Stung Chinit Irrigation and Rural Infrastructure Project

Baseline Income Survey

Preliminary Report: Methodology and Survey Execution

June 12, 2005

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Contents

1. Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2. Project Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.1 Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Irrigation and Drainage Component. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.3 Rural Infrastructure Component . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Agricultural Extension Component. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.4 Baseline Income Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.5 Research Consultant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3. Income Survey Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1 Household Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 Assets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14 3.3 Diversification: Production, Labor, and Sales . . . . . . . . . . . . . . . . . . . . . .16

4. Sampling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.1 Systematic Random Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.2 Level of Significance and Margin of Error of Proportions . . . . . . . . . . . . . . 22 4.3 Level of Significance and Margin of Error of Means . . . . . . . . . . . . . . . . . . 24 4.4 Control Sample Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.5 Household Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26

5. Survey Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .27 5.1 Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.2 Survey Audit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

6. Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6.1 Data Entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

6.2 Non-Parametric Analysis Description of Distributions . . . . . . . . . . . . . . . . 31 6.3 Parametric Analysis Description of Distributions . . . . . . . . . . . . . . . . . . . . 32 6.4 Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32

7. Limitations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 7.1 Design Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 7.2 Survey Limitations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

8. References and Works Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37

Annexes

Annex 1: Research Schedule Annex 2: Survey Instrument

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1.0 Summary The Stung Chinit Irrigation and Rural Infrastructure Project (SCIRIP) is part of the poverty reduction strategy for Cambodia. It has received financing through the Royal Government of Cambodia (RGC) with donor financing from the Asian Development Bank (ADB) and Agence Francaise de Developpement (AFD). Original total project cost is 23.8 million USD and, under a recent finance restructuring, the Cambodian government has offered to contribute an additional 3 million USD. SCIRIP consists of three components: Irrigation and drainage (the main component), rural infrastructure (improvement of roads and improvement of markets), and agricultural extension and organization. SCIRIP is projected to have a 14.4% economic internal rate of return, mostly through increased agricultural income and productivity. This report describes the methodology for the baseline income study conducted for SCIRIP by private research consultancy CamEd. The baseline income survey is the means of verification for the achievement of two output indicators in the logical framework of the third component. A follow up income survey will be conducted in 2008 so that anticipated increases in income can be identified and measured. In order to facilitate preparation for and accuracy of the follow up study in 2008, CamEd will produce a report on the findings of the baseline survey, including detailed description of methodology. That report will be developed from this current draft. The methodological approach for this baseline income survey has three main aspects. First, this baseline income survey records income sources and indicators of income, rather than attempting to determine household net income. Second, the baseline survey includes extensive survey auditing and data verification, including two identical interviews for each household. Third, the sampling technique uses random probability sampling, with the number of households sampled in a village in proportion to the population of the village. The fourth aspect, parametric and non-parametric analysis of data, is undergoing development. At the time of this draft, the baseline survey fieldwork is complete, data entry is finished, data collected is undergoing verification, and the database is being reconfigured. A final description of methodology will be included in the baseline survey report. It is hoped that stakeholders can take the time to review this current draft of methodology and provide feedback, especially on areas of this description that seem inadequate, incorrect or unclear. The comments received will be helpful in producing the final report and description of methodology. Please forward comments to director@cam-ed.com and cc to caseydbarnett@gmail.com.

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2.0 Project Background Poverty reduction is a priority for the Cambodian government. The government strategy emphasizes improvements in agricultural production. This is reflected in the government’s Second Socio-Economic Development Plan (SEDPII) which goes further to emphasize irrigation projects. The Stung Chinit Irrigation and Rural Infrastructure Project (SCIRIP) is part of this strategy and has received financing through the Royal Government of Cambodia (RGC) with considerable donor financing from the Asian Development Bank (ADB) and Agence Francaise de Developpement (AFD). Beneficiaries will also contribute to project costs, mainly with their labor input. A memorandum of understanding was signed between the ADB and the Ministry of Water Resources and Meteorology (MOWRAM) in May 2000, and the project commenced in September 2001. SCIRIP is designed to increase agricultural productivity and boost the local economy through provision of irrigation and drainage, agriculture extension, and rural roads and markets. In this section we provide a brief description of the project area and the project's three components: Irrigation and Drainage, Rural Infrastructure (roads and markets), and Agriculture Extension. 2.1 Area Kampong Thom Province is located about 164 km to the north of Phnom Penh and is accessed by Route 6. The project area covers the Western half of Santuk district of Kompong Thom province, a centrally located province in the Tonle Sap region. The original plan for the irrigation component included coverage of Baray district directly to the South of Santuk district; however, it remains to be seen if and when this phase of the project will be implemented. The area is varied, ranging from high wooded land to a flood plain. In these districts, there are two Tonle Sap tributaries that flow in a Western direction across the districts,

The Stung Chinit river as seen in Santuk District, Kompong Thom.

There is a relatively large population of Cham (Khmer Muslims) in the project area.

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Stung Chinit and Stung Tang Krasang. The environmental impact assessment for SCIRIP describes the area as "an extensive floodplain-cum-delta system with numerous creeks, braided waterways and permanent water bodies such as oxbows and marshes" (Lim et al, p 3). The SCIRIP Agrosystems Analysis goes further to classify the area into four zones: 1. The Lowland Rainfed and Market Zone, 2. The Lowland Rainfed Riceland Zone , 3. The Alluvial Flood Plain Zone, and 4. The Undulating Upland Mixed Crop Zone (SCIRIP, 2002). Santuk district has a population of approximately 58,434 people (UNPF, 1999) whose economic activities are largely agricultural, with the rice as the main product. Other products include watermelons, sweet potato, mango, coconut, cashew, corn, beans, vegetables, and forest products (SCIRIP, 2002). Near main roads, and along National Road No. 6, which crosses the district in a North-South direction, living standards are higher and there are markets and trading activities. 2.2 Irrigation and Drainage Component By and large, this project was conceived and planned by the ADB, supported by research from the 1999 ADB Fact Finding TA under the loan CAM 29247-01. This fact finding research lead the ADB to propose SCIRIP. The Stung Chinit area has long been used for or considered by various irrigation or dam projects. SCIRIP aimed to rehabilitate the Stung Chinit and Tang Krasang weirs (low dams) and related structures. These weirs were originally construction during the Democratic Kampuchea (DK) regime (1975-1979), but gradually fell into disrepair during the subsequent People’s Republic of Kampuchea regime (1979-1993). In the early 1990's the area drew interest from various parties interested in producing hydro-electric power. The ADB provided 0.8 million USD from 1996-1999 under the Japan Special Fund for the Stung Chinit Water Resource Development Project (SCWRDP). Research under the SCWRDP lead Japan, the ADB, and the World Bank to jointly offer funding for the construction of a 6-8 megawatt hydro-electric dam on Stung Chinit. Ultimately, the ADB remained the lead donor interested in pushing development of the Stung Chinit area, but the interest shifted from hydro-electricity to irrigation for rural agriculture. Under the lead of ADB economist Darius Teter, the ADB and the RGC agreed on a rural irrigation project centered on the rehabilitation of the DK era weirs, spillway, flood embankments, canal structures, and earthworks. However, the technical studies conducted under Mr. Teter underestimated the extent and cost of the rehabilitation and the project scope was reduced; the Tang Krasang

The water level drops dramatically during the dry season from December to April.

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river works were eliminated as were Baray district activities (Lahmeyer, 2003). Because of the rescaling of the project, a Irrigation Infrastructure and Irrigation System Management Components Supplementary Analyses was produced in June 2003. This new complementary analyses provides the final economic evaluation.

Together, the SCIRIP infrastructure and irrigation activities are estimated by MOWRAM and the ADB to have a positive economic effect. Under the complementary analysis, which was conducted by the construction and engineering firms Lahmeyer International and SMEC International, the overall project economic internal rate of return (EIRR) will be 14.4% over a 32 year period (Lahmeyer, 2003). Separately, the irrigation component (described in the supplementary analyses as "Phase I") which covers 2,960 ha will have an EIRR of 12.5% on economic costs

of 12.9 million USD. The economic costs includes the cost of construction, resettlement, and cost of land lost (based on annual net income losses based on pre-project agricultural productivity) (Lahmeyer, 2003). The 12.5 % economic return is partly based on estimated additional income from area-wide irrigated rice, from a second rice crop grown on 2,338 ha, and 500 ha planted with a higher-valued non-rice irrigated dry season crop (Lahmeyer, 2003). 2.3 Rural Infrastructure Component The development of rural infrastructure component of this project has also been scaled down due to the underestimation of costs described above; some of the funds allocated to road and market construction were diverted to irrigation and drainage. The remaining funds will go towards 15 person-months of international and 16 person-months of domestic consultants comprising roads engineer, rural infrastructure engineer, and social organizer (Lahmeyer, 2003). The revision for this 4.8 million USD component provides for organization of a project implementation unit at the provincial department of rural development, improvement of 80 km of roads, and rehabilitation of four (4) markets. The aim is to reduce transport and marketing costs. Specifically, the project aims to reduce farm-to-market costs by 30% by 2006. This component of the project aims to achieve this through decreased vehicle operating costs and increased agricultural producer surpluses.

Rebuilding earthworks in early 2005 (photograph taken from FACT report, 2005)

A road that was recently improved under the rural infrastructure component.

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The average vehicle savings is estimated to be 0.02 USD per vehicle kilometer per day. The agricultural producer surplus is an estimation that farmers will adopt better production methods faster; this effect has been observed as the result of similar road improvement projects. The project estimates that, outside of the irrigation project area, these roads will help farmers increase rice production from 1.3 tons per ha to 2.3 tons per ha over the life of the project (Lahmeyer, 2003). The calculation of this project benefit is applied to the area extending for two kilometers from each side of each road. 2.4 Agricultural Extension Component SCIRIP includes an agricultural extension and support component with five areas of responsibility; SCIRIP refers to this as Component 1. They are organization of water user organizations (WUG), agricultural development and research, registration and titling of beneficiaries land, environmental research, and institutional support to the MOWRAM. These are being implemented by Groupe Recherce et d'Echanges Technologiques (GRET) and Centre d'Etude et de Developpement Agricole Cambogien (CEDAC). Technical monitoring system support has been provided by Sogreah. The specific breakdown of financial sponsorship for this component is still somewhat unclear to us. Co-financing for this component has been provided by AFD and MOWRAM; it was a condition under the original ADB agreement with the Royal Government of Cambodia. The original project plan provided for 3.2 million Euro for the implementation of these activities; a 2001 funding report with the Council for the Development of Cambodia recorded an AFD contribution of 664,200 USD under project CKH1052. The goals, objectives, and outputs of this Component 1 have been clearly detailed in the revised logical framework. Under the two main goals of sustained socio-economic growth and poverty reduction, there are five specific objectives (Luthereau, 2004):

1. Effective operation and maintenance of the secondary irrigation and drainage system by the water users organized in a farmer water user committee, and its effective participation in the management of the main canal and reservoir.

2. Improvement and diversification of agricultural production and increase of family revenues.

3. Improvement of land security and facilitation of secondary, tertiary and quaternary construction

4. Institutional strengthening and capacity building of ministry staff

5. Monitor environmental impacts

Simple technical innovations can increase effi- ciency and productivity as with this treadle pump.

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The mandate of this present baseline income study falls under the second objective. The second objective is designed for six outputs. These are increased agricultural income, increased wet season rice production, increased dry season irrigated crops, development of dry season rice, diversified agricultural production, and active farmers and farmer promoters (Luthereau, 2004). The activities that the project will conduct to achieve the outputs fall into the seven categories listed below.

1. Diagnosis • Agronomy (farming systems), analysis on current practices • Assessment of farmers innovations within and outside the project area

2. Irrigation Methods

• Adoption of appropriate crop and irrigation calendar • Land leveling and plots improvement

3. Improved Rice Cropping Systems

• Test and promote appropriate double cropping systems (calendar, soil preparation)

• Seed production and supply • Improved cropping technologies (early transplanting, row planting, etc.) • Integrated Pest Management

4. Fertility Management

• Development of compost production • Test appropriate fertilization methods • Development of green manure

5. Dry Season Crops Diversification

• Test and promote new dry season crops 6. Animal Production Development

• Small livestock development (pigs, chicken, ducks) • Increased fish production

7. Credit

• Sub-contract a study on credit requirements and opportunities • Try to settle a partnership with a rural credit institution in order to implement

a local credit system (Luthereau, 2004)

2.5 Baseline Income Survey This baseline income survey is an external part of the SCIRIP monitoring system that is managed by GRET-CEDAC and was developed with advisory input from Sogreah. The SCIRIP logical framework matrix includes 42 indicators for monitoring the goals and outputs of the project. This baseline income survey, together with a post-implementation income survey scheduled for 2008, is the means of verification for two (2) indicators corresponding to two outputs as described in the table on the following page.

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Indicators 2.1 and 2.5 Design

Summary Performance Targets Means of

Verification Assumption & Risks

Goal Sustained socio-economic growth Reduce poverty

There are no major problems due to natural disaster, and no important national crisis

Object. Improvement and diversification of agricultural production and increase of family revenues

Output (for 2.1)

Increased agricultural income

Agricultural income increases by 40% in SC1 and 10% in SC2 to SC5, depending on access to water during the dry season for 3200 families

Surveys Prices of rice and other variables remain within the ranges used in the economic and financial analyses. PDAFF and DOAFF means are timely and efficiently mobilized. Target beneficiaries will not be alienated from their land.

Output (for 2.5)

Diversified agricultural production

500 farmers increase their livestock income by 50%, including income from increased fish production

Surveys

(Luthereau, 2004) A Stung Chinit Project Complementary Study was conducted in April and May 2000. This study included data relevant to a baseline income survey. However, SCIRIP management decided to outsource an additional separate survey because the Complementary Study was not designed for a post-project comparison. It did not have data coding so that participating households could be identified. Also, the sampling methodology did not control for household access to and use of irrigation (Luthereau, 2004). Therefore, SCIRIP management planned for a 5,000 USD baseline survey over January and February 2005. The budget was revised to 7,500 USD and the survey was implemented in April – May 2005 (Luthereau, 2004). This baseline income survey will be compared against a post income survey to help determine the output, outcome, and impact of the project. Traditionally, impact is defined as the “positive and negative, primary and secondary long-term effects produced by a development intervention, directly or indirectly, intended or unintended.” Outcome is defined as “the likely or achieved short-term and medium-term effects of an intervention’s outputs.” Output is defined as “the products, capital goods and services which result from a development intervention and may also include changes resulting from the intervention which are relevant to the achievement of outcomes." Later sections in this methodology will discuss the strategy to determine output, outcome, and impact that underlie the design of the baseline income survey.

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2.6 Research Consultants SCIRIP management selected the external consultant for the baseline income survey through a process of public competitive bidding. First, bids were solicited by public announcements in local newspapers. Interested consultants were invited to pick up a copy of the baseline income survey terms of reference from the GRET office in Phnom Penh, and then submit a draft of approaches to the baseline income survey, a profile of their consultants and relevant experience, and a description of estimated costs. A shortlist of three consultants was made and SCIRIP management jointly interviewed the three consultants. The interviews were held at the Ministry of Agriculture, Forests and Fisheries in Phnom Penh. The consultant selected was CamEd, with its research team lead by the CamEd director Casey Barnett. CamEd has a team of researchers that have performed a number of rural research projects. Over the past 12 months, CamEd research projects had included the final evaluation of a 1.2 million USD Action Against Hunger food security and sanitation project in Preah Vihear province, an evaluation of the 1.2 million USD Agrisud food security and income generation project in Banteay Meanchey province, and an evalution of the 1.4 million USD Lutheran World Federation food security and income generation project in Kompong Speu.

The same research team members working on these research projects were proposed for the SCIRIP project. However, because of the volume of work, CamEd found that it needed to expand its team to insure a high degree of accuracy conducive to regression analysis. After selection of the consultant, SCIRIP management provided CamEd with project documentation so that CamEd could begin developing draft methodology and a draft survey instrument. An outline

of the methodology and a draft English language survey instrument was developed. The research team then met with Sogreah consultant Francois Luthereau and SCIRIP

Researcher Ouch Soth interviews a young mother in Santuk District.

Researchers Eay Yousos and Puth Khan pilot-test the survey instrument with a family in Central Santuk District.

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management at the MOWRAM office in Kompong Thom to present the draft methodology and draft survey instrument. Suggestions were made and additional project documentation was provided to the research team. After one week, a second meeting was held with select members of SCIRIP management at the project field office in Kompong Thma to review the survey instrument. After this review, the research team pilot-tested the instrument with test sample of five (5) households and made substantial revisions in response to that experience. After this first pilot test, the research team again met with SCIRIP management for additional review, input and discussion. A last round of pilot survey interviews were then conducted after which the survey instrument was finalized (see survey instrument at annex 2).

The household of this young mother and daughter living in a house with bamboo flooring and walls participated in survey interviews.

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3.0 Income Survey Variables The baseline household income survey is the means to verify the increase of agricultural income and the diversification of agriculture income that occur as a result of the Stung Chinit project. This baseline survey was specifically included in the monitoring and evaluation system of the social organization and agricultural extension component of the project. The baseline study, together with the follow up study in 2008, will help indicate whether the project has been able to successfully achieve its goals of reducing poverty and stimulating economic development; the second survey scheduled to be conducted in 2008 will serve as a comparison. Controlling for various factors, changes in type and amount of income between the baseline survey and the final survey should reveal the extent to which the project and its various components have been successful. For the research team, the objective in selecting methodology and determining survey instrument design has been to achieve the most accurate description of household income within the existing technical, logistical, financial, and temporal constraints. The research places greatest emphasis on agricultural income and household characteristics as set forth in the SCIRIP monitoring system. Within this, in order to serve as a means of verifying output in terms of project goals (e.g., reduce poverty, stimulate economic development), the survey collects data on the overall financial position of the household, including assets, external support, access to resources, family composition, labor allocation, and sources of income. The baseline survey variables comprise six broad categories: household composition, assets, labor and sales. However, while many income studies emphasize classifying income sources and wealth indicators, we have instead emphasized only description of the income source or wealth. Also, rather than risk inaccuracies associated with calculating net income (e.g., allocating productive use of capital assets and depreciating those assets; valuing assets such as soil quality; determining a cash value for household labor input or labor derived inputs) this survey asks for gross revenues and yields. Below, we provide an overview of the survey variables and their rationale. The survey instrument itself is attached as annex 2. 3.1 Household Composition The first group of variables requested by the survey instrument are those related to household composition and related factors. In addition to providing essential evaluation data, this information also provides the means for survey auditors or future researchers to locate the household. This information includes household location, age and sex of

Often entire villages specialize in the production of one product, such as furniture, as in the village of this craftsman.

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household members, health, certain household expenses, external support / finance, and self-perception of income trends. The questions on the survey instrument include those for sections 1, 2, 6, 7, 8, 9, 14, and questions 4.6 and 4.7. Household Members In order to evaluate income or change in income for a household, it is important to understand the human resources available to provide labor or social input. Clearly, household income is relative to the number, age, and productivity of household members. Also, it is important to understand the degree to which a household's dependents will absorb or tie up resources which then cannot be used for investment or production (e.g., from illness).

In addition to understanding the human assets and per capita income allocation of households, household composition also will also reveal the financial stability of the household. Our observation has been that rural household composition in Cambodia is highly variable over time, especially during crises. Likewise, in Africa, at least one study of rural households found that, over a one-and-a-half-year period, 20 per cent of the sample households were changed in composition (Haugerud, 1981). Information on the

flexibility of household units over time is an important indicator of their ability to cope with economic stress and change (Jelín, 2004).

This relates to consideration of social networks as an asset, which we have not done in this survey. In rural Cambodia, children and siblings tend to remain close socially and geographically; in this way, while an individual may be living with one household, they are able to draw on the resources of several different households in time of crises. The present survey instrument does not consider this; it would be useful for the follow up study in 2008 to look at these relationships and how they are used as a coping mechanism during crises.

Health / Illness Very similar to the rationale for recording household membership, it is important to document any prolonged illness or health related expenses that may have disrupted any of the household member's ability to be productive. It is important to control for illness and health expenses to avoid misinterpretation of any changes in income. A household who may have actually increased its productivity, but its overall income has dropped due to the loss of labor to illness.

Large, young families suffer from greater instability and require greater economic input.

Good health is a clear economic advantage.

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In the same way, a household’s baseline income may be underestimated because one or more of its members has suffered illness; a subsequent survey of their income might mistakenly perceive the household as having increased its productivity somehow. Household Expenses The baseline survey selects two household expenses as an indicator of income. The survey instrument does not attempt to record household expenses as a means of determining net income or production efficiency. We have selected education and consumption of beef. Expenditure on education is both an indicator of present income and in the long term is an indicator of future income. Recording education also helps us understand to what degree household members are free to provide labor input. It is also an asset. Education (human capital) is probably one of the best means of improving incomes. However, the impact of general education may not be seen within the relatively short (three year) period between this baseline study and the follow up study. Our main use of this will be as an indicator of present income; increased relative education expenses should suggest improved financial stability. This expense is also easy to collect and respondent recall is high. Similarly, the survey asks about beef consumption as an indicator of income. Beef consumption is expected to have high income elasticity; poorer households will eat less, richer households will eat more. Beef is more expensive than other meats, but easily replaced with substitutes such as pork or chicken. In the execution of this survey, we found that many households were strongly influenced by habit and culture in eating beef; despite the dominance of Muslim and Buddhist households, some households had Brahmin beliefs against eating beef. The survey could have done better by asking about several income elastic products, not just beef. Finally, the baseline survey inquires as to any unusually large household expenses. This helps reveal the current financial situation of the family and interpretation of its income. Common large expenses are losses due to theft, purchase of assets (e.g., land, transportation, house), weddings, and ceremonies. External Assistance and Finance Rural households in the area receive both free assistance from family and organizations and purchased assistance from service providers. The free assistance is important as a control factor for interpreting household financial stability and change in income. For example, households with considerable exposure to agricultural extension services may already have a high degree of agricultural productivity. Therefore, such a household would not register as much a difference after the support of capacity building under

Cultural differences mean that Cham people, like this roadside vendor, have more consumption of beef.

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SCIRIP. Likewise, having the free support of external assistance will enable a household to retain assets and household composition in times of crises. External support, either purchased or freely provided, includes financial remittances. This may come in the form of remittance from a daughter working in a garment factory in Phnom Penh or it may come in the form of a high interest loan from a local money lender. The survey instrument records both. External remittances without repayment or interest will most likely improve a household's financial stability. Often this occurs as a transfer of property or livestock; such transfers are recorded separately. As the SCIRIP extension activities include improving access to credit, it will be useful to get a baseline reading of households' debt to income ratios, with 'income' being a composite of certain recorded revenues. The survey will help indicate the degree to which credit, certain rates of interest, and debt affect household incomes positively or negatively. Also, we will be able to ascertain the degree to which credit activities are merely replacing pre-existing local services or are a novel service. Interpretation will need to be careful; for example, an increase in household debt might be either an indication of building poverty or it might be a sign of financial confidence from lenders and borrowers. Self-Perception of Income The survey instrument also asks respondents how they perceive their own financial situation and how they perceive the economic situation in their communities. Most people have a good sense of their income trends. Household responses from the baseline can be easily compared to responses in the 2008 follow up survey and help validate the findings in that final income survey. This baseline survey also asks the households "Why?" The responses will provide insight into each family's individual situation and the forces that affect it. Also, the responses might help validate many of SCIRIP's activities. Likewise, the responses should help validate the rest of the data collected by the survey. 3.2 Assets The second group of variables in the baseline survey are household non-financial assets. Income and change in income are reflected by the assets held by a household. Evaluating income via assets helps reduce the inaccuracy associated with respondent recall. As measures of wealth and income, this baseline survey covers, specifically, house, buildings and structures, irrigation systems, pumps, transportation (e.g., bicycles and motorcycles), tractors, land, and trees. Also, several open questions in the survey invite respondents to describe any additional assets not already specifically listed in the survey instrument.

Local residents are the first to recognize local trends in the economy and employment.

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Ownership of assets is an important behavioural indicator of income. Portfolio theory and diversification theory, emphasizes assets as the subject of choice when a household tries to maximize income and minimize income variability (Barret, 2000). Also, we will be able to determine the contributions made by certain capital assets such as pumps, wells, tractors, and motorcycles. In many instances assets also are a more reliable indicator of welfare than is income as assets can be more directly linked to expenditures (Cohen, 1997). Besides this, evaluating

assets is a cost effective way of measuring change in income, and is therefore suitable to the budget of this baseline survey.

We have chosen to not to collect data on assets that would be difficult to observe (cash, precious metals, soil quality, skills). Regardless, our experience tells us that a majority of a rural household's assets are tied up in capital assets that are productive and have a practical purpose. The survey instrument covers assets in sections 3, 4, 5, 16 and questions 15-1 (land) and 15-2 (irrigation). SCIRIP strategy, and poverty policy in general, aim to improve the asset holdings of the poor, either by endowing them with additional financial, fixed, human, natural, or social assets, by increasing the productivity of assets they already hold, or both. Thus, besides measuring income, a study of assets can also reveal the contribution assets make to overall financial stability and productivity. For purchased assets, the survey records the historical cost and the year of purchase. We have not attempted to divine the present value or useful life of the various assets. However, were this baseline survey to seek net income of certain activities, we would need such information for depreciation. This depreciation would then be an expense allocated and deducted from the gross income of certain activities. Such an effort would be fraught with risk as we would need to determine the excess capacity or usage of the asset; for example, a tractor might be used to plow the farmer's field, but he/she might not be using the plow to its full capacity as a result, we cannot properly determine the profitability of the activity. In another case, the farmer might be plowing his own field, but he/she might also be receiving cash to plow his/her neighbor's field, so the depreciation of

Land and house, like this typical village home, are the principle assets of local people.

Cattle are an asset with productive capacity; the productivity of cattle is applied to a broad range of income generating activities.

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the plow would have to be set against each of the activities, rice production and plowing service. Assets can be a more accurate indicator of welfare because it does not vary as dramatically as earned income. Income is a flow that changes dramatically in relation to external stimuli (Morduch 1994). In terms of income, a household can be poor in one year and not poor in the next even though in most years in the long term it would be either poor or not poor. Therefore, assets can give us a better sense of the household's overall financial situation. The assets recorded by the baseline survey provide an almost infinite number of ways that researchers can measure and evaluate income and changes thereof. Also, the recording of assets allows us to determine which assets can best help perpetuate income growth and rises in productivity.

We will also be able to look at novel information such as whether the quality of the house can be a good signal of future income. We can understand to what degree owning a safe, clean house also feed backs to better health and thus more wealth and human capital. Thus, a review of assets will help us determine if at some point there exists a wealth effect such that rural families become largely immune to the vicissitudes of weather and health.

3.3 Diversification: Production, Labor and Sales The survey instrument mainly measures household revenues and production. The types of wage labour, the amount of labour allocated to wage labour, and the remuneration of wage labour is specifically covered in section 10. Sections 11 and 12 look at foraging and collection of forest products. Services and value-added production is covered in section 13. Section 15, entitled "Land", is largely devoted to measuring household agricultural production capacity; similarly, section 16, on fruit trees, is an indicator of production. Section 17 on crop yields is a more detailed measurement of production and finally section 18 covers purchase, transfer, and sales of livestock. Again, we avoid the inaccuracies associated with calculations of profit by using imputed values. Income is measured in terms of gross revenues and production. It should be understood that while the SCIRIP logical framework sets forth household income

Structure such as this rice storage shed help optimize income by preventing loss and spoilage.

Local production of household good such as baskets is widespread in the target area.

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diversification as an objective, diversification is already often the norm in rural Cambodia; measurement of diversification needs to consider the type and quality of activities. It is important to consider what the households are doing as well as what they are not doing and why.

In rural Cambodia, as in the rural areas of many developing countries, very few people collect all their income from any one source, or hold their wealth in any single asset, or use their assets for just one activity. This is to reduce risks in an uncertain economic and physical environment, realize economies of scope, (diminishing returns to factor use in any given application), respond to crisis, and reduce liquidity constraints, reduce difficulties of highly seasonal income (Barret, 2000; Cohen, 1997).

Diversification is not always a positive impact indicator of welfare or of income; diversification among poor households and micro-enterprises can be due to low labour productivity and employment (Cohen, 1997).This is especially true in rural Cambodia. High transactions costs impel many residents to provide their own goods and services, increasing population pressures result in landholdings too small to absorb household labour, and limited risk-bearing capacity and absence of finance induce households to select a portfolio of activities. This way, they can stabilize income flows to stabilize consumption and minimize the risks. The result is diversified employment and income (Barret, 2000). In fact, if risk aversion decreases as income and wealth increase, then the poor may have greater diversification than the wealthy. However, many studies of households in rural developing countries show that diversification increases as the local economy grows. This is suggested because the poor have fewer barriers to engage in non-farm activities which, because of greater wealth in the local economy, generate increased demand for non-farm goods and services (Reardon 1997). Also, rural development expands market access, encouraging a shift from the production of traditional goods to more modern goods, shifting the rural economy away from its agricultural focus (Stewart and Ranis, 1993). This is often facilitated by infrastructure improvement and increasing population density (Anderson and Leiserson, 1980). For SCIRIP evaluation and monitoring, a measure of diversification is useful because it reveals what income generating options are most attractive to target area households. The baseline survey will reveal for what activities and products, local producers have comparative advantage. This information is important to adjust or identify effective project activities. The survey instrument specifically requests information on products, sales and services. In section 13, it records a description of the activity, the annual duration of the activity, the quantity produced, an estimation of percent of production sold, and the gross revenue generated per unit sold. In other sections, gross revenues are also recorded for labour as well as agricultural production.

Entrepreneurship is common, such as this family owned traditional noodle making business

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This measurement of gross income provides an indication of income levels. Data on gross sales are considerably easier to gather and they are good proxies of income levels (Cohen, 1997). While there are difficulties comparing the gross revenues from disparate sources of incomes, gross sales are useful for overall cash flow stability and comparison of related income generating activities. For wage labour, the survey instrument records the number of laborers per household, a description of the activities, and the daily income. We have chosen a working day as the unit of measurement; our experience is that monthly or hourly wages are a rarity in rural Cambodia. Most wage labour is seasonal agricultural labour associated with rice cultivation. It must be cautioned that the wage rate in itself is not a complete indicator of the real income of a household. We need to consider inflation rates, which determine the real wage; in relation to this, price levels, especially for food and household goods, need to be measured. It will be useful for the baseline survey and the follow up survey in 2008 to record the cost of a selection of common household purchases expressed as a ratio of the wage rate (Sharma, 1992). We will also consider using the figures used by the Cambodian government to calculate the consumer price index (CPI), depending on its reliability and relevance to the SCIRIP target area (i.e., the goods making up the CPI reflect the local food basket).

In the target area, forest products are an important local product and source of income. Likewise, forest and non-forest insects, tarantulas, crabs, snails, reptiles and small birds are an important source of food and income. As in other studies, the agro-ecological region is expected to have a wide range of species which enter the economy, some which may be seldom known outside the area (Nair, 1995).

While these products are a source of steady income for many households, much of the collection of these products is collected on an ad hoc basis. The survey instrument collects data on what is termed food foraging in section 11 and forest products in section 12. Also, for households engaged in the sale of any of these items, this is recorded in section 13. In sections 11 and 12, we focus on merely description of the activity and estimated percent sold; descriptions will be accurate while collection of volume will be inaccurate and negatively impact the efficiency of the survey. In section 13, more detailed questions are asked regarding the quantity, duration, and revenues. Data on forest products is an important measure of income, income diversification and food security. It is also a useful control variable. The role of forest products in income and food security in Cambodia is well documented (Theng, 1998 and many others).

Unskilled temporary wage labor is common throughout the target area, such as this man digging a well by hand.

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Still, if forest products make a material contribution to a household's income, evaluation of the financial needs to take into consideration that households might achieve temporary food or income stability via collection of forest products at the cost of substantial food insecurity in the future. Kampong Thom has a total land area of 1,300,831 hectares with forest cover (Theng, 1998). The main source of fuel in the target area is wood; also, Santuk District is a key source of forest products for the urban markets such as those in Phnom Penh. The amount of charcoal produced annually in Santuk for supply to Phnom Penh is 1,265 tonnes (Theng, 1998). Still, agriculture, rice cultivation in particular, is the main economic activity in the area. It is also the focus of the SCIRIP infrastructure and agricultural extension activities. Because of this central role, the baseline survey gathers production and revenue statistics, including use and type of irrigation. In the survey instrument, sections 15, 16, 17, 18, and 19 request additional details about agricultural production and livestock. The first variable we collect data on is household land ownership, which is also an asset a wealth indicator. Inequality in land distribution has been found to have a strong inverse relationship with economic growth and poverty reduction and has been found to negatively affect future economic growth (Quan and Koo 1985; Deninger and Squire 1998 in Jayne, 2001), and even in the process of growth, poor households appear to benefit less than non-poor households when income and assets are distributed unequally (Gugerty and Timmer 1999 in Jayne, 2001). It will be important to understand how increases in income occur to different degrees according to land ownership and land type. Likewise, it will be important to measure how effective the project is in terms of achieving the overall goal of poverty reduction. Production data is gathered for a broad range of agricultural products, including livestock. For agricultural products that are sold in large quantities and at set times, such as rice, cash crop and livestock, data is collected on type, yield, revenues, and in some cases, certain expenses. However, for agricultural products that are sold in small quantities at irregular intervals, such for vegetables, we have instead focused on recording the type and yield. Agricultural production is detailed not only because this is the area of project intervention, but also because sustained income growth for the poorest households is likely to depend on agricultural growth; low population density reduces the opportunity for larger scale production of household goods or local wage labor. Also, some studies have shown that agricultural productivity growth can best increase income among the poorest and land-constrained households (Jayne, 2001).

A large amount of charcoal is produced in Kompong Thom for local households and also the urban Phnom Penh markets ( Photo taken from FAO, 1998).

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4.0 Sampling The sample size and the resulting research design and budget follow the sample size set by SCIRIP management in the baseline survey terms of reference. The terms of reference stated that the sample size should be at least 150. The researchers accepted this relatively small sample size as reasonable given the budget of the baseline as well as the amount of data that was to be collected for each household interview. We slightly enlarged the original sample size of 150 to 160. This was done to the detriment of the control sample which was reduced from 50 to 40. This was done to focus on the accuracy of the target area data. It is worth noting that most organizations in Cambodia have even smaller sample sizes and do not rigorously impose any particular sampling technique or statistical analysis. This is true for development organizations internationally. A recent study reports "sample sizes commonly range from 25 to 100 respondents. Even when stratified samples sizes were large enough to allow statistical testing, none of the organizations regularly tested the statistical significance" (Späth, 2004). For this reason, and also to facilitate the 2008 follow up study, we have described our sampling methodology in greater detail than normal. Also, we intend to describe certain statistical methods that are otherwise more appropriate for research manuals or textbooks. Our hope is that other organizations can review our approach and help it evolve into a practical tool. 4.1 Systematic Random Sampling For this research, we used a simple probability sample, giving each household in the area an equal probability of being selected. The process of sampling is based on common probability proportional to size sampling approaches. This way we achieve a representative sample, avoiding the risks associated with stratified or weighted samples. The sample allows for straight forward statistical analysis. The sample was made by assigning each household with an equal value, then selecting a skip rate to select among them. First, we determined the number of villages in the target area; the boundaries of the target area are contiguous with those of Santuk District. This was based on review of maps, discussion with district officials, and a report provided by SCIRIP management.

Objective sampling techniques help insure that the economic interests of families have proportionate representation.

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After determining the names and locations of the villages, we determined the number of households in each village, with any outlying households being assigned to the nearest village. It is possible that there are errors in the number of households in the village; however, if the general relative sizes of the villages are correct, then such errors should not affect the accuracy of the sample. We ordered the villages in descending order according to the number of households in each village. The result is shown in the table below.

Table: Village Sampling Apportionment

No. Village No. of

Households

Cumulative No. of House-

holds

No. of Households

Sampled 1 Sivotha 285 285 12 2 Kompong Thmar 273 558 11 3 Banteay Yumreach 253 811 11 4 Chambak Chrum 252 1063 11 5 Khley 236 1299 10 6 Boeng Lvea 210 1509 8 7 Traey Myab 208 1717 9 8 Prasat 178 1895 8 9 Snov 172 2067 7 10 Leav 162 2229 7 11 Toul Sangkae 155 2384 6 12 Tbaeng 153 2537 7 13 Ta Nhaok 141 2678 6 14 Chheu Teal 130 2808 5 15 Thon Mong 130 2938 6 16 Khveck 124 3062 5 17 Prey Plu 96 3158 4 18 Kaoh Bangkov 94 3252 4 19 Kang Sau 94 3346 4 20 Srae Ta Keo 94 3440 4 21 Sumpung 88 3528 4 22 Trapang Tuem 77 3605 3 23 S'ang 71 3676 3 24 Sangkruoh 65 3741 3 25 Trapang Prei 53 3794 2 Total 3794 160

Second, we calculated the total number of households in the target area, as shown in the table above in the column labeled "cumulative number of households". This way, each household in the target area is assigned a number. For example, the first household in Sivotha would be household No. 1 and the last household in Trapang Prei would be household No. 3794. Third, we determined a skip rate to randomly select among the numbers 1-3794. To do this, we simply divided the total number of households, 3794, by the number of samples we aimed to select, 160, to get the number 23.7125. If we start with household number one, we will end up with 161 households sampled. Therefore, we used a random

CamEd 2005 22

number generator to choose a starting number between 1 and 23.7125. Our random start was 16.78. Thereafter, we selected every 23.7125th household. Fractions were rounded to the nearest whole number. The results of this process are shown in the table above in the column marked "No. of Households Sampled". This method of sampling results in a sample that, within margins of error, accurately reflects the population we are studying. Importantly, this method helps us avoid the common risks associated with weighted samples. In a weighted sample, the researcher attempts to include in the sample the various types of households that compose the population, for example, adjust the sample to insure that 10% of the sample households engage in cash crop farming because previous research suggests that 10% of the households plant cash crops. But, of course, this leaves the accuracy of our survey up to the accuracy of the previous research. Given that there has been so little research conducted in the target area, it would be inappropriate to develop a weighted sample based on available statistics. In addition to the high risk of error in compiling a weighted sample, there is also the additional cost of time spent in determining whether households fit the weighted criteria. We have chosen to avoid these difficulties. For this same reason, we have also avoided the temptation of sampling equal numbers of farmers of different varieties of rice or land. If, for example, farmers cultivating floating rice comprise 10% of the target area population, then the data from 90% of the interviews conducted with such farmers would not be able to be included in analyses of the overall population. To compensate for this loss while maintaining the same degree of accuracy, we would need to dramatically increase the sample size beyond the available budget and time constraints. Also, this approach would have created difficulties with random probability selection. 4.2 Level of Significance and Margin of Error of Proportions The margin of error for the sample in this baseline income survey will be relatively large. For samples of proportions, the margin of error will likely vary between 4-20%, depending on the level of response. For determining means, the margin of error will vary between 6-45%. For this study, we will be setting a 5% or 10% level of significance. It should be understood that for a yes/no question which all 200 households respond to, the maximum margin of error will be 5.8%. This margin of error is called the margin of error of proportions; it is the error of a finding that is usually described as a percentage of the population, such as "17% of target area households receive remittances from household members working in Phnom Penh".

Data analysis officer Pollen runs a computer model to speed sample selection.

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It is calculated using the following formula:

In this formula, E is the maximum error of the estimate of the proportion. The term is called the best point estimate of the population proportion, p. What this means is that since we do not know the actual proportion for the population, we simply use the proportion that is drawn in our sample. We call this sample proportion , or "p hat". For example, if we find that 17%, of households receive remittances, then our is 0.17. The next figure, called "q hat" is simply the inverse of "p hat". The letter n in the formula refers to the sample size.

The zα/2 figure is the critical value. It is the z-score that corresponds to α/2, which is the level of significance that we use to determine our confidence interval. For example, if we demand a confidence level of 90%, then our α is 10% (0.10). With our zα at 0.10, then our zα/2 is 0.05. We can then refer to our standard normal (z) distribution table to determine the z-score. This table is based on the probability of a distribution of means. For 0.05, the z-score is 1.64. Therefore, we can determine the maximum margin of error of proportions for our sample size of 200. The largest product for will occur when is 0.5. Therefore, with we have a "q hat" of 0.5. However, we will often find that the product of "p hat" and "q hat" will often be much smaller. We will set a level of confidence of 90%, so we will have a zα/2 of 1.64. Therefore, our maximum margin of error of proportions is calculated as follows:

Therefore, our maximum margin of error of proportions at a 10% level of significance is 5.8%. So, in the case that we were to find that 50% of the households receive remittances, we would say that 50% +/-5.8% of the households receive remittances. Also, we could say that between 44.2% and 58.2% of the households receive remittances. Some common margins of error of proportions that may appear in the baseline income survey report are described in the table on the following page.

nqpzEˆˆ

2α=

p̂

pq ˆ1ˆ −=

p̂

p̂ p̂

p̂

200)5.0)(5.0(64.105798.0 =

CamEd 2005 24

Table 1: Maximum Margin of Error for Sample

Maximum Margin of Error for Proportions

90% degree of confidence (zα/2=1.64)

95% degree of confidence (zα/2=1.96)

Sample Size

20/80 proportion

50/50 proportion

20/80 proportion

50/50 proportion

200 4.6% 5.8% 5.5% 6.9%

180 4.8% 6.1% 5.8% 7.3%

150 5.3% 6.7% 6.4% 8.0%

100 6.5% 8.2% 7.8% 9.8%

40 10.3% 12.9% 12.3% 15.5%

20 14.6% 18.3% 17.5% 21.9%

Doing this exercise, we can also see the implications of demanding a low level of margin of error such as 2%. For a 2% level of error at a high level of confidence, such as 98% confidence, we would need a sample size of 3,393. Therefore, the budget and timing of research have a direct and profound effect on the level of accuracy.

4.3 Level of Significance and Margin of Error of Means The margin of error for mean income in the sample in this baseline income survey will be even larger than that for proportions. The quantitative data collected to determine income levels is reported only for the households that respond positively for an activity. This was a concern of the research team while designing the survey, but because of the budget and time constraints, it was determined to be an unavoidable risk. For the sake of keeping our methodology and analysis transparent and open to feedback, we hope to look at this more thoroughly as we analyze the results of the survey. 4.4 Control Sample Selection There were two main phases to the control sample selection. First, we apportioned control samples to villages. Second, we selected a village with approximate geographic and demographic characteristics as the target area villages. We started this process using proportional probability to apportion control samples among the target area samples; this procedure was the same as the proportional probability method used above except that instead of giving each target area household an equal chance of being selected, we gave each sample an equal chance of being selected. In fact, we could have just as well apportioned the control samples to the households as we did with the target area samples. We did not have any precedent or model for using this approach; it was an ad hoc means of achieving a random sample representative of area households according to village population.

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With 160 target area samples, we apportioned the 40 control area samples using a skip rate of 4 and a random starting point of 2.289885. Rounding to the nearest whole number, we selected 2 (2.289885), then 6 (6.289885) and so on. The results are shown in the table below. Typically, the control sample apportioned to a village is 1/4 of the target area sample.

Table: Control Village Sampling Apportionment

Control Group Village Households

Target Sample

Control Match Sample

Sivotha 285 12 3 1 Kompong Thmar 273 11 3 Banteay Yumreach 253 11 3

2

Chambak Chrum 252 11 2 Khley 236 10 3 Boeng Lvea 210 8 2

3

Traey Myab 208 9 2 Prasat 178 8 2 Snov 172 7 2 Leav 162 7 2 Toul Sangkae 155 6 1

4

5Tbaeng 153 7 2 Ta Nhaok 141 6 1 Chheu Teal 130 5 1 Thon Mong 130 6 1

5

Khveck 124 5 1 Prey Plu 96 4 1 Kaoh Bangkov 94 4 1 Kang Sau 94 4 1 Srae Ta Keo 94 4 1

6

Sumpung 88 4 1 Trapang Tuem 77 3 1 S'ang 71 3 1 Sangkruoh 65 3 1 Trapang Prei 53 2 1

7

Total 3794 160 40 We wanted to select a control sample in villages that were demographically and geographically similar to each of the target area villages. However, from a logistical, financial, and analytical standpoint, it would have been impractical to select control villages where only a single household was to be interviewed. Therefore, we grouped control samples into seven groups of villages that had a relatively similar population. At this first state, it would have been beneficial to also factor in geographic location, but we did not do this until the following stage of control sample selection. The seven control groups are shown in the table above. For example, Sivotha and Kompong Thmar are put together in group 1. Without comprehensive socio-economic data for either the target area or the control area, we then selected control villages based on four general control factors: number of

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households, distance from the nearest major market such as the Kompong Thmar market, distance from a river, and distance from National Road 6. We selected the first target village in each group (each group in the table above) to determine the geographic area. Though we interviewed in over 12 control villages, we only used the data from seven control villages. The other villages were selected by the research team, but in the course of conducting the field research, the interview data was put aside because the villages did not adequately fit the basic requirements described above. The questionnaires for these villages have been retained, but we did not input the data for these villages into the SPSS database. It should be noted that the control villages do not perfectly fit with our basic selection criteria. Faced with a limited choice of villages, we used 'distance from river' as the determining control selection factor. Ultimately, the similarity between the target and control households will be clear once the data has been input and compared. 4.5 Household Selection Household selection was conducted by the teams of interviewers. While the sampling described above assigned each household a number and then selected from those numbers, the assignment of numbers was theoretical and accomplished only apportioning the number of samples to each village. The actual selection of the household had to be done by the interviewers as they visited each village.

First, interviewers identified the outer boundaries of each village along the main axes of the village (roads traversing the village). Second, the interviewers randomly selected one major (often the main road) and one minor village axes (smaller road crossing the village). Axes were selected to best represent the distribution of the population. After determining the bounds of the axes, a count or estimation of the households on the axes was determined and an appropriate skip rate was selected. Then, starting from the bounds of the

axes, the researchers interviewed every nth household according to the skip rate. A few households refused to interview, or all of the family members were absent or unavailable. We are currently reviewing whether the error of replacement is material.

A local household head takes time to discuss her income with Ouch Soth.

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5.0 Survey Execution There were two overlapping phases of survey execution. First, there were the interviews. Second, there was an audit of interviews. Because the length of the first round of interviews had made complete collection of data difficult, the audit of interviews was expanded to every single household and itself constituted a second interview. 5.1 Interviews The interviewing process began with a series of pilot interviews in which the research team conducted interviews as a group. These pilot interviews helped insure that the field researchers could follow and standardize the process of interviewing. After the pilot interviews, the research team held a meeting to discuss the strengths and weaknesses of the procedure as well as the survey instrument itself. The procedure was straight forward. After determining the households to be included in

the sample, the interviewers contacted the household occupants to determine the male and female heads of the family. Once this was done, an appointment to conduct an interview was requested, or, in the case that the household heads were present, an interview was requested immediately. The interviewers identified themselves, their employer, and their role conducting a survey to study the income and family conditions of area households. Initially, the interviewers referred to the project that the survey was for, but after the first auditor raised concerns, we reduced references to the project.

Interviewers were explicit in saying that the household would receive no benefit at all in participating in the survey. We did not give households any material thanks or material token of appreciation as an incentive to answer the questionnaire. This did not pose a problem. With 269 households being interviewed (including pilot interviews and interviews from unaccepted control villages) for 1-2 hours on two separate occasions, only 10 households were reported to complain about not receiving any benefit from the interview. Among these 10 complainants, the main criticism was that they were providing information, but getting nothing in return. They pointed out that the

Researchers share experiences and observations after a day of field work.

While this little boy soon ran away from our researchers, most area people were happy to participate and were easily approached.

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interviewers were getting paid, so the interviewee should get paid as well. Some also commented that other researchers had in the past given gifts such as soap or shampoo. However, because nearly everyone was cooperative and we encountered so few complaints, not giving an incentive appears to have been the best choice. The process of asking questions was fairly systematic, with interviewers following the structure of the survey instrument (see the final survey instrument attached as annex 3). The group pilot testing of the instrument was helpful in standardizing the questions and phrasing. After the pilot testing, the interviews were conducted in pairs for the first two days. This pair work also helped to standardize the questions. Still, our auditors discovered a few irregularities. We were able to correct for the irregularities that our auditors discovered, but it is possible that more irregularities exist. We were aware that more specific, written phrasing of questions would have increased accuracy, but we opted in favor of reducing the number of pages of the instrument. Also, it was our experience that additional phrasing and explanation was inevitable and a strict script would not have improved the accuracy or quality of the survey. Interviewers confirmed description of assets and structures with visual observation. Likewise, interviewers were instructed to act in an investigative manner to note and question any abnormalities that may affect the accuracy or quality of the interview. 5.2 Survey Audits There were five survey audits to insure the accuracy of baseline survey information. Three have been complete at the time of the writing of this draft and a final audit is still to be conducted. The first, second, third and fifth audits were audits in the traditional sense, while the fourth audit was a de-facto second interview.

The first two audits were traditional audits of interview data and interview performance. This audit was conducted by Mr. Sim Huy Choung and was described in the original expression of interest submitted to SCIRIP management. The first audit commenced soon after the formal interviews had started, on April 4th and ended on April 6th. Mr. Chuong randomly selected a sample of questionnaires from each of the researchers and then visited the households and confirmed a sample of the interview questions. After this, Mr. Chuong interviewed the researchers and questioned them on their procedures. Finally,

Mr. Chuong personally accompanied each researcher to observe and compare their performance. This procedure was repeated again on April 9th and 10th. Mr. Chuong also reviewed the contents and responses within 150 questionnaires to check for consistency in the way that the researchers filled in the survey instruments and to look for any irregularities. After each audit, Mr. Chuong met with the researchers to discuss his findings.

Auditor Sim Hoy Chuong reviews the audit activities with researchers.

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He also submitted a candid report to the research manager. After the interviews were complete, the interviewers raised the problem that many respondents provided less and less detailed responses as the interviews progressed in length. To determine the extent of the lack of response, the research manager decided to send another auditor to review the accuracy of the interviews. This second auditor was Ms. Pen Davy. She went to the field with a sample of 30 questionnaires, with each interviewer represented with at least 4 questionnaires. The second auditor Ms. Davy found that the perception of the interviewers was correct and that the later questions were accurate, but lacking less material responses. For example, the very last questions in the survey referred to sale and purchase of animals. Often households had not included certain smaller animals such as chickens and geese, but had bee sure to include cows and buffalo. Similarly, on the second to the last page of the survey instrument, some households failed to mention certain vegetables that they had planted, but they had been sure to include their main crops. For this reason, CamEd decided to interview all of the same households a second time. Using household data from the first survey, the second team had no problem finding each of these households and interviewing them a second time. A fifth and final audit will be conducted during the interpretation stage when researchers return to the target area to consult local stakeholders to solicit interpretation and validation of parametric and non-parametric descriptions of data. Because of the rigor and care of the research team, it is expected that this last audit will confirm a high degree of data quality.

Researchers confirm their appointments with auditor Sim Hoy Chuoung.

CamEd 2005 30

6.0 Data Analysis The process of data analysis began with the review of project documentation that was part of the survey design process described above. After survey design and execution, the survey data was then input into an SPSS database by a team of five data entry personnel working in the CamEd office in Phnom Penh. This team was distinct from the field team of interviewers. Data entry is complete. Now the research team is double-checking data entry and re-configuring the database for ease of analysis. After this, a series of descriptive and statistical reports will be drawn in the form of charts and graphs. The determination of the specific reports to be produced will be determined by the quality of the data, the type of data, and the relevance of the data in helping measure the potential change in household income as a result of the development project. Finally, these descriptive and statistical reports will be interpreted and compiled by our research team. The findings will then be presented to stakeholders and the stakeholders will be asked for their explanation and interpretation. This consultation will help validate or invalidate findings and bring additional input to help understand the dynamics of the local economy. The final stakeholder to be consulted will be SCIRIP management itself; after this, a formal draft survey report will be produced. 6.1 Data Entry An SPSS database spreadsheet that could be used by the data entry personnel. SPSS as a medium was selected at the request of SCIRIP management and was agreed upon by the research team; the research team also proposed to transfer the database into an Excel spreadsheet for the ease of use of the government counterparts who are unfamiliar with SPSS. After laying out the format of the data fields and variables in the database, Drop down values were created so that the data entry personnel would need to do as little typing as possible; essentially, the role of the data entry personnel was simplified to entering numbers and selecting items in drop down boxes. This helped to speed the process of data entry and reduce error. Data entry was conducted from April 17 – June 7. Sok Bunneang coordinated the activities of the data entry team. This process took longer than expected because of additional information collected during the second round of interviews. After the data entry workers input the data into separate databases, the work was consolidated into a single database.

Data entry officers Sokunthea and Tevy transfer data from the questionnaires to an SPSS database.

CamEd 2005 31

Once the database was complete, a sample of the questionnaires was selected for auditing then and compared with the input data. This sample will and the errors found will be checked against the results of a full comparison of every questionnaire. Finally, each household profile will be printed using output from the SPSS database. These profiles will be kept as a softcopy reference and a sample will be selected for further auditing and examination.

For statistical analysis and output of descriptive reports, the research team will

work with a copy of the database to avoid any possible corruption or loss of the original data. 6.2 Non-Parametric Descriptions of Distributions Non-parametric analysis is descriptive analysis. Non-parametric analysis is descriptive because, unlike econometric regression, it does not assume cause and effect between variables. We do not assume a probability distribution. However, in our descriptive analysis, we will include a margin of error in the table/chart description; this is based on the sample distribution. We may consider including a maximum margin of error without reference to the sample distribution. Descriptive analysis is popular among researcher in Cambodia for its simplicity. However, descriptive analyses in rural income studies are often not rigorous. They draw conclusions that are not warranted by the data. Also, most descriptive analyses focus on one aspect of a distribution, the mean. These weaknesses are problems not with descriptive analysis in principle but rather with its use in practice (Schreiner, 1999). To maintain the proper degree of rigour, we will need a lot of data to compensate for the lack of identifying assumptions. Without a lot of data, we cannot control for many variables. In contrast, regression requires less data since it makes extra assumptions about the functional link between variables and about the distribution of the error term. With regression, we just estimate the mean of the dependent variable, depending on independent variables. Descriptive analysis looks at the whole distribution. Our descriptive analysis will include, but not be limited to the median, the mean, variance, the sample distribution itself, and sampling variability. We will also use measurements more specific to our purpose. For example, to examine the income distribution we will use a gini coefficient or a RELGAP measurement. Developed by Gugerty and Timmer (1999), RELGAP is equal to the difference between the top and bottom quartile means, divided by the overall sample mean.

Sok Bunneang inputs data to the SPSS database.

CamEd 2005 32

6.3 Parametric Econometric Regression of Conditional Means This baseline survey is partly designed for regression. Regression analysis is parametric since it assumes a known distribution for the error term. It makes it essential to have robust data on the items that make up the majority of the independent variables. Some of these independent variables include:

• gross income for certain on/off farm activities • amount of remittances • amount of land used • type of roof, walls, and columns of house • number of members in the household • ages and sex of the members of the household • spells of illnesses • presence of financial shocks • employment in off-farm jobs • distance from markets

The survey also collects data useful as dependent variables. Some of these may include:

• agricultural production • income diversification • amount of debt • investment in assets

6.4 Interpretation The baseline income survey report will also provide discussion and interpretation of the parametric and non-parametric descriptions of data. This interpretation will be conducted by research team members, both individually and in groups, contributing their insights as well as additional observations made during the course of the field work. This interpretation will start with the experience and knowledge of the researchers themselves, then will be compared against and interpreted using the findings of other current, relevant studies. After producing a draft report, the research team will consult SCIRIP stakeholders and solicit their interpretation and validation of findings presented in the report. CamEd researchers will incorporate the stakeholders responses as much as possible, providing notation as to the source of the interpretation in the report. This process will culminate with a presentation and discussion with SCIRIP management which will also provide its input. Again, notation will be included to identify conclusions or analyses that is not directly produced by the research team.

What synergistic effects will be observed when a farming household raises livestock; to what degree does it affect income; what is the slope or function of the correlation?

Casey Barnett performs statistical analysis.

CamEd 2005 33

7.0 Limitations The data and interpretations drawn from this baseline study will have to take into careful consideration the constraints, challenges, error in design, and human error that affected the survey. There are many limitations which the researchers have noted; still, we will not try to describe every limitation, but merely discuss the main limitations. During the production of the post-data analysis draft survey report, limitations will be detailed more comprehensively. 7.1 Design Limitations Design limitations arose due to the inherent budget and time constraints set on the survey. It costs a lot to try to measure income. The survey probably does not capture all sources of income and expenses. This is not a weakness of this survey necessarily, but rather a weakness of the survey method. The respondents cannot recall all that they earned and spent in the past year without mistakes. No single measure of net income can be compared. In an effort to keep the survey questionnaire as short as possible expenditure questions were kept to a minimum. As a result, some important expenditure areas such as transport and agricultural inputs were missed. This may lead to an under-estimation of household expenditures. There were several on-going humanitarian NGO activities in the project area during the implementation of the baseline survey. As such, we have no true baseline of what expenditures and coping mechanisms would exist without this assistance. However, at the current time we feel that the level, degree, and extent of the assistance is typical for other similar areas in Cambodia, and will not materially affect our data. For several items on the questionnaire, there was a problem of translation or a misplacement of answers for the first group of households interviewed. Fortunately, the data auditors were able to clarify the answers to most of these questions. The questions we were able to successfully resolve included Q6.2, Q13.5, and Q15. In English, Q6.2 referred to the number of times beef is eaten per month, but the Khmer phrasing failed to mention the period of time. Similarly, for Q13.5, the English phrasing referred to income per unit/period but, the Khmer phrasing referred to total income. For Q15, the questionnaire originally failed to distinguish between land rented to someone and land rented from someone. As a result, some recorded the answer in the wrong columns. There was also misunderstanding of the classification of some crops, particularly cash crops. For example, one researcher recorded cashews as vegetables. However, we were able to correct for this discrepancy at the data entry stage.

Assets developed with household labor, such as this shallow pond, are difficult to value.

CamEd 2005 34

We feel that the collection of non-wood forest products might be less reported than is actually practiced. It would have been helpful to have done more preparatory research to assist in the design and use of the survey questionnaire. In particular, a listing of tree types and their products and a mapping of the tree distribution in the study area would have been helpful.

This baseline income survey also has statistical inaccuracies. For example, the sampling methodology did not adjust for replacement. The care used to draw the sample did not prevent problems in the field. Some households refused to answer questions. Typically, households that do not respond most likely are not the same as the households that do respond. It might have been better to have taken a super-sample of perhaps 300 cases with the goal of completing 240 interviews. After the field work, 240 interviews would be complete. The super-sample could include a primary group and a substitute group. Both groups could be drawn at random, within the frame of the proportional probability perhaps, without replacement. Once drawn, each case in the substitute group could be assigned a number at random. The substitutes could then enter the primary group in the order of this number when a case from the primary group was lost or if the household refused to participate in the survey. This would at least help ensure that the substitute would not be those that are easiest to find but drawn from the population at random. Still, however, sample selection occurs when households refuse to answer or are not found in a non-random way. The sample is still non-random, even if the refused / lost cases are replaced with substitutes drawn at random. Our method of replacement was according to house structure and location and this may have helped mitigate the corruption of data due to replacement. Unfortunately, we did not systematically record the households that did not participate in the survey; when the traits of the lost cases are unknown, there is no way to adjust the analysis. As we continue to review the data and audit the performance of the research team, we hope to determine the degree to which this may affect the overall survey data. 7.2 Survey Limitations A number of limitations arose because of the performance of the research team. At present, we group these limitations into three main categories: question phrasing error, response bias, and household selection error. First, some survey responses are incorrect, non-systematic, or biased due to lack of uniformity in phrasing the questions on the questionnaire. This is because, despite our preparation and coaching, some interviewers understood questions differently than others. This includes questions 7, 9.13, and 15. For question 7, some interviewers asked households to described only assistance provided for the last 12 months (and therefore in conformity with a majority of the other questions) and other interviewers thought that question 7 was not restricted to only the last 12 months and that it could be for many years. Also, for Question 9, sub-item 9.1.3, the interviewers were not clear on the meaning of "month", whether it meant duration or the month that the expense occurred. Lastly, for Question 15, some interviewers understood the question to include the area of the house and others did not. For each of these limitations, we are presently determining the extent and materiality of the limitation.

CamEd 2005 35

Second, there is substantial response error. Responses may be biased because some households anticipate material assistance from the organization funding the baseline survey. The first survey audit conducted revealed that many families thought that interviewers were, as they called them, development officers or "helpers". This might have motivated them to conceal their true number of assets, claiming they have nothing. After this finding, the interviewers

were instructed to be more explicit in telling the households that they are in fact researchers and the data being collected will not result in any form of assistance whatsoever. Also, we returned to interview for a second and third time the households that had been interviewed up to the time of the first audit. All households were compliant and helpful, except for one elderly man who was angry that we were wasting his time. Therefore, we believe our follow up has reduced, but not eliminated, this bias. Another substantial response error is recall error. The survey findings and conclusions are based on self-reported data from respondent households. Response bias by respondents (e.g., underestimating income) is a commonly reported limitation of household surveys. Many of the questions require the respondents to recall expenses and activities over the past year. In many cases, the respondents have simply forgotten certain expenses and activities. In other cases, the respondent might be of the opinion that some assets, activities, and expenses are not material and therefore it was not necessary for them to mention these to the interviewers. Our audits show that this type of error increased in direct correlation with the question number; as the interview dragged on, respondents said less and less, merely identifying what they thought was most material. Recall error in this baseline survey has been exacerbated by the absence of some family members during the time of the interview. Due to time constraints on the number of days interviewers could be in the field, the researchers were permitted to interview either the male or female “Head of Household”. While often the two heads of households were present during the interview, a number of interviews involved only one member. This Head of Household may not be as knowledgeable regarding survey questions as his or her spouse. Likewise, input from the adult children of some households was not always available. Finally, the selection of households themselves was not consistent. Each team of researchers themselves had to determine the bounds of the village and the main roads crossing the village as well as any unique geographically variations. We did not strictly define what were to be considered main roads, secondary roads, or geographic variations. Also, the skip rate for household selection was not consistent; the skip rates nor the boundaries for the determining the rate were recorded by the interviewers.

Most villagers, like this couple, were happy to cooperate with our research team.

CamEd 2005 36

In many cases, interviewers relied on an estimated rather than an actual number of households residing on a road. Our audits have not revealed any material bias due to this, but there is the possibility of bias. In the future, we recommend a more systematic selection procedure; also, the interviewers need to record the boundary markers for each village and the calculation for the selection skip rate.

Recall accuracy of income amounts in 2004 is limited.

CamEd 2005 37

8.0 References & Works Cited Anderson, D.; Leiserson, M.W. "Rural nonfarm employment in developing countries",

Economic Development and Cultural Change, Vol. 28, No. 2., 1980. Asian Development Bank. Memorandum of Understanding between The Royal

Government of Cambodia and the Asian Development Bank for the Stung Chinit Irrigation and Rural Infrastructure Project, Asian Development Bank (ADB), May 2000.

Barret, Christopher; Reardon, Thomas, Asset, Activity, and Income Diversification

Among African Agriculturists: Some Practical Issues, March 2000. Castellanet, Christian. Report of a Monitoring and Planning Mission of the Agricultural

Development Sub-Component in Stung Chinit Project (SCIRIP), GRET, May 2004.

Castellanet, Christian, 2nd Monitoring and Planning Mission of the Agriculture Research

and Development (ARD), GRET, September 2004. Castellanet, Christian; Goessens, Christophe; Fontenelle, Jean-Philip; Khim, Sophara;

Bertone, Francois. Stung Chinit Project Complementary Studies, GRET/CEDAC,, February 2001.

Cohen, Monique; Little, Peter D. Income and Assets as Impact Indicators – Assessing

the Impact of Microenterprise Services (AIMS), USAID Microenterprise Impact Project, Harvard Institute for International Development, February 1997.

Copestake, James; Johnson, Susan; Wright, Katie. Impact Assessment of Microfinance:

Towards a New Protocol for Collection and Analysis of Qualitative Data, Working Paper No. 7, University of Bath, June 2002.

Haugerud, A. Economic Differentiation among Peasant Households: A Comparison of Embu Coffee and Cotton Zones, Institute for Development Studies, University of Nairobi, 1981.

Jayne, T.S.; Yamano, Takashi. Smallholder Income and Land Distributionin Africa: Implications for Poverty Reduction Strategies, Development Department of Economics Paper No. 24, Michigan State University, 2001.

Jelin, Elizabeth; D’az-Mu-oz, Ana Rita. Major trends affecting families: South America in

Perspective, www.worldfamilyorganization.org, 2004. Lahmeyer International, SMEC Cambodia Consulting. Irrigation Infrastructure and

Irrigation System Management Components – Supplementary Analysis, Lahmeyer International, June 2003.

CamEd 2005 38

Lim, Puy; Lek, Sovan, Stung Chinit Irrigation and Rural Infrastructure Project Environmental Impact Assessment, Pre-Impounment Report, Kosan Engineering, January 2005.

Luthereau, Francois. Technical Support to Stung Chinit Scheme Rehabilition (sic) Accompaniment Programme, Sogreah, June 2004.

Makara, Ouk; Sovuthy, Pheav; et al. Project Final Report for Rice Cropping Systems

Study for Stung Chinit Irrigation and Rural Infrastructure Project (SCIRIP), Cambodia Agriculture Research and Development Institute (CARDI), February 2004.

Middleton, Carl; Raingsey, Penn. The Stung Chinit Irrigation and Rural Infrastructure

Project, Kampong Thom Province, Fisheries Action Coalition Team (FACT), March 2005.

Morduch, Jonathan. “Poverty and Vulnerability “in American Economic Review, 1994.

Nair, P.K.R.; Merry, F.D. Development of Non-Wood Forest Products Through Agroforestry: Issues and Strategies, University of Florida, Gainesville. 1995.

Naneyska, Sonja ; Sasho, Stepanovski. Household Expenditure/Consumption Survey Baseline Survey Final Report, Macedonia USAID/FFP Grant, March 2001.

Pramol; Meak, Mengse; Kin, Sopheap; Dr. Reach, Thy; Or, Sreng; Huor, Farming

System Assessment: Complementary Study on Technical Point of Views (sic) Analysis, Submitted to Stung Chinit Irrigation and Rural Infrastructure Project (SCRIP), Centre d’ Étude et de Dévelopment Agricole Cambogien (CEDAC), October 2004.

Reardon, T. "Using Evidence of Household Income Diversification to Inform Study of the

Rural Nonfarm Labor Market in Africa," in World Development, 25 (5), 1997. Salgarolo, Patrice; Degouve, Benjamin; et al. SCIRIP Quarterly Report No. 12,

GRET,/CEDAC, September 2004. Schreiner, Mark; Gonzalez-Vega, Claudio; Beneke de San Felio, Margarita; Shi,

Mauricio A. Notes on Methods Used in a Survey of Rural Clients of Financiera Calpia in El Salvador, Basis Management Entity, The Land Tenure Center, Wisconsin, March 1999.

SCIRIP; Department of Agricultural Extension (Cambodia); et at. An Agroecosystems

(sic) Analysis of the Steung Chinit Irrigation Project, SCRIP, 2004. Seng, Suon; Meng Se, Kin. Farming System Assessment, At the Target of Stoeung

Chinit Irrigation and Rehabilitation Infrastructure Project, Kampong Thom Province, Center d’Etude and de Dévelopment Agricole Cambodgien (CEDAC), March 2003.

CamEd 2005 39

Sharma, R.P. Approaches to Monitoring Access to Food and Household Food Security, FAO Committee on World Food Security, 17th Session, Rome, Marcy 1992.

Sovuthy, Pheav; Reyes, Rudy; et al. Characterization of the Soil at the Stung Chinit Irrigation and Rural Infrastructure Project (SCIRIP), Soil and Water Research Program of the Cambodian Agricultural Research and Development Institute (CARDI), July 2003.

Späth, Brigitte, Current State of the Art in Impact Assessment: With a Special View on

Small Enterprise Development, Swiss Agency for Development and Cooperation, Switzerland, August 2004.

Steward, F.; Ranis, G. “Rural Nonagricultural Activities in Development: Theory and

Application,” in Journal of Development Economics, vol. 40, 1993. Thong, Chay Seng; Chanthoeun, Heng. Woodfuel Flow Study of Phnom Penh,

Cambodia, Food and Agriculture Organization of the United Nations, Bangkok, December 1998.

United Nations Population Fund, General Population Census of Cambodia: Final Census

Results, National Institute of Statistics, Cambodia, July 1999.

CamEd 2005 40

Annex 1

Research Schedule (Revised May 2005) Activity Date(s) 1. Review of existing data We will solicit and review existing data related to the environment, development, livelihoods, political and institutional issues within the target area.

March 21– March 26

2. Preparation of Questionnaire and Interview Methodology Based on the objectives of the baseline agricultural income survey and the local demographics and economic environment, a questionnaire will be prepared; research team members will collaborate on questionnaire design and translate the questionnaire into Khmer and standardize presentation of questions

March 24 – March 27

3. Sampling A sample of 160 target area families and 40 non target area families (control sample) will be selected using proportional representation

March 25 – March 28

4. Trial Interview Researchers will conduct a survey together as a team, observing the relevance and efficacy of survey questions

March 29

5. Revision of Questionnaire Based on the observations of the trial interviews, the questionnaire will be fine tuned to achieve better efficiency and accuracy

March 29

6. Household Survey Initially, researchers conduct interviews in teams of three to help insure standardization of interview technique; after this, researchers conduct interviews in pairs and as individuals

March 30 – April 13, April 18-21

7. Second Round of Interviews and Survey Audits A second round of interviews is conducted with the same households in order to elicit additional information and validate the information from the first round. In addition, a separate team of auditors select a sample of completed questionnaires and meet with the household to validate the data.

April 25- May 30,

8. Data Entry Data entry staff (not including any researchers), input the data into an SPSS database (later, we it will be converted into an Excel spreadsheet for the ease of use by the MOWRAM)

April 19 – June 7

9. Data Proofing & Database Re-configuration Data entry staff cross check all data questionnaires; database configuration is adjusted to facilitate data analysis.

June 8 – June 17

10. Data Analysis Researchers will select and then perform statistical analysis of data and prepare descriptive representation of the data (charts, tables)

June 14- 25 June 30- July 2

11. Meetings to Interpret and Validate Data Researchers will present descriptive findings to a selection of stakeholders, soliciting interpretation and comments

June 26-29

12. Writing of Draft Survey Report The research team will write the survey report, providing a comprehensive statistical and interpretative analysis and description of the findings

July 4 – July 12

13. Presentation of Findings and Draft Report The research team will present descriptive data and findings to SCIRIP management in the form of a draft report.

July 14

14. Revision and Re-submission of Survey Report Considering the feedback, comments, and suggestions of SCIRIP management, the researchers will revise and restructure the final report, then provide SCIRIP management with soft copies and hard copies.

July 26

CamEd 2005 41

Annex 2 Final Draft of Questionnaire

1- Location (TItaMgPUmisaRsþ) 1.1 District (Rsuk): ………… 1.2 Commune (XMu): ………… 1.3 Village (PUmi): …………

1.4 Female House Head (emRKYsarRsI): …………

1.5 Male House Head (emRKYsarRbus)………

2-Number of household members ( cMnYYnsmaCikRKYsar) 2.1 Adults over 60 (mnusScab;BIGayu 60 qñaMeLIgeTA):……

2.2 Males 15-59 (mnusSRbuscab;BIGayu 15-59qñaM):……….

2.3 Females 15-59 (mnusSRsIcab;BIGayu 15-59qñaM):……….

2.4 Children 5-15 (kUnekµgcab;BIGayu 5 - 15):………..

2.5 Children under 4 (ekñgeRkamGayu 4 qñaMcuH): ………….

2.6 Disabled (CnBikar): …………

3-Structures (sMNg;)

i. Questionnaire No………….. ii. Time (from – to): ………

SCIRIP iii. Researcher: ……………… iv. Date: ………. .

Income Survey vi. Reviewed by: ……………… vii. Date Reviewed: …………

viii. Audited by: ……………… ix. Date Audited: ………….

1-House (pÞH) 6-External kitchen (pÞH)ayxageRkA) 2-Latrine (bgÁn;) 7-Cow pen (eRkaleKa) 3-Pig pen(RTugRCUk) 8-Shed for rice mill(eragma:sIunkinRsUv)

4-Chicken coop (RTugman;) 9-Other (epSg) ..............................

5-Rice storage shed (CRgukRsUv) 10-Other (epSg) .............................

CamEd 2005 42

4-House (pÞH) 4.1Column No.

(cMnYnssr) 4.2 Column Type

(RbePTssr) 4.3 Roofing Material

(RbePTdMbUl)

4.4 Walling

(RbePTCBa¢aMg)

4.5 Year of Construction

(sg;qñaMNa)

4.6 House Distance from the market (cMgayBIpÞHeTApSar):………………km 4.7 Are you in the StungChinit irrigation project?

(etIGñksßitenAkñúgKMeragRbB½n§eRsacRsBsÞwgCInit) Yes / No

5. Material Family Assets (RTBüsm,tþiRKYsar) 5.1 No

(cMnYn)

5.2 Assets

(RTBü)

5.3 Cost

(éfø) 5.4 Year

Purchased

(qñaMTij)

4.2. Column Codes

1-Cement (sIum:g;t×) 2-Sawn timber (eQIGa) 3-Round Wood (eQImUl)

4-Bamboo (b¤sSI) 5-Other (epSg²)

4.3 Roofing Codes

1-Zinc (s½gásI) 2-Tile (ek,Og)

3-Cipro (sIuRbU) 4-Palm leaves (søwketñat)

5-Sbov (s,Úv) 6-Other (epSg²)

4.4 Walling Codes

1-Planed wood (kþar)2-Bamboo (b¤sSI) 3-Palm leaves

(søwketñat)

4-Sbov (s,Úv)

5-Other (epSg²)

5.2 Material Family Assets Codes

1-Bicycle (kg;) 9-Motorcycle (mU:tU)

2-Oxcart (reTHeKa) 10-Motorcycle cart (rWm:k)

3-Small irrigation system 11-TV (TUrTsSn_)

(RbB½n§eRsacRsBFntUc) 12-Pump (ma:sIunbUmTwk)

4-Canoe - paddle only (TUk) 13-Small boat – can use

5-Fish pond (RsHRtI) small engine (kaNUt)

6-Traditional well (GNþÚvburaN)

7-Pump well (GNþÚgsñb;) 14-Rice mill

8-Tube well (GNþúglU) (ma:suInkinRsUv)

15-Other (epSg²)

CamEd 2005 43

6- Expenses (cMNay)

6.1 Education (karGb;rM) 6.1.1 Sex

(ePT)

6.1.2 Age

(Gayu) 6.1.3 Daily cost

(cMNayRbcaMéf¶)

6.2 How many times did your family eat beef in the last month?

RKYsarrbs;GñkhUbsac;eKab:unµandgkñúg1Ex ?

7-External Organizational Assistance (karCMnYyBIGgÁkar)

7.1 Organization Type

(GgÁkarNa?)

7.2Year(s)

(qñaMNa?)

8-Serious Illness(es) April 2004-April 2005(CMgWF¶n;F¶rcab;BIExemsa 2004 –emsa 2005) 8.1 Age of

person

(Gayu)

8.2 Illness

(CMgW) 8.3 Duration

(ry³eBlQW) 8.4 Treat cost

(éføBüa)al)

7.1 Organizational Assistance Codes 1-VDC 2-Care 3-District 4-WFP 5-ADRA 6-Hagar 7-Other (Write in)

CamEd 2005 44

9-Perception of Income (karviPaKelIcMNUl) 9.1 Any large expenses or losses in the past 12 months?

(etImankarcMNay rW )at;bg;eRcInkñúg 12 Exknøgmk?) 9.1.1 Expense

(RbePTcMNay)

9.1.2 Cost

(éfø) 9.1.3 Month

(Ex)

9.2.1 How much has your income changed over the past three years?

(etImankarERbRbYlcMNUlya:gem:cenA 3 qñaMknøgeTAsMrab;RKYsarGñk?)

Much Less (ticCagmuneRcIn) Less (ticCagmun) the same (dUcmun) More (Cagmun) Much

more (eRcInCagmun) 9.2.2 Why? ( ehtuGVI :) 9.3.1 Has village income changed over the past three years?

(etImankarERbRbYly:agem:cEdrenAGMLúgeBl 3 qñaMknøgeTAenAkñúgPUmirbs;Gñk)

Much Less (ticCagmuneRcIn) Less (ticCagmun) the same (dUcmun) More (Cagmun) Much

more (eRcInCagmun) 9.3.2 Why? (ehtuGVI :)

9.1.1 Expense Codes

1-Ceremony (buNü) 4-Land (TijdI) 2-Wedding (kar) 5-Other (epSg²)

3-Theft (ecarkmµ)

CamEd 2005 45

10- Labor Income (cMNUl)anmkBIBlkmµ) 10.1 No. of

People

(cMnYnnak;)

10.2 Activity

(skmµPaB)

10.3 Days

(cMnYnéf¶) 10.4 Daily income

(cMNUlRbcaMéf¶)

11-Food Foraging (skmµPaBkaredIrrkstVtUctUc)

11.1 Activity

(skmµPaB)

11.2 Do you sell it?

(etIGñklk;va?)

11.3 % Sold

(PaKrylk;)

11.1Food Foraging Activity codes

1-Tarantula (GaBIg) 7-Crab (kþam)

2-Termite eggs (GRgg) 8-Small Bird (stVsøab)

3-Snail (xügex©A) 9-Rabbit (TnSay)

4-Snake (Bs;) 5-Frog (kEgáb)

6-Large Lizard (GnSg) 10-Other (epSg)

10.2 Labor Activity codes

1-Harvest (RcUt)

2-Plow (P¢Ür) 3-Uproot rice seedlings (dksMNab)

4-Tend cows (emIleKa) 5-Home Construction (eFVIpÞH) 6-General Farm Labor

(EføBlkmµelIcMkar) 7-Re-plant rice seedlings (sÞÚg)

8-Teach (beRgon)

9-Construction (sMNg;) 10-Others (epSg²)

CamEd 2005 46

12-Collection of Forest Products (karRbmUlplmkBIéRBeQI) 12.1

Activity

(skmµPaB)

12.2 Do you sell it?

(etIGñklk;va?)

12.3 % Sold

(PaKrylk;)

13-Product, Sales, Services (plitkmµ lk; esvakmµ) 13.1 Activity

(skmµPaB)

13.2 No. of Months

(ry³eBlEx)

13.3 Quantity

(brimaN)

13.4 Percent sold

(PaKrylk;)

13.5 Income per unit/period

(cMNUltamÉkta)

13.1Product, Sales, Services codes

1-Charcoal (dutFüÚg) 2-Motodop (m:UtUDub) 3-Saw or plane wood (GareQI) 4-Make whiskey (eFVIRsa) 5-Funiture (sgáarwm) 6-Mushrooms (eFVIpSwt) 7-Sculpture (eFVI)av)

8-Silkweaving (t,aj) 9-Sell Medicine (lk;fñaMeBTü) 10-Basket weaving (eFVIkeBa©Ir) 11-Vender (lk;dUr) 12-Cakes (eFVInM) 13-Fishing (rkRtI) 14-Palm sugar/Juice (eFVIsár) 15- Rent land (dICYleGayeK)16-Other (epSg)

12.1 Collection of Forest Products Activity codes

1-Dead wood (eQIgab;) 6-Turtle (GeNþIk)

2-Timber (eQIhub) 7-Forest pig (RCUkéRB)

3-Planed wood (eQIkaþ) 8-Deer (kaþn;) 4-Firewood (GUs) 9-Rattan (epþA)

5-Bamboo (b¤sSI) 10-Other (epSg)

CamEd 2005 47

14-External financing, financial assistance or remittances (luyBIxageRkA)

15-Land (dI) Land Type

RbePTdI 1. Land Area Owned

brimaNdIpÞal;xøÜn

2. Land Area Rented From Someone

(brimaNdI CYlBIeK)

3. Land Area Rented to Someone

(brimaNdICYl eGayeK)

4. Area Planted

(brimaNdIdaM)

5. Irrigation source

(RbBPTwk)

6. Yearly Pumping Expense

(cMNay bUmTwk)

15.1 Seasonally Inundated

Land

dIRsUveLIgTwk

15.2 Rainy Season

Riceland

(RsUvvsSa)

15.3 Dry Season

Riceland

(RsUvR)aMg)

15.4 Cash crop Farmland

(dIcMkar)

15.5 Residential

land

(dIpÞH)

15.6 Other land

(dIepSg)

15.7 Other land

(dIepSg)

14.1 Source/Type

(RbPB)

14.2 Amount

(brimaN)

14.3 Timing

(ry³eBl)

14.4 Interest rate

(karR)ak;)

14.1 Source/Type Codes

1.Loan(kMcI) 2. Relative working away

(bgb¥ÚneFVIkarenAeRkA) 3. Other (epSg)

CamEd 2005 48

16-Fruit (EpeQI)

17. Harvest from April 2004 –April 2005

(karRbmUlplcab;BIExemsa 2004 –emsa 2005)

Crop

(dMNaM) 1. Type

(RbePT) 2. Chemical fertilizer expenses for the year

(cMNayCIsrubkñúgmYyqaMñ) 3. Yield for

the year

(brimaNplsrubkñúg mYy qaMñ)

4. Amount Sold

(brimaNlk; )

5. Sale Price

per unit (kg/ton)

(éf¶lk;) 1.

1.

1. 1. 1. 17.1 Rice

(RsUv) 2.

2. 2. 2. 2.

1.

1.

2.

2.

3.

3.

4.

4.

Vegetables

(bEnø)

5.

5.

1.

1. 1. 1.

2.

2. 2. 2.

3.

3. 3. 3.

Cash Crops

(dMNaMcMkar)

4.

4. 4. 4.

16.1 Tree Type

(RbePTedImeQI) 16.2 No.

(cMnYn)

16.3 Year Planted

(qñaMdaM)

16.1 Tree type codes

1-Mango (sVay) 7-Santol (kMBIgraC)

2-Coconut (dUg) 8-Durien(Fuern)

3-Longine (emon) 9-Rambutan (savma:v)

4-Orange (RkUc) 10-Sapodilla(lµút)

5-Lemon (esda) 11-Banana (eck)

6-Rose apple (Cm<Úr) 12-Others (epSg²)

CamEd 2005 49

18-Animals sold April 2004-April 2005 (stVciBa©wmlk; cab;BIExemsa 2004 –emsa 2005)

18.1 Animals/Breed

(stV) 18.2 No

(cMnYn)

18.3 Original Cost

(éføedImstV1k,al)

18.4 Sale Price

(éfølk;stV1k,al)

19-Animals owned but not sold April 2004-April 2005

(stVciBa©wmEtmin)anlk;cab;BIExemsa 2004 –emsa 2005;)

19.1 Animals/Breed

(stV)

19.2 No

(cMnYn)

19.3 Budget Source

(RbBPfvika)

19.4 Purchase Date

(éf¶Tij)

19.5 Cost

éføedImstV1k,al