_MACHINE LEARNING AND HIGH-FREQUENCY SURVEYS IN PAPUA NEW GUINEA 13_ varying distribution of the month-on-month inflation process as an autoregressive moving average process with fractionally integrated generalized autoregressive conditional heteroskedasticity (ARMA-fiGARCH) following Baillie et al. (1996). The estimation is detailed in the Appendix.

# **III. Application to Papua New Guinea’s Fresh Produce Prices**

With a population of over 11.87 million people (2021), PNG is the largest and most populated island state in the Pacific. Yet, the country is known to be one of the most data-scarce and impoverished in the world. The World Bank’s last poverty assessment was performed well over a decade ago in 2009 and estimated that 38% of the population lived under the US$1.90 poverty line at the time. The country performs poorly on a number of social development indicators, but accurately tracking development trends remains difficult because of a lack of representative primary data (Edmonds et al., 2018).

In PNG, the traditional consumer price index (CPI) is produced at an aggregate level, using data from a few urban areas. This does not properly reflect the majority of the population as the World Development Indicators estimate that 87% of PNG’s population lives in rural areas (2021). Traditional price data collection also aims to follow a deliberate sampling and measurement process that is not well suited for monitoring during crises situations, when price levels may rise and fall rapidly. For instance, at the time of writing, the latest CPI for PNG lags by 6 months.

Scarce analyses point out that food markets in PNG are likely to be poorly integrated and disrupted by recent macro-shocks (Tracer, 2005; Huffaker et al., 2021; Davila et al., 2021), and as such, high volatility due to local shifts in supply and demand can be expected. With 87% of the population in rural areas, and highly localized price dynamics, a local-area CPI would be needed to more adequately describe price trends in different rural or poverty-stricken areas, where the majority of the population resides in fragile situations.

The paper uses end-of-July data available from IFPRI, downloaded from the Papua New Guinea Fresh Food Price Monitoring Tool on August 17, 2022. The data reports monthly prices in Kina (local currency) as measured in 8 different market locations throughout the country (Banz, Goroka, Kokopo, Kundiawa, Lae, Madang, Mt. Hagen and Port Moresby). Raw price data dates back to mid-2009, but there are periods of substantial data gaps, particularly between 2016 and 2018. In total, the data contains 79 unique food items, but most do not have a sufficient number of observations to create a time series.

Since the focus of the paper is on monitoring price inflation, a basket of food items with data coverage over a long time period is essential. The application also aims to understand what the right design may look like for future co-deployment of rapid survey systems and machine learning imputations, and so different data selections are used to investigate the trade-offs in data availability and survey coverage. Three baskets are selected. A 25-item version since 2009 and 2014 that