those critical areas that most choke economic activity (known as the “binding constraints”) will likely be the most cost-effective approach to galvanizing growth. Given that developing countries typically face severe financial limitations, knowing which types of reforms to target is valuable information. This present manual aims to guide green economy practitioners in applying growth diagnostics to identify the most binding constraints to green growth—which is to say, low-carbon, resource efficient, climate-resilient and socially-inclusive growth—in specific country contexts. Specifically, the manual demonstrates how to apply growth diagnostics at the level of a specific “green growth” policy problem such as, for instance, low levels of recycling in a given country, low investments in urban water quality, or over-exploitation of forests, among many other “green economy” challenges. First, the manual provides an overview of the mechanics of the original growth diagnostics method and presents a generalized growth diagnostics tree for analyzing low green growth. Then, the manual provides a step-by-step guide to building trees tailored to a specific green growth policy problem. The following section discusses how to then use evidence to determine “where” the country being analyzed is located on the explanatory tree—in other words, what the most binding constraint(s) might be. To illustrate the method, an example problem, namely low levels of recycling services, is developed throughout the manual.

How  does  growth  diagnostics  work?   The core insight of growth diagnostics is that policy problems—low levels of private investment, for example—depend very much on country context.1 The causes of a policy problem in one country may differ acutely from the causes of the same problem in another country. If the true binding constraint to growth in a country is, say, corruption, spending money on another, less binding constraint such as low human capital, could be wasteful. In that case, improving the national education system would not help accelerate growth—it would simply contribute to the brain drain to other countries, as the more educated find they can make a better living elsewhere. Accordingly, reforms that work in one country could wreak havoc in another. A necessary condition to designing well-functioning, high-impact reforms is to understand the root causes of a problem and determining which ones are the most impactful to address. This is where growth diagnostics comes in. When applying this method, the practitioner must act like a detective: he or she must systematically investigate all possible causes of the green growth problem and use clues—empirical evidence—to determine which one is correct. This entails, as a starting point, understanding the forces affecting the green growth constraint in the country, in order to then build a conceptual flow chart (or “diagnostic tree”) of its ultimate causes. The diagnostic tree is a tool for structuring the complex interaction of factors that have a causal link to the policy problem into a more manageable framework for analysis. The diagnostic tree starts with the fundamental                                                                                                                1 This manual refers to the level of analysis as being at the national level, but the methodology could also be applied at lower political units.

3    

problem being examined (e.g., reasons for low private investment in a country) and then splits into branches, with each branch providing one potential, conceptually distinct reason for the problem observed at the very first node. When possible, each node then splits into a further layer of branches providing distinct explanations for the above node, in a series of cascading branches, like an inverted family tree. The tree progresses downward from the top node—the policy problem stated at a high level of generality—and then progresses with increasing specificity through each stage of explanation, toward ultimate root causes. Once the full tree is developed, practitioners must then analyze price signals and other empirical data to back out the most binding constraints. Hausman, Rodrik, and Velasco, the creators of the original growth diagnostics, created a sample diagnostic tree to investigate potential reasons for low levels of private investment in a given country. Figure 1 provides a slightly modified version of their sample tree, which examines low levels of investment and innovation in “green” activities. Figure 1: Generic Green Growth Diagnostic Tree

 Source: Hausman, Rodrik, and Velasco, 2005. A caveat is in order—the above tree is a non-definitive, non-exhaustive example of how a growth diagnostic tree might be fleshed out for the stated, very general policy problem. There are no definitively “right” or “wrong” trees—the branches in the above example could have been organized in different ways—but there are more or less useful trees. And, given the complex, real-world scenarios that these trees are simplifying, there will always be some degree of overlap between branches: for instance, “insecure property rights” could be conceptualized as a market failure, not a government failure. Some root causes might well re-appear on both sides of a growth diagnostic tree, or there may be some circularity. Remember, a diagnostic tree is a heuristic tool to help structure thinking about a difficult problem; it cannot perfectly capture the complexity of the causality of policy issues. Diagnostic

4    

trees are useful in that they force practitioners to systematically consider i) all the potential causes of a policy challenge, ii) how they might relate, and iii) the range of evidence that could help identify what ill an economy is suffering from—much like a doctor will seize up a patient’s symptoms to determine the causes of a disease, so as to recommend better treatment. There have already been efforts to adapt growth diagnostics for green growth: see, for instance, OECD (2011) and Professor Harald Sander’s critique of this approach (Sander, 2011). The diagnostic trees developed in both approaches remained at high levels of generality, resembling the left-hand (“demand”) side of the tree in Figure 1. This manual pushes the concept further, providing a step-by-step approach, together with examples, to developing sector-specific level diagnostic trees, as well as identifying the type of evidence that could be used to determine where a given economy is on the tree.

A  do-­‐it-­‐yourself  guide  to  green  growth  diagnostics   The steps below guide green economy practitioners through the application of the growth diagnostic methodology with a focus on green growth issues. We use an example—that of low levels of recycling services in an economy—to illustrate each of these steps. This manual focuses on recycling as an example industry because it both stimulates pro-poor growth (by creating jobs) and conserves natural and energy resources, but also tends to be fairly undeveloped in most low- and middle-income countries.

Step 1: Framing the policy problem

The very first step to implementing the growth diagnostic methodology is to define the policy question whose root causes are to be identified. Even though the Growth Diagnostics approach, in its very title, refers to “growth,” the methodology can be applied to policy problems that are not, strictly speaking, related to growth. The issue to be

Box 1: How to read a growth diagnostics tree To illustrate how to read a tree, let’s look at Figure 1 above on low private investment and innovation in green activities. Notice how the driving policy question starts at the very top, at Node 1, because trees are read from top to bottom, and not from side to side. The tree breaks off in the first stage into two distinct potential reasons for the low levels: either the private sector is not able to profitably engage in this activity (Node 2: “low returns to activity”) or the private sector would in theory be able to engage in this activity profitably if it had the funds, but firms are not able to borrow the necessary funds to do so (Node 3: “high cost of finance”). For instance, interest rates may be too high for this activity. We then examine each of nodes 2 and 3 in turn. For node 2, the reasons for low profitability are grouped into two different categories: either an activity yields inherently low returns because there is a lack of complementary factors (e.g. poor infrastructure in the country, an inadequately skilled workforce, or unfavorable climatic or geographical conditions—say, the lack of a deepwater ports or airports) for this activity, or the activity could actually be very profitable but the private sector is unable to appropriate the gains. Reasons for this “low appropriability” (Node 4) could range from risk of government expropriation, corruption, or too-high taxes (represented as “government failures” in Node 7) to market failures (Node 8) such as barriers to competition or network externalities, for instance. On the investment “supply” side (the right-hand side of the tree), the high cost of finance for green investments and/or innovation could be due to poorly-functioning international and domestic credit markets. Financial markets could malfunction if, for instance, domestic savings rates are too low for domestic banks to be able to lend and international banks are also unwilling, or, more specific to green industries, if banks are reluctant to lend because they do not understand how to evaluate the risks of less traditional “green” investments.

5    

examined can also be more specific than the example presented in Figure 1. Green growth diagnostics could very well be applied to say, consistently low levels of adoption of energy efficient appliances in an emerging economy. Broadly speaking, green growth challenges can be broken up into three conceptual categories:

Type 1: Too little of a good thing: This is a problem of low outcomes—for instance, too little investment in water sanitation and transmission services at a city level. A low outcome problem is in essence a “growth” problem, to which the growth diagnostics approach lends itself easily. The question for this kind of green growth problem is, “what is holding this industry or sector back?”

Type 2: Too much of a bad thing: This is the classical externality problem, where pollution occurs as a result of

economic activity. Pollutants are never good in any quantity, but they are emitted because our society values the good and services resulting from polluting production processes. The question here is, “why is this industry producing sub-optimal levels of this pollutant?” Alternately, the question can be rephrased as, “why is this industry not investing (enough) in pollution abatement?” In this sense, this type of problem can be characterized, like the first type, as a “low outcome” problem.

Type 3: Too much of a scarce thing: This is the tragedy of the commons, namely the overexploitation of renewable

resources such as timber, fisheries and wildlife, or agricultural soils. Here, the question is, “why is this industry over-exploiting a scarce resource?” In contrast with the first two types of questions, this is not a low-outcome problem—quite to the contrary, it could be seen instead as “too much” growth in the sector. A diagnostics tree can still be developed for this type of problem—after all, trees are nothing more but root cause analysis diagrams—but they will be distinct from the other two types of “low outcome” trees (see Step 2 below).

Once the green economy challenge to be examined is characterized in this manner, it is informative to understand the issue in the country context, both over the course of the country’s history and across comparable countries. Practitioners may want to refer to Section 2 of UNEP’s manual on Using Indicators for Green Economy Policy Making (2013) about “indicators for issue identification.” The definition of a green economy challenge should consider the following aspects:

• Trends over time: How has the problem changed over time? For natural resources, declining stocks are indicative of a challenge; inversely, for pollutants, increasing trends may be cause for concern. However, no change over time could also indicate a problem—especially when government policy has targeted the issue, such as investments in green technologies or emissions reductions targets. Certain trends, in isolation, may not appear problematic, but are alarming when combined with other indicators. For instance, unchanging per capita water consumption, in conjunction with rapid population increases, still signals impending water shortages.

• International benchmarks: Comparing indicators among similarly-situated economies (when possible) helps give

a benchmark by which to measure whether the indicator is relatively low or high. Even when trends appear positive, if comparable economies show far more positive trends, this could be an indication of a problem, such as untapped opportunities. If, say, an economy is seeing slight increases in the share of renewable energies as part of their energy mix, but similar economies are seeing a boom, there could be a problem.

Understanding the historical and relative position of the country with respect to the policy problem will help better frame the overarching policy question in the first node, regardless of what type it is. Let’s say we were interested in promoting the growth of a recycling industry in a developing country. How should we frame this issue? To start, this would be a Type 1 “low outcome” problem as discussed above: low levels of a particular type of green activity. The issue in question could be characterized as “limited private investments in recycling,” and we could mirror the classical growth diagnostic tree from Figure 1. But we could move the analysis up one level if we reframe the issue as “low levels of recycling services”: this would allow an analysis not just of

6    

private sector behavior, but also of the demand for recycling services—those entities that may want to purchase recycled matter, or even citizens who would like to recycle in the first place. As this example demonstrates, the level at which one frames the question yields a more or less comprehensive view of the sector or industry.

Step 2: Splitting the first node

Now that Node 1 is created, the key action at this stage is to distinguish between different overarching reasons for why the policy problem exists and, when possible, to group them into conceptually similar categories. For low-outcome problems, the fundamental economic question to ask is, “is this a supply issue or a demand issue?” Two countries may have the same, low level of renewable energy penetration, but the reasons underlying this low investment may differ—one could have low supply (of funding) for renewable energy investments, while the other could have low demand for funding for renewable energy projects (few entrepreneurs want to enter this field). These are fundamentally different pathologies. So, for Type 1 “low-outcome” problems, the first step is to divide between supply and demand. The figure below demonstrates the initial split for the recycling industry example discussed above.

Figure 2: Splitting the first node for a recycling growth diagnostics tree

For Type 2 “pollution” problems, a similar division can be made. If the problem is rephrased as “low levels of pollution abatement,” we can still make a distinction between low demand for and low supply of abatement techniques and technologies for the pollutant in question. Type 3 “resource-overexploitation” problems, however, are not easily broken down into supply and demand branches. The problem here is not one of low outcomes; quite to the contrary, it is a question of too-high outcomes, as resources are being unsustainably harvested. To make a compelling diagnostic tree, another means of bundling the different reasons for the problem stated in Node 1 is required. There are different potential ways to do this, which may depend more specifically on the nature of the problem. To conceptualize such a division, it will be helpful to brainstorm some of the potential reasons explaining the overexploitation and to group them together into cohesive, distinct groups—causes that, if one were alleviated, would make little difference to solving the overall problem because the existence of another would prevent any improvement. Along these lines, one way to approach the over-exploitation problem is to consider what, exactly, the problem of over-exploitation entails, namely harvest rates that exceed the resource’s regeneration rate. So Node 1 could be split in two branches: “too high” harvest rates versus “too low” regeneration rates. Of course, what makes a harvest rate “too” high is relative to the regeneration rate. But this division points to two distinct aspects of the problem, namely the separate processes of stock regeneration and stock exploitation. For example, focusing on fishing intensity may not be very useful when the root of the problem is the destruction of spawning habitats of a given species (see, e.g., Figure 4 below).

7    

Step 3: Unfurling the tree from proximate to root causes

After the first decision node is split into distinct branches, the process should be reiterated “down” the tree for each node. Essentially, the process is to ask, “What might cause this?” for every observation at each stage. Much like in Step 2, answers that have a common trait should be grouped together to represent their conceptual similarities. For example, supply and demand branches can be drawn at several different levels of the trees—basically whenever there is a node that relates to low outcomes. Indeed, Figure 3 shows supply and demand branches splitting off at different tree levels. Of course, not all nodes can be broken into supply and demand problems, but other conceptual groupings can be used. Many problems can be categorized as “market failures”—i.e., when something is keeping a market from functioning as efficiently as it could. Often, these problems have to do with uncompetitive markets, externalities, information asymmetries, principal-agent problems, or public goods, among others. Other problems can be classified as “government failures.” For instance, the inability to pass or enforce laws and regulations, poor governance, high crime and corruption rates, and macroeconomic instability all fall in this grouping. Some problems, like poorly-defined property rights, are market failures, strictly speaking; however, diagnostic trees often classify them as government failures because of the intimate role of government in defining, tracking, and defending property rights. Figure 3 below presents the completed sample diagnostic tree for recycling. Note that certain causal factors, like “government failure,” appear on both sides of the tree.

Figure 3: Growth Diagnostic tree for recycling

Box 2: Explanation of selected nodes

Node 7: High cost of recycling investments: Entrepreneurs may not be able to raise the capital they need to build a processing plant, purchase a fleet of trucks to pick up recycling waste, or finance other costs of starting and operating a recycling business. If the domestic financial market is too weak (Nodes 16, 27, and 28) and foreign banks are not lending to make up for it (Node 15), then would-be recycling entrepreneurs may not be able to borrow in the first place. Banks may be unwilling to lend funds, or the interest rates they impose may be prohibitively high. Especially in regions where a recycling industry does not yet exist, lenders may not fully understand the risks of the industry and

8    

may be reluctant to fund it and/or impose high interest rates (Node 17).

Node 8: Weak regulatory environment: The absence of laws compelling or encouraging recycling (for instance, imposing fines for non-compliance, quantity-based pricing of (non-recyclable) waste disposal, and/or mandating separation of recyclable waste) makes it difficult to increase recycling participation. Flat fees for waste disposal are often the norm, which do not incentivize customers to recycle (see, e.g., Reschovsky and Stone, 1994). However, too-high fees on non-recycled waste could incentivize illegal disposal of waste.

Node 9: Perverse subsidies: Any government subsidies to industries producing virgin materials could artificially lower their price relative to recycled matter, which would make it difficult for recycled matter to compete in input markets.

Node 10: Lack of end-use market for recycled materials: Even with laws compelling recycling in place, recycling entrepreneurs may not want to enter the market if there is only low demand for their product (processed recycled matter) in input markets.

Node 11: Consumer behavior externalities: Consumers may not be aware that they have the ability to recycle or of the importance of doing so (Node 18), or they may find it too inconvenient, especially when it comes to separating different types of recyclables (Node 20). This can also be conceptualized as a principal-agent problem, where the government (the principal) would like residents (the agent) to recycle. There may also be split incentives problems, such as if landlords are charged fees for not recycling based on tenants’ behaviors (Node 19). A way to bring the costs and benefits of recycling more into line would be imposing quantity-based pricing of waste disposal, which is linked to the problem in Node 8. Social norms may also affect the willingness of citizens to recycle, even in the absence of fees.

Node 12: Absence of complementary factors: This refers to factors that are out of the recycling entrepreneur’s control but necessary to carry out a thriving business. See explanations for Nodes 21, 22, and 23 below, which are non-exhaustive examples of potentially missing complementary factors.

Node 13: Low or volatile prices of recycled matter on input markets: Prices of recycled materials (on domestic and/or international markets) for sale as inputs can be very volatile and can distort investments (Stromber, 2004). Such volatility increases the riskiness of entering the market. For instance, in South Africa, managers at buy-back centers indicated the difficulty they faced as a result of periodic slumps in the prices they could charge for paper-based recycled matter (Langenhoven and Dyssel, 2008).

Node 14: Low appropriability: This refers to a constraint in which gains from business are not fully captured by the firms, for a host of potential reasons (see, for instance, the explanations for Nodes 32 – 34).

Node 21: Few recyclables in the waste stream: There may not be enough recyclable materials in the waste stream to make the recycling industry profitable. In extremely poor areas, consumer packaged goods and disposable goods in general may not be purchased frequently, and/or those goods that are consumed may not contain materials that can be recycled.

Node 22: Bad infrastructure: In areas with very narrow or poorly-maintained streets, it may be difficult for entrepreneurs to efficiently pick up recyclable waste (e.g. with trucks), especially when coupled with difficult climatic factors or terrain. Likewise, a poorly-functioning electricity network will increase the costs of processing waste and will dampen demand for recycled matter as inputs.

Node 23: Inadequate technologies: Processing of recycling matter may be difficult if entrepreneurs lack the appropriate technologies (such as magnetic and X-ray separation technology for metal and plastics, respectively) to process recyclable waste efficiently and at scale.

Node 24: Skills: The levels of human capital in the economy may be too low to allow large-scale development of a recycling industry, in the sense that there could be a lack of both entrepreneurial and managerial talent as well as skilled labor.

9    

Node 32: Microeconomic risks: Entrepreneurs may face uncertainty in payoff from recycling investments due to government failures. The threats of government expropriation, high levels of crime, overbearing taxes, overregulation (e.g. rigid labor regulations) and corruption may deter would-be entrepreneurs from entering this fairly capital-intensive industry. For instance, in regions around Cape Town in South Africa, some buy-back centers have been forced to hire security guards to protect their suppliers when they drop off recyclable waste (Langenhoven and Dyssel, 2008).

Node 33: Macroeconomic risks: Risks of macroeconomic instability may also affect entrepreneurs’ bottom lines. For instance, if processed recycled materials are sold abroad, foreign exchange instability could be problematic.

Node 34: Self-discovery costs: “Self-discovery” refers to the process by which agents in an economy expand into new industries, with the learning curve that goes along with entering into a new space. There is a risk inherent in being the first to enter a market: entrepreneurs may not know what they have a comparative advantage in, and they fully bear the risks of failure but not all of the benefits of success (since other industries will enter the market to compete with them if they witness their success). There is also a question of coordination failure and network effects: certain industries require the existence of other economic agents in order to be able to thrive.

As another sample diagnostic tree, Figure 4 below presents an example of a “Type 3” diagnostic tree for fisheries overexploitation problems.

Figure 4: Fisheries overexploitation diagnostic tree

Step 4: Using evidence to locate an economy on the tree, or how to identify the most binding constraint

A diagnostic tree maps out, as much as possible, the entire universe of potential causes for an observed problem. The next step—and the most difficult one—is figuring out the location of a particular economy on the tree. This is

10    

equivalent to determining the most binding constraint to achieving a policy goal. The objective at this stage is to locate and analyze information to determine whether a given constraint binds or does not bind. When, at each node, the user selects one reason over others and progresses down a branch, the other branches can be thought of as being “discarded” as less important—in other words, less binding.2 The resulting root cause (the lowest level of the branch) is the policy area that policymakers should target. In practice, this means that the practitioner must identify, for each node and starting from the top, what type of events and signals one would expect to see if a given constraint were binding. To begin, what different signals would we see if the problem were a supply-side one, as opposed to a demand-side one? For supply-demand problems, basic microeconomics holds that if the problem is low demand, the price (of whatever thing is being examined) will be (relatively) low; if the issue is low supply, the price will be (relatively) high. Low supply indicates a binding constraint, whereas low demand does not. With respect to supply and demand for investments, the price is the interest rate; so, for instance, if the issue being examined were low levels of renewable energy investments, the average interest rate for this type of project would be the relevant price to examine in order to determine whether finance is a binding constraint. At any given node, one must select which of the mutually-exclusive branches to go down—where does the empirical evidence best fit the state of the world implied by each branch? Figuring this out involves “detective work,” where the practitioner must draw upon a range of evidence to search for telltale clues that a given constraint is the tightest bottleneck. These clues may involve analyzing price signals (interest rates, wages, etc.), results of policy interventions or natural experiments affecting the constraint, aggregate macroeconomic data, firm-level surveys, and whether patterns of private sector success fit the expected pattern if the examined constraint is indeed binding—among many other types of evidence. There are guiding principles to help in this process.3 Typically, if a constraint is binding, the following four principles will hold:

1. The market or shadow4 price of the constraint should be high. As discussed above, a high price would be an indicator of low supply, and thus of a potentially binding constraint. For instance:

o If credit constraints are a binding constraint, we should expect to see high real interest rates relative

to similarly-situated countries. An example of useful evidence would be the standard deviation of interest rates and how many standard deviations the country is from the mean. The more standard deviations away it is, the more powerful a signal it would be in favor of a “finance as binding constraint” story.

o If low skill levels in the country were a binding constraint, we would expect to see high returns to

education: in other words, all else equal, better-educated individuals should earn more on average than their less educated peers. Useful evidence would here include regression analyses (e.g. regression of wages on years of education or amount of training, while controlling for important factors like gender, region, etc.). Comparing results with other countries (or regions) would aid in understanding the degree to which returns to education are relatively low or high.

2. Relaxing the constraint should ease the problem; tightening the constraint should worsen it. For instance:

                                                                                                               2 Of course, in many cases, the tree is not properly speaking a “tree,” as there may be interactions between different branches, but the heuristic remains overall a useful one. 3 This section draws heavily from Ricardo Hausman, Bailey Klinger, and Rodrigo Wagner, “Doing Growth Diagnostics in Practice: a ‘Mindbook’” Center for International Development working paper 177 (2008). This paper is a great resource to read for readers looking to learn more about growth diagnostics in general. 4 “Shadow price” refers to the price of a good or service that lacks a market price, equivalent to the marginal benefit of relaxing a constraint by one unit.

11    

o If credit constraints are the binding constraint, a reduction in interest rates should increase the overall investment rate. For instance, in the recycling example above, if the problem lies with the high costs of borrowing, we would expect to see that investments in recycling projects are reactive to changes in the interest rate (i.e., a high price elasticity of demand. Conversely, if finance is not a binding constraint, changes in the interest rate should not affect as much the decision to undertake recycling investments—in other words, the price elasticity of demand should be low.

o Relatedly, if credit rationing is a constraint, then an increase in the amount of funding available (for

instance if, say, a development bank opens a credit line for renewable energy projects) should result in greater levels of green investments.

o Comparing the historical performance of an industry before and after the introduction of certain

government policies might be a good indicator of whether government failure might be a binding constraint. For instance, if subsidies to traditional power providers are the binding constraint, then the volume of green investments should react positively to a reduction or removal of subsidies.

3. Agents should be making efforts to get around the constraint if it is a binding one. For instance:

o If the recycling problem is one of low supply, not low demand, then we might observe a makeshift or informal recycling “industry” in place. If there is an existing network of informal waste collectors gathering recyclable waste for resale, this suggests that there is an effort to get around a binding constraint—such as too poor infrastructure to allow recycling trucks to drive through streets for pick-up of recyclable waste, for example.

o If low appropriability due to high taxes is a tight constraint, we might expect to see a greater number

of cash transactions taking place in an economy.

4. Agents that cannot get around a binding constraint should be few and doing poorly; agents that are not very limited by the constraint should be more numerous and thriving. To borrow an example from the creators of the growth diagnostics method: camels thrive in the Sahara because they do not require much water (the binding constraint to the objective of thriving in the desert), but hippopotamuses do not live there. 5

o If credit constraints are a binding constraint, there should be more companies that can self-finance

through cash flows, and fewer companies that are intensive in external finance. To flesh this out more carefully, it would be useful to examine the relative performance of different sectors with varying dependence on external finance.

o In our recycling example, if there are very few industries that require any kind of network of physical

collection or delivery from residences or firms (say, to deliver packages or sell goods door-to-door), this could indicate that road infrastructure is a binding constraint.

Here, one could either start bottom-up, by thinking of which industries would be intensive in the constraint and which less so, or top-down, by looking at which industries are thriving and which are doing surprisingly poorly, and backing out what constraint might be behind any observed patterns.

This stage requires a great deal of careful brainstorming and creativity. To the extent possible, the practitioner should use hard data from surveys, polls, and economic or environmental indicators. However, in many cases where data and economic research are scarce, qualitative evidence or even anecdotes may have to be relied upon. Ultimately, by examining what data suggests about the possible bottlenecks in a given economy, a narrative about the nature of the binding constraint should be developed. At the very least, this stage will force policymakers

                                                                                                               5 See, e.g., Dani Rodrik, “A Manual for Growth Diagnostics” Sep. 10, 2008, available at http://rodrik.typepad.com/dani_rodriks_weblog/2008/09/a-manual-for-growth-diagnostics.html

12    

to consider the full range of potential causes for a policy problem and whether the evidence points in favor of any of these; at best, policymakers applying the growth diagnostic method will be able to pinpoint robust evidence in favor of a particular explanation. No single piece of evidence will definitively diagnose the binding constraint to growth. The “art” of growth diagnostics lies in aggregating a set of often imperfect data that plausibly supports a diagnostic story—more so than any other competing story (i.e. other branches)—and in placing appropriate weight on each piece of data, given the potential problems or biases.6 The more diagnostic “tests” that can be used to support a particular diagnostic story, the better. A suggested way to structure the thought process at this stage could be to create a matrix of tests identifying potential symptoms associated with given binding constraints. The table below represents an example of such a matrix. The table should be read vertically, with the columns represent binding constraints and the cells within the column representing the set of different symptoms one would expect to observe if the constraint is indeed binding. When symptoms are expected to be present over several different binding constraints, the cells stretch across several columns. Note that this table is very much non-exhaustive and is intended to merely provide a suggestion of how to organize economic reasoning at this stage.

Table 1: Symptoms matrix for binding constraints in recycling example

                                                                                                               6 Biases would include the typical biases one might see in regression analysis, such as omitted variable basis, or availability bias: crossing out branches that have a lot of evidence such that you end up choosing, by default, those branches that have very little evidence by which to evaluate them.

13    

Step 5: Using the results of growth diagnostics to refine the set of policy alternatives

The entire point of the growth diagnostic process is to reach, with more certainty than at the outset, a conclusion about the constraint that, if eased, would be the most likely to yield policy improvements. The logical next step is then to devise reforms that would best address the particular problem. Selecting among alternative policy options to address the constraint is no longer the role of growth diagnostics, but rather that of tests like cost-benefit or cost-effectiveness analyses, which operate on the premise that the problem to focus on is already a given. Of course, in certain cases, the most binding constraint cannot be affected by policy levers for some reason (such as administrative or politically infeasibility). In such cases, the practitioner would need to move on to the second most-binding constraint and examine its associated set of feasible policy alternatives. The table below provides a general, non-exhaustive list of potential reforms that could be used to address constraints to growth in the recycling industry example.

Table 2: Potential reforms for selected binding constraints in the recycling example

If#the#binding#constraint#is… Then#potential#policy#reforms#to#consider#are…Perverse&subsidies&for&virgin&materials 3&Decrease&or&remove&subsidies&to&virgin&materialsAbsence&of&legal/regulatory&framework 3&Pass&and&enforce&legislation&compelling&and/or&incentivizing&recycling

Consumer&behavior&externalities

3&Impose&(reasonable)&fees&on&non3recyclers3&Improve&ease&of&recycling&by&providing&bins,&regular&pick3ups,&user3friendly&information&about&how&to&recycle3&Mandate&that&landlords&and&businesses&must&provide&space&for&recycling&bins

Few&end3use&markets&for&recycled&materials

3&Subsidies&or&tax&incentives&for&recycling&industry3&Subsidies&or&tax&incentives&to&industries&using&recycled&materials&as&inputs3&Government&procurement&policies&to&require&use&of&goods&from&recycled&materials

Lack&of&education&and&awareness 3&Education&and&awareness3raising&campaigns,&marketing&campaigns

Low&skills

3&Government&or&PPP&training&programs&for&skills&needed&in&the&recycling&industry3&Experience3sharing&with&other&countries&with&more&developed&recycling&industries

Inadequate&technologies3&Subsidies&or&tax&incentives&to&purchase&better&processing&technologies3&Removal&of&import&taxes&on&foreign&recycling&technologies3&Support&domestic&R&D&on&recycling&technologies&via&grants,&competitions

Bad&infrastructure 3&Improve&road&networks&for&recycling&trucks&to&be&able&to&pick&up&waste3&Improve&reliability&of&electricity&network&to&prevent&black3outs

Low&or&volatile&price&of&recycled&goods&for&resale3&Insurance&mechanisms3&Subsidies&or&tax&incentives&for&recycling&entrepreneurs3&Encourage&exports&of&recycled&materials&to&foreign&markets

High&self3discovery&costs 3&Insurance&in&case&of&non3profitability

Low&access&to&finance

3&Loan&guarantee&programs3&Public3private&partnerships3&Concessional&lending3&Opening&of&credit&lines&for&recycling&investments

Low&demand&for&recycling&services

Low&supply&of&recycling&services

Conclusion    This manual’s green twist on the traditional growth diagnostics approach aims to help green economy practitioners to systematically consider the main bottlenecks to green growth in a particular country and develop more tailored, effective policies. The approach described in the manual keeps the same essential features as the traditional approach, but, by focusing on potential challenges specific to green growth, demonstrates how it can be applied to a range of non-economic growth problems faced by countries.

14    

At the very least, the green growth diagnostics approach will force green economy practitioners to justify their policy decisions or recommendations relative to other options. At its best, the approach will enable practitioners to identify the most urgent problems to address in order to ease their country’s transition to a green economy.

For further reading on the subject and growth diagnostics in general, please review the works cited in the bibliography below.

Bibliography    

• Hausmann, Ricardo, Bailey Klinger, and Rodrigo Wagner. “Doing Growth Diagnostics in Practice: a ‘Mindbook’” Center for International Development Working Paper No. 177 (2008).

• Hausmann, Ricardo, Dani Rodrik, and Andres Velasco. “Growth Diagnostics. The Washington Consensus Reconsidered: Towards a New Global Governance.” Oxford, UK: Oxford University Press, 2008b, 324-355 (2005).

• Langenhoven, Belinda, and Michael Dyssel. “The Recycling Industry and Subsistence Waste Collectors: a Case Study of Mitchell’s Plain." Urban Forum. Vol. 18. No. 1. South Africa, Springer Netherlands (2007).

• OECD. “Tools for Delivering on Green Growth.” Paris, France (2011). • OECD. “Towards Green Growth” Paris, France (2011). • Reschovsky, James D., and Sarah E. Stone, “Market Incentives to Encourage Household Waste Recycling:

Paying for What You Throw Away” Journal of Policy Analysis and Management 13.1: 120-139 (1994). • Sander, Harald. What Can Be Learned from “Green Growth Diagnostics” for Greening the Growth Path of China?

Conceptual Issues and Industry Evidence. No. 2011/23 (2011). • Stromberg, Per. “Market imperfections in recycling markets: conceptual issues and empirical study of price

volatility in plastics.” Resources, Conservation and Recycling 41.4 (2004): 339-364. • UNEP. “Using Indicators for Green Economy Policy Making.” (Forthcoming 2013).