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For useful comments and suggestions on an earlier draft, the authors are grateful to the institutions responsible for the capacity utilization surveys discussed in this paper (Rosstat, REB, IET, and CEA), participants in seminars held at the IMF and the Ministry of Finance of the Russian Federation, and Vladimir Bessonov, Andreas Billmeier, Lorenzo Figliuoli, Neven Mates, Antonio Spilimbergo, Emil Stavrev, Poul Thomsen, and Harm Zebregs. The authors alone are solely responsible for any errors.
. Between 2000 and 2004, the investment share in Russia was roughly 18 percent of GDP, while it was around 23 percent in Central and Eastern European economies, and increased from 17 to 23 percent in other member countries of the Commonwealth of Independent States (CIS). In 2004, the only transition countries with lower investment shares than Russia were Uzbekistan (10 percent), Tajikistan (14 percent), and Macedonia (17 percent), while at least 22 transition countries had higher investment shares than Russia. The transition countries with the highest investment shares were the Czech and Slovak Republics (27 percent), Estonia (28 percent), and Azerbaijan (55 percent).
A similar conclusion was drawn by Gavrilenkov (2003, p. 18), who argued that “the growth mechanism that emerged after the 1998 crisis and contributed to an economic upturn is largely exhausted…. [T]his mechanism was based on increased capacity utilization, but after a number of straight years of growth, most sectors now lack spare capacity.”
We assume that the inflation equation is homogeneous of degree one, so that a doubling in the growth rates of unit labor costs and in unit capital costs leads to a doubling in the inflation rate.
For summaries of the expectations-augmented Phillips curve literature, which goes back to Friedman (1968), see Blanchard and Fischer (1989, chapter 10), or Romer (2001, section 5.4). While we treat utilization rates as exogenous here, it is also possible to make them endogenous, e.g., along the lines of Bils and Cho (1994) or Burnside and Eichenbaum (1996).
A commonly accepted justification for the latter assumption is that the rate of capital depreciation depends on the rate of capacity utilization (e.g., Greenwood and others, 1988; and Burnside and Eichenbaum, 1996).
Alternatively, we could allow the natural rates to increase with productivity growth.
Note that, given the dependence of factor costs on inflation expectations, we would obtain the same vertical Phillips-curve under the extreme assumption of perfect foresight (inflation expectations are equal to actual inflation). However, in that case any inflation path, as long as it was predictable, would be consistent with equation (10).
In addition to these four institutions, Moscow Narodny Bank publishes an additional survey (conducted by NTC Research) that contains indirect estimates of capacity utilization, such as backlogs and supplier delivery times. We do not discuss these estimates here, as they are somewhat difficult to compare with the direct estimates of capacity utilization.
Compared with the IET survey, the REB survey reports a larger share of respondents with “excess” capacity, an approximately equal share of respondents with “insufficient” capacity, and a smaller share of respondents with “sufficient” capacity.
We use core inflation rather than headline inflation in order to eliminate the effects of seasonal food items and administered price adjustments, which are unrelated to underlying inflation. Following Nahuis (2003), we consider monthly changes in the annual (12-month) rate of core inflation in order to eliminate seasonal effects. Since the core CPI index is available only from January 1999, the monthly change in annual core inflation can be computed only from February 2000. We do not plot the changes in core inflation for 2000 and 2001 because these were largely determined by other factors, which we control for in our econometric analysis.
It is somewhat surprising that the REB labor assessments are consistently below the IET labor assessments, while the REB capacity assessments are mostly above the IET capacity assessments. Since the REB survey seems biased toward smaller enterprises and the IET survey appears biased toward larger enterprises (see Appendix I), this suggests that smaller enterprises are more constrained in terms of labor, and less constrained in terms of capital, compared to larger enterprises. Another surprising fact is that the REB’s labor assessment is very low in 1994–95, suggesting that there was not much spare labor in this period, which appears to be inconsistent with the low labor utilization rate reported by the REB for those years. A possible explanation for this is that the REB assesses available labor relative to expected demand during the next 12 months; hence, the reported lack of spare labor may simply reflect overly optimistic expectations regarding overall demand during the following 12 months.
While there is no one-to-one relationship between the NAILU and the NAIRU, it is interesting to note that another study (Bragin and Osakovsky, 2004) estimates that, from 2000 to 2003, the unemployment rate in Russia was also approximately equal to its natural rate. This finding is not based on the labor utilization estimates discussed above, but on an error-correction-type model in which changes in employment are a function of changes in output, changes in inflation, and the difference between actual and natural employment, where the latter is an unobserved variable.
The NAICU estimate of 82 percent for the United States is surprisingly robust (e.g., McElhattan, 1985; Garner, 1994; Corrado and Mattey, 1997; and Emery and Chang, 1997) and is generally used as an indicator of inflationary pressure, by U.S. Federal Reserve banks and private investors alike.
Franz and Gordon (1993) estimate the NAICU for Germany at 84.7 percent. Nahuis (2003) finds NAICUs at around 84 percent for France, Germany, the Netherlands, and the United Kingdom; around 78 percent for Belgium, Greece, and Ireland (with no significant effects for Greece and Ireland); and around 75 percent for Italy. We are not aware of any NAICU estimates for transition or developing economies.
Burnside and Eichenbaum (1996) present a model in which it is optimal for firms to set their capacity utilization rate below 100 percent, because this allows them to immediately increase the effective stock of capital in response to shocks that raise the marginal product of capital.
Oomes and Ohnsorge (2005) conduct unit root tests for a similar inflation model for Russia, and find that the changes in Russian headline inflation, unit labor costs, and the nominal effective exchange rate are stationary for the period 1996-2004. We do not have sufficient observations to run the same unit root tests for core inflation, because data for the core CPI index are available only from January 1999; hence, the monthly change in annual core inflation can be computed only from February 2000. Economic intuition suggests that CU and LU are stationary because they are bounded.
These results are available from the authors upon request.
Another problem that could potentially complicate the estimation of β1 and β2 is potential multicollinearity between capacity and labor utilization, which may lead to biased estimates. A similar point is made by McElhattan (1978, p. 23) concerning the multicollinearity between the NAICU and the NAIRU for the United States. However, multicollinearity was not a problem in our case because of the relative constancy of LU during the sample period.
These estimates are slightly different from those presented in the scatter plots because of the longer sample period. To obtain comparable results and lengthen the sample period for the IET data, we interpolated the quarterly IET estimates by assuming identical capacity utilization rates for the three months within each quarter.
The REB capacity utilization estimate for end-2005 was 79. IET and CEA estimates for end-2005 were not yet available at the time of writing.
A fourth popular method for estimating the output gap, which we do not discuss here, is to identify structural demand and supply shocks in a vector autoregression (VAR), using a Blanchard-Quah type variance decomposition approach. We believe this method is difficult to apply to Russia, given the short time series available, the existence of structural breaks, and the difficulty involved in disentangling demand shocks from supply shocks, given that oil prices are correlated with both.
In fact, the evolution of real GDP in almost all transition economies displays a “V”-shape, with negative real GDP growth rates through the mid-1990s (for Central and Eastern European economies) or even until the end- 1990s (for most CIS countries), and positive growth rates after that. If one were to estimate the output gap for the entire 1990s using trending methods, the output gap would by construction be positive both at the beginning of the sample and at the end of the sample.
Moreover, we assume that the NAICU and the NAILU have been constant over time. While it would be interesting to test this assumption, we currently do not have a sufficient number of observations to do this.
To see this, consider the profit maximization problem max П = PY–WL– RK, s.t. Y = ALα Kβ, where P is the GDP deflator (i.e., PY=nominal GDP), W is the average nominal wage, and R is the average cost of renting capital. It is straightforward to show that the first-order conditions to this problem are α=WL/PY and β=RK/PY.
The category “gross profits and gross mixed incomes” is equal to that part of the value-added component that remains with producers after deducting expenditures related to the compensation of employees and net taxes on production and imports. Since net taxes on production and imports do not accrue to either capital or labor, we exclude them from the definition of total income, so as to ensure that the labor share and capital share sum to one.
Similar observations for Russia have been made by Dolinskaya (2001), Bessonov (2004), and Lissovolik (2004). The same observation applies to U.S. data as well. A number of papers have found that, when variable capital and labor utilization rates are introduced into real business cycle models, the assumed volatility in TFP needed to explain the observed variability in U.S. output is significantly reduced: by 20 percent in Bils and Cho (1994); by 33 percent in Burnside and Eichenbaum (1996); and by 20–40 percent in Baxter and Farr (2005).
Because 2004 data were not yet available during the time of this exercise, the estimate for 2004 is obtained by extrapolation.
This estimate is close to Lissovolik’s (2004) TFP growth estimate of 3.7 percent during 1999–2002.
The assumption of 5 percent is also consistent with the Russian government’s draft medium-term socioeconomic development program for 2005–08, in which it was argued that the Russian economy would not be able to arrive at sustainable rates of GDP growth higher than 4 to 5 percent per year.
At the time this WP was about to be published, REB estimates through end-2005 just became available, and they indicated that average capacity utilization increased from 74 percent in 2004 to 76 percent in 2005. Preliminary regression estimates for the extended sample through end-2005 also suggest a higher NAICU estimate (of almost 78 percent), implying that the output gap may still not have been significantly different from zero in 2005. These estimates, which are based on a slightly modified regression, are available upon request from Hajime Takizawa (email@example.com).
This is similar to the U.S. Federal Reserve Board’s definition of potential capacity as “sustainable maximum output,” that is, “the greatest level of output a plant can maintain within the framework of a realistic work schedule after factoring in normal downtime and assuming sufficient availability of labor and material inputs to operate the capital in place” (Morin and Stevens, 2004a, p. 3; see also Morin and Stevens, 2004b). Morin and Stevens (2004a) argue that it is important that potential capacity be defined as a “sustainable maximum” rather than some higher unsustainable short-run maximum that can be achieved only by postponing routine maintenance or temporarily boosting overtime to produce above capacity, because the latter will be inflationary.
This is similar to the U.S. Institute of Supply Management’s definition of capacity utilization as the ratio of current output to “normal capacity,” where the definition of normal capacity is left to the respondent (Morin and Stevens, 2004a, p. 4).
The IET and CEA surveys do not clearly define the concept of capacity utilization to respondents. In the absence of any other information, their respondents may be likely to use a definition similar to Rosstat’s, especially if they are also part of the Rosstat survey. This is particularly likely for CEA, since the CEA questionnaires are sent as part of a package with Rosstat statistical forms.
Another problem is that none of the surveys appear to include “small businesses,” which are defined as enterprises with less than 100 employees that are not owned by other medium-sized or large enterprises, state, public or religious organizations, charities, or other funds.
Real appreciation or an increase in disposable income can also lead to a fall in demand for low-quality, domestically produced goods, with consumers switching to higher-quality, imported substitutes. However, to the extent that this switch in demand may be temporary, the capital used to produce domestic, low-quality goods may not necessarily be considered economically obsolete. Enterprises should write off their economically obsolete capital, and no longer consider it part of their capacity, only if the switch in demand appears to be permanent.
The 43 goods are the ones for which Rosstat has published capacity utilization estimates since 1990; however, the sample has grown over time, and capacity utilization estimates are currently available for about 70-75 goods in the Rosstat publication “Russia in Figures.” In fact, Rosstat appears to have capacity utilization estimates for as many as 600 goods, but it does not publish these estimates.
This is only a rough estimate, and is obtained by multiplying the share of the sampled goods in total industrial output, as estimated by Bessonov, by the total number of industrial enterprises (except small businesses), as reported by Rosstat. Note that there could be some double counting, in that some enterprises may be producing more than 1 out of the group of 43 goods.
While it would be preferable to weight each good by its share in current output, Bessonov refrained from doing so because the output shares estimated for 1998 and 1999, around the time of the financial crisis, seemed unreliable.
The average annual increase (or decrease) in production capacity is calculated by aggregating annual increases (or decreases) by cause, weighted by the period of time (in percent of the year) during which this cause was effective. As an exception, increases or decreases due to changes in labor intensity are added without weighting.
On January 1, 2005, Rosstat switched to a new industrial classification system (ОКВЭД), and the statistics based on this new system have been revised back to 2003.
Until July 2001, this question had been formulated in a more restrictive way, by asking respondents to choose from eight categories (<30; 30-40; 41-50; 51-60; 61-70; 71-80; 81-90; and >90). The formulations of the other questions have remained unchanged since 1996.
The seven industries are (1) ferrous and nonferrous metals; (2) chemical and petrochemical; (3) machinery and metalwork; (4) forestry, woodworking, pulp and paper; (5) construction materials; (6) light industry; and (7) the food industry. Estimates of capacity utilization are published only for six industries (the ones mentioned above, excluding ferrous and nonferrous metals). The main other industries for which estimates are not available are the electricity industry and the fuel industry.
The population of all registered enterprises is the set of enterprises on the basis of which Rosstat calculates official industrial statistics for the Russian economy. This is by no means the same as the Rosstat sample that is used for capacity utilization estimates, discussed in Section A above.
Nevertheless, the REB sample does contain a number of reasonably large enterprises, given that, among the 20 percent of enterprises with more than 1,000 employees, one-fourth has more than 2,000 employees, and the average number of employees in this group is roughly 3,000 (REB, 2004, Table 2).
To see this, assume for simplicity that the population consists of 999 small enterprises, employing 50 percent of all employees, and 1 very large enterprise, employing the other 50 percent of employees. Taking a random sample with a very small sample size—say, a sample size of one—would imply that, on average, once every 1,000 times the sample is conducted, the sample will include the large enterprise. Thus, while the sample will be unbiased (in the sense that the expected enterprise size in the sample equals the average enterprise size in the population), 999 out of 1,000 times the sample will underestimate the share of large enterprises.
However, following Rosstat’s switch to a new industrial classification system in January 2005, the CEA has started expanding its current sample of 1,200 enterprises (those that respond), adding 3,300 to construct a new sample of 4,500 enterprises. The 1,200 old enterprises will remain part of the new sample only for a transition period.
Some evidence for this is provided by CEA estimates that the average service life of equipment is 20.7 years, while the share of new equipment (purchased in the last five-six years) is only 15 percent.