PPI Data Analysis Workshop - Notes

PPI Data Analysis Training Workshop

Course Design

Pre-Workshop Survey. This survey has to be sent to and collected from the participants

prior to the actual workshop so that the facilitators will have some sense of the

participants’ skill level and thus can tailor-fit the course for them.

Course Objectives. This course is designed so that at the end of the workshop, the

participants should be able to:

1. Compute for poverty distribution of a group;

2. Interpret PPI table;

3. Conduct PPI data analysis; and

4. Communicate PPI results.

This course is intended for data analysts or those who do similar functions: data gathering,

processing, analysis, and communication.

Agenda. The course will run for a day and a half. Time Activity Session objectives

Day 1 1:00-1:30 pm Welcome and introductions Participants are introduced

Expectations are surfaced

Objectives presented

Questions or issues on the agenda clarified and addressed

1:31-2:00 pm Introduction on SPM and contextualizing PPI SPM principles are reviewed

PPI as a tool to support SPM initiative discussed

2:01-2:30 pm Fundamentals of Data Analysis and Interpretation Enumerate the need and benefits of data analysis

2:31-3:00 pm Discussion on PPI Scorecard and Lookup Tables, and Philippine Poverty Lines (national and regional)

Able to use the PPI scorecard, lookup tables and cross-referencing of poverty lines

3:01-3:15 pm Break 3:16-4:30 pm PPI Data Analysis Exercise 1 Prepare a pivot table and graphs

Sample PPI data is analyzed and presented 4:31-5:00 pm Briefing on Exercise 2: ―Take Home Exercise‖ Clarify the assumptions and expectation from

the exercise

Day 2 8:30-8:45 am Recap of previous day Enumerate the key lessons learned by the

participants 8:46-10:00 am Presentation and discussion of the ―Take Home‖

Exercise 2 Data from an MFI is analyzed

Participants presented findings and results of the data analysis

10:01-12:00 pm PPI Data Analysis Exercise 3 Data from an MFI is analyzed

12:01-1:00 pm Lunch 1:00-2:00 pm Presentation and discussion of Exercise 3 Data from an MFI is analyzed

Participants presented findings and results of the data analysis

PPI Data Analysis Workshop

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QuickT ime™ and aTI FF (Uncompressed) decompressor

are needed to see this picture.

2:01-4:30 pm PPI Data Interpretation Exercise PPI data interpretation is communicated with the stakeholders

4:31-5:00 pm Closing and evaluation


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Day 1

Session 1 Introduction (30 minutes)

Session 2 Introduction to SPM and Contextualizing PPI (30 minutes)

Review of the SPM concepts:

Social Performance (SP) is defined as “the effective translation of an institution's social

mission into practice in line with accepted social values.”

Social Performance Management (SPM) is “taking the steps to deliberately manage

and achieve desired results.”

Note: Key message is that SPM is not accidental and automatic; we need effort to

structure and design the organization to ensure meeting of both financial and social goals.

Why PPI? Many organizations take on PPI as a project. But project has an end. It is,

however, part and parcel of SPM. It is a tool that helps us measure and monitor what we

want to do.

Session 3 PPI Overview and Fundamentals (30 minutes)

What is PPI?

Note: Since APIS changes over time, it is important that MFIs work closely with its MIS

so as to keep tracking in tact despite the versioning of the PPI tool.


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Insight from participant: What happened to those clients who dropped out? Chances are

their lives did not improve thus they left. If you do not track them, chances are again, you

will get only positive impact on those who stayed in the MFI. One can track the dropout

even only by sampling.

Poverty table must correspond to the benchmark the MFI chooses. If one uses different

standards over time, then your poverty data are not comparable. One has to articulate in

the report which standard one used in what particular period.

Note: How do we know which table to choose? The sector used to refer to only one table

but some MFIs have interest on other segments of the poor thus the Grameen Foundation

came up with different tables based on different poverty definitions.

Session 4 Fundamentals of Data Analysis and Interpretation (30 minutes)

The data analysis cycle

To determine what is important to an MFI, use the PPI Implementation Plan Outline. Then

identify what data do the stakeholders need.


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In data collection, how do we ensure accuracy of data? The monitoring checklist helps one

ensure accuracy of data—a sort of ―are-things-going-well‖ test.

After identifying data needs, identify possible sources of data.


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How often do we need to collect data?

How do we do sampling? There is an Excel program designed to compute the ideal sample

size for you specific needs---confidence level, etc.

Data quality—simply put, it is making sure that all 10 questions are answered. There are

Guidelines and Data Integrity on page 3 of the PPI Implementation Plan Outline.


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Verification is checking the data collection process; validation is checking the accuracy of

the data collected. Data cleaning is just part of data verification—that is, when some items

are not filled out and you cannot do anything about it any more.

Seat Work

Data Analysis Exercise 1

Note: Exercises are designed such that they build on one another; their level of difficulty

increases as the workshop progresses.

Note: Issue raised—how to compute for the average loan size? If there is only one data

point, use only the look-up table; if more, use the poverty distribution program.

Some pointers in data analysis:


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Exercise 1 – PPI Data Analysis Exercise 1

Session 5 PPI Data Analysis Exercise 1 (90 minutes)

Exercise 2.1 – Beginners group

Exercise 2.2 – Advance group

Day 2

Session 6 Presentation and Discussion of Take Home Exam/ Exercise 2 (105 minutes)

PPI Data Analysis Exercise 2

Possible answers:

Analysis of Pivot Table 1 – Loan Cycle

Clients in the earlier loan cycle have higher likelihood of being below the poverty line.

This could mean that as the clients stay in the program, they move up the poverty

ladder.

The number of clients per cycle decreases as the loan cycle increases. This could be

indicative of dropout or other intervening factors that may describe the figures—are

there other products offered to ―older‖ or ―bigger‖ clients?


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―Startdate‖ is 2009—is this the start date of the program or start date of the survey? If

the former case, then the loan cycle data becomes invalid (i.e. how many loan cycles

could there be in a year?) If the latter case, then the table is simply a profile of the

client base and not indicative of movement out of poverty in the absence of a baseline.

Background information on the organization and its program this becomes critical in

data presentation.

Analysis of Pivot Table 2 – Age

As the client age group increases, the likelihood of poverty decreases. Thus, retaining

clients make more sense in that the MFI can help the move out of poverty. On the

other hand, this could also explain the ―drop-out‖ in the program—because people

move out of poverty, they also do not find the value of staying in it.

Average loan size is correlated with age. However, the difference in the loan size

between age 28 and 57 is minimal, so the correlation may not be that high. Still on the

other hand, maybe loan size is not a factor of movement out of poverty.

The table does not tell you ―why‖ but can only lead you to ask more questions—find

more interesting things or validate your earlier assumptions.

Looking at the number of clients per age group, more clients fall in the 28-57 bracket.

In terms of targeting, will the MFI target the younger, poorer ones or the bigger

population, older groups?

Analysis of Pivot Table 3 – Number of Business

The higher number of business of clients, the less if the poverty likelihood. Worth

looking at is the type of businesses they engage in—are they complementing? Are they

all viable? Further, will the MFI target those clients with multiple business? Or should

it help its clients engage in many businesses to increase their chances of moving out of

poverty?

The data on single-business clients could also be indicative of competition if they are

in the same business, e.g. sari-sari stores.

The table could also be analyzed vis-à-vis the loan cycle in the pivot table 1 to see if

the number of client business increases with the loan cycle.

Analysis of Pivot Table 4 – Geographical location

Average loan amounts of rural and urban do not vary much. However the issue of

PAR comes out—who has better repayment record? But this leads to more

questions—how is your product designed? Are there other intervening factors why one

group could be ―better payer‖ than the other?

Analysis of Pivot Table 5 – Loan products


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Fully secured loan has the lower poverty likelihood—this could mean the clients who

avail are not really poor such that they have assets to secure. Note further the average

loan size.

The ILP and IND (individual loans) – clients in these groups are less likely to poor.

Comparing with the group loan data, it does make sense to have different product

types to suit the client needs.

Agri-loan and AKP (group loan) have clients with the highest poverty likelihood.

Should the MFI serve/target this type of clients?

The table also leads to asking, are multiple loans allowed? –but maybe not because the

total number sums up to the total clients.

Note: Emphasize that at the end of the day, the MFI has to decide which data it needs, so

that they do not falling to the trap of collecting everything but not really use it. Highlight,

too, the need for a benchmark against which a certain data set will be compared, e.g.

poverty incidence in the area of operation vis-à-vis poverty levels of the clients. What

does that mean if your clients’ poverty level is lower (meaning less poor) than the area

poverty level? It means that the MFI is not yet hitting the poor market.

Session 7 PPI Data Analysis Exercise 3 (150 minutes)

Exercise 3

PPI Data Analysis Exercise 3

Possible answers:

Analysis of Pivot Table 1 – Savings data

Line 3 - 32% of the 37 clients with savings of between 500 and 999 are likely below

the national poverty line.

Analysis of Pivot Table 2 – Age data versus loan size

As age of clients increases, the less likely they are below the poverty line. Top

borrowers are between the age of 27 and 56.

Line 8 – if there is only one client, then the 22% is not longer the percentage of the

group, but the actual probability of that person being below poverty.

Analysis of Pivot Table 3 – Start date with MFI

Not realistic because client count in year 7430.

Poverty likelihood increases with the loan cycle. Question is, were the data taken from

amongst the new clients in each year only or from the entire client base? If the former,


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then the MFI is more likely recruiting clients who are poor. If the latter case, then it

means that the clients as a whole are getting worse off as they stay in the program.

As the clients stay longer in the program, they save less and borrow less. And

beginning 2004 enrollees, the percentage of the poor belonging to the below NPL class

increases each year.

Session 8 Communicating PPI Results (90minutes)

At the stages of data analysis, identify the uses of the information generated and the users

of this information.

E-copy file: Sample PPI Report.xls

Note: Data presented must be aligned with the social goals of the MFI. Researchers have

to exercise judgment what data need to be presented.

Some things that you want to consider in making your report:

INSERT NAME OF ORGANIZATION<INSERT> Date of Report Social Goals

<INSERT> Date range of the data in the report

MFI Loan Products

Confidence Level and IntervalPPI Collection Process

BENCHMARKS

MFI Name No % Below NPL% Below

$1/day

% Below

$1.5/day

% Below

$2/day

Poverty Rates/Incidence

(households)

% Below

NPL

% Below

$1/day

% below

$1.5/day

% below

$2/day

New Clients 86,922 17.1% 25.8% 59.8% 79.4% National Level 17.0 25.4 56.9 74.9

Existing Clients 55,214 12.0% 17.3% 49.2% 71.9% Rural 17.5 30.8 66.2 84.1

All (Total) Clients 142,136 15.1% 22.5% 55.7% 76.5% Urban 15.4 10.0 30.3 48.9

State/Province Level

Loan Cycle

(1 cycle = 50 weeks)No % Below NPL

% Below

$1/day/PPP

% below

$1.5/day/PPP

% below

$2/day/PPP Rural 11.8 23.3 55.4 79.1

1 86,922 17.1% 25.8% 59.8% 79.4% Urban 18.6 8.9 26.0 41.4

2 22,154 13.3% 19.5% 52.2% 74.1%

3 15,633 11.2% 16.0% 47.1% 70.2% Example Graph

4 11,345 10.2% 14.1% 44.8% 68.7%

5 3,728 11.7% 16.8% 49.1% 72.0%

6 1,578 13.2% 19.7% 54.7% 77.2%

7 544 15.7% 24.3% 61.1% 81.8%

8 149 13.9% 20.9% 57.3% 79.7%

9 62 11.6% 16.3% 48.3% 72.0%

10 21 8.6% 10.6% 39.4% 66.1%

All Clients 142,136 15.1% 22.5% 55.7% 76.5%

Occupation No % Below NPL% Below

$1/day

% below

$1.5/day% below $2/day

Agriculture 72 16.8% 26.2% 62.3% 81.6%

Animal/ Husbandry 37,276 21.5% 33.4% 70.0% 86.5%

Assets 47 10.3% 14.1% 46.0% 69.9%

Cultivation 4,203 12.4% 17.9% 49.4% 71.6%

Production 11,601 11.7% 16.6% 48.0% 71.1%

Service Sector 8,792 11.9% 17.0% 48.2% 71.0%

Trading 71,419 13.5% 19.6% 52.0% 73.9%

Transportation services 8,726 10.7% 14.7% 45.8% 69.9%

All Clients 142,136 15.1% 22.5% 55.7% 76.5%

<INSERT> 1. New cl ient out reach of at least 17% below NPL

<INSERT> 2. After three years with the program, 5% of clients move above the

<INSERT> Group loan

<INSERT>Census of al l new and renewal clients. Collected with loan

application.

<INSERT> 95% confidence with +/- 0.95 percentage points (based on total number of cl ients)

Poverty Level Distribution by Loan Cycle

0%

10%

20%

30%

40%

50%

60%

70%

1 - 2

(N=712)

3 - 4

(N=636)

5 - 6

(N=292)

7 - 8

(N=137)

> 9

(N=123)

Total Sample

% Below NPL% Below $4/Day /PPP


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Exercise 4 – PPI Communication Plan

How one can communicate PPI results to various stakeholders:

Documents

PPI Data Analysis Workshop - Notes