Explaining Russia's Output Collapse: Aggregate Sources and Regional Evidence

Irina Dolinskaya; Irina Tytell

doi:10.5089/9781451843408.001.A001

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Explaining Russia's Output Collapse

Aggregate Sources and Regional Evidence

Author: Ms. Irina Dolinskaya and Ms. Irina Tytell

Contributor Notes

Author’s E-Mail Address: idolinskaya@imf.org

Publication Date:: 01 Feb 2001

eISBN:: 9781451843408

Language:: English

Keywords:: WP; capital stock; private sector; production function; productivity gain; TFP estimate; output share; productivity effects of transition; income share; observed productivity growth

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This paper explores sources of the output collapse in Russia during transition. A modified growth accounting framework is developed that takes into account changes in factor utilization typical of the transition process. The results indicate that declines in factor inputs and productivity were both important determinants of the output fall. The contribution of the productivity drop was critical, but smaller than previously reported. Possible causes of the reduction in productivity are assessed using data on sub-national regions within Russia. Privatization and entry of private firms are found to have generated productivity gains, while lack of exit of unviable enterprises constituted a drag on efficiency.

Abstract

I. Introduction

All the transition economies of Eastern Europe and the Baltics, Russia, and other former Soviet Union countries (henceforth “BRO”) have experienced major declines in output after the launch of economic reforms. The magnitude and the duration of the “transition recession” varied greatly across countries, from Poland that lost 14 percent of output over 2 years to Ukraine that lost 58 percent over 6 years². The BRO countries were generally hit harder during the reform period, with the average output fall nearly twice as large as that in Eastern Europe. As much as 75 percent of the total BRO output during this period was accounted for by Russia, by far the largest economy in the region. The reforms in Russia started in 1992 and were accompanied by an output fall that continued uninterrupted till 1997, resulting in a cumulative output loss of 40 percent relative to 1991.

Russia’s growth performance had already worsened prior to the beginning of economic transformation. The reason for the slowdown was the nature of the Soviet economic growth that was based on mobilization of resources, rather than increases in productivity. It is well established in the growth theory literature, that this kind of input-led growth is unsustainable in the long run (see Romer, 1996 for a review). The standard neo-classical model shows that in the absence of technological progress diminishing returns to capital lead to eventual wearing out of the economy’s growth potential. Technological progress, however, results from innovation and adoption of new technologies, for which the central planning could not provide the appropriate incentives.

High growth of the Russian economy in the 1950s was achieved by means of rising labor force participation rates and working hours, increasing labor quality through education and, most importantly, heavy investment. The Soviet Union still had one of the highest investment rates in the world in the 1960s, 1970s and 1980s, although its growth performance deteriorated. The Soviet economy had become more and more inefficient over time with productivity growth showing a strong declining trend since the 1960s (Easterly and Fischer, 1994). After the initial period of high returns to capital accumulation, the economy got saturated with capital, and continued investment led to over-accumulation. The capital was inefficiently allocated and increasingly outdated (as indicated by its very poor substitutability for labor), thus resulting in rapidly falling returns on investment starting in the 1960s. The slowdown resulted not only from extensive growth as such, but also from an extraordinarily low payoff to it. The situation was aggravated by the burden of defense spending and demoralization resulting from the distorted incentives of central planning.

Thus, the stage for the output decline of the 1990s was set by the extensive growth of the preceding decades. However, the output collapse during transition was by far too dramatic. So what was the driving force behind the “transition recession”? Was it a result of factor unemployment or a productivity drop? It is widely acknowledged that while reallocation of resources across sectors and individual firms that accompanies the transformation to a market economy ultimately improves efficiency, it is subject to considerable transition costs (Blanchard, 1997). Market-oriented reforms undermine the functioning of the state firms, while the new private sector takes time to emerge. This causes a rise in unemployment, as a drop in the labor demand of state firms is not immediately compensated by a rise in the labor demand of private firms. Besides, the old state sector workers often need to be re-trained before they can be employed in the new private sector. In addition, the process of transition is likely to cause underemployment of capital (Hernandez-Cata, 1997). While the declining state sector releases its capital stock, the emerging private sector cannot employ it unless it undergoes some time-consuming restructuring. In fact, part of the capital stock inherited from the socialist times is so outmoded that it will never be used again and thus has to be replaced, which requires time and resources.

The productivity effects of transition may be mixed over the short term. While market liberalization can boost the development of more productive private firms, it can further reduce productivity of the remaining state enterprises. An important consequence of the transition process is a breakdown of economic relations among firms, or “disorganization” (Blanchard and Kremer, 1997). A prominent feature of central planning had been the prevalence of highly specific relations among economic agents. The effect of transition was to eliminate the enforcement mechanism that made such structures work, before the market-based contract enforcement mechanism was in place. Thus, market liberalization opened up new private opportunities that undermined established production networks. Roland and Verdier, 1997 put forward a similar argument based on search frictions generated by the liberalization of supplier-customer relations. If enterprises do not undertake capital investment until a long-term partner is found, output may fall due to the fall in investment and the failure to replace outdated capital.

Some recent empirical work (De Broeck and Koen, 2000) found that a productivity drop played the dominant role in the “transition recession” in the BRO countries. The analysis below shows that the productivity effect – while very important – tends to be overstated in such studies. This paper looks closely at Russia’s growth performance during the transition period with the aim of gaining a deeper understanding of the reasons behind the output fall. It also goes further in trying to explain the reasons behind the observed productivity decline. Explaining the collapse is important for developing the appropriate policy response, particularly in light of the financial crisis of 1998 that highlighted fragilities of Russia’s economy. The recent recovery has so far been driven by the ruble depreciation that gave a boost to both import-competing and exporting sectors, as well as by a surge in world energy prices. Sustainability of this recovery cannot be insured unless internal structural imbalances are eliminated and a solid foundation for economic growth is put in place.

The paper is organized as follows: section 2 describes the approach and presents the results of a growth accounting exercise, section 3 evaluates the sources of the productivity drop with the help of a regression analysis on a regional data set, and section 4 concludes.

II. Aggregate Sources: Growth Accounting

A. Measurement Issues

In transition economies in general, and in Russia in particular, output tends to be poorly measured, even though certain corrections have been made by the statistical authorities. Official statistics reflect mainly the performance of declining large and medium-sized state (or formerly-state) enterprises and tend to neglect newly emerging private activities. This, as well as under-reporting due to notorious tax evasion, leads to overstatement of the output fall. It is exacerbated by the likely over-reporting that took place under the central planning. Informal activities are widespread and include, for example, street trading, small-scale renovations and repairs, small-scale smuggling and so on. Allowances made in the statistics to account for the informal economy are largely arbitrary, since its size is hard to measure. It is worth noting that the presence of informal economy distorts measurement of factor inputs, as well as that of output. Labor and capital used in the informal sector are often not properly registered and hence not properly accounted for. It may actually be conceivable that unrecorded output is produced from unrecorded inputs. Finally, inflation and relative price shifts introduce difficulties in assessing the real values, which appear to be sensitive to different sets of prices used for deflating the nominal values (Gavrilenkov and Koen, 1994).

A number of proxy indicators of output change have been suggested in the literature, the most popular being electricity consumption (Dobozi, 1995). It has been used, in particular, to estimate the size of the shadow economy in transition countries, which is unaccounted for in the official statistics (Kaufmann and Kaliberda, 1996). The use of this indicator is based on the conjecture that in market economies the electricity-GDP elasticity is close to one. However, it is not uniformly the case and in transition economies departures from this conjecture are particularly likely (De Broeck and Koen, 2000). One might expect a rise in electricity efficiency, since the newly-emerging private sector is likely to be more energy-efficient, while traditional power-intensive sectors were hit hard by reforms, and since energy prices rose substantially as a result of liberalization (although non-payment of energy bills is widespread). On the other hand, a drop in capacity utilization and lack of basic maintenance could diminish the efficiency of electricity utilization, while a significant portion of electricity consumption is realized through lighting and heating (including residential) and hence has little relation to output.

In addition, in the transition environment output measures may not be good indicators of welfare. Thus, fewer unwanted goods do not make consumers worse off, while more varieties and better quality of available goods make them better off. Average money income, for example, has been put forward as a better measure of the standard of living in Russia (Gavrilenkov and Koen, 1994). This indicator reflects significant adjustments in wages and other social payments, as well as emergence of new kinds of income, such as entrepreneurial earnings and dividends. However, real income data is highly sensitive to inflation and suffer from misreporting as a consequence of tax evasion and widespread payment arrears and in-kind remuneration.

Fig. 1 shows the dynamics of GDP as compared to that of electricity consumption and real money income over the transition period. While per capita income was considerably more volatile and electricity consumption experienced a visibly weaker decline than GDP in 1992 – 1994, overall all three indicators exhibit broadly similar trends. Since it seems that using proxies may pose more dangers than using GDP as reported by the statistical authorities, the analysis below is based on the official data.

B. Methodology

Basic Framework

Growth accounting is a technique based on the standard neo-classical growth model (Solow, 1956), that seeks to identify sources of economic growth. The standard aggregate production function that gives rise to the growth accounting equation is:

\begin{matrix} Y = AF (L, K), & (1) \end{matrix}

where Y denotes output, A (total factor productivity, or TFP) measures the efficiency level at which factor inputs are transformed into output, L and K are labor and capital inputs, respectively. The production function is assumed to have constant returns to scale, and markets are assumed to be perfectly competitive.

In this framework, the growth rate of output can be represented as:

\begin{matrix} \frac{\dot{Y}}{Y} = \frac{\dot{A}}{A} + (\frac{\partial Y}{\partial L} \frac{L}{Y}) \frac{\dot{L}}{L} + (\frac{\partial Y}{\partial K} \frac{K}{Y}) \frac{\dot{K}}{K}, & (2) \end{matrix}

and using the perfect competition assumption:

\begin{matrix} \frac{\dot{Y}}{Y} = \frac{\dot{A}}{A} + (\frac{w L}{Y}) \frac{\dot{L}}{L} + (\frac{r K}{Y}) \frac{\dot{K}}{K}, & (3) \end{matrix}

where w stands for real wage and r stands for real rental rate of capital.

In other words, the growth rate of output can be decomposed into the growth rates of inputs weighted by their respective income shares and the growth rate of the total factor productivity. TFP growth is a residual that represents the component of growth that is not explained by increases in the factors of production, but rather is attributed to productivity gains.

The weights take somewhat different form depending on the particular form of the production function. In the most widely used (constant returns) Cobb-Douglas representation, which is:

\begin{matrix} Y = A L^{α} K^{1 - α}, & (4) \end{matrix}

the growth accounting equation takes the form:

\begin{matrix} \frac{\dot{Y}}{Y} = \frac{\dot{A}}{A} + α \frac{\dot{L}}{L} + (1 - α) \frac{\dot{K}}{K} & (5) \end{matrix}

For use in discrete time (relevant for most practical applications) this becomes:

\begin{matrix} [\frac{Δ Y_{t}}{Y_{t - 1}}] = [\frac{Δ A_{t}}{A_{t - 1}}] + \bar{α} [\frac{Δ L_{t}}{L_{t - 1}}] + (1 - \bar{α}) [\frac{Δ K_{t}}{K_{t - 1}}], & (6) \end{matrix}

where $\bar{α}$ is the average labor share over the periods t-1 and t³.

Extended Framework

As will become apparent below, it is crucial to explicitly account for factor utilization in the context of transition economies. Within the basic framework changes in factor utilization would be picked up in the TFP, which could make interpretation of results especially difficult. The massive reallocation of resources from the state to the private sector during transition is associated with significant under-utilization of labor and capital. In this case the basic framework would tend to understate the decline in inputs and overstate the decline in TFP.

In order to reflect changes in factor utilization explicitly, the standard production function is modified in the following way:

\begin{matrix} Y = AF (u^{L} L, u^{K} K), & (1') \end{matrix}

where u^L and u^K denote the shares of, respectively, labor and capital actually utilized. Accordingly, equation (3) becomes⁴:

\begin{matrix} \frac{\dot{Y}}{Y} = \frac{\dot{A}}{A} + (\frac{w L}{Y}) (\frac{\dot{L}}{L} + \frac{{\dot{u}}^{L}}{u^{L}}) + (\frac{r K}{Y}) (\frac{\dot{K}}{K} + \frac{{\dot{u}}^{K}}{u^{K}}), & (3') \end{matrix}

and taking discrete approximation in the Cobb-Douglas case yields the following modified version of equation (6):

\begin{matrix} [\frac{Δ Y_{t}}{Y_{t - 1}}] = [\frac{Δ A_{t}}{A_{t - 1}}] + \bar{α} [\frac{Δ L_{t}}{L_{t - 1}} + \frac{Δ u_{t}^{L}}{u_{t - 1}^{L}}] + (1 - \bar{α}) [\frac{Δ K_{t}}{K_{t - 1}} + \frac{Δ u_{t}^{K}}{u_{t - 1}^{K}}] & (6') \end{matrix}

Another important consideration not captured by the basic framework is that transition process affects different economic sectors differently. Thus, labor tends to shift from lower paid less productive sectors to higher paid more productive sectors. This also implies different labor utilization rates in different sectors. Likewise, capital utilization also differs across sectors. In addition, modernization of the capital stock occurs slower in declining sectors and faster in emerging ones, implying increasingly different quality of capital. A failure to explicitly account for the differences in quantity and quality of production factors across economic sectors leads to biases in estimated TFP⁵.

Barro, 1998 describes extensions of the basic growth accounting framework that allow disaggregation across different factor types. Incorporating multiple factor types into the utilization-augmented production function gives:

\begin{matrix} Y = A F (u_{1}^{L} L_{1}, \dots, u_{n}^{L} L_{n}; u_{1}^{K} K_{1}, \dots, u_{n}^{K} K_{n}), & (1 ″) \end{matrix}

Where i indexes economic sectors. Under the perfect competition assumption this yields:

\begin{matrix} \frac{\dot{Y}}{Y} = \frac{\dot{A}}{A} + \sum_{i = 1}^{n} (\frac{w_{i} L_{i}}{Y}) (\frac{{\dot{L}}_{i}}{L_{i}} + \frac{{\dot{u}}_{i}^{L}}{u_{i}^{L}}) + \sum_{i = 1}^{n} (\frac{r_{i} K_{i}}{Y}) (\frac{{\dot{K}}_{i}}{K_{i}} + \frac{{\dot{u}}_{i}^{K}}{u_{i}^{K}}) & (3 ″) \end{matrix}

or, under Cobb-Douglas specification in discrete time:

\begin{matrix} [\frac{Δ Y_{t}}{Y_{t - 1}}] = [\frac{Δ A_{t}}{A_{t - 1}}] + \sum_{i = 1}^{n} {\bar{α}}_{i} [\frac{Δ L_{i t}}{L_{i t - 1}} + \frac{Δ u_{i t}^{L}}{u_{i t - 1}^{L}}] + \sum_{i = 1}^{n} {\bar{β}}_{i} [\frac{Δ K_{it}}{K_{it - 1}} + \frac{Δ u_{it}^{K}}{u_{it - 1}^{K}}] & (6 ″) \end{matrix}

where ${\bar{α}}_{i}$ and ${\bar{β}}_{i}$ denote, respectively, average labor and capital shares in sector i. Under the assumption of constant returns $\sum_{i = 1}^{n} {\bar{α}}_{i} + \sum_{i = 1}^{n} {\bar{β}}_{i} = 1 .$

Some methodological and measurement issues should be noted at this point (Barro and Sala-i-Martin, 1995). It should be understood that TFP reflects a whole range of factors - in fact, it is everything that is not accounted for by input growth. It is hard to distinguish the effect of technological change from that of improved resource allocation or even from biases resulting from general model deficiencies and poor data quality. Thus, TFP estimates are affected by scale economies and sensitive to data perturbations. The growth accounting technique therefore should be treated with caution and regarded as a useful way of examining the data rather than a model of the growth process.

C. Data

Overview

The main data source for the present exercise is the 1999 Yearbook of the State Committee for Statistics of the Russian Federation (Goskomstat, 1999), which is supplemented with data of the Russian Economic Trends (RET, 2000) and the IMF (IMF, 2000). Both aggregate data and data by economic sector (industry, agriculture, construction and services⁶) are used.

Fig. 2 presents output dynamics by sector as measured by sector value added in constant prices. It shows that construction has experienced the largest drop, followed by industry, while agriculture and services fell less than the total output (the decline in agriculture was relatively slow, and the decline in services largely bottomed out by 1993). Table 1 shows shares of each sector in the overall GDP in current prices. While the shares of all goods sectors – and in particular industry – have declined since the beginning of economic reforms, that of the service sector has increased dramatically. This reflects the natural transition from an over-industrialized economy with neglected service sector to a more balanced one.

Table 1:

Sector shares in overall GDP

Table 1:

Sector shares in overall GDP

	1991	1992	1993	1994	1995	1996	1997
Industry, %	37.6	34.5	32.4	31.5	27.0	27.1	26.0
Agriculture, %	13.7	7.2	7.4	5.8	6.3	6.3	5.7
Construction, %	9.3	6.4	7.5	8.7	7.9	7.7	7.2
Services, %	39.4	51.9	52.7	53.9	58.8	58.9	61.1

Table 1:

Sector shares in overall GDP

	1991	1992	1993	1994	1995	1996	1997
Industry, %	37.6	34.5	32.4	31.5	27.0	27.1	26.0
Agriculture, %	13.7	7.2	7.4	5.8	6.3	6.3	5.7
Construction, %	9.3	6.4	7.5	8.7	7.9	7.7	7.2
Services, %	39.4	51.9	52.7	53.9	58.8	58.9	61.1

Measuring Labor Input

Representation of the labor input by overall employment is misleading in the Russian context due to labor hoarding. It has become common to put workers on shortened working days or compulsory leave, while formally maintaining employment. Hence the official employment statistics overestimate actual labor input, which has fallen faster than formal employment. Table 2 shows that underemployment has indeed been substantial, as compared to official unemployment data.

Table 27:

Official and Hidden Unemployment

Such hidden unemployment may to some extent be a reflection of unemployment that had effectively existed under the central planning. Disguised unemployment – defined as employment in very low productivity occupations (see Eatwell, 1997) – had been a common feature of the central planning that strived to provide jobs to all. This means that some of the unemployment observed during transition had been present before the launch of reforms and was revealed once the systemic transformation began. A more detailed examination of this issue requires additional research and is beyond the scope of this paper.

The data on compulsory leaves and shortened working hours are scarce, so only a rather crude adjustment is feasible. The available data (Goskomstat, 1999; RET, 2000) cover large and medium-sized enterprises (where the kind of underemployment in question mostly takes place) starting from 1993. The data on shortened working hours do not specify the actual number of hours, so a uniform half-day assumption is maintained in the adjustment⁸.

In order to obtain labor utilization rates during the entire period of interest, underemployment was assumed away in 1991 and extrapolated for 1992. Utilization rates by sector were estimated by fitting a linear function to the aggregate utilization and output growth rates:

\begin{array}{l} {\dot{u}}^{L} / u^{L} = 0.12 \dot{Y} / Y (R^{2} = 0.56) & (7) \\ (0.04) \end{array}

The estimated relationship is rather strong (though based on very few data points), but imprecision stemming from its uniform application to economic sectors should be kept in mind when interpreting the results.

Table 3 shows employment levels by sector and utilization rates estimated from the aggregate utilization rate using equation (7). Industry and construction have experienced the largest falls in both employment and labor utilization, while the service sector has actually gained employment and the estimated utilization drop was slight. However, these observations suggest that movement of labor between sectors has been sluggish: laid off workers seem to join the unemployment pool rather than seeking alternative jobs in more prosperous sectors. However, some shifting of workers, particularly from industry to services, have taken place (see Fig. 3).

Table 3:

Employment And Labor Utilization by Sector

Overall estimation error (average mismatch between u^LL and

\sum_{i} u_{i}^{L} L_{i}

): 0.5%.

Table 3:

Employment And Labor Utilization by Sector

			1991	1992	1993	1994	1995	1996	1997	Total change
Industry:
	- employment, mill.		22	21	21	19	17	16	15	-32%
		- utilization, %	100	98	96	94	93	93	93	-7%
Agriculture:
	- employment, mill.		10	10	10	10	10	9	9	-10%
		- utilization, %	100	99	98	97	96	95	95	-5%
Construction:
	- employment, mill.		8	8	7	7	6	6	6	-25%
		- utilization, %	100	96	95	92	91	89	89	-11%
Services:
	- employment, mill.		33	33	33	33	33	34	35	+6%
		- utilization, %	100	98	96	96	96	96	96	-4%
Total:
	- employment, mill.		74	72	71	68	66	66	65	-12%
		- utilization, %	100	98	96	94	95	94	96	-4%

Overall estimation error (average mismatch between u^LL and

\sum_{i} u_{i}^{L} L_{i}

): 0.5%.

Table 3:

Employment And Labor Utilization by Sector

			1991	1992	1993	1994	1995	1996	1997	Total change
Industry:
	- employment, mill.		22	21	21	19	17	16	15	-32%
		- utilization, %	100	98	96	94	93	93	93	-7%
Agriculture:
	- employment, mill.		10	10	10	10	10	9	9	-10%
		- utilization, %	100	99	98	97	96	95	95	-5%
Construction:
	- employment, mill.		8	8	7	7	6	6	6	-25%
		- utilization, %	100	96	95	92	91	89	89	-11%
Services:
	- employment, mill.		33	33	33	33	33	34	35	+6%
		- utilization, %	100	98	96	96	96	96	96	-4%
Total:
	- employment, mill.		74	72	71	68	66	66	65	-12%
		- utilization, %	100	98	96	94	95	94	96	-4%

Overall estimation error (average mismatch between u^LL and

\sum_{i} u_{i}^{L} L_{i}

): 0.5%.

Measuring Capital Input

While there are reasons to believe that the value of the fixed capital stock is underestimated (similarly to GDP) due to under-coverage of the emerging private sector, the official data still grossly overstate the actual size of the capital input in production, since a large part of the fixed capital stock is so outmoded that it has become obsolete. The data on the share of idle capital is scarce, so only an imperfect adjustment is feasible. Some survey-based capacity utilization data are available from 1992 onwards for industrial companies (RET, 2000), which allows to amend the fixed capital stock figures. According to these data, capacity utilization in industry fell from 75 percent of its “usual level” in 1992 to 60 percent of that level in 1997. For the purpose of the exercise capacity utilization was assumed to be at its “usual level” in 1991. The overall utilization rate and those for the rest of the sectors were estimated similarly to those for labor by fitting a linear function to utilization and output growth rates in industry:

\begin{array}{l} {\dot{u}}^{K} / u^{K} = 0.75 \dot{Y} / Y (R^{2} = 0.42) & (8) \\ (0.26) \end{array}

Table 4 shows real changes of the capital stock by sector and utilization rates estimated from the industry utilization rate using equation (8) (note that the obtained estimates are slightly less precise than those for labor). All sectors have undergone major drops in capacity utilization. Construction has experienced the largest fall, followed by industry, while agriculture and services have had the smallest, but still significant fall. The capital stock in industry and services has been growing throughout the period, while the other sectors have experienced declines, most notably agriculture. The observed capital growth in industry points to the over-accumulation inertia inherited from the Soviet times. The growth of the capital stock in services is more likely to reflect the expansion and modernization of the sector and thus may be of better quality. However, this difference is very hard to quantify.

Table 4:

Capital Stock and Capacity Utilization by Sector

Overall estimation error (average mismatch between u^KK and

\sum_{i} u_{i}^{K} K_{i}

): 1.8%.

Table 4:

Capital Stock and Capacity Utilization by Sector

			1991	1992	1993	1994	1995	1996	1997	Total change
Industry:
	- capital stock, index		100	101.9	102.6	102.7	102.8	102.8	101.8	+1.8%
		- utilization, %	100	75	69	64	57	54	60	-40%
Agriculture:
	- capital stock, index		100	101.1	99.5	95.1	91.3	88.4	84.1	-15.9%
		- utilization, %	100	93	90	82	77	74	75	-25%
Construction:
	- capital stock, index		100	103.3	103.1	103.8	100.0	97.9	96.6	-5.4%
		- utilization, %	100	73	69	56	54	47	45	-55%
Services:
	- capital stock, index		100	102.0	102.9	103.3	104.7	105.3	106.0	+6.0%
		- utilization, %	100	85	79	78	77	76	77	-23%
Total:
	- capital stock, index		100	101.9	102.4	102.1	102.3	102.2	101.8	+1.8%
		- utilization, %	100	85	79	72	70	68	68	-32%

Overall estimation error (average mismatch between u^KK and

\sum_{i} u_{i}^{K} K_{i}

): 1.8%.

Table 4:

Capital Stock and Capacity Utilization by Sector

			1991	1992	1993	1994	1995	1996	1997	Total change
Industry:
	- capital stock, index		100	101.9	102.6	102.7	102.8	102.8	101.8	+1.8%
		- utilization, %	100	75	69	64	57	54	60	-40%
Agriculture:
	- capital stock, index		100	101.1	99.5	95.1	91.3	88.4	84.1	-15.9%
		- utilization, %	100	93	90	82	77	74	75	-25%
Construction:
	- capital stock, index		100	103.3	103.1	103.8	100.0	97.9	96.6	-5.4%
		- utilization, %	100	73	69	56	54	47	45	-55%
Services:
	- capital stock, index		100	102.0	102.9	103.3	104.7	105.3	106.0	+6.0%
		- utilization, %	100	85	79	78	77	76	77	-23%
Total:
	- capital stock, index		100	101.9	102.4	102.1	102.3	102.2	101.8	+1.8%
		- utilization, %	100	85	79	72	70	68	68	-32%

Overall estimation error (average mismatch between u^KK and

\sum_{i} u_{i}^{K} K_{i}

): 1.8%.

In general, it is plausible that new capital investment undertaken during the transition period is of higher quality than that of the central planning. If indeed the quality change was significant, a failure to account for it would bias the TFP estimates. In order to address this issue, the growth rate of capital can be split into an investment and a depreciation component in line with the standard perpetual-inventory accounting method⁹. This approach allows to represent the capital stock data by the following formula:

\begin{matrix} K_{t} = I_{t} + (1 - δ_{t - 1}) K_{t - 1} & (9) \end{matrix}

where K stands for the capital stock, I is investment and δ is the rate of depreciation. In order to account for the increasing quality of investment during transition, a quality factor ε_t≥1 can be introduced into the formula:

\begin{matrix} K_{t} = I_{t} ε_{t} + (1 - δ_{t - 1}) K_{t - 1} & (9') \end{matrix}

This approach is somewhat similar to vintage-capital models (see, for example, Bliss, 1968), where output is obtained from investments of various “vintages”. However, measuring ε_t is problematic given the available data, hence in the current analysis the improved quality of capital will be picked up in the TFP estimates.

Fig. 4 shows the dynamics of utilization-augmented capital stock by sector. In utilization terms the overall capital stock dropped by about between 1991 and 1997, with industry exhibiting the steepest decline.

Measuring Factor Shares

The weights are estimated under the constant returns to scale assumption¹⁰. According to the national accounts data, employees’ gross wages on average amounted to 45 percent of GDP in 1991–97. This figure probably grossly underestimates the labor share, since it does not include the compensation of entrepreneurs, self-employed, etc., but data on mixed incomes are generally unavailable. In view of the questionable quality of the wage data, in what follows the typical factor shares of 0.7 for labor and 0.3 for capital are assumed. Note that in Section 3 (see below) the factor shares are estimated on a regional cross-section from the Cobb-Douglas production function under the constant returns to scale restriction. While admittedly crude, the exercise yields the shares of 0.7 for labor and 0.3 for capital, thus providing some support for this assumption.

In order to capture changes in relative productivity across sectors, sectoral labor shares were taken proportional to sectoral wage shares, while sectoral capital shares were taken proportional to sectoral output shares in constant prices. This way the differences in labor remunerations across sectors are taken into account, while the capital remuneration is effectively assumed to be the same in all sectors¹¹. To ensure comparability with the aggregate case, the coefficients of proportionality were scaled so as to preserve the aggregate labor share of 0.7 and the aggregate capital share of 0.3. Table 5 shows that labor income shares in industry, agriculture, and construction declined dramatically over the period, while that in services significantly increased.

Table 5:

Labor and Capital Income Shares by Sector

Table 5:

Labor and Capital Income Shares by Sector

			1992	1993	1994	1995	1996	1997
Industry:
	- labor share		.26	.23	.18	.20	.20	.21
		- capital share	.11	.11	.10	.10	.10	.10
Agriculture:
	- labor share		.11	.10	.08	.07	.06	.06
		- capital share	.05	.05	.05	.05	.05	.05
Construction:
	- labor share		.11	.08	.06	.06	.05	.04
		- capital share	.02	.02	.02	.02	.02	.02
Services:
	- labor share		.22	.29	.37	.37	.39	.39
		- capital share	.12	.12	.13	.13	.14	.14
Total:
	- labor share		.7	.7	.7	.7	.7	.7
		- capital share	.3	.3	.3	.3	.3	.3

Table 5:

Labor and Capital Income Shares by Sector

			1992	1993	1994	1995	1996	1997
Industry:
	- labor share		.26	.23	.18	.20	.20	.21
		- capital share	.11	.11	.10	.10	.10	.10
Agriculture:
	- labor share		.11	.10	.08	.07	.06	.06
		- capital share	.05	.05	.05	.05	.05	.05
Construction:
	- labor share		.11	.08	.06	.06	.05	.04
		- capital share	.02	.02	.02	.02	.02	.02
Services:
	- labor share		.22	.29	.37	.37	.39	.39
		- capital share	.12	.12	.13	.13	.14	.14
Total:
	- labor share		.7	.7	.7	.7	.7	.7
		- capital share	.3	.3	.3	.3	.3	.3

D. Results

The results of the growth accounting exercise are shown in Tables 6–9. Table 6 reports the outcome of the basic setup without corrections for factor utilization. In this framework 82 percent of the output drop is due to a collapse in TFP. The average contribution of labor is 19 percent, while that of officially recorded capital is even slightly negative.¹² A sensitivity analysis based on alternative assumptions on factor shares yielded the following range for the TFP estimate: from 79 percent (labor share 0.8, capital share 0.2) to 85 percent (labor share 0.6, capital share 0.4). The TFP estimate is therefore sufficiently robust and, as expected, substantially overstates the actual TFP drop, as demonstrated below.

Table 6.

Decomposition of GDP Growth in the Basic Framework

Notes: growth rates of labor and capital are weighted

Table 6.

Decomposition of GDP Growth in the Basic Framework

	1992	1993	1994	1995	1996	1997	Average	Contribution
Output growth	-19.4%	-10.4%	-11.6%	-4.2%	-3.4%	0.9%	-8.0%	100%
Labor growth	-1.6%	-1.2%	-2.4%	-2.1%	-0.4%	-1.5%	-1.5%	19%
Capital growth	0.6%	0.2%	-0.1%	0.1%	0.0%	-0.1%	0.1%	-1%
Total Factor Productivity	-18.4%	-9.4%	-9.1%	-2.1%	-2.9%	2.5%	-6.6%	82%

Notes: growth rates of labor and capital are weighted

Table 6.

Decomposition of GDP Growth in the Basic Framework

	1992	1993	1994	1995	1996	1997	Average	Contribution
Output growth	-19.4%	-10.4%	-11.6%	-4.2%	-3.4%	0.9%	-8.0%	100%
Labor growth	-1.6%	-1.2%	-2.4%	-2.1%	-0.4%	-1.5%	-1.5%	19%
Capital growth	0.6%	0.2%	-0.1%	0.1%	0.0%	-0.1%	0.1%	-1%
Total Factor Productivity	-18.4%	-9.4%	-9.1%	-2.1%	-2.9%	2.5%	-6.6%	82%

Notes: growth rates of labor and capital are weighted

Table 7.

Decomposition of GDP Growth in the Extended Framework