Effects of COVID-19 and other shocks on Papua New Guinea’s food economy: A multi-market simulation analysis

In this study, we use a multi-market simulation model of the PNG food economy that explicitly includes production, consumption, external trade and prices of key agricultural commodities to quantify the likely impacts of a set of potential shocks on household welfare and food security in PNG. We have built the model to be flexible in order to explore different potential scenarios and then identify where and how households are most affected by an unexpected shock. The model is designed using region and country-level data sources that inform the structure of the PNG food economy, allowing for a data-driven evaluation of potential impacts on agricultural production, food prices, and food consumption. Thus, as PNG confronts different unexpected challenges within its agricultural economy, the model presented in this paper can be adapted to evaluate the potential impact and necessary response by geographic region of an unexpected economic shock on the food economy of the country.

We present ten simulations modeling the effects of various shocks on PNG’s economy. The first group of scenarios consider the effects of shocks to production of specific agricultural commodities including: 1) a decrease on maize and sorghum output due to Fall Armyworm; 2) reduction in pig production due to a potential outbreak of African Swine Fever; 3) decline in sweet potato production similar to the 2015/16 El Niño Southern Oscillation (ENSO) climate shock; and 4) a decline in poultry production due to COVID-19 restrictions on domestic mobility and trade. A synopsis of this report, which focuses on the COVID-19 related shocks on the PNG economy is also available online (Diao et al., 2020).1

The second group of simulations focus on COVID-19-related changes in international prices, increased marketing costs in international and domestic trade, and reductions in urban incomes. We simulate a 1) 30 percent increase in the price of imported rice, 2) a 30 percent decrease in world prices for major PNG agricultural exports, 3) higher trade transaction costs due to restrictions on the movement of people (traders) and goods given social distancing measures of COVID-19, and 4) potential economic recession causing urban household income to fall by 10 percent. Finally, the last simulation considers the combined effect of all COVID-19 related shocks combining the above scenarios into a single simulation.

A key result of the analysis is that urban households, especially the urban poor, are particularly vulnerable to shocks related to the Covid-19 pandemic. Lower economic activity in urban areas (assumed to reduce urban non-agricultural incomes by 10 percent), increases in marketing costs due to domestic trade disruptions, and 30 percent higher imported rice prices combine to lower urban incomes by almost 15 percent for both poor and non-poor urban households. Urban poor households, however, suffer the largest drop in calorie consumption - 19.8 percent, compared to a 15.8 percent decline for urban non-poor households. Rural households are much less affected by the Covid-19 related shocks modeled in these simulations. Rural household incomes, affected mainly by reduced urban demand and market disruptions, fall by only about four percent. Nonetheless, calorie consumption for the rural poor and non-poor falls by 5.5 and 4.2 percent, respectively.