Unpacking the ‘Black Box’ of Public Expenditure Statistics; Lessons from a Diagnostic Analysis of Agricultural Sector Public Expenditures

1. Agriculture Public Expenditure Workshop
8. October 2014, Golden Peacock Hotel
Lilongwe, Malawi
Unpacking the ‘Black Box’ of Public
Expenditure Statistics
Lessons from a Diagnostic Analysis of
Agricultural Sector Public Expenditures

2. Objective of this Research Programme
• Several major policy initiatives requiring measurement
and tracking of public expenditures in support of the
agriculture sector (e.g. CAADP, country strategies, IDPs)
• However, how to measure the quantity of agricultural
expenditures?
• Inconsistencies: Different reports and databases report different
figures (for the same country and year)
• Non-transparent aggregates: Not always clear what “ingredients
went into the soup”
• This research programme seeks to offer approaches for
country analysts to quantify agPEs in a consistent and
transparent way
• 4 country cases: Ghana, Kenya, Malawi, Mozambique

3. Tracking Aggregate Ag. PE over Time
– Growth of Funds or of Coverage?
11
10
9
8
7
6
5
4
3
2
1
0
2000
2001
2002
2003
Ghana
2004
2005
2006
2007
2008
2009
Ag pub. exp. as %
of total pub exp.
CAADP guideline: 10 % ag
spending share
• Data in earlier years didn’t include public expenditures
related to cocoa, debt servicing; subsequently included
• Most recent data started including local government
funds, and feeder roads

4. What the Research Programme Is Not
• What this IFPRI programme is not primarily about:
• Econometric analysis of the returns to and impact of public
expenditures in agriculture
 other longstanding research on this in IFPRI and elsewhere
• A database or dataset of public expenditures in agriculture
 several initiatives have generated such datasets (especially
cross-country), by IFPRI, IMF, FAO, OECD, etc.
• Descriptive review of trends and patterns of agricultural
expenditures
 other well established work on this through World Bank
AgPERs, and through other initiatives (however,
complementary, and Malawi AgPER to be presented here)

5. Agriculture Public Expenditure Workshop
8. October 2014, Golden Peacock Hotel
Lilongwe, Malawi
Unpacking the ‘Black Box’ of Public
Expenditures in Agriculture:
A Case Study of Mozambique
Tewodaj Mogues, Senior Research Fellow, IFPRI, Washington DC
(Collaboration w/ Leonardo Caceres, Francisco Fernandes, Mariam Umarji)

6. Different figures in different reports
World Bank AgPER:
PE in Ag. 2007
3,281 MMT
2,773 MMT (excluding OILL)

7. Different figures in different reports
IMF Article IV Consultation: PE in Ag. & Rural Dev’t 2007
2,067 MMT

8. Proposal to undertake a “DIY” approach, using
the existing government public accounts
Step 1:
Decide on the scope of expenditures to fall
under “agriculture”

9. Step 2:
Understand nature of expenditure data along
the budget process

10. Understanding expenditure data along the
budget process

11. Step 3:
Understand the types and quality of the
classification systems in use

12. A multiplicity of classification systems:
Too much of a good thing?

13. Functional classification:
COFOG (IMF GFSM 2001)
COFOG Level 1

14. Functional classification:
COFOG (IMF GFSM 2001)
COFOG Level 2 COFOG Level 3 No int’l coding for Level 4
Not

15. Functional classification:
COFOG (IMF GFSM 2001)

16. Functional classification:
COFOG (IMF GFSM 2001)

17. Functional classification:
COFOG (IMF GFSM 2001)

18. Functional classification: Great in principle …
but limited usefulness as practiced
Mozambique applies Level 4 codes
Not used in the budget data, but only in the execution and
actual expenditure data

19. Administrative classification: Detailed, and of
relevance to government for its operations
However, changing coding system over time, and no dedicated
codes for units within a ministry

20. Programmatic classification
Useful, though not a comprehensive system

21. Step 4:
Reconstruction of agricultural public
expenditures using the most appropriate
classification system(s)

22. Illustration of reconstruction using
administrative and programmatic classification

23. Illustration of reconstruction using
administrative and programmatic classification

24. Illustration of reconstruction using
administrative and programmatic classification

25. Step 5:
Accounting for domestic and donor spending
that is not captured in the public accounts

26. Comparison with international and national
expenditure data sources (2010)

27. Comparison with international and national
expenditure data sources (2011)

28. Some key take-aways
• Analysts and others wanting to obtain time-consistent
information on how much PE is going to
agriculture need to work with the appropriate
classification and coding systems of government
accounts
• The administrative system of classification tends to
be most versatile for reconstruction of agPE
• But coding system should be more detailed, follow a
clear logic, and be consistent over time
• IDPs should be aware of capacity constraints, and
asking for too many classification systems reduces
their quality

29. Agriculture Public Expenditure Workshop
8. October 2014, Golden Peacock Hotel
Lilongwe, Malawi
Unpacking the ‘Black Box’ of Public
Expenditures in Agriculture:
A Case Study of Mozambique
Presented by:
Tewodaj Mogues, Senior Research Fellow, IFPRI, Washington DC