Agricultural Transformation

Regional implications of public investments and external shocks in Papua New Guinea: An economywide analysis

Improvements in telecommunications have enabled a tremendous increase in the flow of information across Papua New Guinea. Although transport costs remain high between various parts of the main island of Papua New Guinea, as well as between the main island and other parts of the country (e.g. the islands of New Britain and Bougainville to the East), phone and internet conditions enable government, traders and entrepreneurs to share market information that reduces transactions costs and improves market function. Nonetheless, there remain major differences in agricultural production, income sources and demand patterns across regions that are important to take into account in analyzing the impacts of external shocks and formulating development strategies.

Earlier work on PNG by Schmidt et al., (2021) explored the effects of Covid-related income and world rice price shocks with a partial equilibrium model of PNG’s rice economy. Diao et al., (2021) used a regional multi-market model to analyze broader effects of Covid-19 and other shocks to agriculture and household incomes in four regions of PNG. Various other studies, including Dorosh and Pradesha (2022) and Diao et al. (2024), used national computable general equilibrium (CGE) models of PNG to analyze exchange rate, terms of trade, and productivity shocks on various household groups. In this paper, we extend these earlier analyses through use of a new regional CGE model that utilizes recent household survey data, including the 2023 IFPRI PNG rural household survey.

The plan of this paper is as follows. The next section provides an overview of agriculture and economic growth in PNG over the last decade, highlighting the almost constant share of agriculture in GDP and very low per capita agricultural GDP growth rate. Section 3 describes the regional disaggregation of the PNG economy and the structure of household incomes and expenditure as reflected in the economywide database, the Social Accounting Matrix (SAM), used in this analysis. Section 4 then presents a summary of the regional economywide model. (Details of the model are found in the annexes and in the references included in the paper.) Design of the model simulations and simulation results covering investments in agriculture and transport sectors, as well as world price shocks are discussed in Section 5. Chapter 6 concludes with a summary of the main findings and policy implications, as well as suggested areas for further work.

Papua New Guinea Rural Household Survey, 2023

The Papua New Guinea Rural Household Survey (2023) collected detailed household-level data on agricultural production, food and non-food consumption and expenditure, and livelihood strategies across 14 provinces, covering communities in the highlands, lowlands, and islands of Papua New Guinea (PNG). The survey was designed using a purposive sampling strategy based on defined agro-ecological zones, which allows for the analysis of key factors influencing rural households and communities. It is important to note that the survey is not nationally representative; however, given the careful random selection of survey areas, we expect that generalizable relationships between variables affecting socio-economic and other development outcomes in rural PNG communities will be consistently observed across representative samples and in this survey. These factors include those that contribute to more resilient local food systems, diversified employment opportunities, and improved household wellbeing. The survey encompasses 2,699 households in 270 communities, spanning five agroecological zones. It features detailed modules on a wide range of topics relevant to rural livelihoods, agricultural production, and household wellbeing.

Papua New Guinea: Systematic analysis of domestic production and world market shocks

Using an economy-wide, multi-sectoral model, this study explores potential vulnerability of Papua New Guinea (PNG) to economic shocks and identifies factors that contribute most to economic uncertainty. Economic scenarios were developed using historical data on volatile domestic sectors (e.g., primary agriculture and hydropower electricity generation) and world prices of goods and services in which PNG trades. The Computable General Equilibrium (CGE) model of PNG was used to simulate a range of potential economic outcomes under these scenarios. In addition, data mining and machine learning methods were applied to quantify the contribution of each shock to the uncertainty of economic outcomes. Key findings suggest that Papua New Guinea’s economy is predominantly exposed to external risks, with world prices and foreign capital volatility together accounting for approximately two-thirds of the potential variation in GDP and three-fourths of the potential variation in private consumption, poverty, and undernourishment (domestic production volatility contributes the remaining one-third and one-fourth, respectively). The current structure of the PNG economy underpins these results. While agriculture is the most uncertain sector, it is relatively less important compared to the overall economy; export earnings from energy and metals, as well as volatile foreign exchange capital inflows, play a much more significant role in the country’s current economic risks. Understanding how potential shocks might impact various segments of the PNG economy and population is a critical first step in facilitating discussions on relevant risk mitigation strategies, such as increasing sectoral productivity or diversifying production away from high-risk sectors.

The agrifood system in PNG: Structure and drivers of transformation

Although the economy of Papua New Guinea is heavily influenced by the oil and natural gas sector, which accounts for 30 percent of GDP and most of the country’s foreign exchange earnings, small-scale agriculture continues to be the major source of livelihoods for most of the population. Much of the food crop production (particularly starchy staples such as sweet potatoes, cassava, yams and sago) is not traded internationally; however, oil palm, coffee and cocoa are major exports. A large share of agricultural production undergoes little value-added through processing and much of it is consumed by farm households themselves. Thus, there would appear to be substantial scope for increases in employment and incomes through further development of the broader agrifood system, including agroprocessing, trade and transport, and food services.

Subsistence farming typically dominates agriculture during the earliest stages of development; as agricultural productivity rises; however, farmers start to supply surplus production to markets, thus creating job opportunities for workers in the nonfarm economy both within and outside of agrifood sectors (Haggblade, Hazell, and Dorosh 2007). Rising rural incomes generate demand for more diverse products, leading to more processing, packaging, transporting, trading, and other nonfarm activities. In the early stages of agricultural transformation, the agriculture sector serves as an engine of rural and national economic growth. Eventually, urbanization, the nonfarm economy, and nonagricultural incomes play more dominant roles in propelling agrifood system development, with urban and rural nonfarm consumers creating most of the demand for agricultural outputs via value chains connecting rural areas to towns and cities (Dorosh and Thurlow 2013). The exact nature of this transformation process varies across countries because of the diverse structure of their economies and the unique growth trajectories of their various agrifood and nonfood subsectors.

This paper describes the current and changing structure of PNG’s agrifood system (AFS) and evaluates the potential contribution of different value chains to accelerate agricultural transformation and inclusiveness. We start by offering a simple conceptual framework of the AFS and then compare PNG’s AFS to that of other countries at different stages of development. We go on to disaggregate PNG’s AFS across agricultural value chains, taking into consideration their different market structures and historical contribution to economic growth and transformation. Finally, we use a forward-looking economywide model to assess the diverse contributions that specific value chains can make to each of a set of broad development outcomes. We conclude by summarizing our main findings.

Papua New Guinea rural household survey (2023): Synopsis of selected results

From May to December 2023, IFPRI implemented the 2023 PNG Rural Household Survey which was designed to understand rural livelihoods and welfare across different areas of PNG (Schmidt et al., 2024). Given the rural nature of the survey sample, almost all surveyed households depend on their own-farm production (predominantly starchy roots and tubers) to meet daily caloric needs. On average, households reported utilizing about 1.6 hectares of land for agriculture cultivation at the time of the survey. The survey collected a detailed account of the quantity of food types consumed by the household in order to estimate the average caloric intake per adult equivalent. Comparing the estimated caloric intake reported by surveyed households, with a recommended calorie intake suggests that only 45 per cent of individuals in surveyed households meet the recommended daily caloric intake for a lightly active individual. The survey also collected anthropometry data for children under five years of age and found that 36 percent of surveyed children were stunted in their growth. The 2023 Rural Household Survey represents an important effort in collecting a wide breadth of information about rural livelihoods. However, greater investments of in-depth data collection and analysis should be undertaken to examine specific components of PNG household livelihood strategies.

2023 PNG Rural Household Survey Report

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.

Constraints facing rural poultry production in PNG: The role of input suppliers

Context: The average consumption of protein foods in Papua New Guinea (PNG) remains insufficient to meet nutritious diet guidelines, especially in rural areas. While an expanding literature has demonstrated that poultry is a cost-efficient animal source food to increase protein intake, rural households in PNG face high prices at the market for poultry meat. Similarly, the high price of poultry production inputs constrains greater uptake of rural poultry production. PNG’s heavy reliance on feed (and feed input) imports, as well as high transportation costs and insufficient rural manufacturing and processing infrastructure creates limited opportunities for rural subsistence and commercial poultry production growth. Objective: There is a lack of value chain studies to understand the feasibility of expanding the local mini livestock feed mill model in PNG to increase poultry feed supply in underserved areas. This study builds from earlier work on rural livestock feed infrastructure programs, and aims to fill the knowledge gap on the opportunities and constraints for expanding domestic livestock feed production and distribution via rural mini feed mills. Method: The International Food Policy Research Institute (IFPRI) and the National Agriculture Research Institute (NARI) conducted in-depth qualitative interviews with 8 mini mills and 13 poultry farmers across 4 highland provinces during October and November 2022. We synthesize the interview transcriptions of the qualitative interviews in tandem with quantitative analysis of food consumption and agri-food trade data, as well as the authors’ own field observations in this paper. Results: The in-depth interviews showed that the poultry farmers who purchased from local mini feed mills substantially lowered their feed costs, resulting in greater gross profits compared to rural poultry farmers that only sourced feed from commercial feed suppliers. However, the mini feed mills that we interviewed outlined a series of challenges in sustaining rural feed mills in PNG. The main challenges of running a successful mill included feed mill equipment procurement, electricity reliability, reliable raw ingredient supply, mini mill retailing to secure a client base, and adequate information about feed formulation. We identified two potential approaches that have overcome many of the identified challenges, that could be replicated and adapted to expand mini feed mill operations in the Highlands. The first approach is a farmer cooperative model that incorporates credit and feed delivery services to cooperative farmers. In doing so, they are able to better estimate volume demand for processed feed and accommodate feed production accordingly. The second approach follows a lead firm model, whereby a local farm supply retail outlet is expanding its business to include livestock feed production and supply, overcoming equipment procurement constraints given their previously developed business model focused on farm implement supply. Our evaluation provides detailed costs and benefits of both approaches for potential expansion of these livestock feed producer and distributor models.

Synopsis: Improving agricultural productivity in Papua New Guinea: Strategic and policy considerations

In a recent working paper, we examine staple and cash crop production yields in Papua New Guinea (PNG). In doing so, we assess the yield gap (difference between the crop yields farmers commonly obtain and what they might realize with optimal inputs and crop management) for the main staple food crops in PNG. The yield gap for sweet potato is the smallest (1/4 - 1/3 less than attainable yields), while banana shows the largest yield gap at about ¾ less than what might be achieved under intensive cultivation. In addition, we compare PNG agricultural output with areas of similar growing conditions in Indonesia to provide insight into potential investments to further spur agricultural productivity in PNG. Finally, we assess current sector policies in PNG that aim to support agricultural development as an engine for economic growth.

Improving agricultural productivity in Papua New Guinea: Strategic and policy considerations

If smallholder farming households in Papua New Guinea achieve higher crop productivity levels, progress will be made along several dimensions of the development vision for PNG – increasing GDP for the agricultural sector and the overall economy; driving growth, diversification, and transformation of local rural economies; improving food consumption; and reducing poverty. In this paper, we examine recent data on yields for the most important crops grown in PNG, assess what yields might be achieved based on productivity data from areas of Indonesia with similar growing conditions, and sketch where policy reforms could provide incentives and access to technologies to achieve higher crop yields by all farmers across PNG.