Catastrophic depopulation in Russia and Japan

The World Bank provides population projections for all countries through 2050. Figures 1 and 2 display the evolution of population in Russia and Japan. Both projections look catastrophic. Population shrinks with acceleration. The birth rate is always lower than the death rate.

Figure 1. Depopulation of Russia and Japan: 2010 to 2050.

Figure 2. The rate of birth and death in Russia and Japan: 2010 to 2050

 

Russia will never catch up

Labor productivity is the economic parameter that is practically the best to characterize the level of technical development and human capital. The Total Economy Database lists various estimates of productivity for many countries. For Russia,  only GDP per person employed in 1990 Geary-Khamis 1990 $ is available since 1989. For the USSR, the TED provides a virtual time series since 1960. Here, we address the question of evolution of labor productivity in Russian Federation (RF) relative  to the USA. Figure 1 depicts two original curves which reveal the sadness of the current evolution of labor productivity in Russia: the slope of the Russian curve is lower than that for the USA curve. In the long rung these curve diverge. Thus:

  

Labor productivity in Russia will never catch up that in the USA.

 

Figure 2 presents a different view on these curves. The difference in labor productivity had been increasing linearly for the USSR and suffered an accelerated increase between 1990 and 2005. A few years of extremely high oil price corrected the deviation down but the current evolution is likely returning to the long term trend - the gap grows with time.

 

Figure 3 demonstrates that in relative terms labor productivity in Russia is still lower than it was in the USSR. It will take another ten years to get to 1/3 of the US productivity. Discouraging story.

 

 

Figure 1. The evolution of GDP per person employed in the USA and Russia.

 

 

Figure 2. The differences in labor productivity in the USA and  Russia/USSR.

Figure 3. The ratios of labor productivity in the USA and  Russia/USSR.     













  

The error of the CPI estimates in Japan runs away

A month ago I wrote about the difference between two inflation measures as expressed by the consumer price index (CPI) and the GDP deflator in Japan. It was shown that the difference between these two indices increases over time. In this post, I add couple graphs to demonstrate that the difference diverges as time squared. Figure 1 displays the difference between the GDP deflator and CPI since 1985. One may observe that the slope of the difference increases with time approximately every five years, i.e. when new basket for CPI is introduced. In Figure 2, we approximate the difference with a quadratic function of time. The difference is running away, i.e. the error in the CPI estimate runs away since the GDP deflator completely includes the CPI.

 

Figure 1. The difference between the CPI and GDP deflator

 

 

Figure 2. Approximation of the difference between the CPI and GDP deflator by a quadratic function of time

 

This is how the Japan Statistics explains the difference between CPI and the GDP deflator:

 

Compared with the recent movement of the CPI and that of the GDP deflator, the range of drop of the GDP deflator has been larger than that of the CPI. The discrepancy between the CPI and the GDP deflator is mainly ascribable to the different things they cover. Other causes of the discrepancy include, among others, the different calculation formulae employed.

 

(1) The Target
While the CPI focuses only on household consumption, the GDP deflator covers business investments in equipment, etc., in addition to household consumption. Since much of the investment in equipment today is made in information technology goods, whose quality is rapidly improving, price falls in such goods considerably affect the deflator. For this reason, the change ratios of the deflator tend to be lower than those of the CPI.
Also, while the prices of petroleum products and other imported goods are rising, the CPI is usually pulled up. On the other hand, the deflator tends to drop until such price hikes are all reflected in the relevant product prices. Thus, the discrepancy between the two grows wider.
If the scopes of the two indices are narrowed down to cover the same items as far as possible, i.e., if we compare the CPI for “All items” with the GDP deflator for “Final household consumption expenditure” alone, the two indices show quite similar fluctuations.

(2) The Formula
While the CPI calculation employs the Laspeyres formula, the GDP deflator employs the Paasche formula. Generally, the Paasche formula, which calculates a weighted average using the quantitative weights at the time of comparison, tends to provide a lower index, while the Laspeyres formula, which employs quantitative weights at reference period, usually produces higher values. In addition, since quality improvement is reflected in the form of an increase in volume, Paasche formula gives a larger weight to an item whose price has fallen due to quality improvement. For this reason, the rate of decline of the GDP deflator, which employs Paasche formula, tends to be getting larger.
Also note that the GDP deflator employs a “chain method” with the reference periods it updates weights annually, to minimize the bias accompanying calculation of the index. Such a chain method is also used with the CPI as well, to provide and publish an additional, referential value to the index.

Does Banque de France control inflation and unemployment?

A PDF version of the paper is available here.

We re-estimate statistical properties and predictive power of a set of Phillips curves, which are expressed as linear and lagged relationships between the rates of inflation, unemployment, and change in labour force. For France, several relationships were estimated eight years ago. The change rate of labour force was used as a driving force of inflation and unemployment within the Phillips curve framework. Following the original problem formulation by Fisher and Phillips, the set of nested models starts with a simplistic version without autoregressive terms and one lagged term of explanatory variable. The lag is determined empirically together with all coefficients. The model is estimated using the Boundary Element Method (BEM) with the least squares method applied to the integral solutions of the differential equations. All models include one structural break might be associated with revisions to definitions and measurement procedures in the 1980s and 1990s as well as with the change in monetary policy in 1994-1995. For the GDP deflator, our original model provided a root mean squared forecast error (RMSFE) of 1.0% per year at a four-year horizon for the period between 1971 and 2004. The same RMSFE is estimated with eight new readings obtained since 2004. The rate of CPI inflation is predicted with RMSFE=1.5% per year. For the naive (no change) forecast, RMSFE at the same time horizon is 2.95% and 3.3% per year, respectively. Our model outperforms the naive one by a factor of 2 to 3. The relationships for inflation were successfully tested for cointegration. We have formally estimated several vector error correction (VEC) models for two measures of inflation. In the VAR representation, these VECMs are similar to the Phillips curves. At a four year horizon, the estimated VECMs provide significant statistical improvements on the results obtained by the BEM: RMSFE=0.8% per year for the GDP deflator and ~1.2% per year for CPI. For a two year horizon, the VECMs improve RMSFEs by a factor of 2, with the smallest RMSFE=0.5% per year for the GDP deflator. This study has validated the reliability and accuracy of the linear and lagged relationships between inflation, unemployment, and the change in labour force between 1970 and 2012.