World Bank Knowledge for Change Program – Full Proposal Templet
Basic Data:
Title Monitoring the Sustainable Development Goals: Identification, Data, and Dissemination
Linked Project ID (if available) Product Line RA
Applied Amount ($) 150,000 Est. Project Period 11/15/2018 -06/15/2021 Team Leader(s) Hai-Anh Dang (TTL); Managing Unit DECSU Umar Serajuddin (co- TTL)
Contributing unit(s) DECDD, GEDGE, GWA08
Funding Window Innovation in Data Production, Analysis and Dissemination
Regions/Countries World
General:
1. What is the Development Objective (or main objective) of this Grant?
The World Bank is currently responsible for monitoring the progress of 20 indicators of the 230 or so Sustainable Development Goals (SDGs) indicators, and involved in the production of an extended group of 23 additional indicators. The main objective of this work is to conduct research on the key 20 SDG indicators that the World Bank is in charge of, and to provide guidance for further improvement on data collection and analysis. We aim to improve and refine the list of these indicators to provide a more useful assessment, together with a dissemination tool that can better serve the Bank’s twin goals (both of which are prominent SDG targets as well).
More specifically, our proposed work aims to
i) examine the 20 indicators that the Bank are currently responsible for, and investigate to what extent these indicators can form a subset that represent the 232 indicators ii) identify a core group of indicators that can help track the progress of all the 232 indicators, which provides insights on potential changes and modifications to the 20 indicators the Bank is responsible for iii) employ relevant statistical methods to construct a richer and more complete database of this core group of indicators that enables tracking a country’s SDG progress over time.
2. Summary description of Grant financed activities
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World Bank Knowledge for Change Program – Full Proposal Templet
We propose two sets of activities under this Grant. The first set of activities conducts research on the Bank’s current list of SDG indicators. We will study a number of challenges facing the construction and monitoring of the Bank’s indicators, and all the SDG indicators in general. These challenges consist of the following i) the extent that the current indicators the Bank is responsible for represents all the SDG indicators ii) whether we can identify a core group of indicators that allows useful analysis and facilitates data collection efforts iii) how we interpret the progress of the SDG indicators, and iv) the best-practices solutions to address the issue of missing values.
The second set of activities focuses on capacity building and dissemination. In particular, we will disseminate the outputs obtained in the first set of activities above in a variety of outlets, including ▪ policy discussions with our counterparts who are in charge of monitoring the SDGs at the United Nations and other international organizations ▪ professional conferences and academic seminars related to the SDGs ▪ World Bank products on the SDGs that we are in charge of, including the World Bank’s SDG Atlas and websites We will also actively engage in social media, including writing blog posts, to further disseminate our results and build capacity on a world-wide, wholesale basis. One particular blog that we will contribute posts to is the Open Data Blog, which is currently the most read blog of the World Bank.
3. What are the main risks related to the Grant financed activity? Are there any potential conflicts of interest for the Bank? How will these risks/conflicts be monitored and managed?
There are minimal risks associated with the proposed activities. The main source of risk is that there are currently a number of missing values for the SDG indicators. This is a data challenge that will need to be satisfactorily solved for better monitoring of the trends of the indicators over time. But this issue of missing values is in fact one of the research questions that we propose to address under this Grant. We will minimize this risk by applying the latest analytical techniques in the relevant statistical and economic literatures. We will also obtain advice from our advisors and other leading experts in academia for further improvement.
4. (Optional question) What can/has been done to find an alternative source of financing, i.e. instead of a Bank administered Grant?
In addition to the proposed Grant financing, we have secured some additional fund, which include staff time of $50,000 from the Bank’s budget (BB).
KCPIII Specific:
1. How does (do) the objective(s) of this proposal align with the World Bank Group’s twin goals? What are the key thematic research questions being addressed in this research?
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World Bank Knowledge for Change Program – Full Proposal Templet
In September 2015, the United Nations General Assembly formally adopted the 2030 Agenda for Sustainable Development, which is expected to guide global action over the next 15 years. Consisting of 17 Sustainable Development Goals (SDGs) and 169 associated targets, this new agenda builds on the achievements of the Millennium Development Goals (MDGs), but is far wider in scope and ambition. To monitor these 169 targets, 232 official indicators have now been selected. The implementation of the SDGs will require a solid monitoring framework and a high-quality database of indicators to inform policy and ensure accountability of stakeholders.
However, different from the MDGs, the more ambitious SDGs include very diverse measures of welfare outcomes. In particular, the themes of the SDGs broadly cover “people, planet, prosperity, peace, and partnership”, and the targets associated with these themes are even more general. The World Bank is directly responsible for monitoring the progress of 20 indicators of these 232 indicators (see Table 1), and involved in the production of an extended group of 23 additional indicators.
The main objective of this work is to conduct research on the 20 indicators that the Bank are currently responsible for, and to provide guidance for further improvement on data collection and analysis. We aim to improve the list of these indicators to provide a more useful assessment and monitoring tool that can better serve the Bank’s twin goals. More specifically, our proposed work aims to
i) examine the 20 indicators that the Bank are currently responsible for, and investigate whether, and to what extent, these indicators can form a subset that represent the 232 indicators ii) identify a core group of indicators that can help track the progress of all the 232 indicators, which provides insights on potential changes and modifications to the 20 indicators the Bank is responsible for iii) employ relevant statistical methods to construct a complete database of this core group of indicators that enables the tracking of a country’s SDG progress over time. Our proposed research for tracking the SDGs is closely linked to the Bank’s twin goals and key operational priorities. In particular, the SDGs provide broader measures of welfare outcomes that include and place higher priority on the Bank’s twin goals. Specifically, the very first target of the first SDG goal on poverty calls for eliminating poverty by 2030 as currently defined by the $1.90 a day line (target 1.1). Moreover, the first target of the tenth SDG goal on inequality is related to the Bank’s goal of boosting shared prosperity, with the specification that the bottom 40 grow faster than the average (target 10.1). The Bank is, in fact, the organization responsible for providing estimates for these two targets. More generally, our proposed work on the SDGs is consistent with the strong focus at other international organizations, as well as at the country level, that employs the SDGs framework for policy discourses.
2. Describe analytic design & methodology. Elaborate on hypotheses, conceptual framework, data (survey design if applicable).
We describe below two main activities that we propose to implement under this Grant, together with more specific research questions in this Section.
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1. Research on the Bank’s current list of SDG indicators and constructing a core group of indicators
We propose to investigate a number of issues surrounding the construction of the SDG indicators in general, and the Bank’s SDG indicators in particular. Below is a brief discussion of some key issues. i. To what extent does the current Bank’s selection of indicators represent all the SDG indicators?
Even though 232 indicators have now officially been selected to monitor the 169 SDG targets, tracking all these indicators presents no small challenge for both technical reasons (i.e., many indicators have missing values) and logistical reasons (i.e., it is a resources-consuming process to put together a high-quality database on this large number of indicators over time). The selection process of the 20 core indicators is based on the Bank’s expertise as well as the data that the Bank can mobilize. While this process results in a relatively more manageable database of indicators of high quality, as yet it is unclear to what extent these 20 indicators can represent all the SDG indicators. And it is also unclear which aspects of the SDGs that these indicators can better capture. Put differently, what can we say about the general progress with the SDGs if we are to look only at these 20 indicators that the Bank can update? In a bad-case scenario, if these current 20 indicators offer a biased picture of the SDGs, then how biased is this picture (e.g., too much focus on economic growth rather than the environment)?
The insights into these questions will have much operational relevance to the Bank in general as a leading organization on implementing the SDG goals, and DECDG in particular as the department that coordinating with other global practices within the Bank to produce and curate data on the Bank’s list of SDG indicators. We thus propose to analyze these indicators, quantify their contribution, and validate the uniqueness of each of the indicators. Furthermore, we will explore if this current list can be further improved (say, by adding or replacing certain indicators) such that it can better represent all the SDG indicators. ii. How many indicators do we actually need? Can we identify a core group of indicators (that facilitates data efforts and reduces missing values)?
The SDGs are composed of 232 indicators that address various aspects of societal development. Compared to the MDGs, the SDGs have far more indicators. Still, questions can be raised over whether all these indicators should be expanded, or reduced, or further refined.
Indeed, our preliminary analysis of these questions for all the 232 indicators has uncovered several different issues (Dang, Fu, and Serajuddin, 2018). First, some of these indicators can overlap with each other, which can result in confusion in communications, and even potential duplication with monitoring efforts. As an example, both indicators 1.5.2 and 11.5.2 aim to measure the direct disaster economic loss in relation to global gross domestic product (GDP), and both indicators 11.7.2 and 16.1.3 tracks the proportion of persons that are victim of physical or sexual harassment. This finding is consistent with the popular notion among many development practitioners that the list of SDG indicators can be further refined.1
1 For example, the UN recently acknowledged on their website about nine group of indicators that are identical to each other Page 4 of 24
World Bank Knowledge for Change Program – Full Proposal Templet
Second, some indicators may lead to potentially conflicting goals. For example, how good is a country’s performance if it reduces poverty (Goal 1) but increases inequality (Goal 10)? Or what is the tradeoff between strong economic growth (Goal 8) and deteriorating environmental quality (Goal 11)? For example, China has performed spectacular economic growth and dramatically reduced poverty in the past decades, but its environment quality has worsened considerably. Figure 1 graphs the trends of GDP against the PM 2.5 matter for China, and Norway for comparison, over the past 25 years (from 1990 to 2015). While China’s GDP has solidly climbed up, its environmental quality has also steadily gone down in this period. This stands in sharp contrast to the opposite pattern of improving good economic performance and preserving the environment of Norway.
The solutions to these conflicting goals may not be straightforward, and may even place national priorities against global ones. But perhaps the first step to solving any challenge starts with obtaining consistent and comparable data that facilitates a well-informed decision-making process. We thus propose to examine the contribution of the selected indicators, as well as their correlation, to make necessary adjustments. Indicators that highly overlap with each other should be consolidated, or even removed. We will specifically examine the core group of the Bank’s indicators, and also the extended group of indicators. We also propose to explore the construction of an alternative core set of indicators, if necessary, that explains most of the variations in data. The advantages of this subset of indicators are at least twofold: first, it offers a simpler, easier-to-analyze and easier-to-interpret alternative to the whole set of indicators, and second, we can reduce potential missing data issues in collecting this (far) reduced list of indicators. iii. How do we interpret the progress of the SDG indicators?
Even if we can identify a core group of indicators (say, 30 indicators) that do not overlap, the next question to ask is how best to interpret the changes with all these indicators? Should we just weight all indicators equally, such that we count up the number of positive changes to compare with the number of negative changes? Or should we give more weight to certain indicators when aggregating them? And how should these weights be defined (e.g., arithmetically or geometrically)? Should we look at the changes in absolute numbers or relative numbers over time, or both (i.e., the number of the poor in the population versus the percentage of the poor in the population)? What is a reasonable interval length to study the trends: is it a five-year or a ten-year period? Is the trend more important or the present level of achievement of welfare more important (e.g., how do we compare the performance of a very poor country with a solidly decreasing poverty rate in the past decade versus a not so poor country with a slow reduction, or even upward ticks, in poverty?)
Table 2 provides a brief investigation of one of the questions above, where comparable poverty numbers for the most recent year from the Word Development Indicators (WDI) are shown for four countries Brazil, China, Ethiopia, and India. To keep the figures comparable, we use the data in 2011 for all countries, except for Ethiopia
https://unstats.un.org/sdgs/indicators/indicators-list/ Page 5 of 24
World Bank Knowledge for Change Program – Full Proposal Templet
where data are in 2010, and the international poverty line of PPP$ 1.9/day. Judging by the headcount poverty rate (row 1), or the percentage of the population that are poor, Brazil and China have the lower rates at 6 percent and 8 percent respectively. In contrast, the poverty rate is more than twice higher for India at 21 percent, and more than four times higher for Ethiopia at 33 percent. Brazil and China are thus clearly strong performer in terms of having a smaller percentage of the population that are in poverty. Yet, these four countries vary widely in population size, which leads to the interesting result that one percent of the population in India (12 million) is far more than ten percent of the population in Ethiopia (9 million) (row 5). Thus, although Ethiopia has a half times more poverty rate than India, the former has less than one-eighth of the number of poor people living in the latter country (row 2). Similarly, Ethiopia has more than four times the poverty rate of China, but its number of poor people is less than one-third of the latter.
As such, while driving down the poverty rate by one additional percentage point may represent the same amount of progress with decreasing the headcount poverty rate for each country, the same figure when translated into the number of people living in poverty offers a quite different measure. Using either of these measures would clearly lead to different interpretation results with the pace of poverty reduction. It can thus be useful to report statistics on the number of poor people as an additional indicator for SDG 1 on poverty reduction. For example, the absolute number of the people living in poverty—at least globally or regionally—can be reported alongside the relative versions (i.e., the proportion of the population living in poverty).
When measuring poverty in terms of proportions, another important issue that merits consideration is the time dimension. The numbers discussed above are static and provide only a snapshot look at poverty at a single point in time. If we consider the historical time trends, more interesting results emerge. We show the averaged poverty reduction rate for each country in two periods, one for the past decade (row 3) and the other dating back to the earliest years—the early 1980s—that we have available data (row 4). For the past decade, China is the best performer given its average poverty reduction of 13 percent per year, which is then followed by India, Brazil, and Ethiopia in a decreasing order of poverty reduction. But this order changes when we consider the past three decades: China and Ethiopia are still respectively the best and weakest performer, but Brazil and India now switch their position.
In short, the overall question is, how do we develop a coherent approach to measure and monitor countries’ progress toward the SDGs? One possible solution is to carefully discuss and then balance a country’s progress on different dimensions as suggested by the simple example above. Another solution is to construct an index based on the core indicators. It may also be useful to construct another index that is based on all the indicators for comparison purposes. To achieve this goal, we can implement data reduction techniques to obtain these indexes such as principal component analysis (see, e.g., Jolliffe (2002)), cluster analysis (see, e.g., Everitt et al., 2011), and other relevant techniques. We may also experiment with replicating and building on the SDG index that is recently offered by Sachs et al. (2017). iv. What is the best way to address the issue of missing values?
Our example immediately above points to the crucial role of time trends with interpreting poverty reduction. The same holds true for other development outcomes. Indeed, we examine the different patterns of GDP per capita growth over a five-year period during 2011-2015 for all countries in the WDI database and show these patterns Page 6 of 24
World Bank Knowledge for Change Program – Full Proposal Templet
in Figure 2. We divide growth patterns during this 5-year interval into two groups: decreasing (Panel A) or increasing (Panel B). While the notion of increasing GDP growth is most likely continuous growth over all five years for most readers (Figure 2, Panel B, group 1), in fact, the real growth patterns in practice turned out to be much more complicated. It can include other growth patterns such as a decrease in GDP per capita in the first year, and subsequent continuous increase for the remaining years (Panel B, group 2). Or it can be continuous growth for the first two or three years, then a decrease in the remaining years (Panel B, group 4 and group 5). Or it can be even continuous growth for the first three years, followed by a decrease in the fourth year, and then an increase in the fifth year (Panel B, group 3).
These results help highlight the key role that missing data can potentially play in interpreting the SDG progress. More precisely speaking, missing data can result in heavily biased and even incorrect interpretations of the actual trends. For an extreme example, if the data for group-4 and group-5 countries for the five-periods are somehow missing except for the last two years, the resulting growth pattern in this example would be decreasing despite the general increase over the longer period. The pattern can be even more complex if not all countries have data in the same years, and some groups have data in some years only—which is true for many SDG indicators. In such cases, missing data can clearly lead to severe misinterpretations of progress over time.
We propose a two-layered approach to constructing a database for the SDG core indicators. For the first layer, we construct a dataset by focusing on the Bank’s current 20 indicators that builds on the existing WDI database. One remarkable strength of this dataset is that it offers both data coverage (i.e., for all countries) and data depth (i.e., consists of enough time series to allow analysis of trends). For the second layer, after further research, we will construct a similar dataset for our proposed core SDG indicators. As discussed above, one particular challenge with constructing such large datasets is that there can be missing data for a number of indicators. To address this issue, we will also experiment with applying multiple imputation methods (see, e.g., Little and Rubin, 2002) to help fill in the data gaps for certain indicators. If micro-level survey data are available, more recent imputation methods (Dang, Lanjouw, and Serajuddin, 2017; Dang, Jolliffe and Carletto, 2017) may also be employed to generate better poverty estimates.
v. Is there any synergy between monitoring of the SDGs at the global level vs. at the country level? If yes, how best to combine the monitoring activities at these two levels?
There is a strong relationship between monitoring the SDGs at the global level and the country level. In fact, the SDG progress at both levels is just two sides of the same coin: progress at the country level implies progress at the global level, and the opposite relationship should hold for most countries. But, there is, surprisingly, very little research on this key relationship.2
2 A recent exception is the study by Amin-Salem et al. (2018), who benchmark a country’s current outcomes against the global averages and then apply this framework to the case of Egypt. However, given the nascent literature on the SDGs as discussed earlier, this is perhaps not very surprising. Page 7 of 24
World Bank Knowledge for Change Program – Full Proposal Templet
We propose further research into this interesting relationship. In particular, we will attempt to categorize countries into different groups, based on their past and potential performance. By focusing on these homogenous groups of countries instead of all countries, we can perhaps better identify the characteristics that drive stronger performance. We can also look more closely into certain good performers, which can provide useful lessons and growth trajectories for the weaker performers. In short, we will combine our team’s in-depth country expertise (e.g., Bangladesh, China, Vietnam) and global expertise to provide further analysis into these research questions.
2. Policy discussion, dissemination and developing local capacity
The SDG database for the core indicators will be integrated into the WDI data on an experimental basis. The WDI is one of the most popular (data) products of the Bank. For example, the number of downloads of the WDI database is estimated to average 2.5 million downloads per month in recent years. Consequently, our proposed work offers a public good that can potentially reach a large number of users all across the globe.
This work is highly relevant for our counterparts in the UN as well. For example, the UN Statistics Division (UNSD) is coordinating efforts to monitor SDGs and our team has an ongoing policy discussion with them. They are naturally interested in the outcomes of this work, specially our proposed work on measuring progress and employing relevant statistical methods to construct a database of a core group of indicators to track a country’s SDG progress. The current paucity of data poses a big challenge to monitoring the SDG agenda, and even more so implementing policies to achieve them. Our dialogue with the UNSD, which houses the secretariat of the formal SDG monitoring body—the Inter-agency and Expert Group on SDG Indicators (IAEG-SDGs)—will subsequently permeate to the country level. The Bank is an observer at the IAEG-SDGs, and has regular interactions with IAEG-SDG members who are representatives from NSOs.
We will also organize training courses and seminars, as well as present our research at various fora, for further dissemination. This can help make the estimation of the proposed SDG indicators more widely accessible. For a specific example, the TTL of this project has been invited to teach a graduate course in international development topics at Georgetown University, Department of Economics. He has also been recently invited to provide a key note lecture at the National Autonomous University of Mexico (UNAM), one of the premier higher education institutions in Mexico. We will use these opportunities to help with disseminating the results of our proposed work. For another example, our team members are affiliated with various think tanks and research institutes in developing countries such as Russia’s Moscow Higher School of Economics and Vietnam’s Academy of Social Sciences. Our strong working relationship with staff at these institutes will facilitate our efforts to build local capacity.
3. Provide a literature review & explain study’s intellectual merit.
Given that the SDGs recently came into being, the academic literature on the SDGs is, unsurprisingly, still growing. We provide below a brief overview of the most relevant literature.
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Previous studies existed that investigate interpretation issues with the MDGs (e.g., Easterly, 2009; Waage et al., 2010), but the SDGs are far more encompassing and present a new host of other issues. Some studies have recently emerged, and have highlighted certain issues such as a need to have more health indicators (Waage et al., 2015) or the strong role of governance for a successful implementation (Joshi, Hughes, and Sisk, 2015). Some criticism has also been voiced that the SDGs, among other things, are inconsistent, difficult to quantify, implement and monitor (Spaiser et al., 2017; Bali Swain, 2018). Recent studies also paid attention to the inter- connections and interactions among the SDGs (Le Blanc, 2015; Nilsson, Griggs, and Visbeck, 2016; Hall et al., 2017), which may be further analyzed with network theory to help set country priorities (El-Maghrabi et al., 2018). An alternative framework that benchmarks a country’s performance against those of others, and subsequently projects a country’s future indicators in 2030 based on GNI capita is offered by Gable, Lofgren, and Osorio Rodarte (2015).3
On the other hand, international organizations such as the United Nations and the World Bank produce reports on the current status and progress with the SDGs on a regular basis (UN, 2018; World Bank, 2018), which have resulted in increasingly more public attention on the SDGs.
While these studies highlight the need to provide more in-depth study of the SDGs, they offer little, if any, discussion on the research questions we raised above. Consequently, to our knowledge, our proposed work will be among the first that will rigorously examine the proposed research questions. We thus will make original contributions to the nascent literature on the SDGs on both the conceptual and empirical front. Our proposed work on constructing a group of core SDG indicators to be curated by the Bank will contribute to Bank’s position as leading development agency in this area. Our proposed software will also be the first to help local researchers produce a number of SDG indicators straightforwardly, which can have feedback benefits on policy makers in developing countries by facilitating the tasks of monitoring their country’s SDG indicators.
4. Describe Implementation arrangements. Identify timeline, key team members and their roles. If the partnership is involved, describe the partnership arrangements, and the respective responsibility of Bank units and partners.
Within the World Bank, the work will be done in close collaboration between staff in the Survey Unit and the Sustainable Development Statistics Unit, both in the Development Data Group (DECDG). The team will work closely with our colleagues in all the relevant Global Practices and Cross Cutting Solutions Areas, given the wide-ranging scope of the SDGs. The team will also be working with Bank staff in the country offices to help fine tune the indicators.
The TTLs will lead and supervise all activities and manage the resources. Our team are strongly engaged in work programs related to the SDGs. For example, the co-TTL (Umar Serajuddin) is the team leader that is responsible
3 Another report by the Overseas Development Institute (ODI) produced scorecards that offers projections at the regional level for the achievement of the 2030 Agenda (Nicolai et al., 2015). Page 9 of 24
World Bank Knowledge for Change Program – Full Proposal Templet
for coordinating SDG-related activities between DECDG, other units within the Bank, and the Bank’s counterparts at international organizations. The team member Tariq Khokhar is the team leader for the Bank’s SDG Atlas report, who is also in charge of maintaining the Open Data Blog.
The project will be implemented in close collaboration with colleagues at and outside the Bank, including in academia and think tanks. The team will benefit from valuable advice from the project advisors, who consist of Steve Knack (Lead Economist, DECMG) and Jos Verbeek (Special Representative to the WTO and UN, WBOGV). We will also actively seek further guidance from leading experts in the fields of development and econometrics/ statistics if necessary as the project develops.
The work will take place starting from FY 2019. The background research paper(s) will be published in the World Bank Policy Research Working Paper series starting around October 2019, and will be submitted to a leading academic development journal soon afterwards. We will provide dissemination events (i.e., training courses, workshops) throughout the life of the project.
Team Members with proposed responsibilities (in alphabetical order)
Husein Abdul-Hamid (Team member, Senior Economist, GEDGE): contribute to analyzing the data, research papers and briefs
Luis Andres (Team member, Lead Economist, GWA08): contribute to analyzing the data, research papers and briefs
Hai-Anh Dang (TTL, Economist, DECSU): manage project, take lead in analyzing the data, writing the research papers and technical briefs, provide training and presentations
Tariq Khokhar (Team member, Senior Economist, DECDD): contribute to dissemination activities, provide training and presentations, writing blogs
Misha Lokshin (Manager, DECSU): provide overall guidance and advice to the team, provide presentations
Umar Serajuddin (co-TTL, DECDD): manage project and coordinate dissemination activities, contribute to analyzing the data, writing the research papers and technical briefs, provide training and presentations
Consultants (to be recruited) to help with data collection and analysis.
5. Outline the expected outputs (working paper, publication, computational/analytical tools, datasets, etc.) and specify the expected date of delivery for each output.
The expected outputs include:
• Research outputs o 2 to 3 policy research papers that will be disseminated in the WB Policy Research Working Papers series, and subsequently submitted to academic journals Page 10 of 24
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o Shorter dissemination pieces such as policy briefs, technical notes, and blogs • Data o A SDG database of our proposed core indicators, which consists of data for all WDI countries. This database will offer time series data on most of the indicators. • Dissemination and capacity building o (Ongoing) Policy discussions with other Bank units and our counterparts at the UN and other international organizations (that are officially responsible for monitoring progress with the SDGs) o A workshop that will bring together stakeholders from international organizations, academia, and Bank staff for further dissemination o Training courses and seminars provided for development practitioners and researchers from developing countries.
6. Describe the beneficiary of the research, the relevance for policy in developing (or transition) countries and for WBG Operations. Outline dissemination plans, including plans to reach policy makers.
Given the high-profile of the SDGs, our proposed work can help address various fundamental issues in international development facing the Bank clients and operations. We briefly discuss some salient policy areas below.
First, monitoring progress at national (and sub-national) levels with respect to SDGs provide useful knowledge and can inform Bank clients what areas they need to focus on and direct policy efforts towards. Clients can refer to the Bank for guidance and data on their own progress with the SDGs, as well as comparison with other countries. This has direct beneficial impacts on the Bank operations.
Second, for the Bank itself, the Bank’s twin goals are enshrined in the SDGs very prominently, the goal of eliminating extreme poverty listed as the first of the 169 targets. As such, monitoring the SDG progress can be considered an expanded version of the Bank’s key mission of poverty reduction and increasing shared prosperity.
Third, the Bank has traditionally been the leading international organization in terms of producing data and analysis. The Bank is particularly well-known for its work on offering the most complete and high-quality (monetary) poverty statistics, among other topics. However, there is an increasing trend nowadays to consider other non-income aspects of poverty. Tracking progress of the SDGs can be a response in this direction. Since there is still relatively little rigorous analytical work on the SDGs, early investment by the Bank in this area can yield high returns and help further enhance the role of the Bank as a leading research center on development issues in the future.
7. Describe the capacity building components, including the collaboration with local partners, researchers from developing countries.
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As discussed above, the second set of activities under this Grant consists of a strong component for dissemination and building capacity on a world-wide, wholesale basis. Indeed, our proposed work on the SDGs offers a global public goods that will specifically benefit researchers in developing countries and staffs at national statistical agencies. In particular, taking advantage of the large audience of the Open Data Blog, our blog posts can contribute to knowledge transfer on a mass scale.
There are two ways we can provide capacity building. First, we will organize some training sessions directly for local researchers to present our work on tracking the SDGs. One promising direction is to further explore, with staff at national statistical agencies, the possibilities of creating an SDG Atlas at the country level. This can model after the current global Atlas. For example, the TTL (Hai-Anh Dang) is affiliated with Vietnam’s Academy for Social Sciences and is regularly invited to provide training to the staff in that organization as well as Vietnam’s General Statistical Office. We plan to continue this support and provide further assistance to local researchers and research institutions in developing countries under our proposed grant activities. Second, we will provide some training to Bank staffs that work directly with micro-survey data in the country offices that will in turn promote these modules to local researchers.
Please also see our discussion above (Section 2.2) related to our interaction with other UN agencies, and other capacity building activities with local higher education institutions. We also document some evidence of local capacity building and dissemination in Section 2 of the Appendix.
8. Document evidence of the consultation process with relevant research and operations units. E.g. consultation conducted, comments received, & how comments were addressed. TTLs should also describe plans to maintain operational and research consultation.
Our team coordinate with the different Global Practices (GPs) and Cross-cutting Solutions Areas (CCSAs) in highlighting SDG trends in its annual World Development Indicators (WDI) publication. Our team significantly contribute to vetting most of the indicators with the relevant GPs and CCSAs before the final inclusion in the WDI database. This collaboration is thus crucial both in terms of constructing the SDG database and evaluating progress across a variety of goals and targets. We have also been discussing collaboration on the SDGs with colleagues in other Bank units including OPCS units, which are responsible for operationalizing the Bank’s (internal corporate) priorities and programs. DECDG also hosts the interactive SDG dashboard (http://datatopics.worldbank.org/sdgs/) that can help disseminate the indicators and database. We plan to reach out to researchers or research institutions in low and middle-income countries, as well in high income countries, since tracking the SDGs is a universal agenda.
The experience we have gained from outside the Bank is perhaps even more valuable. The Bank is an observer at the IAEG-SDGs, and has regular interactions with IAEG-SDG members who are representatives from NSOs across the world. The Bank is viewed as a leader in the SDG monitoring agenda and has been regularly asked to present in various Inter-agency meetings. The Bank has presented in the IAEG-SDG meetings, in the UN Statistical Commission’s side events, in various side-events surrounding the UN’s annual High Level Political Forum (HLPF). Through interactions and discussions with various colleagues at these meetings, there is abundant
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evidence that indicates strong support for our proposed activities. Our proposed experimental approach to SDGs monitoring is also a useful way to push forward the development agenda since its research nature precludes any political agenda.
Please see the Appendix (Section 1) for a brief documentation of the co-TTL’s strong engagement with other UN agencies in shaping up the SDGs’ monitoring activities.
9. If this is an impact evaluation study, please answer the following:
a. Why is this project a research project and not an impact evaluation project? b. Is the project linked to the Bank lending project? If so, provide the project number. c. Will this project produce new knowledge or fill the gap of current literatures?
Disbursement Projection
From Date To Date Amount 11/15/2018 05/15/2018 40,000 05/16/2018 11/15/2019 40,000 11/16/2019 05/15/2020 35,000 05/16/2020 11/15/2020 35,000
References
Amin-Salem, H.; El-Maghrabi, M.H.; Osorio Rodarte, I.; Verbeek, J. (2018). Sustainable Development Goal Diagnostics : The Case of the Arab Republic of Egypt. Policy Research Working Paper # 8463. World Bank: Washington, DC.
Bali Swain R.B. (2018). “A Critical Analysis of the Sustainable Development Goals”. In: Leal Filho W. (eds) Handbook of Sustainability Science and Research. World Sustainability Series. Springer, Cham
Dang, Hai-Anh, Haishan Fu, and Umar Serajuddin. (2018). “Tracking the Sustainable Development Goals: Emerging Measurement Challenges and Further Reflections”. Working paper.
Dang, Hai-Anh, Dean Jolliffe, and Calogero Carletto. (2017). "Data Gaps, Data Incomparability, and Data Imputation: A Review of Poverty Measurement Methods for Data-Scarce Environments". World Bank Policy Research Paper # 8282. World Bank: Washington, DC.
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Dang, Hai-Anh, Peter Lanjouw, Umar Serajuddin. (2017). “Updating Poverty Estimates at Frequent Intervals in the Absence of Consumption Data: Methods and Illustration with Reference to a Middle-Income Country.” Oxford Economic Papers, 69(4): 939-962.
Easterly, William. (2009). “How the Millennium Development Goals are Unfair to Africa”. World Development, 17(1): 26-35.
El-Maghrabi, M. H., Gable, Susanna Elisabeth, Osorio-Rodarte, Israel, Verbeek, Jos. (2018). “Sustainable development goals diagnostics: an application of network theory and complexity measures to set country priorities”. Policy Research working paper no. WPS 8481. Washington, D.C.: World Bank
Everitt, Brian S., Sabine Landau, Morven Leese, and Daniel Stahl. (2011). Cluster Analysis. Chichester, UK: JohnWiley & Sons.
Gable, Susanna, Lofgren, Hans, and Osorio Rodarte, Israel. (2015). Trajectories for Sustainable Development Goals: Framework and Country Applications. Washington, DC: World Bank.
Hall, Ralph P., Shyam Ranganathan, and Raj Kumar GC. (2017). "A General Micro-Level Modeling Approach to Analyzing Interconnected SDGs: Achieving SDG 6 and More through Multiple-Use Water Services (MUS)." Sustainability, 9(2): 314.
Jolliffe, I. T. (2002). Principal Component Analysis. 2nd ed. New York: Springer.
Joshi, Devin K., Barry B. Hughes, and Timothy D. Sisk. (2015). "Improving governance for the post-2015 sustainable development goals: scenario forecasting the next 50 years." World Development, 70: 286-302.
Le Blanc, D. (2015). “Towards Integration at Last? The Sustainable Development Goals as a Network of Targets”. Sustainable Development, 23(3): 176–187.
Little, Roderick J. A. and Donald B. Rubin. (2002). Statistical Analysis with Missing Data. 2nd Edition. New Jersey: Wiley.
Nicolai, S., Hoy, C., Berliner, T. and Aedy, T. (2015). Projecting Progress: Reaching the SDGs by 2030. London: Overseas Development Institute.
Nilsson, M.; Griggs, D.; Visbeck, M. (2016). “Map the interactions between sustainable development goals”. Nature, 534, 320–322.
Sachs, J., Schmidt-Traub, G., Kroll, C., Durand-Delacre, D. and Teksoz, K. (2017). SDG Index and Dashboards Report 2017. New York: Bertelsmann Stiftung and Sustainable Development Solutions Network (SDSN).
Spaiser, Viktoria, Shyam Ranganathan, Ranjula Bali Swain & David J. T. Sumpter. (2017). “The sustainable development oxymoron: quantifying and modelling the incompatibility of sustainable development goals”. International Journal of Sustainable Development & World Ecology, 24(6): 457-470. Page 14 of 24
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United Nations. (2018). The Sustainable Development Goals Report 2018. New York: United Nations.
Waage, Jeff, Christopher Yap, Sarah Bell, Caren Levy, Georgina Mace, Tom Pegram, Elaine Unterhalter, Niheer Dasandi, David Hudson, Richard Kock, Susannah Mayhew, Colin Marx, and Nigel Poole. (2015). "Governing the UN Sustainable Development Goals: interactions, infrastructures, and institutions." The Lancet Global Health, 3(5): e251-e252.
Waage, Jeff, Rukmini Banerji, Oona Campbell, Ephraim Chirwa, Guy Collender, Veerle Dieltiens, Andrew Dorward, Peter Godfrey-Faussett, Piya Hanvoravongchai, Geeta Kingdon, Angela Little, Anne Mills, Kim Mulholland, Alwyn Mwinga, Amy North, Walaiporn Patcharanarumol, Colin Poulton, Viroj Tangcharoensathien, Elaine Unterhalter. (2010). "The Millennium Development Goals: a cross-sectoral analysis and principles for goal setting after 2015." The Lancet, 376(9745): 991-1023.
World Bank. 2018. Atlas of Sustainable Development Goals 2018: From World Development Indicators. Washington, DC: World Bank.
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Table 1: Indicators that the Bank Directly Reports on (alone or with partner agencies)
Indicator Indicator Name Tier Possible Partner World Bank (as of Custodial Agency Unit April Agency (ies) (ies) 20, 2017)
1.1.1 Proportion of population below the international 1 World Bank ILO Poverty GP poverty line, by sex, age, employment status and geographical location (urban/rural)
1.2.1 Proportion of population living below the 1 World Bank UNICEF Poverty GP national poverty line, by sex and age
1.3.1 Proportion of population covered by social 2 ILO World Social protection floors/systems, by sex, distinguishing Bank Protection children, unemployed persons, older persons, and Labor GP persons with disabilities, pregnant women, newborns, work-injury victims and the poor and the vulnerable
1.4.2 Proportion of total adult population with secure 3 World Bank, FAO, DECRG: tenure rights to land, with legally recognized UNSD, Agriculture & documentation and who perceive their rights to UN-Habitat UN Rural Devt land as secure, by sex and by type of tenure Women, (DECAR) UNEP,IF AD
3.8.2 Number of people covered by health insurance 2 WHO World Health, or a public health system per 1,000 population Bank Nutrition and Population GP
5.1.1 Whether or not legal frameworks are in place to 3 UN Women, OHCHR DECIG promote, enforce and monitor equality and non‑discrimination on the basis of sex World Bank,
OECD
Development
Centre
7.1.1 Proportion of population with access to 1 World Bank IEA, Energy & electricity Extractives GP
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UN- Energy
8.1.1 Annual growth rate of real GDP per capita 1 UNSD World DECDG Bank
8.10.2 Proportion of adults (15 years and older) with an 1 World Bank UNCDF DECRG: account at a bank or other financial institution Finance & or with a mobile-money-service provider Priv Sec Devt (DECFP)
9.1.1 Proportion of the rural population who live 3 World Bank UNEP, Transport & within 2 km of an all-season road UNECE ICT GP
10.1.1 Growth rates of household expenditure or 1 World Bank Poverty GP income per capita among the bottom 40 per cent of the population and the total population
10.2.1 Proportion of people living below 50 per cent of 3 World Bank Poverty GP median income, by age, sex and persons with disabilities
10.7.1 Recruitment cost borne by employee as a 3 ILO, World DECMR – proportion of yearly income earned in country of Bank Migration and destination Remittances
10.c.1 Remittance costs as a proportion of the amount 3 World Bank Finance and remitted Markets GP
16.5.2 Proportion of businesses that had at least one 2 World Bank, DECEA – DEC contact with a public official and that paid a UNODC Enterprise bribe to a public official, or were asked for a Analysis bribe by those public officials during the previous 12 months
16.6.1 Primary government expenditures as a 1 World Bank Governance proportion of original approved budget, by GP sector (or by budget codes or similar)
17.3.2 Volume of remittances (in United States dollars) 1 World Bank DECDG as a proportion of total GDP
17.4.1 Debt service as a proportion of exports of goods 1 World Bank DECDG and services
17.17.1 Amount of United States dollars committed to 3 World Bank PPP CCSA public-private and civil society partnerships
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Table 2: Poverty Reduction as Measured by Different Methods
Country Outcomes Brazil China Ethiopia India Headcount poverty rate ($1.90/ day; percent) 5.5 7.9 33.5 21.2 Number of poor people (million) 11.0 106.2 30.1 264.5 Average reduction rate from initial period (percent) -4.8 -7.7 -2.5 -3.3 Average reduction rate within the past decade (percent) -7.1 -12.7 -4.5 -8.1 Population (million) 200.5 1,344.1 89.9 1,247.4 Notes: All estimates are based on the WDI dababase.
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Figure 1: Trends of GDP and PM 2.5 Matter for Two Countries, 1990-2015
Norway
10.5
100000
10
80000
9.5
60000
9
40000
PM2.5 air pollution air PM2.5
8.5
GDP per capita (current US$) (current capita GDP per 20000 1990 1995 2000 2005 2010 2015
China
58
8000
56
6000
54
4000
52
2000
50
PM2.5 air pollution air PM2.5
0 48
GDP per capita (current US$) (current capita GDP per 1990 1995 2000 2005 2010 2015
GDP per capita(current US$) PM2.5 air pollution
Figure 2: Different Patterns of GDP per capita Growth over Time, 2011-2015
Panel A: decreasing Panel B: increasing
2011 2012 2013 2014 2015 2011 2012 2013 2014 2015
group 1 group 2 group 1 group 2 group 3 group 4 group 3 group 4 group 5 group 5
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Appendix: Additional Support Documents
1. Documentation of our team member (co-TTL)’s active engagement in the Bank’s SDG work with the United Nations
1.1. with the UN Inter-Agency and Expert Group for SDG Indicators
Dear Umar, 1. Please travel to Bahrain to attend the sixth meeting of the UN Inter-Agency and Expert Group for SDG Indicators (IAEG SDG Indicators). The meeting will follow the same format as the 5th IAEG-SDG meeting, with a Members’ meeting taking place during the first two days (11-12 November) and a Plenary Session taking place during the second two days (13-14 November). During the plenary session, all countries, international and regional agencies and entities, and other stakeholders are invited to attend. 2. The meeting’s objectives are to: (i) review of tier classification of indicators; (ii) review progress made on methodological development of Tier III indicators; (iii) review any proposed annual refinements of indicators; (iv) review guidelines on data flows and global data reporting; (v) discuss the data collection calendar; (v) discuss data disaggregation; and (vi) decide on work plan and next steps. The agenda for the meeting is available here: https://unstats.un.org/sdgs/meetings/iaeg-sdgs-meeting-06/. 3. The Bank is responsible for (co)reporting on about 20 SDG indicators (see attached document). You will be discussing the Tier upgrading prospects of indicators 1.4.2 (land tenure rights), 10.c.1 (remittance costs), and 17.17.1 (public-private partnerships) with the IAEG members on November 12. When the tier status of indicators are reviewed during the plenary session (Nov 13-14), please intervene on indicators of relevance to the Bank, based on inputs from GP staff where possible. 4. Please circulate a brief back-to-office report when you return. With best wishes, Haishan
Haishan Fu Director Development Data Group T 202 473 2359
W www.worldbank.org A 1818 H Street, NW, Washington DC 20433, USA
Dear Haishan and Colleagues, I attended the sixth meeting of the UN Inter-Agency and Expert Group (IAEG) for SDG Indicators in Bahrain. The meeting was organized by UN Statistics Division. I presented at the closed-door session of IAEG members
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on November 12, and following that I attended the plenary session of the meeting on November 13-14. The plenary session was attended by several UN Member States (represented by their national statistical offices), various international and regional agencies and entities, and stakeholders from civil society and the business community. From the Bank’s perspective, this meeting was important because of the discussions involving several indicators the Bank is (co-)custodian of. I coordinated with the relevant focal points in the Bank before and during the meeting and have already briefed them about the outcomes. Below are some highlights of the meeting.
Tier 3 status update A tier reclassification exercise took place, mostly for indicators currently classed as Tier 3. I made presentations to a closed-door session of IAEG members on three SDG indicators that the Bank is (co-) custodian of. - The indicator on land tenure security (SDG indicator 1.4.2) – jointly presented with UN Habitat – was upgraded to Tier 2 status. Within the Bank, DECAR (DECRG: Agriculture & Rural Devt.) is the focal point for this indicator. The indicator has components on tenure documentation and on tenure security perceptions. A couple of IAEG members suggested that the perceptions component of the indicator be optional for countries to collect. - The indicator on remittances costs (SDG indicator 10.c.1) was upgraded to Tier 2. The Finance and Markets GP produce the statistics for this indicator. IAEG members requested country specific meta-data for the indicator. - The indicator on public private and civil society partnerships (SDG indicator 17.17.1) remained a Tier 3 indicator. Following the Banks’ request, the indicator will be split in two parts separating public private and civil society partnerships. The PPP group (GTPPP) produces the statistics for the public private component of the indicator. The IAEG members recommended that in addition to the current data on infrastructure, the Bank collect data on PPP investments on education and health.
Updating Work Plans of Tier 3 Indicators - Ahead of the IAEG meetings the Bank provided update workplans of several Tier 3 indicators: rural access index (SDG indicator 9.1.1), proportion of population living below 50% of median income (SDG indicator 10.2.1), and recruitment costs of workers migrating abroad (SDG indicator 10.7.1). - In the plenary session I made two presentations on the indicator on legal frameworks for promoting gender equality (SDG indicator 5.1.1) and on the macroeconomic dashboard (SDG indicator 17.4.3). The Bank is making progress in developing these indicators and plans to request for a Tier upgrade (from Tier 3 to Tier 1 or 2) in 2018. The meeting requested more NSO consultation in the development of indicator 5.1.1.
Other Updates - The SDG indicator for the Bank’s shared prosperity measure (SDG indictor 10.1.1) was reclassified to Tier 2 due to insufficient coverage in Africa and the Asia Pacific regions. However, the Bank can continue to report on it and regional aggregation for this indicator is not expected. In general, it is hoped that capacity building efforts will intensify for indicators reclassified this way. - The indicator on government expenditures as a proportion of original approved budget (SDG indictor 16.6.1) remains Tier 1 but the expectation is that by June 2018 the Bank will report the raw numbers rather than the current PEFA classifications. Page 21 of 24
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- The indicator on national multidimensional poverty measures (SDG indicator1.2.2) remained an ‘orphan indicator’ (i.e. without agency custodianship). In the sidelines of the IAEG-SDG meeting a few agencies (UNDP, UNICEF, the Bank) and OPHI (Oxford Poverty and Human Development Initiative) discussed the issue of custodianship. I will follow up with the Poverty GP regarding the Bank’s involvement in this. - The indicator on the coverage of essential health services (SDG indicator 3.8.1) remains Tier 3 because a component of it is currently Tier 3 (SDG indicator 3.b.2). - The measure on safely managed sanitation services (SDG indicator 6.2.1) was reclassified to Tier 2 because of the three data series ‐ safely managed sanitation services, open defecation, and handwashing facility – data coverage for safely managed sanitation services and handwashing facility are low. - Going ahead, the IAEG SDG is proposing to have a Webex meeting every two months to discuss pressing issues and fast tracking tier reclassifications.
On broader reporting issues it is worth mentioning that data flow or reporting mechanism for SDG indicators still remains unresolved, with several member countries discussing the roles international agencies should play in reporting. In general, NSOs want greater involvement in both data collection and reporting. This will be a topic of discussion in IAEG SDG meetings in the near future as well. Apologies for the delay is issuing the BTOR but I hope it is useful.
Best regards, Umar
1.2 with the UNICEF
From: Haishan Fu Sent: Thursday, June 21, 2018 12:35 PM To: Umar Serajuddin
Dear Umar,
On June 20 you will travel to New York City to participate in a workshop on the SDG indicator on multidimensional poverty, hosted by UNICEF. You will join Carolina Sanchez Paramo (Senior Director, Poverty GP) and Joao Pedro Wagner De Azevedo (Lead Economist, Poverty GP) in this daylong workshop to discuss the way forward in monitoring SDG Indicator 1.2.2 (Proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions). The World Bank along with UNICEF and UNDP are partner agencies responsible for monitoring this indicator. The agenda for the workshop is provided below.
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Special partner agency role - Our ToRs 11am - 12:30pm Data flow for SDG 1.2.2 Demo of PRIMUS 12:30pm - 2:00pm Lunch break Guide on reporting procedures Minimum quality assurance requirements 2:00pm - 3:30pm Inventory of existing methodologies on multi-dimensional poverty Initial list of technical notes 3:30pm - 4:00pm Coffee break Overview of existing capacity development efforts 4:00pm - 5:30pm Gaps and opportunities for collaboration 5:30pm Next steps
Upon your return, you will circulate a brief back-to-office report.
Best wishes, Haishan,
Haishan Fu Director
Development Data Group T (202) 473-2359
W www.worldbank.org
2. Invitation letter to give a keynote lecture at National Autonomous University of Mexico
From: LUKASZ CZARNECKI [mailto:[email protected]] Sent: Friday, June 29, 2018 11:57 AM To: Hai-Anh H. Dang
Dear Mr. Hai-Anh H. Dang
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My name is Lukasz Czarnecki. I am a researcher at the University Program on Asian and African Studies, UNAM, in Mexico City, which was recently created in 2017. The main objective of this institution is to strengthen cooperation between Asian and African countries and Mexico. Within this broad interest I am personally in charge of boosting research on Vietnam. We would like to organise the First Seminar on Vietnam, on 5th September this year. It would be a great honor if you would participate as a Keynote Lecturer.
I am looking forward to hearing from you. Thank you in advance.
Best
Lukasz
Dr. Lukasz Czarnecki National Autonomous University of Mexico University Program on Asian and African Studies Ciudad Universitaria, 04510 México CDMX skype: lucamexicooo linkedin.com/in/czarneckilukasz
Newest publication:
Guest Editor of Special Issue Journal Acta Sociológica dedicated to Ágnes Heller: http://www.revistas.unam.mx/index.php/ras/issue/view/4942
Czarnecki, Lukasz and Vargas-Chanes, Delfino, “Chapter 3. Welfare Regime, Neoliberal Transformation, and Social Exclusion in Mexico 1980-2015”, in Social Welfare Responses in the Neoliberal Era: Policies, Practices and Social Problems, edited by Mia Arp Fallov and Cory Blad, to be published by Brill, 2018 (forthcoming).
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