Data response analysis june 21 2012 r maseko

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


Content

• Introduction

• Rational

• Data Collection Process

• Observations – Data collection & collected data

• Results of Analysis

• Suggestions


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


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


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


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.


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


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


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


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



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


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

)





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%




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%




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%


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


Q & A

Data response analysis june 21 2012 r maseko

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