Development of a Cost-Effective
Poverty Assessment Tool
Contractor’s Final Report
Lima, Peru. October 2004
Acknowledgements
We wish to thank Ms. Julia Johannsen and Mr. Manfred Zeller for their help and
valuable guidance in every stage of the survey, especially in relation to quality
assurance procedures for data gathering and processing.
We are also very thankful to all the staff who was directly involved in the project,
including interviewers who had to work long hours under harsh weather and even
unsafe public conditions. This survey would not have been possible without their
valuable efforts.
Contents
Key Staff in Peru .............................................4
Definitions .....................................................4
Territorial / administrative divisions ...................................................................... 4
Geographical Regions ................................................................................................... 4
Other Terms.................................................................................................................. 5
Survey Organization and Execution ..........................5
Functions ........................................................................................................................ 5
Selection and Training of Survey Personnel .......................................................... 6
Fieldwork .......................................................6
Fieldwork Planning and Programming ....................................................................... 6
Fieldwork Execution .................................................................................................... 7
Fieldwork Control and Supervision ........................................................................... 7
Data Entry and Validation ....................................8
Sampling ........................................................8
Sample of MFI Clients ................................................................................................ 8
Sample of Non-Clients ................................................................................................ 8
Routes ........................................................................................................................... 10
Description of routes and assigned personnel .................................................... 10
Supervision ................................................... 14
Routes selected for supervision. ............................................................................ 14
Supervision Procedure ............................................................................................... 14
Supervisors’ Reports ................................................................................................. 15
Major Findings ............................................... 15
Learning Curve............................................................................................................. 15
Improved Wording Needed ..................................................................................... 15
Estimated workload based on the actual sample ................................................ 15
Errors in the lists of entering clients in areas of recent expansion ............. 16
Most common errors when filling questionnaires out ........................................ 16
Difficulties found in MFI selection ....................................................................... 16
Curious happenings during fieldwork ..................................................................... 18
The Case of the CRAC Cruz de Chalpón. ............................................................... 18
3
Final Report
Key Staff in Peru
The Peruvian
professionals:
staff
involved
in
the
project
included
the
following
M.Sc. Luis Castillo Quintana. Project Manager and Director
Econ. Pedro Llontop Ledesma. Fieldwork Manager,
Est. Mario Reyna Farje Espinoza. Statistician.
Definitions
Many terms are peculiar to a given country, i.e. the meaning they are given there may
not be the same –or as common– as elsewhere.
Territorial / administrative divisions
The Peruvian territory is administratively divided first into departments
(departamentos) and therefore a department is the largest territorial division.
Departments are in turn broken down into provinces (provincias), and finally
provinces are subdivided into districts (distritos.)
As the decentralizing regionalization process consolidates, a further governance tier
is currently in progress –that of regions. For the time being, however, regions and
departments encompass the same territories and consequently we will no longer
consider regions in the discussion that follows.
Geographical Regions
Traditionally, the Peruvian territory has been divided into three geographical regions
as a result of the influence of the Andes, the large mountain range that runs NorthSouth throughout the territory. Accordingly, such regions take the shape of three
North-South longitudinal strips. The strip between the Sea and the Western slopes
of the Andes is called the Coast. The highlands crowned by the Andes, with its
fertile and habitable valleys are called the Sierra. The land East of the Andes where
tropical forests and rainforests thrive is called the Jungle, and is sometimes
referred to as the Amazonian region/area. Climatic and cultural differences are
believably assumed to influence and/or determine their inhabitants’ distinct
patterns of living.
4
While such names refer directly to dominant geographical features, it should
not be understood as restricted to them, but only as labels of such extensive
tracts of land. It is within such a context that we should understand such
apparent oxymora as “Urban Jungle” (neither should it be thought of as
equivalent to “asphalt jungle”, but rather as the urban areas within the
geographical region called “the Jungle”) and the like.
Other Terms
Route. An established or selected course of travel and the assigned territory to be
systematically covered
CRAC. Initials of Caja Rural de Ahorro y Crédito, Spanish for Rural Savings and
Credit Union
CMAC. Initials of Caja Municipal de Ahorro y Crédito, Spanish for Municipal Savings
and Credit Union
CAC. Initials of Cooperativa de Ahorro y Crédito, Spanish for Savings and Credit
Union
EDPYME. Initials of Entidad de Desarrollo de la Pequeña y Micro Empresa, Spanish
for Micro and Small Business Development Institution.
Survey Organization and Execution
Functions
Instituto Cuánto was responsible for the overall implementation of the survey in
compliance with the instructions by, and in coordination with, the IRIS Center. The
survey direction and management was structured as follows:
100
200
300
400
Project Manager. Responsible for both technical and managerial direction of
the survey in all stages. He also comprehensively assesses, controls, and
supervises the progress of the survey.
Translator. Responsible for translating and back-translating documents in
Spanish into English and vice versa, as well as facilitating communication
between IRIS researches visiting Lima and local staff.
Statistician. Responsible for preparing the sampling frame, as part of the
survey design, as well as for determining the procedures leading to the
selection of sampling units.
Fieldwork Manager. Responsible for implementing the survey fieldwork, from
staff training and planning to actual fieldwork to the submission of
completed questionnaires for data entry.
5
500
600
700
Regional Supervisors: Responsible for coordinating the fieldwork in each area
surveyed.
Logistics Manager: Responsible for delivering any materials on a timely
manner, and managing current expenses.
Data Processing Officer. Responsible for processing data, designing the data
entry shell, and validating the data entered.
Selection and Training of Survey Personnel
This involved two stages: First, personnel recruiting and technical training for
fieldwork supervisors and interviewers, and second, a course for critics and
codification operators.
Instituto Cuánto has available a large directory of experienced interviewers, which
take part of social and economic studies on a regular basis.
Pretest Survey
This survey will allow us to administer the questionnaires proposed for the study so
as to make it possible to correct any errors, measure the time required to complete
a questionnaire per household, as well as to improve the instructions given on the
manner both conglomerates and households were to be located.
Fieldwork
This part of the survey refers to the execution of a number of tasks, subtasks, and operations intended to gather data from selected households to
meet survey objectives.
Fieldwork involved the following tasks:
Fieldwork Planning and Programming
This task involved providing information on fieldwork routes, time required to
complete the fieldwork, detailed determination of goods and services required for
the successful completion of each task, fieldwork budgeting and allocation of funds
to each route. Planning and programming required taking into account sample size and
distribution. One of the basic elements of this process is determining the time
required to get to the selected sampling unit. In this regard, the following
movements should be taken into account:





Within the route
Between routes
Between districts
Between provinces
Between departments
6
To that end, district-, province-, and department-level maps, roadmaps and street
maps of the districts selected, as well as auxiliary information indicating distances,
types of roads, and the usual means of transportation were provided. It was very
important the broad expertise and knowledge of the staff involved in this task.
Fieldwork Execution
This task involves gathering information from selected households by administering
the questionnaires designed for this purpose. The main guidelines to execute the
survey are listed below:
The information from each selected household is gathered through direct interview.
Fieldwork schedule is flexible, depending on circumstances and the appointment
arranged by the interviewer or Team Leader with the household head.
The Team Leader allocates the workload to each interviewer
Work is done Monday to Saturday. Only previously appointed interviews are carried
out on Sundays, as required, to complete the weekly workload or to retrieve
information.
As the information from individual households is completed, each interviewer
submits all completed questionnaires to his/her Team Leader for data cleaning and
validation
The Team Leader supervises interviews to correct interviewers’ initial mistakes and
check for compliance with methods and procedures.
As Team Leaders are responsible for the technical and administrative performance
of survey fieldwork, they had to be continually in contact with the Project Manager,
who consequently controls and supervises the overall progress of the survey.
Team Leaders are required to submit fieldwork progress reports every other day,
either via email o telephone.
Fieldwork Control and Supervision
It involves checking the survey fieldwork for adequate technical performance and
compliance with methods, budget, and scheduled deadlines as planned for the
fieldwork. Control and supervision is performed by the Team Leader.
7
Data Entry and Validation
This stage involves gathering, coding, and entering the data from completed
questionnaires. Data entry is carried out under the Data Processing Officer
in a roomy and adequately equipped computer room by experienced data entry
operators. Double entry of data is performed to prevent potential data entry
errors.
Sampling
Factors to be taken into consideration for sample selection
Sample of MFI Clients
Step 1.
Once we had available the lists provided by the selected MFI, entering clients were
sorted by district. Then two districts were selected by applying PPS sampling.
Step 2.
Where selected districts had over 100 clients each, 100 were chosen from each to
get a sample size of 200.
Where districts had fewer than 100 clients, two districts were randomly selected
and the sampling units were allocated to both districts according to their sizes.
There was only one exception to this rule. The CRAC Cruz de Chalpón had too few
new clients. As a result, we had to select 5 districts to cover the total sample of
200 clients.
Step 3.
Sample units were allocated as shown in Table 1.
Sample of Non-Clients
Step 1. (Using PPS method)
At the beginning we decided to select 6 departments from which the sample will be
taken. The following departments were selected:
-
Arequipa
Cusco
La Libertad
Lima (twice)
Piura
8
Table 1. Allocation of the MFI-Client sample among selected MFIs.
MFI
Department Province
District
Lima
Huaral
Chancay
CAC San Isidro
Lima
Huaral
Huaral
Total
Chincha
Chincha Alta
CMAC de Chincha Ica
Ica
Chincha
Pueblo Nuevo
Total
Huánuco
Huánuco
Amarilis
NGO Caritas del
Junín
Chanchamayo Chanchamayo
Perú
Total
Lima
Lima
Chorrillos
Edpyme Edyficar
Lima
Lima
San Juan de Lurigancho
Total
Lambayeque Chiclayo
Chiclayo
CRAC Cruz de
Cajamarca
Jaén
Jaén
Chalpón
Lambayeque Ferreñafe
Ferreñafe
Lambayeque Lambayeque Lambayeque
Total
Apurímac
Chincheros Ranracancha
CAC San Pedro
Apurímac
Chincheros Anco – Huallo
Total
Total Sample Backup
57
32
3
299
168
17
356
200
20
349
100
10
244
100
10
593
200
20
412
111
11
331
89
9
743
200
20
252
100
10
783
100
10
1035
200
20
136
119
12
52
46
5
24
21
2
16
14
1
228
200
20
98
25
3
700
175
17
798
200
20
Step 2
Since Lima was taken twice, we decided to draw one more department out of the
remaining ones. The selected department was Cajamarca
Step 3.
When we analyzed the sample, we observed that no department in the Amazonian
region had been selected. To solve this, we applied PPS sampling to the departments
in the Amazonian region alone. The selected department was Loreto.
Step 4
With a sample of 7 departments, we allocated the 800 non-client sample among the
selected domains as shown in Table 2.
Table 2. Non-Client sample allocation among selected domains.
Domain
Total % Population
Coast and Lima
400
0.518
Metropolitan Lima
200
0.289
Urban Coast
132
0.178
Rural Coast
68
0.052
Sierra
266
0.350
Urban Sierra
99
0.126
Rural Sierra
167
0.224
Jungle
134
0.131
Urban Jungle
66
0.060
Rural Jungle
68
0.071
TOTAL
800
1.000
% Sample
0.500
0.250
0.165
0.085
0.333
0.124
0.209
0.168
0.083
0.085
1.000
9
Table 3. Districts selected
Arequipa
Cerro Colorado
Mariano Melgar
Tiabaya
La Libertad
Chao
La Esperanza
Trujillo
Cajamarca
Cajamarca
Encañada
Querocoto
Loreto
Iquitos
Punchana
Yurimaguas
Cusco
Echarate
Quiquijana
Wanchaq
Piura
Chulucanas
Pariñas
Sullana
Lima
Ate
El Agustino
Lima
Rímac
San Juan de Miraflores
Santiago de Surco
Step 5. (Using PPS Sampling)
We selected 6 districts from Lima and 3 from all other departments. Table 3 shows
the selected districts.
Step 6
Since there is no recent information about which districts have rural areas,
we selected randomly, according to our needs, which districts would be rural
and which urban. The districts that were chosen as rural were verified is
they indeed have rural areas from were to take the rural sample.
Step 7
The sample was finally allocated as shown in Table 4:
Routes
Fourteen routes were required to cover all selected areas from which the sample
was to be taken. Table 5 shows the routes covering the MFI-Client sample areas and
Table 6 shows the routes covering the Non-Client sample areas..
Description of routes and assigned personnel
Only two out of the fourteen routes (routes 13 and 14) required the use of
Quechua-speaking interviewers. Most interviews were therefore held in Spanish.
Except for the team who covered route 6 only, all teams covered two routes, one
after the other. Table 7 details the sequences followed.
Route 1 with 2. Route 3 with 4 and 5. Route 6 with 7. Route 8 with 9 and 10. Route 11
alone. Route 12 alone. Route 13 with 14.
10
Table 4. Detailed allocation of Non-Client Sample among selected districts
Total
Coast
Urban
497
332
Rural
303
68
TOTAL
800
400
Arequipa
100
Urban
66
Mariano Melgar
33
Tiabaya
33
Rural
34
Cerro Colorado
34
Cajamarca
100
Rural
100
Cajamarca
34
Encañada
33
Querocoto
33
Cusco
100
Urban
33
Wanchaq
33
Rural
67
Echarate
34
Quiquijana
33
La Libertad
100
100
Urban
66
66
La Esperanza
33
33
Trujillo
33
33
Rural
34
34
Chao
34
34
Lima
200
200
Urban
200
200
Ate
33
33
El Agustino
33
33
Lima
33
33
Rímac
34
34
San Juan de Miraflores
34
34
Santiago de Surco
33
33
Loreto
100
Urban
66
Iquitos
33
Punchana
33
Rural
34
Yurimaguas
34
Piura
100
100
Urban
66
66
Pariñas
33
33
Sullana
33
33
Rural
34
34
Chulucanas
34
34
Sierra
99
167
266
100
66
33
33
34
34
100
100
34
33
33
66
33
33
33
33
-
Jungle
66
68
134
34
34
34
100
66
33
33
34
34
-
11
Table 5. Routes for MFI-Client areas
Route 1. Lima: 200 households
District
Province
Huaral
Huaral
Chancay
Huaral
Route 2. Ica: 200 households
District
Province
Chincha Alta
Chincha
Pueblo Nuevo
Chincha
Route 3. Lima: 200 households
District
Province
Chorrillos
Lima
San Juan de Lurigancho
Lima
Route 4. Junín: 89 households
District
Province
Chanchamayo
Chanchamayo
Route 5. Huánuco: 111 households
District
Province
Amarilis
Huánuco
Route 6. Lambayeque: 154 households
District
Province
Chiclayo
Chiclayo
Ferreñafe
Ferreñafe
Lambayeque
Lambayeque
Route 7. Jaén: 46 households
District
Province
Jaén
Jaén
Route 8. Lima: 200 households
District
Province
Ate
Lima
El Agustino
Lima
Lima
Lima
Rímac
Lima
San Juan de Miraflores
Lima
Santiago de Surco
Lima
Route 14. Apurímac: 200 households
District
Province
Anco – Huallo
Chincheros
Ranracancha
Chincheros
Department
Lima
Lima
Domain
Urban Coast
Urban Coast
Department
Ica
Ica
Domain
Urban Coast
Urban Coast
Department
Lima
Lima
Domain
Metropolitan Lima
Metropolitan Lima
Department
Junín
Domain
Rural Jungle
Department
Huánuco
Domain
Rural Sierra
Department
Lambayeque
Lambayeque
Lambayeque
Domain
Coast rural
Coast rural
Coast rural
Department
Cajamarca
Domain
Rural Jungle
Department
Lima
Lima
Lima
Lima
Lima
Lima
Domain
Metropolitan Lima
Metropolitan Lima
Metropolitan Lima
Metropolitan Lima
Metropolitan Lima
Metropolitan Lima
Department
Apurímac
Apurímac
Domain
Sierra rural
Sierra rural
12
Table 6. Routes for Non-Client areas
Route 9. Arequipa: 100 households
District
Province
Department
Mariano Melgar
Arequipa
Arequipa
Tiabaya
Arequipa
Arequipa
Cerro Colorado
Arequipa
Arequipa
Route 10. Piura: 100 households
District
Province
Department
Chulucanas
Morropón
Piura
Pariñas
Talara
Piura
Sullana
Sullana
Piura
Route 11. La Libertad - Cajamarca: 200 households
District
Province
Department
La Esperanza
Trujillo
La Libertad
Trujillo
Trujillo
La Libertad
Chao
Virú
La Libertad
Cajamarca
Cajamarca
Cajamarca
Encañada
Cajamarca
Cajamarca
Querocoto
Chota
Cajamarca
Route 12. Loreto: 100 households
District
Province
Department
Iquitos
Maynas
Loreto
Punchana
Maynas
Loreto
Yurimaguas
Alto Amazonas Loreto
Route 13. Cusco: 100 households
District
Province
Department
Wanchaq
Cusco
Cusco
Echarate
La Convención Cusco
Quiquijana
Quispicanchi
Cusco
Domain
Urban Sierra
Urban Sierra
Sierra rural
Domain
Urban Coast
Urban Coast
Coast rural
Domain
Urban Coast
Urban Coast
Urban Coast
Sierra rural
Sierra rural
Sierra rural
Domain
Urban Jungle
Urban Jungle
Rural Jungle
Domain
Urban Sierra
Rural Jungle
Rural Sierra
Table 7
Route
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Supervisor
1
1
1
1
1
1
1
2
1
1
1
1
1
1
Interviews
5
5
6
2
3
5
0
6
3
3
3
2
5
5
Working Days
30
30
26
29
27
27
30
27
27
29
46
30
21
30
13
Supervision
A number of supervising visits to most fieldwork teams were carried out intended
mainly to observe the their performance as they administered the Composite
Questionnaire to respondents, as well as to verify the quality of the information
provided by MFIs that took part in the survey.
Routes selected for supervision.
The following routes were visited by Mr. Luis Castillo, Project Manager and Director.
Department of Lambayeque:
Route of MFI CRAC Cruz de Chalpón’s clients (cities of Chiclayo and Lambayeque.)
Department of La Libertad:
Non-Client Route. (Cities of Trujillo and Moche)
Department of Loreto:
Non-Client Route. (City of Iquitos y Town of Punchana.)
Department of Ica:
Route of MFI CMAC de Chincha’s clients. (City of Chincha, Town of Pueblo Nuevo.)
Department of Arequipa:
Non-Client Route. (City of Arequipa and Cerro Colorado)
Department of Lima:
Route of MFI CAC San Pedro’s clients. (Cities of Huaral y Chancay)
All fieldwork teams in Metropolitan Lima were supervised by Mr. Pedro Llontop,
Head Survey Officer. Mr. Llontop had also been commissioned with supervising
fieldwork teams in Cusco and Apurímac; however, these two cities could not be
supervised as flights to Cusco and Apurímac were cancelled due to harsh weather
conditions.
Supervision Procedure
The following steps were followed in every province visited:
Initial Briefing with the Supervisor
An overall briefing on fieldwork progress and Q&A on matters directly related with
the Composite Tool.
Initial review of all completed questionnaires:
Those which required further clarification were set aside.
Determining the average time required for interviewers to complete a questionnaire.
First Overall Meeting with Fieldwork Teams
Exchange of opinions and Q&A related to the Composite Questionnaire.
On-Site Fieldwork Visit
A given area was chosen only when work was in progress at that time. The distances
to be covered by interviewers were calculated, as well as the time required to
14
complete them. The daily workload was checked for completion, and a sample
questionnaires were partially filled for illustrative purposes.
Completion of Review of All Questionnaires
Intended to assess the individual performance of each interviewer.
Final Briefing with the Fieldwork Team.
Discussion of main findings of questionnaire review. Individual instructions intended
to improve team performance were given. In this last meeting administrative issues
were discussed, largely related to increased operational costs.
Supervisors’ Reports
After the first inspection visits we determined the need that each supervisor
prepared a report detailing the main occurrences in their work. This would be used
as input for a planned meeting with Ms. Julia Johannsen, Such feedback would be
useful to pinpoint the main concerns and challenges found in the application of the
Composite Tool.
These reports were timely submitted to Ms. Julia Johannsen and were personally
elaborated in the meeting held in Lima with the attendance of all supervisors and
interviewers. Such reports were extended with the conversation with interviewers,
who described the challenges and facilities found in each fieldwork area.
Fieldwork supervision was deliberately carried out when the Composite
Questionnaire was being applied. We did not intend to supervise interviewers’
performance in administering the LSMS Expenditure Module (Benchmark
Questionnaire.)
Major Findings
Learning Curve
It was surprising how readily interviewers eventually were able to administer the
Composite Questionnaire, averaging less than 90 minutes to complete it.
Improved Wording Needed
On the other hand, it was quite clear to us that some effort should be done to
improve the wording of some questions; especially those who were intended to elicit
information on housing rent or sale value from respondents
Estimated workload based on the actual sample
Peru is a very diverse country, and a range of distinct reactions to survey interviews
is normally found.
15
Table 8.
Domain
Clients
Non-Clients
Urban
Coast
2-3
2-3
Urban
Sierra
2-3
3-4
Urban
Jungle
N/A
3-4
Rural Coast Rural Sierra Rural
Jungle
2-3
2-3
N/A
2-3
3-4
3-4
Table 8 summarizes the modules of the Composite Questionnaires that may be
applied per workday:
Errors in the lists of entering clients in areas of recent expansion
All MFIs were requested the same information: a list with the names and addresses
of entering clients belonging to branches where a recent expansion had been taken
place, with entering clients meaning people who had been their clients for at most 6
months. However it seems that the concept of “being a client for at most 6 months
was not clear enough, as there were cases in which:
The list included underage people with savings accounts.
The list included clients of the MFI who had repaid a first loan and had been
granted a new loan after a few months, which diverted from what had been
requested.
It was not rare the existence of clients who.
Applied for, and were granted, a loan using a relative’s address, but the borrower
himself lived elsewhere, even abroad!
Declared the house they were renting as their domicile but had moved elsewhere
without reporting their new addresses.
Most common errors when filling questionnaires out
Supervision visits revealed that errors were limited to a few questions. Table
9 shows the most common errors found.
Some errors found in the supervision could be attributed to interviewer’s confusion.
This in turn may have been the result of insufficient number of examples during
training sessions, and the fact that supervisors did not hold ongoing updating
briefings with their teams.
Difficulties found in MFI selection
We contacted friends and colleges in USAID by means of Mr. Jaime Gieseke, who
referred us to Mr. Armando Pillado from COPEME. COPEME provided us with a list
containing:
 Some of the main Municipal Savings and Credit Unions (CMAC), Micro and
Small Business Development Institutions (EDPYMEs) and Rural Savings and
Credit Unions (Cajas Rurales)
 Contact people.
 Some indications on how to locate them.
16
Table 9. Most common errors when completing questionnaires.
Page Question
Page Question
B12
Terms
were
not
clearly
D1a-b Either there was no real estate
understood
market or people refused to
2
5
B15
Unlikely high amounts for poor
provide
property
value
households
estimates.
E15
The cultural peculiarities of some
E17
Both
respondent
and
11
populations made it difficult to 11
interviewer
misreport
the
correctly ask this question
social program providing aid.
G1.20 Respondents felt bothered as they
H6
Answers were often not
considered the question to be
15
18
realistic considering the area
repetitive and often did not
surveyed.
answer it.
H7, 9 Respondents provided confusing
Questions I10 onwards:
answers. They confused their
Confusion with amounts and
20
21
economic situation with their
names of NGOs providing free
political discomfort
services was a constant error.
We contacted some of the main domestic NGOs providing microcredit. We visited
directly to Chief Officers responsible for microcredit programs.
We started conversations with senior officers of Banco del Trabajo, Banco de
Crédito and tried to meet with a senior officer of Mi Banco. However, and despite all
our efforts, we could not manage to get any support from these institutions.
At the same time, IRIS provided us with information and contacts from the
Federation of Savings and Credit Unions.
We established the first contacts with them and prepared a short list of financial
institutions willing to cooperate with us.
Generally speaking, financial institutions showed a strong opposition to provide us
with the requested information. They claimed it was not considered to be public
information. We were reminded that people with moderate or high economic or
financial position had been victims of theft and kidnappings. In a few cases, the
information on “who is who” or “how much they hold” was provided to criminals by
personnel from financial institutions themselves.
Despite those difficulties, we managed to get support from:
NGO Caritas del Perú,
EDPYME Edyficar,
Credit Unions San Isidro de Huaral and San Pedro de Andahuaylas.
CRAC Cruz de Chalpón de Chiclayo.
CMAC de Chincha.
In all cases we had to make use of our professional and social relationships to
contact people who may be willing to support our survey, as we requested. But we
were not able to check the quality of the lists we were given which make it even
more difficult to locate MFI clients.
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Curious happenings during fieldwork
Although this issue may have been extensively reported by Ms. Julia Johannsen, we
would like to highlight only two facts:
That one of our fieldwork teams was interviewed by a Peruvian TV station
because they had been bearing freezing temperatures for over 48 hours
inside a bus that was prevented from moving forward by the snow.
That a campaign of enforced collection of debts took place in Chincha at the
same time we were carrying out the interviews, which caused lots of clients
to “disappear” temporarily or even to move to other address.
Remarkably enough, respondents continually expressed their discomfort
towards some questions that tried to depict their living conditions. Most
people felt offended because they felt their privacy was being invaded. Some
respondents even decided to discontinue the interview and refused roundly to
resume it.
The Case of the CRAC Cruz de Chalpón
We planned to complete 200 interviews to clients of this MFI. The fieldwork was
carried out at the same time as the Copa América Perú 2004 Football Tournament
was played in several cities of Peru.
Chiclayo was one of the venues of this continental football tournament. As a result
of this, the number of crimes increased because criminals, especially thieves, from
other places came to the city.
In the city of Chiclayo, during the development of fieldwork a number both minor
and serious crimes were perpetrated and extensively covered by the local media.
This alarmed local residents against strangers’ visits.
The CRAC Cruz de Chalpón provided us with a relatively small list of clients in the
city of Chiclayo.
The number was small due to the fact that its clients are persons somehow linked to
agriculture and the trade of agricultural products. This year there was a severe
drought in Northern Peru and as a result there were only a small number of new
credits approved between January and May 2004.
These factors affected the performance of interviewers assigned to this route, to
the extent that it was the first place the Project Manager had to visit for
supervision.
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The interviewers made up to 4 attempts to have MFI clients participate.
Nevertheless, in spite of such multiple attempts, we were not able to complete the
number of interviews assigned.
All possible backup respondents were used so as to complete all the assigned
questionnaires. However, out of all 200 interviews to clients of the MFI assigned for
the entire route, we were able to complete only 175.
This was the only route we were not able to complete the assigned workload. In many
cases respondents refused to answer the second questionnaire, in spite of having
promised to do so.
The combination of these factors affected severely the performance of our work.
And undoubtedly small, incomplete lists were the principal cause of our failure to
meet objectives as planned in this area.
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