Can Mobile Phone Based Household Surveys In Rural Papua New Guinea Generate Information Representative Of The Population Surveyed

Conducting household surveys through face-to-face interviewing in rural Papua New Guinea is beset with difficulties and high costs. With phone network coverage spreading across PNG, using mobile phones to obtain information from respondents can allow such surveys to be done more quickly and at significantly lower cost. However, not all rural households own mobile phones. In this Project Note, an assessment is made of whether survey information collected by calling respondents on their mobile phones will be representative of the population surveyed or, rather, might be subject to systematic biases. This assessment is done by analyzing the characteristics of households in four rural areas of PNG that were interviewed in a field survey in mid-2018.

Papua New Guinea (PNG), a nation of now almost nine million people, continues to evolve and adapt. While there is no shortage of recent data and research on PNG, the two most recent social science volumes on the country were both written more than a decade ago. Since then, much has changed and much has been learnt. What has been missing is a volume that brings together the most recent research and reports on the most recent data. Papua New Guinea: Government, Economy and Society fills that gap. Written by experts at the University of Papua New Guinea and The Australian National University among others, this book provides up-to-date surveys of critical policy issues for PNG across a range of fields, from elections and politics, decentralisation, and crime and corruption, to PNG’s economic trajectory and household living standards, to uneven development, communication and the media. The volume’s authors provide an overview of the data collected and research undertaken in these various fields in an engaging and accessible way. Edited by Professor Stephen Howes and Professor Lekshmi N. Pillai, Papua New Guinea: Government, Economy and Society is a must-read for students, policymakers and anyone interested in understanding this complex and fascinating country.

From May to December 2023, the International Food Policy Research Institute (IFPRI) implemented a rural household survey that collected detailed data on rural household food consumption and expenditures, agricultural production practices, employment profiles, child and mother 24-hour diet recall, and child anthropometry measurements in Papua New Guinea (PNG). The research team carried out the survey, which used location-based sampling, across five agroecological study areas, of which four of the areas were defined using elevation and rainfall variation. The five agroecological survey areas were seasonal highlands, nonseasonal highlands, seasonal lowlands, nonseasonal lowlands, and islands (the islands survey sample was not disaggregated by elevation or precipitation patterns). In identifying seasonal and nonseasonal survey areas, we adapted the rainfall seasonality categories established by Bourke and Harwood (2009), who evaluated the relative difference in rainfall between the wet and the dry season using resource mapping units defined by the PNG Resource Information System (PNGRIS). The areas of the country that experience large seasonal variation in rainfall (heavy to light, depending on the season) are classified as seasonal, whereas the areas that experience moderate to continuously heavy rainfall throughout the year are classified as nonseasonal (see Figure A1.1 in the appendix for the survey seasonality classification by area). In nonseasonal areas, agricultural growing conditions remain similar year-round, whereas seasonal rainfall areas have agricultural conditions that necessitate a variety of production strategies. Lowland and highland areas were defined using elevation data; areas 1,000 meters or more above sea level were classified as highlands, and those below 1,000 meters were classified as lowlands. The survey collected data from 270 communities across 14 provinces, from a total of 2,699 households. It is important to note that the survey is not nationally representative. Rather, we chose a purposive sample using criteria that would enable analysts of the data to understand the key factors that interact within rural households and communities to create more resilient local food systems, more diversified employment profiles, and improved well-being. Generalizable relationships between variables that affect socioeconomic and other development outcomes in rural PNG communities should be seen consistently in both representative and unrepresentative survey samples.

Between May and July 2018, the International Food Policy Research Institute (IFPRI) implemented a household-level survey in four areas of PNG: the Autonomous Region of Bougainville (South Bougainville near Buin), Madang (Middle Ramu near Kwanga Station), East Sepik (near Maprik) and West Sepik (near Nuku). The survey investigated the food systems of rural households and how they assure sufficient food to meet the nutritional needs of their household members. The household questionnaire for the survey focused on agricultural production systems and health outcomes and included modules on: production; consumption and expenditure; labor activities (farm and non-farm); nutritional status; and the experience of the survey households with recent agricultural production or other shocks that impacted their livelihoods. This report provides descriptive results from the survey and discusses key indicators and actions to improve agricultural systems and nutrition in PNG.

The first round of the Myanmar Household Welfare Survey (MHWS)–a nationwide phone panel consisting of 12,100 households–was implemented between December 2021 and February 2022. The objective of the survey was to collect data on a wide range of household and individual welfare indicators–including wealth, livelihoods, unemployment, food insecurity, diet quality, health shocks, and coping strategies–in a country exceptionally hard hit by conflict, severe economic collapse, and several damaging waves of COVID-19. The respondents interviewed in the MHWS were purposely selected from a large phone database aimed at being representative at the region/state level and urban/rural level in Myanmar. In this paper, we discuss two key steps taken to ensure that the MHWS is nationally and subnationally representative at the state/region and urban/rural level. First, we used a quota-based sampling strategy by setting survey quotas for respondents’ geography, education, farming status, gender, and rural/urban residence. This sampling strategy is used to address the well-known drawbacks of phone survey samples (e.g., the over-sampling of more educated respondents) and the survey’s particular interest in over-sampling farm households and equally sampling men and women. Second, we constructed household, population, and individual level weighting factors to further ensure that the survey generates nationally and subnationally representative statistics. To assess the effectiveness of these two strategies on achieving representativeness and consistency with previous surveys, we compare results from the MHWS to earlier nationally representative datasets, focusing on sample sizes of interviewed households for each state/region, and on education levels, farm/non-farm occupation, urban/rural residence, as well as respondents’ housing characteristics, which are unlikely to change substantially over short periods of time. We show that the phone-based MHWS has broader geographical coverage than previous national surveys, reaching 310 of Myanmar’s 330 townships. Moreover, our sampling approach was generally effective in reducing the education bias of phone surveys, except for a handful of states/regions. The MHWS is also unique in providing equal representation of male and female respondents. Additionally, the MHWS sampling and weighting strategies produce statistics on key indicators that closely mirror results from the two most recent national surveys in Myanmar. Overall, the results suggest that these strategies are successful in generating a subnationally representative phone survey that collected data on a rich array of household welfare indicators in exceptionally difficult political and economic circumstances.

Papua New Guinea (PNG) is the country with the largest rural population share in the East Asia and Pacific region. In addition, PNG is affected by El Niño Southern Oscillation (ENSO) climatic events that in severe years cause significant food insecurity due to failed agricultural production. Shrinking land holdings for agricultural production due to the growing population together with climate risk motivates many rural households to explore off-farm income generating opportunities. The analysis reported on here evaluates the importance of nonfarm employment in rural areas in PNG in diversifying risk to household welfare (associated with weather shocks, crop pests and diseases, agricultural price fluctuations, etc.), in smoothing seasonal income fluctuations, and in absorbing excess labor in households with limited agricultural resources. Our study draws upon a wide-ranging literature focused on the role of nonfarm enterprises in the livelihood strategies of rural households globally, e.g., de Janvry and Sadoulet 2001; Lanjouw and Lanjouw 2001; Jayne et al. 2003; Barrett et al. 2005; Haggblade et al. 2007; Lay et al. 2008. We use data collected from rural households in PNG between May and July 2018 by the International Food Policy Research Institute (IFPRI). The survey was administered to 1,026 households in 70 communities across specific districts in East Sepik, Madang, and West Sepik provinces and in the Autonomous Region of Bougainville. Respondent households were asked detailed questions about any nonfarm enterprises (NFE) in which they were involved, including questions on ownership, labor characteristics, and income generation. We explore how engaging in an NFE affects household welfare. In doing so, we evaluate two questions: 1) What barriers to entry exist for male- versus female-owned NFEs? 2) Do the welfare effects of NFE ownership differ by the sex of the owner? Overall, we find that households with an NFE have significantly higher annual per capita consumption compared to matched households without an NFE, amounting to an average increase in the value of annual household consumption of 180 kina (PGK) per capita. In addition, households with an NFE consume approximately 9.5 grams more protein per person per day and achieve greater diversity in their diets compared to households without an NFE. We find that the positive effects of NFE ownership on welfare outcomes are largely driven by male-owned and jointly-owned (male and female) NFEs. The welfare outcomes attributable to female-owned NFEs are much smaller.

Report on a survey of households in eleven urban areas of Papua New Guinea, 1974 - discusses population characteristics partic. Employment, housing, income, population growth, unemployment, educational level, level of qualification, internal migration, human settlements, economic growth and contact with the rural areas. Graphs, map, references, statistical tables.