Global food prices and inflatable BRICs
Although the recent food price spike has been significant, global food reserves and good harvests in other crops will likely prevent it from causing mass starvation. Nonetheless, food inflation is now putting the heat on global central banks as they consider whether they need to tighten monetary policy to maintain inflation credibility, or whether they should stick to maintaining short term growth instead. The BRIC countries stand on a dangerous precipice, as their real growth in recent months has slowed dramatically. The Brazilian Central bank is dealing with inflation on its doorsteps as its own employees are striking and demanding a 23% wage hike to compensate for higher cost of living. India's growth engine is also losing its spark and global food prices coupled with an already poor monsoon season could push inflation further beyond the central bank's comfort levels. China is barely holding on, and monetary tightening at this juncture would have serious implications for both broad growth and the stability of the shadow banking sector. Russia is dealing with its own drought and its central bank is also under pressure from IMF officials calling for a monetary tightening. These food inflation problems are compounded by a rise in the value of the dollar, making purchases of U.S. corn, whose futures are 6% more expensive than their 2008 peak, even more costly.
No doubt, the current situation is quite severe, but what can history tell us about how food price affect inflation in the BRIC countries? Econometric evidence suggests that world food prices are a key driver, more so than oil, of global inflation, but can we generalize to the BRIC countries in the current situation? The first thing to note is that global food prices, as measured by the IMF food price index, have been on a secular rise since 2000, but that in June, the last measured month, food prices were still below where they were during the 2008 or 2011 food price crises.
Given that average food expenditure as a percentage of income for Brazil, Russia, India, and China all hover around 25 to 35%, we should expect that increases in food price growth should quickly show up in each country's inflation rates . However, by looking at the time series for each CPI and the IMF food price index, we see that the time series do not match up well and that the real story is a bit more complex. In each graph, CPI year over year growth rates are plotted on the left axis, while food index year over year growth rates are plotted on the right axis.
I split the countries in the above two groups for more than aesthetic reasons. If you look carefully at the time series, you can see that in the first group, China and Russia, food price growth and inflation rates seem to move together at all levels of food price growth. Over the entire period, China's inflation rate and food price growth had a correlation value of 0.7, which is enough at the 99% confidence level. Russia's correlation is more limited, as inflation only starts to move in sync with food prices after 2007. But in the period of time since 2007, the correlation value is 0.18, which is enough at about the 90% confidence level. I call this the unconditional inflation group, as the correlation between food prices and inflation is not conditional on the rate of food price growth.
On the other hand, if you look at the second graph, there's less of a discernible pattern for India or Brazil. Food prices spike in 2004 and 2008, but neither of the magnitudes of the two countries' change in inflation match the large swing in food price. However, the time series do start to line up in times of crisis, such as in 2009. This is especially evident for India, as from 2009 on, its inflation rate seemed to move in tandem with the food price growth rate. I call this group the conditional inflation group, as the correlation between food prices and inflation seems to be conditional on whether food price growth is sufficiently high.
To test this hypothesis, we can generate 2-year backwards looking rolling correlations and see how they evolve through time. These price correlations are plotted below, with the value of the 2-year rolling price correlation plotted on the left axis and year over year change in the IMF food index on the right.
China and Russia:
In these graphs, we see the difference between the groups in a different light. The value of the food correlation for China and Russia seem quite uncorrelated with food prices, whereas for India and Brazil the correlation between food prices and inflation is higher when food prices are higher. With further analysis, it can be shown that we can reject the null hypothesis that food prices don't affect the value of the food correlation for Brazil and India, but we fail to reject the same null hypothesis for China and Russia. This is the reason why Brazil and India are grouped together as conditional inflation countries. Food price changes affect their inflation rate only if food prices are growing quickly enough. On the other hand, Russia and China are unconditional inflation countries, as food prices strongly affect their inflation rates at all levels of food price growth. The scatter plots of correlation versus food price growth for India and Russia are particularly illustrative of this difference. First, India:
While India's food correlation values look to be affected by food price growth, Russia's food correlations seem to just cluster horizontally around values of y=-0.75 and y=0.5. With more detailed regression analysis of India's results, we obtain a 95% confidence interval of (-0.25, -0.05) for the intercept and a 95% confidence interval of (0.0053, 0.0156) for the slope. A similar regression for Brazil returns a 95% confidence interval of (-0.35, -0.18) for the intercept and a 95% confidence interval of (0.0022, 0.0141) for the slope. Both these numbers suggest that the effect is real: higher food price inflation is associated with a tighter positive relationship between food prices and inflation. Food prices are a convex predictor: little effect when prices are low, much stronger effect when they are high.
What implications does this have for food inflation and the BRIC countries? First, we should expect China's and Russia's inflation rates to be hit the hardest by any food price growth. They unconditionally inflate, which means that the historical relationships suggest that a rise in food prices will directly translate into higher inflation rates for those two countries. On the other hand, India and Brazil only conditionally inflate. Statistically significant relationships are unlikely to form at current food price growth levels, and we need to be looking at at least 10% year over year growth in food prices before we should expect each country's inflation to becomes statistically linked to global food prices. Therefore, their inflation rates will likely only rise after China and Russia's inflation rates rise. However, this analysis does not say anything about welfare costs to these countries. Given that India and Brazil have higher inflation rates than China or Russia, convex costs to inflation may end up leading to more damage in the conditional inflators than in the conditional inflators. Nonetheless, it shows that the relationship between food prices and broad inflation is not so clear cut, and that some statistical manipulation can be invaluable in teasing out the connection.