Abstract
In this chapter, first we demonstrate new estimates of GDP growth for the Russian Empire and the Russian Republic for 1860–1990 (Sects. 11.1 and 11.2). Second, we provide an estimate of informal economy for the Russian Republic for 1960–1990 (Sect. 11.3). Third, featuring the present national accounts, we show a reappraisal of GDP data by industry for the present Russia for 1989–2015 (Sect. 9.4). International comparisons of our estimates are made along with volatility. We suggest that heavy volatility of GDP growth rates led to the revolution in 1917. We also suggest that growth slumps with negative total factor productivity and bad quality of GDP contents resulted in the second revolution in 1991. Growth paths of Russia and Japan for 1860–2017 are compared.
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- 1.
- 2.
Goldsmith (1961) also pointed out that the preference of material production in the Soviet statistical methodology stemmed from the statistical methodology of the Russian Empire.
- 3.
- 4.
This figure was set to correspond to the consumption rate of fixed capital in the GDP of the entire Russian Empire. The final estimation results of service GDP changed little if we changed the figure from 1% to 10%. The simplified method, therefore, seems acceptable. Falkus’ (1968) ratios of consumption of fixed capital to the gross value added were 9.4%, 3.6%, and 2.6% for large industry, agriculture, and small industry, respectively. The figures for large industry were taken from TsSU (1921) and those for agriculture and small industry from Prokopovich (1931).
- 5.
This assumption has the effect of decreasing the volatility more strongly than the assumption setting the same growth rates for both large and small industries. Under the assumption taken, the signs of the growth rates of large and small industries can be opposite.
- 6.
It may be possible to estimate trade using Strumilin’s (1963) data on employment and fixed capital in the trade sector and using the wages paid in large industry.
- 7.
The two regression equations for 1886 to 1913 are as follows:
$$ \log (trade)=0.761\log (ind)+2.489; $$$$ \log (service)=0.535\log (ind)+2.791. $$The White heteroskedasticity-consistent t-values are 27.273 and 11.498 for the independent and constant terms, respectively, in the first equation. Adjusted R2 for the first equation is 0.956. The White heteroskedasticity-consistent t-values are 17.278 and 11.857 for the independent and constant terms, respectively, in the second equation. Adjusted R2 for the second equation is 0.885. All coefficients are significant at the 1% level.
- 8.
Another factor that may influence the volatility is small industry. Small industry might grow countercyclically to the growth in large industry, and thus, the “true” volatility might have been lower. However, there is no reliable data on the small industry growth. Fragmental data on small industry seem to suggest that the inclusion of small industry may increase the volatility. Gukhman and Strumilin (Gukhman 1927; Kaufman 1962, Table 6) estimated the gross nominal production of large and small industries using the tax data. The year, total industry, large industry, and small industry are given below (in million rubles).
(Year) Total Large Small
(1900) 5463 3761 1702
(1901) 5322 3906 1416
(1902) 5413 4029 1384
(1903) 5408 4162 1246
(1904) 5637 4217 1420
(1905) 5527 4230 1297
(1906) 5294 4136 1158
(1907) 5836 4621 1215
(1908) 6105 4800 1305
(1909) 6337 4982 1355
(1910) 6429 4977 1452
(1911) 6931 5451 1480
(1912) 7900 6194 1706
(1913) 8832 6882 1950
Production data are in million rubles. The average annual growth rates were 3.8%, 4.8%, and 1.1% for total, large industry, and handicraft, respectively. The signs of growth rates were different between large and small industries only in 1901, 1902, 1903, and 1905 because of slumps in small industry. The nominal growth rates of small industry were around one-fifth of those of large industry; the standard deviations in the log-transformed growth rates were 0.047, 0.106, and 0.054 for large industry, small industry, and the total industry, respectively. Thus, the volatility increases if small industry is included. This result does not provide a clear conclusion because the data are nominal gross production data. It nevertheless indicates that more data and further investigation are necessary before it can be accepted that small industry moved countercyclically to large industry.
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Russian GDP includes GDP of the Crimean Republic from the second quarter of 2014 and after. This means the territory of the Russian Republic returned to the territory before the cession of the Crimean Republic to Ukraine in 1954.
- 10.
Appendix Tables 11.2.1 and 11.2.2 show all data on the nominal value-added structure. The former shows the series at market prices and the latter at basic prices.
- 11.
Due to the nature of smuggling, it is difficult to measure its volume. These activities may be, however, reflected at least in household incomes (earned from illegal exports) and expenditures (spent on illegally imported goods). It is also difficult to separate domestic and import components of consumption goods from household expenditures, although here we assume that the consumption of illegally imported goods was marginal compared to the total volume of consumption. Net exports in the 1970s ranged from 1 to 5 billion rubles (Ministerstvo vneshnikh ekonomicheskikh sviazei SSSR 1991, p. 6), which accounted for, at most, 1% of Soviet net material products. According to Alexeev and Sayer’s (1987) study using an immigrant survey, the goods brought from abroad by friends or family members accounted for another 3–5 billion rubles; the size of the illegal market for foreign-made goods in the late 1970s is estimated to have been 13–15 billion rubles per year.
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State enterprises settled their payments on books and cash transactions were strictly regulated. They were permitted to procure only a limited amount of materials via an officially approved small-scale wholesale trade system (melkii opt). It is possible that they illegally obtained goods without permission and increased their inventories. These illicit transactions in the consumer markets are called the “siphoning effect,” that is, unplanned spillover of effective demand from the planned sector to the consumer sector (Kim 2002, pp. 111−115). Although enterprises engaged in these activities, the total amount of retail turnover reported to the authority did not change. Measurement problems are, thus, undetectable.
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The systematic sampling method reduced the probability that those who are not in the list, such as students, pensioners, or families with single workers, are selected as the object of the survey (Matiukha 1967, pp. 11−40). For example, the probability of a single-worker family (including single persons) to be selected was half of that of a two-worker family (Belova and Dmitrichev 1990, p. 26).
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One of the key differences between Soviet surveys and other European countries’ surveys is that the response rate in the former is almost 100% (Dumnov and Riik 1978).
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The list of archival materials used in this paper is shown in Appendix Table 11.3.1.
- 18.
RP, RS, and K can be disaggregated into more detailed groups according to industrial sectors and regions (location). The data integration and adjustment process is described in Appendix Table 11.3.2.
- 19.
Money expenditures in the balance data comprise two sectors: (A) monetary expenditures at state and cooperative retail shops; (B) transactions among citizens.
- 20.
Balance data used here is author’s own estimation. Estimation methodology and primary sources are described in Shida (2015, Appendix 1). Appendix Table 11.3.3 shows official data and author’s estimation results based on balance data for 1960−1990. Appendix Table 11.3.4 shows the estimated results based on household budget survey. Appendix Table 11.3.5 shows population and “total household population.” Appendix Table 11.3.6 shows the estimated balances and the constructed household budget survey data.
- 21.
Author’s calculations using data from Kashin and Mikov (2006) who, using Bank of Russia’s archival materials, compiled data from balances at the USSR level.
- 22.
See also Kuboniwa (1997).
- 23.
The growth indices of informal GDP deflated by the four deflators A, B, C, and D used for the official GDP in 1990 are 346.0, 216.5, 303.7, and 265.0, respectively. All estimates except B are biased upward.
- 24.
The average growth rates, calculated as in footnote 23, for A, B, C, and D are 4.4%, 2.7%, 3.9%, and 3.4%, respectively.
- 25.
See the Rosstat’s website (http://www.gks.ru/free_doc/new_site/vvp/letter_vvp.pdf, accessed on November 16, 2016).
- 26.
See the national classification on the website of the United Nations for industrial classification of each country (http://unstats.un.org/unsd/cr/ctryreg/default.asp?Lg=1)
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Kuboniwa, M., Shida, Y., Tabata, S. (2019). Gross Domestic Products. In: Kuboniwa, M., Nakamura, Y., Kumo, K., Shida, Y. (eds) Russian Economic Development over Three Centuries. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-13-8429-5_11
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