Unpacking the ‘Black Box’ of Public Expenditure Statistics� by Tewodaj Mogues

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

Transcript of Unpacking the ‘Black Box’ of Public Expenditure Statistics� by Tewodaj Mogues

  • 1. Agriculture Public Expenditure Workshop8. October 2014, Golden Peacock HotelLilongwe, MalawiUnpacking the Black Box of PublicExpenditure StatisticsLessons from a Diagnostic Analysis ofAgricultural Sector Public Expenditures

2. Objective of this Research Programme Several major policy initiatives requiring measurementand tracking of public expenditures in support of theagriculture sector (e.g. CAADP, country strategies, IDPs) However, how to measure the quantity of agriculturalexpenditures? Inconsistencies: Different reports and databases report differentfigures (for the same country and year) Non-transparent aggregates: Not always clear what ingredientswent into the soup This research programme seeks to offer approaches forcountry analysts to quantify agPEs in a consistent andtransparent way 4 country cases: Ghana, Kenya, Malawi, Mozambique 3. Tracking Aggregate Ag. PE over Time Growth of Funds or of Coverage?111098765432102000200120022003Ghana200420052006200720082009Ag pub. exp. as %of total pub exp.CAADP guideline: 10 % agspending share Data in earlier years didnt include public expendituresrelated to cocoa, debt servicing; subsequently included Most recent data started including local governmentfunds, 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 publicexpenditures 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 (especiallycross-country), by IFPRI, IMF, FAO, OECD, etc. Descriptive review of trends and patterns of agriculturalexpenditures other well established work on this through World BankAgPERs, and through other initiatives (however,complementary, and Malawi AgPER to be presented here) 5. Agriculture Public Expenditure Workshop8. October 2014, Golden Peacock HotelLilongwe, MalawiUnpacking the Black Box of PublicExpenditures in Agriculture:A Case Study of MozambiqueTewodaj Mogues, Senior Research Fellow, IFPRI, Washington DC(Collaboration w/ Leonardo Caceres, Francisco Fernandes, Mariam Umarji) 6. Different figures in different reportsWorld Bank AgPER:PE in Ag. 20073,281 MMT2,773 MMT (excluding OILL) 7. Different figures in different reportsIMF Article IV Consultation: PE in Ag. & Rural Devt 20072,067 MMT 8. Proposal to undertake a DIY approach, usingthe existing government public accountsStep 1:Decide on the scope of expenditures to fallunder agriculture 9. Step 2:Understand nature of expenditure data alongthe budget process 10. Understanding expenditure data along thebudget process 11. Step 3:Understand the types and quality of theclassification 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 intl coding for Level 4Not 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 practicedMozambique applies Level 4 codesNot used in the budget data, but only in the execution andactual expenditure data 19. Administrative classification: Detailed, and ofrelevance to government for its operationsHowever, changing coding system over time, and no dedicatedcodes for units within a ministry 20. Programmatic classificationUseful, though not a comprehensive system 21. Step 4:Reconstruction of agricultural publicexpenditures using the most appropriateclassification system(s) 22. Illustration of reconstruction usingadministrative and programmatic classification 23. Illustration of reconstruction usingadministrative and programmatic classification 24. Illustration of reconstruction usingadministrative and programmatic classification 25. Step 5:Accounting for domestic and donor spendingthat is not captured in the public accounts 26. Comparison with international and nationalexpenditure data sources (2010) 27. Comparison with international and nationalexpenditure data sources (2011) 28. Some key take-aways Analysts and others wanting to obtain time-consistentinformation on how much PE is going toagriculture need to work with the appropriateclassification and coding systems of governmentaccounts The administrative system of classification tends tobe most versatile for reconstruction of agPE But coding system should be more detailed, follow aclear logic, and be consistent over time IDPs should be aware of capacity constraints, andasking for too many classification systems reducestheir quality 29. Agriculture Public Expenditure Workshop8. October 2014, Golden Peacock HotelLilongwe, MalawiUnpacking the Black Box of PublicExpenditures in Agriculture:A Case Study of MozambiquePresented by:Tewodaj Mogues, Senior Research Fellow, IFPRI, Washington DC