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Sylvia Szabo, Sinead Mowlds, Joan Manuel Claros, et al.

donors, and private sector investors alike (IEAG, 2014). Yet the extent and quality of data for analy-
sis and progress tracking require significant improvement. As part of the ONE Campaign-facilitated
accountability working group’s mandate, the authors undertook extensive analysis and expert con-
sultations in order to identify specific challenges faced by data users, which are likely to hamper ac-
countability and thus, progress towards the achievement of the SDGs. Here, we provide specific
examples of data challenges identified within selected global and national projects and activities re-
lated to monitoring progress in commitments to improve food and nutrition security. A table summa-
rising key challenges is provided as supplementary material.

2.1 Ending Rural Hunger Project
The Ending Rural Hunger project is a first attempt at providing a tool to review and follow-up on
Sustainable Development Goal 2: end hunger, achieve food security and improved nutrition and pr-
omote sustainable agriculture. The Ending Rural Hunger project was created by the Global Economy
and Development division of the Brookings Institution in 2015. The project was a collaborative ef-
fort benefiting from the input of over 120 experts. The project gathers and curates the data necessary
to review and follow-up on a key aspect of SDG2: Ending Rural Hunger. In the developing coun-
tries’ needs assessment, the analysis is directly tied to the specific targets of SDG2 (2.1, 2.2, 2.3, 2.4).
Before arriving at the database with 106 indicators for 145 countries, an assiduous review of availa-
ble sources was undertaken, to exclude data deemed inaccurate or unreliable. Three over- arch-
ing challenges were encountered: availability, reliability, and difficulty in measurement.
First, some crucial food and nutrition security (FNS) indicators are not measured and available
(see Table 1). While the SDGs explicitly call for doubling the productivity of small-scale farms, at
present there are no comparable, cross-country data specifically on the productivity of small-scale
farms. Similarly, very little country-specific data are available on how much food is lost or wasted
(post-harvest or post-market) in developing countries, although rough regional estimates have be-
en compiled. Systematic data on domestic private investment in agriculture, a key driver of progress,
are not available. Very few agricultural indicators are disaggregated by gender, even though many
key FNS indicators may vary systematically between men and women. An initial database on access
to rural insurance has been discontinued on the grounds that it did not adequately reflect ground
realities. Other variables are available for certain countries or regions but have limited coverage. Of
the 80 indicators used, 15 were available for fewer than half of developing countries.
Second, even where data are available, reliability is an issue in terms of quality and comparability.
The statistics collected and published by the Food and Agricultural Organization (FAO) are based on
reporting from national statistical agencies. But due to a lack of reliable reporting from mem-
ber countries, FAO data experts have had to generate their own estimates of basic production data for
nearly 70 percent of African countries (FAO, 2008). This means that even straightforward production
data for most African countries could be unreliable. This presents a challenge to strengthen national
statistical offices, something that the Paris 211 initiative and the new Global Partnership for Sus-
tainable Development Data are responding to.
Data on more complex or nuanced issues such as under nutrition, the capital stock in agriculture,
or the environmental impact of agricultural production are often derived from modelling and extra-
polation rather than real data collection. Data on governments’ domestic public spending on agricul-
ture are also out of date and of questionable comparability because the various statistical agencies
take different approaches to include or exclude line items like “rural roads” that serve multiple pur-
st
poses (FAO, 2008, pp.8,36). According to the Partnership in Statistics for Development in the 21
Century, a number of issues and priorities are important for FNS but are inherently difficult to meas-
ure and quantify. For example, strong leadership — among politicians, government bureaucrats, and
entrepreneurs in the private sector — is a crucial ingredient in designing and implementing a suc-
cessful national strategy for ending hunger, but good metrics for capturing leadership are hard to find.
And when it comes to trying to estimate the effects of climate change on agricultural productivity, so
many factors and assumptions must be built into agro-climatic models that ultimately we must ac-
cept that there will always be high levels of uncertainty in such projections.

International Journal of Population Studies | 2016, Volume 2, Issue 1 67