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 corresponding goals to

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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 indicates

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

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- 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.

• 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

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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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

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

3 Largely based on the methodology of Gable, Lofgren, & Osorio Rodarte, (2015). 4 See World Bank, 2018, for more details on calculations.

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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: Clean

Water and Sanitation; SDG 09: Industry, Innovation, and Infrastructure; and SDG 03: Good Health and

Well-Being.

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-performing

2. 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 j

4. The country’s current position in the SDG-network

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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.

• 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

6 World Bank Group, 2018.

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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

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).

HIGH CENTRALITY LOW CENTRALITY

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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 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.

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 in nine important goals – SDG 01: No Poverty; SDG 03: Good Health and Well-Being; SDG 06: Clean Water

and Sanitation; SDG 07: Affordable and Clean Energy; SDG 09: Industry, Innovation and Infrastructure; SDG 10: Reduced

Inequalities; SDG 11: Sustainable Cities and Communities; SDG 14: Life Below Water; SDG 15: Life on Land.

Alternatively, five indicators appear to be under-performing; SDG 2: proportion of wasted children and underweight

children; SDG 8: proportion of youth not enrolled in education, employment, or training; SDG 8, the unemployment rate;

and SDG 17, the debt to export ratio. Under-performance may be attributed to country-specific conditions that require

more concerted attention or funding, and they point to areas in which the payoffs from policy and institutional change may

be high. Of the five indicators currently performing worse, four have deteriorated faster than Egypt’s GNI per capita growth

rate between 2000-2014, requiring immediate consideration by policy-makers and key stakeholders: (due to limited data,

two goals, SDG Goal 13: Climate Action and SDG Goal 16: Peace, Justice, and Strong Institutions, are not examined.)

The diagnostics also investigates financing for development space in the context of Egypt. According to the analysis,

Egypt appears to be overinvesting in areas where it is on target to meet its SDG obligations (as well as the objectives

outlined in the Sustainable Development Strategy (SDS): Egypt Visions 2030) and underinvesting in areas where it is not.

Figure 3: Egypt’s current SDG performance

Note: Red indicates under-performance relative to Egypt’s peers; green indicates over-performance; and black indicates progress is as expected. Source: World Bank, 2018.

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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 percentile rankings,

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

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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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.

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

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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.

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

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. 12 Assessing Egypt’s SDG Progress, Spending and Implementation Gaps, World Bank, 2018

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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 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

13 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

Page 13 of 17

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

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.

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

Den

sity

Centrality

Tradeoffs

Page 14 of 17

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 SPEIG Trust

Fund. Any questions should be directed towards Robert Beschel at (202) 458-0140 or

robert.beschel@worldbank.org.

17 Maghrabi, et. al. 2018, Sustainable development goals diagnostics, World Bank Group 18 Overview of institutional arrangements for implementing the 2030 Agenda at national level. Policy Brief. UN DESA. 2016 19 Oberai, Institutionalizing SDG Implementation: Lessons from the OECD and MENA Region, 2019

Page 15 of 17

Annex 1. UNDESA SDG network analysis 20

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).

Page 16 of 17

Annex 2. Methodology for the IGES study

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.

22

capabilities. But, where to begin? We show below how the product space can be used to identify

which products require capabilities that are most similar to those that the country already has.

Opportunities for Structural Transformation

The products that a country currently exports not with RCA comprise its opportunity set for further

structural transformation. In the context of the product space, the ease of acquiring RCA in these

products depends on: (i) how diversified the country’s current export basket is; and (ii) how close

the current export basket of the country is to its opportunity set. Hausmann and Klinger (2006)

capture this notion of distance between each of the goods in the opportunity set and those currently

exported with RCA by calculating the density.16

This is a proxy for the probability that a country

successfully exports a new product, given its current set of capabilities.

Figures 9 and 10 show two different representations of the opportunity set for four SSA

countries: Ethiopia, Mozambique, Nigeria, and Senegal. Figures 9a and 9b (comparison with

Germany, Korea and Singapore) show the set of opportunities in the sophistication-distance space,

where sophistication represents the income or productivity level associated with a commodity and

distance is the inverse of the density so that products with distance close to zero are relatively

nearby. We say “relatively nearby” because density is country-specific. The products that are

“nearby” for a specific country are the ones that are the closest relative only to the country’s export

basket. Figure 10, on the other hand, shows where the opportunity set lies in the product space,

grouped by distance.

Figure 9a shows that while Nigeria has the highest number of products in its opportunity set,

these are “far” when compared with the products in the opportunity sets of Ethiopia, Senegal, and

Mozambique. This is because Nigeria exports with RCA very few products. In 2007, Nigeria

16

The density of commodity j, a product not exported with comparative advantage, is the sum of proximities between

product j and all products that are exported with comparative advantage, scaled by the sum of all proximities leading to

product j:

å

å=

i

ij

ci

i

ij

cj

x

densityj

j

where îíì ³

=otherwise.0

1 if1 ci

ci

RCAx and ijf denotes the proximity between goods i and j. The density of any product lies

between 0 and 1. The higher the density of a product not exported with RCA, the closer its required capabilities are to

the country’s existing capabilities. 22

capabilities. But, where to begin? We show below how the product space can be used to identify

which products require capabilities that are most similar to those that the country already has.

Opportunities for Structural Transformation

The products that a country currently exports not with RCA comprise its opportunity set for further

structural transformation. In the context of the product space, the ease of acquiring RCA in these

products depends on: (i) how diversified the country’s current export basket is; and (ii) how close

the current export basket of the country is to its opportunity set. Hausmann and Klinger (2006)

capture this notion of distance between each of the goods in the opportunity set and those currently

exported with RCA by calculating the density.16

This is a proxy for the probability that a country

successfully exports a new product, given its current set of capabilities.

Figures 9 and 10 show two different representations of the opportunity set for four SSA

countries: Ethiopia, Mozambique, Nigeria, and Senegal. Figures 9a and 9b (comparison with

Germany, Korea and Singapore) show the set of opportunities in the sophistication-distance space,

where sophistication represents the income or productivity level associated with a commodity and

distance is the inverse of the density so that products with distance close to zero are relatively

nearby. We say “relatively nearby” because density is country-specific. The products that are

“nearby” for a specific country are the ones that are the closest relative only to the country’s export

basket. Figure 10, on the other hand, shows where the opportunity set lies in the product space,

grouped by distance.

Figure 9a shows that while Nigeria has the highest number of products in its opportunity set,

these are “far” when compared with the products in the opportunity sets of Ethiopia, Senegal, and

Mozambique. This is because Nigeria exports with RCA very few products. In 2007, Nigeria

16

The density of commodity j, a product not exported with comparative advantage, is the sum of proximities between

product j and all products that are exported with comparative advantage, scaled by the sum of all proximities leading to

product j:

å

å=

i

ij

ci

i

ij

cj

x

densityj

j

where îíì ³

=otherwise.0

1 if1 ci

ci

RCAx and ijf denotes the proximity between goods i and j. The density of any product lies

between 0 and 1. The higher the density of a product not exported with RCA, the closer its required capabilities are to

the country’s existing capabilities.

Page 17 of 17

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