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Strengthening the Skills Pipeline for Statistical Capacity Development to Meet the Demands of Sustainable Development: Implementing a Data Fellowship Model in Colombia



The UN Sustainable Development Goals (SDGs) create a global need to deliver on an agenda which relies heavily on quantitative data. This creates a demand for human capital with the requisite statistical and data skills to work on the challenges represented by the SDGs. Increasingly, long-term domestic solutions to the building of statistical capacities are being sought, in order to decrease the dependency on external support and ensure a sustainable flow of qualified professionals. In this paper we argue for the value of thinking of statistical capacity as a pipeline which needs to begin in the education system, and illustrate how investment in this end of the pipeline can deliver a more sustainable and long-term pathway to building up the holistic skills-base to enable the challenges of the SDGs to be tackled. We describe an existing triedand-tested experiential learning model called Q-Step based on developing skills in the workplace, and propose that the successes of this partnership-driven model illustrate how ‘data fellowships’ can fulfil some of the unmet capacity needs of the data revolution for sustainable development. We illustrate our argument through a practical exploration of the development of such a learning model in Colombia. Although there are challenges in ensuring that educational access is equal, we demonstrate that there are significant opportunities and a data fellowships model opens doors for addressing local skills gaps to help deliver the SDGs.
Digital Development
Working Paper Series
The Digital Development (formerly Development Informatics) working paper series
discusses the broad issues surrounding digital data, information, knowledge,
information systems, and information and communication technologies in the
process of socio-economic development
Paper No. 89
Strengthening the Skills
Pipeline for Statistical
Capacity Development to
Meet the Demands of
Sustainable Development:
Implementing a Data
Fellowship Model in Colombia
Centre for Digital Development
Global Development Institute, SEED
University of Manchester, Arthur Lewis Building, Manchester, M13 9PL, UK
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Table of Contents
ABSTRACT ...................................................................................................................... 1
A. Introduction ............................................................................................. 2
B. Statistical Capacity Building and the SDGs ................................................ 3
B1. BACKGROUND: THE SDGS AND THEIR STATISTICAL IMPLICATIONS ....................................... 3
B2. STATISTICAL CAPACITY DEVELOPMENT ........................................................................... 4
B3. PARTNERSHIPS FOR BUILDING STATISTICAL CAPACITIES ..................................................... 6
B4. SUMMARY ............................................................................................................... 7
C. A Data Fellowship Model for Building Statistical and Data Literacies ......... 7
C1. STATISTICAL LITERACY AND DATA LITERACY .................................................................... 7
C2. DATA FELLOWSHIPS AND THE VALUE OF EXPERIENTIAL LEARNING ....................................... 9
D. Practical Steps for Implementing Data Fellowships for the SDGs ............. 11
D1. THE NEED FOR COUNTRY-LED INTERVENTIONS ............................................................. 11
D2. FUTURE IN-COUNTRY RESEARCH IN COLOMBIA ............................................................. 12
E. Conclusions ............................................................................................. 15
REFERENCES ................................................................................................................. 16
Manchester Centre for Digital Development Working Paper 89
Strengthening the Skills Pipeline for
Statistical Capacity Development to Meet
the Demands of Sustainable Development:
Implementing a Data Fellowship Model in
Pete Jones1, Jackie Carter1, Jaco Renken2 & Magdalena Arbeláez Tobón3
1Department of Social Statistics, University of Manchester, UK
2Centre for Digital Development, University of Manchester, UK
3Data-Pop Alliance
The UN Sustainable Development Goals (SDGs) create a global need to deliver on an agenda
which relies heavily on quantitative data. This creates a demand for human capital with the
requisite statistical and data skills to work on the challenges represented by the SDGs.
Increasingly, long-term domestic solutions to the building of statistical capacities are being
sought, in order to decrease the dependency on external support and ensure a sustainable
flow of qualified professionals.
In this paper we argue for the value of thinking of statistical capacity as a pipeline which
needs to begin in the education system, and illustrate how investment in this end of the
pipeline can deliver a more sustainable and long-term pathway to building up the holistic
skills-base to enable the challenges of the SDGs to be tackled. We describe an existing tried-
and-tested experiential learning model called Q-Step based on developing skills in the
workplace, and propose that the successes of this partnership-driven model illustrate how
‘data fellowships’ can fulfil some of the unmet capacity needs of the data revolution for
sustainable development.
We illustrate our argument through a practical exploration of the development of such a
learning model in Colombia. Although there are challenges in ensuring that educational
access is equal, we demonstrate that there are significant opportunities and a data
fellowships model opens doors for addressing local skills gaps to help deliver the SDGs.
Manchester Centre for Digital Development Working Paper 89
A. Introduction
The ability to collect, handle and analyse quantitative data has never been more central to
civic participation and social progress. The United Nations’ 2030 agenda for sustainable
development has established the Sustainable Development Goals (SDGs) as the primary
framework through which societal progress is defined globally (UN, 2015). The associated
Global Indicator Framework, largely the work of statisticians, seeks to measure these goals
through a host of quantitative indicators, a strategy which places data and statistics at the
heart of the international agenda for social, economic and environmental development (UN,
2020). There is a global ambition to deliver on these goals, yet the majority of countries
continue to face challenges in their access to a workforce with the requisite statistical and
data literacies to meet these demands. In this paper we aim to address the question of
where these literacies are going to come from by highlighting the role university-based
education interventions can play.
Our goal is to provide a critical review of the relevant literature for thinking about how
statistical education initiatives can contribute to the delivery of the data-oriented agenda of
the SDGs. We argue that the existing capacity development literature has primarily
considered statistical capacity at the level of providing training and support to those
working in national statistical offices (NSOs). However, we suggest that there are unmet
needs in this area, and that there is much to be gained from thinking more about capacity
development in terms of strengthening the pipeline for statistical and data literacies and
skills. We propose that leveraging the SDGs as an educational tool and building partnerships
with organisations working on SDG-related social challenges can help to build engaging,
incentivised pathways for the development of home-grown statistical literacy. Focusing on
Colombia, we offer a practical example to illustrate this discussion by exploring the
opportunities and challenges of adapting an established internship-based experiential
learning model for the development of statistical and data literacy. This focus on Colombia
is the result of a partnership between researchers at the University of Manchester and Del
Rosario University in Bogotá. Ultimately, we argue that how the SDGs and the global need
for quantitative skills can be used to mobilise statistical education is an open question, but
that there are a number of open doors and opportunities for strengthening the statistical
capacity pipeline in Colombia and beyond.
The rest of the paper is structured as follows. In Section B, we discuss the connection
between the SDGs and statistical education programmes in more detail. First, we provide
some context on the SDGs, their importance, and their statistical implications. We then
review the literature on statistical capacity development in order to highlight the growing
recognition that more human-centred understandings of statistical capacity are needed to
address the skills shortages many countries face. In Section C we explore the value of a
university-based research internship or ‘data fellowship’ model for strengthening the
statistical and data literacy pipeline in countries. We reflect on the statistical and data
literacy literature, and the kinds of skills and competencies most needed and coveted for
working on data-driven approaches to addressing social challenges. We describe an
effective and proven internship-based model for experiential learning in the UK, and
highlight how this model taps into many of the currents and unmet needs outlined in
Section B. We then open up the discussion to consider how this model can be expanded
Manchester Centre for Digital Development Working Paper 89
outside of the UK, arguing for the need for country-led implementations tailored to the
particular national and regional needs therein. In Section D we explore the challenges and
opportunities of developing this model outside of the UK more directly through a focus on
Colombia. We briefly consider the baseline regional capacity, before zooming in on
Colombia as a case study for evaluating how this kind of statistical education programme
can achieve the highest impact in that country.
B. Statistical Capacity Building and the SDGs
B1. Background: The SDGs and their Statistical Implications
In 2015, the United Nations launched its 2030 Agenda for Sustainable Development (UN,
2015). The Agenda constitutes a plan for sustainable development which centres on 17
overarching goals, comprising 169 associated targets and 231 indicators with which to
measure progress. These goals build on the earlier Millennium Development Goals (MDGs),
widely seen to have been well-intentioned but hindered by data gaps, measurability issues,
and an inattentiveness to the needs and perspectives of global South governments (Attaran,
2005; Fukuda-Parr and McNeill, 2019; Merry, 2019; OECD, 2015; Yayboke et al, 2017). The
SDGs eclipse the MDGs in quantity (compare the SDG indicator framework’s 231 indicators
with the MDGs’ 60), breadth and ambition, taking a much more holistic approach to
sustainable development which applies to all countries and not just those considered low-
income, while acknowledging that the particular path to realising them will vary country to
country (UN, 2015). As such, there is a truly global need and desire to deliver on the SDGs.
This brings significant implications for the statistical education research community, as
statistics and data skills and competencies are at the heart of the 2030 agenda. In the most
direct sense, an unprecedented amount of data is required to populate the SDG indicators,
and this creates new demand for access to individuals with the capacity to collect, handle
and analyse such data. However, there are more indirect ways in which countries need to
draw on a statistically skilled talent pool in order to deliver on the SDGs. A significant
number of indicators are classified as “Tier 3”, which means there is no established
international standards or methodology for measuring them. This only increases the burden
at a national level to identify appropriate data sources and proxies, a task which is
complicated by the increasing consideration being given to “unofficial” statistics and data,
either for SDG monitoring specifically or for evidence-based policy and social innovation
more generally (eg see Lokanathan et al, 2017; MacFeely, 2019; Stuart et al, 2015). These
new forms of data open up questions of data justice, ownership and privacy (Heeks and
Renken, 2018). Thus, in addition to statistical analysis skills, being able to critically evaluate
the value of quantitative data, as well as understanding the legal and ethical issues which
such data can present, are skills which are much needed.
Moreover, the effect of the SDGs on the statistical landscape creates the need not only for
more skills, but also for new skills. The next generation of statisticians and data
professionals working on SDG problems need not only to possess critical data skills, but also
other competencies required to navigate the new landscape. These competencies include
domain knowledge about sustainability, including understanding of the complex,
Manchester Centre for Digital Development Working Paper 89
interconnected nature of the goals, targets and indicators in the SDG framework (Barbier
and Burgess, 2019; Dang and Serajuddin, 2020). In addition, working on the SDGs entails
multiple levels of analysis (organisational, sectoral, national, regional, global) which
introduces further analytical complexity and practical challenges. Moreover, efforts at
addressing the SDGs involve a diversity of stakeholders, including private organisations,
governments, civil society, NGOs and multilateral development bodies. A combination of
analytical, technical, professional and communicative skills are therefore required in order
to navigate the multi-disciplinary configurations which often mark work related to delivering
the SDGs. This combination of factors implies a workforce which is equipped not only with a
strong textbook understanding of statistics, but also the ability to engage with the fluidity
and complexity of real-world research contexts by drawing on a hybrid skillset of
In sum, the SDGs require a steady supply of statistically and data literate social researchers.
In reality, these skills are in short supply in many countries. As such, the UN Independent
Expert Advisory Group on a Data Revolution for Sustainable Development has
acknowledged that delivering the current agenda requires “a huge increase in the capacity
of many governments, institutions and individuals” (UN IEAG, 2014: 4), given that national
statistical systems around the world face significant challenges in the form of limited
budgets, resources, and access to staff with the required statistical and data literacies to
gather and analyse the relevant data (Fisher and Fukuda-Parr, 2019). What role can statistics
education programmes play in achieving this necessary increase in capacity?
B2. Statistical Capacity Development
In order to explore how educational interventions can contribute in the space of statistical
capacity development, we first need to establish that the term statistical capacity refers to
“the ability of a country’s national statistical system, its organisations and individuals to
collect, produce, analyse and disseminate high quality and reliable statistics and data to
meet users’ needs” (PARIS21, 2019: 13). As we argue above, the implications of the SDGs
mean that this capacity is marked by a hybrid skillset incorporating analytical, technical and
professional competencies. Efforts at evaluating and developing statistical capacity are
primarily coordinated by multilateral organisations such as the World Bank, the UN
Statistical Division, and the Partnership in Statistics for Development in the 21st Century
(PARIS21), as well as national governmental bodies such as the UK’s Office for National
Statistics, Statistics Canada and the French INSEE.
There is an increasing recognition in the capacity development literature that efforts at
building statistical capacity need to be focused not on importing capacities from outside,
but empowering those in-country in order “to provide the long-term foundation for
transformation” (Stafford-Smith et al, 2017: 914). As MacFeely and Barnat put it, “capacity
building is most effective when it is home-grown, long-term in perspective and managed
collectively by those who stand to benefit" (MacFeely and Barnat, 2017: 897). Ensuring that
there is a supply of statistical and data skills sufficient to meet the demand of the SDGs is a
critically important pillar in this regard. In order to have home-grown capacities in place for
statistical measurement and reporting, more attention needs to be placed on connecting
Manchester Centre for Digital Development Working Paper 89
the higher-order development outcomes with the earlier stages of the pipeline of a
statistically and data literate workforce capable of addressing the challenges of the SDGs.
However, statistical capacity is generally evaluated at the level of the national statistical
system (NSS), without regard for the flow of sufficiently skilled talent into the system. For
example, the World Bank’s Statistical Capacity Indicator assessment system evaluates
nations’ capacity only in terms of methodology, data sources and periodicity (World Bank,
n.d.). This effectively frames statistical capacity as the ability to provide data that can fulfil
international monitoring standards, detaching data from the individuals that use (and stand
to benefit from) the data within countries. We see two shortcomings of this perspective on
statistical capacity which can be addressed through an increased role for higher education.
Firstly, capacity development is often thought of as an interaction between national
statistical systems and other actors such as NGOs and foreign aid and multilateral
organisations. Focusing on the NSS without regard for the statistical and data literacy
pipeline that feeds into it seems to either implicitly assume that the relevant capacities will
be built from outside the system (e.g. by international actors providing training for NSS
staff), or fail to account for the realities of statistical education in many countries, where
general statistical competency rates may be low and very unequally distributed through the
population (North et al, 2014; Stuart et al, 2015). Such a perspective therefore overlooks the
role that investment in statistical education interventions can play in strengthening the in-
country pipeline and reducing dependency on external support.
Secondly, data alone will not be sufficient for meeting the challenges of the SDGs, and
countries require investment in data literacy and skills in order to sustainably improve the
capacity for effective use and understanding of data (PARIS21, 2015; Stuart et al, 2015).
While the benefits of investing in data production, infrastructure and technology are well-
known and understood, there is still comparatively little energy directed towards the need
to invest in the individual capabilities necessary to make effective use of these data and
technologies. In a recent survey of national statistical offices (NSOs) around the world on
statistical capacity assessment, PARIS21 found that “while only 2% of assessed capabilities
target the individual, 32% of countries expressed that individual capabilities need to be
improved to rise to new data ecosystem challenges” (PARIS21, 2019: 16). Moreover, Keijzer
and Klingebiel caution against “the production of data and information at the expense of
statistical capacity” (2017: 10), suggesting that care must be taken to ensure that the short-
term desire to populate the SDG indicators does not divert resources away from the longer-
term development of the overall system capacity (MacFeely and Barnat, 2017).
Recent advances in the capacity development literature begin to address these
shortcomings by acknowledging that a more human-centred vision of statistical capacity is
needed to respond to the current global demands. This is reflected in the “Capacity
Development 4.0” (CD4.0) framework recently developed by PARIS21, which aims to “go
beyond the traditional production-side interventions to also include the strengthening of
data use, literacy and results” (PARIS21, n.d.). CD4.0 offers a conceptual refinement of
statistical capacity in light of the 2030 agenda, which recognises that data alone are
insufficient to generate the insight and knowledge required to form effective evidence-
based policy, and that more work needs to be done to improve the skills base on which the
delivery of the SDGs relies.
Manchester Centre for Digital Development Working Paper 89
Thus, raising the level of individual skills is now being made a priority in the statistical
capacity discourse, with a mixture of professional, technical and analytical skills being
promoted. This addresses prior concerns that one of the barriers to the data revolution is
that there is “too little investment in people and skills” (PARIS21, 2015: 19). By focusing
more on developing the skills and resources necessary for the effective use of data, the
CD4.0 approach acknowledges that both the short-term monitoring requirements and the
longer-term development goals can be met by promoting in-country skills development.
On a practical level, CD4.0 remains focused on the national statistical system and, as such,
does not explicitly consider the role of educational interventions in helping to build the
statistical capacities it calls for. However, it usefully emblematises the appetite for new ways
of thinking about statistical capacity, as well as the clear need for the relevant skills and
competencies to be built. We argue that connecting the desired outcomes of this capacity
building perspective with the education system through novel approaches and partnerships
can help build a stronger, more direct pipeline for a workforce with the required skills and
competencies to tackle the SDGs.
B3. Partnerships for Building Statistical Capacities
While much of the focus of statistical capacity building has been on NSOs and the broader
NSSs, there is growing recognition that there is a lot of valuable expertise and data which
lies outside of these systems. As such, statistical capacity building is increasingly focused on
the broader “data ecosystem” (PARIS21, 2020), which includes the NSO, the wider NSS, the
private sector, academia, civil society and individual citizens. This means not only
considering non-traditional data sources, but also the vital role of partnerships between
different sectors to capitalise on the skills and experience that already exist in countries
looking to develop their statistical capacity (Oburu and Yoshikawa, 2018). The SDGs often
cut across traditional realms of expertise and require co-ordination between multiple actors
within the data ecosystem. As MacFeely and Barnat argue, "availing of new opportunities
arising from new data sources or meeting the challenges of greater demands for climate
related data, geo-spatial information, better visualisation or improved explanations will
require new strategic partnerships, both nationally and internationally in order to get the
mix of skills and experience necessary" (MacFeely and Barnat, 2017: 898).
In fact, partnerships are represented explicitly by Goal 17 in the SDGs, a broad goal targeted
at improving the means of implementation of the SDG framework, which includes capacity
building and engaging “effective public, public-private and civil society partnerships, building
on the experience and resourcing strategies of partnerships” (UN, 2020). The UN’s A World
That Counts report frames such partnerships as a key aspect of how the data revolution can
be achieved, listing among its hopes for the future that “governments, civil society,
academia and the philanthropic sector work together to raise awareness of publicly
available data, to strengthen the data and statistical literacy (“numeracy”) of citizens” (UN,
2014: 18).
This follows the recognition in the statistical capacity literature that the SDGs require
building capacities at deeper, more foundational levels in society in order to bring about the
data revolution for sustainable development (Jütting, 2016; Sachs, 2012). Thus, one of the
Manchester Centre for Digital Development Working Paper 89
ways in which the current development agenda can be more sustainable is by increasing the
role of partnerships with education providers to create stronger pipelines for the skills
needed to work on the SDGs. As Akinsola and Sharma argue, “as we move towards 2030,
what is becoming increasingly clear is that traditional approaches to promoting partnerships
might not be enough to meet the objectives of SDGs. Bilateral partnerships that focus on
increasing capacity at the grassroots and decreasing dependency on external support need
to be promoted” (Akinsola and Sharma, 2018: 416). We suggest that, in this way,
partnerships between higher education institutions and organisations producing or working
with data related to the SDGs can be leveraged as an effective means for sustainably
developing statistical capacity. In Section C, we discuss how this can be achieved in more
B4. Summary
The SDGs create a global need to deliver on an agenda which relies heavily on quantitative
data. This creates a demand for human capital with the requisite statistical and data skills to
work on the challenges represented by the SDGs. However, the dominant paradigm of
statistical capacity development is focused on the national statistical office, including the
training of the staff therein by external actors. Increasingly, more home-grown and long-
term solutions to the building of statistical capacities are being sought, in order to decrease
the dependency on external support and ensure a sustainable flow of qualified labour from
the grassroots level. This shift to a more human-centred and holistic vision of statistical
capacity, along with an increased recognition of the value of cross-sector partnerships for
building statistical capacity, opens doors for novel statistical education initiatives to play a
role in strengthening the pipeline for statistical capacities. In the next section, we suggest
and discuss an experiential learning-based intervention which offers a promising model for
achieving impact in this area.
C. A Data Fellowship Model for Building Statistical and Data
C1. Statistical Literacy and Data Literacy
Before proceeding further, it is worth clarifying how we are using the terms “statistical
literacy” and “data literacy” here, in order to establish the kinds of skills that are most
important for an educational intervention that can address the unmet needs discussed in
Section B. In the education community, questions of data provenance and handling have
typically been considered components of “data literacy”, while the more analytical and
conceptual questions of quantitative analysis of data have been considered under
“statistical literacy” (e.g. Schield, 2004). From this perspective, we can understand statistical
literacy as a combination of needs and competencies comprising “the ability to produce,
analyse and summarise detailed statistics in surveys and studies” and “the ability to read
and interpret summary statistics in the everyday media: in graphs, tables, statements and
essays” (Schield, 2010: 135), and data literacy as a broader “ability to collect, manage,
evaluate, and apply data, in a critical manner” (Ridsdale et al, 2015). Separating out these
literacies assists us in considerations of the way in which these could be addressed in the
Manchester Centre for Digital Development Working Paper 89
curriculum, either in the school or university classroom, or through training in the
However, the demands of the data revolution and the rise of more holistic data science
approaches to statistical research have begun to erode the boundaries between the two,
along with distinctions between the needs of producers and users of data. Statistical literacy
scholarship increasingly recognises that "as forms of communication and ways of presenting
and receiving data change so too does what it takes for someone to be statistically literate"
(Wild, 2017: 31). The critical data skills that are typically thought of as comprising data
literacy are now being argued to be necessary parts of the statistical curriculum (Gould,
2017). The current age of big data, open data and new forms of data requires a more holistic
skill set which includes and goes beyond understanding the basic formulas, principles and
analytical aspects of statistical reasoning. In order to both leverage the power and
understand the limits of the varied data sources which now permeate our world, a kind of
literacy is needed which includes “a critical appreciation of data provenance and quality”
(Ridgway et al, 2013), “understanding issues of data privacy and ownership” (Gould, 2017),
and “understanding how data are stored” (ibid.). These skills are in addition to, rather than
instead of conventional statistical literacy skills.
The conceptual, mechanical and critical aspects of doing statistics with real data are all
important elements of the skillset required to work on understanding and addressing social
challenges such as those represented by the SDGs (Engel, 2017; Gould, 2017; Prodromou
and Dunne, 2017). Critical data skills are important as, unlike textbook data, “real data
about society are often more complex and messy” (Engel, 2017: 46). If learners are to use
their statistical skills to work on SDG problems, they need to be prepared to work with such
real-world data and develop the various data literacy requirements that this entails. This is
particularly important for ensuring that data sources which fall outside the traditional
sphere of official statistics are adequately understood when these are utilised towards the
SDGs. Likewise, the ability to draw insights from such data also requires statistical literacy:
the social problems represented by the SDGs are highly complex, and understanding them
therefore “requires the ability to explore, understand, and reason about complex
multivariate data, because social phenomena do not happen in a vacuum, and their
understanding requires awareness of how variables co-vary, or affect each other, or are
situated in a network of causal factors that may change over time in manifold ways” (Engel,
2017: 45).
The recognition of the need for both sets of competencies does not mean that the
distinction should be dispensed with altogether, or that it is appropriate to assume that the
terms may be used interchangeably. By referring only to “data literacy”, one may implicitly
centre the tools and perspectives of data science at the expense of the important principles
of statistical reasoning that remain central in statistics education. Similarly, referring only to
“statistical literacy” may privilege the analytical dimension of working with data at the
expense of the critical and practical aspects which frequently define such work outside of
the classroom. We do not aim (nor claim) to reconcile these definitional issues in this paper.
For these reasons, we generally refer to “statistical and data literacies”, as this collapsing of
the two effectively captures that hybridity is at the core of how quantitative education
curricula can contribute to the unmet needs of the data revolution. Moreover, as our
Manchester Centre for Digital Development Working Paper 89
proposed model is premised on the application of statistics in the workplace, we regard the
need to have the full set of data and statistical literacies to provide an inclusive set of skills
which will have most benefit in strengthening the skills pipeline nationally.
In the following section, we set out how an experiential learning model can help to capture
the value of a hybrid approach by giving learners hands-on experience with real data in
addition to classroom-based learning.
C2. Data Fellowships and the Value of Experiential Learning
The model we propose is designed to build on the success of an established model from the
UK called Q-Step (see Carter et al, 2017). Q-Step was a national educational step-change
programme developed as a strategic response to the shortage of quantitatively-skilled social
science graduates in the United Kingdom (Nuffield, 2014). The programme funded 15 Q-
Step Centres within higher education institutions. As an experimental programme, centres
were encouraged to trial and evaluate different approaches to training undergraduates in
statistics and data analysis. The University of Manchester Q-Step Centre, with many
successful partnerships already established with industry, developed a paid internship
programme which placed 250 undergraduates since 2013 in organisations including local
and national government, charities, statistical institutes and research consultancies.
Students spent two months working on real quantitative data-driven projects that could
help them to practice and develop their professional and analytical skills. The data-driven
research projects, which were co-designed by the host organisation, had to matter and have
relevance to the host. Although not formally assessed as part of the student’s
undergraduate degree, the interns were required to develop a poster at the end of their
placement. A half-day conference was then held to provide an opportunity to celebrate the
placement learning, with prizes being awarded for talks and posters, the external judges
panel comprising practitioners, academics and former interns. The intention was to share
the value of the experiential learning opportunities, and to make visible the analytical and
professional learning that took place. Many of these internships resulted in graduate job
offers, and even more led to valuable connections between the partner organisations and
the university.
The value of the Q-Step paid internship model is that it exposes students not only to
classroom-based instruction in the basic statistical principles and analytical techniques
which underpin statistical literacy, but also hands-on experience working with real data.
Students participating in the programme first receive intensive training in the classroom to
develop their statistical and data literacy skills, before moving to the workplace where they
undertake real quantitative data-driven projects. In these research internships, students are
able to practice and develop the skills learned in the classroom while building the
accompanying professional and analytical skills that are necessary for working with and
communicating about data. These placements provide a win-win scenario, where partner
organisations receive an extra pair of hands to work on a short-term project of value to
them, while the student gets to hone and develop their skills doing research that matters.
Whilst we named these ‘internships’, they form the basis of the data fellows programme
that we propose here.
Manchester Centre for Digital Development Working Paper 89
The data fellowship model is an example of experiential learning, a pedagogical approach
rooted in the theory of Kolb (1984), of which “a key part is personal hands-on practical
experience in the learning process” (Purdam, 2016: 258). Experiential modes of learning
such as internships, study away programmes and project-based learning are “proving to be
critical and powerful in the overall quality and meaning of the undergraduate experience”
(Bass, 2012: 24), with growing evidence that there are material benefits to both learners
and educators (Roberts, 2018). This is particularly important for the teaching of quantitative
skills to social science students, where there is a growing recognition that “the learning of
social science research skills should be closer to the reality of social science research in
practice” (Purdam, 2016: 266). This means getting students to practice and develop the
hybrid set of data and statistical literacies required through hands-on experience with real
data and real research problems.
This experiential learning can help to bridge the gap between the formulae learned and the
conceptual understandings underpinning them by putting statistics into the context of front-
line social research. A key challenge for statistics education is to ensure that students
develop more than just the ability to recall and apply formulae and calculations from
textbooks, but also a higher-level understanding of the principles of making sense of data.
As Gail Burrill observes, “you could go to a class you have of undergraduates and ask them
what the mean is, and you’re probably going to get an answer like ‘you add them up and
divide’. So, there’s really not a conceptual foundation. It’s a recipe” (Burrill, 2020).
This is illustrated by Hobden (2014), who reports on a teacher re-skilling programme in
South Africa which aimed to provide educators with the quantitative foundations to teach a
course in Mathematical Literacy. When asked to provide an interpretive explanation of a
sample sentence reporting the median survival times for men and women once infected
with HIV, the trainees displayed generally low levels of conceptual understanding which led
them to misunderstand the underlying health information being communicated in the
sentence. Hobden notes that by focusing on a free-text, explanatory task rather than a
mechanical application of a formula or calculation, the study draws “attention to the chasm
that exists between the procedural computation of basic statistical measures and the
conceptual understanding that enables and informs important opinions” (Hobden, 2014:
80). This chimes with Chris Wild’s assessment that capacity building in statistical literacy
ought to prioritise “conceptual understandings that enhance human understanding of data
and what data can and cannot do, elements that are empowering and of lasting value to
citizens” (Wild, 2017: 36, emphasis in original). Crucially, the hybrid skillset needed to close
this gap between procedural knowledge and effective understanding for everyday use is
“unlikely to develop without opportunity to learn through exposure to practice, and this
seems to be the key” (Hobden, 2014: 81).
We propose that the Q-Step model developed at the University of Manchester can provide a
valuable intervention in light of the challenges identified in Section B by helping to develop
the unmet skills needs and thus contribute to the development of the next generation of
quantitative social researchers. The approach achieves this in a number of ways. First, the
paid internship model incentivises students to take an interest in statistical and data-driven
approaches to solving social challenges, providing motivation for more students to embark
on pathways into quantitative careers. Moreover, as the learners are substantively
interested in complex social phenomena, as social science students, they are able to bring
Manchester Centre for Digital Development Working Paper 89
their new knowledge and understanding back to their studies and use this to direct their
interests and future career choices. Second, the two-way feedback between the partner
organisations and the educators helps build understanding of which skills are most valued
and expected by prospective employers working on data-oriented projects. Importantly, this
includes those professional and communicative skills which we have argued are particularly
needed for working on the SDGs. Third, by working with partners in relevant sectors, the
model capitalises on the already existing skills and data available in-country. This not only
reduces the dependency on external actors for capacity building support, but also ensures
that learners will be honing their data skills on problems that are relevant to their society.
With the potential value of this model in mind, we consider in the next section how to apply
it practically in Colombia.
D. Practical Steps for Implementing Data Fellowships for the
D1. The Need for Country-Led Interventions
The Q-Step programme offers an encouraging example of how partnerships and workplace
learning-based interventions can build a pipeline of statistical capacity for quantitative social
research. In the remainder of this paper we wish to explore in more practical terms how this
model can be developed in other countries to address the unmet needs identified in Section
B. In doing so, we must first acknowledge that what works in the UK will not necessarily
work elsewhere, and it is critically important to consider how the approach might serve the
needs of a particular country. Both the UN (2015) and PARIS21 (2015) acknowledge that the
particular social, economic and political environments in each country mean that progress
towards the SDGs and efforts at statistical capacity development must be country-led
processes. Language, culture, political will, economic factors, and the structure of the
education system all contribute to determining what kind of implementation is practical and
likely to have the desired impact in a given context.
We have argued the importance of partnerships in the process of building the in-country
capacity pipeline, specifically aimed at supporting the SDG framework. However,
partnerships is not an end in itself the development sector is very familiar with tokenistic
approaches to partnerships and participation potentially leading to the “the unjust and
illegitimate exercise of power” (Cooke and Kothari, 2001: 1). Partnerships that lead to
transformative outcomes in the capacity pipeline require in-country ownership and
leadership, supplemented by international actors. The role of those trans-national actors
should revolve around offering technical competence and service, whereas local actors take
the lead thereby ensuring that the local context remains central to capacity development
As such, our argument is not that the Q-Step model can be imported into other countries
prefabricated. What we are arguing for is the value of countries building on the successes
and strengths of this model to tailor an intervention to the particular unmet needs of that
country. In practice, this means identifying where opportunities exist, such as determining
where there is political will to address skills gaps in certain sectors, and working with
Manchester Centre for Digital Development Working Paper 89
universities with expertise in statistical education to develop relevant learning materials.
However, it also means acknowledging the challenges that may exist in a particular country,
for example where access to higher education is particularly skewed, or where the baseline
of statistical expertise, capacity and infrastructure required for such a programme to be
impactful is not already in place.
D2. Future In-Country Research in Colombia
To this point we have argued that there are a number of unmet needs in the current
statistical capacity development landscape, particularly in relation to the SDGs, and that a
country-led, experiential learning-based data fellowship model can help to address these
needs. In this section we wish to illustrate this argument in more practical terms through a
discussion of the development of a data fellows programme in Colombia. We focus on
Colombia as our research on this topic is the result of a partnership between the University
of Manchester and Data-Pop Alliance, a research laboratory led by Harvard, MIT and the
Overseas Development Institute that has worked extensively on data literacy in Latin
America and maintains a strong network and knowledge base in the region, including in
Colombia. This facilitates identifying the strengths and weaknesses of the Colombian
statistical system, as well as the practical opportunities and challenges of implementing the
kind of model described in Section C. Our analysis in this section is also informed and
supplemented by pilot research we conducted in several Latin American countries, including
an initial workshop in São Paulo in May 2019, followed by interviews with a number of key
actors in the statistical community of three countries (Mexico, Colombia and Brazil), drawn
from academia, civil society and the public sector (for more details see Higgins et al, 2019).
This pilot research led to a partnership with colleagues at Universidad Del Rosario in Bogotá,
who will lead the development of a data fellowships programme in Colombia. Here, we
report some of the emerging challenges and opportunities that will shape this development.
First, we focus on Colombia’s statistical and data capacity for the SDGs, before turning to
the Colombian pipeline for developing statistical and data literacies.
Statistical and Data Capacity for the SDGs
One of the strengths of the Colombian statistical system for developing a data fellowships
programme is that Colombia is one of several countries in the region that already have well-
established and resourced national statistical and academic systems, which allows more
runway for getting a programme of the nature we describe up and running. The national
statistical office is relatively well-resourced and developed by international standards. For
example, Colombia scores well on the World Bank’s Statistical Capacity Index, remaining
well above the regional average as well as the average for middle-income countries over the
last 15 years (World Bank, 2020). Moreover, with specific regard to the SDGs, interviewees
described Colombia as an example in the region for taking the lead on the development of
country indicators for measuring progress against Tier 2 and Tier 3 indicators (those where
established methodologies and international standards either do not exist or are not widely
used) (Higgins et al, 2019).
However, while statistical and data capacity is relatively high by international data
monitoring standards, Colombia’s capacity is more limited when considered from a more
Manchester Centre for Digital Development Working Paper 89
human-centred perspective. Statistical and data literacy levels remain relatively low in
Colombia. Colombia has been improving its national results in standardised tests such as
PISA in recent years, as well as results in the national test Pruebas Saber. However, PISA test
results are still lower than the average for OECD countries for mathematics (391 for
Colombia vs 489 for OECD) and sciences (420 vs 488 for OECD). Interviewees described a
need for more data literacy training across academia, particularly at the undergraduate
level, as well as in the public sector and civil society. Previous research illustrates how this
lack of statistical and data literacy training manifests in skills gaps in Colombia. In a survey
led by the National Planning Department in 2017 concerning the use of data science in
public entities, the results show low levels of exploitation of data for the generation of value
in public administration. National entities state that lack of training in data analysis and
applications is the main barrier (63%) to data exploitation (DNP, 2018a). This relatively low
level of human capital in statistical and data skills is part of the wider issue of Colombia
having an underdeveloped data culture. In many sectors, data skills are low and their value
poorly understood (Higgins et al, 2019). As such, there is not yet a strong culture of using
data to find innovative solutions to social problems, restricting the development of
expertise in the field. In response to this shortage of qualified data professionals, the
government has explicitly called for action to generate mechanisms to materialise the value
of data in the public sector and to institutionalise the exploitation of data in public decision
making (DNP, 2018a).
Indeed, understanding the political will and appetite for these skills and competencies in
Colombia helps highlight the opportunities for Colombian higher education partners to
develop a programme which is able to achieve the most impact by addressing the pressing
sectoral needs and skills gaps. In this sense, the National Development Plan of the
Colombian government presents a number of open doors. In particular, the Ministry of
National Education, together with the National System of Science, Technology and
Innovation, is committed to supporting the development of high-level human capital as a
matter of objective priority (DNP, 2018b). There is strong will among policymakers to foster
high-level professionals able to contribute in the ‘knowledge society’, and to support young
people and professionals into education pathways that can contribute in this arena. As we
have argued, the data fellowship model promotes a holistic skillset which helps develop
well-rounded statisticians and data professionals, and there is clear demand for these skills
from the Colombian government. Partnering with key organisations and public institutions
in which shortages of statistical and data literacy have been identified helps to ensure that
the programme builds talent pipelines directly into these gaps.
Statistical and Data Capacity Development Pipeline
A key strength of the capacity development pipeline in Colombia is that the country has a
relatively strong academic sector from which expertise in teaching can be leveraged.
However, there is a somewhat limited offer of courses in relevant subjects: in 2017 there
were only 75 formal education programs related to data science and analytics in the
country, with only five of these at postgraduate level and just one PhD programme, while
only 2.5% of the scholarships offered by the Colombian government were in data skills-
related subjects (Higgins et al, 2019: 19). In particular, teaching expertise in statistical and
data skills tends to fall more under the umbrella of mathematical education subjects which
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do not always develop the hybrid set of social research skills which the SDGs demand; social
science subjects, on the other hand, tend to be more oriented towards theory than data
analysis (Higgins et al, 2019). Interviewees from the statistical community thus emphasised
“the need to build data literacy capacities towards fostering ‘hybrid’ professionals that can
understand, use and analyze data for ‘social sciences’ purposes, i.e. evidence-informed
policy, journalism, activism etc.” (Higgins et al, 2019: 32). Through its focus on developing
hybrid skills for working with data on complex social problems, the proposed model can
help fill a clear skills gap in Colombia with regard to the supply of professionals capable of
using data to find innovative solutions to social problems.
The government’s SDG strategy recognises that academia, through both research and
teaching, has a significant role to play in the field of innovation and training for the
sustainability of future generations (DNP, 2018c). This presents an opportunity for
educators to highlight how the data fellowship model fits within the current national
development priorities and can contribute in the space of capacity development for the
SDGs. The national SDG strategy also emphasises the need to establish mechanisms for
dialogue with non-governmental actors, and calls for partnerships for knowledge, financing
and mobilisation towards the SDGs. Moreover, the government has highlighted stimulus for
the interaction between university and business as a strategic objective in its Pact for
Sciences, Innovation and Technology. The data fellowship model can thus provide a
platform for strengthening university-industry partnerships and bolstering the country’s
human capital in data-oriented fields and public innovation, both of which are development
priorities in Colombia. These partnerships are at the heart of how data fellowships can
achieve impact, as they involve a range of stakeholders who understand the particular
needs and realities that can vary country to country. This places Colombian universities in a
strong position to play a key role in supplying statistically and data literate talent for the
delivery of the SDGs.
However, one of the key challenges for Colombia is that there are pre-existing inequalities in
education access which structure opportunities to develop statistical and data literacies and
skills. There are generally high levels of dropout and low graduation rates. In 2016, the
cohort dropout indicator stood at 45.1% for university students and 53.2% for technicians
and technologists, while the graduation rate was 37.4% and 27.1%, respectively (DNP,
2018b). In 2015, only 62% of young adults had finished high school, and only 38% of high
school graduates progressed into higher education (ibid.). This gap in access to education is
driven by structural inequalities. In Colombia, 27% of the population is low-income and 7.8%
is in extreme poverty (DANE, 2018). By 2017, young people in the top quintile of the income
distribution had almost four times greater access to higher education than young people
from the bottom quintile. This is not only a question of high- and low-income, but also
urban and rural. The population in rural areas of Colombia accumulate an average of 6 years
of education, whereas in urban areas this average goes up to 9.7 years (DNP, 2018b). The
gap in access to primary education is around 10% and increases to 16% for tertiary
education. These gaps mean that developing the data fellowship model through
partnerships between higher education institutions (particularly private universities in urban
areas) and industry, civil society or government are at risk of “empowering the empowered”
(Gurstein, 2011) and leaving behind the rural population when it comes to statistical and
data literacy. To combat this, there must be active efforts targeting the involvement and
Manchester Centre for Digital Development Working Paper 89
recruitment of marginalised populations into a data fellowship programme. On a practical
level, this could involve partnerships between the large private universities and regional
universities with a student pool drawn from less privileged backgrounds. Another strategy is
to aim to partner with organisations that work in rural areas, in order to better target the
needs of those communities when designing projects for data fellows. The use of digital
course delivery methods such as MOOCs could also help to decentralise the educational
components of the programme and improve rural access. Ultimately, evaluation
mechanisms must be built into the intervention to ensure that partners are able to identify
what works and what needs improvement to make the data fellows model work in
E. Conclusions
The ever-expanding reach of statistics and data in the digital age means that there is a
global need for capacity in the critical quantitative skills required for understanding and
working with data. The Sustainable Development Goals, the most ambitious exercise in the
quantification of social progress we have seen, are a clear example of where demand for
such skills is outstripping supply. This demand can be a galvanising force for the
development of statistics education programmes aimed at achieving impact where these
skills are most needed. However, it is important that progress in this arena is not contained
to the high-income countries of the Global North. Partnerships can be a key tool in building
bridges between sectors and countries, to ensure that the data and expertise that already
exist can be utilised in developing and motivating the next generation of quantitative talent.
In this paper we have argued for the value of thinking of statistical capacity as a pipeline
which begins in the education system, and illustrated why investment in this end of the
pipeline can be a more sustainable and long-term pathway to building up the holistic skill-
base needed to tackle the challenges of the SDGs. Through our review of the practical
conditions underlying the development of such a programme in Colombia, we have
illustrated that, although there are challenges in ensuring that educational access is equal,
there are significant opportunities and open doors for addressing local skills gaps through
data fellowships. The next step of this research is thus to begin placing students within
partner organisations in Colombia and learning what works well, what needs to be refined,
and what may be scalable to other countries beyond Colombia.
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... Studies have shown that one of the key challenges faced by the NSS of the majority of developing countries is producing the data required for SDG monitoring and reporting (Jones et al., 2021;OECD, 2017). This challenge arises mainly because data collection and reporting are not primary functions of most line ministries. ...
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This study reviews the level of statistical skills and competencies of staff of National Statistical Offices (NSOs) of the Caribbean as a critical step in enhancing the statistical capacity of National Statistical Systems (NSSs) of the subregion. It is important to take stock of the skills gaps that exist at the NSOs and the level of statistical literacy of NSO stakeholders in ministries, departments, and agencies of government. As producers and users of official statistics, NSO stakeholders’ understanding of data and statistics is key to evidence-based decision making. In general, improving statistical literacy among the Caribbean population will contribute to a better understanding and use of statistical data by all users, including businesses, mass media, civil society, academia, and the ordinary citizen. Building on discussions of statistical literacy in national policies and regional frameworks such as CARICOM’s Regional Strategy for the Development of Statistics (RSDS), this study explores statistical literacy and competencies at the NSOs using self-reported responses ofNSO staff to a structured questionnaire that focused on technical expertise of staff and their perception of the statistical skills of their colleagues at the NSO and within the NSS. Results of the survey provide an encouraging indication that NSOs facilitate continuous education for staff to enhance their skills and are actively engaged in promoting statistical literacy among the general population. NSO staff perceive the staff of other government ministries, departments, and agencies with whom they interact on data needs and requests to have good statistical literacy. However, the study's results are also indicative of the need to improve the statistical literacy of staff who do non-statistical work at the NSOs and suggest the prevalence of misuse and misinterpretation of data and statistics by journalists and the ordinary citizens.
... The second challenge is the pervasive inequality in education among subgroups of the population. To overcome these inequalities, efforts must be made to target the recruitment of marginalised sub-groups (such as women, those with lower incomes and those in rural communities) into a data fellowship programme [41]. ...
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EmpoderaData – from the Spanish word empoderar ‘to empower’ – is a partnership research project between the University of Manchester (UK), Fundação Getulio Vargas (Brazil), Universidad del Rosario (Colombia) and Data-Pop Alliance (US and France). The project builds upon a successful data-driven, research-led paid internship programme in the UK (Q-Step) which enables undergraduate social science students to practise data skills through immersion in the workplace. Two-hundred and fifty students have benefited from the Q-Step programme in six years, many graduating into analytical careers in civic society and industry. EmpoderaData aims to build on this experiential learning initiative by developing a data fellowship programme in order to foster and develop data literacy skills in Latin America, led by the need to address society’s most pressing issues and using the framework of the Sustainable Development Goals (SDGs). EmpoderaData Phase 1 explored whether the internship model would have relevance and usefulness within the context of three Latin American case study countries (Brazil, Colombia and Mexico). The team set out to establish a baseline of the state of data literacy and existing training programs in Brazil, Colombia and Mexico. As part of a ‘Big Data for the Common Good’ event, a workshop was held in São Paulo with thirty participants representing data literacy advocacy or policy formation and drawn from civil society, academia, the private and public sector. The main conclusions from this first phase are: (1) the most requested data literacy training need is for basic skills, including introductory statistics, foundation data analysis and methodological skills; (2) paid data fellowship models are acknowledged as a useful intervention; and (3) the notion of a ‘hybrid’ professional to build data literacy capacities for ‘social science’ purposes provides a practical way forward. In the EmpoderaData Phase 2 project our focus was on Colombia to explore the challenges and opportunities of developing a pilot data fellowship model there. Engaging with national, regional and international capacity development efforts, this highlighted a demand for partnerships between universities and organisations working on the social challenges represented by the SDGs. Partnerships ensure that the in-country data literacy pipeline is strengthened in a home-grown, self-sustaining way, producing a steady flow of data literate graduates into the institutions and sectors where critical data skills are most needed. We report on how the EmpoderaData project is exploring working with students studying STEM degrees at the Universidad del Rosario, to improve the application of statistical methods to the social sciences. The aim is to strengthen STEM skills and develop youth empowerment across Colombia, urban and rural areas, to improve the quality of statistical education at the national level, and support the skills needed to deliver the SDGs. In parallel, the Fundação Getulio Vargas (FGV) Business School in São Paulo agreed to trial the work-placement programme in their undergraduate business and public policy degrees through a programme entitled ‘The FGV Q-Step Center to improve quantitative skills in undergraduate business students’. This two-year-long funded study will enable us to explore the transferability of the internship model from the UK to Brazil. The paper will discuss how the programme was established (following the lessons learned from EmpoderaData), explain how this model will be implemented in FGV, especially paying attention to how the curriculum will develop to support it, and how the impact of the programme will be monitored. The knowledge exchange generated from this study will complement the research conducted through the EmpoderaData project. The paper will cover the progress of the EmpoderaData project and FGV-Q-Step Center to date and explore how we are developing these initiatives, the challenges we have faced, and how through partnership working we are developing capacity building in statistical and data skills training.
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The Sustainable Development Goals (SDGs) recently adopted by the United Nations represent an important step to identify shared global goals for development over the next two decades. Yet, the stated goals are not as straightforward and easy to interpret as they appear on the surface. Review of the SDG indicators suggests that some further refinements to their wordings and clarifications to their underlying objectives would be useful. We bring attention to potential pitfalls with interpretation, where different evaluation methods can lead to different conclusions about country performance. Review of the United Nations' SDG database highlights the overwhelming challenge with missing data: data are available for just over half of all indicators and for just 19 percent of what is needed to comprehensively track progress across countries and over time. We offer further reflections and propose some simple but cost-effective solutions to these challenges.
This special issue explores the workings of global goals as an instrument of global governance by numbers. These goals can alter power relations, affect the distribution of resources, reorganize national and local priorities, create perverse incentives for performance, and produce narratives that shape thinking and communication. As the articles in the 2014 JHDC special issue showed, the MDGs had complex, often distorting, consequences which were often in tension with the (intangible and difficult to quantify) principles of equity, human agency and participation as the cornerstone of development. This issue focuses on SDGs and includes five case studies of this localization process in aid programming in the Valencia, national reporting by Sweden, farming collectives in South Africa, indigenous communities in Australia and New Zealand, and infrastructure development in Ecuador and Pakistan. A sixth paper examines the role of metrics in including Neglected Tropical Diseases in the SDGs. These papers are diverse in the research questions they ask but engage with the common themes of global goals as a tool of global governance and their disruptive effects on power structures. Using the framework of data infrastructure—means of collection and analysis, social structures amongst actors, knowledge systems—this introduction highlights the insights that emerge.
Assessment of the 17 Sustainable Development Goals (SDGs) has largely focused on formulating appropriate targets and indicators for each goal. Much less attention has been devoted to estimating possible tradeoffs and complementarities in attaining the various SDGs. Yet such tradeoffs and complementarities clearly exist. We develop an analytical model to estimate the welfare effects of progress in attaining one SDG while accounting for interactions in achieving other SDGs. We assess quantitatively progress in the SDGs over 2000–2016 at the global level and for low-income countries, using a representative indicator for each goal. We then estimate the welfare changes for improvements in No Poverty (SDG1) net of any welfare gains and losses in attaining each of the remaining 16 goals. For the world, we estimate the per capita welfare change of reductions in 2000–2016 poverty rates net of any gains or losses in attaining each of the remaining 16 goals is $12,737 per capita. This is more than double the welfare change of $5671 per person for poverty reduction alone from 2000 to 2016. However, once interactions with other SDGs are taken into account, the net welfare change for poverty reduction in poor economies from 2000 to 2016 is $244 per person, which is almost 20% lower than the welfare estimate of $299 per capita of poverty reduction on its own. Such an analysis helps policy makers prioritize improvements towards one goal or set of goals, and show explicitly the net gains and losses for achieving one goal while impacting others. For example, over 2000–2016 the world may have come closer to attaining the No Poverty goal, but at the expense of other critical SDGs, especially those associated with the environment. This research has important implications for achieving sustainability through pursuit of the SDGs.
This brief article comments on the special issue on SDGs ‐ Knowledge and Politics in Setting and Measuring the SDGs. Acknowledging that the articles in the issue show how reliance on indicators changes the way development is conceived, Merry asks why is it so difficult to produce better indicators. If they are too narrow, why not simply produce more? She argues that conceiving of measurement as an infrastructure provides important insight into these questions. Measuring development is clearly an important strategy for promoting it, but it should not be the only one, particularly considering how it shapes what development means and how it is implemented. Such a heavy reliance on quantification ignores the constraints on accurate knowledge resulting from the infrastructure of measurement.
This chapter discusses the roles of transnational multiple stakeholder partnerships in addressing development and implementation challenges affecting youth and children in both rich as well as low- and middle-income countries. We first discuss each of five major sets of stakeholders –national governments; community members; civil society organizations; the private sector; and researchers – in terms of their stakes in working towards SDG progress. Then we present how networks across these groups (e.g. at national, regional and global levels, or Multiple Stakeholder Partnerships (MSPs), can help achieve progress, with several current examples. Throughout we balance discussion of challenges, strengths and opportunities in both individual stakeholder approaches and MSPs. We also place special emphasis on the role of research in general and developmental science in particular, in the work of MSPs on the Sustainable Development Goals.
The scope and magnitude of the sustainable development goals (SDGs) set out by the United Nations provides new opportunities for nations to collaborate and take action. This chapter focuses on goal 17 of the SDGs, with capacity building as one if its dedicated target, as a possible course of action to achieve the goals for the 2030 Agenda. The chapter deliberates on how developmental science can facilitate progress on the SDGs from a life span perspective along with exploring shifts in the approach which are required in order to enhance capacity development of researchers at the grassroots and decreasing their dependency on external support. Case examples of developmental science programs influencing policy decisions related to children and youth are explored so as to set the stage for the importance of investing in professional training of developmental scientists. We link the story of gains made by developmental science and it’s far reaching impact with existing programs at some of the leading universities in both the minority and majority world in order to better understand the need for investment at the foundation stage and examine what makes for a successful graduate program. We also detail the capacity building initiatives of key developmental science societies and understand how they encourage collaboration among researchers across countries, especially early career scholars. We conclude the chapter by reemphasizing that building capacity would require acquiring the right skills and knowledge and how developmental scientists are having an increasingly important role in helping shape the future.