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ABSTRACT
This study focuses on smallholders in Siem Reap, Cambodia to identify determinants of income levels and strategies for poverty reduction based on qualitative and quantitative sources. Data was collected through 60 quantitative questionnaires, ten long-form qualitative surveys, and eight key informant interviews. Results indicate that growing higher volumes of vegetables is strongly positively related to higher household income, yet nearly 90% of households reported reluctancy to grow vegetables because it requires technical skills, capital, and market risk. These results suggest that creating an environment in which smallholders can pursue horticulture with less risk could be a strong poverty alleviation measure.
RÉSUMÉ
Dans cet article, nous nous proposons d’identifier les déterminants du niveau des revenus des petits exploitants de Siem Reap, au Cambodge, ainsi que les stratégies mises en œuvre pour réduire leur pauvreté, en nous basant sur des sources qualitatives et quantitatives. Notre collecte de données a été effectuée au cours de 60 questionnaires quantitatifs, dix longs questionnaires qualitatifs, et huit entrevues avec des informateurs clés. Nos résultats indiquent qu’il y a une forte corrélation positive entre faire pousser des quantités de légumes plus importantes et des revenus des ménages plus élevés, bien que 90% des ménages aient indiqué des réticences à faire pousser des légumes, car cela requiert des compétences techniques, du capital, et de prendre des risques sur le marché. Ces résultats suggèrent que créer un environnement dans lequel les petits exploitants peuvent pratiquer l’horticulture en prenant moins de risques pourrait être une mesure efficace pour réduire la pauvreté.
Acknowledgements
The authors would like to thank Mare Kim for his enumeration of both the questionnaire and the long-form response survey. We also would like to thank the participants who gave their time and information to the study. Thank you to the George Washington University Undergraduate Research Grant and the team at Sa’akom Farms. We thank two anonymous reviewers for critically reading the manuscript and suggesting substantial improvements.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 Farming inputs defined within this study as seeds, fertilizers, capital, wage labor, and other agricultural technology used to enhance farm yields. Animal farming inputs were not included in this study.
2 In this study, farming households are defined as households that cultivate rice or vegetables. In the scope of this paper, the amount and type of vegetables were studied rather than a predominant focus on rice. Animal farming is considered as an additional source of income, separate from crop and vegetable farming.
3 Commonly cited as households with over 5 people.
4 Typically more than 1 ha of land, but varies depending on region.
5 Higher levels of education most often refer to completing at least primary education if not secondary education.
6 Whereas contract farming implies that individual farmers are contracted by a collector or wholesaler, agricultural cooperatives signify that a group of smallholders are selling their produce as a group rather than individuals. Alternatively, the members of an agricultural cooperative might not sell their produce as a group but instead pool their inputs or other resources. While cooperatives are often contracted, they can function outside of a contracting arrangement. In Cambodia, most smallholders are reluctant to sign contracts at the start of the season due to fear that they will not be able to meet the arrangement at the end of the season, yet centralized models of contract farming in which an agribusiness buys produce from many farmers and maintains quality and quantity control have been used (Sreymom and Pirom Citation2015). Agricultural cooperatives in Cambodia are informal, and relatively uncommon as commonly lack enforcement mechanisms or other institutional power or technology to be productive organizations (Ofori, Sampson, and Vipham Citation2019).
7 Institutional Review Board (IRB) approval was obtained to ensure that there were adequate privacy protections for participants and ethical standards were met. Participants were notified of the purpose of the study, their ability to refuse to answer questions at any time, and that all appropriate efforts would be made to ensure their privacy was maintained and their answers would not be directly attributable to them. A comprehensive timeline of the study steps can be viewed in supplementary figure 1.
8 Every 26th household along the main road was selected to be surveyed, and the number 26 was randomly selected through a random number generator between 20-35.
9 All data collection tools were originally written in English and then translated into Khmer by the enumerator, and question and sentence structure were revised with enumerator before initial data collection period to ensure maximum relevance and understanding.
10 These measures of income were selected based on the likelihood of the respondent having a most accurate and confident measure, since this is when data collection took place. Participants could more confidently state their highest, lowest, and most recent income as opposed to overall yearly income or the income from each month as it is uncommon for detailed sales or consumption records to be maintained.
11 Each participant had up to five days to fill out their form via a written response, and if participants were unable to read or write they had a family member record their responses. Participants were compensated for their time with a $5 USD mobile top-up.
12 Interviewees were invited to suggest or recommend additional informants that could offer relevant and topical information to the study.
13 In STATA/BE.
14 Since any tourism-related wage work was severely limited during the period of data collection, any conclusions about the earnings comparing farm versus non-farm work would need to be substantiated with further research. Similarly, in-person research would need to be undertaken to gain more sensitive data, such as levels of indebtedness.
15 The conversion factor from riels to USD used in the study is based on the rate on April 7th, 2022 when 4030 riels is equivalent to $1.00.
16 The sample included 52 women and 8 men.
17 For selected independent variables statistics broken down by household size, see supplementary table 8.
18 Riels spent on crop inputs: Inputs include fertilizer, seeds, pesticides, and other tools that households use to grow their vegetables. When controlling for the actual square meters of vegetable crops, this variable acts as a proxy for horticulture production. However, it is not a perfect proxy for total production because households that have the exact same cultivation area might spend different amounts on inputs if one household either invests more capital per unit of output or a household uses their inputs inefficiently.
19 Sum of Melons and other Leafy Greens: These were measured together under an ‘other vegetables’ category that included mustard greens, choy sum, bok choy/pak choy, curly cabbage, winter melon, and watermelon.
20 Average Yearly 1: The average yearly income which was calculated by averaging the highest maximum value for a three month period and the smallest minimum value for a three month period, and multiplying that average by four to represent all twelve months.
21 Average Yearly 2: This measure of average yearly income was calculated by multiplying household income during the past month by twelve to account for the entire year.
22 Maximum Seasonal Earnings: Participants were prompted to select the three month period in which they earn the greatest income, and then provide a range of values in riels in which they were certain that their true value fell. The average of the range was used as the maximum earnings.
23 Minimum Seasonal Earnings: Participants were prompted to select the three month period in which they earn the least income, then provide a range of values in riels in which they were certain that their true value fell. The average of the range was used as the minimum earnings.
24 In the literature review, there was no previous research found that identified relationships between specific vegetable production and household income. In our multivariate analyses, lettuce was the only vegetable that was found to have a significant impact on household income. While lettuce production was not significant against maximum income and average yearly income based on seasonal maximum and minimums, it was highly positively significant against past month income and minimum income. This significance could indicate that lettuce was only relevant since it was harvested and sold within the previous months, yet lettuce cultivation was also significant when regressed against minimum income. Furthermore, only four participants recorded the November and December months as their lowest earnings period which provides some evidence that lettuce is relevant beyond household income in the months of November and December (Supplementary Table 16).
25 The education variable was dropped due to potential challenges in remote data collection.
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Notes on contributors
Rachel Brown
Rachel Brown is a Bachelor of Science Candidate in Economics at the Elliott School of International Affairs in Washington, D.C., and she also works as a Research Assistant at George Washington University’s Institute for International Economic Policy. Her research interests include agricultural economics, microfinance, and the economics of poverty alleviation.
Samuel Ledermann
Samuel Ledermann, PhD is an Assistant Professor of Practice of International Affairs in the International Development Studies program at the Elliott School of International Affairs at George Washington University, Washington, D.C.. Since 2022, he also serves as the Director of the Elliott School's Global Capstone Program. He is an economic geographer with an extensive background in sustainable and agricultural development, and a recognized expert in sustainable development practice across Eastern and Southern Africa.