This document provides the details of the sampling for the monitoring and evaluation component of the MoreMilkIT 1 project. The overall M&E Strategy for the project is described in detail in the below embedded document.
MLE for
MoreMilkiT.docx
The project focus population is described in the overall M&E strategy (see embedded document). The project is focused on 30 villages covering 4 districts of Tanzania (Appendix A); in each village an appropriate hub type is being piloted, chilling plant or milk trader hub. To provide the counter-factual group of households who have no access or opportunity to participate in the project, non-project villages have been identified and are included in the monitoring & evaluation activity. There is one ‘control’ village representing each District, and hence both Hub Types. The original baseline included both cattle-keepers and non-cattle keepers, this latter group was to provide baseline information on the level of engagement of the overall population in cattle-keeping activities (e.g. feed sellers), including milk consumption.
However, for ongoing monitoring & evaluation the focus is on cattle-keeping households only.
Stratification & ‘Types’ of household
District (Handeni, Kilosa, Lushoto, Mvomero) and Hub Type (chilling plant, milk trader) are used as the two main stratification factors to ensure sufficient monitored households in each District x Hub Type combination for estimation of key variables and indicators, Figure 1.
Figure 1: Sampling Structure – Stratification & Clustering levels
Clustering
Districts
Villages
Households
District – Handeni, Kilosa, Lushoto, Mvomero
Chilling Plant (CP) Milk Trader (MT)
Group member
Non-Group member
Group member
Non-Group member
No Hub
All cattle-keeping households
Stratification
District
Hub Type
Group
Membership
Household ‘types’: Milk sales to the hub (yes / no), Accessing inputs & services from hub (yes / no) and Group membership (yes / no) = 8 combinations
The project would also like to evaluate and compare changes in indicators according to the level of participation of households in the hub, in terms of: sales of milk to the hub, access of inputs and services from the hub and
membership of producer organisations (PO). We therefore have 8 ‘types’ of households, plus cattle-keeping households in non-project villages (Table 1).
1 Adapting dairy market hubs for pro-poor smallholder value chains in Tanzania (MoreMilkIT), funded by Irish Aid

At the start of the project milk sales to hub and access of inputs & services from the hub are zero as an initial project activity is creation of the hub. However, producer organisations (PO) were already in existence in some project villages and hence this is the only factor it is possible to stratify the sample by (Figure 1). In stratifying by PO membership we ensure sufficient and equal (most efficient) replication to enable comparisons of indicators for members versus nonmembers of POs. Note that membership of groups in non-project villages was not used to stratify households as there was limited knowledge of whether there were existing POs in the villages.
Table 1: Monitored Household ‘Types’
Project Villages
No
Yes
No
No
Yes
Yes
No
Yes
No
No
No
Yes
No
Yes
No
Yes
Yes
No
No
No
No
Yes
No
Yes
Yes
Yes
Yes / No
1
2
5
6
3
4
7
8
9 Non-project villages
Note that in Table 1, households with the ‘Yes’ level of each factor have, to some extent, self-selected. Hence, any analysis including these factors must attempt to explain and adjust for the factors influencing this self-selection prior to estimating these factor effects.
Additionally, households may move between levels of a factor in different rounds of the survey – e.g. households may join a PO after three rounds of M&E; households may start to access inputs & services from the hub after the first round of M&E. It is hoped that households will generally move from the No level to the Yes level but it is also possible that households will move in / out of the Yes level – e.g. households only access inputs & services from the hub in half of the rounds of M&E; a household chooses to leave a PO. These movements will need to be considered in analyses, for example, by incorporating the complete profile of the household in terms of household type over the course of the project into final analysis of changes attributable to the project.
Counterfactual population
The counterfactual population will include different types of non-participating project household: households in project villages who choose not to participate in any aspect of the hub (household type 1), households in project villages who don’t participate in one or two aspects of the hub (household type 2 – 7) and households in non-project villages who are cattle-keepers but in no way involved in the project (household type 9).
Sample Size
The appropriate number of households for ongoing monitoring was calculated for each District by Hub Type combination using MoreMilkIT baseline survey data to estimate potential differences / changes in key variables and expected variation. Key variables considered were: milk production per household per day (litres) and gross margin from dairy per household per year (USD).
Standard normal sample size calculations for comparing two groups were used to calculate the required sample size per group (district x hub type combination). As it is unknown which households will become each household type (Table 1) the sample size calculated was assumed to be for a single household type and then multiplied by the number of types. A

compromise sample size for the two key variables was used. In addition, the number of households for the non-project village household type was inflated to provide sufficient information for the counter-factual.
No adjustment was carried out for intra-village correlation (ICC) because sampling was carried out at the district x hub type level, i.e. possible households to survey were combined across villages in each district x hub type combination to form the sampling frame for random selection. Village level sampling, adjusting for ICC was considered (see greyed out calculations in embedded spreadsheet) but ruled out as could not guarantee sufficient villages in each district for each hub type.
Equal versus proportional sampling (according to the number of cattle-keepers in the area) was compared, see embedded spreadsheet. For comparison of districts and hub type, equal sampling is the most efficient. However, given highly variable numbers of cattle-keepers across the districts and hub types it was preferable to apply proportional sampling; this means that project level estimates for indicators do not need to be post-weighted later, for simple summaries or in models, to adjust for this variable population size.
Details of the sample size calculations are shown in the embedded spreadsheet below and a summary of the numbers of households participating in the project M&E is shown in Appendix A.
MoreMilkITME-Sampl eSizeCalcsJul14_Mar15updateJP.xlsx
Sampling Process

For each District, group the project villages by Hub Type.

For every project village in each district and hub type, obtain a list of all cattle keepers classified into group members
(members of hub/ MoreMilkIT groups) and non-members of group. Do the same for each non-project village.

Combine together the household lists for all villages in the same district x hub type combination into one list of group members and one of non-members of group.

Finally randomly select the required number of households from each list – for project villages you should have 16 lists of sampled households – 8 group members and 8 non-members of group; 4 of these (2 in each group membership type) will be for the same District and 8 of these (4 in each group membership type) will be for the same Hub Type. For non-project villages you should have 1 list of all cattle-keepers.

District Ward Village
Hub
Type 2
Number of households with cattle
Local cattle
Improved cattle
Total by village
No. HH for M&E
Group NonTotal HH's in members members M&E 1st round
Lushoto Ubiri Ubiri 1 * CP
Lushoto Kwemashai Kwemashai MT
Lushoto Soni
Lushoto Shume
Lwandai
Viti
MT
CP
Lushoto Mwangoi Mwangoi
Lushoto Mbuzii Mbuzii 1 *
CP
MT
Lushoto Lushoto Ngulwi
Lushoto Bumbuli Wena*
Lushoto
CP
CP
Mbokoi (non-project)
53
65
125
17
40
44
12
55
167
152
73
133
110
102
120
58
220 6
217 7
198 5
150 9
150 5
146 2
132 7
113 6
Handeni
Handeni
Handeni
Vibaoni
Misima
Misima
Lushoto Total
Konje*
Msomera
Kibaya 1 *
MT
MT
MT
390
208
200
Handeni Vibaoni Kwabaya
Handeni Sindeni Sindeni 1 *
CP
Handeni Kiva Kweditilibe MT
Handeni Chanika Kwediyamba* CP
MT
Handeni
Handeni
Segera
173
120
87
Masatu* MT
86
52
Magamba (non-project)
Handeni Total
Mvomero Hembeti Kambala CP
Mvomero Dakawa WamiSokoine 1 CP
Mvomero Dakawa WamiDakawa* CP
Mvomero Dakawa WamiLuhindo CP
Mvomero Melela
Mvomero Melela
Mela*
Mangae*
MT
MT
Mvomero Diongoya Manyinga 1 * MT
Mvomero Kidudwe (non-project)
Mvomero Total
562
579
605
603
527
156
109
0
0
1
0
15
1
0
10
76
53
18
19
47
28
42
1,326
390 2
208 13
201
86
62
4
173 1
135 2
88 5
9
5
47
1,343
638 11
41
632 7
623 4
622 3
574 6
184 4
151 4
3,424 39
Kilosa Rudewa Twatwatwa 1 MT 191 2 193 3
Kilosa
Kilosa
Kilosa
Kilosa
Kilosa
Kilosa
Kilosa
Dumila Mabwegere CP
Madoto Madoto CP
Madoto Mbwade 1 * CP
Dumila Kwambe* CP
Mikumi IhombweMfilisi MT
Ulaya Kibaoni* MT
Kiduhi (non-project)
97
78
47
44
37
34
0
0
2
2
3
1
99
81
48
44
37
36
4
1
16
4
1
2
Kilosa Total 538
Overall Total 6631 158
1 Selected by MilkIT; 2 MT = Milk Trader, CP = Chilling Plant; *Included in baseline household survey
31
12
21
9
19
30
9
13
21
20
12
18
7
6
15
8
14
7
18
14
24
13
28
8
17
9
2
7
20
6
8
10
9
7
20
461
154
105
98
104
5
13
3
5
2
18
10
5
3
107
64
3
17
2
5
4
5
3
20
59
6
14
4
10
25
7
6
15
20
10
24
7
1
5
1
5
20
73
303