Prioritizing SDG Implementation Utilizing Network Analysis – A Preliminary Analysis

September 2019

Section 1. Approaches to analyzing SDG interlinkages

The Sustainable Development Goals (SDGs) have inherent linkages that reflect the intent of United Nations member countries to better integrate development objectives and reflect synergies across sectors. As such, the goals and targets can be seen as a network, in which links explicitly refer to multiple goals (UNDESA, 2015). Untangling and mapping these interlinkages, as well as identifying any casual relationships between them, may offer important institutional and developmental insights—insights which in turn could be used to facilitate better integration and policy coherence across sectors, or to more effectively allocate scarce resources towards the pursuit of SDG objectives. This note explores the feasibility of such efforts, using the Egyptian context to highlight how they could potentially be applied both in that country and more broadly.

To date, several approaches have been piloted in the effort to define measurement standards, identify causality and understand the inter-relationships between various SDG targets within a given country. Drawing upon this work, the first part of this note assesses three potential methodologies for conducting a network analysis in Egypt. In essence, these methodologies build on each other and provide different approaches to network analysis. The UNDESA study provides a mapping of the SDGs, while the IGES and World Bank studies provide tools for identification of the most influential SDGs and the prioritization of SDGs. Section 2 of the note also introduces the application of the final methodology discussed on the case of Egypt and presents some initial findings.

Approach No. 1: Towards integration at last? The SDGs as a network of targets (UNDESA)

METHODOLOGY

Prepared for the United Nations Department of Economic and Social Affairs (UNDESA), David Le Blanc (2015) examines the linkages across the SDGs by constructing a network of targets based on the interpretation of the wording for targets as given in each goal. This results in a network which is a “political mapping” built on consensus amongst stakeholders. This approach highlights targets that are well connected to others, as well as areas in which connections are theoretically weaker. It should be noted that the mapping does not consider important economic or physical links between goal areas, but instead focuses purely on the presence of key words in the label of the respective target. The results are shown graphically in Annex 1.

RESULTS

Some key findings of the study are:

(1) For each area covered by the SDGs, there are core as well as “extended” targets (i.e. targets linked with the concerned SDG that are located under other goals).

Of the 107 targets under examination, 60 explicitly refer to at least one other goal, and 19 targets correspond to three goals or more, creating indirect linkages among the SDGs. For example, under SDG 03: Good Health and Well-being, Target 3.8, which relates to achieving universal health coverage, is linked to both inequality and poverty. Le Blanc therefore associates the above-referenced target to both SDG 01: No Poverty and SDG 10: Reduce Inequalities, despite it not belonging to either goal. As an additional example, in the area of health, covered by SDG 03, there are eight

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corresponding goals to which the health goal is connected (excluding the implementation-related SDG 17 on partnerships to achieve the goal).

(2) There are unequal networks between SDGs (i.e. some are denser than others)

The analysis reveals that several thematic areas covered by the SDGs are well connected to others; however, some parts of the network have weaker connections with the rest of the system. SDG 12: Responsible Consumption and SDG 10: Reduce Inequalities, for example, have the greatest number of interlinkages within the network, totaling 14 and 12, respectively. Alternatively, SDG 14: Life Below Water, is connected with fewer than two goals, thus indicating that the SDGs are unequally connected.

Le Blanc argues that despite the unequal distribution of interlinkages, the SDGs overall are more connected than their predecessors (i.e. the Millenium Development Goals). He asserts that the strong interdependencies and trade-offs within the network offers a new opportunity for policy-makers to depart from the orthodox “pick-and-choose” approach, as actions or measures taken to achieve one goal may be mutually reinforcing or contradictory with achieving other goals. The adjoining nature of the SDGs could enable more integrated policies so long as synergies and silos across thematic areas are considered in both the design and implementation process.

LIMITATIONS

Unfortunately, there are a number of key limitations associated with this approach. The first and perhaps most serious is that the criteria used to determine the presence of linkages are limited in robustness. The criteria are based on interpretations of targets and goals that have been worded subjectively; and evaluations of links between targets have been examined by only a few coders (the author and one other person). Beyond these semantic linkages, there are no empirically established connections. As a result, some important links based on natural and social science are not directly captured through the SDG network. They include examples such as energy and industrialization, energy and climate change, and oceans and climate change.

This framework does not explicitly reflect the multiplicity of links that often matter for policy purposes. For example, an effort to reduce traffic fatalities would involve coordinating activity between the traffic police, ministries of transport, state and local governments and emergency personnel. Thus in practice, it may be of limited use in providing guidance for the policy making process. Finally, the study was produced prior to the complete adoption of the SDGs in 2015, and it therefore does not address a number of indicators that have been added subsequent to the inception of the 2030 Agenda.

Approach No. 2: SDG Interlinkages and Network Analysis: A Practical Tool for SDG Integration and Policy Coherence (IGES)

METHODOLOGY

Expanding upon Le Blanc’s theoretical assessment, the Institute for Global Environmental Strategies (IGES) (2017) developed an analytical framework to determine interlinkages between SDG targets. The study uses Social Network Analysis (SNA) techniques to identify causal relationships between the SDG targets and further quantify the linkages. The report uses correlation analysis in an effort to determine how strong the links are and to isolate strategic targets to determine those that contribute to the achievement of others. The study covers Bangladesh, Cambodia, China, India, Indonesia, Japan, Korea, the Philippines, and Vietnam. See Annex 2 for details on the methodology.

RESULTS

This networking exercise enables the visualization of linkages between different SDG targets to inform how they interact with indicators associated to other goals. The ranking results of SDG targets

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indicates that the following indicators are the most influential in the network across the countries studied:

- Target 2.3: Double agriculture productivity- Target 2.4: Build sustainable food production systems- Target 6.1: Universal access to safe drinking water- Target 6.2: Universal access to sanitation and hygiene- Target 7.1: Universal access to energy- Target 9.1: Develop resilient targets

The targets above are considered the most “influential” based on different metrics used to measure the inter-connectedness of various SDG targets. This means that these targets play central roles in the network in terms of having wider connections with other targets by both exerting influences and receiving influences, acting as important intermediates in bridging unconnected targets, and placing at strategic positions in connecting with influential targets.

The report underscores that a silo approach towards SDG implementation may be inappropriate, as the targets form a dense and united network. Negating inherent synergies may deliver a local optimum rather than a system optimum, thereby delivering a sub-optimal solution. Therefore, the interactions of the SDG targets requires policy-makers to take a more integrated approach. The report also encourages the replication of the exercise on a national level, as it will allow decision-makers to pinpoint targets for which policies and actions need to be prioritized, thus contributing to maximizing synergies and minimizing trade-offs. Additionally, the results can serve as input in the review of existing national institutional arrangements, as well as influence the efficient allocation of fiscal resources based on identified SDG linkages.

LIMITATIONS

The paper concludes by presenting a number of limitations that constrain the effective use of the approach as a practical tool to support policy integration:1

Difficulties in identification of SDG interlinkages especially at the national level: the general structure of the SDG interlinkages network is built upon a binary linkage between each pair of 169 targets, which is assumed homogenous across all countries. In other words, it does not take into account the diversified nature of national contexts and priorities, calling for more knowledge on SDG interlinkages on both the regional and national levels.

Challenges in well‐defined indicators with reliable data: the effectiveness of the methodology is only as reliable as the data. Given the obscure language and conceptually unclear nature of some indicators, many countries grapple with adequately measuring the SDGs. Nevertheless, improving the SDG indicators is an open-ended process, thus the quality of the quantification of SDG interlinkages can be enhanced.2

Challenges in finding data which is reliable and can be tracked for the quantification: conducting reliable correlation analysis, the basis for the quantification of linkages, requires full trackable time-series data for all indicators. Due to substantial gaps in data, 51 indicators (including proxies when exact data is not available) are mapped with 108 targets (out of 169). This yields weak quantification and sub-optimal results from the network analysis.

1 Source: IGES (2017).2 In March 2015, the Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs) was created and tasked to periodically develop and refine indicators as new data became available and methodological development improved. Since inception, nine meetings of the IAEG-SDGs have been held, drastically improving conceptual clarity of indicators, and in turn, ensuring that targets are well-defined.

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Challenges in defining the functions of the SDG network and selection of appropriate metrics for the structural analysis of the SDG network: defining the functions of the SDG network is critical to guide practical policy-making; however, lack of knowledge and information on the functions of the SDG network limits the ability to derive useful policy implications from the results.

Approach No. 3: An Application of Network Theory and Complexity Measures to Set Country Priorities (World Bank Group)

METHODOLOGY

Building on the above, the World Bank Group (WBG) publication, An Application of Network Theory and Complexity Measures to Set Country Priorities, (El-Maghrabi et al. 2018) presents a methodology that can assist policy-makers in the prioritization of SDG targets. The report hypothesizes that if a set of positive development outcomes are observed frequently across countries, then the mechanisms of SDG delivery are very similar. This implies that the probability of succeeding in an SDG target can be estimated conditionally on the observed progress on all other targets.

Using global databases from the United Nations and the WBG to build a measure of SDG progress at the indicator level,3 the methodology proposes a set of metrics based on network theory and economic complexity to test the hypothesis. The methodology relies on three key concepts to derive the inter-connectedness of SDGs: proximity, centrality and density, as explained below.

Proximity

The ease with which capacities can be used between SDGs depends on their degree of commonality, conceptualized in the proximity between them. For example, the commonalities between the indicators “number of physicians, per 1,000 people” and “malnutrition, prevalence in children under 5 years old” is expected to be larger than between “number of physicians, per 1,000 people” and “the share of protected marine areas.” As such, they would enjoy greater proximity.

Centrality

Another important concept is an SDG’s centrality. The centrality of an SDG is the sum of all their SDGs pair-wise proximities; as such, it is used as a measure of overall connectedness. High centrality indicates that the SDG has a multitude of SDGs in its proximity (i.e. if a country is successful in that SDG, it is likely that it will be successful in many others). It should be noted given that we have yet to introduce country-specific concepts, an SDGs centrality is singular across all countries. It is expected that central SDGs, meaning those that appear to be better connected with the rest of the network; are fundamental to scaling-up SDG-delivery mechanisms and making a greater contribution to the overall SDG agenda.

Density

Density in a given SDG is a country- and SDG-specific concept. In the context of the SDG network and for a particular SDG, the ease for a country of becoming an over-performer depends on: (i) in which other SDGs is the country over-performing; and (ii) on the proximity from the target SDG to each of the others on which the country is over-performing. Formally, the density for a country c in SDG j, on which is under-performing, is the sum of proximities between SDG j and all other successful SDGs, scaled by the sum of all proximities leading to SDG j (i.e. scaled by its centrality). A formula for density is provided in Annex 3.

It is important to note that the resulting proximity and centrality matrix are universal and common to 3 Largely based on the methodology of Gable, Lofgren, & Osorio Rodarte, (2015).

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all countries.4 However, not all countries are created equal, thus, the concept of density is introduced as a measure to address the country-specific dimension. Density measures how close a country is from turning a non-successful SDG into a successful SDG (Figure 2). It is dependent upon the position of the country in the SDG network, and the relatedness of capacities between the under-performing and over-performing SDGs. The under-performing and over-performing SDGs for a specific country are derived from trajectory analysis presented in a World Bank diagnostic below (see Box 2). Such capacities include human, physical, and institutional capital, productive factors, infrastructure, and natural resources, to name a few.5

The density of any SDG lies between 0 and 1; the closer the density of a specific SDG is to 1, the higher the likelihood of it becoming a successful SDG as the capacities required for improvement are aligned with a country’s existing capabilities. Alternatively, the farther it is from 1, the less likely it is to improve as achievement is statistically not within reach. Therefore, countries with a dense SDG network are more likely to achieve the SDGs than those with a sparse network as the former is highly inter-connected and capacities are transferable across competing SDGs.

RESULTS

The methodology builds on the notion that the SDGs are products of both (i) a country’s current capacities, and (ii) a contributor to future capacities. Table 1 below presents a ranking of SDGs based on centrality. Using pairwise correlations, the SDGs that exhibit high centrality, or overall connectedness with other SDGs, would include: SDG 07: Affordable and Clean Energy; SDG 06:

4 See World Bank, 2018, for more details on calculations.5 Hausmann and Klinger (2006).

Figure 2: Important terms defined

The ease through which a country can realize a given SDG depends on:

1. The capacities of the SDGs in which the country is over-performing2. The proximity of SDG i to each of the other SDGs for which the country is over-

performing (SDG j)3. The connectedness of capacities between SDG i and SDG j4. The country’s current position in the SDG-network

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Clean Water and Sanitation; SDG 09: Industry, Innovation, and Infrastructure; and SDG 03: Good Health and Well-Being.

Table 1: Ranking of Centrality by SDGs6

It should be noted that although some SDGs appear to be far less-connected than others, this does not suggest that they are irrelevant and unimportant. Rather, it simply implies that the capacities necessary for achievement have little overlap with other SDGs in its proximity and/or do not align with countries current capacities.

Putting the approach into practice, the report states that if a country is faced with two policy options related to the SDGs, stakeholders are suggested to examine the country-specific measure density (“ease of success”) for each SDG indicator weighted against centrality, the cross-country measure (“connectiveness”). Countries should prioritize SDGs that:

Are within reach (high density, implies that a country has most of the capacities necessary to reach the objective); or

Offer higher scope for positive externalities (high centrality, indicating that if the country is successful in achieving this SDG, it will more likely be capable of fulfilling other SDGs).

A country may be faced with a trade-off between an SDG with a higher probability of success given current capacities (higher density), and an SDG with a higher probability of further SDG achievements given successful outcome (higher centrality). In practice, such trade-offs may be more complex and require political as well as technical factor considerations.Further analyses on additional factors such as financing for development space, technological sophistication, factor endowments, etc. are therefore suggested.

LIMITATIONS

The distribution of data coverage may play a crucial role on the credibility of the results: it is hard to quantify the importance of unknown data. Arguably, the current distribution of data coverage reflects the evolving interest of the development community and policy-makers. Under this argument, it is not surprising that coverage is biased towards the indicators included in in the original MDGs, since most countries established the MDGs as benchmarks for progress.

6 World Bank Group, 2018.

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The report does not go into great detail about policy implications as the methodology is limited to a few country case studies: the suggestions related to prioritization between competing SDG targets should be seen as an input to discussion and must be complemented with further analyses. For example, redeploying resources to invest in new capacities versus investing in “low-hanging fruit” requires supplemental research on the country-in-question’s ability to move to an SDG that is likely to achieve other SDGs in the future.

The approach assumes that all SDG targets have equal value in themselves, while in practice, those values may differ between SDG targets and countries: the report would benefit greatly on more country-case studies as well as an assessment of policy as a separate component.7

RATIONALE FOR SELECTION OF APPROACH

There are several advantages to the Bank’s methodology. First, unlike the other reports, the WBG methodology presents the SDG-specific context using proximity and centrality as well as the country-specific dimension. This approach allows policy-makers to assess different angles of prioritizing a given SDG. Second, this methodology takes advantage of the fact that the SDG network is fixed and the relationship between SDGs is the same across all countries. Finally, there are no losers. The WBG methodology allows a country to empirically assess its position in the network and identify the direction that will offer the most success in the context of their national priorities.

Section 2. Results of applying the network framework to Egypt

Thia section applies the WBG’s network application to the context of Egypt. The WBG network analysis complements the findings of the trajectory analysis, “Sustainable Development Goal Diagnostics: The Case of the Arab Republic of Egypt” (see Box 2).8 The diagnostics serves as a starting point in prioritizing the SDG by identifying goals for which Egypt is performing better than that of its peers as well as areas where performance is weak. The outputs of the diagnostics and the results drawn from the centrality matrix are merged together below (see Figure 2). Consequently, the SDGs for which Egypt is over-performing in goals are also those that have the highest degree of centrality (as seen in Table 1), indicating that exceling in these indicators have, on average, a higher probability of success in the achievement of other goals. This indicates that exceling in these indicators have, on average, a higher probability of success in the achievement of other goals. Egypt is thus well-positioned in the SDG network.

Figure 2: Level of performance in SDGs versus degree of centrality, Egypt

Conducting density analysis for Egypt. As a result of density calculations for each SDG, a density distribution graph is derived. This probability distribution of densities for Egypt, as seen in Figure 4, reveals a narrow shape

7 During FY2019-FY2020, the World Bank Group launched the SDG Acceleration Toolbox, a five-pillar project piloted in Kazakhstan, Vietnam, as well as Egypt. In partnership with the Republic of Korea, Yonsei University (Ban Ki-moon Center for Sustainable Development) in Seoul, Korea, the project applies the (i) trajectory analysis; (ii) the networking exercise; and (iii) a policy/governance sectoral study to assist the aforementioned countries in the prioritization of the SDGs and more broadly, accelerating progress in the global goals. For more information, see: https://moderndiplomacy.eu/2019/04/15/world-bank-government-of-korea-join-forces-to-support-achievement-of-sdgs/ 8 Amin-Salem, H., M.H. El-Maghrabi, I. Osorio Rodarte, & J. Verbeek. (2018).

Box 2: SDG Diagnostics for the case of Egypt

The World Bank Group publication “Sustainable Development Goal Diagnostics: the case of the Arab Republic of Egypt” (Amin-Salem et al. 2018) seeks to assist prioritization efforts by providing an initial picture of the challenges that the 2030 Agenda pose for Egypt. The analysis uses cross-country regressions to benchmark Egypt’s progress in the SDGs against those of other countries—given levels of gross national income (GNI) per capita—and projects its levels in 2030 when a statistically significant relationship between GNI per capita exists. The exercise identifies SDGs for which Egypt is over-performing, under-performing, and performing as expected relative to that of its peers.

As illustrated below, Egypt’s performance appears to be mixed across competing SDGs. Egypt is over-performing relative to its peers

UNDERPERFOMING

OVERPERFOMINGLOW CENTRALITYHIGH CENTRALITY

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of the distribution relative to other countries. This indicates that many SDGs across Egypt’s network share similar, high levels of density and thus a similar potential for success given individual country capacity. Egypt therefore has high possibilities of becoming an over-achiever when pursuing the SDG agenda.

Conversely, a broad probability distribution compared to other countries implies that density differs significantly across SDGs, indicating that a country has a high potential of achieving some goals, but a lower potential of achieving others.

Figure 4: Distribution of SDG densities in Egypt

In the context of Egypt, the indicators that have the highest average density are those related to SDG 04: Quality of Education and SDG 03: Good Health and Well-Being, as indicated by Table 2 below.

Table 2: SDGs with the highest average density

Prioritization between two SDGs

While Egypt has a similar potential for success across the SDGs given the narrow relative distribution of densities, in practice, resources are constrained, and some level of prioritization is needed even if the SDGs are connected. Therefore, as a next step, the network analysis is applied to sequence the deployment of resources between two SDGs in which Egypt is underperforming. For example, Egypt’s progress in SDG 02: Zero Hunger and SDG 08: Decent Work and Economic Growth is significantly worse than that of its peers (see Figures 5 and 6). In fact, proportion of wasted children as well as unemployment rate are two targets that have exhibited the largest decline in country

Box 2: SDG Diagnostics for the case of Egypt

The World Bank Group publication “Sustainable Development Goal Diagnostics: the case of the Arab Republic of Egypt” (Amin-Salem et al. 2018) seeks to assist prioritization efforts by providing an initial picture of the challenges that the 2030 Agenda pose for Egypt. The analysis uses cross-country regressions to benchmark Egypt’s progress in the SDGs against those of other countries—given levels of gross national income (GNI) per capita—and projects its levels in 2030 when a statistically significant relationship between GNI per capita exists. The exercise identifies SDGs for which Egypt is over-performing, under-performing, and performing as expected relative to that of its peers.

As illustrated below, Egypt’s performance appears to be mixed across competing SDGs. Egypt is over-performing relative to its peers

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percentile rankings, indicating that Egypt has deteriorated in these areas at a rate faster than GNI per capita growth.9

Figure 5: Proportion of Wasted Children

Figure 6: Unemployment Rate, Ages 15-24

For the purpose of this study, the networking exercise was replicated to (i) determine where Egypt is positioned in the SDG network with respect to these two indicators; and (ii) identify which of the two ensures the highest easiness of success given current capacities. Calculating for centrality, it found to be 280 for the proportion of wasted children, and 240 for the unemployment rate between the ages of 15-24. Holding all else equal, prioritizing proportion of wasted children over the unemployment rate is suggested as the former has higher centrality indicating that achieving success in this indicator will bring a higher probability of achieving other SDGs in tandem.

9 Amin-Salem et al. 2018. It should be noted that despite the under-performance, the government’s economic reform policies led to increased GDP growth and a gradual reduction in the employment rate. See: http://www.egypttoday.com/ Article/3/64707/Unemployment-rate-drops-to-8-9-in-Q4-2018.

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Given Egypt’s current capacities and its position of successful SDGs within the network, calculating density for each of these SDGs allows policy-makers to determine which indicator is likely to be more obtainable. Using Stata, the density of each is calculated and it is found that SDG (a) proportion of wasted children is closer to capacities since its density is 0.536 relative to SDG (b) unemployment rate at 0.523.10 Therefore, ceteris paribus, the GOE could potentially consider prioritizing SDG 02: Zero Hunger over SDG 08: Decent Work and Economic, as it will be easier for Egypt to become an over-performer in reducing malnourishment given its current capacities (see Table 3).

Table 3: Prioritization of 2 SDGs in Egypt

Figure 7 re-examines the distribution of all SDG densities for the case of Egypt presented earlier in the note. As presented by the graph, SDG (a) is positioned at a point in the distribution which is above SDG (b). SDG (a) therefore has a higher possibility of improvement given measures of both centrality and density. This would imply that SDG (a) would be a preferred choice for prioritization.

Figure 7: Distribution of SDG densities 11

Section 3. Conceptualizing the relationship between SDG network analysis, coordination and budgeting

As the discussion above indicates, findings from the trajectory and network analysis can help inform policy makers in their decision making on how certain sets or groups of SDGs should be approached in an integrated manner either for puposes of policy and operational coordination or for budget allocations. In the first step, policy makers may use data from the trajectory analysis to assess spending patterns and review their achievements towards SDG targets. Policy makers may then use findings from the network analysis to further direct their resources and coordination efforts towards certain high impact SDGs depending on their capabilities.

The trajectory analysis could be used as a first step by policy makers to understand where their country is on track or not on track to achieve its SDG targets. It basically analyzes the statistical relationship between economic growth and progress on a given set of SDGs, such as maternal mortality and morbidity. It then relies upon long-term economic projections to estimate where a given country is likely to end up in meeting its SDG targets given a projected rate of economic growth.

10 While the small difference is small for policy making purposes, this methodology provides a tool for prioritization between two SDGs.11 Authors’ calculations, 2018.

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The next step is to review investment spending through the filter of a trajectory analysis to assess the extent to which the capital budget is consistent or inconsistent with these projections. In case of Egypt, a combination of trajectory and expenditure analysis suggests that Egypt is overinvesting in areas where it is already on target to meet its SDG targets and underinvesting in areas where it is not.12 Egypt seems to have directed resources heavily towards infrastructure, utilities, and economic affairs and overperforms in 11 out of the 14 indicators tracked under SDG 09: Industry, Innovation and Infrastructure, reflecting impressive strides to redress infrastructure gaps, including those in the power sector and roads13. On the other hand, Egypt seems to be under-performing in some social sectors, such as education and health where public as well as ODA investment remains low. This data suggests that the GOE would need to increase its investment in lagging sectors to improve the relevant SDG outcomes. It suggests that a greater share of resources could be allocated towards those SDGs where Egypt is underperforming, notably the human capital sectors where the greatest developmental gains can be realized.

Policy makers may further target their decisionmaking by complementing data from the trajectory analysis with findings from the network analysis to direct resources towards achieving SDGs that have higher impact based on the centrality of the SDG and the capability of the country to achieve it (density). A larger sample of country experience, as well as empirical assessment of coordination mechanisms and budget allocations on meeting SDG targets, will be required for more specific policy recommendations.14 Yet the findings from the network analysis suggests that some SDGs are central to helping countries meet the overall SDG targets—i.e. if a country is successful in achieving a specific SDG, it is likely that it will be successful in achieving others as well. Therefore scaling-up resources and delivery mechanisms for SDGs with high centrality may be useful for achieving a given country’s overall SDG agenda.

The network analysis also identifies country specificity based on the density, or the capacity of the country to achieve a particular SDG. Countries with narrower distribution of densities will likely be more successful in pursuing the broader SDG agenda while countries with a broader distribution of density have a higher probability of achieving some goals but a lower potential of achieving others.15

Prioritizing Resource Allocation

Findings from network analysis suggest that countries may be more successful in achieving the SDG agenda if they prioritize SDGs that are central and within reach i.e., the SDGs that have a higher impact on other SDGs (high centrality) and SDGs that the country has higher capacity to achieve (high density). As the capacity to achieve a specific SDG differs among countries, conceptually it could be argued that countries may be more successful in prioritizing SDGs depending on where they fall on a centrality and density matrix (see Figure 8 below). Increasing effort in a dense part of the network could mean that the country’s current capacities can be used to achieve a wider range of SDG and the probability of success may be limited if the focus is on a sparse part of the network.

Indicators in the upper right quadrant display both the possibility of high impact (with resources generating positive externalities for other SDGs, which are likely to move together) as well as high-capacity and likelihood to be achieved (in the form of higher density ratings). One could argue that investing in tightly-linked, higher density indicators is likely to be more cost effective, as there will be significant externalities and positive spill-over effects as tightly clustered SDGs will invariably move together.

Indicators in the lower right quadrant involve SDGs with high centrality and low density scores—areas where investments are likely to lead to significant positive spill-over effects, but capacity will be 12 Assessing Egypt’s SDG Progress, Spending and Implementation Gaps, World Bank, 201813 Assessing Egypt’s SDG Progress, Spending and Implementation Gaps, World Bank, 2018 14 The Bank is proposing to apply the network analysis in a wider range of countries and is seeking additional funding to assess the implications for budget allocation and coordination mechanisms for SDGs networks. 15 Maghrabi, et. al. 2018, Sustainable development goals diagnostics, World Bank Group

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an issue. Any investments in this area will need to be accompanied by significant oversight and efforts towards capacity building and strengthening.

Indicators in the upper left quadrant could best be described as “low-hanging fruit”—areas where centrality ratings are low, but density ratings are high. Governments would have the ability to engage successfully with a solid possibility of success, but the rewards in doing so would be modest in terms of having a broader impact upon multiple SDG indicators. It may be sensible to move forward for a variety of reasons—to achieve success and build momentum for further reforms downstream, or because an individual SDG is a particularly important priority even though its links with others are modest.

Figure 8: Centrality and Density Matrix

Finally, SDGs with low centrality and low density rankings in the lower left quadrant, would require most effort and would have lower impact compared to other SDGs. In many countries, they would probably be among the last set of goals to be pursued.

While such analyses can provide valuable input into the SDG budgeting process, several factors need to be considered while directing resources and coordination effort to SDGs with a higher probability of success. These include: the country’s development goals; fiscal space; sector strategies and interventions; assessment of sequencing between competing priorities; political dynamics; fiscal, social and economic costs; and economic rate of returns of spending interventions. Regardless of whether countries build the SDGs into their national development plans or not, budgets may prioritize some sectors, such as health and education because of strong constitutional, political or legal provisions. Prioritization and trade-offs may also factor in fiscal costs of achieving SDGs which may vary significantly, as some SDGs may require low cost policy changes while others may require more extensive capital investment construction.

The heavy technical and data requirements as well as limitations of the network analysis should also play a role in how the findings may be used. Countries may differ in the extent to which data is available and to extent to which their budget information is detailed and disaggregated, and that the type of disaggregation may also vary across countries. They may not be able to separate out data for specific targets, such as the primary education budget from the aggregate education budget. In some cases, countries that have a budget program or line item named primary education, may still not cover all expenditures on primary education. For example, it may exclude teacher salaries, teacher training, or infrastructure.16

16 Tracking Spending on the SDGs: What Have We Learned from the MDGs? May 2017, International budget Partnership (IBP)

SDGs with high impact; high country capacity to meet target

SDGs with low impact; high country capacity to meet target

SDGs with low impact; low country capacity to meet target

SDGs with high impact; low country capacity to meet target

Density

Centrality

Tradeoffs

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The budget process, which strives to achieve a balance between ambitious development plans, fiscal policy objectives and the available fiscal space, is about prioritization and trade-offs. While political decision-making in the absence of any sound technical inputs can lead to disastrous outcomes, it is unrealistic to expect a purely technical prioritization of expenditures to be feasible or good practice. The network analysis could help increase allocative efficiency by helping governments better understand the realities of what may be achievable and to address priorities strategically. It can, therefore, be a valuable input for informing inter-sectoral allocations and spending prioritization debate where political, policy, social and economic trade-offs should be carefully considered.

Coordination and Implementation Mechanisms

Similarly, findings from the network analysis suggest that establishing inter-sectoral coordinating and implementation arrangements around SDGs that are closely interlinked and with higher density could be beneficial given the fact that effort and progress in one area is likely to impact another. The analysis suggests that traditional ‘silo’ approach to development taken by many countries in the past should be reviewed and modified to take advantage of possible positive externalities among SDG targets.17 The UNDESA study also finds that the silo approach to implementation has been counterproductive and undermines the integrated planning approach that is necessary for achieving sustainable development.18 Countries are adjusting their implementation and coordination frameworks for the implementation of the SDGs. There is anecdotal evidence that for SDGs that are closely interlinked, having coordination arrangements that include stakeholders across multiple co-dependent SDGs can help design more comprehensive plans to achieve their SDG agenda.19 This requires strong capacity for inter-agency coordination to ensure that a country’s existing development strategies align with the SDGs and there is consistency in implementation among different planning frameworks.

However, given limited resources and skills, countries should use greater selectivity in how implementation arrangements are established. Greater selectivity will allow them to use their resources and capacity strategically on a manageable sub-set of indicators. Several OECD countries have demonstrated selectivity coordination mechanisms. Furthermore, targets may not be dynamically related but may be responding to progress in a third area. For example, GDP growth could have a robust positive impact on both maternal mortality and morbidity and the environmental protection of fisheries, but it may not make sense for a government to establish coordination mechanisms between agencies working in these sectors.

Implementation arrangements for achieving the SDG agenda in each country should be based on the country context that should factor governmental, institutional and political situation in the country. Understanding the planning and budget process, budgetary institutions, capacity, stakeholder dynamics, development priorities and mandates, and other factors that affect implementation of the SDGs will be essential in defining the coordination mechanisms. Successful implementation arrangements should be designed considering the key political economy dynamics that shape the country’s debate on development objectives.

About this Note

This paper was drafted by a World Bank Team consisting of Robert Beschel (Lead Public Sector Specialist), Shilpa Pradhan (Senior Public Sector Specialist), Ali Halawi (Senior Public Sector Specialist); Mariam Hoda El-Maghrabi (Policy Analyst); and Rina Oberai (World Bank Consultant). It was prepared under the auspices of the Bank’s MENA Global Governance Practice (Renaud Seligmann, Practice Manager). Support was provided by the British Embassy in Cairo through the 17 Maghrabi, et. al. 2018, Sustainable development goals diagnostics, World Bank Group18 Overview of institutional arrangements for implementing the 2030 Agenda at national level. Policy Brief. UN DESA. 201619 Oberai, Institutionalizing SDG Implementation: Lessons from the OECD and MENA Region, 2019

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SPEIG Trust Fund. Any questions should be directed towards Robert Beschel at (202) 458-0140 or robert.beschel@worldbank.org.

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Annex 1. UNDESA SDG network analysis 20

Annex 2. Methodology for the IGES study

20 Cited from David Le Blanc, “Towards Integration at Last? The Sustainable Development Goals as a Network of Targets,” United Nations Department of Economic and Social Affairs Working Paper No. 141 (March 2015).

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Figure 6 outlines the methodology of the IGES assessment. First, a comprehensive literature review is conducted to identify theoretical relationships between SDG targets. The identification of binary linkages between the 169 SDG targets are conducted in this stage by synthesizing existing scientific research and relevant policy documents to ascertain pronounced correlations. In a parallel process, time series data between 2001-2014 is collected for the nine countries understudy and then mapped with the SDG targets. Subsequently, identified interlinkages are quantified using correlation analysis, and country-specific networks are produced thereafter. Using SNA, the structure of the interlinkages network for each country is analyzed to highlight the most strategic and influential targets using various measures of centrality.

Figure 6: IGES SDG interlinkages analysis and visualization tool

Source: Strategic and Quantitative Analysis Centre (QAC), IGES, 2017.

Annex 3. The Formula for Density

The following formula is utilized for calculating density:

where denotes the proximity between SDG i and j. The density of any SDG lies between 0 and 1. The higher the density of a non-successful SDG, the closer its required capacities are to the country’s existing ones. Hence, density is defined on the basis of the proximities of the SDG to other SDGs in which the country is successful.

Source: El-Maghrabi, M. H., S. Gable, I. Osorio Rodarte, & J. Verbeek. (2018), pp.

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