_MACHINE LEARNING AND HIGH-FREQUENCY SURVEYS IN PAPUA NEW GUINEA 21_ ing this, the low-cost of imputations, and the major gaps in primary data in the Pacific Islands, these results suggest that the methods could be deployed in the Pacific Island region to provide unique insight into local price dynamics.

The methods have wider applicability and could help to fill crucial data gaps in data scarce regions such as the Pacific Islands, especially in conjunction with specifically designed continuous surveys. Potential indicators include tracking broad price indices, but possibly the same strategies could be used for other development indicators. Sub-national estimates of prices may also improve smallarea estimates of poverty and welfare, which is an important topic actively being explored by Lange et al. (2021) and Newhouse et al. (2022).

The encouraging results set the stage for a real-time evaluation of the methods along with external validation. Below are a few directions for future efforts.

First, the high intra-month volatility in prices suggest that supply, demand and prices are fluid in PNG markets and economically meaningful price action may be observed in the higher frequency domains. The application showed that it worked well with a short time series of data gathered from 2017 onward. This suggests that a system that gathers data at a weekly interval may also work if the data exhibit meaningful variation. Similarly, a daily interval approach may generate enough survey data to process and yield results within weeks or months after deployment. Such high frequency systems may be particularly valuable to monitor trends after natural disasters. One approach could be to maintain such systems at low cost by sustaining a limited data gathering operation, but quickly ramp up data gathering during periods of heightened interest such as when inflation is high or once a disaster has emerged and intense price action is anticipated.

Market price data gathering can be inexpensive if enumerators can reach multiple markets in a short time. However, geographically highly dispersed markets as in many Pacific Islands can make such an effort unfeasible. At the same time, phone surveys are increasingly used to collect household information across wide geographical areas, but at low cost. Continuous household-based phone surveys can create an opportunity to collect a variety of prices across large areas at almost no additional cost. Systematically assigning different items across households can create exactly the kind of data set with systematically missing items, which the proposed algorithm takes as an input to impute price indices. Thus, merging the efforts of household-based data collection via phones with price data collection is promising. However, the impact of challenges like measurement error from reporting prices through households, as well as an implicitly biased sample of markets (which households with phones use) will need to be carefully evaluated.

Second, the number of markets covered by the application was relatively low when compared to earlier related efforts in FCS countries. Moreover, correlations in prices across markets was very low in PNG. On the one hand this suggests that inflation tracking in PNG may benefit from data gathering at additional markets. On the other hand, the fact that the imputation results were still reasonably ac-