In rural Indonesia, rice accounts for almost one-quarter of the total expenditures of the poor. However, food prices have been on a rollercoaster ride because of unreliable production data. For decades, rice production data relied mainly on subjective measures of eye-estimate (relying on no device other than one’s own eyes to measure distances), which created unreliable evidence on which to base critical policy decisions.
Sensing this, in 2016, the Indonesian President Joko Widodo responded by instructing the Central Bureau of Statistics (BPS) to be the only agency responsible for collecting and releasing food production data. Since then, BPS, together with the Agency for Assessment and Application of Technology (BPPT) and other agencies, has improved the calculation of the various stages of the rice production data collection. Four major parameters and methodology were revised: the total rice field areas (revised from 7.75 million ha in 2013 to 7.11 million ha in 2018); the adoption of the Area Sampling Framework methodology to collect data on the vegetation phases of the sampled areas using an android-based application; land productivity, which is now sampled from the Area Sampling Framework areas only; and lastly, the two conversion rates from dry threshed paddy to unhusked dry rice ready for milling, and from unhusked dry rice ready for milling to milled rice.
Two years later, in October 2018, Vice President Jusuf Kalla acknowledged the newly revised rice production data, which was 30 percent lower than previous estimates. The latest data vindicates policymakers who tried to stabilize rice prices through imports. The new data shows that there is only 2.85 million tons of rice surplus in 2018, compared to the previously claimed 10 million tons of rice surplus every year, and 44 percent of which are estimated to be held by producer-households, 18 percent by traders, etc., leaving insufficient amount for the national buffer stock, especially in non-harvest months.
What will the newly improved rice production data mean to Indonesia? Technology-based measurement should enhance transparency, accuracy, and accountability of rice production data that will further help policymakers and the private sector make better decisions concerning domestic supply to minimize rice price volatility and shocks. The fact that data is collected monthly with GPS-enabled, geospatial-coordinate accuracy allows policymakers and the private sector to know precisely when and where rice is being harvested and the vegetative growth phases of rice production in each location. Such statistical-geospatial data also enables the management of the regional distribution of rice, the forecasting of rice balance and import needs, as well as early mitigation of pest attack, flood, and drought. In the medium and longer-term, more accurate data improves budget efficiency and farmers’ as well as consumers’ welfare through better production management and less volatile producer and consumer prices.
What can other countries learn from Indonesia's experience in improving food production data? This reform would not have been possible without political commitment at the highest level that led to across-the-board collaboration among key government agencies. To a lesser extent, this reform might not be possible without the implicit agreement of “data amnesty” so no one can be held accountable for any incorrect data in the past. The idea is to be forward-looking and not looking back, and this is critical for such a significant reform, which otherwise would have received a major backlash.
Although this reform is monumental, it is only the beginning. The Indonesian government should apply this new methodology to other food commodities and continuously improve its way of collecting food production data, especially with emerging availability of and accessibility to advanced technology such as big data, machine learning, artificial intelligence, and the Internet of Things. Most importantly, the new rice production data should be used in every possible way to improve policymaking decisions, for example, budget efficiency, forecasting, and stock management.