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Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
2011 CAADP M&E :Data Response Analysis
Regional Strategic Analysis and Knowledge Support System for Southern Africa (ReSAKSS-SA)
By
Raymond Nkululeko Maseko
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
Content
• Introduction
• Rational
• Data Collection Process
• Observations – Data collection & collected data
• Results of Analysis
• Suggestions
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
In June 2011 SADC country consultants were contracted to collect data for the purpose of Monitoring and Evaluating CAADP process; in particularly, progress made towards achieving the 10% allocation of national budget to agriculture and 6% growth in agricultural output.
Introduction
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
The main objective of the response analysis is to establish:
a. the overall response rate for all the SADC countries that collected
data which are Angola, Botswana, DRC, Lesotho, Malawi,
Mozambique, Namibia, South Africa, Swaziland, Tanzania, Zambia
and Zimbabwe;
b. a response rate per question and section with a view to
identifying gaps in the data;
c. which critical questions and sections are affected by gaps in the
data;
Rational
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
Step 1
Finalise questionnaire preparation
Step 2
Normalise questionnaire
(Format, questions, validation)
Step 4
Design Computer Database Structure
Step 3
Develop Questionnaire Completion Checklist
Step 5
Issue an electronic Questionnaires and
Checklist
Step 6
Workshop Questionnaire Methodology
Step 7
Develop Resource Schedule by Country
Step 8
Setup data Collection Appointment
Schedule
Step 9
Confirm Schedule
Step 10
Collect Data and Complete
Questionnaire
Step 11
Perform High Level Data Validation, Complete Checklist
and provide weekly status update
Step 12
Carry out spot checks
Step 14
Submit electronic Questionnaire and
Checklist
Step 15
Project Coordinator Record Receipt of
Questionnaires
Step 16
Capture Data into a Regional Database
Step 18
Update Checklist
Step 19
Handover Database to Analysts
Step 13
Complete Checklist
Step 17
Validate Captured Data
Data Collection Process
IWMI IWMI & Consultants Consultants
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
Observations on data collection and collected data
1. Most country consultants outsourced collection of data or submitted requests to various government departments to fill out the questionnaire;
2. In some cases there is no evidence to suggest that the questionnaire was thoroughly discussed with subcontractors or departments that were requested to complete the questionnaire or sections of the questionnaire;
3. Not all countries responded to all spot check issues that were raised with them. In fact some consultants choose to address the data issues in their country reports;
4. There is no evidence to suggest that some country consultants checked data before it was submitted to IWMI Project Co-ordinator;
5. When country consultants presented their draft reports during the workshop, most of the reports were not based on collected data but a different data source;
6. Questions that were asked by some country consultants during the second data workshop suggested that either the questionnaire was not clear or there was a communication breakdown / problem;
7. Some country consultants could not explain some of the ambiguities in the data because it was transcribed from source as is and without any explanation;
8. It is not clear: a. if data is not available; or b. at source it is not stored / collected in a manner that can easily relate to the way questions
are structured in the questionnaire; or c. there is inadequate skill to extract data in the manner it is required on the questionnaire.
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
Impact of gaps in the data
It is not possible to produce comprehensive combined regional statistics for meaningful analysis and the table below is one of the
exampleAgriculture expenditure as a percentage of AgGDP
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Botswana 57.39536 58.87476 67.53936 53.47036 55.69629 59.10841 49.41439 41.81901 40.43799 32.32517
Malawi 28.81457 34.73428 49.74135 46.966 3.422355 8.573295 13.47078 17.45058 19.05711 26.43682
Swaziland 8.329403 11.11626 11.0481 15.94108 17.66625 14.57973 13.32836 25.01819 30.36949 24.40279
South Africa 16.44749 16.23911 13.92657 16.20926 18.05014 22.92123 24.14941 26.36115 24.57718 23.77911
Zambia 3.696304 6.429125 5.2664 6.855124 6.573276 7.733492 9.25641 13.05252 16.52246 10.52671
Lesotho 15.36789 8.462462 14.15812 13.62344 13.40708
Mozambique 1.904167 6.363125 5.811887 8.506668 11.40644 10.37076 10.26641 5.553242 6.75931
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
Indicator Angola
Botswana
DRC
Lesotho
Malaw
i
Mozambique
Namibia
South Africa
Swaziland
Tanzania
Zambia
Zimbabwe
Average
B1. OVERVIEW OF REVENUES AT NATIONAL LEVEL 52%
60%
49%
56%
59%
51%
8% 62%
52%
30%
33%
60%
48%
B2. BUDGET ALLOCATION AT NATIONAL LEVEL 67%
50%
41%
67%
67%
61%
33%
67%
83%
64%
61%
67%
60%
B3. BUDGET ALLOCATION BY AGRICULTURAL SUB-SECTOR 25%
23%
26%
30%
78%
18%
49%
52%
74%
5% 58%
44%
40%
B4. BUDGET ALLOCATION BY FUNCTION/DEPARMENT 7% 53%
26%
41%
0% 43%
30%
27%
70%
10%
46%
55%
34%
B5. ACTUAL PUBLIC EXPENDITURE AT NATIONAL LEVEL 53%
50%
0% 48%
67%
44%
15%
68%
83%
39%
64%
38%
47%
B6. ACTUAL PUBLIC EXPENDITURE BY AGRICULTURAL SUB-SECTOR 0% 23%
0% 20%
75%
18%
0% 50%
74%
21%
7% 36%
27%
B7. ACTUAL PUBLIC EXPENDITURE BY FUNCTION/DEPARMENT 0% 43%
10%
33%
0% 42%
0% 42%
75%
0% 0% 39%
24%
B8. PRIVATE SECTOR EXPENDITURE ON AGRICULTURE 0% 0% 25%
0% 0% 48%
0% 27%
0% 0% 0% 0% 8%
B9. PRIVATE SECTOR EXPENDITURE ON AGRICULTURE BY SUBSECTOR
0% 0% 20%
0% 0% 0% 0% 0% 0% 10%
0% 0% 2%
B10. INWARD FOREIGN DIRECT INVESTMENT 0% 0% 50%
38%
0% 48%
10%
32%
50%
49%
38%
0% 26%
B11. INWARD FDI ON AGRICULTURE BY SUBSECTOR 0% 0% 80%
0% 0% 0% 0% 0% 0% 5% 0% 0% 7%
B12. NON-GOVERNMENTAL ORGANIZATIONS INVESTMENT 6% 0% 13%
0% 0% 0% 0% 25%
0% 0% 0% 11%
5%
B13. NON-GOVERNMENTAL ORGANIZATIONS INVESTMENT BY SUBSECTOR
0% 0% 30%
0% 0% 0% 0% 0% 0% 0% 0% 5% 3%
Average 16%
23%
28%
26%
27%
29%
11%
35%
43%
18%
24%
27%
26%
The percentage in each column represents available data measured against an expected 100% response rate for each indicator. This table must be read in
conjunction with indicator bar charts showing gaps in the data
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
B1.1 Internally generated B1.2 Externally generated
B1.1.1 Tax-revenueUsd
B1.1.2 Domestic loansUsd
B1.2.1 grantUsd
B1.2.2 loan (Doações) Usd
2000 17,43 ND 188,5 30,0
2001 45,6 ND 324,5 ND
2002 162,46 ND 528,5 24,0
2003 288,8 ND 34,9 55,0
2004 496,7 ND 5,54 117,4
2005 95,0 248,1 496.7 32,0
2006 14,0 360,9 3.21 ND
2007 2,1 323,2 260.6 ND
2008 3200,0 1288,5 5223,6 52,0
2009 1900,0 3000,0 4801,8
2010 2290,0 3118,6 4910,4 383,5
Note: Tax revenue includes for example taxes on income and profits, payroll and workforce, domestic goods and services, taxes on international trade and transactions as well as stamp duties and fees
Note: Specify currency ___Millions USD______________________________________________ in: Thousands (1,000) Millions (1,000,000) Billions (1,000,000,000)
Extract from country questionnaire
Specify Calendar Year: __________ or Fiscal Year from: month __________ year________ to month __________ year________Please note: All monetary values should be in the Local Currency Unit (LCU). In case an alternative currency is used, please state explicitly. Agriculture is defined to include crops, livestock, fisheries (captured and farmed) and forestry.
B1. OVERVIEW OF REVENUES AT NATIONAL LEVEL
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
B1.1 Internally generated B1.2 Externally generated B1.1 Internally generated
B1.2 Externally generated
B1.1.1 Tax-revenue
B1.1.2 Domestic loans
B1.2.1 grant B1.2.2 loan (Doações) Usd
B1.1.1 Tax-
revenue
B1.1.2 Domestic
loans
B1.2.1 grant
B1.2.2 loan
(Doações) UsdUsd Usd Usd Usd Usd Usd
2000 17,43 ND 188,5 30,0 2000 17.43 ND 188.5 30 235.932001 45,6 ND 324,5 ND 2001 45.6 ND 324.5 ND 370.12002 162,46 ND 528,5 24,0 2002 162.46 ND 528.5 24 714.962003 288,8 ND 34,9 55,0 2003 288.8 ND 34.9 55 378.72004 496,7 ND 5,54 117,4 2004 496.7 ND 5.54 117.4 619.642005 95,0 248,1 496.7 32,0 2005 95 248.1 496.7 32 871.82006 14,0 360,9 3.21 ND 2006 14 360.9 3.21 ND 378.112007 2,1 323,2 260.6 ND 2007 2.1 323.2 260.6 ND 585.92008 3200,0 1288,5 5223,6 52,0 2008 3200 1288.5 5223.6 52 9764.12009 1900,0 3000,0 4801,8 2009 1900 3000 4801.8 9701.82010 2290,0 3118,6 4910,4 383,5 2010 2290 3118.6 4910.4 383.5 10702.5
Note: Tax revenue includes for example taxes on income and profits, payroll and workforce, domestic goods and services, taxes on international trade and transactions as well as stamp duties and feesNote: Specify currency ___Millions USD______________________________________________
in: Thousands (1,000) ð Millions (1,000,000) Billions (1,000,000,000)
Issues that need clarification1. What does ND mean?2. What does a blank mean?3. Are the figures in yellow expected, i.e. the fluctuation?4. Are the figures real or nominal?5. If real which one is used as the base year?6. Please specify source?
Spot check report
B1. OVERVIEW OF REVENUES AT NATIONAL LEVEL
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
Question No. / Question 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
B1.a Overview of revenues - Specify Calendar year
B1.b Overview of revenues - Specify Year
B1.c Overview of revenues - Specify Currency USD$ USD$ USD$ USD$ USD$ USD$ USD$ USD$ USD$ USD$ USD$
B1.d Overview of revenues - Specify accounting denomination (1 000 or 1 000 000 or 1 000 000 000)
1000000
1000000
1000000
1000000
1000000
1000000
1000000
1000000
1000000
1000000
1000000
B1.e Overview of revenues - Specify if nominal or real values
B1.f Overview of revenues -If real, which one is used as the base year?
B1.1.1 Overview of Revenues at National Level – Internally generated Tax revenue
17.43 45.6 162.46
288.8 496.7 95 14 2.1 3200 1900 2290
B1.1.2 Overview of Revenues at National Level – Internally generated Domestic loans
248.1 360.9 323.2 1288.5
3000 3118.6
B1.2.1 Overview of Revenues at National Level – Externally generated grants
188.5 324.5 528.5 34.9 5.54 496.7 3.21 260.6 5223.6
4801.8
4910.4
B1.2.2 Overview of Revenues at National Level – Externally generated loans
30 24 55 117.4 32 52 383.5
Database Extract
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
B1.1.1 Overview of Revenues at National Level – In-ternally generated Tax revenue
17.43 45.6 162.46 288.8 496.7 95 14 2.1 3200 1900 2290
B1.1.2 Overview of Revenues at National Level – In-ternally generated Domestic loans
NaN NaN NaN NaN NaN 248.1 360.9 323.2 1288.5 3000 3118.6
B1.2.1 Overview of Revenues at National Level – Ex-ternally generated grants
188.5 324.5 528.5 34.9 5.54 496.7 3.21 260.6 5223.6 4801.8 4910.4
B1.2.2 Overview of Revenues at National Level – Ex-ternally generated loans
30 NaN 24 55 117.4 32 NaN NaN 52 NaN 383.5
500
1500
2500
3500
4500
5500
USD$
(Mill
ions
)
B1. OVERVIEW OF REVENUES AT NATIONAL LEVEL
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
The percentage in each column represents available data measured against an expected 100% response rate for each indicator. This table must be read in
conjunction with indicator bar charts showing gaps in the data
Indicator Angola
Botswana
DRC
Lesotho
Malawi
Mozambique
Namibia
South Africa
Swaziland
Tanzani
a
Zambia
Zimbabwe
Average
C1. USE OF IMPROVED VARIATIES AND CHEMICAL (INORGANIC) FERTILIZER BY CROP
26%
20%
90%
7% 0% 41%
0%
32%
7% 85% 18%
0% 27%
C2. TOTAL AREA UNDER IMPROVED LAND MANAGEMENT 45%
0% 0% 3% 100%
27%
0%
39%
6% 100%
33%
9% 30%
C3. USE OF IMPROVED LIVESTOCK TECHNOLOGY 0% 7% 50%
75%
0% 74%
0%
0% 31%
100%
0% 0% 28%
C4. USE OF AGRICULTURAL INPUTS 5% 12%
86%
6% 20% 21%
3%
29%
23%
49% 24%
20%
25%
C5. HUMAN CAPITAL 25%
53%
31%
68%
25% 11%
2%
18%
54%
50% 0% 20%
30%
Average 20%
18%
51%
32%
29% 35%
1%
24%
24%
77% 15%
10%
28%
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
The percentage in each column represents available data measured against an expected 100% response rate for each indicator. This table must be read in
conjunction with indicator bar charts showing gaps in the data
IndicatorAngola
Botswana DRC
Lesotho
Malawi
Mozambique
Namibi
a
South
Africa
Swaziland
Tanzania
Zambia
Zimbabwe
Average
D1. LAND AND LABOUR 41% 36% 50% 55% 50% 43% 27% 91% 0% 80% 95% 5% 48%
D2. GDP BY SECTOR 70% 55% 42% 75% 60% 59% 45% 80% 85% 80% 62% 65% 65%
D3. AGRICULTURE GDP BY SUB-SECTOR 50% 48% 36% 59% 63% 52% 45% 61% 72% 75% 50% 0% 51%
D4. OUTPUT/PRODUCTION BY CROP 38% 13% 45% 20% 90% 34% 44% 70% 17% 69% 51% 46% 45%
D5. LIVESTOCK PRODUCTION BY LIVESTOCK TYPE 40% 23% 59% 15% 50% 54% 43% 57% 40% 80% 9% 30% 42%
D6. TOTAL FISHERIES PRODUCTION 20% 0% 40% 4% 100% 37% 16% 64% 0% 0% 47% 22% 29%
D7. TOTAL FORESTRY PRODUCTION 17% 0% 17% 6% 17% 18% 12% 48% 74% 100% 0% 0% 26%
D8. AGRICULTURAL TRADE 35% 42% 13% 45% 75% 66% 58% 75% 63% 0% 75% 50% 50%
D9. AGRICULTURAL TRADE VOLUME BY CROP 8% 39% 78% 62% 23% 38% 0% 59% 19% 0% 77% 12% 35%
D10. MEAT TRADE 17% 6% 77% 3% 0% 23% 17% 75% 41% 0% 92% 19% 31%
D11. FISHERIES TRADE (both aquaculture and captured fish) 2% 55% 32% 5% 50% 11% 35% 18% 13% 0% 75% 5% 25%
Average31%29%44%32% 52% 40%31%63%38% 44% 58%23%40%
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
The percentage in each column represents available data measured against an expected 100% response rate for each indicator. This table must be read in
conjunction with indicator bar charts showing gaps in the data
Indicator Angola
Botswana
DRC
Lesotho
Malawi
Moza
mbique
Namibia
South Africa
Swaziland
Tanzania
Zambi
a
Zimbabwe
Average
E1. MACRO-ECONOMIC INDICATORS 25%
7% 30%
78%
100%
35%
45%
72%
64%
100%
67%
31%
54%
E2. POPULATION STRUCTURE 50%
0% 60%
15%
62% 82%
10%
92%
35%
5% 80%
68%
47%
E3. NUMBER OF PEOPLE LIVING WITH HIV/AIDS 0% 2% 10%
5% 0% 60%
10%
37%
60%
0% 4% 18%
17%
E4. NUMBER OF PEOPLE LIVING BELOW THE NATIONAL POVERTY LINE 0% 2% 0% 3% 33% 2% 9% 9% 36%
0% 5% 0% 8%
E5. NUMBER OF PEOPLE LIVING WITH DIETARY ENERGY CONSUMPTION BELOW 2100 KCAL PER DAY
0% 1% 0% 2% 0% 33%
0% 0% 36%
0% 0% 0% 6%
E6. NUMBER OF CHILDREN UNDER THE AGE OF 5 WHOSE WEIGHT-FOR-AGE IS LEASS THAN MINUS TWO STANDARD DEVIATIONS FROM MEDIAN OF THE WHO REFERENCE POPULATION
0% 2% 0% 10%
100%
5% 0% 0% 36%
0% 0% 0% 13%
E7. NUMBER OF CHILDREN UNDER THE AGE OF 5 WHO ARE STUNTED 0% 1% 0% 4% 100%
0% 0% 8% 36%
0% 5% 0% 13%
Average 11%
2%
14%
17%
56% 31%
11%
31%
44%
15% 23%
17%
23%
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
Suggested Data Sources
Section Possible Data Source as per CAADP Framework
A. CAADP implementation process i. CAADP Focal pointB. Expenditure and investment indicators i. Ministry of Finance
ii. Accountant General’s Officeiii. Ministry of Agricultureiv. Donor Officesv. Chamber of Commerce
C. Output indicators (Agricultural technology, diffusion, and human capital indicators
i. Ministry of Agricultureii. Environmental protection
Agenciesiii. National Statistics Office
D. Agricultural sector performance indicators (Agricultural production and trade indicators)
i. Ministry of Agricultureii. Ministry of Tradeiii. Food Balance Sheets iv. Export promotionsv. National accounts
E. Macro- and socio-economic indicators (Welfare indicators)
i. Ministry of Financeii. Ministry of Tradeiii. National accountsiv. Ministry of Health
F. Agricultural development strategies, policies and / or plan
i. Ministry of Agricultureii. Ministry of Finance
Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)
Q & A