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Towards a data users’ framework to advance Sustainable Development Goal 2

Table 2. Summary of solutions for data users to ensure accountability and achieve SDG2
Examples of challenges Proposed solution Related principle

Unavailable or unmeasured indicators More rigorously designed and implemented household and agri- I. Standardisation/
cultural surveys have potential for better measuring the produc- alignment
Small scale farms: No comparable, cross-country data specifically on tion and consumption of small-scale farms.
their productivity.
II. Institutional
Little or no country specific data on Food Loss and Waste (FLW), Efforts to create agreed protocols on how to measure food loss framework
post-harvest or post-market, but rough regional estimates exist. and waste.
III. Sustainability
Systematic data on domestic private investment in agriculture. Increasing political attention is being devoted to the issue. For
example, the UN has recently launched an Inter-Agency and
Agricultural indicators are not disaggregated by gender. Expert Group on Food Security, Agricultural and Rural Statistics
to document good practices and guidelines on concepts, methods,
Access to rural insurance indicators has been discontinued. and statistical standards.

Poor data availability, e.g., out of 80 indicators in the Ending Rural
Hunger (ERH) developing country database, 15 are available for fewer
than half of developing countries.

Reliability is a challenge in terms of quality and comparability Paris 21 initiative and the new Global Partnership for Sustainable I. Standardisation/
Development Data are responding to the need to strengthen na- alignment
The nature of self-reported data, e.g., due to a lack of reliable reporting tional statistical offices.
from member countries. FAO data experts have had to generate their
own estimates of basic production data for nearly 70 percent of Afri- II. Institutional
can countries. New technologies such as cellphones may decrease data collec- framework
tion costs.
Presents a challenge to strengthen national statistical offices. III. Sustainability
Data on more complex or nuanced issues such as undernourishment,
the capital stock in agriculture, or the environmental impact of agri-
cultural production are often derived from modelling and extrapolation
rather than real data collection.

Data on governments’ domestic public spending on agriculture 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out of date and of questionable comparability because the various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 purposes.
Inherent difficulty of measurement and quantification Satellite imaging can potentially provide cheaper, more accurate, Institutional frame-
and more regionally disaggregated data on physical and envi- work
Strong leadership is a crucial ingredient in designing and implementing ronmental issues.
a successful national strategy for ending hunger, but good metrics
for capturing leadership are hard to find. Innovation

Effects of climate change on agricultural productivity; because many
factors and assumptions must be built into agro-climatic models that
ultimately there will always be high levels of uncertainty in such pro-
jections.

Data are not open GODAN is promoting opening data sets for transparency Transparency

In some countries, data of budgetary allocations and spending are not Institutional frame-
publicly available and difficult to obtain. work

5 Conclusions

This paper summarises the findings of the data user working group on the accountability framework
for SDG2 facilitated by the ONE Campaign. The paper describes key accountability challenges with
specific programmatic examples and proposes a novel framework for professionals working with
data. Finally, the paper provides concrete operational and policy solutions to address accountability
obstacles by applying a chart of principles approach. Accountability constitutes a critical part of ef-

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