M&E Presentstion

1. KEM LEY | Principal investigator
NHIM DALEN |Consultant
BORAY BORALIN | Data Analyst
UMAKANT SINGH | Advisor
Professional Training

2. To intensify the M&E skills and expertise of researchers and
improve the impact on general public and development.
Main
Objective
Specific
Objectives
Expected
Results
Impact
1. Building the capacity and skills of researchers on M&E system and
development evaluation
2. Strengthening the capacity of researchers to be able to develop M&E
framework and tools
3. Strengthening the capacity of researchers to be able to conduct program and
project evaluation
4. Equipping researchers with M&E skills and expertise
1. Become familiar with concepts and practices of M&E
2. Be able to develop M&E framework and Tools
3. Be able to conduct program/project evaluation
4. Equipped with M&E Skills and expertise
1. M&E Specialist
2. Professional Research Consultant

3. M&E Framework andTools Development
Module 1: M&E Rapid Assessment
Module 2: M&E framework development
Module 3: Monitoring tools development
Module 4: M&ETools Pilot and Review
Module 5: Finalized M&E Framework andTools
Module 6: Roll-out Plan and M&E Costed Capacity Plan
Development Evaluation
Module 1: Objectives of Evaluation
Module 2: Focus and Scope
Module 3: Select Indicators
Module 4: Chose Study Design
Module 5: Data collection Plan
Module 6: Data Enumerators Train
Module 7: Data Collection/Field Work
Module 8: Data processing and analysis
Module 9: Data organization and interpretation
Module 10: Evaluation ReportWriting

4. OUTPUTS
PROCESS
INPUTS
OUTCOMES
Efficiency
Effectiveness
IMPACTS
OBJECTIVES
Input Monitoring
Process
Monitoring
Outputs Monitoring
Outcomes Monitoring
and/or Evaluation
Impact Monitoring and/or
Evaluation

5. Roll out Plan
M&E Tools pilot and
review and finalized
tools
Monitoring Tools
Development
M&E Framework
Development
M&E Rapid
Assessment

6. Conceptual Framework
Results Framework
Logical Framework
Interaction of various
factors
Logically links inputs,
processes, outputs,
and outcomes
Logically linked program
objectives

7. Community action and results for health and
non health
Activities/services for
communities
Systems
develop & manage
that they use to deliver
Commune
Committee for
Women and
Children
Community &
Health Actors
Outputs
Health outcomes Other outcomes
Impacts on
health and
reduction of
vulnerability of
OVC
Resulting in:
which in turn contribute to
that lead to

8. % of current school
attendance among
double orphans and
non orphans aged 10-
14
% of double orphans
who received
education assistance
and scholarship;
# of OVC and
community people
involved in parental
association and
education for all
committee
# of school offering
breakfast
% of double orphans
whose households
received economic
support
# of OVC whose HH
received economic
and food support

9. Narrative
Summary
Objectively
verifiable
indicators
Means of
Verification
Important
assumptions
Overall Goal
Project Purpose
Outputs
Activities Inputs
Pre-Conditions

10. Type of
Framework
Brief Description Program
Management
Basis for Monitoring
and Evaluation
Conceptual Interaction of
various factors
Determine which
factors the program
will influence
No. Can help to explain
results
Results Logically linked
program objectives
Shows the causal
relationship between
program objectives
Yes – at the objective
level
Logic model Logically links
inputs, processes,
outputs, and
outcomes,
Shows the causal
relationship between
inputs and the
objectives
Yes – at all stages of
the program from
inputs to process to
outputs to outcomes/
objectives

11. Strategy1:
Objectives Activity
Domain
Core
Indicators
Baseline Target Data
Collection
Methods
Responsible
Institution
Reference
Indicator
Strategy2:
Goal: Strengthen the coordination, systems, coverage and quality, of services needed to mitigate the impact of HIV on
the lives and futures of Cambodian children, while also addressing the underlying issues to vulnerable children.
Impact Indicators: % of Birth Registration, Proportion of Current School attendance , stunt, underweight and wasted

12.  Select indicator standard
 Reporting Format
 Instruction Guide
 Data Flow and Management
 M&E Data Collectors Train
 Piloting and updating
 Roll out plan
 Data Base System
 Data Use Plan

13. A good Indicator should meet the following six
standard;
 The indicator is needed and useful
 The indicator has technical merit
 The indicator is fully defined
 Its feasible to measure the indicator
 The indicator has been field tested or used
operationally.
 The indicator set is coherence and balanced (
relevant to indicator sets only)

14.  STANDARD 1: THE INDICATOR IS NEEDED AND
USEFUL
 Question 1: Is there evidence that this indicator is needed at the
appropriate level?
 Question 2: Which stakeholders need and would use the
information collected by this indicator?
 Question 3: How would information from this indicator be used?
 Question 4: What effect would this information have on planning
and decision-making?
 Question 5: Is this information available from other indicators
and/or other sources?
 Question 6: Is this indicator harmonized with other indicators?

15. STANDARD 2: THE INDICATOR HAS
TECHNICAL MERIT
 Question 1: Does the indicator have
substantive merit or technically sound and significant or
measure something significant and important within particular field
 Question 2: Is the indicator reliable and
valid?
 Question 3: Has the indicator been peer
reviewed?

16. STANDARD 3: THE INDICATOR IS FULLY DEFINED
 Title and definition
 Purpose and rationale
 Method of measurement
 Data collection methodology
 Data collection frequency
 Data disaggregation
 Guidelines to interpret ad use data
 Strengths and weaknesses
 Challenges
 Relevant sources of additional information

17. STANDARD 4: IT IS FEASIBLE TO COLLECT AND ANALYSE DATA
FOR THIS INDICATOR
 Question 1: How well are they systems, tools and
mechanisms that are required to collect, interpret and use
data for this indicator functioning?
 Question 2: How would this indicator be integrated into a
national M&E framework and system?
 Question 3: How what extend are the financial and
human resources needed to measure this indicator
available?
 Question 4: What evidence exists that measuring this
indicator is worth the cost?

18. STANDARD 5: THE INDICATOR HAS BEEN FIEL-
TESTED OR USED OPERATIONALLY
 Question 1: To what extend has the
indicator been field-tested or used
operationally?
 Question 2: Is this indicator part of a
system to review its performance in
ongoing use?

19. STANDARD 6: THE INDICATOR SET IS COHERENCE
AND BALANCED (Relevant to indicator sets only)
 Question 1: Does the indicator set give and overall
picture of the adequacy or otherwise of the response
being measured?
 Question 2: Does the indicator set have an appropriate
balance of indicators across elements of the response?
 Question 3: Does the indicator set over different M&E
levels appropriately?
 Question 4: Does the set contain an appropriate number
of indicators?

20. Consistency or dependability of data and
evaluation judgments, with reference to quality of
the instruments, procedures and analysis used to
collect and interpret evaluation data
Indication defines clearly what we should be
measured. It defines the variables that help
measure change within a given situation as well
as describe the progress and impact.
The extent to which something is reliable and
actually measures up to or make a correct claim.
The process of cross-checking to ensure that the
data obtained from one monitoring method are
confirmed by the data obtained from a different
method
INDICATOR PROTOCOLS
INDICATOR PROTOCOLS
REQUIRES
• Definition
• Measurement
• Strengths
• Limitations
• Reliability
• Precision
• Validity
• Objective
• Owned
• Accessible
• Useful

21. M&E FRAMEWORK &
TOOLS
DEVELOPMENT
M&E Rapid
Assessment
M&E Framework
Development
Monitoring Tools
Development
M&E Tools Pilot
and Review
Roll-out Plan and
M&E Costed
Capacity Plan
Finalize M&E
Framework and
Tools

22.  What is instruction guide?
 Instruction guide is a reference tool formulated tends to provide clear
explanation on how to accurately complete the reporting format.
 How to develop instruction guide?
 Identify purpose of the instruction guide
 State purpose of the reporting form
 Data sources
 Who prepare the report
 Frequency of reporting
 Reporting period
 Name of agency completing the report
 District
 Province
 Indicators

23. Indicators:
 For example: Total number of OVC whose households received economic support (income
generation activities, livelihood support, regular cash transfer)
 Write the total number of OVC whose households received economic support during the
reporting period.
Definition: Economic support (IGAs and livelihood) has been defined as:
 Home gardening
 Animal husbandry
 Provision of agricultural seeds
 Small business development
 Money management training
 Emergency cash support
 Regular cash transfers
 Access to loan/microfinance
 Other
Disaggregation:
 This data is disaggregated by gender. Write the total number of male OVC in the “Male”
column and the total number of female OVC in the “Female” column. Then write the total
number of OVC (male + female) in the “Total” column.

24.  When mapping the flow of data, please consider
the following issues:
 Who will be responsible for data collection?
 Who will provide the data?
 Who will be responsible for supervision of data
collection?
 Who will be responsible for compiling and aggregating
data?
 How often are data collected, compiled, reported, and
analyzed?
 How are data sent from one level to the next?
 How is feedback on reported data provided?

25. Ministry of Social Affairs, Veterans and Youth
Rehabilitation (MoSVY)
(Child Welfare Department)
Youth Rehabilitation /
Drug Rehabilitation
Alternative
Care Centers
Provincial Department of Social Affairs, Veterans and
Youth Rehabilitation (PoSVY)
DoSVY
Commune Council (via
CDB)
Quarterly
Quarterly
Quarterly
Quarterly PoSVY
Report on OVC
Provincial Department of Planning
Ministry of Planning
CCWC
POVCTF
Service
Providers
(NGOs)
Data flow
Feedback
Supportive Supervision
NOVCTF
Village Council (via
CBD)
Annual
Annual
Annual
Annual
Law
Enforcement
(police,
prison, courts
)
PHD
MoH

26. When developing role and responsibility of all key players
involve in data collection, some important point that you
should consider:
 What type indicator they need to collect and report?
 How many indicator they need to collect and report?
 How they collect those data (source of data –
registration book)?
 Which reporting form they use?
 How frequency that they should report – when?
 Who they should report to?

27.  Transposition—An example is when 39 is entered as 93.
Transposition errors are usually caused by typing mistakes.
 Copying errors—One example is when 1 is entered as 7; another
is when the number 0 is entered as the letter O.
 Coding errors—Putting in the wrong code. For example, an
interview subject circled 1 = Yes, but the coder copied 2 (which =
No) during coding.
 Routing errors—Routing errors result when a person filling out a
form places the number in the wrong part or wrong order.
 Consistency errors—Consistency errors occur when two or more
responses on the same questionnaire are contradictory. For
example, if the birth date and age are inconsistent.
 Range errors—Range errors occur when a number lies outside the
range of probable or possible values.

28.  First, determine the source of the error.
 If the error arises from a data coding or entry
error
 If the entry is unclear, missing, or otherwise
suspicious
 Once the source of the error is identified,
the data should be corrected if
appropriate.

29.  Feedback should be constructive and not punitive
 Feedback should be useful to data collectors and help
them improve their work
 Errors should be pointed out and corrected
 The M&E supervisor should talk to the data collector to
find out the cause of the error so it can be prevented in
the future
 The M&E supervisor should discuss how data quality
and reports can be improved in the future

30.  Provide both positive and negative feedback (e.g. you
do X very well but can improve Y)
 Provide feedback in a timely manner
 Help data collectors understand the problem so they
know how to correct it in the future
 Be helpful and collaborative

31.  Builds relationship between data collectors and users at all levels
 Important element of management and supervision
 Leads to greater appreciation of data
 Improves data quality
 Improves information use
 Improves service delivery and benefits the target population and the
community
 Improve program reporting- data collectors understand trends in
data and understand reasons behind numbers
 Incentivizes and motivates data collectors

32.  Set criteria for selecting pilot province
 Provide training on M&E reporting tools to
all data collectors
 Provide on the job training to all data
collectors

33.  Objective:
 Aim to take an in-dept look at the quality of the
data that was collected during the pilot period
and to assess the systemic factors that affect
M&E performance and to gather direct input
on the M&E tools and system.

34.  Step in conducting the review:
 Develop assessment tools
▪ Data transmission, accuracy, processing and analysis
▪ Data transmission
▪ Data accuracy
▪ Data processing and analysis
▪ Data use
▪ Some qualitative questions added
 Provide training to assessment team
 Conduct assessment
 Conduct consultation meeting on the findings

35.  Key point affecting the finalization of M&E
framework and mechanics
 Indicators
▪ Does these indicators are feasible to collect?
▪ Does these indicators are feasible to analyze and use?
▪ Is there any evidence that financial and human
resources are available to allow an indicator to be
measured and that the benefits of measuring the
indicator are worth the costs?
A good indicator needs to be one that is feasible to measure with
reasonable levels of resources and capacity.

36. The situation may change meaning that an indicator needs to be
changed, discarded or added.
 M&E system mechanics
 Does the data collection tools are applicable?
 Does the reporting formats are applicable?
 Does the instruction guide (guideline) is user friendly?
 Data management process
 How well functioning of the data flow of the system?
 Does existing human resource have an appropriate capacity to
manage the data flow?
 How clear the roles and responsibility of department or person
involved in M&E system?
 Does the frequency of data collection and reporting are
appropriate at each level?

37.  Revise M&E framework, with revised
indicator, M&E mechanics, and data
management process
 Conduct consultative meeting among M&E
team and relevant stakeholders to finalize
M&E framework and system
 Get approval from top level of management
(decision makers, policy makers).

38. Share
Data with
Partners
Reporting/
Accountability
Program
Improvement

39. Data Analysis-,
Interpretation and
report
Data Cleaning,
entry, Processing,
Sampling
Technique
Sample Size
Calculation
Objectives , Scope
and Steps for
Evaluation &
Research

40. Royal
Government
of Cambodia
Development
Partners and Civil
Society
Threatened
Communities
• Unfair
Compensation and
worsen living
condition
• Loss of job
• High Service cost
for relocated site
• There is no
available legal,
social and health
services
Positive Impact of
development
• Beautification
• Development
• Employment
• GDP Growth
• Economic Growth
• Survive people from
Slum
Negative impact of development
• Human Rights Violation
• Inadequate housing rights
• Unfair Compensation
• Unfair development
• Inequality of profits distribution

43. Objectives
Focus & Scope
Select Indicators
Chose Study design
Data Collection Plan
Data collection/Field Work
Data Cleaning & Verification
Data Processing & Aggregation
Data Analysing & Organization
Data Interpretation & Report
1
2
3
4
5
7
8
9
10
Data Enumerators Train
Data Use and Data Translation
11
12
6

44.  The overall objective of the program
evaluation of HRTF is to assess the
social economic impact of Cambodia
Forced eviction in urban areas of
Phnom Penh Municipality.
 The specific objectives of the program
evaluation is to know the status of
economic, education, health, employment,
food security and environment of
threatened and relocated communities.

45. Socio Economic
Impact
Relocated
Households
Economic Status
Education
Status
Health Status
Employment
Environment
Threatened
Household
Economic
status
Education
Status
Health
Employment
Environment
Poverty and
Quality of
live among
relocated
Households
and
threatened
Households

46. Selected Indicators Relocated
Households
Threatened
Households
1. Percentage of Children drop out of school
2. Percentage of households whose income below poverty line
3. Percentage of households consumption
4. Percentage of households with debt
5. Percentage of household access to registered MFI
6. Percentage of households with food shortage
7. Percentage of house members whose access to health
services in the past three months
8. Percentage of households have experienced physical attack
9. Percentage of household have experienced stigma and
discrimination
10. Percentage of respondents have lost job due to forced
eviction

47.  Qualitative and quantitative study design (Cross
Sectional Study)
 Household Survey (Cluster Sampling-Lot division)
 Key Informant Interview(KII)-Relevant Stakeholders
 Focus Group Discussion (FGD)-RS and TS HH
 Desk Study and Literature Review
▪ Cambodia Legal Frameworks
▪ National and International Research Findings
▪ NSDP and JMI 2009-2013, MoP
▪ Pro-Poor Policy and National Safety Net Strategy, CoM
▪ HRTF Baseline Survey 2010
▪ HRTF Program and strategy documents
▪ HRTF Strategic Plan 2011-2015
▪ CCHR Survey on land and housing Issues 2011
▪ Draft of National Housing Policy 2011
▪ Country Report _Special reporters 2009, 2010, 2011
▪ Others

48. SDV Z Z2 p q e e2 n
99% 2.58 6.6564 0.5 0.5 0.01 0.0001 16641
98% 2.33 5.4289 0.5 0.5 0.02 0.0004 3393
95% 1.96 3.8416 0.5 0.5 0.05 0.0025 384
90% 1.64 2.6896 0.5 0.5 0.10 0.01 67
85% 1.44 2.0736 0.5 0.5 0.15 0.0225 23
80% 1.28 1.6384 0.5 0.5 0.20 0.04 10
Sample size (n) for Precision (e) of:
Size of Population +/- 3% +/- 5% +/- 7% +/- 10%
500 a 222 145 83
600 a 240 152 86
700 a 255 158 88
800 a 267 163 89
900 a 277 166 90
1,000 a 286 169 91
2,000 714 333 185 95
3,000 811 353 191 97
4,000 870 364 194 98
5,000 909 370 196 98
6,000 938 375 197 98
7,000 959 378 198 99
8,000 976 381 199 99
9,000 989 383 200 99
10,000 1,000 385 200 99
15,000 1,034 390 201 99
20,000 1,053 392 204 100
25,000 1,064 394 204 100
50,000 1,087 397 204 100
100,000 1,099 398 204 100
Over 100,000 1,111 400 204 100

49.  Confidence Level: The standard confidence level is
95%. This means you want to be 95% certain that your
sample results are an accurate estimate of the
population as a whole.
 Precision: This is sometimes called sampling error or
margin of error. We often see this when results from
polls are reported.
 Confidence Interval: We can say that we are 95%
certain (this is the confidence level) that the true
population's average salary is between 1,800 and 2,200
(this is the confidence interval).

50. Populatio
n size
Sample
size
Populatio
n Size
Sample
Size
10 10 550 226
20 19 600 234
40 36 700 248
50 44 800 260
75 63 900 269
100 80 1,000 278
150 108 1,200 291
200 132 1,300 297
250 152 1,500 306
300 169 3,000 341
350 184 6,000 361
400 196 9,000 368
450 207 50,000 381
500 217 100,000+ 385
N
n= ----------
1+(N(e)2
2
2
e
q
p
z
n 
SDV Z Z2 p q e e2 n
99% 2.58 6.6564 0.5 0.5 0.01 0.0001 16641
98% 2.33 5.4289 0.5 0.5 0.02 0.0004 3393
95% 1.96 3.8416 0.5 0.5 0.05 0.0025 384
90% 1.64 2.6896 0.5 0.5 0.10 0.01 67
85% 1.44 2.0736 0.5 0.5 0.15 0.0225 23
80% 1.28 1.6384 0.5 0.5 0.20 0.04 10

51. Population size Sample size Population Size Sample Size
10 10 550 226
20 19 600 234
40 36 700 248
50 44 800 260
75 63 900 269
100 80 1,000 278
150 108 1,200 291
200 132 1,300 297
250 152 1,500 306
300 169 3,000 341
350 184 6,000 361
400 196 9,000 368
450 207 50,000 381
500 217 100,000+ 385

52. N
n= ----------
1+(N(e)2
n: Sample Size
N: Population Study
e: Level of precision
Yamane (1960) formula assumes a degree
of variability (i.e. proportion) of 0.5 and a
confidence level of 95%.
SDV Z Z2 p q e e2 n
99% 2.58 6.6564 0.5 0.5 0.01 0.0001 16641
98% 2.33 5.4289 0.5 0.5 0.02 0.0004 3393
95% 1.96 3.8416 0.5 0.5 0.05 0.0025 384
90% 1.64 2.6896 0.5 0.5 0.10 0.01 67
85% 1.44 2.0736 0.5 0.5 0.15 0.0225 23
80% 1.28 1.6384 0.5 0.5 0.20 0.04 10

53. 2
2
e
q
p
z
n 
SDV Z Z2 p q e e2 n
99% 2.58 6.6564 0.5 0.5 0.01 0.0001 16641
98% 2.33 5.4289 0.5 0.5 0.02 0.0004 3393
95% 1.96 3.8416 0.5 0.5 0.05 0.0025 384
90% 1.64 2.6896 0.5 0.5 0.10 0.01 67
85% 1.44 2.0736 0.5 0.5 0.15 0.0225 23
80% 1.28 1.6384 0.5 0.5 0.20 0.04 10
n= sample size
p = the approximate proportion you expect to find in the population
q = 1-p
e = the level of precision you can tolerate (plus or minus 10%, etc.)
z = the z-value from a table for the level of confidence you want

54. LQAS
LOT5= 19
LOT1= 19
LOT2= 19
LOT5= 19
LOT3= 19
LOT4= 19
1.
• Can be used locally
• Can provide an accurate measure of coverage (
benchmark)
• Can be used for quality assurance
• is a simple, low cost random sampling
methodology
• Small sample
• Meet the quality standards
• Statistically determined sample size
 LQAS = Lot Quality Assurance Sampling
• Developed in the 1920’s
• In 1980’s, method was adapted to measure health program coverage:
• Immunization
• Malaria
• Neonatal tetanus elimination
• Leprosy elimination
• Family planning,
• HIV/AIDS prevention
• In Cambodia World Vision , CONCERN , ADRA, and other

55. Sample size for LQAS
where
n= sample size
p = the approximate proportion you expect to find in the population
q = 1-p
e = the level of precision you can tolerate (plus or minus 10%, etc.)
z = the z-value from a table for the level of confidence you want
n = (1.96)
2
(0.5 x 0.5) / (0.1) 2
n = (3.84) (0.25)/(0.01)
n = 96
2
2
e
q
p
z
n 

56. Non Random
Sampling
Purposive
Convenient
Snowball
Quota
Accidental
Random
Sampling
Simple
Random
Sampling
Systematic
Random
Sampling
Cluster
Sampling
Stratified
Random
Sampling
Combination
Cluster sampling is a multi-step way or we may
want to take a stratified sample of farmers at
various distances from a major city
you do not have a complete list of everyone in the
population of interest
combinations of methods are used
we want to select 100 files from a population of 500?

57. Name of
Village Population
Cumulative
population
Sampling
Interval
Random
number
Sample
Size
A 510 510
B 750
C 910
D 570
E 800
F 750
G 600
H 450
K 530
L 900
Total 6770 385

58. Commune 1: Pres Klang (Control Area)
Name of ADP
Number of Samples Seleced
Name of village Population
Cumulative
population
Sampling
Interval
Random
Number (0-5 month) (15-45yrs)
Mor Seth 914 914 169 105
5 5
274
443
612
781
Okleng Por 769 1683 950
5 5
1119
1288
1457
1626
Sromouve 643 2326 1795
4 4
1964
2133
2302
Krang Doung 357 2683 2471
2 2
2640
Anlong Svay 631 3214 2809
3 3
2978
3147
Total 19 19

59. Methods Source Advantage Disadvantage
1. Desk Study & Literature Review
2. Population Base Survey
3. Qualitative Data Collection
3.1. Key Informant Interview-KII
3.2. Focus group discussion-FGD
3.3. Case Study
3.4. Best Practice
3.5. Observation
3.6. Self-administered questionnaires
3.7. Exit Interview
4. Routine Program Monitoring

60. Coding and
Entry
• Analyzing
Editing
Checking
• DATA ORGANIZATION
• DATA INTERPRETATION
• REPORTING
• DATA USE

61. Table
Chart
Graphs
DATA
Description
Opinion or
View

62. Male Female Total
Age n 131 91 222
5-9 13.0% 9.9% 11.7%
10-14 72.5% 69.2% 71.2%
15-18 14.5% 20.9% 17.1%
Current school attendant 76.7% 67.4% 72.9%
Level of education n 133 102 223
Never attend school 29.5% 23.1% 26.9%
Primary school 65.2% 67.0% 65.9%
Secondary school 4.5% 9.9% 6.7%
High school 0.8% 0.0% 0.4%
Type of education attended n 129 89 210
Formal education 43.4% 48.3% 45.4%
Non-formal education 11.6% 10.1% 11.0%
Both formal and non-formal education 39.5% 40.4% 39.9%
Current living status n 132 93 225
Residential care 18.9% 17.2% 18.2%
Non-residential care 81.1% 82.8% 81.8%
Status of children n 133 93 226
Orphan 26.9% 32.3% 29.1%
Street children 58.5% 55.9% 57.4%
Children in conflict with the law 6.2% 2.2% 4.5%
Chronically ill parent/caregiver during month of the last 12
months 23.1% 22.6% 22.9%
Abused and exploited children 1.5% 2.2% 1.8%
Children addicted to drugs 0.8% 0.0% 0.4%
Children with physical disabilities 0.0% 1.1% 0.4%
Children infected by HIV 0.0% 0.0% 0.0%
Children living with poor HH 44.6% 38.7% 42.2%
Title does not say:
what, when, where
A mistake of using
row and column
Footnote is
needed
Interpretation

63. Status of orphans and vulnerable in Kamreing and Battambang Province,
Cambodia, 2010
Ref. Definition, MoSVY 2010, An orphan is a child who has lost one or both parents.
A maternal orphan is a child whose mother has died. A paternal orphan is a child whose father
has died. A double orphan is a child who has lost both parents.
Note: Types and Definition of
OVC, MoSVY 2010
Male Female Total
Type of orphan n 35 30 65
Maternal orphan 17% 17% 17%
Paternal orphan 46% 60% 52%
Double orphan 37% 23% 31%
Total 100% 100% 100%
Male Female Total
Overlap risk of children n 133 93 226
Once 45% 48% 47%
Double 50% 51% 50%
Triple 5% 1% 3%
Total 100% 100% 100%

64. 87.5%
77.4% 76.0%
92.0%
Orphan Non-Orphan Orphan Non-Orphan
MPK 2010 CDHS 2005
Percentage of children aged 10-14 who currently attending school
Title does not say: what, when, where
Reference
Footnote is
needed
Interpretation

65. 87.5%
77.4% 76.0%
92.0%
Orphan Non-Orphan Orphan Non-Orphan
MPK 2010 CDHS 2005
%
of
respondent
Type of study
MPK 2010 Orphan
MPK 2010 Non-Orphan
CDHS 2005 Orphan
CDHS 2005 Non-Orphan
Comparison of school attendant among orphan and non-orphan
aged 10-14 between MPK 2010 and CDHS 2005
MPK: Meatho Phum Kohma
CDHS: Cambodia Demographic and Health Survey
Ref. End of project evaluation of MPK in 2010 in Battambang Province
with two district (Battambang and Kamrieng).
CDHS 2005, the nationwide study.

66. Poverty/work
67%
To by my own
16%
Mother/father coming
here
11%
Orphan
2%
DV, abuse and
exploitation
1% Other
3%
Main reason of being away from home

67. 54%
65%
22%
37%
47%
16%
70%
70%
10%
50%
70%
60%
Education
Health care
Economic
Food and nutrition
Psychological
Other support
Essential Service for OVC given by MPK
compared to NPA Review 2008
MPK 2010 NPA Review 2008

68. KEM LEY | Principal investigator
NHIM DALEN |Consultant
BORAY BORALIN | Data Analyst
UMAKANT SINGH | Advisor
Employment
Rate
Poverty
Line
Income
per capital
• 25% or 1/3 are under poverty
line ( RKR, PVH and ST >40%
(MoP 2010)
• 12% food insecurity to 20% or
2,8 millions (CDRI 2008)
• School drop out rate from 13%
to 22%
• Underweight:28%
• Stunt : 40%
• Wasted : 11%
Source: CAS 2008 and CDHS 2010
• 23% or 3.5 m of young population
• 72 of 100 people aged 15-24 are job
seekers
• 30,000 to 30,000 have entered job
market but 67,000 new job created or
27%
• Reason: Skill mismatch
Source: ILO and CAMFEBA
• Income per capita 285 in
1997 to 593US$ in 2007
• More than 80% are farmers
and 91% are living in rural
areas and account for 48% of
total poor
• Benefits have not been
equitably distributed
• Gaps between rich and poor
(the difference in share of consumption
between the richest 20% of
Cambodians and the poorest 25 &
reveals a dramatic and widening gap in
wealth)
Data Interpretation
Fragility of
Cambodia Development

69. Employment
Rate
Agriculture
Sector
Industrial
Sectors
Service
Sector
• Income: 70% from self
employment income, 27%
from wage and salary, 2%
from transfer received and 1%
from other,
• Labor Forces or working age
(15-64) is 84% or 7.5 millions
• Child under 18 is 41% and
child labor (5-14) is 45%
• Expenditure: 49%, food, 19%
(House, water, electricity, 10%
health and others
CSES 2009, MoP
30-40,000 seek job
but absorption is
67,000 Job/27% or

70. Cambodia Population
13,395, 682 or
young population
Working Age
Population (15-64 )
84% or 7.5 millions
Adult working
Population (64%)
including Old Age
Working Population
Old AgeWorking
Population(…)
Young Population(15-24)
23% or 3.5 Million of 200
per village (14 ,073) or 2000
per c/s )1621 C/S)
23% ofWorking
population
Children (5-17): 4.3
Millions or 35%
1.5 millions (5-14)
were working
children or 45%
 Sources: ILO, 2009
 CCA 2009
 CDB, MoP 2009
 CSES 2009
 Good Governance and Social
Accountability,TAF 2011, 8 Provinces
 Average INCOME: 120 US$ per
month per family
Average Expenditure: 150 US$ per
month per family

71. Increased employment
Young Population
Agriculture Sector
Self-Employment
Access to credit
Farming System
market integration
Reformed School
Curriculum
VocationalTrainings
Minimum Wage Policy
for Workers
( Labor Law)
Industrial Sector
Service Sector
Domestic Workers
Retired and Old Age
Population
Management
Early Retired
Population
Reduced Child Labor
D&D –Practicing Decentralization
EmploymentYoung Population
with CC,Village Councils and other
lines offices of Ministries
USA, Singapore, Thailand
Employment and minimum wage
policy and policy enforcement
108 NGO study: 40, 0000 or equal to all
factory workers
NGO Sector-CARE, Plan Int..
Good Governance

73. Total employment workers (15-64) is 7.5 millions but
 Paid employee : 22.8%
 Self employed: 51.7%
 Unpaid family workers: 25.1%
 Employer: 0.3%

74. Food
Education
Motorcycle
49% Cell-Phone
44%
Social Services
Housing/Water and
Electricity
Health
Legal Services
TV
60%.

75. • Radio : 42.5%
• TV :59.6%
• Video tape/recorders/Players : 28.7%
• Stereo : 13.5%
• Cell phone : 43.8%
• Satellite Dish :1%
• Bicycle : 67.7%
• Motorcycle :49%
• Car : 3.8%
• Jep/Van :1%
• PC : 3.4%
CSES 2009, MoP, RGC

76. Household, Capita-
US$
Cambodia 94 21
Phnom Penh 307 65
Urban 134 54
Rural 79 18
8% are negative income among formers
________________________________________
______
• Self employment income :70%
• Wage and Salary :27%
• Transfer received : 2%

77. Description Cambodia Urban RR
• Food 30$ or 49% 38$ 45.% 27$ or 52%
• Housing/Water/Ele 12$ or 19% 19$ or 23% 8$ or 15%
• Health 5$ or 7% 5$ or 5% 5$ or 9%
• Education 2$ or 2% 2$ or 3% 1$ or 1%
• Other 23% 24% 24%
Total 62$ 85$ 51$
CSES 2009, MoP, RGC

78. Average Monthly
Income
(Rural Area)
18 US$
Expenditure
Average monthly
expenditure per
capita
In Rural Area
51 US$
Average Monthly
Saving per capita
-33 US$ Debt
Landless
 Migrants
Child Labor
School Drop Out
 SexWorkers
 Fragile Population
Others
Poverty
Social
Insecurity