Data response analysis june 21 2012 r maseko

2011 CAADP M&E :Data Response Analysis By Raymond Nkululeko MasekoRegional Strategic Analysis and Knowledge Support System for Southern Africa (ReSAKSS-SA)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 • SuggestionsStrategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)

Introduction 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.Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)

Rational 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;Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)

Data Collection Process IWMI IWMI & Consultants Consultants Step 1 Step 6 Step 7 Step 8 Finalise questionnaire Workshop Questionnaire Develop Resource Setup data Collection preparation Methodology Schedule by Country Appointment Schedule Step 2 Step 5 Step 10 Step 9 Normalise Issue an electronic Collect Data and Confirm Schedule questionnaire (Format, Questionnaires and Complete Questionnaire questions, validation) Checklist Step 3 Step 11 Step 4 Step 12 Develop Questionnaire Perform High Level Data Design Computer Carry out spot checks Completion Checklist Validation, Complete Checklist and Database Structure provide weekly status update Step 16 Step 15 Step 14 Step 13 Capture Data into a Project Coordinator Record Submit electronic Complete Checklist Regional Database Receipt of Questionnaires Questionnaire and Checklist Step 17 Step 18 Step 19 Validate Captured Data Update Checklist Handover Database to AnalystsStrategic 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 example Agriculture expenditure as a percentage of AgGDP 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Botswana 57.39536 58.87476 67.53936 53.47036 55.69629 59.10841 49.41439 41.81901 40.43799 32.32517Malawi 28.81457 34.73428 49.74135 46.966 3.422355 8.573295 13.47078 17.45058 19.05711 26.43682Swaziland 8.329403 11.11626 11.0481 15.94108 17.66625 14.57973 13.32836 25.01819 30.36949 24.40279South Africa 16.44749 16.23911 13.92657 16.20926 18.05014 22.92123 24.14941 26.36115 24.57718 23.77911Zambia 3.696304 6.429125 5.2664 6.855124 6.573276 7.733492 9.25641 13.05252 16.52246 10.52671Lesotho 15.36789 8.462462 14.15812 13.62344 13.40708Mozambique 1.904167 6.363125 5.811887 8.506668 11.40644 10.37076 10.26641 5.553242 6.75931Strategic 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 Mozambique South Africa Zimbabwe Swaziland Botswana Tanzania Namibia Average Lesotho Malawi Zambia Angola DRC 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 0% 0% 20% 0% 0% 0% 0% 0% 0% 10% 0% 0% 2% SUBSECTOR 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 0% 0% 30% 0% 0% 0% 0% 0% 0% 0% 0% 5% 3% SUBSECTOR Average 16% 23% 28% 26% 27% 29% 11% 35% 43% 18% 24% 27% 26%Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)

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 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 Usd Usd 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)Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)

Spot check report B1. OVERVIEW OF REVENUES AT NATIONAL LEVEL B1.1Internallygenerated B1.2Externallygenerated B1.1Internally B1.2Externally generated generated B1.1.1Tax- B1.1.2Domestic B1.2.1grant B1.2.2loan B1.1.1 B1.1.2 B1.2.1 B1.2.2 revenue loans (Doações)Usd Tax- Domestic grant loan revenue loans (Doações) Usd Usd Usd Usd Usd Usd Usd 2000 17,43 ND 188,5 30,0 2000 17.43 ND 188.5 30 235.93 2001 45,6 ND 324,5 ND 2001 45.6 ND 324.5 ND 370.1 2002 162,46 ND 528,5 24,0 2002 162.46 ND 528.5 24 714.96 2003 288,8 ND 34,9 55,0 2003 288.8 ND 34.9 55 378.7 2004 496,7 ND 5,54 117,4 2004 496.7 ND 5.54 117.4 619.64 2005 95,0 248,1 496.7 32,0 2005 95 248.1 496.7 32 871.8 2006 14,0 360,9 3.21 ND 2006 14 360.9 3.21 ND 378.11 2007 2,1 323,2 260.6 ND 2007 2.1 323.2 260.6 ND 585.9 2008 3200,0 1288,5 5223,6 52,0 2008 3200 1288.5 5223.6 52 9764.1 2009 1900,0 3000,0 4801,8 2009 1900 3000 4801.8 9701.8 2010 2290,0 3118,6 4910,4 383,5 2010 2290 3118.6 4910.4 383.5 10702.5 Taxrevenueincludesforexample taxesonincomeandprofits,payrollandworkforce,domesticgoodsandservices,taxeson internationaltradeandtransactionsaswell asstampdutiesand Note: fees Note: Specifycurrency___MillionsUSD______________________________________________ in:Thousands(1,000) Millions(1,000,000) Billions(1,000,000,000) Issuesthatneedclarification 1.WhatdoesNDmean? 2.Whatdoesablankmean? 3.Arethefiguresinyellowexpected,i.e.thefluctuation? 4.Arethefiguresrealornominal? 5.Ifrealwhichoneisusedasthebaseyear? 6.Pleasespecifysource?Strategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)

Database Extract 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 100000 100000 100000 100000 100000 100000 100000 100000 100000 100000 100000 (1 000 or 1 000 000 or 1 000 000 000) 0 0 0 0 0 0 0 0 0 0 0 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 17.43 45.6 162.46 288.8 496.7 95 14 2.1 3200 1900 2290 generated Tax revenue B1.1.2 Overview of Revenues at National Level – Internally 248.1 360.9 323.2 1288.5 3000 3118.6 generated Domestic loans B1.2.1 Overview of Revenues at National Level – Externally 188.5 324.5 528.5 34.9 5.54 496.7 3.21 260.6 5223.6 4801.8 4910.4 generated grants B1.2.2 Overview of Revenues at National Level – Externally 30 24 55 117.4 32 52 383.5 generated loansStrategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)

B1. OVERVIEW OF REVENUES AT NATIONAL LEVEL 6000 5000 4000 USD$ (Millions) 3000 2000 1000 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 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 52 383.5Strategic 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 Mozambique South Africa Zimbabwe Swaziland Botswana Tanzania Namibia Average Lesotho Zambia Malawi Angola DRC C1. USE OF IMPROVED VARIATIES AND CHEMICAL (INORGANIC) FERTILIZER 26% 20% 90% 7% 0% 41% 0% 32% 7% 85% 18% 0% 27% BY CROP 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 Mozambique South Africa Zimbabwe Swaziland Botswana Tanzania Namibia Average Lesotho Zambia Malawi Angola DRC Indicator 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% Average 31% 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 Mozambique South Africa Zimbabwe Swaziland Botswana Tanzania Namibia Average Lesotho Zambia Malawi Angola DRCE1. 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 0% 1% 0% 2% 0% 33% 0% 0% 36% 0% 0% 0% 6%BELOW 2100 KCAL PER DAYE6. NUMBER OF CHILDREN UNDER THE AGE OF 5 WHOSE WEIGHT-FOR- 0% 2% 0% 10% 100% 5% 0% 0% 36% 0% 0% 0% 13%AGE IS LEASS THAN MINUS TWO STANDARD DEVIATIONS FROM MEDIANOF THE WHO REFERENCE POPULATIONE7. 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 point B. Expenditure and investment indicators i. Ministry of Finance ii. Accountant General’s Office iii. Ministry of Agriculture iv. Donor Offices v. Chamber of Commerce C. Output indicators (Agricultural technology, diffusion, and human i. Ministry of Agriculture capital indicators ii. Environmental protection Agencies iii. National Statistics Office D. Agricultural sector performance indicators (Agricultural production i. Ministry of Agriculture and trade indicators) ii. Ministry of Trade iii. Food Balance Sheets iv. Export promotions v. National accounts E. Macro- and socio-economic indicators (Welfare indicators) i. Ministry of Finance ii. Ministry of Trade iii. National accounts iv. Ministry of Health F. Agricultural development strategies, policies and / or plan i. Ministry of Agriculture ii. Ministry of FinanceStrategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)

Q&AStrategic Analysis and Knowledge Support System for Southern Africa (SAKSS-SA)

Data response analysis june 21 2012 r maseko

by African Regional Strategic Analysis and Knowledge Support System (ReSAKSS) on Jul 03, 2012

Accessibility

Categories

Upload Details

Usage Rights

1 Embed 65

Statistics