Analysis of SDVC DataMarch 2009 - April 2012
Presented May 31, 2012
ARTIFICIAL INSEMINATIONUSAGE RATES OVER TIME
mar-09 jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12
8.6% 7.1% 7.4% 13.8% 11% 10.3% 8.8%23.3%
PERCENT OF DEWORMING USAGE RATES OVER TIME
mar-09 jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12
58% 51% 42% 41% 34% 31% 47%79%
wealthy
at least3%
DEWORMING
Use of Artificial Insemination increases for all households. The average household can expect to see at least a 3% increase in household income from milk if they use artificial insemination.
Vaccination of cattle is most beneficial for more wealthy households. In poorer households, the use of vaccination does not seem to increase income. However, in wealthier households, income can increase by about 3% if the cattle are vaccinated.
Deworming of cattle has a very positive effect on household income for all households. The average household can expect an increase in income of between 5 and 10% if they deworm their cattle.
Whether or not a household uses Artificial Insemination is strongly predicted by the availability of the service, the economic status of the household and the skills of the household’s Livestock Health Worker.
Households that have lower then average incomes use CARE Livestock Health Workers or Other Livestock Health Workers or a Government Vet. Households that have higher than average incomes tend to use their own family members to provide vaccinations.
Interestingly, a household with a low knowledge score and a high level of confidence in their LHW and a household with a high knowledge score but a low level of confidence in their LHW are both about equally likely to deworm their cattle. Both are about 15% less likely to deworm their cattle than the high knowledge and high confidence household.
The strongest predictor of whether or not a household chooses to deworm their cattle is their overall knowledge score and the level of confidence they feel in their Livestock Health Worker. A household with a high knowledge score and a high level of confidence in their Livestock Health Worker is 30% more likely to deworm their cattle than a household with a low knowledge score and a low level of confidence in their Livestock Health Worker.
However, for all households – the household income is related to vaccination provider choices.
income increase
at least3%
5% 10%-
VACCINATION
ARTIFICIAL INSEMINATION
GROUP LEADER BY GROUP COMPOSITION
Overall, Households within Learning Groups with Female Leaders have incomes that are 3-6% higher.
Learning Groups with Female Leaders do relatively better as the Phase progresses
FARM LEADER GENDERIMPROVES INCOME FROM MILK
phase 1
7%
phase 2
5%
phase 3
2%
phase 4
0%
female leader
percent improvedperformance over male leaders
In Phase 1, the groups with female leaders do 7% betterIn Phase 2, the groups with female leaders do 5% betterIn Phase 3, the groups with female leaders do 2% better than groups with male leadersIn Phase 4, there is no difference in income between female led groups and male led groups.
GROUP COMPOSITIONLEADER GENDER
12%
2%
5%
or
farmleader gender
percentincrease
groupcomposition
Learning Groups with a high percentage of women producers with a female group leader perform the best overall.
Learning Groups with a high percentage of men producers do moderately well regardless of group leader gender.
Learning Groups with a high percentage of women producers and a male group leader perform the least well.
higher income
3% 6%-
GROUP LEADER BY GENDER
GROUP LEADER BY PHASE
wealthier district poorer district
5% 7%
better inearning income
-
po
or
po
or
Market Linkage by household economic status and presence of a group selected
MARKETINFORMAL
poor wealthy
MARKETS
MV RD PRAN BRAC
wealthy wealthy
MARKETS
GRAMEENDANOON AKIJ
At MV & RD, the households making the most money are wealthy and do not have their own collectors.On the informal markets, the
poorer households with their own collectors do the best. At PRAN and BRAC, the households
doing the best are wealthy with their own milk collector.
At Akij, it seems to be irrelevant if you have your own collector or not.
At Grameen Dannon, most households do the same – with the very significant exception of the very poor households without their own collector. These households do very poorly at this market.
milk collectorWHAT PERCENT DO HOUSEHOLDS FROMRICH GROUPS DO BETTERTHAN HOUSEHOLDS FROM POOR GROUPS
MV6.19%
RD5.48%
PRAN4.78%
BRAC4.21%
AKIJ3.49%
GRAMEENDANNON
2.05%
INFORMALSECTOR
1.47%
OTHER0.88%
wealthy
GROUP ECONOMIC STATUS LARGER ECONOMIC CONTEXTThe initial economic status and the larger economic environment of a group has a heavy influence on their milk income.
In general, if a group is poor initially, their progress is better if they operate within a wealthy District.
A poor Learning Groups that operate within one of the wealthier Districts do 5-7% better in earning income than equivalent poor Learning Groups that operate within one of the poorer Districts.
And in general, a group that is more wealthy to begin with a operates within a wealthy District does the best overall – a full 10% - 12% better than even an equivalently rich group that operated in a poor District.
MARKET LINKAGE How and where a household sells their milk significantly affects their income.
MARKETINFORMAL
poor wealthy
MARKETS
MV RD PRAN BRAC
5-8%
poor wealthy
MARKETS
GRAMEENDANOON AKIJ
poor wealthy
The poorest households do the same as the wealthier households when selling their milk in an informal market.
However, the rich households do much better than the poor households when they sell their milk to the MV, RD, PRAN, and BRAC markets. When all else is equal, a rich household makes between 5-8% more money than a poor household when selling milk in these markets.
There is a slight advantage to the wealthier households is the Grameen Danoon and Akij markets, but it is much less consistent.
Market Linkage by household economic status
AN
D
10% 12%-
better in earning money
PERMISSION TO ATTEND FAR AWAY MEETINGS
mar-09 jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12
0.58 0.68 0.78 0.88 0.98 1.08 1.17 1.27
2.56 2.48 2.38 2.27 2.17 2.07 1.96 1.86
0.37 0.47 0.57 0.67 0.77 0.86 0.96 1.06
2.37 2.27 2.16 2.06 1.96 1.85 1.75 1.65
high incomelearning group
low incomelearning group
HOUSEHOLDS IN WHICH WOMEN OWN CATTLE
CATTLE SELLING DECISIONS
PERMISSIONS TO ATTEND MEETINGS
Households where women own cattle do about 10% better in earning money than do households where women do not own cattle.
However, this relationship is complex and is changing over time.
Households in which women own cattle and women make the cattle selling decisions are more likely to sell cattle and are more likely to have higher incomes overall.
Whether or not women producers need permission to attend meetings, both within and outside of their village is influenced by whether or not they own cattle, the economic status of their group and time.
Women who own cattle are less likely to need permission to attend meetings far away.
Women in high income learning groups are slightly more likely to need permission to attend meetings.
However, the rates of women needing permission to attend meetings is dropping amongst women who don’t own cattle.
GENDER: GROUP AND HOUSEHOLD
mar-09 jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12
-0.22 -0.13 -0.04 0.04 0.13 0.22 0.31 0.4
-0.21 -0.11 -0.028 0.06 0.14 0.23 0.33 0.41
-0.45 -0.36 -0.27 -0.18 -0.09 -0.004 0.08 0.17
-0.34 -0.25 -0.17 -0.08 0.01 0.1 0.19 0.28
group has fewhouseholds wherewomen own cattle
group has manyhouseholds wherewomen own cattle
PERMISSION TO ATTEND MEETINGS
mar-09 jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12
-1.45 -1.25 -1.1 -0.97 -0.8 -0.6 -0.5 -0.45
-1.6 -1.35 -1 -0.75 -0.48 -0.25 0.05 0.4
-1.2 -1 -0.9 -0.8 -0.6 -0.47 -0.3 -0.07
-1.43 -1.05 -0.82 -0.5 -0.3 -0.05 0.4 0.55
high incomelearning group
low incomelearning group
Women who own cattle need less permission to attend meetings.
10%
better in earning money
LIVESTOCK HEALTH WORKERS
MILK COLLECTORS
TRAINING BY SEX IS IMPORTANT
SEX BY RECEIVE LOAN IS IMPORTANT
Livestock Health Workers income is influenced by:
• the gender of the worker• the training the worker received• whether or not the worker received a loan.
LIVESTOCK HEALTH WORKERS INCOME
BASIC
ADVANCE
BOTH
LEVEL OF TRAININGFEMALE LHW IMPROVEMENTover MALE LHW with
the same training
33%
22%
17%
LOANFEMALE LHW
35%
24%
IMPROVEMENTover MALE LHW withthe same loan status
Female LHW with loans have a 35% higher increase in income than men.
Female LHW without loans have a 24% higher increase than men.
Milk collectors income is most influenced by the sex of the collector in combination with the market linkage of the collector
Grameen DannonWomen milk collectors who sell here can expect a 30% higher income increase over time than men collectors selling here.
MILK COLLECTORS INCOME
BRAC
AKIJ
INFORMAL
WOMENMILK COLLECTOR
100%
80%
-10%
MARKET INCREASE OVER MEN MC
GRAMEENDANNON 30%
NAMV PRANRD
RD & PRANDo not have enough women selling milk here to discuss.
Informal Women selling here had an income increase that was 10% lower than men (3%)MVVery few women collectors sell milk here. The few that do achieve a much higher income increase than the male milk collectors.
BRAC Women milk collectors who sell here can expect a 100% higher income increase over time than men collectors selling here.
AkijWomen milk collectors who sell here can expect a 80% higher income increase over time than men collectors selling here.
Female LHW with basic training achieve a 33% higher income increase than men.
Female LHW with advanced training achieve a 22% higher income increase than men.
Female LHW with both basic and advanced training achieve a 17% higher income increase than men.
FEED SOURCE COMPARED
CA
RB
OH
YD
RA
TES
PRO
TEIN
SV
ITA
MIN
S &
MIN
ERA
LSO
THER
increaseper litre
% of householdsusing this feed
feed source average costper kg in taka
4.4%
45.4%
0.30.19 0.0294.9%
58.6%
BDT %
0.50.05 0.30
BDT %
00.04 0.30
BDT %
0.30.06 0.60
BDT %
0.8 0.605.5%
21.4%
0.04
BDT %
0.8 0.300.03
BDT %
0.126.1% 0.200.17
BDT %
0.13.6% 0.100.08
BDT %
% increase inmonth milk income
RICE BRAN
WHEAT BRAN
PULSE HUSK
BROKEN RICE
OIL CAKE
M. OIL CAKE
VITAMINS& MINERALS
READY FEED
per monthly 10 kg increase
For the best nutrition, cattle need a combination of Carbohydrates, Proteins and Vitamins and Minerals.
CATTLENUTRITION
Carbo
hydra
tes
Miner
als
ProteinsVitamisn
The most cost effective and beneficial forms of carbohydrates seems to be Wheat Bran and Broken Rice.
The most cost effective and beneficial forms of proteins are various forms of Oil Cakes.
Vitamins and minerals are very important for the health and milk production of cattle.
Over time, our farmers have increased their regular use of vitamins and minerals by about 20% overall.
Over time, our farmers have increased their Wheat Bran use from 50% to 75% of all households. And our farmers have held their rates of Broken Rice steady over time. About half of all households use broken rice.
Over time, our farmers have increased their use of various types of oil cakes by about 10% overall.
FEED SOURCEPRICE OVER TIME
RICE BRAN
WHEAT BRAN
PULSE HUSK
BROKEN RICE
OIL CAKE
M. OIL CAKE
VITAMINS& MINERALS
READY FEED
0.25
BDT
0.06
BDT
0.05
BDT
0.09
BDT
0.06
BDT
0.00
BDT
0.23
BDT
0.25
BDT
jun-09
0.20
BDT
0.06
BDT
0.05
BDT
0.07
BDT
0.04
BDT
0.04
BDT
0.03
BDT
0.20
BDT
oct-09
0.19
BDT
0.06
BDT
0.05
BDT
0.05
BDT
0.05
BDT
0.04
BDT
0.02
BDT
0.19
BDT
mar-10
0.21
BDT
0.05
BDT
0.04
BDT
0.04
BDT
0.03
BDT
0.04
BDT
0.57
BDT
0.21
BDT
jul-10
0.16
BDT
0.04
BDT
0.04
BDT
0.04
BDT
0.04
BDT
0.04
BDT
0.03
BDT
0.16
BDT
jan-11
0.16
BDT
0.04
BDT
0.04
BDT
0.05
BDT
0.04
BDT
0.04
BDT
0.31
BDT
0.16
BDT
jul-11
0.15
BDT
0.04
BDT
0.04
BDT
0.05
BDT
0.04
BDT
0.04
BDT
0.02
BDT
0.15
BDT
apr-12
0.19
BDT
0.05
BDT
0.04
BDT
0.06
0.04
BDT
BDT
0.03
BDT
0.19
BDT
0.19
BDT
overallaverage
FEED SOURCEPROPORTIONS
RICE BRAN
WHEAT BRAN
PULSE HUSK
BROKEN RICE
OIL CAKE
M. OIL CAKE
READY FEED
jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12
18% 57% 31% 10% 8% 9% 7%
4% 10% 9% 3% 2% 2% 3%
4% 7% 4% 1% 0.7% 0.8% 0.9%
0% 0.7% 0.3% 0% 0.1% 0.2% 0.3%
0% 2% 1% 0.3% 0.3% 0.3% 0.4%
0.8% 0.2% 62% 2% 72% 0.2% 0.1%
2.3% 3.6% 3.2% 1.1% 2.3% 1.2% 1.1%
CA
RB
OH
YD
RA
TES
PRO
TEIN
S
VITAMINS& MINERALS
4% 0.6% 0.5% 0.9% 0.4% 0.1% 0.1%
VIT
AM
INS
&M
INER
ALS
OTH
ER
WHEAT BRAN
OIL CAKES
VITAMINS MINERALS
75%
10%
20%
Overview of Household Compostion - Entire Dataset
Respondents' GenderCount Percent
1 Women 7290 80.15%2 Men 1805 19.85%Total 9095 100.00%
Count of In-milk Local Breed Cows
Count Percent0 4192 46.09%1 4138 45.50%2 690 7.59%3 75 0.82%Total 9095 100.00%
Count of In-milk Cross Breed Cows
Count Percent0 8093 88.98%1 800 8.80%2 173 1.90%3 29 0.32%Total 9095 100.00%
Overview of Entire Dataset:Household Overview
Count of Total In-Milk Cows in Household
Count Percent0 3302 36.31%1 4734 52.05%2 935 10.28%3 124 1.36%Total 9095 100.00%
Count Percent1 Yes 1202 13.22%2 No 6248 68.70%Total 7450 100.00%
Count of Households that have Cattle Owned by Women
Count of Households who Dewormed Cattle
Count Percent1 Yes 3589 39.46%2 No 5382 59.18%Total 8971 100.00%
Overview of Entire Dataset:Vet Practices
Count of Households Who Got AI for Cattle
Count Percent1 Yes 943 13.81%2 No 5884 86.19%Total 6827 100.00%
Type of Treatment Provider, in general
Count Percent
1 CARE LHW6093 66.99%
2 Other LHW1305 14.35%
3 Govt Vet344 3.78%
4 Other people of DLS
107 1.18%
5 Milk Processor Vet
39 0.43%
6 Medicine/Feed Compant Vet
10 0.11%
7 Kabiraj30 0.33%
8 Own Family Member
7 0.08%
9 Others63 0.69%
Total7998 100.00%
Overview of Entire Dataset:Financial Practices
Count of Households that Got Loans
Count Percent1 Yes 126 1.39%2 No 8969 98.61%Total 9095 100.00%
Count Percent1 Relatives 7 5.56%2 MFI 87 69.05%3 Commercial Bank 7 5.56%4 Merchent 2 1.60%5 Govt Institution 2 1.59%6 Milk Processing Company2 1.59%7 Milk Trading Association9 7.14%8 Other Association 7 5.56%9 Others 3 2.38%Total 126 100.00%
Source of Loans for Households that Got Them
Count Percent1 Yes 1165 55.19%2 No 946 44.81%Total 2111 100.00%
Count of Households that Engaged in Group Savings
Overview of Entire Dataset:Gender Roles
Gender of Person Engaged in Cow Rearing
Count Percent1 Women 5037 55.38%2 Men 892 9.81%3 Both 3166 34.81%Total 9095 100.00%
Gender of Person Engaged with Milk Selling
Count Percent1 Women 2279 25.06%2 Men 2765 30.40%3 Both 871 9.58%Total 5915 100.00%
Count Percent1 Yes 3670 40.35%2 No 3898 42.86%Total 7568 100.00%
Count of Women Who Need Permission to Attend Group Meetings
Count Percent1 Yes 6534 86.34%2 No 1034 13.66%Total 7568 100.00%
Count of Women Who Need Permission to Attend Meetings at a Distance
Gender of Person Engaged in Feed Purchase
Count Percent1 Women 653 7.18%2 Men 6529 71.79%3 Both 1051 11.56%
Overview of Entire Dataset:Cattle Productivity
Local Breed Cow Productivity (Daily Litres) Over Time and According to Phase
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12
Phase 1 Phase 2 Phase 3 Phase 4
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12 Phase 1 Phase 2 Phase 3 Phase 4
Cross Breed Cow Productivity (Daily Litres) Over Time and According to Phase
Overview of Entire Dataset:Knowledge & Practical Education
0
1
2
3
4
5
6
7
8
Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12
Phase 1 Phase 2 Phase 3 Phase 4
Total Knowledge Score Over Time and According to Phase
Total Practical Score Over Time and According to Phase
0
2
4
6
8
10
12
Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12
Phase 1 Phase 2 Phase 3 Phase 4
Overview of Entire Dataset:Feed Costs & Milk Income
0.00
200.00
400.00
600.00
800.00
1000.00
1200.00
Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12
Phase 1 Phase 2 Phase 3 Phase 4
0.00
200.00
400.00
600.00
800.00
1000.00
1200.00
Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12
Phase 1 Phase 2 Phase 3 Phase 4
0.00
0.50
1.00
1.50
2.00
2.50
Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12
Phase 1 Phase 2 Phase 3 Phase 4
Monthly Feed Costs per Cow (taka) Over Time and According to Phase
Monthly Income per Cow (taka) Over Time and According to Phase
Ratio of Milk Income to Feed Costs Over Time and According to Phase
Overview of Entire Dataset:Cattle Productivity
0%
10%
20%
30%
40%
50%
60%
70%
Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12
Phase 1 Phase 2 Phase 3 Phase 4
Percent of Women Who Need Permission to Attend Group Mee Over Time and According to Phase
Percent of Women Who Need Permission to Attend Group Mee Over Time and According to Phase
0%
20%
40%
60%
80%
100%
Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12
Phase 1 Phase 2 Phase 3 Phase 4
Overview of Entire Dataset:Where Milk is Sold
Phase One Groups Over Time
29%
0% 5%
5% 12%
0% 2%
47%
1 2 3 4 5 6 7 8
25%
0% 3%
14%
1%
15% 7%
35%
1 2 3 4 5 6 7 8
24%
0% 3%
13%
1% 13%
3%
43%
1 2 3 4 5 6 7 8
20%
0% 4%
11%
0% 10%
2%
53%
1 2 3 4 5 6 7 8
25%
0%
3% 14%
2%
16% 5%
35%
1 2 3 4 5 6 7 8
26%
0% 4%
10%
1% 10% 7%
42%
1 2 3 4 5 6 7 8
27%
0% 3% 9%
1% 15% 6%
39%
1 2 3 4 5 6 7 8
29%
0% 6%
10%
1%
11%
10%
33%
1 2 3 4 5 6 7 8
24%
0% 6%
12%
1%
17%
9%
31%
1 2 3 4 5 6 7 8
27%
0%
2%
6%
2% 15% 21%
27%
1 2 3 4 5 6 7 8
Phase Four Groups Over Time
October 2009
April 2012August 2011
April 2012
June 2009
August 2011
March 2009
August 2010 January 2011
March 2010
1= Percent milk consumed by household 2=Percent milk spoiled 3= Percent milk sold to neighbors 4=Percent milk sold on open markets 5= Percent milk sold to tea shops 6= Percent milk sold to milk collector 7= Percent milk sold to sales point 8= Other
Summary of Statistical Models Used
Data Collectiong & Variables
SDVC has collected and analyzed over 350 variables encompassing 863 groups, 45 field facilitators and 2 regions spanning 4 years.
The data has been collected at the household level, the static group level, and the dynamic group level (which changes over time) over eight waves from 2009-2012.
Given this, advanced statistical methods are required to produce accurate results.
Household ID Count of all cows
Milk product.
Group ID Phase Region PPT Round 1
PPT Round 2
PPT Round 3
737 1 .25 10111 1 1 35 47 75
1601 1 1.6 10111 1 1 35 47 75
2492 3 4.25 20245 2 1 NA 57 90
4962 2 2.5 30865 3 2 NA NA 82
Household Level Data Sta=c Group Level Data Dynamic Group Level Data
Summary of Statistical Models Used
Data Analysis Methods Generalized Linear Mixed-Effects models
To accurately analyze the evidence on how SDVC interventions are working, we built statistical models that looked at all the levels simul-taneously and controlled for the context in which the household exists (in this case, we included various group and program level vari-ables).
Most of the trends and effects presented in the findings have controlled for many confounding and mediating variables in addition to the primary variables of interest, including: Geographic variables (upazila, region)Group effect (group number, group contextual variables)Household differences (family size, number of cows, breed of cows).
We used the R software for statistical computing. R is a free software environment that is widely used by statisticians. R is powerful and uses the most up to date algorithms available due to its open source nature. The R packages contain functions for working with the com-plex type of data that is involved in this project. These functions are not established in most other statistical packages.
We primarily used R to build mixed-effects regression models with both fixed and random effects. This we essential for accuracy as this data has both nested effects (such as households within groups within regions) and crossed effects (such as groups within phases within PPT rounds).
Due to the complex nature of the data, all of the models in this analysis were done using generalized linear mixed-effect models. Each of the models in this presentation control for the size of the household cattle herd, the phase of the learning group of the household, the ef-fects of time on the outcomes, and the contextual difference between the household’s results and the group’s results (ie. the within-house-hold trend and the between-household trend.)
Generalized linear mixed-effect models (GLMMs) are a class of models designed for the analysis of clustered and longitudinal data with non-normal dependent variables. In our models we have used a binomial link funtion and a penalized quasi-likelihood methods. All our models include both a random intercept and a random slope. Each model includes fixed effects such as size of herd, time of collection and phase of group. Each model also includes a series of random effects including the learning group and time. This method properly controls for the fact that each group is meaured repeatedly as well as the fact that the data is clustered in several dimensions (ie. phase and geography)
The acceptable significance level for all of our models is alpha = 0.05.