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Zimbabwe Harmonized Social Cash Transfer Programme (HSCT)

Analysis of the process and results of targeting labour

constrained food poor households in the first 10 districts

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Prepared by: Bernd Schubert and Richard Chirchir (Consultants) Email: Schubert@teamconsult.org In cooperation with: Officers of the Ministry of Labour and Social Services on national and district level Officers of UNICEF Zimbabwe

          Harare, April 2012  

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Summary

In the first scale up phase of the Harmonized Social Cash Transfer Programme (HSCT) 10 districts have been covered. 19,827 labour constrained food poor households have been approved and are now receiving regular cash payments. Inclusion errors are estimated to be approximately 15 percent. Exclusion errors are estimated to be approximately 20 percent. This is consistent with international standards. However, the estimates regarding inclusion and exclusion errors are not based on representative statistical data. Such data can only be generated by a randomized sample survey. The estimates are based on triangulation of the limited evidence available which is documented in chapter 2 and 3 of the report. Activities to further reduce inclusion and exclusion errors in the 10 districts covered are ongoing.

The comparison of the social and demographic profile of beneficiary households with national data from the DHS 2010/11 (see Table 5) indicates that HSCT beneficiary households are significantly more deprived compared to average households. 62% are female headed compared to the national average of 45%. The number of elderly household members is three times above average. The percentage of children in beneficiary households is 63% compared to the average of 53%. Also beneficiary households care for more orphans than other households. In summary: The comparison indicates that HSCT beneficiary households are in a number of ways more disadvantaged compared to non-beneficiary households. At the same time they contain a high number of members of the so called vulnerable groups like elderly, disabled and children. The percentage of orphans in beneficiary households is twice the national average. 85% of HSCT beneficiary households care for children.

Operational costs are approximately 12.5% (see Chapter 6) signaling high cost-effectiveness1. In summary the first phase of the HSCTP has been implemented successfully. This has been achieved by the commitment and the tireless and highly professional efforts of the officers and organizations that managed and implemented the targeting process.

Factors that caused most of the challenges faced in the targeting and approval process were the decision to conduct the targeting process simultaneously in all 10 districts with an army of about 500 enumerators, supervisors and data input clerks and having to work under extreme time pressure. Main recommendations based on lessons learned:

• Instead of covering 10 districts simultaneously in two months, the targeting and approval process should be organized as a sequential learning process over a period of 6 to 8 months. Instead of 500 enumerators and other personnel the programme should work with three teams of 50 moving from district to district getting more experienced and more competent as they proceed

• Strengthen the MIS unit and the capacity of the DSS district offices. Decentralize the targeting and management process as foreseen in the Manual. DSSOs supervised by PSSOs and provincial NAP II coordinators should play a central role in planning, supervision and management. Data input and processing should be done on district level. Forms should not be sent to HQ but stay at district level. Each district having its own data base while the central data base contains all district data. Only the pay roles and beneficiary cards should be produced on central level

Detailed recommendations for improving the Manual of Operations, the MIS and the verification process are given in chapter 7.

                                                            1 The cost calculation does not include the UNICEF 7% HQ overhead costs 

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1. Background and purpose of this paper

During September to December 2011 the Department of Social Services (DSS) started the HSCT by organizing a targeting process in 10 districts. The districts chosen were identical with the districts recommended in the study “Design of the Zimbabwe Harmonized Social Cash Transfer Programme” done by DSS and UNICEF in 2011 (see Table 1).  

Table 1: Number of wards, number of households, estimated number of food poor labour constrained households and estimated annual programme costs in the poorest districts of the 8 rural and 2 urban provinces to be covered in year 1 Province District No. of

wards Total No. of hhs (2002 census)

No. of labour constrained food poor hhs

Annual costs in USD

Manicaland Makoni/Rusape 12 49,768 4,977 1,493,100

Mash Central

Rushinga 19 13,427 1,343 402,900

Mash East Goromondzi 25 32,724 3,272 981,600

Mash West Kariba 12 7,687 769 230,700

Masvingo Chivi

25 31,088 3,109 932,700

Mat North Umguza 18 16,118 1,611 483,300

Mat South Mangwe 12 14,655 1,466 439,800

Midlands Zishavane 18 13,790 1,379 413,700

Harare Epworth 6 22,814 2,281 684,300

Bulawayo Bulawayo Wards 8,14,18,19,27,29

6 29,402 2,940 882,000

Totals 153 231,657 23,166 6,949,800

Average 15 23,166 2,317 694,980

The process was guided by the “Manual of Operations for the Zimbabwe Harmonized Social Cash Transfer Programme” and the “Operational Guide for the Management Information System for the Zimbabwe Harmonized Social Cash Transfer Programme”. While DSS supervised the targeting process, the implementation was tendered out to JIMAT Development Consultants and ZIMSTAT.

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Table 2 shows the actual targeting results: 19,827 households (8.4% of all households interviewed) meet the eligibility criteria2. Payments to the approved households started February 21st.

Table 2: District level analysis of targeting results

District 

A. Number of households interviewed with Form 0  

B. Labour constrained households according to Form 1 

C . Labour constrained households in % of all households interviewed with Form 0 

D. Households that are labour constrained and food poor according to Form 3 

E.  Households meeting both criteria in % of all households interviewed with Form 0 

   

Bulawayo       20,151        2,080   10.32%         860  4.27%     3.3

Chivi       32,757        6,978   21.30%      4,386  13.39%     16.

Epworth       25,437        1,202   4.73%          397  1.56%     1.5

Goromonzi       33,876        4,478   13.22%      1,857  5.48%     6.8

Kariba Rural         6,625        1,043   15.74%          503  7.59%     5.6

Kariba Urban         4,205            218   5.18%         132  3.14%     14.

Makoni       52,495      10,085   19.21%      5,725  10.91%     16.

Mangwe       11,953        3,142   26.29%     1,997  16.71%     6.6

Plumtree         2,258            274   12.13%            40  1.77%     12.

Rusape         6,160            581   9.43%         268  4.35%     13.

Total     236,458      37,223   15.74%    19,827  8.38%     10.

By end of April 2012 the first payments have been delivered to all approved beneficiary households except for one Ward in Goromonzi and one Ward in Bulawayo where councilors did not agree to the beneficiary lists. Government will resolve this issues making sure that beneficiaries will not be negatively affected.

The purpose of this paper is:

• To identify the magnitude of inclusion and exclusion errors and the factors that caused these errors and other potential performance deficits

• To assess what can be done to reduce these errors and to avoid them in future • To assess the cost-effectiveness of targeting and delivery • To identify changes required in the design of the ZHSCT (Manual of Operations, Forms,

MIS)

                                                            2 In the 2010-11 ZDHS, a household referred to a person or group of related and unrelated persons who lived together in the same dwelling unit(s), who acknowledged one adult male or female as the head of the household, who shared the same housekeeping arrangements, and who were considered a single unit. 

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• To identify how the next scale up phase should be organized based on lessons learned from phase 1. This includes recommendations how DSS and UNICEF should manage the targeting process and how targeting can be improved by using advanced technology

• To assess verification activities done by DSS and UNICEF and to recommend how verification should be done in future

2. Data and information sources used for the analysis

A. Reports from JIMAT consultancy and discussion with JIMAT officers B. Harmonized Social Cash Transfer Verification Report by DSS and UNICEF C. Reports from field trips done by DSS and UNICEF officers while the data collection process

was ongoing in November, December 2011 D. District and ward level analysis giving the number of households interviewed using Form 0,

percentage of households classified as labour constrained and percentage of households classified as labour constrained and ultra poor

E. District and ward level profiles giving percentages of different types of head of beneficiary households (female headed, elderly headed etc) and percentages of different types of members of beneficiary households (gender, children, orphans, etc)

F. Reports from verification visits by DSS and UNICEF officers conducted in selected districts and Wards in 2012 G. A workshop with officers from DSS on national, provincial and district level held in Harare on

2-3 April 2012 and follow up discussions on 4 April H. Discussion with ZIMSTAT management

3. Assessment of the effectiveness of targeting

3.1 Inclusion error

Table 3 summarizes the results of household level verification visits done in March 2012 by UNICEF officers in selected Wards in 7 districts.

Table 3: Results of household level verification visits in selected Wards

District Ward No. of approved HHs visited

No. not labour constrained

No. not food poor

No. neither lc nor food poor

No. not eligible

% not eligible

Kariba 6 4 0 0 0 0 0 10 6 0 0 0 0 0 2 5 0 0 0 0 0 12 3 1 0 0 1 33.3% 5 2 0 0 0 0 0 Epworth3 6 6 1 1 1 1 17% 5 4 0 0 0 0 0 2 5 0 0 0 0 0 4 1 0 0 0 0 0 Rushinga 13 12 0 0 0 0 0                                                             3 8 households could not be located 

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8 12 0 0 0 0 0 Bulawayo4 19 11 1 0 0 0 9% 8 11 1 0 0 0 9% Goromonzi (Domboshava)

3 14 6 7 7 7 50%

Chivi 1 10 1 1 1 1 10% 7 11 2 2 2 2 18% 8 7 1 1 1 1 13% Total 134 15 14 13 15 11%

According to Table 3 the inclusion error is 11%. Without the extremely high inclusion error in Goromonzi, Ward 3 (which still has to be followed up) the inclusion error of the remaining 125 households is only 6%. This is an excellent result. However, the sample of 134 is small and the Wards visited were selected at random, not focusing on Wards that were flagged as potential problem Wards by the beneficiary profile analysis (see Table 4). The interviewers also were not able to identify why ineligible households were approved because they had no access to the Forms 2 on which the approval has been based.

3.2 Exclusion error

The following observations signal that the targeting process has resulted in a significant number of exclusion errors:

• The number of approved households was only 7.9% of all households covered whereas the estimate regarding the number of labour constrained ultra poor households is 10%. The audit helped to correct a number of exclusion errors raising the total number of approved households to 8.4% (see chapter 4.3)

• The number of approved households in all urban areas is only 2%. Exclusion errors discovered by the audit raised it to 3%.

Exclusion errors have been caused by the following factors:

a) A number of villages in some areas and a number of households in some villages have not been covered by Form 0 or have been covered by Form 0 but not by Form 2 (see field visit report in Annex 3). DSSOs should identify these villages and households, document the household structure and poverty status of the missed out households using Forms 0 and 2 (see Annex) and transfer this information to DSS for approval of those households that are eligible

b) Destitute people living on the road and having no house or hut have not been covered. DDS should decide if these” households” should still be targeted by the respective DSSOs

c) About 25,000 households required call backs because the respondents were absent. After three unsuccessful visits these households were left out. A breakdown on how many of these cases refer to Form 0 and how many refer to Form 2 is not available. It is also not

                                                            4 3 households could not be located due to migration 

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known how many of these cases have been solved and how many are still pending. The exclusion error resulting is estimated at approximately 1,000

d) About 1,600 Forms 2 were rejected by the MIS because they either contained no information on the age of the head of household or contained no identity card number. DSSOs try to get these data in order that these forms can be processed

e) The audit revealed that when ZIMSTAT processed Form 2 data from the 3 urban areas 6 of 10 poverty scores were left out of the analysis resulted in not approving 850 eligible urban households (see chapter 4.3 b)

f) When designing a separate Form 2 for urban areas, the last indicator “Do you or someone living in this household own this dwelling? ( 1 ) own ( 0 ) rent” should have been coded ( 0 ) rent (1) own (see improved Form 2U in Annex 11). The targeting error resulting from this coding error was 395 (see chapter 4.3 c)

As a result of all these factors it is estimated that the exclusion error (including the errors under e) and f) discovered by the audit) may be approximately 20%.

3.3 Identification of Wards with questionable targeting results

To identify targeting errors the data from some of the sources listed in Chapter 2 have been triangulated. These data are:

a) Ward level data on number of households interviewed and percentages of households identified as labour constrained plus food poor (see Annex 1). From this table the Wards were flagged that had either less than 3% eligible households or more than 19% eligible households (blue colour). Additionally those Wards were flagged that have a 50% lower or 100% higher percentage of eligible households compared to the average of their respective districts (blue color)

b) Ward level data on the profile of approved households by different types of heads of households (see Annex 1). From this table the Wards were flagged that have percentages that vary more than 50% from the district average (brown color)

c) Ward level data on the profile of the members of approved households by types of members like elderly persons, children, orphans, etc (Annex 2). From this table the Wards have been flagged where percentages vary more than 50% from the district averages (brown color).

By triangulating these data a number of Wards were identified which stood out. For these Wards evidence to explain the reasons for the variance should be collected. In case the variance cannot be explained by geographic, demographic or socio-economic characteristics of the respective Ward, verification on household level should be done to check the incidence of data collection errors. For Wards with high variance it should also checked if data in the MIS data base match the data on the data collection forms (Form2).

The following paragraphs explain how a triangulation of different types of Ward level profiling of targeting results has been used to identify Wards with potential targeting errors. The results of the triangulation are summarized in Table 4.

In the table giving ward level percentages of approved households (see Annex 1) those Wards were flagged which had a 100% higher approval rate or a 50% lower approval rate compared to the district average approval rate. These Wards are:

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• Bulawayo (average 4%): Ward 18 (8%) • Chivi (average 13%): Ward 2 (22%), 8 (21%), 30 (0%), 31 (0%) • Epworth (average 2%): Ward 30 (0%) • Goromonzi (average 5%): Ward 1 (10%), 5 (11%), 6 (1%), 7 (2%), 8 (2%), 10 (15%), 11

(10%), 13 (2%), 14 (1%), 15 (13%), 24 (1%), 25 (0%) • Kariba rural (average 8%): Ward 0 (4%), 1 (2%), 3 (4%), 4 (14%), 5 (14%), 6 (20%), 8 (1%),

9 (2%), 10 (16%), 12 (1%), 13 (0%) • Kariba urban (average 3%): Ward 3 (12%), 4 (1%), 5 (1%), 8 (9%), 9 (0%), 40 (0%) • Makoni (average 11%): Ward 4 (4%), 6 (4%), 11 (5%), 23 (18%), 32 (3%), 33 (4%) • Mangwe (average 17%): Ward 2 (4%), 11 (8%), 12 (7%) • Plumtree (average (2%): Ward 4 (0%), 5 (0%) • Rusape (average 4%): Ward 1 (0%), 2 (0%), 3 (2%), 3 (2%), 7 (0%), 11 (13%), 12 (13%) • Rushinga (average 6%): Ward 1 (0%), 2 (18%), 3 (13%), 5 (0%), 6 (15%), 9 (0%), 19 (1%),

16 (2%), 17 (2%) • Umguza (average 11%): Ward 1 (2%), 6 (3%), 7 (3%), 11 (28%), 18 (20%) • Zvishavane (average 10%): Ward 5 (0%), 14 (5%), 15 (19%), 17 (3%)

The comparison of ward level percentages of different types of heads of beneficiary household like female-headed, elderly-headed, etc with the district average percentages (see Annex 1) identified the Wards that had a more than 50% variance. Wards with extreme variations are:

• Chivi: Wards 9, 14, 18, 27, 30, 31 • Epworth: Wards 3, 5, 7, 31 • Goromonzi: Wards 3, 6, 7, 8, 10, 14, 17, 19, 21, 23, 24, 25 • Kariba rural; Wards: 0, 2, 3, 9, 12, 13 • Kariba urban: Wards 1, 2, 7, 9, 40 • Makoni: Wards 4, 6, 11, 12, 22, 32 • Mangwe: Wards 11, 12 • Plumtree: Wards 4, 5 • Rusape: Wards 1, 2, 3, 7, 8 • Rushinga: Wards 1, 5, 9, 12, 15, 16, 17 • Umguza: Wards 1, 2, 3, 4, 5, 6, 7, 12, 17 • Zvishane: Wards 1, 5, 8, 13, 16, 17

The comparison of ward level percentages of different types of members of beneficiary households like children, orphans, elderly, etc (see Annex 2) identified the Wards that had a more than 50% variance from the district average. These Wards are:

• Bulawayo: Ward 27 • Chivi: Wards 14,17,18,27,30,31 • Epworth: Wards 5, 30 • Goromonzi: Ward 3, 5, 6, 8, 11, 17, 19, 25 • Kariba rural: Wards 0, 1, 2, 3, 8, 9, 10, 11, 13 • Kariba urban: Wards 4, 5, 7, 9, 40 • Makoni: Wards 4, 10, 13, 22, 32, 33 • Mangwe; none • Plumtree: Wards 4, 5 • Rusape: Wards 1, 2, 7, 8, 9, 11 • Rushinga: Wards 1, 5, 9, 15, 17, 19

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• Umguza: Wards 1, 5, 10, 13 • Zvishavane; Wards 5, 17

Table 4 provides a synopsis of the 4 sets of data analyzed above. Column 2 gives the ward number. In column 3 those Wards are marked were the percentage of households approved is more than 100% higher or more than 50% lower compared with the district average. Columns 4 and 5 show which Wards have extreme variations compared to the district averages regarding the percentages of different types of heads of beneficiary households and regarding different types of household members. Column 6 shows which Wards had high inclusion errors identified by household level verification visits (see table 3). Column 7 flags those Wards which have 4 or more “red flags”. These Wards require an auditing of the data collection and management process to establish the reasons for the deviations and to decide if the targeting process has to be repeated. Wards that have ?? in column 7 are Wards for which the data base contains no information.

Table 4: Triangulation of ward level targeting profiles to identify potential targeting errors

1. District 2.Ward

3. % of approved hhs

4. Variations from averages reg. heads of hhs

5. Variations from averages reg. hh members

6. Verification visits – more than 15% not eligible

7. No. of “red flags”

Bulawayo 18 8/4 1 27 Elderly 9/17

Disabled 5/14 2

Chivi 7 22/13 2/11 = 18% 2 8 21/13 1 9 Children 0/3 1 14 Children 0/3 Fit 4/7 2 17 Fit 3/7 1 18 Disabled 6/55 Disabled 1/13 2 27 Disabled 31/55 Elderly 9/18

Disabled 6/13 3

30 0/13 All categories 0% All categories 0% ?? 31 0/13 All categories 0% All categories 0% ?? Epworth 3 Children 0/4 1 5 Children 8/4 Fit 7/14 2 7 Children 2/4 1 30 0/3 All categories 0% All categories 0% ?? Goromonzi 1 10/5 1 3 Children 0/2 Unfit 1/8 7/14 = 50% 3 5 11/5 Unfit 4/8 2 6 1/5 Disabled 11/54

Children 11/2 Disabled 2/14 4

7 2/5 Women 17/59 2 8 2/5 Elderly 28/54 Elderly 7/17

Fit 13/8 4

10 15/5 Children 1/2 2 11 10/5 Fit 7/12 2 13 2/5 2 14 1/5 Women 25/59

Children 0/2 3

15 13/5 3 17 Elderly 27/58

Disabled 20/54 Elderly 7/17 Disabled 5/14

5

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Children 7/2 19 Elderly 100/58

Disabled 100/54 Male 29/47 Orphans 0/25 Disabled 43/14

5

21 Children 7/2 1 23 Children 0/2 1 24 1/5 Women 14/59

Children 14/2 3

25 0/5 Disabled 23/54 Women 31/59 Children 0/2

Orphans 9/25 5

Kariba rural 0 4/8 Elderly 67/41 Women 100/64 Disabled 0/63

Male 100/47 Children 0/61 Disabled 100/15

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1 2/8 Elderly 6/12 2 2 Elderly 21/41 Elderly 7/12 2 3 4/8 Elderly 22/41

Children 4/2 Elderly 6/12 4

4 14/8 1 5 14/8 1 6 20/8 1 8 1/8 Elderly 23/12

Unfit 3/14 1

9 20/8 Children 10/2 Unfit 6/14 3 10 16/8 Elderly 21/12 2 11 Unfit 6/14 1 12 1/8 Disabled 33/63

Women 33/64 All categories have round figures

1/3 = 33% 6

13 0/8 All categories 0% All categories 0% ?? Kariba urba 1 Elderly 16/28 1 2 Elderly 17/28 1 . 3 12/3 1 4 1/3 Elderly 29/9

Orphans 29/15 Unfit 0/24

4

5 1/3 Orphans 6/15 2 7 Elderly 100/28

Women 0/47 Male 83/46 Elderly 83/9 Children 17/54

5

8 9/3 1 9 0/3 All categories 0% All categories 0% ?? 40 0/3 All categories 0% All categories 0% ?? Makoni 4 4/11 Disabled 100/60

Children 0/3 Disabled 44/15 Fit o/8

5

6 4/11 Children 7/3 2 10 Fit 6/15 1 11 5/11 Women 38/63 2 12 Children 6/3 1 13 Elderly 32/21

Disabled 23/13 Fit 4/8

3

22 Children 8/3 Unfit 2/7 2 23 18/11 1 32 3/11 Elderly 40/71

Disabled 40/60 Elderly 11/21 Fit 14/8

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Women 80/63 Children 10/3

Unfit 2/7

33 4/11 1 Mangwe 2 4/17 1 11 8/17 Elderly 36/62

Children 0/3 3

12 7/17 Children 0/3 2 Plumtree 4 0/2 All categories 0% All categories 0% ?? 5 0/2 All categories 0% All categories 0% ?? Rusape 1 0/4 All categories 0% All categories 0% ?? 2 0/4 All categories 0% All categories 0% ?? 3 2/4 Elderly 71/38

Disabled 86/56 Women 86/66 Children 0/5

Elderly 38/12 Children 28/59 Orphans 0/20

8

7 0/4 All categories 0% All categories 0% ?? 8 Elderly 18/38

Disabled 31/56 Children 15/5

Elderly6/12 4

9 Elderly 23/12 Fit 15/8

2

11 13/4 Elderly 44/12 Children 28/59 Disabled 44/14

4

12 13/4 1 Rushinga 1 0/6 Elderly 100/64

Disabled 100/42 Women 60/100

Disabled 33/13 Fit 0/8 Unfit 0/10

7

2 18/6 1 3 13/6 1 5 0/6 Elderly 100/64

Disabled 100/42 Women 60/100

Male 0/48 Orphans 0/24 Disabled 50/13

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6 15/6 1 9 0/6 All categories 0% All categories 0% ?? 10 1/6 Orphans 11/24 2 12 Disabled 25/42 1 13 14/6 1 15 1/6 Women 100/60 Elderly 7/19

Fit 0/8 Unfit 20/10

5

16 2/6 Elderly 89/64 2 17 2/6 Elderly 92/64 Disabled 7/13

Unfit 5/10 5

Umguza 1 2/11 Disabled 85/59 Elderly 36/17 Disabled 30/14

4

2 Children 0/2 1 3 Children 0/2 1 4 Children 0/2 1 5 Children 5/2 Fit 0/8

Unfit 4/11 3

6 3/11 Children 0/2 2 7 3/11 Children 0/2 2 11 28/11 1 12 Children 0/2 1

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13 Male 77/53 Orphans 8/25

2

17 Children 0/2 1 18 20/11 1 Zvishavane 1 Children 0/2 1 5 0/10 All categories 0% All categories 0% ?? 8 Children 0/2 1 13 Children 0/2 1 14 5/10 1 15 19/10 1 16 Children 0/2 1 17 3/10 Children 0/2 Orphans 15/28

Fit 2/8 4

In summary there are 22 Wards which have 4 or more red flags indicating that there may be serious targeting errors. Of these 6 are in Goromonzi, 5 in Kariba, 4 in Rushinga, 3 in Rusape, 2 in Makoni and 1 each in Umguza and Zvishavane.

3.4 Comparison of beneficiary profiles with data from the Zimbabwe Demographic and Health Survey 2010-11

The fact that only 2.9% of urban households meet the eligibility criteria has raised doubts with regard to the suitability of the poverty scoring used for HSCT targeting (see Form 2U in Annex 11). However, Table 5 reveals that according to the DHS 2010-11 there are hardly any urban households in the two lowest wealth quintiles. This signals that the low percentage of urban HSCT beneficiary households may well reflect the actual incidence of labour constrained urban households.

Table 5: Comparison of national demographic parameters (DSH 2010 - 11) with HSCT targeting results

Parameters Source Rural Urban Total 1. Poverty incidence Percent of labour constrained food poor households HSCT 10.2% 2.9% 8.4% Population by wealth quintile

• Lowest quintile • Second quintile • Third quintile • Forth quintile • Highest quintile

DHS 2010-11 28.8% 28.3% 25.2% 12.6% 5.1%

0.0% 1.1% 8.2% 36.9% 53.9%

20% 20% 20% 20% 20%

2. Households headed by women HSCT 62% 59% 62% DHS 2010-11 44.1% 45.3% 44.6%

3. Households caring for children HSCT 85% 4. Households headed by children HSCT 3% 5. Household members Percentage of elderly (60+) HSCT 18% 15% 18%

DHS 2010-11 8% 4% 6.5% Percentage of children 0 - 18 HSCT 63% 59% 63% Percentage of children 0 - 19 DHS 2010-11 56% 45% 53% Orphans in percent of all children HSCT 41% 36% 41%

DHS 2010-11 22% 18% 21%

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The comparison of other parameters given in Table 5 all indicate that HSCT beneficiary households are significantly more deprived compared to average households. 62% are female headed compared to the average 45%. The number of elderly household members is three times above average. The percentage of children in beneficiary households is 63% compared to the average of 53%. Also beneficiary households care for more orphans than other households. In summary: The comparison indicates that HSCT beneficiary households are in a number of ways more disadvantaged compared to non-beneficiary households. At the same time they contain a high number of members of the so called vulnerable groups like elderly and children. The percentage of orphans in beneficiary households is twice the national average. 85% of HSCT beneficiary households care for children, 3% are child headed.

4. Main challenges experienced and how to avoid them in the next phase

4.1 Overall organizational approach The four factors that caused most of the challenges faced in the targeting and approval process were (1) the decision to conduct the targeting process simultaneously in all 10 districts with an army of about 500 enumerators, supervisors and data input clerks. (2) To work under extreme time pressure. Reading through the reports on field visits by DSS and UNICEF it seems that most stakeholders involved in the targeting process were overwhelmed by the tasks they had to face and suffered from the stress they experienced. (3) DSS officers feel that not enough budgetary support was provided to the exercise. They think that most logistical issues could have been resolved by adopting the original budget or streamlining it slightly. (4) Capacity at district level, as pronounced in the Manual of Operations should have been strengthened before rolling out the programme. Main problems resulting from these factors were:

• unrealistic deadlines • shortcuts to safe time (skipping or “simplifying” essential steps laid down in the Manual of

Operations and in the MIS guidelines – see Chapters 4.2 and 4.3) • neglect of PSSOs and DSSOs (information, involvement, transport) • delays in paying allowances to enumerators • weaknesses in the data input, transfer and processing chain partly caused by lack of control

and supervision (see 4.3) • conflicts between DSS/UNICEF and the consulting companies on the amount of additional

funds required because the number of households in 9 of the 10 districts was higher than had been anticipated (compare table 1 and table 2) leading to lengthy negotiations and (according to JIMAT) delays of payments to the consortium5

• management deficits of the consortium with regard to situation analysis, planning, control, communication, coordination and logistics

• poverty scoring (Form 2) as foreseen in the Manual not suitable for urban households. However, DSS in cooperation with UNICEF solved this problem by designing a separate Form 2 for urban areas before starting data collection.

Reports on monitoring visits conducted while targeting was ongoing repeat the same deficits over

                                                            5 DSS officers feel that not enough budgetary support was provided to the exercise 

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and over (delayed Form 1, no quality control of Form 1, delayed provision of blank Forms, delayed payments, lack of information and coordination, no follow-up on child abuses) indicating that follow-up of monitoring results and corrective action were inappropriate. It seems that there was neither time nor capacity for using monitoring results to improve the performance. In addition the fact that four organizations (DSS on national, provincial and district level, JIMAT, ZIMSTAT and UNICEF) were involved in the process created numerous interfaces which made it difficult to ensure a smooth information and cooperation process and resulted in prolonged decision making. Lessons learned for the next scale up phase:

• Instead of covering 10 districts simultaneously in two months, the targeting and approval process should be organized as a sequential learning process over a period of 6 to 8 months. Instead of 500 enumerators and other personnel the programme should work with three teams of 50 moving from district to district getting more experienced and more competent as they proceed (more detailed recommendation are given in Chapter 7.1)

• Decentralize the targeting and management process as foreseen in the Manual. DSSOs supervised by PSSOs and provincial NAP II coordinators should play a central role in planning, supervision and management. Data input and processing should be done on district level. Forms should not be sent to HQ but stay at district level. Each district having its own data base while the central data base contains all district data. Only the pay roles and beneficiary cards should be produced on central level. To prepare for this, capacity at district level needs to be strengthened as foreseen in the Manual of Operations (Chapter 7.3)

• Use Personal Digital Assistance (PDAs) for data collection and transmission • Consider to tender targeting in the next phase to only one organization • Base planning and budgeting on the latest demographic information available.

4.2 The Manual of Operations

The design of the Manual is based on a baseline survey in Ward 5 of Goromonzi District, a situation analysis of ongoing social protection programmes in Zimbabwe and on the results of a test run in Ward 15 of Goromonzi District. Both Wards 5 and Ward 15 are rural Wards. As a result it turned out that the poverty scoring in Form 2 was not tailored to the requirements of identifying the poverty status of urban households.

In order to cope with this situation stakeholders agreed to design and use a separate Form 2 for urban areas. The urban Form 2 keeps 6 of the poverty indicators used for urban areas and complements them by 4 indicators tailored to urban poverty manifestations. The rural Form 2 was also slightly improved. Both Forms 2 and Form 0 were digitized in order to facilitate easy data entry. The changes made with regard to Forms 0 and 2 were necessary and appropriate and should be kept for the next phase, except that one of the urban indicators was wrongly coded (see 3.2 f).

The following paragraphs list those parts of the Manual of Operations which were skipped or ‘simplified’ mainly because of time pressure, because of centralizing functions which may be better done on district level and in some cases because of lack of funds:

a. Form 0 and Form 2 have a field to document the GPS coordinates for each household. However, GPS coordinates have not been recorded. The lack of GPS coordinates made it

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difficult to find households for the second interview (Form2) or for verification visits leading to exclusion of households that were not found

b. Chapter 4.1, paragraphs 3. to 7.of the Manual describe how to prepare the targeting process at district level. It lists the numerous tasks of the DSSO to inform and train different stakeholders. Some of these activities were not done (like training enumerators with regard to child protection protocols). Others were done on central level but not on district level (like setting up DSS District level data banks)

c. Chapter 4.2.2a) foresees that ‘To eliminate data entry errors, data entry has to be done twice with subsequent verification and correction of inconsistencies’. This essential activity has been skipped leading to data input errors

d. Chapter 4.2 2c) and d) state that completed Forms 0 are handed over to the DSSO which facilitates that cases of abuse documented on the back of Form 0 can immediately be followed up. Instead all Forms were transported to ZIMSTAT and were never accessible at district level

e. Chapter 4.2 4a) and b) state that Forms 0 and Forms 2 have to be compared to identify discrepancies. Where Form 0 and Form 2 not match, the household has to be revisited for verification. The comparison was neither done at district level (because the Forms 0 went straight to the national level and were never returned to the districts) nor on central level. Discrepancies were not identified

f. Chapter 4.2 5a) states that DSSO verifies data entry quality by comparing a sample of Forms 2 with the data in the data bank and triggers off a second data verification in case of inconsistencies and discrepancies.. This was neither done on district nor on national level

g. Chapter 8.2 lists the records to be kept by the DSSO which include all Forms that have been used for targeting and case management. Systematic documentation and filing is essential for effective administration and management on district level. The filing system at DSSO level has still to be established.

Assuming that the next phase is done using a decentralized approach, it is recommended that the programme is implemented fully in accordance with the Manual of Operations. To facilitate this the Manual should be printed, distributed to all stakeholders and used for training. Simultaneously district capacities have to be strengthened in accordance with the Manual.

Based on lessons learned, the Manual of Operations will be updated, especially with regard to Form 0 and Form 2 (see Annexes 9, 10 and 11). There is now Form 2R for rural areas and Form 2U for urban areas. The Manual now also contains precise instructions who to organize and document call backs.

However, it has been suggested that some of the poverty indicators in Form 2R for rural areas may be replaced by indicators that are district specific. This may be done in cooperation with the respective DSSOs when planning the scale up in the next districts.

During the discussions with DSS officers it was suggested that instead of the two phased approach first using Form 0 and subsequently Form 2, it should be tested if Forms 0 and 2 could be combined. This would save time and costs and would lead to having data on the household structure and poverty status of all households in the respective districts, which could be used for a number of purposes. The disadvantage is that it would eliminate the possibility of verifying the data on household structure by comparing Forms 0 and 2 (see Manual 4.2, 2a). It is suggested that combining Forms 0 and 2 is piloted in two districts in the next phase.

It has been further suggested that the national population census planned for August 2012 could be used for the targeting of the next 10 districts. Mr. Nelson Taruvinga, Director Income Analysis at

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ZIMSTAT, informed that this may be possible and would have to be discussed in the interministerial Technical Committee.

4.3 The MIS

During the design phase Team Consult developed a Management Information System (MIS) that enables data to be stored and monitored electronically as part of the HSCT. The developed MIS is designed to support Ministry of Labour and Social Services (MoLSS) coordination efforts and complement existing cash transfer schemes that target beneficiaries at household level such as BEAM. Additionally, overlaps with existing welfare cash transfers that provide predictable incomes to household members would be reduced by cross referencing and triangulating the targeted beneficiaries of HSCT and other welfare schemes. During the HSCT programme scale-up, the MIS was used to perform a number of cash transfer functions (i) digitization of the pre-targeting survey – Form 0 (ii) digitization of the targeting survey – Form 2 (iii) computation of labour constrained households (iv) listing of labour constrained households - Form 1 and (v) calculation of poverty scores, determination of households whether eligible or not and production of payroll. The HSCT MIS was originally conceptualized, designed and implemented to operate as a distributed system at district level with the provisions of data transfer to the central database at Harare. However, because of rapid scale-up, data entry was centrally done at ZIMSTAT. Additionally, ZIMSTAT wanted to make use of their data entry system – CSPRO – a tool that is mainly used for statistically analysis - as they normally do in other surveys. It should be noted that CSPRO is not a standard relational database and does not have robust database controls e.g. validation of dates. In order to facilitate the data transfer to the HSCT MIS, a standard protocol of data exchange was developed between ZIMSTAT and MoLSS. A comma delimited values file (CSV) template was developed as set out in Annex 6 of this report. To enable export of data from CSPRO, ZIMSTAT built data export functionality. Additionally, HSCT MIS was customized to allow direct importation of the comma delimited values file. These changes were made in late to early December 2011.

The performance of the HSCT MIS during the scale up to the initial ten districts was overall impressive. The programme was able to undertake all the automated functions as set out on the programme and MIS guidelines. However, in order to prepare for the next phase of the programme, it was deemed necessary to audit the MIS functionality, especially automated functions so as to flesh out any potential hiccups that may add up to targeting inclusion and exclusion errors. The audit was undertaken between March and April 2012 with an in country review mission between 1st and 6th of April 2012. The methodology involved an in-depth data analysis as well as process checks. Discussions were also held with key stakeholders at MoLSS, ZIMSTAT and JIMAT.

The review focused on the following key MIS processes: • Data input at ZIMSTAT of both pre-targeting (Form 0) and targeting (Form 2) • Data processing and export from ZIMSTAT using CSPRO software • Data import on HSCT MIS • Data processing using HSCT MIS

a) Data Input at ZIMSTAT. Because of the short duration of the audit, it was not possible to

pull a large sample of the captured forms – i.e. Form 0 and Form 2. However, a more comprehensive work had already been done by MoLSS MIS team including a visit to some

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districts to compare the data on the forms and data inputted on the MIS. As already covered in JIMAT final report, it was not possible for ZIMSTAT to do double data capture for each Form 0 or Form 2 because of the short digitization period. There was no time to thoroughly check to what extent the quality of the data may have been compromised by the absence of this double data capture. It was however noted that some of the data exported had inaccuracies e.g. invalid dates i.e. dates that do not exist in Gregorian calendar. And, 14 percent of household member names were not captured. This may be a problem to the overall referral function especially if individual household members have to be followed up.

b) Data processing and export from ZIMSTAT. After digitization, data was exported to the comma delimited values file (csv) and sent to MoLSS. As part of the audit, ten poverty indicators were analyzed. The second survey questionnaire was designed such that a food poor household would score 3 or more of 10 points while a relatively well to do household would score a zero. To explore if the scores were uniformly filled up across districts, the scores were summed up for rural and urban districts. The Annex 12 to this report shows the percentages of the indicators and how they were populated for both urban and rural areas. It is evident that six indicators that were common to both rural and urban Form 2s were not analyzed to arrive at the scores for the households in urban areas. This was triangulated with checks on the ZIMTATs CSPRO system and a strong grievance from the urban areas welfare officers on the suitability of the targeting system in identifying food poor urban households. After the audit ZIMSTAT corrected the bug on the query that was used to extract information for the urban areas. The data was subsequently imported into the HSCT MIS. Overall there was an increase of 850 eligible food poor households from urban areas, specifically from Bulawayo, Epworth and Plumtree.

c) Data import on HSCT MIS. Because CSPRO was used to capture the data, the HSCT MIS was customized to allow for importation. The importation function was tested and assessed for accuracy and consistency. Overall, the functions performed as expected. However, there were two gaps that were noted: (i) main material of the house walls indicator was being replaced with the indicator on land (ii) the scores on the urban question i.e. “Do you or any member of your household own or rent this house” had the scores switched inadvertently to give more poverty weight to owners of household as opposed to those who rent. The two errors were fixed and the data for both urban and rural areas were re-imported. An addition of 395 beneficiaries was found to be labour constrained and food poor taking the additional number of excluded beneficiaries to 1245. Because of the recalculation of the poverty scores as a result of the two errors, 157 households were found to be ineligible because of low poverty scores.

d) Data processing and reporting using HSCT MIS. The HSCT MIS was used to determine whether households are eligible because they are labour constrained and at the same time food poor. The MIS also automatically produced the payroll of beneficiaries and other essential processes. This function was performed by the MIS team at MoLSS. The natural results of input and processing in any information system are reports. The MIS was built to produce the essential reports. However, as a result of the collection of a register of 236, 471 households, there is need to built standard analytical reports. There is already great demand for some reports including those on child protection and other exceptions. It should be possible to reduce the pressure on the MoLSS MIS team because other users within the network should be able to access the MIS and mine the data off the MIS without relying on specialists to run ad hoc queries. Of course, the MIS can only produce reports on the basis of information collected by the programme i.e. what is collected on Form 0 and Form 2

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questionnaires including payment records and other processes defined in the operational manual.

5. The verification process

Apart from the verification done as part of this mission, the following verification activities have been done by DSS on national and district level and by UNICEF:

• Monitoring visits by DSS and UNICEF officers to observe the targeting process • Verification visits to samples of approved households by DSSOs • Verification of eligibility of approved households during the first pay point meetings using

Form 11 leading to the elimination of households that do not meet the eligibility criteria • DSSOs listing households that claim to meet the eligibility criteria but have not been

approved. These households will be visited by DSSOs to fill in Form 2 which then will be processed by the MIS

• Verification trips to a sample of households in selected Wards jointly done by DSS and UNICEF officers (see results in Table 3 and Annex 3). Additional verification trips are planned focusing on Wards flagged in Table 4.

The main results of these activities are:

• Information on how targeting was implemented and what challenges were faced during the targeting process

• Information required to decide if the quality of targeting is good enough to go ahead with payments

• Eliminating approved households that do not meet the eligibility criteria in order to reduce the inclusion error

• Listing households that may meet the eligibility criteria but have not been approved in order to reduce the exclusion error.

The activities listed above focused on the data collection process and on verifying the targeting results. Little attention has been paid to supervise and control the data input, transfer and processing process leading to undetected data management problems that resulted in significant exclusion errors (see 3.2 and 4.3). Due to lack of double checking, lack of supervision and lack of control of data management, 50% of eligible urban households would have been excluded if the current audit would not have discovered the data management errors at ZIMSTAT.

If in the next phase the targeting process is fully done in accordance with the Manual of Operations (chapter 4.2 lists the parts that have been omitted), a number of verification activities will already be done as part of the targeting. This will reduce the necessity to do extensive verification activities to verify and correct the targeting results. In addition the planned external evaluation should provide reliable evidence with regard to the effectiveness of targeting.

To meet the remaining verification needs a sequence of activities is recommended in chapter 7.4.

6. Cost analysis

During the design phase it has been estimated that 80% of total programme funds will go to the beneficiary households while 20% are required for operational costs (tables below are copied from the design report).

Table 6: Average annual costs per beneficiary household as estimated in the design report

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In USD In % of total costs

Average annual transfer payments per beneficiary household: 12 times USD 20

240 80%

Annual operational costs per beneficiary household

60 20%

Total annual costs per beneficiary household 300 100%

Table 7: Breakdown of annual operational costs per beneficiary household as estimated in the design report

In USD In % of total costs

Annual costs of targeting by private service provider assuming retargeting every second year6

21 7%

Annual costs for bi-monthly of delivery of transfer payments by private service provider: 6 times USD 17

6 2%

Annual costs of DSS district and ward level programme activities8

15 5%

Annual costs of DSS provincial level programme activities9 3 1%

Annual costs of DSS national level programme activities10 15 5%

Total annual operational costs per beneficiary household 60 20%

The operational costs for the first year (October 2011 to September 2012) are given in Table 7. They consist of the actual costs spent until March 2012 and the estimated costs for the period April to September 2012.

Table 8: Breakdown of HSCT costs during the period October 2011 to September 2012

In USD In % of total costs

Transfer payments to beneficiary household: 6 times average USD 40 for 20,000 households

4,800,000 87.5%

Targeting costs: 50% of payments to the consortium 356,456 6.5%                                                             6 Estimate of the costs of targeting are based on the quotation of RMT for targeting Goromonzi, Ward 15 7 Cost estimate for delivery is based on a quotation from Safeguard 8 Costs on district level involve allowances, costs for operation, maintenance and depreciation of vehicle, computer, printer, copier, communication, stationary and assistance to ward level CPCs in terms of bicycles, stationary etc 9 Provincial level activities training, guidance, supervision and control of implementing officers on district level 10 Costs on national level include costs of additional staff (e.g. MIS expert), allowances, training courses, operation, maintenance and depreciation of vehicles, computers, printers, copiers ,stationary, communication, technical assistance, external evaluations

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JIMAT/ZIMSTAT11 Costs for delivery of transfers 6 times including bank charges

180,000 3.3%

DSS costs at district level including USD 50,000 spent for training and supporting CPCs

Approximately USD 150,000

2,7%

DSS costs at provincial level DSS costs at HQ level Total costs 5,486,456 100%

According to the calculation given in Table 8 the beneficiaries receive 87.5% of the total HSCT funds while 12.5% are spend on operational costs which are significantly lower compared to the estimates in Tables 6 and 7. This means that the programme is highly cost effective12. Costs for targeting have been like estimated. Delivery was nearly twice as costly as projected. Costs at DSS level are much lower than predicted. This means that there is scope for allocating additional funds for strengthening DSS capacities e.g. with regard to the MIS (see chapter 7.3 and the district offices..

7. Summary and recommendations

In the first scale up phase of the HSCT 10 districts have been covered. 19,827 labour constrained food poor households have been approved and are now receiving regular cash payments. Inclusion errors are estimated to be approximately 15 percent. Exclusion errors are estimated to be approximately 20 percent. This is consistent with international standards. Further activities to reduce inclusion and exclusion errors are ongoing. Operational costs are approximately 12.5% signaling high cost-effectiveness. In summary the first phase of the HSCTP has been implemented successfully.

The estimates regarding inclusion and exclusion errors are not based on representative statistical data. Such data can only be generated by a randomized sample survey. The estimates are based on triangulation of the limited evidence available which is documented in chapter 2 and 3 of the report.

In summary the estimate of 15 percent inclusion errors is based on the verification by DSS and UNICEF officers of 134 approved households in different districts and wards (see table 3) and on field reports from DSS and UNICEF officers. The estimate of 20 percent exclusion errors is partly based on the fact that before the data input and processing audit done by Team Consult, the percentage of approved households was only 7.9% while the estimate of the incidence of food poor labour constrained households in Zimbabwe is 10%. However, as a result of the audit additional households will be approved increasing the number to 8.4%. This will reduce the estimated exclusion error to 15%. Other indications of exclusion errors are listed in chapter 3.2. They involve villages and households not interviewed with Form 0, destitute people who have no permanent address, households interviewed with Form 0 but could not be interviewed with Form 2 (unsuccessful callbacks), households which were not processed by the MIS because essential data

                                                            11 As retargeting is done only every second year annual targeting costs are only 50% of the costs encored in the year the targeting is done 12 The cost calculation does not include the 7% UNICEF HQ overhead costs 

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are missing. DDS is in the process of correcting a number of these errors which will further reduce the percentage of exclusion errors.

This has been achieved by the commitment and the tireless and highly professional efforts of the officers and organizations that managed and implemented the targeting process. They pulled through in spite of the pressure and stress caused by extremely tight deadlines, a situation which in the next phase should be avoided.

A summary of the main lessons learned and of improvements recommended for the next scale up phase are given in the following paragraphs.

7.1 Approach

Organize the next phase using a decentralized approach as foreseen in the Manual of Operations. Ensure early that the next 10 districts are equipped with the human resources, equipment, offices and funds required for their tasks.

Instead of targeting all 10 districts simultaneously stagger the process in 4 phases of 2 months. In the first phase only one district should be covered in order that the organization entrusted with implementing the targeting process can gain experience. In each of the next three phases three districts can be covered. Assuming that the next phase will start in July 2012 the following schedule is recommended:

July 2012 Preparation and training August and September 2012 Targeting in 1 district October and November 2012 Targeting in 3 districts December 2012 Review of performance January and February 2013 Targeting in 3 districts March and April 2013 Targeting in 3 districts

Decide early if you want to experiment with only one form/household visit (a combination of Forms 0 and 2) in two districts. In case it is decided to do that experiment it might be appropriate to tender the targeting in these two districts separately.

Also decide early if you want to use the planned Population Census as a targeting tool. If the Population Census is not used, keep in mind that ZIMSTAD enumerators will be occupied implementing the census and may not be available for targeting.

7.2 Manual of Operations

Forms 0 and 2 have been changed and a few minor adjustments have been made in the Manual of Operations to capture lessons learned. The forms are now coded and poverty indicators have been adjusted to better reflect the manifestations of rural and urban poverty (see Annexes 9, 10 and 11). In fact there are now separate Forms 2 for rural and for urban areas. Procedures and forms on how to organize, document and monitor call backs have also been included. The Manual should now be printed, distributed to stakeholders and used for training.

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7.3 MIS

From the review findings it is evident that the MIS by and large performed to the expectations given the rapid scale of the programme to the 10 districts and the short implementation period during which e.g. data capture, data digitization and data processing had to be accomplished. The process errors that were identified during the review mission have been corrected. In fact, a new version of the MIS was deployed by the end of the review. Going forward, it would be reasonable to learn from the first phase and strengthen systems to cope with full blown scale up. Some of the recommendations include:

a) Decentralized and technological-supported data management. MIS should be construed more broadly to encompass the information flow from the community to the national level. The current HSCT MIS could be accessed at the district level using the current expanded MoLSS wide area network. Challenges such as lack of electricity, inadequate computers and training should be addressed as part of programme setup costs and should not be bundled as costs of targeting. Challenges of low connectivity – or low bandwidth – could be supplemented with software that have been developed to allow connections in areas with erratic connections e.g. Citrix. For data entry, technology components such as Personal Digital Assistants (PDAs) could be used. In fact in Zimbabwe, a number of surveys – e.g. ZIMVAC – are already implemented using PDAs. The usual challenge with PDAs is procuring a large set of smart-phone like devices and not finding use after census/surveys. But, there are other alternatives available, especially in Zimbabwe e.g. renting the PDAs at a cost of 3 USD per day. Of course a cost benefit analysis should be done. And proper planning and budgeting should be completed in advance by the HSCT so that the technology components are embedded to the survey processes and maintenance of the PDAs is guaranteed. Annex 8 gives a comparative analysis of using paper, desktop, laptop and Personal Digital Assistants for data capture.

b) Build MIS Capacity. Although a number of MIS functions - especially those bordering on report production - will gradually be transitioned to the users, the two MIS resource persons will continue to perform pertinent roles in the HSCT programme. Already, there is greater demand for their services from not only core HSCT related work but also from other competing initiatives e.g. BEAM, web site administration, hardware maintenance and general support. Yet, with the vision outlined in NAP II, the HSCT MIS will act as a “single register” of poor households. Such a database could be used for a number of other areas e.g. identification of referral cases on persons who are on abuse cases, planning and designing programmes for labour endowed households who are food poor and add on targeted programmes for disabled and other vulnerable groups. To achieve this vision and mission, we recommend that three critical resource persons are required (i) MIS Architect leading the role of planning, coordination and management of all MIS related activities. This position will liaise with both software and hardware vendors and will lead in thinking on possible solutions and ensure that requirements of HSCT are technologically driven. With strong analytical skills, this position may assist with programme monitoring and ensuring overall accuracy of all outputs coming out of the MIS. (ii) Database Administrator will ensure the availability of the MIS. Critical backups should be done as specified on the MIS guides. And the position will dedicate time and resources to full time user support and may do ad hoc queries on behalf of the users. (iii) Network Administrator will focus on design and availability of the network. The wide area network of MoLSS connecting districts and provinces has to be monitored to ensure availability and security. In effect, we recommend

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an addition of an extra MIS resource person. As a matter of fact, this pales significantly when compared with other countries e.g. Uganda and Malawi where each district is setup with an MIS Officer in charge of database and MIS administration.

7.4 Verification

Organize verification as a systematic step by step process:

1. Use the data base to do an analysis of district and ward level data like the one described in chapter 3.3 resulting in a table that triangulates ward level targeting outcomes (see Table 4). In this way Wards are identified that may have severe targeting errors

2. For the Wards identified in step 1 compare a random sample of 50 Forms 2 from the respective Wards with the data in the data base to determine data input or data transfer errors

3. In case the variations identified in step 1 cannot be explained by data input or data transfer errors, audit the data processing process. Remember that the extremely low percentage of urban beneficiaries (see 3.2 e) was caused by a data processing error

4. If the variations discovered in step 1 are not explained by data input or data processing errors, do household level verification of a randomized sample of at least 50 households in the respective Ward. Fill in the verification Form (see Annexes 4 and 5) for the sampled households. The verification form documents a comparison of the data in Form 2 with the information discovered during the verification visit. To be able to do this comparison the interviewer has to get access to Form 2 of the respective household before doing the verification visit.

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Annex 1: Percentages of eligible households, disaggregated by type of head of household by district and ward

Beneficary households in District by type of household head

District  Ward 

Beneficiary households in % of all households 

Elderly headed in % of all beneficiary hhs 

Disabled or Chronically Sick headed in $ of all beneficiary hhs 

Female headed in % of all beneficiary hhs 

Child headed in % of all beneficiary hhs 

   Totals  8%  64% 55% 62% 3% 

   Rural  10%  66% 55% 62% 3% 

   Urban  3%  49% 54% 59% 5% Bulawayo   

Average  4%  56% 55% 61% 6%              8  4%  57% 77% 60% 3%           14   4%  51% 54% 66% 7%           18   8%  54% 56% 60% 8%           19   3%  56% 41% 66% 3%           27   2%  37% 22% 57% 8%           29   3%  74% 60% 61% 3% 

Chivi   Average   13%  66% 55% 66% 3%              1   11%  70% 63% 68% 1%              2   16%  63% 57% 66% 3%              3   16%  68% 65% 63% 1%              4   18%  75% 69% 60% 4%              5   9%  78% 64% 75% 0%              6   11%  76% 71% 59% 2%              7   22%  71% 66% 66% 3%              8   21%  66% 38% 68% 1%              9   8%  82% 78% 62% 0%           10   10%  65% 64% 61% 3%           11   16%  72% 34% 67% 2%           12   6%  75% 58% 61% 3%           13   10%  68% 44% 63% 2%           14   7%  58% 53% 71% 0%           15   18%  68% 52% 60% 5%           16   12%  64% 50% 61% 5%           17   11%  75% 67% 58% 5%           18   14%  62% 6% 66% 4%           19   19%  64% 65% 65% 4%           20   13%  69% 66% 67% 3%           21   14%  72% 71% 62% 2%           22   18%  64% 61% 70% 2%           23   15%  65% 68% 68% 2%          24   17%  61% 51% 72% 5% 

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          25   10%  61% 42% 64% 4%           26   17%  55% 46% 72% 4%           27   11%  43% 31% 74% 3%           28   10%  64% 52% 75% 1%           29   12%  51% 45% 62% 3%           30   0%  0% 0% 0% 0%           31   0%  0% 0% 0% 0% 

Epworth   Average   2%  49% 47% 52% 4%              1   1%  63% 46% 63% 2%             2   2%  43% 57% 47% 6%              3   2%  44% 50% 58% 0%              4   2%  51% 42% 47% 5%              5   2%  59% 49% 51% 8%              6   1%  40% 40% 45% 5%              7   1%  43% 45% 51% 2%           30   0%  0% 0% 0% 0% 

Goromonzi   Average   5%  58% 54% 59% 2%              1   10%  58% 69% 60% 2%              2   5%  55% 59% 66% 3%              3   10%  72% 54% 51% 0%              4   7%  58% 54% 61% 2%              5   11%  61% 63% 71% 2%              6   1%  44% 11% 44% 11%              7   2%  50% 44% 17% 6%              8   2%  28% 40% 64% 0%              9   4%  36% 62% 36% 5%           10   15%  67% 58% 66% 1%           11   10%  61% 52% 74% 2%           12   5%  65% 60% 61% 2%           13   2%  41% 65% 35% 6%           14   1%  38% 63% 25% 0%           15   13%  57% 36% 52% 2%          16   6%  57% 49% 56% 2%           17   4%  27% 20% 67% 7%           18   3%  40% 48% 74% 2%           19   5%  100% 100% 67% 0%           20   7%  50% 58% 40% 5%           21   5%  52% 41% 33% 7%           22   5%  50% 44% 40% 6%           23   5%  54% 46% 46% 0%           24   1%  86% 43% 14% 14%           25   0%  85% 23% 31% 0% 

Kariba Rural 

 Average   8%  41% 63% 64% 1% 

 0   4%  67% 100% 0% 0% 

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             1   2%  29% 57% 29% 0%              2   11%  21% 69% 44% 2%              3   4%  22% 60% 71% 4%              4   14%  40% 61% 67% 1%              5   14%  56% 64% 61% 0%              6   20%  38% 62% 72% 0%              7   9%  36% 54% 79% 0%              8   1%  83% 83% 50% 0%              9   2%  40% 60% 60% 10%           10   16%  63% 77% 66% 0%           11   8%  63% 67% 74% 0%           12   1%  33% 33% 33% 0%           13   0%  0% 0% 0% 0% 

Kariba Urban 

 Average   3%  28% 63% 47% 0%              1   2%  16% 37% 68% 0%              2   4%  17% 76% 55% 0%              3   12%  38% 66% 31% 0%              4   1%  67% 33% 33% 0%              5   1%  20% 80% 70% 0%              6   3%  20% 70% 40% 0%              7   1%  100% 50% 0% 0%              8   9%  40% 47% 33% 0%              9   0%  0% 0% 0% 0%           40   0%  0% 0% 0% 0% 

Makoni   Average   11%  71% 60% 63% 3%              1   14%  74% 72% 59% 2%              2   11%  65% 49% 60% 5%              3   11%  67% 58% 60% 5%              4   4%  75% 100% 75% 0%              5   14%  84% 71% 66% 1%              6   4%  63% 42% 48% 7%              7   12%  70% 60% 66% 2%              8   6%  73% 42% 61% 2%              9   10%  62% 57% 62% 4%           10   7%  86% 68% 55% 4%           11   5%  72% 41% 38% 2%           12   11%  72% 62% 65% 6%           13   6%  75% 70% 50% 0%           14   13%  72% 64% 68% 1%           15   14%  74% 63% 70% 3%           16   14%  78% 62% 72% 1%           17   7%  68% 60% 43% 1%           18   11%  74% 68% 57% 5%           19   11%  78% 67% 70% 2%           20   7%  76% 58% 69% 4% 

  28

          21   17%  73% 66% 59% 3%           22   4%  71% 66% 68% 8%           23   18%  71% 63% 65% 4%           24   13%  70% 57% 71% 5%           25   14%  76% 63% 66% 1%           26   17%  68% 58% 61% 3%           27   12%  72% 62% 70% 2%           28   20%  74% 70% 62% 2%           29   13%  73% 67% 58% 2%           30   15%  76% 57% 61% 3%           31   16%  53% 46% 66% 2%           32   3%  40% 40% 80% 10%           33   4%  72% 40% 64% 4%           34   8%  56% 45% 67% 4%           35   9%  71% 56% 63% 3% 

Mangwe   Average   17%  62% 49% 64% 3%              1   15%  62% 52% 62% 2%              2   4%  74% 49% 67% 2%              3   19%  64% 57% 68% 3%              4   23%  61% 47% 61% 3%              5   17%  64% 50% 63% 3%              6   20%  60% 56% 64% 5%              7   19%  65% 39% 65% 2%              8   17%  60% 30% 67% 2%              9   18%  58% 54% 61% 3%           10   22%  65% 48% 63% 2%           11   8%  36% 51% 51% 0%           12   7%  83% 50% 17% 0% 

Plumtree   Average   2%  40% 45% 60% 8%              2   3%  50% 44% 75% 6%              3   3%  33% 46% 50% 8%              4   0%  0% 0% 0% 0%              5   0%  0% 0% 0% 0% 

Rusape   Average   4%  38% 56% 66% 5%              1   0%  0% 0% 0% 0%              2   0%  0% 0% 0% 0%              3   2%  71% 86% 86% 0%              4   7%  53% 61% 67% 4%              5   3%  36% 45% 45% 0%              6   5%  33% 53% 75% 5%              7   0%  0% 0% 0% 0%              8   4%  18% 31% 54% 15%              9   3%  60% 40% 40% 0%           10   4%  44% 83% 67% 0% 

  29

          11   13%  78% 78% 44% 0%           12   13%  32% 60% 71% 4% 

Rushinga   Average   6%  64% 42% 60% 1%              1   0%  100% 100% 100% 0%              2   18%  69% 54% 61% 0%              3   13%  73% 42% 61% 1%              4   8%  67% 37% 56% 2%              5   0%  100% 100% 100% 0%              6   15%  70% 41% 62% 1%              7   9%  56% 36% 60% 1%              8   6%  53% 47% 56% 4%              9   0%  0% 0% 0% 0%           10   1%  67% 50% 33% 0%           11   10%  67% 49% 67% 0%           12   4%  49% 25% 70% 2%           13   14%  51% 40% 53% 2%           14   3%  52% 41% 59% 4%           15   1%  33% 67% 100% 0%           16   2%  89% 63% 63% 5%           17   2%  92% 31% 38% 0%           18   5%  70% 49% 51% 0%           19   7%  60% 50% 75% 0% 

Umguza   Average   11%  61% 59% 50% 2%              1   2%  77% 85% 31% 0%              2   6%  52% 69% 52% 0%              3   11%  57% 57% 53% 0%              4   8%  68% 63% 55% 0%              5   8%  84% 53% 53% 5%              6   3%  65% 50% 70% 0%              7   3%  64% 93% 36% 0%              8   9%  57% 32% 48% 3%              9   13%  53% 62% 42% 2%           10   19%  65% 65% 55% 1%           11   28%  59% 71% 51% 1%           12   12%  58% 66% 63% 0%           13   15%  56% 46% 54% 3%           14   13%  61% 53% 42% 3%           15   12%  67% 51% 58% 2%           16   13%  69% 67% 42% 2%           17   9%  69% 69% 23% 0%           18   20%  68% 46% 49% 4%           19   16%  57% 45% 54% 2% 

Zvishavane   Average   10%  72% 47% 64% 2%              1   13%  88% 50% 63% 0% 

  30

             2   13%  73% 46% 59% 1%              3   9%  71% 36% 69% 2%              4   14%  71% 42% 59% 4%              5   0%  0% 0% 0% 0%              6   11%  73% 43% 63% 4%              7   10%  72% 38% 67% 2%              8   8%  72% 37% 63% 0%              9   8%  76% 64% 62% 4%           10   13%  72% 35% 58% 1%           11   15%  73% 46% 61% 4%           12   10%  70% 48% 65% 2%           13   13%  76% 43% 63% 0%           14   5%  58% 50% 65% 4%           15   19%  70% 59% 69% 2%           16   13%  68% 46% 72% 0%           17   3%  80% 63% 74% 0%           18   8%  71% 54% 60% 3% 

Annex 2: Percentage of different categories of members of beneficiary households by district and ward

Profile of household members 

District  Ward  Male  Female  Elderly  Children  Orphans

Disabled or chronically ill adults 

Fit (19 to 64) 

Unfit (19 to 64) 

   Total  48% 52%  18% 63% 26% 14% 8% 9% 

   Rural  48% 52%  18% 63% 26% 13% 8% 8% 

   Urban  46% 54%  15% 59% 21% 15% 9% 14% Bulawayo   

Average  45% 55%  17% 61% 23% 15% 9% 11%              8   47% 53%  18% 53% 24% 23% 9% 17%           14   42% 58%  15% 63% 23% 15% 8% 12%           18   45% 55%  17% 60% 23% 17% 8% 12%           19   41% 59%  16% 65% 22% 11% 10% 5%           27   51% 49%  9% 70% 19% 5% 13% 6%           29   46% 54%  21% 62% 26% 14% 8% 6% 

Chivi   Average   46% 54%  18% 64% 27% 13% 8% 7%              1   47% 53%  18% 63% 27% 14% 7% 8%              2   48% 52%  15% 65% 26% 13% 8% 9%              3   48% 52%  19% 62% 30% 15% 10% 8%              4   45% 55%  22% 60% 26% 18% 8% 8% 

  31

             5   47% 53%  22% 64% 29% 18% 7% 6%              6   52% 48%  21% 60% 23% 18% 6% 9%              7   49% 51%  20% 61% 19% 17% 8% 10%              8   47% 53%  16% 61% 29% 9% 8% 11%              9   51% 49%  22% 59% 28% 17% 7% 9%           10   49% 51%  18% 67% 24% 16% 7% 7%           11   45% 55%  20% 61% 31% 8% 8% 9%           12   47% 53%  21% 62% 36% 13% 6% 7%           13   46% 54%  21% 62% 28% 10% 6% 6%           14   43% 57%  18% 66% 21% 13% 11% 4%           15   47% 53%  18% 64% 26% 12% 7% 8%           16   47% 53%  18% 66% 29% 12% 10% 5%           17   47% 53%  23% 65% 31% 17% 7% 3%           18   46% 54%  16% 65% 27% 1% 10% 8%           19   46% 54%  17% 66% 28% 16% 9% 7%           20   46% 54%  19% 63% 30% 17% 8% 7%           21   45% 55%  19% 63% 26% 17% 9% 6%           22   48% 52%  18% 63% 25% 14% 8% 7%           23   45% 55%  18% 66% 26% 17% 8% 6%           24   45% 55%  15% 66% 23% 12% 8% 8%           25   45% 55%  16% 66% 26% 10% 9% 8%           26   45% 55%  14% 68% 27% 10% 10% 7%           27   49% 51%  9% 73% 28% 6% 11% 6%           28   42% 58%  18% 65% 24% 12% 9% 6%           29   48% 52%  13% 69% 30% 10% 10% 6%           30   0% 0%  0% 0% 0% 0% 0% 0%           31   0% 0%  0% 0% 0% 0% 0% 0% 

Epworth   Average   49% 51%  16% 58% 20% 13% 9% 14%              1   47% 53%  18% 58% 19% 12% 11% 12%              2   48% 52%  15% 57% 22% 19% 10% 16%              3   56% 44%  15% 57% 13% 13% 7% 19%              4   49% 51%  16% 58% 20% 11% 8% 15%              5   49% 51%  22% 59% 23% 16% 7% 7%              6   50% 50%  11% 58% 19% 11% 13% 15%              7   45% 55%  16% 55% 24% 14% 8% 15%           30   0% 0%  0% 0% 0% 0% 0% 0% 

Goromonzi   Average   47% 53%  17% 59% 25% 14% 8% 12%              1   46% 54%  18% 56% 22% 21% 6% 16%              2   46% 54%  15% 63% 27% 14% 8% 12%              3   45% 55%  23% 45% 21% 13% 1% 8%              4   45% 55%  16% 59% 23% 13% 8% 10%              5   46% 54%  15% 57% 30% 13% 4% 12%              6   60% 40%  10% 60% 13% 2% 10% 19%              7   59% 41%  16% 57% 21% 14% 5% 16%              8   47% 53%  7% 68% 23% 9% 13% 12% 

  32

             9   51% 49%  12% 54% 18% 17% 6% 21%           10   48% 52%  19% 61% 26% 16% 8% 10%           11   44% 56%  18% 64% 28% 13% 9% 7%           12   47% 53%  18% 62% 25% 15% 8% 10%           13   53% 48%  11% 61% 21% 21% 9% 19%           14   47% 53%  10% 61% 14% 12% 10% 20%           15   46% 54%  18% 57% 33% 10% 11% 15%           16   48% 52%  16% 60% 27% 12% 8% 13%           17   54% 46%  7% 62% 33% 5% 9% 18%           18   50% 50%  11% 68% 29% 12% 10% 11%           19   29% 71%  71% 14% 0% 43% 0% 14%           20   50% 50%  16% 62% 20% 16% 6% 15%           21   53% 47%  13% 63% 25% 10% 10% 12%           22   49% 51%  15% 54% 23% 12% 9% 18%           23   53% 47%  20% 51% 16% 16% 9% 13%           24   50% 50%  33% 50% 17% 17% 8% 8%           25   47% 53%  38% 40% 9% 7% 9% 11% 

Kariba Rural 

 Average   47% 53%  12% 61% 20% 15% 10% 14%              0    100% 0%  67% 0% 0% 100% 0% 33%              1   53% 47%  6% 66% 13% 13% 9% 13%              2   47% 53%  7% 59% 23% 19% 11% 21%              3   45% 55%  6% 62% 18% 14% 9% 19%              4   47% 53%  10% 62% 23% 14% 11% 14%              5   51% 49%  17% 56% 21% 17% 9% 14%              6   47% 53%  12% 63% 13% 16% 10% 13%              7   46% 54%  9% 65% 18% 12% 13% 12%              8   50% 50%  23% 53% 13% 20% 10% 3%              9   39% 61%  11% 67% 30% 11% 13% 6%           10   45% 55%  21% 54% 19% 21% 10% 14%           11   43% 57%  16% 64% 13% 14% 8% 6%           12   40% 60%  10% 50% 20% 10% 10% 20%           13   0% 0%  0% 0% 0% 0% 0% 0% 

Kariba Urban 

 Average   46% 54%  9% 54% 15% 20% 10% 24%              1   44% 56%  4% 56% 19% 12% 6% 29%              2   46% 54%  6% 57% 20% 23% 12% 24%              3   46% 54%  12% 49% 14% 21% 11% 24%              4   29% 71%  29% 57% 29% 14% 14% 0%              5   47% 53%  6% 49% 6% 20% 10% 27%              6   50% 50%  8% 53% 8% 20% 10% 28%              7   83% 17%  83% 17% 0% 50% 0% 0%              8   46% 54%  16% 57% 11% 14% 11% 16%              9   0% 0%  0% 0% 0% 0% 0% 0%           40   0% 0%  0% 0% 0% 0% 0% 0% 

Makoni   Average   48% 52%  21% 63% 29% 15% 8% 7% 

  33

             1   47% 53%  23% 59% 31% 21% 8% 8%              2   51% 49%  17% 66% 27% 11% 9% 6%              3   49% 51%  21% 64% 27% 16% 7% 7%              4   44% 56%  33% 56% 22% 44% 0% 11%              5   47% 53%  27% 59% 34% 20% 6% 6%              6   50% 50%  19% 63% 29% 11% 9% 7%              7   51% 49%  19% 64% 29% 14% 9% 7%              8   49% 51%  20% 65% 32% 9% 7% 6%              9   50% 50%  18% 62% 23% 15% 9% 9%           10   45% 55%  23% 57% 24% 17% 4% 4%           11   54% 46%  23% 57% 26% 12% 9% 9%           12   50% 50%  23% 63% 31% 16% 6% 6%           13   58% 42%  32% 55% 27% 23% 4% 8%           14   49% 51%  19% 63% 25% 15% 7% 8%           15   45% 55%  22% 61% 29% 16% 6% 7%           16   49% 51%  24% 61% 26% 17% 7% 6%           17   49% 51%  20% 61% 24% 16% 10% 7%           18   49% 51%  24% 63% 31% 19% 7% 5%           19   42% 58%  23% 64% 35% 18% 7% 4%           20   45% 55%  23% 62% 26% 18% 8% 6%           21   48% 52%  24% 59% 24% 20% 8% 9%           22   46% 54%  24% 63% 28% 20% 7% 2%           23   47% 53%  20% 65% 32% 16% 7% 6%           24   46% 54%  20% 65% 28% 15% 7% 6%           25   43% 57%  21% 63% 25% 15% 8% 5%           26   49% 51%  19% 64% 31% 14% 8% 8%           27   48% 52%  21% 61% 30% 17% 8% 8%           28   48% 52%  22% 62% 26% 19% 7% 8%           29   47% 53%  24% 62% 31% 18% 6% 6%           30   46% 54%  21% 64% 34% 13% 7% 5%           31   48% 52%  12% 70% 26% 9% 10% 7%           32   39% 61%  11% 73% 27% 9% 14% 2%           33   46% 54%  24% 63% 43% 13% 7% 2%           34   43% 57%  14% 70% 30% 10% 10% 5%           35   48% 52%  22% 63% 32% 15% 8% 6% 

Mangwe   Average   49% 51%  15% 66% 24% 10% 9% 9%              1   49% 51%  14% 68% 24% 10% 8% 9%              2   44% 56%  16% 65% 28% 9% 7% 9%              3   48% 52%  15% 66% 28% 12% 8% 9%              4   47% 53%  17% 64% 21% 10% 9% 9%              5   49% 51%  16% 65% 25% 11% 10% 8%              6   51% 49%  15% 66% 21% 12% 9% 9%              7   48% 52%  15% 66% 24% 8% 10% 8%              8   48% 52%  15% 66% 28% 6% 9% 8%              9   52% 48%  15% 66% 17% 11% 9% 8%           10   48% 52%  17% 64% 24% 11% 7% 10% 

  34

          11   48% 52%  9% 65% 18% 11% 13% 11%           12   48% 52%  16% 62% 24% 12% 10% 10% 

Plumtree   Average   42% 58%  12% 63% 22% 14% 11% 13%              2   39% 61%  15% 54% 15% 16% 10% 21%              3   44% 56%  11% 69% 27% 12% 11% 8%              4   0% 0%  0% 0% 0% 0% 0% 0%              5   0% 0%  0% 0% 0% 0% 0% 0% 

Rusape   Average   44% 56%  12% 59% 20% 16% 8% 18%              1   0% 0%  0% 0% 0% 0% 0% 0%              2   0% 0%  0% 0% 0% 0% 0% 0%              3   31% 69%  38% 31% 0% 46% 0% 31%              4   51% 49%  15% 56% 22% 17% 6% 21%              5   41% 59%  8% 59% 20% 8% 11% 15%              6   37% 63%  11% 60% 23% 14% 6% 19%              7   0% 0%  0% 0% 0% 0% 0% 0%              8   47% 53%  6% 69% 13% 9% 11% 13%              9   54% 46%  23% 46% 15% 15% 15% 15%           10   48% 52%  17% 57% 24% 24% 5% 16%           11   50% 50%  44% 28% 11% 44% 6% 22%           12   42% 58%  12% 61% 23% 19% 9% 18% 

Rushinga   Average   48% 52%  19% 60% 24% 13% 8% 10%              1   67% 33%  33% 67% 33% 33% 0% 0%              2   45% 55%  20% 57% 26% 15% 9% 10%              3   45% 55%  23% 60% 29% 11% 10% 7%              4   46% 54%  20% 58% 23% 13% 7% 13%              5   0% 100%  50% 50% 0% 50% 0% 0%              6   51% 49%  19% 63% 26% 12% 9% 8%              7   43% 57%  17% 60% 20% 11% 10% 9%              8   47% 53%  17% 65% 24% 13% 10% 7%              9   0% 0%  0% 0% 0% 0% 0% 0%           10   44% 56%  11% 61% 11% 17% 11% 17%           11   46% 54%  19% 61% 23% 14% 8% 9%           12   43% 57%  14% 66% 29% 8% 11% 8%           13   51% 49%  17% 58% 21% 13% 7% 13%           14   51% 49%  19% 59% 27% 10% 9% 12%           15   60% 40%  7% 73% 53% 13% 0% 20%           16   52% 48%  28% 53% 34% 17% 7% 7%           17   51% 49%  24% 59% 31% 7% 8% 5%           18   59% 41%  26% 56% 17% 16% 5% 9%           19   50% 50%  16% 63% 10% 11% 6% 10% 

Umguza   Average   53% 47%  17% 62% 25% 14% 8% 11%              1   55% 45%  36% 42% 17% 30% 6% 13%              2   54% 46%  15% 62% 28% 18% 7% 13% 

  35

             3   47% 53%  15% 63% 26% 13% 9% 12%              4   60% 40%  18% 63% 16% 14% 6% 11%              5   49% 51%  21% 57% 22% 11% 0% 4%              6   55% 45%  15% 66% 21% 9% 5% 13%              7   49% 51%  20% 58% 28% 23% 8% 11%              8   56% 44%  17% 58% 27% 9% 9% 10%              9   50% 50%  15% 65% 26% 13% 9% 10%           10   53% 47%  17% 67% 27% 14% 8% 7%           11   52% 48%  17% 63% 30% 17% 7% 12%           12   51% 49%  14% 64% 24% 15% 8% 10%           13   77% 23%  16% 66% 8% 12% 10% 8%           14   49% 51%  18% 60% 26% 12% 9% 10%           15   51% 49%  15% 62% 31% 10% 8% 12%           16   55% 45%  24% 54% 20% 21% 6% 12%           17   56% 44%  20% 58% 20% 15% 8% 12%           18   48% 52%  17% 61% 28% 10% 9% 10%           19   49% 51%  16% 60% 19% 11% 10% 12% 

Zvishavane   Average   47% 53%  19% 64% 28% 11% 8% 7%              1   46% 54%  24% 63% 32% 12% 6% 5%              2   49% 51%  20% 62% 36% 11% 7% 9%              3   50% 50%  20% 62% 22% 9% 10% 5%              4   47% 53%  20% 61% 24% 10% 9% 9%              5   0% 0%  0% 0% 0% 0% 0% 0%              6   44% 56%  22% 64% 26% 11% 8% 5%              7   44% 56%  22% 64% 31% 10% 10% 5%              8   50% 50%  22% 63% 27% 9% 8% 7%              9   48% 52%  22% 63% 27% 15% 7% 6%           10   48% 52%  21% 60% 28% 8% 10% 7%           11   44% 56%  21% 65% 27% 12% 7% 5%           12   53% 47%  18% 65% 26% 11% 9% 8%          13   47% 53%  21% 61% 26% 11% 8% 7%           14   46% 54%  14% 69% 20% 11% 10% 6%           15   44% 56%  17% 66% 29% 12% 9% 6%           16   47% 53%  17% 69% 30% 10% 7% 6%           17   61% 39%  18% 71% 15% 11% 2% 8%           18   45% 55%  18% 62% 29% 12% 7% 10% 

  36

Annex 3: Example of a field monitoring report

1. Name: Marshall Karidozo  2. Division/Section: Child Protection3. Field visit objectives: Harmonized Social Cash Transfers: 1. Triangulate ward level MIS ward profiles with field observations 2. Monitor the cash transfer process – planning and implementation  

 4. Places visited: Chivi wards 1, 2, 21, 19, 16, 12, 8, 20, 22, 32, 4  

5. Dates: 9 – 14 April  2012                              6.  TA N°: 006830497 

7. Category of Persons/Stakeholders met: Provincial Social Services Officer  ‐ Chivi Provincial NAP Coordinator‐ Chivi Chivi District Social Services Officer Chivi Social Services Officer Deputy Director – Family and Child Welfare, DSS NAP II National Coordinator  

8. Achievements/progress – observed results of accomplished activities/actions:Quantitative description and data source: • Monitored transfers in 11 wards • Developed ward level profiles for 11 Chivi wards. 

 Qualitative description: •  9. Bottlenecks assessment through discussions with stakeholders (what are the main constraints/challenges)?  8a‐Enabling Policy environment barriers: • NONE 8b‐ Supply bottlenecks: • NONE  8c‐Financial bottlenecks: • NONE 

 8d‐Social bottlenecks:  

 • Community perception of criteria for participation is in some instances, different from programme design. The 

organization is designed to assist food poor and labour constrained households. However, some community members are of the opinion that vulnerable households include widow headed households. ACTION: DSS to develop strategies for informing community members about who qualifies to participate in the programme. 

• Some elderly beneficiaries are failing to distinguish the different US dollar notes. ACTION: DSS and UNICEF to design and implement a strategy for educating beneficiaries on the different US dollar notes and their values. 

  

8e‐Other Constraints: • Planning – Implementation of a cash transfer programme requires pre planning processes like production of 

monitoring forms and lists prior to the distribution date. This ensures that program staff are able to travel early to distribution sites. This was not the case with Chivi officers. ACTION: DSS to ensure that all monitoring forms are decentralized to district offices.  

• Timing – Chivi currently has the second highest number of beneficiary households. There was thus, the need to allocate more distribution time ( days)to the distribution process. This ensures that cash is not distributed late into the night. ACTION 1: DSS to allocate more distribution time to districts that have high beneficiary numbers. ACTION 2: There is need by DSS and UNICEF to involve the cash distribution agent ( SECURICO) in planning so as to tap in from the experience in previous distributions. 

• Pre distribution speech – The pre distribution speech currently focuses on cash transfers as a single intervention. 

  37

ACTION: There is need to bring out the holistic nature of the social protection programme and the centrality of cash distributions and its link to other social protection interventions such as BEAM, AMTOs by DSS.     ACTION 2: DSS to ensure that all district staff, child protection committees and all relevant stakeholders are conversant with the holistic nature of NAP II and the centrality of cash transfers to all the other social transfer assistance initiatives. 

• Child protection Referrals – in as much as there were very few recorded child protection issues in Chivi, the haphazard nature in which the issues were addressed requires further review. ACTION: DSS and UNICEF to URGENTLY develop and disseminate a standardized strategy for handling child protection issues that are coming through as a result of the cash transfers intervention.  

• Exclusion errors – 11 villages in at least 1 (in this case ward 12) were not verified. This was confirmed both by the ward representative (councillor) as well as information in the MIS. The situation is almost similar in all the wards. In some cases, certain households within villages were not enumerated for form 2. ACTION: DSS, UNICEF and all relevant stakeholders to urgently agree on a way forward for the identified sources of exclusion errors. 

10. Recommendations/follow up actions required: • Based on field observations made, it is recommended that DSS and UNICEF quickly adopt recommendations from 

the recent cash transfers review process meeting so as to improve efficiency of the distribution process.   

11. Signature of Traveller:   

12. Date: 15 April 2012

13. Supervisor comments:   Report put on M Drive for access/sharing: Yes/No13 14. Supervisor Signature:  15. Date: 

                                                            13 NB: All field visit report soft copy must be put into each section M drive and bottlenecks discussed during Section meeting for problem solving; soft copy of all field visit report must be put on sharing M drive‐folder ”Adhoc activities” for consolidation by PMU. 

  38

Annex 4: Form for verification of Form 2R District: _____________ Ward: ____________________ Village: ________________ Name of head of household: ______________________ ID Nr. __________________ Names of interviewer that filled in Form 2: ________________________________ Name of member of verification team:_______________________Date: __________ GEOCODE: HH No. A. Household structure on Form 2 in reality 1. Total number of household members ... 2. Total number of adults (19 to 64 years) ... 3. Number of adults fit for work and not schooling 4. Number of adults not fit for work or schooling 5. Number of severely disabled/chron. sick hh memb. .................... ................ Give detailed reasons why adults are not fit for work: .. Is the household labour constrained (no fit adult or more than 3 dependents per fit adult or more than 2 dependents in case of a severely disabled/chron. sick household member)? Write YES or NO: B. Poverty scores given (either 1 or 0) on Form 2 in reality 1. For meals per day 2. For grain from last harvest lasted . ... . 3. For livestock 4. For blankets ................... ................ 5. For number of sleeping rooms ................... ............... 6. For condition of the house . 7. For main sources of livelihood 8. For remittances .................... ................ 9. For valuable assets .................... ............... 10. For owning land ................... ................ Poverty score: ................... ................ C. Conclusion: Does household meets the eligibility criteria? Write YES or NO: ............... Signature of member of the verification team:

  39

Annex 5: Form for verification of Form 2U

District: _____________ Ward: ____________________ Village: ________________Name of head of household: ______________________ ID Nr. __________________ Names of interviewer that filled in Form 2: ________________________________ Name of member of verification team:_______________________Date: __________ GEOCODE: HH No. A. Household structure on Form 2 in reality 1. Total number of household members ... 2. Total number of adults (19 to 64 years) ... 3. Number of adults fit for work and not schooling 4. Number of adults not fit for work or schooling 5. Number of severely disabled/chron. sick hh memb. .................... ................ Give detailed reasons why adults are not fit for work: .................. Is the household labour constrained (no fit adult or more than 3 dependents per fit adult or more than 2 dependents in case of a severely disabled/chron. sick household member)? Write YES or NO: B. Poverty scores given (either 1 or 0) on Form 2 in reality 1. For meals per day 2. For main sources of livelihood . ... . 3. For average income/remittances ................ . 4. For % of income for rent ............... .............. 5. For water/electricity disconnected ................ ............... 6. For number of sleeping rooms . 7. For type of fuel mainly used ..... . 8. For material of walls ................. .............. 9. For rented or owned dwelling ............ . ............... 10. Possession of assets ................... ............... Poverty score: ................... ................ C. Conclusion: Does household meets the eligibility criteria? Write YES or NO: ............... Signature of member of the verification team:

 

  40

Annex 6: Form 0 data importation form Field  Data Type  Format  Sample Data 

PROVINCE  text  Free Text  Bulawayo 

DISTRICT  text  Free Text  Bulawayo Urban 

WARD  text  Free Text    

VILLAGE  text  Free Text    

GEO_CODE  text  ID Format    

HH_NO  text  Free Text    

HHH_IDNO  text  ID Format    

NAME_INTERVIEWER  number  Valid dd/mm/yyyy    

INTERVIEW_DATE  date  Valid dd/mm/yyyy    

DISTRICT_CODE  number  3 codes    

HOUSE_ID  number       

LINE_NO  number       

HH_MEMBER_FIRSTNAME  text  No numbers  GWERUME 

HH_MEMBER_MIDDLENAME  text  No numbers  1 

HH_MEMBER_SURNAME  text  No numbers  65 

RELATIONSHIP  number  1 to 9    

AGE  number  1 to 120    

DOB  date  Valid dd/mm/yyyy    

SEX  text  M/F    

DISABLED  text  Y/N    

SCHOOL_ATTENDANCE  text  Y/N    

SCHOOL_NAME  text       

GRADE  text       

DISABILITY_DESCRIPTION  text  Y/N  N 

CANNOT_WORK  text  Y/N  N 

CANNOT_LEAVEHOUSE  text  Y/N  N 

REQUIRES_CARE  text  Y/N  N 

BEDRIDDEN  text  Y/N    

PHYSICAL_ABUSE  text  Y/N    

SEXUAL_ABUSE  text  Y/N    

EMOTIONAL_ABUSE  text  Y/N    

NEGLECT_ABUSE  text  Y/N    

CHILD_MARRIAGE  text  Y/N    

CHILD_ABUSE  text  Y/N    

ABUSE_REASONS  text  Free Text    

 

  41

Annex 7: Form 2 data importation form Field  Data Type  format  Sample Data 

PROVINCE  text  Free Text  Bulawayo 

DISTRICT  text  Free Text  Bulawayo Urban 

WARD  text  Free Text  8 

VILLAGE  text  Free Text  J70 

HHH_IDNO  text  ID Format  50‐023781Z50 

NAME_INTERVIEWER  text  Free Text  THABISO MAGONYA 

INTERVIEW_DATE  date  Valid dd/mm/yyyy 27/09/2011

DISTRICT_CODE  number  3 codes  021 

HOUSE_ID  number     211000000000 

LINE_NO  number    1

HH_MEMBER_FIRSTNAME  text  No numbers  TENDAI 

HH_MEMBER_MIDDLENAME  text  No numbers  A 

HH_MEMBER_SURNAME  text  No numbers  GWERUME 

RELATIONSHIP  number     1 

AGE  number  1 to 120  65 

DOB  date  Valid dd/mm/yyyy  dd/mm/yyyy 

SEX  text  F/M  F 

DISABLED  text  Y/N  Y 

ORPHANHOOD  text  Y/N  P 

SCHOOL_ATTENDANCE  text  Y/N  N 

SCHOOL_NAME  text       

GRADE  text  Grade & Number    

DISABILITY_DESCRIPTION  text     HIGH BLOOD PRESSURE 

CANNOT_WORK  text  Y/N  N 

CANNOT_LEAVEHOUSE  text  Y/N  N 

REQUIRES_CARE  text  Y/N  N 

BEDRIDDEN  text  Y/N  N 

RECEIVE_SUPPORT  text  Y/N  N 

PENSION  text  Y/N  N 

WELFARE  text  Y/N  N 

AMTO  text  Y/N  N 

NGO  text  Y/N  N 

NGO_NAME  text       

REGULAR_REMITTANCES  text  Y/N  N 

OTHER  text       

RECEIVE_BEAM  text  Y/N  N 

MEALS  number  1/0  1 

ASSETS  number  1/0  1 

BLANKETS  number  1/0  1 

HOUSE  number  1/0  2 

LIVELIHOOD  number  1/0  3 

MATERIALS  number  1/0  4 

HARVEST  number  1/0  5 

  42

LIVESTOCK  number  1/0  6 

SUPPORT  number  1/0  7 

LAND  number  1/0  8 

INCOME  number  1/0  9 

RENT  number  1/0  10 

FUEL  number  1/0  11 

OWNERSHIP  number  1/0  12 

  43

Annex 8: An analysis of hardware options Model Technology Implementation Feasibility

PDA Based Distributed Model

The beneficiary list is captured on PDA (Personal Digital Assistant) on a real-time or near real-time basis. The PDA is a portable computer that can be fitted with a rugged casing to protect it from harsh environmental conditions. Some PDA models also have smart card capability while others ship with biometric fingerprint scanners and cameras. PDAs are installed with an operating system e.g. Windows Mobile and can host applications such as standard MIS solutions. The PDA’s battery can last 48 hours on continuous use.

The model requires a central database server and distributed PDA’s at the community level.

The PDA’s are then configured to send data from community to national level servers using the GSM/GPRS/EDGE/3G mobile phone connectivity.

The PDA is portable and allows registration to be real-time. It merges several technologies thus reducing the costs of procuring separate gadgets such as laptops and cameras. They come with a GSM functionality thus allowing for data export from remote areas. The disadvantage is that there are fewer developers who provide solutions targeted at this platform. There is also the risk of data loss. GPRS/EDGE data transfer can reduce this risk.

Laptop Based Distributed Model

The beneficiaries are registered on a laptop on real-time or near real-time basis. Laptops are portable and relatively light weight computers. They can be installed with standard operating systems and applications software. Laptops have a rechargeable battery that can last for 3 hours on continuous use.

The laptops installed with the MIS system are distributed to the community level. Information is sent to the national servers via synchronisation or connectivity options.

The laptop is portable but not lighter when compared with a PDA. This implies that registration could be managed from mobile registration points. There are many developers that provide solutions that run on this platform. The risk associated with this model is data loss through theft or breakdown of equipment.

Desktop Based Distributed Model

The beneficiaries are registered on paper-forms. The information is then entered on the nearest computer.

The Desktops are installed with the MIS system at the either District or any other administrative level. Data is collected from the beneficiaries using designed forms. The data captured in the computers is forwarded to the District for consolidation. The data is then forwarded from the District to the national level for final consolidation.

The use of paper reduces the chances of data loss because there is a paper trail of the registered households. There is duplication of effort in inputting of information in both paper and computer.

 

  44

    

Annex 9: Updated Form 0 Annex 10: Updated Form 2R Annex 11: Updated Form 2U

45 

 Form

 0:  Ba

selin

e Form

    

  GE

OCO

DE  

    

  HH

 NO 

                                                                                                                                                              

    

    

    

    

    

    

   D

istr

ict_

____

____

____

____

____

___W

ard_

____

____

____

____

____

____

_Vill

age_

____

____

____

____

____

____

____

____

____

Nat

iona

l ID

of h

ouse

hold

hea

d___

____

____

____

____

____

____

____

GP

S ..

......

......

.....

Nam

e of enu

merator____________________________________________________

 Date of in

terview  ___________/___________/201

1 

F1

00

F101

F1

02

F103

F1

04

F105

F1

06

F109

F1

10

F111

  N

ames

of h

ouse

hold

mem

bers

(sta

rt w

ith

head

of h

ouse

hold

) R

elat

ion

to

hous

ehol

d he

ad

Age

in

co

mpl

- ete

d ye

ars

Dat

e of

Birt

h Is

(nam

e)

mal

e/

fem

ale?

1.

Yes

2.

No

Is (n

ame)

di

sabl

ed o

r ch

roni

cally

ill

? (1

. Yes

, 2.

No)

Is (n

ame)

en

rolle

d in

scho

ol?

1.Y

es

2. N

o N

ame

of sc

hool

G

rade

/ Fo

rm

DD

M

MY

YY

Y

1   

1   

    

    

    

    

 1       2 

 1       2 

 1       2 

   

2   

 2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

   

3   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

   

4   

2  3  4  5 6  7  8  9

    

    

    

    

   1 

      2 

 1       2 

 1       2 

   

5   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

   

6   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

   

7   

2  3  4  5 6  7  8  9

    

    

    

    

   1 

      2 

 1       2 

 1       2 

   

8   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

   

9   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

   

10 

  2  3  4  5 6  7  8  9

    

    

    

    

   1 

      2 

 1       2 

 1       2 

   

11 

  2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

   

12 

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 13

 2  3  4  5 6  7  8  9

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 14

 2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 15

 2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 16

 2  3  4  5 6  7  8  9

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 17

 2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 18

 2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 Codes fo

r F102: (1) Head (2) Spo

use (3) Son

/Daughter (4) Brother/Sister (5

) Nep

hew/ N

iece/Cou

sin (6

) Grand

child

 (7) P

aren

t (8) Other re

lative (9) N

ot re

lated 

Codes fo

r F115: (00) P

re‐schoo

l/nursery; (10) G

rade

 Zero; (1

1‐17) G

rade

 One

 – Grade

 Seven

; (21

 – 26) Form One

 ‐  Form Six 

46 

 Dis

able

d an

d/or

chr

onic

ally

ill o

f hou

seho

ld m

embe

rs

F115

F1

12

F113

F1

14

Oth

er a

ttrib

utes

(s

ee c

odes

des

crip

tions

bel

ow)

1.

Yes

2.

N

o L

ine

No

Nam

e D

escr

iptio

n

    

  1

23

4

    

   1   2 

1   2

 1   2 

 1   2 

    

   1   2 

1   2

 1   2 

 1   2 

    

   1   2 

1   2

 1   2 

 1   2 

    

   1   2 

1   2

 1   2 

 1   2 

    

   1   2 

1   2

 1   2 

 1   2 

Codes fo

r F115 (1

) Is u

nable to do hard work like plou

ghing, digging

 or h

erding  (2) Canno

t leave th

e ho

use alon

e   (3) Req

uires d

aily care of m

ore than

 2 hou

rs   (4) Is p

ermanen

tly bed

 ridd

en    

Obs

erva

tions

on

spec

ific

soci

al p

rote

ctio

n an

d/or

chi

ld p

rote

ctio

n ne

eds m

ade

by e

num

erat

or

F116

F1

17

F118

(cir

cle

appl

icap

le)

F119

L

N o

f chi

ld

Nam

e of

chi

ld

Type

of a

buse

R

easo

ns fo

r su

spec

ting

abus

e

1

2

3

4

5

6

1

2

3

4

5

6

1

2

3

4

5

6

1

2

3

4

5

6

1

2

3

4

5

6

Cod

es fo

r F11

8 (1

) Phy

sica

l abu

se; (

2) S

exua

l abu

se; (

3)Em

otio

nal a

buse

; (4)

Neg

lect

; (5

) Chi

ld m

arria

ge;

(6) C

hild

labo

ur

Enu

mer

ator

N

ame

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

. Si

gnat

ure

......

......

......

......

......

......

......

......

......

......

....

Dat

e __

____

/___

____

_/ 2

011

Wha

t act

ion

has b

een

take

n (F

illed

by

DSS

O)

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

____

__

DSS

Offi

cer

Nam

e __

____

____

____

____

____

____

____

____

____

____

____

____

Sign

atur

e __

____

____

____

____

____

____

____

____

____

____

____

__

Dat

e __

____

_/__

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__/2

011

Stam

p

47 

   Form

 2R: In

form

ation regarding a labo

ur con

strained

 rural hou

seho

ld  

    

GEOCO

DE  

    

  HH

 NO 

    

    

    

    

    

    

    

Dis

tric

t___

____

____

____

____

____

_War

d___

____

____

____

____

____

___V

illag

e___

____

____

____

____

____

____

____

____

__

ID o

f hou

seho

ld h

ead_

____

____

____

____

__ G

PS

.....

......

......

..

Nam

e of

Inte

rvie

wer

____

____

____

____

____

____

____

____

____

____

____

Dat

e___

___/

____

____

_/20

12

F20

0 F2

01

F202

F2

03

F204

F205

F206

F207

F2

08F2

09F2

10F2

11

  

Nam

es o

f hou

seho

ld

mem

bers

(sta

rt w

ith h

ead

of

hous

ehol

d)

Rel

atio

n to

ho

useh

old

head

Age

in

com

pl-

eted

ye

ars

Dat

e of

Birt

h Is

(n

ame)

m

ale/

fe

mal

e?

1.Y

es

2.N

o

Is (n

ame)

di

sabl

ed o

r ch

roni

cally

ill

? (1

. Yes

, 2.

No)

Is (n

ame)

's

mot

her

aliv

e?

1.Y

es

2. N

o

Is

(nam

e)'

fath

er

aliv

e?

1. Y

es

2. N

o

Is (n

ame)

en

rolle

d in

scho

ol?

1.Y

es

2. N

o N

ame

of sc

hool

G

rade

/ Fo

rm

DD

M

M

YY

YY

1   

1   

    

    

    

    

 1       2 

 1       2 

 1       2 

 1       2 

 1       2 

   

2   

 2  3  4  5 6  7  8  9

   

    

    

    

    

 1       2 

 1       2 

 1       2 

 1       2 

 1       2 

   

3   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

4   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

5   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

6   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

7   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

8   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

9   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

10 

  2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

11 

  2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

Codes fo

r F202: (1) Head (2) Spo

use (3) Son

/Daughter (4) Brother/Sister (5

) Nep

hew/ N

iece/Cou

sin (6

) Grand

child

 (7) P

aren

t (8) Other re

lativ

e (9) N

ot re

lated 

Codes fo

r F211: (00) P

re‐schoo

l/nursery; (10

) Grade

 Zero; (1

1‐17) G

rade

 One

 – Grade

 Seven

; (21

 – 26) Form One

 ‐  Form Six

F212

H

ow m

any

regu

lar m

eals

a d

ay (m

eals

with

sadz

a) d

oes t

he h

ouse

hold

ty

pica

lly e

at?

( 1

) no

mea

l or o

ne m

eal

( 0

) tw

o or

mor

e m

eals

1

0

F213

H

ow m

any

mon

ths d

id th

e gr

ain

from

the

last

har

vest

last

? ( 1

) le

ss th

an 3

mon

ths

( 0

) mor

e th

an 3

mon

ths

1

0

F214

W

hat l

ives

tock

doe

s the

hou

seho

ld p

osse

s?

( 1 )

has n

o liv

esto

ck o

r les

s tha

n 6

chic

ken

or le

ss th

an 3

goa

ts

( 0

) has

6 o

r mor

e ch

icke

n or

3 o

r mor

e go

ats o

r ca

ttle

1

0

48 

 F215

H

ow m

any

blan

kets

per

per

son

does

the

hous

ehol

d po

sses

?

( 1 )

less

than

1 b

lank

et

( 0

) 1 a

nd a

bove

bla

nket

s 1

0

F216

H

ow m

any

room

s in

the

hous

e ar

e us

ed fo

r sle

epin

g?

( 1 )

1 o

r non

e (

0 ) 2

or a

bove

1

0

F217

M

ain

mat

eria

l of t

he h

ouse

wal

ls?

( 1

) na

tura

l or r

udim

enta

ry (p

ole

&

dagg

a, m

akes

hift,

woo

d, c

ardb

oard

( 0

) F

inis

hed

bric

k w

all (

bric

k/st

one

with

ce

men

t/lim

e)

1

0

F218

M

ain

sour

ces o

f liv

elih

ood

of th

e ho

useh

old?

(

1 ) b

eggi

ng o

r pie

ce w

ork

(0 )

agric

ultu

re, o

ther

bus

ines

s, pe

nsio

n 1

0

F219

A

vera

ge m

onth

ly re

mitt

ance

s the

hou

seho

ld re

ceiv

es fr

om re

lativ

es o

r ot

hers

?

( 1 )

less

than

$ 2

5

(0 )

$ 25

or m

ore

1

0

F220

D

oes t

he h

ouse

hold

ow

n an

y of

the

follo

win

g pr

oper

ties (

in w

orki

ng

cond

ition

): R

adio

, TV

, mob

ile p

hone

, ani

mal

dra

wn

cart,

mot

orcy

cle,

car

)?

( 1

) ha

s no

valu

able

ass

ets

( 0

) ha

s val

uabl

e as

sets

1

0

F221

D

oes t

he h

ouse

hold

ow

n la

nd?

( 1 )

land

less

or o

wns

up

to o

ne a

cre

(

2 )o

wns

mor

e th

an o

ne a

cre

1

0

Dis

able

d an

d/or

chr

onic

ally

ill o

f hh

mem

bers

(Circle all that app

ly) 

F225

 F226

 F227

 F228

 

Other attrib

utes (see

 cod

es 

descrip

tions below

 1.

Yes 

2. No  

Line

 No 

Nam

e De

scrip

tion 

F222:Doe

s the

 hou

seho

ld re

ceive supp

ort? 

    

12

34

 Pen

sion from

 Governm

ent o

r priv

ate sector 

1   

    

 1   2 

1   2

 1   2 

1   2

Welfare paymen

ts from

 the De

p. of Social Service 

2 1   2 

1   2

1   2 

1   2

AMTO

 3 

1   2 

1   2

1   2 

1   2

NGO

 or chu

rch, if YES sp

ecify: .................................................... 

4   

    

 1   2 

1   2

 1   2 

1   2

 Regular re

mittances, if YES from

 who

m: ............................. 

5   

    

 1   2 

1   2

 1   2 

1   2

Other: .....................................................................................  

6   

    

 1   2 

1   2

 1   2 

1   2

1.

Unable to perform

 hard work like plou

ghing, digging, 

herding cattle 

2.

Cann

ot leave the ho

use alon

e F223:Do any of th

e children living in th

is ho

useh

old receive 

assis

tance from

 BEA

M? (1. Yes, 2. N

o) 

  3.

Re

quire

s daily care of m

ore than

 2 hou

rs  

F224: If Y

ES, give names of the

 children receiving BE

AM 

assis

tance: 

  4.

Is perman

ently

 bed

 ridd

en 

NAM

E LN

        

  

49 

    Form 2U: Information regarding a labo

ur con

strained

 urban

 hou

seho

ld 

    

  GE

OCO

DE  

    

  HH

 NO 

    

    

    

    

    

    

    

Dis

tric

t___

____

____

____

____

____

_War

d___

____

____

____

____

____

___V

illag

e___

____

____

____

____

____

____

____

____

__

ID o

f hou

seho

ld h

ead_

____

____

____

____

__ G

PS

.....

......

......

..

Nam

e of

Inte

rvie

wer

____

____

____

____

____

____

____

____

____

____

____

Dat

e___

___/

____

____

_/20

12

F20

0 F2

01

F202

F2

03

F204

F2

05

F206

F2

07

F208

F2

09

F210

F2

11

  

Nam

es o

f hou

seho

ld

mem

bers

(sta

rt w

ith h

ead

of

hous

ehol

d)

Rel

atio

n to

ho

useh

old

head

Age

in

com

pl-

eted

ye

ars

Dat

e of

Birt

h Is

(nam

e)

mal

e/

fem

ale?

1.

Mal

e 2.

Fem

ale

Is (n

ame)

di

sabl

ed o

r ch

roni

cally

ill

? (1

. Yes

, 2.

No)

Is (n

ame)

's

mot

her

aliv

e?

1.Y

es

2. N

o

Is

(nam

e)'

fath

er

aliv

e?

1. Y

es

2. N

o

Is (n

ame)

en

rolle

d in

scho

ol?

1.Y

es

2. N

o N

ame

of sc

hool

G

rade

/ Fo

rm

DD

MM

YY

YY

1   

1   

    

    

    

    

 1       2 

 1       2 

 1       2 

 1       2 

 1       2 

   

2   

 2  3  4  5 6  7  8  9

   

    

    

    

    

 1       2 

 1       2 

 1       2 

 1       2 

 1       2 

   

3   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

4   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

5   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

6   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

7   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

8   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

9   

2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

10 

  2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

11 

  2  3  4  5 6  7  8  9 

    

    

    

    

   1 

      2 

 1       2 

 1       2 

 1       2 

 1       2 

   

Codes fo

r F202: (1) Head (2) Spo

use (3) Son

/Daughter (4) Brother/Sister (5

) Nep

hew/ N

iece/Cou

sin (6

) Grand

child

 (7) P

aren

t (8) Other re

lativ

e (9) N

ot re

lated. Rem

ark: Ask 207

 t0 209

 only for childrenl 

Codes fo

r F211: (00) P

re‐schoo

l/nursery; (10

) Grade

 Zero; (1

1‐17) G

rade

 One

 – Grade

 Seven

; (21

 – 26) Form One

 ‐  Form Six

F212

H

ow m

any

regu

lar m

eals

a d

ay (w

ith sa

dza)

th

e ho

useh

old

typi

cally

eat

s?

( 1

) no

mea

l or o

ne m

eal

( 0

) tw

o or

mor

e m

eals

1

0

F213

M

ain

sour

ces o

f liv

elih

ood

of th

e ho

useh

old?

(

1 ) b

eggi

ng o

r pie

ce w

ork

(0 )

form

al/in

form

al e

mpl

oym

ent

1

0

F214

A

vera

ge in

com

e/re

mitt

ance

s the

hou

seho

ld re

ceiv

es p

er m

onth

? ( 1

) le

ss th

an $

25

( 0

) $25

or m

ore

1

0

F215

W

hat p

erce

ntag

e of

you

r inc

ome

goes

to re

nt a

nd ra

tes?

( 1

) 75

% a

nd a

bove

(

0 ) b

elow

75%

1

0

Com

men

t [u

1]:

Will th

is be

 asked

 children be

low 18 years? 

Com

men

t [u

2]:

As p

er agreemen

t on 

From

 0 we have assum

ed th

is will be 

asked to children of 5 years and

 abo

ve.  

50 

 F216

Is

you

r wat

er o

r ele

ctric

ity d

isco

nnec

ted

beca

use

of o

utst

andi

ng b

ills?

( 1

) is

dis

conn

ecte

d ( 0

) n

ot d

isco

nnec

ted

1

0

F217

H

ow m

any

room

s are

use

d fo

r sle

epin

g?

( 1

) tw

o an

d be

low

(0

) m

ore

than

two

1

0

F218

W

hat t

ype

of fu

el d

oes y

our h

ouse

hold

mai

nly

use

for c

ooki

ng?

( 1 )

firew

ood

or w

aste

(0 )

elec

trici

ty, c

oal,

gas o

r par

rafin

1

0

F219

M

ain

mat

eria

l of t

he h

ouse

wal

ls

( 1 )

Nat

ural

or r

udim

enta

ry (p

ole

& d

agga

, m

akes

hift,

woo

d, c

ard

boar

d or

met

al)

( 0) F

inis

hed

wal

l (br

ick/

ston

e w

ith c

emen

t/ lim

e)

1

0

 

F220

D

o yo

u or

som

eone

livi

ng in

this

hou

seho

ld o

wn

this

dw

ellin

g?

( 1 )

Ren

t

(0 )

Ow

n

1

0

F2

21

Doe

s the

hou

seho

ld p

osse

ss a

ny o

f the

follo

win

g as

sets

: Rad

io, t

elev

ision

, m

obile

tele

phon

e, re

frig

erat

or, b

icyc

le, m

otor

cycl

e, c

ar, o

r tru

ck

(1)

No

(0)

Yes

1

0

Dis

able

d an

d/or

chr

onic

ally

ill o

f hh

mem

bers

(Circle all that app

ly) 

F225

 F226

 F227

 F228

 

Other attrib

utes (see

 cod

es 

descrip

tions below

 1.

Yes 

2. No  

Line

 No 

Nam

e De

scrip

tion 

F222:Doe

s the

 hou

seho

ld re

ceive supp

ort? 

    

12

34

 Pen

sion from

 Governm

ent o

r priv

ate sector 

1   

    

 1   2 

1   2

 1   2 

1   2

Welfare paymen

ts from

 the De

p. of Social Service 

2 1   2 

1   2

1   2 

1   2

AMTO

 3 

1   2 

1   2

1   2 

1   2

NGO

 or chu

rch, if YES sp

ecify: .................................................... 

4 1   2 

1   2

1   2 

1   2

 Regular re

mittances, if YES from

 who

m: ............................. 

5   

    

 1   2 

1   2

 1   2 

1   2

Other: .....................................................................................  

6   

    

 1   2 

1   2

 1   2 

1   2

5.

Unable to perform

 hard work like plou

ghing, digging, 

herding cattle 

6.

Cann

ot leave the ho

use alon

e F223:Do any of th

e children living in th

is ho

useh

old receive 

assis

tance from

 BEA

M? (1. Yes, 2. N

o) 

  7.

Re

quire

s daily care of m

ore than

 2 hou

rs  

F224: If Y

ES, give names of the

 children receiving BE

AM 

assis

tance: 

  8.

Is perman

ently

 bed

 ridd

en 

NAM

E LN

        

  

 

Com

men

t [u

3]:

What is illegal m

aterial 

51 

  Ann

ex 1

2: A

naly

sis

of th

e ru

ral a

nd u

rban

pov

erty

indi

cato

r sco

ring

Rural Form 2 

Distric

t 

Gene

ral Q

uestions 

Rural Spe

cific

 Que

stions 

How m

any 

meals a day 

does th

e ho

useh

old 

typically 

eat? (1

) no 

meal o

r 1 

meal (0) 

two or 

more meals 

Does th

e ho

useh

old ow

n an

y of th

e follo

wing assets: 

radio, te

levision

, mob

ile te

leph

one, 

anim

al drawn cart, 

bicyle, m

otorcycle, 

car, or truck? (1

) Ye

s (0) No 

How m

any 

average 

blan

kets 

per p

erson 

does th

e ho

useh

old 

possess? 

(1) less 

than

 1 (0

) more than

 1 

How m

any 

room

s are 

in use 

includ

ing 

kitche

n an

d ba

throom

? (1) 1

 or 

none

 (0) 

more than

 1 

Main 

Sources o

f livelihoo

d of th

e ho

useh

old?

 (1)  be

gging 

or piece 

work (0) 

agric

ulture, 

othe

r bu

sine

ss, 

pension 

Main 

material of 

the ho

use 

walls (1

) na

tural o

r rudimen

tary 

(0) b

rick 

How m

any 

mon

ths d

id 

the grain 

from

 the 

last harvest 

last? (1) 

less th

an 3 

mon

ths (0) 

more than

 3 mon

ths 

Wha

t livestock 

does th

e ho

useh

old 

posses? (1) h

as 

no livestock or 

less th

an 6 

chicken or less 

than

 3 goa

ts (0

) ha

s 6 or m

ore 

chicken or 3 or 

more goats o

r cattle 

Average 

mon

thly 

remittan

ces 

househ

old 

receives from

 relativ

es or 

othe

rs (1

)  less th

an $25

 or non

 (0) 

$25 an

d ab

ove 

Does th

e ho

useh

old 

own land

? (1) 

land

less 

or owns 

up to

 one

 acre (0

) ow

ns 

more than

 1 acre 

Chivi 

2%13% 

14%

1%8%

4%19

%12

%23

%4%

Goromon

zi 

2%13% 

7%3%

10%

10%

9%16

%20

%10

%Ka

riba 

3%6%

 9%

4%10

%8%

13%

14%

17%

8%Makon

i 3%

11% 

8%2%

14%

6%11

%14

%25

%6%

Man

gwe 

5%14% 

5%2%

12%

5%21

%10

%20

%5%

Rushinga 

3%8%

 10

%3%

10%

5%20

%15

%22

%5%

Umguza 

4%12% 

6%2%

13%

6%16

%14

%20

%6%

Zvisha

vane

 3%

14% 

10%

1%9%

4%19

%12

%24

%4%

Urban

 Form 2 

Distric

t Ge

neral Q

uestions 

Urban

 Spe

cific

 Que

stions 

52 

 

How

 man

y meals a day 

does th

e ho

useh

old 

typically 

eat? (1

) no 

meal or 1

 meal (0) 

two or 

more meals 

Does th

e ho

useh

old ow

n an

y of th

e follo

wing assets: 

radio, te

levision

, mob

ile te

leph

one, 

anim

al drawn cart, 

bicyle, m

otorcycle, 

car, or truck? (1

) Ye

s (0) No 

How

 man

y average 

blan

kets 

per p

erson 

does th

e ho

useh

old 

possess? 

(1) less 

than

 1 (0

) more than

 1 

How m

any 

room

s are 

in use 

includ

ing 

kitche

n an

d ba

throom

? (1) 1

 or 

none

 (0) 

more than

 1 

Main 

Sources o

f livelihoo

d of 

the 

househ

old?

 (1)  be

gging 

or piece 

work (0) 

agric

ulture, 

othe

r bu

sine

ss, 

pension 

Main 

material of 

the ho

use 

walls (1

) na

tural or 

rudimen

tary 

(0) b

rick 

How m

uch 

average 

income 

does th

e ho

useh

old 

earn per 

mon

th? (1) 

Less th

an 

$25 (0) $

25 

and ab

ove 

Wha

t percentage 

of you

r incom

e goes to

 rent and

 rates? (1

) 75%

 an

d ab

ove (0) 

below 75%

 

Wha

t type of 

fuel doe

s your 

househ

old 

use for 

cooking? (1

) Firewoo

d or 

waste (0

)  Electricity

, gas, paraffin

 or coa

l 

Do you

 or 

someo

ne 

living in 

this 

househ

old 

own this 

dwellin

g? 

(1) o

wn 

(0) ren

t Bu

lawayo 

0%0%

 0%

0%0%

0%20

% 

38%

5%38%

Epworth 

0%0%

 0%

0%0%

0%19

% 

16%

48%

16%

Karib

a 3%

6% 

9%4%

10%

8%1%

 2%

2%2%

Rusape

 0%

0% 

0%0%

0%0%

21% 

25%

29%

25%