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CAADP Biennial Review (BR) Process

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By Samuel Benin

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CAADP Biennial Review (BR) Process

  1. 1. 2019 CAADP Biennial Review (BR) Process: Overview of the Process and Preliminary Results of the BR-Support Pilots to Improve Data Systems and Quality for CAADP implementation Samuel Benin, IFPRI
  2. 2. Outline: 2-part presentation Overview of the 2019 CAADP BR process oActivities and outputs up to the draft report presented to the AU’s Specialized Technical Committee (STC) in Addis Ababa on 21-25 October 2019 BR-support pilots in five countries (Kenya, Malawi, Mozambique, Senegal, and Togo) oSummary of activities and results—impact on reporting rate and quality of data reported
  3. 3. Recommitment to the principles and values of the CAADP process Enhancing investment finance in agriculture Ending hunger by 2025 Enhancing resilience of livelihoods & production systems to climate variability and other shocks Strengthening mutual accountability to actions and results The BR derives from… 1 7 6 5 4 3 2 Boosting intra-African trade in agricultural commodities & services Reduce poverty by half, by 2025, through inclusive agricultural growth and transformation
  4. 4. AFRICA’S AGRICULTURE TRANSFORMATION GOALS Aspiration 1: A prosperous Africa based on inclusive growth & sustainable development Health & nutrition Ag. productivity & production Environment & resilience UN Sustainable Development Goal #2 1 7 6 543 2
  5. 5. CAADP (Maputo Declaration) July 2003, Maputo, Mozambique MALABO DECLARATION June 2014, Malabo, Equatorial Guinea 1ST BIENNIAL REPORT (2015–2016 Data) January 2018 2ND BIENNIAL REPORT (2015–2018 Data) January 2020 FINAL REPORT January 2026, Addis Ababa The CAADP BR Timeline GOAL FOR ACHIEVING MALABO COMMITMENTS 3RD & 4TH REPORTS
  6. 6. THE CAADP BIENNIAL REVIEW: EVIDENCE-BASED AND PEER-DRIVEN OBJECTIVE Evaluate country performance in achieving the CAADP Malabo goals and targets for agricultural growth and transformation in Africa by 2025 7 thematic areas 23 performance categories 43 indicators 7 thematic areas 24 performance categories 47 indicators 1st Biennial Review (2017) 2nd Biennial Review (2019) +4 +1
  7. 7. KEY MOMENTS IN THE 2ND BR PROCESS Kigali, February 2019: training of trainers on tools Accra, March 2019 Continental training on tools March- June 2019 support to countries & RECs on report April 2019 updated technical notes on scores J’burg, May 2019 structure of the report & products June to August 2019 validation in countries & RECs; eBR Sept 2019 writeshop (Lusaka) comm plan (Nairobi) Addis Ababa, February 2020 release of report
  8. 8. REPORTING ON the 1st & 2nd BRs Submitted a report Did not submit a report 1st Biennial Review 2nd Biennial Review New member states reporting • Eritrea • Guinea-Bissau • Somalia • South Sudan Did not report • Algeria • Comoros • Libya • Sahrawi • Egypt** • Sao Tome and Principe** ** Reported in 2017 47 8 49 6 Member states that submitted Members states that did not submit
  9. 9. Next Steps in the 2019 BR 1.The draft 2019 CAADP BR Report and accompanying Africa Agriculture Transformation Scorecard (AATS) were presented to and endorsed by the 3rd Specialized Technical Committee (STC) on Agriculture, Rural Development, Water and Environment (ARDWE) in Addis Ababa, 21-25 October 2019. 2.The report will be presented to the Executive Council and then to the AU Assembly in February 2020. 3.The AU Commission and the STC will hold consultative meetings with the RECs and member states on the two communication products that have been developed: a Dashboard with 22 highlight indicators and a Toolkit (an online user platform).
  10. 10. Introduction and objectives of the BR-support pilots  Introduction: BMGF provided funding for IFPRI-ReSAKSS to strengthen data systems and capacities in 5 pilot countries  Goal: improve data systems and quality of data available for policymaking in CAADP implementation  Objectives: improve quality of data used in the 2019 BR process: 1.Identify gaps and challenges related to data, methodologies, capacities, and systems 2.Strengthen human and institutional capacities in above 3.Improve the quality (accuracy, consistency, traceability, and validity) of the data and reporting 4.Support countries to deliver a high-quality 2019 BR report 5.Develop a roadmap for countries to fill missing data for future BRs 6.Strengthen capacities in ICT for BR data management and sharing 7.Conduct strategic analysis for achieving key targets and objectives Countries: • Kenya • Malawi • Mozambique • Senegal • Togo • Mozambique • Senegal
  11. 11. Overview of pilot activities and results (1) Submission (eBR) •July 15 •August 31 Assessment of the inaugural (2017) BR process and report BR Teams (core, data clusters, review) Training (general, focus on gaps from assessment) •Data collection and compilation •Report preparation and revision Validation (review team, senior mg’t, ASWG, all stakeholders)
  12. 12. Overview of pilot activities and results (2) 2017 parameters reported of 166 Date; Participants (F, NSA, data clusters) Data compilation retreat dates Date; Participants (F, NSA) 2019 parameters reported of 266 KEN 146 (88%) 33 (9, 12, 7) 6/7, 6/18 58 (23, 24) 244 (92%) MWI 143 (86%) 23 (6, 2, 6) 7/1-2, 7/4-5 32 (7,12) 237 (89%) MOZ 135 (81%) 50 (22, 2, 5) 5/27, 6/27 60 (15, 15) 218 (82%) SEN 131 (79%) 27 (6, 3, 5) 6/11-14, 6/25 52 (10, 6) 239 (90%) TGO 134 (81%) 41 (0, 0, 5) 6/6-8, 6/20 116 (18, 10) 247 (93%) SubmissionAssessment BR Teams Training •Data •Report Validation
  13. 13. Methods for assessing impact of pilot activities  Change in reporting rate (RR): difference-in-difference (DID) oRR = data parameters reported as percent of total required oCalculate change in RR between 2017 and 2019, and compare change for pilot countries with comparable non-pilot (or like-pilot) countries oLike-pilot = non-pilots with RR2017 similar to pilots o+/- 1, 2, and 3 standard deviations of RR2017 mean for pilots DID = (RR2019 – RR2017)pilots – (RR2019 – RR2017)like-pilots  Improvement in quality of reporting (QR): oAnalyze data issues (accuracy, consistency, validity, traceability) in 2019, count and compare incidence for pilots vs. like-pilots
  14. 14. Pilot vs non-pilot countries: some x’ics at 2017 List of countries (45) Reporting rate (%) BR score ASCI agVA (% GDP) Pilot Kenya, Malawi, Mozambique, Senegal, Togo (5) 83.0 4.52 60.1 21.7 Like-pilot +/- 1sd Botswana, Cote d'Ivoire, Eswatini, Gambia, Lesotho, Madagascar, Mauritius, Namibia, Tanzania (9) 81.7 3.78 53.4** 16.3*** Like-pilot +/- 2sd Like-pilot 1sd plus Benin, Burkina Faso, Cabo Verde, Djibouti, Ethiopia, Morocco, South Africa, Uganda, Zimbabwe (18) 82.4 4.06 53.2*** 9.7*** Like-pilot +/- 3sd Like-pilot 2sd plus Burundi, Gabon, Ghana, Mali, Seychelles, Zambia (24) 83.2 4.07 53.8*** 10.6*** All non- pilots Like-pilot 3sd plus 16 others (40) 73.9*** 3.54** 51.8*** 15.2***  Compared to like-pilots, pilots had similar BR score in 2017, but higher ASCI and agVA/GDP. ASCI = Agricultural Statistics Capacity Index agVA = agriculture value added ** & *** = significant at 5% and 1% level
  15. 15. Number of data parameters required has increased overall, with declines in two themes All Thematic area 1 2 3 4 5 6 7 2017 166 28 20 63 21 16 7 11 2019 266 27 28 153 29 16 8 5 Change (%) 60 -4 40 143 36 0 14 -55
  16. 16. Impact or improvement in BR reporting rate 2017 2019 Change (%pts) Pilots 83.0 89.1 6.1 Non-pilots All 73.9 79.0 5.0 Like-pilots (+/- 1sd) 81.7 78.9 -2.8 Like-pilots (+/- 2sd) 82.4 80.0 -2.5 Like-pilots (+/- 3sd) 83.2 81.5 -1.7 Difference between pilots and non-pilots: (%pts) All 9.1*** 12.5*** 1.0 Like-pilots (+/- 1sd) 1.3 10.2*** 8.9** Like-pilots (+/- 2sd) 0.6 9.1** 8.5** Like-pilots (+/- 3sd) -0.2 7.6** 7.8** DID Compared to like-pilot countries, BR-pilot support activities helped raise their reporting rate by 8 to 9 %pts on average. *, **, *** = 10%, 5% and 1% significance level
  17. 17. Relative improvement in BR reporting rate in the pilot countries 2017 2019 Change %pts % over 2018 Pilots (all) 83.0 89.1 6.1 7.3 Kenya 88.0 91.7 3.8 4.3 Malawi 86.1 89.1 3.0 3.4 Mozambique 81.3 82.0 0.6 0.8 Senegal 78.9 89.8 10.9 13.9 Togo 80.7 92.9 12.2 15.1  The improvements were largest in Senegal and Togo;  Moderate in Malawi and Kenya; and very little in Mozambique.
  18. 18. Improvement in reporting rate, by thematic area (%pts) 1 2 3 3 – fs 4 5 6 7 Change: 2017 to 2019 Pilots 6.4 12.7 3.8 6.1 4.7 10.0 2.5 -2.5 Non-pilots Like-pilots (+/- 1sd) 6.3 1.0 -4.5 -0.5 -8.6 -1.4 2.6 3.4 Like-pilots (+/- 2sd) 2.9 4.9 -3.9 -0.7 -5.4 1.0 -4.6 1.9 Like-pilots (+/- 3sd) 2.9 3.4 -4.0 -1.6 -1.2 5.2 -2.9 0.4 Difference-in-difference Like-pilots (+/- 1sd) 0.1 11.7** 8.4*** 6.6** 13.3 11.4 -0.1 -6.0 Like-pilots (+/- 2sd) 3.5 7.8 7.7** 6.8* 10.1 9.0 7.1 -4.5 Like-pilots (+/- 3sd) 3.5 9.3* 7.9*** 7.7*** 5.9 4.8 5.4 -2.9  Bulk of gains derived from themes 2 (agriculture investment) and 3 (food security and nutrition; with or without the new food safety indicators). excl. food safety *, **, *** = 10%, 5% and 1% significance level
  19. 19. Relative improvement in BR reporting rate in pilot countries, by thematic area (2017 to 2019, %pts) 1 2 3 3 – fs 4 5 6 7 Pilots (all) 6.4 12.7 3.8 6.1 4.7 10.0 2.5 -2.5 Kenya 0.0 7.9 5.7 5.5 2.8 -25.0 30.4 -20.0 Malawi 0.0 20.0 0.9 10.3 1.0 12.5 1.8 0.0 Mozambique 0.0 7.9 8.0 7.6 -7.2 0.0 -35.7 9.1 Senegal 32.1 2.9 3.1 3.1 -8.0 31.3 16.1 -20.0 Togo 0.0 25.0 1.4 3.9 35.1 31.3 0.0 18.2  Kenya’s overall moderate progress due to declines in themes 5 and 7  Malawi’s overall moderate progress due to no show in food safety indicators  Low overall performance in Mozambique driven mostly by decline in theme 6  Senegal (lowest RR in 2017) could have achieved even more, but for theme 7  Improvements in Togo (same RR in 2017 as Mozambique) is laudable
  20. 20. Quality of data reported in 2019: pilots vs. like-pilots 5 8 9 12 5 3 8 5 1 8 10 20 7 13 11 16 9 8 8 12 3 1 3 12 0 5 10 15 20 1sd 2sd 3sd 1sd 2sd 3sd 1sd 2sd 3sd 1sd 2sd 3sd 1sd 2sd 3sd 1sd 2sd 3sd Pilots Like-pilots Pilots Like-pilots Pilots Like-pilots Pilots Like-pilots Pilots Like-pilots Pilots Like-pilots All Theme 1 Theme 2 Theme 3 Theme 4 Theme 5 Data parameters with issues, % of data parameters reported  Overall, quality is higher in pilots than in the non-pilot countries; and especially for themes 2 and 3.  Mixed results for themes 1, 4, and 5; depending on the comparative like-pilot.  No data issues or differences for themes 6 and 7 (results not shown).
  21. 21. Quality of data reported in 2019: pilot countries Data parameters with issues, % of data parameters reported  Overall, Kenya had the least issues, followed by Malawi, Togo, and Senegal; with Mozambique having the most issues.  Larger differences for themes 1, 4, and 5 (recall mixed results for these when comparing with like-pilots).  No data issues or differences for themes 6 and 7 (results not shown). 2 4 9 5 4 0 0 7 3 13 5 0 4 0 0 1 6 7 7 5 25 0 18 3 0 0 3 21 0 0 0 5 10 15 20 25 KEN MWI MOZ SEN TGO KEN MWI MOZ SEN TGO KEN MWI MOZ SEN TGO KEN MWI MOZ SEN TGO KEN MWI MOZ SEN TGO KEN MWI MOZ SEN TGO All Theme 1 Theme 2 Theme 3 Theme 4 Theme 5
  22. 22. Lessons and implications (1)  Main factors contributing to the overall higher performance in the pilot countries: oCritical assessment of the gaps in the inaugural BR oData clusters and targeted training and strategy to address the gaps oEngagement of relevant stakeholders (including women, NSAs, and non-agriculture experts) in the training and data compilation and validation processes As approach used in pilots shared with all member states  the additional resources and hand-holding are critical oIt is why the lower performance of Mozambique seems surprising; cited lack of investments in data collection at national scale as main issue Engaging all potential data providers (cluster idea) is critical
  23. 23. Lessons and implications (2)  These raise a key challenge: oHow to sustain the clusters to continuously update the data? Engage them in using BR data to conduct policy analysis for achieving national objectives  financial support and technical assistance  Some data inaccuracies/inconsistencies in 2019 will remain: oNo time to internalize post-validation analysis to make corrections oLearning curve with eBR, as first time being implemented Analysis of the data issues will be useful for improving the data collection template and the eBR in the next round (2021)

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