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“You say you want a [data] revolution”: A proposal to use unofficial statistics for the SDG Global Indicator Framework

September 2019
Statistical Journal of the IAOS

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Abstract. This paper argues that the Global Indicator Framework required to support the 2030 Agenda Sustainable Development Goals will not be successfully populated, using only existing approaches and mechanisms. Official statistical systems must adapt and consider new approaches if only partial success is to be averted. This paper presents a proposal to accredit unofficial statistics as official for the purposes of compiling sustainable development goal indicators. While there may be some reluctance, and there are certainly risks with this proposal, the arguments put forward highlight the potential for collaboration. Keywords: 2030 Agenda, accreditation, risk management, fundamental principles of official statistics

Net official development assistance (total) as a % of gross national income, 2002-2016. Source: OECD DAC: https://data.oecd.org/ oda/net-oda.htm and authors own calculations.

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A proposed future: Using unofficial data and statistics to compile SDG indictors.

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(National/Global) statistical and data ecosystems.

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Statistical Journal of the IAOS 35 (2019) 309–327 309

DOI 10.3233/SJI-180486

IOS Press

“You say you want a [data] revolution”: A

proposal to use unofﬁcial statistics for the

SDG Global Indicator Framework

Steve MacFeelya,b,∗and Bojan Nastava

aUnited Nations Conference on Trade and Development, Geneva, Switzerland

bCentre for Policy Studies, University College Cork, Cork, Ireland

Abstract. This paper argues that the Global Indicator Framework required to support the 2030 Agenda Sustainable Development

Goals will not be successfully populated, using only existing approaches and mechanisms. Ofﬁcial statistical systems must adapt

and consider new approaches if only partial success is to be averted. This paper presents a proposal to accredit unofﬁcial statistics

as ofﬁcial for the purposes of compiling sustainable development goal indicators. While there may be some reluctance, and there

are certainly risks with this proposal, the arguments put forward highlight the potential for collaboration.

Keywords: 2030 Agenda, accreditation, risk management, fundamental principles of ofﬁcial statistics

1. Introduction

‘We always overestimate the change that will occur

in the next two years and underestimate the change that

will occur in the next ten’ – Bill Gates [1].

In 2015, the United Nations (UN) launched its most

audacious and ambitious development plan; The 2030

Agenda and corresponding Sustainable Development

Goals (SDGs). That agenda covers sixteen separate di-

mensions of development ranging from eradication of

extreme poverty, achieving gender equality, ensuring

sustainable consumption and production to combating

climate change. It also includes a seventeenth multi-

dimensional goal to address implementation. This goal

comprises ﬁve operational sub-dimensions: ﬁnance,

technology, capacity-building; trade and systemic is-

sues.

Unlike the previous development programme, the

Millennium Development Goals (MDGs), the SDGs

∗Corresponding author: Steve MacFeely, United Nations Con-

ference on Trade and Development, Palais des Nations, Ch-1211

Geneva 10, Switzerland. Tel.: +41 22 917 57 34; E-mail: steve.

macfeely@un.org.

explicitly require statistical performance indicators to

be compiled. The resulting Global Indicator Frame-

work (GIF) was adopted by the UN General Assem-

bly in July 2017. The broad scope of the 2030 Agenda

means that (currently) 232 performance indicators are

required. Many of these indicators are not produced

regularly if at all. In fact, the Inter-Agency and Ex-

pert Group on Sustainable Development Goal Indica-

tors (IAEG-SDGs) calculated in April 2019 that less

than half of the selected indicators for the GIF could

be populated.

Various agencies and economists have attempted

to put a cost on populating the GIF. The estimates

vary enormously, but all are far in excess of existing

funding [2]. In an environment of faltering multilat-

eralism, it seems unlikely that available funding will

match requirements. Yet political expectations appear

to be very high; perhaps irrationally so, considering

the scale and complexity of the SDG targets and the

resultant indicators. Historic difﬁculties in populating

the more modest MDG indicators suggest these expec-

tations may be very optimistic. Therefore, in order to

meet expectations new, or supplementary, approaches

will be required.

1874-7655/19/$35.00 c

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310 S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF

One supplementary approach could be the introduc-

tion of a mechanism to certify unofﬁcial statistical in-

dicators as ofﬁcial. Although the approach discussed

in this paper is consistent with philosophy of the data

revolution and the established trend of endorsing ‘un-

ofﬁcial’ scientiﬁc discovery through accreditation and

validation by a recognized authority, we nevertheless

make this suggestion somewhat reluctantly. Our hes-

itancy arises as we believe the ideal situation is one

where National Statistical Ofﬁces (NSOs), National

Statistical Systems (NSSs) and International Organisa-

tions (IOs) are mandated and properly funded and re-

sourced to compile all required national and interna-

tional ofﬁcial statistics respectively. However, as this

is not the case, and it is hard to envisage a sudden and

dramatic improvement, then alternative solutions must

be considered. The pessimistic viewpoint sees this as

the thin edge of a dangerous wedge, where funding

to NSOs may be further reduced and the standing of

NSSs and ofﬁcial statistics is further undermined. A

more optimistic perspective recognizes the opportuni-

ties to develop the mandate of ofﬁcial statistics beyond

its current role.

It is useful at this juncture to clarify what we mean

by the term ofﬁcial statistics. An ofﬁcial statistic is any

statistic produced by a mandated institution or system

in accordance with the UN Fundamental Principles of

Ofﬁcial Statistics [3] or internationally accepted na-

tional or international code of practice or principles,

such as, Principles Governing International Statistical

Activities [4].1

The idea of using unofﬁcial data to compile ofﬁ-

cial statistics, be they national or international is noth-

ing new. NSOs use unofﬁcial data everyday as inputs

to compiling ofﬁcial statistics (see also Section 2.2).

IOs must also resort to using unofﬁcial data to com-

pile global aggregates. In fact, the Committee for the

Coordination of Statistical Activities (CCSA) has pub-

lished guidelines Recommended Practices on the Use

of Non-Ofﬁcial Sources in International Statistics [5]

on the topic. The practice of using unofﬁcial data is ex-

pected to grow in the coming years as NSOs and IOs

are increasingly reliant on administrative data and per-

haps even Big Data to compile ofﬁcial statistics. But

what if we were to go a step further? Rather than sim-

ply using unofﬁcial data as inputs to derive or compute

ofﬁcial statistics, what if we could use already com-

piled unofﬁcial statistics to ﬁll some of the gaps in of-

1These terms are deﬁned and discussed more detail in Section

2.1 – A Proposal.

ﬁcial statistics? In other words, use statistics that have

been compiled by institutions other than those formally

mandated by States to compile statistics for national

or international purposes. At this point it may be use-

ful to clarify another very important point. Data and

statistics are not the same thing. While the terms are

frequently (and incorrectly) used inter-changeably or

synonymously, they are in fact two different things.

Data are basic elements or single pieces of informa-

tion. Statistics are numerical data that have been or-

ganized through mathematical operations in line with

conceptual frameworks.

The idea of using unofﬁcial statistics to compile of-

ﬁcial statistics is not a new idea either. Several papers

have raised this issue before, either directly or indi-

rectly, questioning whether there is a new role for of-

ﬁcial statistics as a certiﬁcation authority [6–9]. Ham-

mer et al. [10, p. 19] summarise the issue succinctly:

‘statistical agencies could consider new tasks, such as

the accreditation or certiﬁcation of data sets created by

third parties or public or private sectors. By widening

its mandate, it would help keep control of quality and

limit the risk of private big data producers and users

fabricating data sets that fail the test of transparency,

proper quality, and sound methodology.’

In this paper we discuss, whether such a mechanism

might be useful in the speciﬁc context of compiling

indicators for the 2030 Agenda. From the outset, we

would like to clarify that the proposal put forward in

this paper is not driven by any ideological position but

rather by a desire to ﬁnd a pragmatic, yet professional,

solution to what we perceive as a serious problem. In

making this proposal our intention is not to be sub-

versive or iconoclastic. We have no desire to under-

mine or corrode ofﬁcial statistics. We are not setting

out to promote or argue for the privatization or ‘uber-

ﬁcation’ of ofﬁcial statistics. Nor are we advocating a

completely open wiki-stat approach. On the contrary

– we are staunch defenders of the need for impartial,

independent ofﬁcial statistics. But given the pace of

progress, the cost of developing the SDG indicators

and the weight of expectation, we feel it is necessary to

ask whether there are other approaches? Speciﬁcally,

we are asking whether there might be a way to collab-

oratively harness the intellectual power of those out-

side the ofﬁcial statistics tent to avoid needless delays,

duplication and expenditure. Bordt and Nia [11, p. 1]

argue that populating the GIF is an ‘adaptive challenge

requiring us to go beyond any one authoritative exper-

tise to discover and generate new capacity, new exper-

tise, and new ways of doing things.’ We agree. We also

S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF 311

argue that in a post-truth era, ofﬁcial statistics might do

well to take more control of a rapidly fragmenting and

complicated information environment. Our fear is that,

as Gates has warned, we (the statistics community) are

underestimating the changes underway in the world of

data and statistics. In brief, this is a risk management

strategy. The details of the proposal are outlined in Part

2 – A proposal for a system to certify unofﬁcial statis-

tics.

This proposal is in keeping with the inclusive spirit

of the 2030 Agenda and the idea of holistic data

ecosystems. To date, many of the discussions regarding

the GIF have placed ofﬁcial national statisticians, ofﬁ-

cial international statisticians and other statistical com-

pilers as competitors. But perhaps there is a way to col-

laborate rather than compete? This latter aspect of col-

laboration and data sharing is at the heart of recent rec-

ommendations of the Bogota Declaration of The UN

Global Working Group on Big Data [12].

The paper is divided into two parts. Part 1 (Back-

ground and Context) outlines some of the background

issues, such as measurement difﬁculties and the likely

costs associated with populating the GIF to help read-

ers understand the scale of the challenge facing the

global statistical community. Part 2 (A Proposal for a

system to accredit unofﬁcial statistics) outlines the pro-

posal and argues the approach is consistent with the

philosophy of the 2030 Agenda. The arguments put

forward are also consistent with notions of the wider

data revolution and a longer historic trend of embrac-

ing ‘unofﬁcial’ scientiﬁc discovery through accredita-

tion and validation by a recognized authority.

2. Part 1 – Background and context

‘The data demands arising from the SDGs are huge

and cannot be realistically met by ofﬁcial data alone’ –

M. Kituyi, UNCTAD Secretary General [13].

2.1. Measuring the SDGs

From a statistical perspective the measurement chal-

lenges arising from the 2030 Agenda are huge. To as-

sess progress, each of the 169 complex, multi-faceted

targets requires a statistical indicator. In fact, many of

the targets are so complex, 232 indicators have been

agreed. In truth, if all aspects of the targets were cov-

ered properly, then arguably twice that number would

be required [14].

The MDG requirements were modest in comparison

with the SDGs. Nevertheless, at the end of the MDG

lifecycle in 2015, countries could populate on average,

only 68 per cent of MDG indicators [2]. In their ﬁnal

MDG progress report, the United Nations [15, p. 10]

warned that ‘Large data gaps remain in several devel-

opment areas. Poor data quality, lack of timely data and

unavailability of disaggregated data on important di-

mensions are among the major challenges. As a result,

many national and local governments continue to rely

on outdated data or data of insufﬁcient quality to make

planning and decisions.’

The far reaching ambition of the 2030 Agenda has

led to development targets that are well ahead of avail-

able ofﬁcial statistics and statistical concepts. In many

cases, appropriate statistical methodologies do not yet

exist from which to generate indicators. To elaborate

this problem and facilitate the population of the GIF

the IAEG-SDG has classiﬁed all SDG indicators in to

three tiers on the basis of their conceptual development

and availability of data [16]. The tiers are:

Tier 1: the indicator is conceptually clear, has an

internationally established methodology, standards

are available, and data are regularly produced by

countries for at least 50 per cent of countries and of

the population in every region where the indicator

is relevant.

Tier 2: the indicator is conceptually clear, has an

internationally established methodology, standards

are available, but data are not regularly produced

by countries.

Tier 3: no internationally established methodology

or standards are yet available for the indicator, but

methodology/standards are being (or will be) de-

veloped or tested.

In April 2019, the IAEG-SDG reported that 44 per

cent of the selected indicators were classiﬁed as Tier

1 (see Table 1). Furthermore, they reported that 15 per

cent of the indicators remained classiﬁed as Tier 3.

While Table 1 shows the not inconsiderable improve-

ments in conceptual development and data availabil-

ity that has been made since 2016, it also highlights

the magnitude of the task still facing the global sta-

tistical community. The pace of transition of indica-

tors through the tiers to reach Tier 1 is likely to slow

as presumably the low hanging fruit has now been

picked. Table 1 suggests this is indeed the case, as the

conversion rate to Tier 1 was slower between Decem-

ber 2017–2018 than between December 2016–2017.

There were no additional conversions between Decem-

ber 2018 and April 2019.

312 S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF

Table 1

Number of SDG indicators by Tier

Tier classiﬁcation December 2016 December 2017 December 2018 April 2019

Number %Number %Number %Number %

1 81 35 93 40 101 44 101 44

2 57 25 66 28 84 36 91 39

3 88 38 68 29 41 18 34 15

Multiple 4 2 5 2 6 3 6 3

Total 230 100 232 100 232 100 232 100

Source: Derived from IAEG-SDG [16]. https://unstats.un.org/sdgs/iaeg-sdgs/tier-classiﬁcation/.

2.2. Who measures?

Countries understandably guard and protect their

reputations preciously. Consequently, countries can be

quite sensitive about what is measured and who does

the measurement. This sensitivity has often led to ten-

sions between ofﬁcial national statistics compilers and

external compliers. For example, IOs may from time

to time, alter national statistics to ensure they align

with international standards to facilitate international

comparisons – the main purpose of international statis-

tics. Understandably this can lead to disagreements

and tensions. Tensions can also arise between govern-

ments and Non-Governmental Organisations (NGOs),

universities or other countries for a variety of reasons.

In the context of the 2030 Agenda, this has led to some

tensions as to whose data should be used in the compi-

lation of the SDG indicators.

Countries, perhaps not unreasonably, are anxious

that only ofﬁcial national data are used to populate

the SDG indicators. Equally, IOs are anxious that

national statistics are internationally comparable and

have therefore put forward arguments as to why their

data should be used. Kapto [17, p. 135] notes, ‘A tense

debate is taking place on data ﬂows from national to re-

gional to global levels, and on custodian agencies’ role

in harmonizing national data for global comparability,

as countries assert their sovereignty over national data.’

What he describes as a ‘cease-ﬁre’ between countries

and IOs; an agreement on data ﬂows has been brokered

by the IAEG-SDG [18]. But there are some circum-

stances where this approach may be sub-optimal. In

thinking about this, it is useful to remember that the

primary purpose of the GIF is to produce global and

internationally comparable indicators.

The ﬁrst reason to query the ‘country data’ approach

is where speciﬁc national ofﬁcial statistics do not ex-

ist. Unfortunately, this is not an uncommon problem.

It makes perfect sense to use good quality national of-

ﬁcial statistics when they exist but if they don’t, and

there is insufﬁcient data to populate global indicators

(see Tier 2 – Table 1), then other approaches must

be found. The demand for SDG indicators has exac-

erbated this problem as many of the targets (and con-

sequent indicators) fall far outside the scope of tra-

ditional ofﬁcial statistics and thus are not guided by

agreed international measurement standards (see Tier

3 – Table 1).

A second problem with the ‘country data’ approach

is where problems with the national ofﬁcial statistics

exist. Problems could mean anything from incomplete-

ness, errors or inaccuracies, non-adherence to interna-

tional standards, inconsistencies over time, or imbal-

ances. A good example of where this might arise is

the asymmetries that frequently exist between bilateral

trade datasets. From a global perspective, unbalanced

trade data are not especially useful, and so steps are

taken by IOs to remove these asymmetries. This may

lead to a mismatch between ofﬁcial national statistics

and ofﬁcial international statistics. This issue is not

unique to international trade, problems with national

data exist across all statistical domains. Despite the

best efforts of NSOs and IOs, internationally compara-

ble data will be a real challenge for the GIF.

A third and more delicate issue is that of impartial-

ity. Targets, such as for example, 16.5 or 16.6 which

deal with corruption, bribery and institutional account-

ability provide perfect examples of why it might make

sense to use external data sources other than ofﬁcial

statistics provided by a NSS. There have clearly been

cases where ofﬁcial national statistics could not be

trusted to provide an independent or impartial picture.

This is not to say, that all national data are untrustwor-

thy. On the contrary – most national ofﬁcial statistics

are trustworthy. But there are cases (either indicator or

country speciﬁc) where arguments can be made that

more independent and trustworthy data may exist.

Another exception to the ‘country data’ approach

arises from what can be termed the data revolution. To-

day our day-to-day dependence on technology is leav-

ing a bewildering array of ‘digital footprints’. This in

turn has created a deluge of digital data. Some of these

S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF 313

Fig. 1. Net ofﬁcial development assistance (total) as a % of gross national income, 2002–2016. Source: OECD DAC: https://data.oecd.org/

oda/net-oda.htm and authors own calculations.

new digital datasets are global in scope offering the

possibility of compiling genuinely harmonised global

statistics. In such cases, where a single data source

might provide more consistent and timely data than the

amalgamation of multiple individual country datasets,

it would seem insensible to discount their use for the

purpose of global reporting. This might be applicable

to targets such as 15.1 that deal with forest, drylands,

wetlands and mountain regions governed by interna-

tional agreements. Arguably superior quality and in-

ternationally comparable data could be derived from

satellite imagery than from multiple national datasets

of which many will be based on irregular sample sur-

veys of varying quality. For other examples, see the

mapping of projects in the UN Big Data Inventory

and the UN Global Pulse to SDG goals undertaken by

MacFeely [9].

2.3. The cost of measurement

Unlike the MDGS, the SDGs are universal. The

SDGs are also much broader in scope, far beyond sim-

ply reducing extreme poverty, to encompass the sur-

vival of our planet, improving equity and freedom

in our societies and trying to develop a more sta-

ble and sustainable economic model. One of the im-

plications of such a broad and ambitious develop-

ment agenda is the price tag. Estimates vary, but Am-

bassador Macharia Kamau of Kenya, who co-chaired

the SDG intergovernmental consultative process, an-

ticipates that implementing the 2030 Agenda could

cost somewhere between $3.5 and $5 trillion per

year [19]. Ibrahim Thiaw [20], United Nations Assis-

tant Secretary-General and Deputy Executive Director

of the United Nations Environment Programme, esti-

mates it will cost at least an additional US$1.5 trillion

annually over the Millennium Development Goals. The

intergovernmental committee of experts on sustainable

development ﬁnancing [21] estimated the value of in-

vestment in infrastructure required to achieve the erad-

ication of poverty alone at between $5 and $7 trillion

annually.

To put these numbers in perspective, total Ofﬁcial

Development Assistance (ODA) contributions from

the OECD Development Assistance Committee mem-

bers’, averages about $113 billion per year (in current

prices).2Since Monterrey in 2002, when the wealthier

nations of the world, promised to contribute 0.7% of

their Gross National Income to ODA [22], the cumu-

lative shortfall in ODA (2002–2016) has risen to $2.4

trillion (current prices) or $2.9 trillion in 2016 constant

prices. Since 2015, and the commencement of Agenda

2030, the average country effort hasn’t changed appre-

ciably (see Fig. 1) and remains well short of the 0.7%

target.

Developing the statistical concepts and collecting

the data required for the GIF will not be inexpensive

either. The Global Partnership for Sustainable Devel-

opment Data estimates around $650 million per year

is needed to collect data to support the 2030 Agenda,

of which only $250 million is currently funded [23].

PARIS21 [24, p. 11] has estimated that ‘funding for

statistics needs to be increased from current commit-

ments of between US$300 million and 500 million to

between US$1 billion and 1.25 billion by 2020’. The

PARIS21 estimates cover a wider remit than just SDG

indicators, and presumably this explains some of the

2Authors own calculations based on OECD Development Assis-

tance Committee Statistics (Table 1: Net Ofﬁcial Development As-

sistance) 2002–2014.

314 S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF

difference in scale between the two estimates. But ir-

respective of which estimate of costs is used; all esti-

mates clearly exceed existing funding. More recently

PARIS21 [25] has estimated that ODA devoted to data

and statistics ($591 million in 2015 and $623 million

in 2016) is only one third of 1% of ODA.

2.4. Summary of challenges

Part 1 has provided some of the background and

context that are relevant to the proposals put forward

in Part 2. The universal and broad scope of the 2030

Agenda present real measurement challenges for the

global statistical community. Populating the GIF will

be a challenging and complex task with enormous re-

source implications, even for developed countries with

sophisticated statistical systems. History suggests that

it is highly unlikely that by 2030 all of the 232 indi-

cators will be populated. Today, only 44% of the SDG

indicators can be populated. One of the risks with the

Tier system is that it has created a vacuum, and as the

saying goes: ‘nature abhors a vacuum’. Who will ﬁll

that vacuum and how? At a time when multilateral-

ism is faltering, when funding is not matching ambi-

tion, and where the ‘data revolution’ has brought new

competition, we see countries clinging to an anachro-

nistic view, prioritising ‘country data’ or even aban-

doning the use of statistics as a policy support instru-

ment, and international organisations jealously laying

claim to indicators to attract or safeguard funding. If

the development and statistical communities are seri-

ous about populating the GIF then it is time to consider

alternative approaches.

3. Part 2 – A proposal for a system to certify

unofﬁcial statistics

‘One of the greatest tasks of our era may be ﬁgur-

ing out how to unlock and harness the value of [pri-

vate and civil sector] data to provide actionable in-

sights for positive social and economic impacts’ Ste-

faan Verhulst [26].

As outlined in Part 1, the data demands arising from

the 2030 Agenda are enormous. If the history of MDG

data is any indication of future outcomes, then it sug-

gests that a large portion of the GIF could remain

empty for much of the remaining time between now

and 2030. Addressing the data gaps using only tradi-

tional approaches will realistically not achieve success.

For this reason, we propose, not only using existing un-

ofﬁcial data as inputs to derive SDG indictors but also

using already compiled unofﬁcial indicators or statis-

tics.

The rationale behind this proposal is straight-

forward. The demand for data to populate the SDG

GIF far outstrips supply from traditional sources. Yet

there are no shortage of data and indicators in exis-

tence; if anything, the opposite is true, we are awash

with both. The statistical and information landscape

has changed utterly over the past decade. Today there

are an unimaginable range of statistical indicators be-

ing compiled by a wide variety of producers: civil so-

ciety; academia; NGOs; and the private sector. For the

purposes of the GIF many of these indicators have not

been considered to date. Bearing in mind the scale of

challenge facing the statistical community, we argue, it

is time to rethink this approach.

3.1. A proposal

An agreed recognized and mandated body, with the

authority and competence to certify statistics as ‘ﬁt

for purpose’, would review unofﬁcial statistics to see

whether they can be certiﬁed as ‘ofﬁcial’ for the pur-

poses of populating the SDG GIF. Statistics certiﬁed

‘ﬁt for purpose’ could be accredited and used as of-

ﬁcial statistics. For the purposes of this discussion

‘Fit for purpose’ means that an indicator or statis-

tic meets pre-deﬁned quality and metadata standards

and has been compiled in an impartial and indepen-

dent manner. Those pre-deﬁned standards and crite-

rion must be open and transparent to all. The term

quality can be interpreted in the broadest sense, en-

compassing all aspects of how well statistical pro-

cesses and outputs fulﬁl expectations as a SDG indi-

cator. In more concrete terms, ‘ﬁt for purpose’ would

mean that any statistic must be relevant, accurate, re-

liable, coherent, timely, accessible, and interpretable.

The statistic must be produced using sound method-

ologies, concepts and reliable systems. The statistic

must also be compiled within an institutional environ-

ment that recognises the need for objectivity, impartial-

ity and transparency. This last point is important. For a

statistic to be designated ofﬁcial, neither the input data

nor the methodologies can be proprietary but must be

available to all and open to scrutiny (subject to obvious

conﬁdentiality constraints).

This proposal envisages the SDG GIF being popu-

lated from a combination of ofﬁcial statistics and un-

ofﬁcial (but certiﬁed as ofﬁcial) statistics. By pooling

all available indicators an improved completion rate

S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF 315

Fig. 2. A proposed future: Using unofﬁcial data and statistics to compile SDG indictors.

will be achieved. To ensure a level playing ﬁeld and

maintain quality standards a formal accreditation sys-

tem is required. By combining ofﬁcial and accredited

unofﬁcial sources into a single high-quality ‘pool’ the

chances of successfully populating the GIF will in-

crease (See Fig. 2).

In this new regime the indicator pool would com-

prise of:

1. Ofﬁcial national statistics. These are statistics

produced by the NSO in accordance with the UN

Fundamental Principles of Ofﬁcial Statistics [3]

and other national codes of practice, other than

those explicitly stated by the NSO not to be ofﬁ-

cial; and all statistics produced by the NSS in ac-

cordance with the UN Fundamental Principles of

Ofﬁcial Statistics and national codes of practice

i.e. by other national organisations that have been

mandated by national government or certiﬁed by

the head of the NSS to compile statistics for their

speciﬁc domain.

2. Unofﬁcial national statistics that are accredited

as ‘ofﬁcial national statistics’ by the NSS for the

purposes of supplying statistics to populate the

SDG – MGF.

3. Ofﬁcial international statistics. These are statis-

tics, indicators or aggregates produced by a UN

agency or other IO in accordance with the Prin-

ciples Governing International Statistical Activi-

ties [4] and other institutional codes of practice. It

is often necessary for a UN agency, or other inter-

national organisation, to modify ofﬁcial national

statistics that have been provided by an NSO or

another organisation of a NSS, in order to har-

monise statistics across countries, to correct evi-

dently erroneous values or to reconcile with inter-

national standards. Furthermore, in the absence

of an ofﬁcial national statistic, a UN agency or

other international organisation may compile es-

timates. Thus, it is not sufﬁcient to deﬁne ofﬁcial

international statistics as simply the reproduction

of ofﬁcial national statistics.

4. Unofﬁcial international unofﬁcial statistics that

are accredited as ‘ofﬁcial international statistics’

by the body mandated by the UN Statistics Com-

mission for the purposes of supplying statistics to

populate the SDG GIF.

This supplementary approach would only be used

when particular conditions apply. Firstly, it should be

a measure of last resort, and only considered when

all other ofﬁcial options have been exhausted. Speciﬁ-

cally, when:

1. Tier 3 indicators (i.e. indicators with no inter-

nationally established methodology or standards

are available) remain unpopulated and when re-

alistically, no methodology or standards will be

developed in time. The concept of ‘in time’ will

need to be speciﬁed – perhaps by 2025 would be

a reasonable cut-off.

2. when Tier 2 indicators (i.e. where the indicator

is conceptually clear, has an internationally es-

tablished methodology and standards are avail-

able) remain unpopulated and data are not being

systematically produced. Here too, a cut-off date

will be needed. Again 2025 might be sensible.

Secondly, compilers of unofﬁcial indicators hoping

to secure accreditation must demonstrate their adher-

ence to the principles of ofﬁcial statistics. For national

accreditation this means observance of the UN Fun-

damental Principles of Ofﬁcial Statistics [3]. In par-

ticular, principles 1 (impartiality), 2 (professionalism),

3 (scientiﬁc standards), 6 (conﬁdentiality), 9 (inter-

national classiﬁcations) are of special relevance and

316 S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF

should be rigorously tested. Principle 5 (quality and

other aspects of data) is also extremely important. For

global accreditation it would mean adherence to the

Principles Governing International Statistical Activi-

ties [4].

Thirdly, unofﬁcial indicators will be required to

meet a deﬁned set of quality standards. For national ac-

creditation, the indicator would be required to meet the

same standards and conditions as set out in the national

code of practice or national statistical quality frame-

work. For international accreditation, the indicator will

be expected to meet the quality standards as deﬁned in

the UN Statistical Quality Assurance Framework [27].

Furthermore, clear metadata standards should be set

for accreditation. In cases where standards don’t yet

exist, the UNECE Common Metadata Framework [28]

sets out suitable generic standards that could be used

as criteria for accreditation.

Finally, prospective compilers of ofﬁcial SDG indi-

cators must be able to guarantee that they can supply

those indicators for, at least, the lifetime of Agenda

2030. In practical terms, this means being able to sup-

ply, at a minimum, the statistic on an annual basis for

the years 2010–2030. While sufﬁcient funding is im-

portant, in line with the UN Fundamental Principles

of Ofﬁcial Statistics [3], funding must be free of any

political or ideological conditions or inﬂuence. Access

to the indicator itself must also be open and constraint

free.

One could view the conditions outlined above as

overly rigid and with too many criteria. The counter

argument might be to just let compilers bid against

each other and whichever indicator or statistic can be

demonstrated to have the best quality (however de-

ﬁned) would be selected. While such an egalitarian ap-

proach might be intuitively logical and attractive, it

might be seen as contravening the spirit of the ‘country

ﬁrst’ principle agreed by the IAEG-SDG in 2019 [16].

Hence, for the purposes of this paper, a set of reason-

able, albeit conservative, conditions are envisaged. It

also means that the results of the homologation would

be binary i.e. only two possible outcomes are envis-

aged – a statistic is either accredited or it is not. One

could also make the argument that such a binary re-

sult is itself too rigid, and one could perhaps envis-

age a wider set of choices, for example, an interme-

diate or experimental certiﬁcation might be possible.

If the scope of the discussion were broadened to of-

ﬁcial statistics more generally, this indeed would be

a worthwhile consideration. However, in the context

of the SDGs, which already have the complication of

Tiers, an experimental category might muddy the wa-

ters too much.

3.2. How does this differ from the current situation?

Unofﬁcial or ‘non-ofﬁcial’ data sources are already

being used as inputs in the compilation of ofﬁcial

statistics all around the world, both at national level

and international level. At national level, for example,

unofﬁcial data are frequently used to supplement ofﬁ-

cial survey data in the derivation of consumer price in-

dex expenditure weights, retail sales index trading day

weights, and in many aspects of compiling national ac-

counts. Typically, at national level, there are no ofﬁ-

cial guidelines or accreditation systems used in these

processes. Depending on the quality and detail of the

metadata, the reliance of an individual statistic on un-

ofﬁcial data may or may not be clear. As noted above,

NSOs will be guided by their own national codes of

practice and the UN Fundamental Principles of Ofﬁ-

cial Statistics [3], in particular, principle 5 which states

that ‘Data for statistical purposes may be drawn from

all types of sources, be they statistical surveys or ad-

ministrative records...’

The same is true at international level, except that

IOs are directed by the Committee for the Coordina-

tion of Statistical Activities guidelines on the use of

unofﬁcial data. Those guidelines, Recommended Prac-

tices on the use of Non-Ofﬁcial Sources in Interna-

tional Statistics [5], provide direction on the use of un-

ofﬁcial source data. No formal accreditation system is

necessary when using unofﬁcial data as they are effec-

tively subsumed into ofﬁcial aggregates and thus are

covered by the formal ‘ofﬁcial’ label applied to the de-

rived indicator. In other words, accreditation of unofﬁ-

cial data is implicit. The guidelines however stay silent

on the use of fully developed indicators.

Both NSOs and IOs already regularly use unofﬁ-

cial source data to compile ofﬁcial statistics. This prac-

tice is expected to grow as statistical agencies are now

looking beyond survey data and administrative records

to investigate whether big data is a useful source of

data for compiling ofﬁcial statistics. In 2018, 34 NSSs

from around the world had registered 109 separate big

data projects on the Big Data Project Inventory3com-

piled by the UN Global Working Group on Big Data.

IOs had logged a further 91 projects [9]. NSOs and

IOs are investigating a wide range of big data sources,

from satellite imagery to mobile phone CDR records to

3https://unstats.un.org/bigdata/inventory/ [examined on 27 April,

2018]. These numbers are a best estimate. Projects are not always

well deﬁned or explained on the inventory. Some projects seem to

incorporate several projects or big data sources.

S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF 317

augment or supplant existing data sources or generate

completely new statistics. The question now is how all

of this activity will be integrated with the compilation

of ofﬁcial statistics more generally.

This proposal goes a step further than existing prac-

tices and frameworks, in that it anticipates using, in the

speciﬁc cases outlined in Section 2.1, unofﬁcial statis-

tics to create a larger ‘pool’ from which SDG indi-

cators can be selected. This pool would comprise of

not only unofﬁcial source data to derive ofﬁcial statis-

tics, but also using already developed unofﬁcial indi-

cators or statistics (but reclassiﬁed as ofﬁcial) – see

Fig. 2. Now compilers of statistics (ofﬁcial and unofﬁ-

cial) would submit bids (proposals) to the IAEG-SDG

for consideration. Bids would only be considered if

they adhere to agreed quality and metadata standards

and broader principles of ofﬁcial statistics.

3.3. Risks associated with adopting this proposal

No doubt persuasive counter-arguments can be made

against implementing this proposal. After all such a

move will introduce risks. But not adapting to the mod-

ern data world runs the risk of achieving only a par-

tially populated GIF, which in turn risks tarnishing the

reputation of the global statistical community. A busi-

ness as usual approach also puts NSSs, particularly

those in developing countries, under unnecessary pres-

sure to compile a range of new statistics.

This section outlines some of the most obvious risks

in adopting this proposal. There are legal concerns,

reputational risks and practical implementation issues,

such as costs, to be considered. Some of these issues

are discussed brieﬂy.

3.3.1. Legal issues

In theory accreditation could be done at national

level or at global level. At national level, it will be

important that compilers of unofﬁcial indicators can

demonstrate that they adhere to the same standards as

compilers of national ofﬁcial statistics. In most coun-

tries, the national accrediting body will most likely be

(but not necessarily) the head of the NSS, or if a for-

mal system does not exist, then the head of the NSO.

In some countries this may be the same person. Here

some legal hurdles might need to be jumped. For ex-

ample, not only might the unofﬁcial statistic itself need

to be accredited as an ofﬁcial statistic, but the compil-

ing agency might also need to be certiﬁed as a pub-

lic body or a recognized statistical agency or author-

ity in order to comply with national statistical legisla-

tion and/or national codes of practice. For example, in

some countries ofﬁcial statistics are deﬁned as statis-

tics compiled by the NSO or other public institutions.4

Such a broad accreditation might be seen as a bridge

too far. However, this caveat might be circumvented by

outsourcing the actual compilation of the statistic (un-

der license) to a third party but the statistic itself would

be disseminated by a recognized body of the NSS or

the NSO itself. This approach would also satisfy the

UN Fundamental Principles of Ofﬁcial Statistics [3].

At global level, as no head of the global statisti-

cal system exists, an accreditation body would need to

be mandated. However, as the UN Statistical Commis-

sion (UNSC) has been mandated to compile the SDG

indicators, the UNSC would seem to be the obvious

and appropriate body to mandate such an accredita-

tion board. One could imagine that they might ask the

IAEG-SDG to take on this additional task. Assuming

the IAEG-SDG is mandated as the statistical accred-

itation body, they would most likely need additional

statistical support (as the indicators in question will

most likely fall outside the expertise of traditional NSO

statisticians), in particular from IOs who can provide

both technical, professional and secretarial support.

Equally, at the global level there is no statistical

law to impose constraints. The UN Fundamental Prin-

ciples of Ofﬁcial Statistics [3] discussed above apply

only to ofﬁcial national statistics, and so, do not have

anything to say regarding the compilation of ofﬁcial in-

ternational statistics. The Principles Governing Inter-

national Statistical Activities [4], which are the equiv-

alent of the fundamental principles for compilers of

ofﬁcial international statistics, are also silent on who

exactly can compile international statistics or who is

a member of the international statistical community.

As the CCSA has expanded considerably over recent

years, there is clearly some ﬂexibility regarding the in-

terpretation of how ‘international statistical commu-

nity’ can be interpreted.5There is also some ambiguity

as to what an ofﬁcial international statistic is. The UN

Statistics Quality Assurance Framework [27, p. 9] de-

4A similar approach is adopted by the European Union where

European ofﬁcial statistics are by deﬁnition and law (Regulation

223/2009) those, and only those, that are disseminated by Eurostat.

5Membership of the CCSA comprises international and suprana-

tional organizations, whose mandate includes the provision of inter-

national ofﬁcial statistics in the context of the Principles Governing

International Statistical Activities [4], and which have a permanent

embedded statistical service in their organization and regular con-

tacts with countries. At the inaugural meeting in 2003, there were 25

agencies. By 2017, the CCSA had expanded to 45 member agencies.

318 S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF

ﬁnes ofﬁcial International statistics as ‘statistics, indi-

cators or aggregates produced by a UN agency or other

international organisation in accordance with the Prin-

ciples Governing International Statistical Activities [4]

formulated by the Committee for the Coordination of

Statistical Activities’. But this quality assurance frame-

work applies only to UN agencies and thus does not

prescribe the activities of other non-UN IOs.

There is no doubt more to be said on this matter.

Nevertheless, a preliminary assessment suggests that

there are no absolute legal barriers sufﬁcient to prevent

either national or global accreditation mechanisms be-

ing put in place, should that be desired. Nor would

such mechanisms, if done carefully, breach the letter or

the spirit of the UN Fundamental Principles of Ofﬁcial

Statistics [3] or the Principles Governing International

Statistical Activities [4].

3.3.2. Reputational risks

Whether certifying unofﬁcial statistics will under-

mine or enhance the reputation of the ofﬁcial statistics

brand is difﬁcult to predict. But there will naturally be

concerns that certifying unofﬁcial statistics as ofﬁcial

may ultimately undermine or tarnish the ofﬁcial statis-

tics brand. A valid argument can be made that by using

unofﬁcial statistics, the line between ofﬁcial and un-

ofﬁcial statistics may become blurred and the reputa-

tion of ofﬁcial statistics will be damaged or put at risk.

Such a risk must be anticipated and mitigated as of-

ﬁcial statistics have many unique qualities and enjoys

a reputation worth preserving and delineating. Conse-

quently, it will be very important that the protection of

the ofﬁcial statistics brand is carefully considered.

There may also be concerns that in allowing some

unofﬁcial sources to be designated as ofﬁcial, this may

be the thin end of a dangerous wedge, whereby the

compilation of ofﬁcial statistics is slowly outsourced

or privatized and incrementally taken away from NSOs

and NSSs. Some may fear also that this is somehow

an admission of failure – that ofﬁcial statisticians can-

not deliver. There may be concerns too that in an era

of data revolution, but reduced funding for ofﬁcial

statistics, that ofﬁcial statistics is already surrendering

ground to other information providers and this pro-

posal will only add fuel to the ﬁre. In other words, ef-

fectively outsourcing the production of ofﬁcial statis-

tics may further drain funding from NSSs and IOs.

Perhaps so, but a (cold) data war is already under-

way. There is a growing asymmetry in the resources

available for the compilation of public/ofﬁcial and

private/unofﬁcial statistics and indicators. In a world

where ofﬁcial estimates are increasingly being chal-

lenged by alternate facts it may be unwise to take

the future of ofﬁcial statistics for granted. This may

sound alarmist, but developments in Greece [29,30];

Canada [31]; Norway [32]; and most recently in Tanza-

nia [33,34]; South Korea [35]; or Russia [36] provide

sobering reminders that the impartiality and indepen-

dence of ofﬁcial statistics can be surprisingly fragile.

The reputational risks outlined in this section are

not trivial and must be carefully considered and miti-

gated. Ofﬁcial statistics must adapt in a way that allows

ofﬁcial national and international mechanisms to take

some control (or at least exert more inﬂuence) over

a rapidly fragmenting information landscape. Reputa-

tion is a double-edged sword. If there are risks of repu-

tational damage arising from certiﬁcation of unofﬁcial

statistics must be balanced against the risk of reputa-

tional damage to ofﬁcial statistics failing to deliver on

the expectations arising from Agenda 2030. Of course,

a counter argument could be made that by being proac-

tive and showing leadership, the ofﬁcial statistics brand

might enjoy a heightened reputation.

3.3.3. Double standards

To certify unofﬁcial indicators as ofﬁcial, a level

playing ﬁeld will be essential. Careful thought must to

be given to ensuring that quality standards are com-

parable, so that neither unofﬁcial nor ofﬁcial compil-

ers are placed at a disadvantage. It will be very im-

portant that unofﬁcial statistics don’t enjoy light touch

regulation vis-a-vis their ofﬁcial counterparts or vice-

versa. If unofﬁcial statistics are to be used, then they

must adhere to the same high-quality standards as of-

ﬁcial statistics. The dimensions of those quality stan-

dards, for the purposes of compiling UN statistics, are

deﬁned by the UN Statistics Quality Assurance Frame-

work [27, p. 22] as: relevance; accuracy; reliability; co-

herence; timeliness; punctuality; accessibility; and in-

terpretability.

Adherence to the principles of ofﬁcial statistics must

also be a condition for accreditation. Although the

principles themselves are not overly speciﬁc in tech-

nical terms, their importance cannot be overstated. In

particular: principles 1 (impartiality); 2 (professional-

ism); and 6 (conﬁdentiality) are of paramount impor-

tance. More technical in nature but no less important

are principles 9 and 5 which deal with use of inter-

national classiﬁcations and quality standards respec-

tively. Thus, adherence to the UN Fundamental Prin-

ciples of Ofﬁcial Statistics [3] must apply to all com-

pilers. In particular, unofﬁcial statistics must adopt the

S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF 319

same standards of openness and transparency of meta-

data.

In order to accredit unofﬁcial statistics as ofﬁcial,

these quality dimensions and principles must be as-

sessed and judged ‘ﬁt for purpose’ for SDG indicators.

Indicators must also be available for the entire dura-

tion of the 2030 agenda. Ideally this means from 2010–

2030. Any indicator selected as an SDG indicator must

provide certainty on this issue. As noted in Section 2.1

this proposal only allows for a binary outcome – pass

or fail. If this debate were expanded beyond the SDGs

to ofﬁcial statistics more broadly, then a more nuanced

set of options that allows for experimental statistics

might be necessary. But this is beyond the scope of the

discussion in this paper.

3.3.4. Data neutrality

Conﬂict of interest is always a risk when consumers

of data become compilers. The reasons should be ob-

vious. Rosling et al. [37, p. 236] explains it with a sim-

ple analogy – ‘a long jumper is not allowed to mea-

sure her own jumps.’ Advocacy or ideology may en-

courage compilers to achieve a certain result or out-

come. The impartiality or agnosticism of ofﬁcial statis-

ticians is one of its key strengths. The European Statis-

tics Code of Practice [38], the draft Statistics Quality

Assurance Framework for the African Statistics Sys-

tem [39], the Caricom Data Quality Assurance Frame-

work [40] and the UN’s Fundamental Principles of Of-

ﬁcial Statistics [3] all stress the need for ofﬁcial statis-

tics compiled free from political and external interfer-

ence.

The counter argument is that there is no such thing

as neutral information [41] and that consumers prob-

ably know the context better and so can compile bet-

ter, more nuanced, statistics. These are not invalid ar-

guments. Behind every statistic there were people who

made decisions and these decisions are often unstated

and undocumented [42,43]. They may even be uncon-

scious. The translation from words to numbers, in-

volves assumptions and theories which may be ob-

scured behind a veil of a technocratic objectivity [44].

As the title of Gitleman’s book [45] eloquently puts it

“‘Raw Data” Is an Oxymoron.’ The choice of indica-

tor may inadvertently reﬂect a political ideology, but

it may also be a deliberate attempt to control a narra-

tive. Mahajan [46, p. 110] sums up the situation well

– ‘measurement is never an innocent matter where as

it were, the facts speak for themselves. What is mea-

sured, who ﬁnances and does the measuring, how data

are collated, interpreted, and disbursed, how they are

harnessed to decision-making and program implemen-

tation, and how other measures and ways of collect-

ing information are displaced – all these are contested

matters because they are linked with the speciﬁc ori-

entation of institutions and policies, the outcomes that

they aspire to, and the forms of knowledge that they

privilege.’

Every statistic comprises several conscious and sub-

conscious decisions – how to treat outliers, how to im-

pute for missing values or what level of aggregation

should be chosen? The list of decisions is almost end-

less. So, no statistic is strictly neutral in the sense that

choices have unavoidably been made during compila-

tion. But perhaps the more relevant question is whether

the statistics were compiled with the intention of pro-

viding impartial information or to advocate for a spe-

ciﬁc objective? Not always an easy question to an-

swer. The purpose of ofﬁcial statistics is the former –

to provide statistics and information, that in as far as

is possible are free from any political agenda. The ar-

gument as to whether other agents can compile bet-

ter statistics than ofﬁcial statisticians is at the heart of

the debate as to whether centralised or decentralised

statistical systems are better. There are strengths and

weaknesses with either approach. Centralised statisti-

cal systems are typically seen as strong on indepen-

dence and impartiality but sometimes struggle with rel-

evancy, owing to their remoteness from policy debate.

Decentralised statistical units often produce highly rel-

evant statistics but are more susceptible to political in-

terference and pressure to present statistics relating to

ministerial policies and outcomes in a favourable light,

or to schedule publications to suit political considera-

tions, thus compromising the credibility of the statis-

tics [47,48]. From the perspective of accrediting unof-

ﬁcial statistics, all compilers must be able to demon-

strate adherence to principle 1 of the UN Fundamental

Principles of Ofﬁcial Statistics [3].

The risks associated with users compiling statistics

already exist. These risks can arguably be mitigated

through implementation of codes of practice, quality

standards, transparent metadata, open data standards

and peer reviews.

3.4. The risks associated with not adopting the

proposal

Some of the risks associated with implementing the

proposed approach have been outlined above. But there

are also risks in not considering such an approach.

It is also important to carefully consider these. The

main risks would appear to be those arising from un-

addressed competition.

320 S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF

3.4.1. Competition

We live in a world where development funding is

not exclusively provided by States. Philanthropic fund-

ing is now increasingly important, with funds, such

as, the Gates, Ford, Hilton and Rockefeller founda-

tions making enormous sums of money available. Un-

fortunately, relatively little is known about these phil-

anthropic funds, what they fund or how they decide

what gets funded. Salazar [49] estimated that in 2009,

the top 10 philanthropic foundations made US$5.6 bil-

lion available, of which, US$3.6 billion was given to

‘global development’. In 2016, Viergever and Hen-

driks [50] estimated that the 10 largest philanthropic

funders of health research together funded research

costing $37.1 billion, constituting 40% of all public

and philanthropic health research spending globally.

They note the need for increased transparency about

who the main funders are globally.

The danger for ofﬁcial statistics is philanthropically

funded projects may inadvertently be counterproduc-

tive; competing with ofﬁcial statistics and the SDG

GIF. See Mahajan [46, p. 110] for a graphic example

in the health sector, where she argues that the Institute

for Health Metrics and Evaluation funded by the Gates

Foundation has led to the ‘relative sidelining of inter-

national agencies and especially the World Health Or-

ganization.’ In the growing world of online collabora-

tion, competition to the SDG GIF could emerge at any

time. If other data compilers in civil society or the pri-

vate sector feel disenfranchised or frustrated with the

ofﬁcial approach they may develop competing frame-

works. Arguably this has begun already. The Sustain-

able Development Solutions Network (SDSN),6the

Global Partnership for Sustainable Development Data

(GPSDD)7and the United Nations Global Pulse8are

all, in one way or another, competing with the UN Sta-

tistical Commission. They are all competing for fund-

ing and other resources to improve data and statistics

for development. Take the GPSDD for example – re-

portedly a network of more than 280 members, includ-

ing governments, the private sector, civil society, in-

ternational organizations, academic institutions, foun-

dations, statistics agencies, and other data communi-

ties, it was established to fully harness the data revolu-

tion for sustainable development. Their ambition is to,

among other things: strengthen inclusive data ecosys-

tems; drive data collaborations; drive global collab-

6http://unsdsn.org/.

7http://www.data4sdgs.org/.

8https://www.unglobalpulse.org/about-new.

oration to improve production and use of data; de-

velop global data principles and protocols for shar-

ing and leveraging privately held data; Bring together

data communities at global and national level to spur

innovation and collaboration; harmonize data speci-

ﬁcations and architectures; and ensure the interoper-

ability of technology platforms for assembling, access-

ing, and using data. These all seems like sensible am-

bitions. The risk of course is that, in doing so, it may

undermine the global structure established by countries

to do exactly this – the United Nations. The risk also,

is that, several of the organisations who have joined the

network, may have done so under duress, as they can’t

risk being excluded or being seen to be irrelevant. The

distinction between voluntary collaboration and forced

cooptation is often blurred.

In terms of addressing the threat of competition, ar-

guably it is better that ofﬁcial statistics takes control

and propagates statistical standards, rather than build-

ing a wall in an attempt to shelter or safeguard ofﬁ-

cial statistics from other compilers. Proactive coopera-

tion could mitigate the detrimental risks of involuntary

cooptation. In the rapidly changing data environment

that we live in today, not adapting may be the bigger

risk.

3.5. Ideological arguments

It is clear there is resistance in many countries to

governments collecting more data. The argument un-

derlying this resistance is supposedly fears of a Big

Brother state [51,52]. Despite statistical legislation and

the UN fundamental principles, respondents, but most

particularly ﬁrms, don’t always trust NSOs to safe-

guard their data from other arms of government or not

to use their data for non-statistical purposes. As an

aside, MacFeely [9] notes the incongruity of these con-

cerns and the lack of concern regarding the emergence

of a corporate or private sector Big Brother. But there

is ideology at play here. The neo-liberal agenda aims

to minimise the role of the public sector. Landefeld [7]

warns, even in the data sphere, there will be resistance

by industry to expanded government oversight.

Thus, one can anticipate ideological arguments

against accreditation, along the lines that this is an ex-

pansion of the role of government. But as Reich [53,

p. 5] correctly points out ‘Government doesn’t “in-

trud” on the “free market”. It creates the market.’

Polanyi [54, p. 61] too notes the importance of the

‘deus ex machina of state intervention’ for the forma-

tion of markets. The UN or national government must

S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF 321

set normative standards, whether, deﬁning post codes,

tax numbers, personal identiﬁcation numbers or statis-

tical classiﬁcations – these are all part of a nations data

infrastructure [55] and essential to the efﬁcient func-

tioning of a market. Even Hayak [56], the godfather of

modern liberal economics, understood this, explaining

that in line with liberal principles, the State should ex-

ercise control of weights, measures and statistics.

In any event, challenging the establishment of na-

tional or global accreditation mechanisms on the

grounds of such ideology is a specious argument. An

accreditation system will facilitate wider participation

of the private sector, along with academia, NGOs and

civil society in the 2030 Agenda. It opens a doorway,

for indicators that have traditionally been excluded

from consideration, to compete for recognition as ofﬁ-

cial SDG indicators.

3.6. Consistent philosophy

The 2030 Agenda emerged from a globally inclu-

sive, open and democratic process. In line with this

philosophy, contributions on the compilation of SDG

indicators could also be open and inclusive. To an ex-

tent they already are, in that anyone can propose indi-

cators, or comment on existing proposals. But to date,

it has been envisaged that compilation will be the ex-

clusive permit of ofﬁcial statisticians (either national

or international). But what if the power and knowledge

of unofﬁcial data and unofﬁcial statisticians could be

harnessed? This indeed would be a data revolution.

The idea of an accreditation system is not incon-

sistent with the philosophy underlying the UN Funda-

mental Principles of Ofﬁcial Statistics [3]. In particular

principle 5 which states:

‘Data for statistical purposes may be drawn from

all types of sources, be they statistical surveys or

administrative records. Statistical agencies are to

choose the source with regard to quality, timeli-

ness, costs and the burden on respondents’.9

In other words, statistical agencies should in princi-

9‘Data for statistical purposes may be drawn from all types of

sources, be they statistical surveys or administrative records’ is a cu-

rious sentence, as ‘all types’ is presented as a choice between sur-

vey or administrative data. This we think was simply a reﬂection

of the reality at the time. Today a much greater variety of potential

data sources exist. One imagines the next update of the Fundamental

Principles will need to adopt more generic terms, such as, primary

and secondary data to reﬂect this abundance. See MacFeely and Bar-

nat [48] for a more developed discussion on this point.

ple use the widest variety of data sources possible to

compile ofﬁcial statistics provided the quality of those

data are sufﬁciently good and the costs are not pro-

hibitive. Why not go one step further, and argue that

statistical agencies should in principle use, not only the

widest variety of data, but also the widest variety of

statistics for the purposes of providing ofﬁcial statistics

to feed the SDG GIF?

The idea is also broadly consistent with the spirit

of the 2030 Agenda itself, which states ‘Data and in-

formation from existing reporting mechanisms should

be used where possible’ [57, p. 48]. So, like the fun-

damental principles, the 2030 Agenda recognises the

importance of reusing existing data and information

from other ofﬁcial systems. Again, one could argue

that what we are proposing is simply an extension or

relaxation of this condition – in particular, a relaxation

of the term ‘existing reporting mechanisms.’ The 2030

Agenda also noted that any ‘global review will be pri-

marily based on national ofﬁcial data sources’ (our em-

phasis) [57, p. 74a]. Thus, it was recognized from the

start the GIF might require data from outside national

ofﬁcial sources. The document wisely didn’t set any

conditions or limitations on what these sources might

be.

This proposal is also consistent with the broad

philosophy or vision put forward by the Indepen-

dent Expert Advisory Group on a Data Revolution

for Sustainable Development in their report ‘A World

That Counts’. In this report, they state ‘New insti-

tutions, new actors, new ideas and new partnerships

are needed, and all have something to offer the data

revolution. National statistical ofﬁces, the traditional

guardians of public data for the public good, will re-

main central to the whole of government efforts to

harness the data revolution for sustainable develop-

ment. To ﬁll this role, however, they will need to

change. . . and strong collaboration between public in-

stitutions and the private sector’ [58, p. 9]. The report

stresses the need to create incentives for private sec-

tor participation and comes tantalizingly close a num-

ber of times to proposing something quite radical, but

it never quite does10 – it highlights the importance of

data sharing but never statistics. In short, they advo-

cate a vibrant ‘global data ecosystem’ [58, p. 17] and

an extended concept of statistical systems. We inter-

pret (global or national) data ecosystems as something

much broader than (global or national) statistical sys-

tems – See Fig. 3.

10No doubt this was deliberate. But it is nevertheless ironic in a

report discussing revolution.

322 S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF

Fig. 3. (National/Global) statistical and data ecosystems.

The NSOs mapped in Fig. 3 don’t require any ex-

planation. A NSS is the collection of statistical insti-

tutions or units within a country that collects, com-

piles and disseminates ofﬁcial statistics on behalf of

national government. For the purposes of this argu-

ment, we understand a data ecosystem to be the amal-

gam of all data and statistical actors in a country, in-

cluding ofﬁcial statistics and holders of public sector or

administrative data, private and commercial sector data

holdings and indicators, research data, civil society and

non-governmental data holdings. We acknowledge that

in an era of globalising data imposing a distinction be-

tween a national and a global data ecosystem is perhaps

somewhat archaic. The idea of constraining global dig-

ital data to a ‘country’ or that data will respect national

borders is anachronistic. Thus, we acknowledge that

data ecosystems may need to be international or global

by default. The important point is that data ecosystems

are much broader than ofﬁcial statistical systems.

The ofﬁcial statistical system, whether national, re-

gional or global, should retain control of the process

for standards and certiﬁcation. Thereafter, there is no

reason why NSOs or NSSs could not accredit unofﬁ-

cial statistics or indicators for the purposes of compil-

ing SDG indicators. Furthermore, with the evolution

of modern, globalised data sources, there is no rea-

son why IOs or the UN could not establish regional

or global accreditation systems to facilitate the use of

good quality unofﬁcial statistics.

3.7. Lessons from history

Scientiﬁc discovery has always relied on amateur

inventors or scientists. Many important contributions

were made ‘by men with minimal scientiﬁc educa-

tion’ [59, p. 201]. John Harrison, a clock maker, in-

vented the famous H1ships chronometer used to

estimate longitude; Michael Faraday discovered dia-

magnetism, electrolysis, and electromagnetic induc-

tion; Gregor Mendal, a Czech Augustinian monk, pi-

oneered experiments on dominant/recessive qualities

of genes in peas; William Herschel, an amateur as-

tronomer forged the development of telescopic lenses

and discovered the planet Uranus; and Charles Dar-

win was the legendary amateur naturalist famous for

his contribution to the theories of evolution. Wein-

berger [60] points out, the reason that amateurs such

as these could make such important contributions and

have them recognised was that there were bodies, such

as the Royal Society, the Royal Astronomical Society

or the Académie des Sciences to test and validate their

work.

There are lessons we can learn from this approach.

Just as professional scientists did not have the

monopoly on scientiﬁc wisdom in the past, ofﬁcial

statisticians do not have the monopoly on information

today. In fact, when it comes to mining new forms of

digital data, ofﬁcial statisticians are for the most part

far behind their unofﬁcial counterparts. Today, many

unofﬁcial statistics are produced by a wide variety of

compilers, ranging from: journalists; researchers; so-

cial media outlets; civil society; academia; commer-

cial enterprises; lobby groups; and NGOs. The qual-

ity of these statistics varies enormously, from one end

of the quality spectrum to the other. In many cases the

quality is hard to determine, as the underlying data and

methodologies are proprietary and shrouded in mys-

tery. In other situations, the statistics are clearly of

good quality and are accompanied by supporting meta-

data. It seems unwise therefore to tar all unofﬁcial

statistics and indicators with the same brush.

Is there a way to sift and sort this effort in such a way

as to harness it? Could NSOs (at country level) or the

UN (at the global level) provide mechanisms that could

test and validate unofﬁcial statistics and accredit them

for the purposes of the SDG GIF? That is the question

posed in this paper. Without such a system, new statis-

tics will emerge daily, leaving the public unclear as to

their quality and utility. But by providing a quality as-

surance stamp, NSOs at country level and the UN at the

global level could say which statistics are ‘facts’. The

UN could become today, what the Académie des Sci-

ences was to the Victorian era, in terms of validation.

Winning such recognition might provide the necessary

incentive for many compilers to become less propri-

etary with their data, methodologies and algorithms.

S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF 323

3.8. Lessons from the data ecosystem

Ofﬁcial statisticians are not the only experts grap-

pling with the challenges of homologation. Across

the wider data ecosystem, data scientists and re-

searchers too have been developing their own princi-

ples,11 certiﬁcation standards and accreditation proce-

dures. Some examples include: The Network of Ex-

pertise in long-term Storage and Accessibility of Dig-

ital Resources (NESTOR) seal;12 the Trusted Digi-

tal Repository checklist (ISO standard 16363) https://

www.crl.edu/archiving-preservation/digital-archives/

metrics-assessing-and-certifying/iso16363; and most

recently (2016) the CoreTrustSeal or Core Trustworthy

Data Repositories Requirements,13 launched jointly by

the International Council for Science World Data Sys-

tem (ICSU-WDS) and the Data Seal of Approval.

While the focus of these standards has been primarily

the certiﬁcation of digital data repositories rather than

on the compilation of statistics, they nevertheless offer

examples of how communities have developed gover-

nance structures and certiﬁcation standards to ensure

that data can be trusted. No doubt there are lessons to

be learned from these scientiﬁc communities.

The fact that certiﬁcation standards and accredita-

tion procedures are being developed by communities

in the wider data ecosystem suggests that perhaps, our

concerns and thinking inside the national and global

statistical system are not so different from those out-

side. In other words, ofﬁcial statisticians, data scien-

tists, researchers, and other scientists are all concerned

with the quality of data, statistics and analyses. Per-

haps optimistically, it suggests, that compilers of unof-

ﬁcial statistics will not ﬁnd adherence to accreditation

standards an alien or overly challenging concept.

4. Conclusion

The demands made by the SDG GIF are colossal

with enormous implications for NSSs. In April 2019,

the IAEG-SDG stated that at best only 44 per cent

of the selected indicators for the SDG GIF could be

populated. The costs of populating the GIF exceed

11For example, the FAIR (Findable, Accessible, Interoperable,

and Reusable) principles for scientiﬁc data management and stew-

ardship were published in 2016. See https://www.go-fair.org/fair-

principles/.

12See German Institute for Standardization (DIN) standard 31644

https://www.prestocentre.org/resources/nestor.

13https://www.coretrustseal.org/why-certiﬁcation/requirements/.

existing funding. It seems unlikely that funding will

increase sufﬁciently to match requirements. Yet the

global statistical system is expected to deliver a fully

populated GIF to support the 2030 Agenda. Although

these expectations are probably not realistic, failure to

deliver could nevertheless result in signiﬁcant reputa-

tional damage to that system, with far reaching reper-

cussions.14

It is time to consider new approaches to populating

the SDG GIF. Experience from the MDGs tells us that

by 2030 many of the SDG indicators will not be pop-

ulated. Without considerable investment, most Tier 2

and 3 indicators, are unlikely to become Tier 1 indi-

cators. Few countries will be capable of producing the

country level data required for the foreseeable future.

While it is very important that countries feel owner-

ship of the SDG process, the insistence on prioritising

country statistics may ultimately be self-defeating; the

focus should be on the best available statistics. There

is a risk that in taking a rigid position on the source of

statistics, countries are simply trying to hold back the

tide. The data deluge will overcome them eventually.

Hence the proposal for a supplementary approach.

To recap – an agreed recognized and mandated body,

with the authority and competence to certify statistics

as ‘ﬁt for purpose’, would review unofﬁcial statistics

to see whether they can be certiﬁed as ‘ofﬁcial’ for the

purposes of populating the SDG GIF. Statistics certi-

ﬁed ‘ﬁt for purpose’ could be accredited and used as

ofﬁcial statistics. In other words, the SDG GIF would

be populated from a combination of ofﬁcial statistics

and unofﬁcial (but certiﬁed ofﬁcial) statistics. There

will naturally be concerns that the proposal outlined in

this paper may contribute to a wider corrosion of of-

ﬁcial statistics, multilateral systems and public goods.

There may be fears that this is the vanguard for the pri-

vatization of ofﬁcial statistics. There will be concerns

too regarding the quality of any ‘outsourced’ indica-

tors, and even whether they have been compiled free of

political or advocacy pressures. These are all valid con-

cerns that must be addressed if an accreditation system

is to be introduced. But as already stated, this is not an

argument for the privatisation or ‘uberﬁcation’ of ofﬁ-

14This of course presupposes that 232 is the optimal number of

indicators. It is possible of course that by 2030 the UNSC and the

IAEG-SDG may have concluded that there were too many metrics

rather than too few. Therefore, to assess partial completion as failure

is to uncritically accept that 232 was in fact the correct number of

indicators to begin with. One could make the argument that a more

select set of indicators would be better – just as one could also argue

that more would have been better.

324 S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF

cial statistics, nor is it an attempt to subvert NSOs or

NSSs. Quite the contrary, the argument is that in order

to protect ofﬁcial statistics and NSSs, those systems

must evolve and adapt.

The approach proposed here is consistent with the

open philosophy adopted during the consultation and

negotiation phase of the 2030 Agenda. One could think

of it as democratizing the SDG GIF but in a controlled

way with clear rules. It would harness the intellectual

power of NGOs, civil society and the private sector,

giving them an incentive to share their data. In a world

of ‘alternative facts’ it might also allow NSOs and the

UN to assert their mandate and protect their legitimate

role as custodians of knowledge and protectors of de-

liberative public spaces.

It is time for a data revolution. The Dubai Declara-

tion, drafted at the conclusion of the 2018 UN World

Data Forum acknowledges ‘that the data demands for

the 2030 Agenda require urgent new solutions that

leverage the power of new data sources and technolo-

gies through partnerships between national statistical

authorities and the private sector, civil society, and the

academia and other research institutions.’ [61, p. 7].

The UN Statistics Division, summarizing the debate of

a special session at the 50th session of the UN Statis-

tics Commission in 2019 on ‘The Future of Economic

Statistics’, state ‘In an apparent paradigm shift, there

is now an overriding consensus that both developed

and developing statistical system should actively pur-

sue and accelerate the integration of these new data

sources in their statistical products and services [62,

p. 2].’ They also note ‘Statistical agencies alone do not

have the capacity to meet these challenges, they need

to work with academia, private sector agencies, NGOs

and other government agencies to obtain the data, tools

and know-how required [62, p. 3].’ So there appears

to be general acknowledgement that greater collabora-

tion is the way forward. This paper provides one pos-

sible mechanism for how that collaboration might be

achieved and managed.

The information environment is changing. Ofﬁ-

cial statisticians must remain vigilant – complacency

will create vulnerabilities. The proposal outlined here

brings risks, but it may be necessary to open up and

surrender a position of dominance or monopoly today

in order to survive tomorrow. With every bold initia-

tive there are risks. It is essential that such a system

not be adopted blindly but only after careful consider-

ation, and if adopted, known risks must be mitigated.

As Diamond [63, p. 433] points out, all ‘decisions in-

volve gambles, because one often can’t be certain that

clinging to core values will be fatal, or (conversely)

that abandoning them will ensure survival.’ For better

or worse, the Tier 2 and Tier 3 indicators have cre-

ated a vacuum and if this vacuum is not ﬁlled by of-

ﬁcial statistics, then it will be exploited by someone

else. In a rapidly changing and increasingly competi-

tive data world, ofﬁcial statisticians may need to col-

laborate with a variety of actors from the wider data

ecosystems or perish. In doing so it may not be easy

to decide what core values or principles to discard and

which to cling on to. But given the experience with the

MDG indicators, it is highly improbable that by 2030,

the majority of the SDG indicators will be populated.

The question for ofﬁcial statisticians is whether it is

time to try something different or just keep doing the

same thing over and over again, hoping for a different

result; a practice Einstein purportedly deﬁned as insan-

ity.

The proposal here is that ofﬁcial statistics switch

from a purely production or manufacturing based

model to a mixed business model: one combining the

manufacture of ofﬁcial statistics with the franchising

of production under license. One could think of this

approach as a decentralized supply chain model. This

is not a wiki approach but rather a spoke – hub, or

HQ – subsidiary model. This proposal envisages the

creation of a regulated market place, where compilers

bid to populate SDG indicators. NSOs at national level

and the UN at international level, as independent bro-

kers of information, would be the quality controllers.

The beneﬁts of such an approach would be the enor-

mous human and organizational capital that could be

harnessed from all around the world. It would allow of-

ﬁcial statistics to tap into and avail of immense creativ-

ity and innovation, possibly accelerating change and

reducing duplication, but in a controlled way. This ap-

proach positions NSOs and IOs as the guardians of

public trust, the data stewards for the 21st century, safe-

guarding data and statistics as public goods.

This proposal is not a panacea. Myriad problems

will remain, new and unforeseen ones will arise. But

it may unleash the untapped productivity and creativ-

ity of a wider data ecosystem. It should be stressed

that this proposal is speciﬁc to addressing gaps in the

SDG GIF, and consequently the scope is limited to

populating SDG indicators. The approach discussed in

this paper, is scalable beyond the SDGs. The same,

or similar, approach could be applied to ofﬁcial statis-

tics more broadly. Although this wider debate falls

outside the scope of this paper, one could neverthe-

less consider the SDGs a petri dish where a limited or

S. MacFeely and B. Nastav / “You say you want a [data] revolution”: A proposal to use unofﬁcial statistics for the SDG GIF 325

conﬁned experiment could be trialed. The danger of

course, is that by 2030, as Gates warned in the opening

quote, we will have underestimated the changes that

will occur over the next 10 years and the data landscape

may have changed dramatically, and ofﬁcial statisti-

cians may look back wistfully and wish they had been

braver and more proactive. Furthermore, in this paper

we limit our discussion to the homologation of statis-

tics. One could have a parallel debate about the certiﬁ-

cation of institutions to be inducted into a formal ofﬁ-

cial data ecosystem. Again, this is a related, but differ-

ent, discussion and that also falls outside our immedi-

ate scope.

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Priorities, Scale and Insights: Opportunities and Challenges for Community Involvement in SDG Implementation and Monitoring

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Mar 2023

Monitoring progress towards the achievement of the Sustainable Development Goals (SDGs) mainly relies on national voluntary review mechanisms, which often depend on regional spatial data and statistics. While it is critical that governments take ownership of SDG implementation and reporting, many communities are proactively driving changes towards sustainability through local action. This paper explores the potential implications of bridging national and local implementation and reporting through the lens of SDG indicators data. It presents a community-driven case study for sustainability monitoring in the Republic of Ireland, exploring how local priorities and associated data scalability and insights provide opportunities and challenges towards a comprehensive and accurate understanding of SDG progress in implementation and achievement. Systemic data availability and scale limitations weaken the evidence-base needed for informed community-driven sustainable development initiatives. Similarly, local efforts to track changes on relevant indicators are uncommon but necessary for filling in data gaps and contributing to a more accurate national reporting. The achievement of the SDGs requires invested commitment across national, regional, local, and community levels. The implementation of sustainability interventions and tracking any changes these may enact on relevant indicators is equally a joint effort, which calls for strategic data and knowledge exchange partnerships.

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Citizen science for monitoring the health and well-being related Sustainable Development Goals and the World Health Organization's Triple Billion Targets

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Aug 2023

Achieving the health and well-being related Sustainable Development Goals (SDGs) and the World Health Organization’s (WHO) Triple Billion Targets depends on informed decisions that are based on concerted data collection and monitoring efforts. Even though data availability has been increasing in recent years, significant gaps still remain for routine surveillance to guide policies and actions. The COVID-19 crisis has shown that more and better data and strengthened health information systems are needed to inform timely decisions that save lives. Traditional sources of data such as nationally representative surveys are not adequate for addressing this challenge alone. Additionally, the funding required to measure all health and well-being related SDG indicators and Triple Billion Targets using only traditional sources of data is a challenge to achieving efficient, timely and reliable monitoring systems. Citizen science, public participation in scientific research and knowledge production, can contribute to addressing some of these data gaps efficiently and sustainably when designed well, and ultimately, could contribute to the achievement of the health and wellbeing related SDGs and Triple Billion Targets. Through a systematic review of health and well-being related indicators, as well as citizen science initiatives, this paper aims to explore the potential of citizen science for monitoring health and well-being and for mobilizing action toward the achievement of health and wellbeing related targets as outlined in the SDG framework and Triple Billion Targets. The results demonstrate that out of 58 health and well-being related indicators of the SDGs and Triple Billion Targets covered in this study, citizen science could potentially contribute to monitoring 48 of these indicators and their targets, mostly at a local and community level, which can then be upscaled at a national level with the projection to reach global level monitoring and implementation. To integrate citizen science with official health and well-being statistics, the main recommendation is to build trusted partnerships with key stakeholders including National Statistical Offices, governments, academia and the custodian agencies, which is mostly the WHO for these health and well-being related targets and indicators.

The contributions of citizen science to SDG monitoring and reporting on marine plastics

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Sep 2023
SUSTAIN SCI

The accumulation of plastic litter in marine environments is a major environmental challenge along with the difficulties in their measurement because of the massive size of the oceans and vast circulation of plastic litter, which is being addressed as part of the United Nations (UN) Sustainable Development Goals (SDGs). Citizen science, public participation in scientific research and knowledge production, represents a potential source of data for SDG monitoring and reporting of marine plastic litter, yet there has been no evidence of its use to date. Here, we show how Ghana has become the first country to integrate existing citizen science data on marine plastic litter in their official monitoring and reporting of SDG indicator 14.1.1b for the years 2016–2020, which has also helped to bridge local data collection efforts with global monitoring processes and policy agendas by leveraging the SDG framework. The results have been used in Ghana’s 2022 Voluntary National Review of the SDGs, and reported on the UN SDG Global Database, as well as helping to inform relevant policies in Ghana. In addition, here, we present a pathway that can be adopted by the relevant government authorities in other countries that have an interest in following a similar citizen science data validation and reporting process for this indicator and potentially others.

Mapping the Landscape of Citizen Science in Africa: Assessing its Potential Contributions to Sustainable Development Goals 6 and 11 on Access to Clean Water and Sanitation and Sustainable Cities

Article

Full-text available

Jun 2023

Data are vital for and creating knowledge-based solutions to development challenges facing Africa. As a result of gaps in government-funded data collection, and in the interest of promoting community engagement, there is a global movement towards consideration of nontraditional sources of data, including citizen science (CS) data. These data are particularly valuable when collected at a high resolution over large spatial extents and long time periods. CS projects and infrastructure are abundant and well documented in the Global North, while needs for participatory projects to fill environmental monitoring gaps may be greater in the Global South. The paper explores the contributions of citizen science projects originating in Africa for two Sustainable Development Goals (SDGs), namely SDG 6, and SDG 11 which are particularly important to the millions of low-income residents of cities across Africa. Using a mixed methods approach that involves online surveys, interviews, expert conference panels, and a desk review, we analyze a total of 53 CS projects focusing on water, sanitation, and urban planning. The paper addresses CS in Africa and CS for SDGs, and documents evidence for participatory and CS data collection in Africa. It also describes the survey methods, including approaches to training of volunteers, sources of funding, data collection methods, and objectives of the tools and projects. Finally, it provides reflections on the challenges of integrating CS into official statistics in Africa, and some lessons learnt from CS projects in Africa. This paper recommends the establishment of an open-source database, creation of a network of CS projects for communication and collaboration, the uptake of citizen-generated data, and continuous funding for such projects in Africa.

How Certain is Good Enough? Managing Data Quality and Uncertainty in Ordinal Citizen Science Data Sets for Evidence-Based Policies on Fresh Water

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Jun 2023

This study investigates surface water quality in Luxembourg with the help of citizen scientists. The fundamental question explored relates to uncertainty and judgements on what constitutes adequate data sets, comparing official data and citizen science. The case study evaluates how gaps and uncertainties in official data for the United Nations Sustainable Development Goal 6 (UN SDG 6), Indicator 6.3.2 on water quality, and the EU Water Framework Directive (WFD), can be served with citizen science. In two Water Blitz sampling events organised in collaboration with the NGO Earthwatch, participants sampled water bodies at locations of their choice, using field kits to estimate nitrate (NO3--N) and phosphate (PO43–-P) concentrations. Samples were collected (428 in total) over two weekend events, providing snapshots in time with a good geographic coverage of the water bodies across the country: 35% of nitrate and 29% of phosphate values were found to exceed thresholds used by the European Environment Agency to classify the nutrient content in water as good. Our study puts forward recommendations on how citizen science data can complement official monitoring by national agencies with a focus on how such data can be represented to serve the understanding and discussion of uncertainties associated with such ordinal data sets. The main challenge addressed is high levels of natural variation in nutrient levels with both natural and anthropogenic multi-factorial causes. In discussing the merits and limitations of citizen science data sets, the results of this study demonstrate that a particular strength of citizen science is the identification of pollution hotspots in small water bodies, which despite being critical for ecosystem wellbeing are often overlooked in official monitoring. In addition, citizen science increases public awareness and experiential learning about factors affecting surface water quality and policies concerning it.

Citizen Science: What is in it for the Official Statistics Community?

Article

Full-text available

Jun 2023

Citizen science data are an example of a non-traditional data source that is starting to be used in the monitoring of the United Nations (UN) Sustainable Development Goals (SDGs) and for national monitoring by National Statistical Systems (NSSs). However, little is known about how the official statistics community views citizen science data, including the opportunities and the challenges, apart from some selected examples in the literature. To fill this gap, this paper presents the results from a survey of NSS representatives globally to understand the key factors in the readiness of national data ecosystems to leverage citizen science data for official monitoring and reporting, and assesses the current awareness and perceptions of NSSs regarding the potential use of these data. The results showed that less than 20% of respondents had direct experience with citizen science data, but almost 50% felt that citizen science data could provide data for SDG and national indicators where there are significant data gaps, listing SDGs 1, 5, and 6 as key areas where citizen science could contribute. The main perceived impediments to the use of citizen science data were lack of awareness, lack of human capacity, and lack of methodological guidance, and several different kinds of quality issues were raised by the respondents, including accuracy, reliability, and the need for appropriate statistical procedures, among many others. The survey was then used as a starting point to identify case studies of successful examples of the use of citizen science data, with follow-up interviews used to collect detailed information from different countries. Finally, the paper provides concrete recommendations targeted at NSSs on how they can use citizen science data for official monitoring.

GDP and the SNA : Future Challenges

Chapter

Jan 2023

The system of national accounts (SNA) and gross domestic product (GDP) are progeny of the great depression and World War 2. Forged in the fires of these seismic events, they emerged to become global standards for measuring our economies. But today concerns are being expressed that GDP has outlived its usefulness and that the SNA is out of date as it does not adequately capture important changes in economy and society, notably globalization and digitalization. Other criticisms focus on the inappropriate use of GDP as a measure of well‐being and sustainability. This chapter examines the most important of these criticisms and how they are shaping the future development of the SNA (and by extension business and social statistics). The chapter also explores the challenges in an indicator that is deeply embedded in our economic and statistical system, and still enjoys economic hegemony and massive cultural authority, and some of proposed alternatives to GDP.

Data Partnerships for the SDGs: The Potential of Federated Information Systems

Chapter

Sep 2022

Quality data collection and distribution are fundamental to SDG achievement. However, the scope of the SDG indicator framework coupled with the potentially conflicting needs of the data producers and data consumers renders the SDGs an unprecedented global data reporting challenge. The magnitude of the challenge is such that international data partnerships are required should robust global SDG reporting be achieved. This chapter aims to describe one of the lifelines to global SDG reporting that results from an international data partnership between the UN and ESRI—the federated information system for the SDGs (FIS4SDGs). While the data partnership has proven fruitful insofar as national SDG data hubs have been successfully launched in five partnering nations, it does not come without certain limitations, the majority of which stem from barriers associated with the systems’ political, financial, or technological context. The FIS4SDGs serves as a potent example of the benefits that may be reaped from international data partnerships, catalyzing national SDG reporting and localization; however, its true power is yet to be realized. Indeed, the FIS4SDGs has the potential to revolutionize current SDG reporting acting as the medium through which citizen science-generated data and big data might be incorporated into the SDG data system.KeywordsData partnershipsSDG dataFederated information systemsFederated information system for the SDGs (FIS4SDGs)UN Open Data SDG Hub

The UN 2030 Agenda for Sustainable Development

Chapter

Sep 2022

In this chapter, we provide an overview of the UN 2030 Agenda for Sustainable Development. In doing so, we offer an overview of the Sustainable Development Goals (SDGs) and other key elements of the 2030 Agenda. These include a vision, means of implementation, and follow-up and review processes. In addition, we discuss the role of partnerships in the Agenda, outlining their historical and future role in the architecture for implementing sustainable development at global and nation levels.KeywordsThe UN 2030 AgendaPartnershipsHistory and futures

Layers of Politics and Power Struggles in the SDG Indicators Process

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Jan 2019

Serge Kapto

The process of designing the 2030 Agenda for Sustainable Development was intensely political, as it can be expected of United Nations negotiations of that magnitude. Inevitably, those politics have spilled over into the technical process of formulating global indicators to monitor the Sustainable Development Goals. This commentary explores some of the tensions between competing priorities and various constituencies that affect the design of the global SDG indicator framework. By revealing the politics behind the selection of SDG indicators, the articles in this special issue can inform the design of more pragmatic and actionable national frameworks for monitoring the 2030 Agenda.

Knowledge and Politics in Setting and Measuring the SDGs: Introduction to Special Issue

Article

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Jan 2019

The 2030 Agenda: An Unprecedented Statistical Challenge

Article

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Nov 2018

Steve MacFeely

The SDGs represent the first ever democratically forged agreement on universal development. They will guide the global development agenda until 2030. Contrary to the MDGs, which were written by the UN Secretariat in 2000 to shape development up to 2015, the SDGs came about not through the distillation of policy agreements from previous UN conferences but through governments negotiating. Although the 2030 Agenda's goals were agreed by all UN Member States, the process of selecting the performance indicators was effectively delegated to the global statistical community, which means that statisticians are defining the meaning of the 2030 Agenda targets and will thus be the ones to determine whether the Agenda is ultimately pronounced a success, a failure or something in-between. This paper offers insights into the unprecedented statistical challenge presented by this measurement framework and possible unintended consequences for countries, their statistical systems and the broader information ecosystem.

Statistical capacity building for sustainable development: Developing the fundamental pillars necessary for modern national statistical systems

Article

Full-text available

Oct 2017

This article argues that to prioritise the data requirements of the Sustainable Development Goal (SDG) monitoring framework, requiring 232 global indicators and spanning the full spectrum of development issues, over the development of national statistical systems would be a mistake. Rather, countries and international organisations should prioritise the development of efficient national statistical systems that are sufficiently flexible, responsive and affordable to satisfy the enormous appetite of the SDG monitoring framework but also national and regional information requirements. The growing recognition of the importance of good quality, independent official statistics to support development and progress, provides a unique opportunity to make a real and long-lasting investment to improve national statistical systems. But this will require coordinated investment and political support from countries, donors and international organisations. The three core pillars necessary for a modern statistical system are detailed: a robust legal framework; functioning institutional coordination; and a logical data infrastructure. Without these pillars countries will not be able to build statistical systems appropriate to a data driven world. Nor will they be able to meet existing and future demands for information, including the SDG monitoring framework.

Technical report of the ESS Big Data Event, Rome 2014

Technical Report

Full-text available

May 2014

Technical report of the ESS Big Data Event, Rome 2014

SDG Implementation - what to do when it’s not clear what to do?

Research

Jan 2019

Many tools have been developed to support SDG implementation. Some focus on data needs, while others focus on setting governance priorities. This paper presents a framework to assist the statistical and governance communities of practice to decide on which are suitable under different conditions. The Data/Priority Matrix classifies selected SDG implementation tools into one or more of four quadrants depending on whether data are available and whether priorities are clear and agreed. Further criteria are suggested for documenting SDG implementation tools so that potential users can better understand the appropriate conditions and expected outputs.

The IHME in the Shifting Landscape of Global Health Metrics

Article

Jan 2019

Manjari Mahajan

The rise of the Institute for Health Metrics and Evaluation (IHME) has augured profound changes in the landscape of global health metrics. Primarily funded by the Bill and Melinda Gates Foundation, the IHME has offered donors a platform for assessing many health‐related Sustainable Development Goal (SDG) indicators and a toolkit to measure the progress of different countries. The IHME's increasing influence reveals the relative sidelining of international agencies and especially the World Health Organization which has long been central to global health metrics production. This shift reflects a growing conflict between the expertise and norms of national and intergovernmental statistical production on the one hand, and the distinct epistemologies and logics of new non‐state data actors. These transitions – from an international world of statistics to a more plural, global realm of data – have acute implications for the politics and accountability of knowledge production related to the SDGs and development writ large. Even as the SDGs embrace the rubric of ‘no one left behind’, the emerging data politics might be eroding the ability of poorer states to know and act upon their development problems on their own terms. The IHME might be exacerbating the problem of data gaps from poor countries because of its move away from an exclusive reliance on government statistical data and towards collection of data from private sources.

The Big (data) Bang: Opportunities and Challenges for Compiling SDG Indicators

Article