The latest economic worry, as countries begin to recover from the Covid pandemic, is that the broader economy could be severely restricted by rising prices and shortages. This has already been seen in such areas as new and rental cars, where prices have soared due to computer chip shortages.
One issue is how are current price increases affected by spatial patterns and processes and to what extent this affects populations. Research has demonstrated that there can be ways to study the spatial effects and relationships between location and inflation. In fact, it might be necessary to study inflation using spatial methods because of the differential impacts it can have on different parts of the population.
Research on how the pandemic impacts prices globally is still developing, but some results for different regions have been evident as results come in.
Spatial Patterns of Pandemic-driven Inflation
In West Africa, for instance, research has shown there is a broad spatial pattern to inflation based on the pandemic using instrument variable (IV) and ordinary least squares (OLS) regression for different countries. Broadly, it has been demonstrated that countries responding to the pandemic have affected prices differently.
Locally, if countries applied mitigation efforts to minimize the pandemic’s effects, such as social distancing, then prices initially declined as the pandemic spread. However, as other countries also applied similar mitigation to the pandemic, then prices in other countries began to rise as supply became severely limited and affected what could be exported.
In effect, country policy not only affects an individual country but it can affect neighboring countries as exports and prices are affected for traded items. The results suggest that countries should coordinate their Covid mitigation strategies to minimize rapid price increases and declines.
Inflation can also vary in terms of inputs from regional factors within countries. In the UK, the Centre for Economic Performance looked at regional factors of inflation. The results show there are some impact on prices based on regional policies and differential effects, particularly supply chains.
For instance it was found that in Northern Ireland price increases were more pronounced because of supply chains being more disrupted by the pandemic. Lockdown easing and restrictions were also applied differently in the nations of the UK, where price increases or declines reacted to these events slightly unevenly depending on the timing of when policies came into effect.
Using GIS to Analyze Inflation
While we are only beginning to get more data on inflation and its relationship to the pandemic, there are GIS-based methods that are available that could help to ensure approaches can better capture the likely differential impact of inflation and aid policy.
As an example of a useful method, in one study researchers showed the effects of food price inflation on mortality in Ethiopia. Using regional data, and nearest neighbor analysis that linked survey data on mortality and food prices with regional hubs, it was shown that there is a correlation of increased mortality for pre-natal and post-natal mortality, with 10% inflation on food prices affecting mortality by more than 5%.
In effect, these metrics and methods of linking local mortality data with local food price data can help to show a direct linkage between how regional variation in prices have differential impacts on health for different segments of the population. For policy makers, this means that monitoring regional variations on vulnerable communities in food poverty regions sensitive to price fluctuations are needed to best target regional inflation impacts.
Inflation Data in the United States
Inflation data associated with Covid’s impact in the United States is still developing, although we know some sectors are being affected, the spatial relationship still needs formal analysis. However, there are methods that have been recently developed that can aid in better understanding the effects of inflation on the United States in key areas.
In particular, the relationship between house price inflation and gentrification has been studied and developed using conditional autoregressive (CAR) modeling and applying the American Community Survey (ACS) as a data source for community housing change.
In an example, it was shown that house prices increased even faster, compared to broader house price measures, in parts of northern Manhattan and Brooklyn, which have significantly accelerated gentrification in those areas since 2000 as previous communities’ population changes due to price pressure and changes.
This could suggest that house prices, which are increasing in parts of the United States, could have very different impacts on different socio-economic levels in the United States as people begin to take advantage of low interest rates. This is something that will have to be monitored closely as regional differences in house prices are observed.
We are beginning to see that inflation is a cause of concern, at least in some areas, even while economists debate if the price increases we are seeing are only temporary. We have seen from previous spatial approaches and methods that food and house price increases, in particular, can have very detrimental impacts to those who are economically vulnerable. Policy should account for regional impacts of inflation, while researchers should utilize approaches available to get a better sense of differential impacts of inflation on the wider population for countries and states.
 For more on how West Africa’s economies and prices were affected by different Covid-19 policies, see: Coulibaly, Seydou. 2021. “COVID‐19 Policy Responses, Inflation and Spillover Effects in the West African Economic and Monetary Union.” African Development Review 33 (S1). https://doi.org/10.1111/1467-8268.12527.
 For more on spatial effects of inflation in the UK based on a recent report, see: https://cep.lse.ac.uk/pubs/download/cepcovid-19-017.pdf.
 For more on food inflation and mortality, including regional effects, see: Kidane, Daniel, and Andinet Woldemichael. 2020. “Does Inflation Kill? Exposure to Food Inflation and Child Mortality.” Food Policy 92 (April): 101838. https://doi.org/10.1016/j.foodpol.2020.101838
 For more on the methodology and use of CAR modeling for house price inflation, see: Johnson, Glen D., Melissa Checker, Scott Larson, and Hanish Kodali. 2021. “A Small Area Index of Gentrification, Applied to New York City.” International Journal of Geographical Information Science, June, 1–21. https://doi.org/10.1080/13658816.2021.1931873.