A Strong Connection
Producer and consumer prices are measured through PPI and CPI. There should be a relation between the two, given a pass-through effect, from the former to the latter (PPI -> CPI). The producer and consumer price indexes can be seen as prices of different goods along the production chain. Higher producer prices should lead to higher consumer prices: this is because changes in the price of crude materials should pass through to prices of intermediate and finished goods – hence to consumer prices.
It is evident, especially in the case of durables, how the CPI moves in line with movements in the PPI, considering your changes since we want to account for seasonality effects (Figure 1). This is evident in the years 2000-2004 and 2010-2014. Also, the CPI is shown to react with some lag, like it is more apparent from the case of non-durables: this demonstrates our initial guess, i.e. CPI changes after PPI movements. An anomaly shows up in the economic cycle 2005-2009 for durables: an increase in the CPI precedes the one in the PPI. Also, in 2016, there is a spike in the CPI Durables, while PPI remains low. This is probably why the regression below (Figure 2) shows a low R^2 despite the charts showing that PPI explains quite well the variability of the CPI Durables.
However, it was clear from the tables above; there is a significant positive relation between the PPI and the CPI. A change of 1% in the PPI reflects in a change of around 0.11% in the CPI of both types of goods.
Durables And The EU Recovery
The recent spike in the CPI Durables, mentioned above, is due to the recovery in durable good consumption which gained momentum: the primary driver of the recovery in core inflation in the Eurozone has been the price of durables, as it is explicit from the chart below (Figure 3). In 2015, which should coincide with the start of the recovery following the depression, the CPI spikes while the PPI stays flat: this has been due to a high demand for durable goods.
Additionally, the PPI is more volatile than the CPI. Our regression below (Figure 4) suggests that import prices, usually quite volatile, affect produce prices through a positive and significant relationship. The R^2 is high, demonstrating that import prices alone explain around 1/3 of the PPI’s volatility.
Another Case Study
This relationship is interesting since it allows for an explanation of one of the ways in which shocks in one economy transmit to another one. Let us take the example of China, a country with many State-owned firms which were often over-supplying the market. Over the past years, it was possible to observe continuously falling prices in China. In particular, over the past 50 months, deflation was dragging down prices not only in China but also out of it through exports. Therefore, this explains even more, how China’s slowdown has been affecting the global economy, starting already in 2H2015. Worries about a slowdown in the Chinese growth are justified, among other things, by this strong link, also considering its largest trading partners are the EU and the US.
In conclusion, we would like to understand how late the CPI reacts to movements in the PPI. Exploiting now the data for non-durable goods, which seems not to exhibit anomalies which may distort our results, we want to find what is the lag that explains most the variance in the dependent variable i.e. CPI (Figure 5). We will follow an approach which tends to align PPI’s movements with CPI’s ones, by lagging PPI by a certain number.
According to our results, the optimal lag is given by 9, i.e. the CPI reacts with a delay of 9 months to the movements in the PPI. This is because the 9th delay of PPI explains the variability in CPI the most. Therefore these results may help us predicting how and when the CPI will be moving, following movements in the PPI.
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