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Table 3.Results of Assessments of Remainder Growth Rates for the Period between 1997 and TFP (So- TFP (Direct TFP (dual TFP (dual min max low) method) method 1) method 2) 1 2 3 4 5 6 Russian Federation 3.45 2.85 1.14 1.14 3.Altay Krai 0.80 2.71 0.87 2.53 2.53 2.Amour Oblast 0.51 1.74 4.07 2.18 0.51 4.Arkhangel' Oblast 4.38 4.44 3.44 1.14 1.14 4.Astrakhan Oblast 6.79 7.42 3.20 3.20 7.Belgorod Oblast 4.63 4.08 1.07 0.43 0.43 4.Bryansk Oblast 2.23 2.91 3.06 0.77 0.77 3.Vladimir Oblast 2.90 2.57 3.31 2.71 2.57 3.Volgograd Oblast 2.62 5.62 0.87 2.70 2.70 5.Vologda Oblast 2.91 2.54 2.06 0.18 0.18 2.Voronezh Oblast 1.96 2.85 0.61 1.29 1.29 2.the city of Moscow 4.92 2.81 4.44 7.69 7.69 4.the city of Saint4.36 2.82 1.94 2.64 1.94 4.Petersburg Jewish Autonomous 0.74 0.19 0.16 0.74 0.Oblast Ivanovo Oblast 0.97 0.89 3.05 0.54 0.54 3.Irkutsk Oblast 1.93 1.74 6.89 0.18 1.93 6.Kabardino-Balkar Re5.63 7.56 2.25 2.20 2.20 7.public Kaliningrad Oblast 2.53 2.64 4.60 3.49 4.60 2.Kaluga Oblast 1.62 1.44 2.81 2.43 1.44 2.Kamchatka Oblast 2.36 0.94 1.41 5.36 5.36 1.Karachay-Cherkessya 3.75 4.27 0.97 2.49 0.97 4.Republic 1 2 3 4 5 6 Kemerovo Oblast 2.29 2.49 2.53 0.70 0.70 2.Kirov Oblast 0.13 0.11 3.11 0.53 0.13 3.Kostroma Oblast 2.32 2.77 0.18 0.18 2.KrasnodarKrai 2.08 1.68 0.47 1.92 1.92 2.Krasnoyarsk Krai 2.06 2.68 2.74 2.65 2.65 2.Kurgan Oblast 1.69 2.95 0.84 1.83 1.83 2.Kursk Oblast 2.94 3.51 1.45 1.45 3.Leningrad Oblast 5.71 5.50 2.40 2.40 5.Lipetsk Oblast 1.99 3.35 1.72 2.47 2.47 3.Magadan Oblast 1.03 0.06 2.36 2.36 0.Moscow Oblast 3.93 4.05 4.94 0.05 0.05 4.Murmansk Oblast 2.20 2.96 2.24 2.24 2.Nizhny Novgorod 4.05 4.04 0.39 3.03 3.03 4.Oblast Novgorod Oblast 4.18 4.43 0.03 1.15 0.03 4.Novosibirsk Oblast 3.84 4.94 4.88 1.30 1.30 4.Omsk Oblast 1.38 2.36 4.57 1.37 1.37 4.Orenburg Oblast 1.52 1.56 1.12 1.60 1.60 1.Orel Oblast 5.39 6.08 4.04 1.52 1.52 6.Penza Oblast 1.93 3.67 3.27 1.28 1.28 3.Perm Oblast 3.27 3.32 2.10 0.00 0.00 3.Primorsky Krai 0.18 0.73 0.92 0.35 0.92 0.Pskov Oblast 0.81 1.95 0.90 1.21 1.21 1.Republic Adygeya 0.41 1.68 1.71 1.83 0.41 1.Altay Republic 1.78 2.60 1.26 0.82 0.82 2.Republic Bashkortostan 1.52 2.74 2.31 0.90 0.90 2.Republic Buryatia 4.63 6.10 0.48 0.42 0.48 6.Republic of Dagestan 3.55 4.41 0.67 0.05 0.67 4.Republic of Ingoushetia 3.79 1.86 11.39 11.39 1.Republic of Kalmykia 6.06 11.40 7.44 7.44 11.Republic Karelia 3.31 2.76 1.32 0.56 0.56 3.Republic Komi 1.28 2.03 3.99 0.76 0.76 3.Republic Mary-El 0.28 2.73 3.66 0.02 0.28 3.Republic Mordovia 4.27 4.43 0.06 0.06 4. 1 2 3 4 5 6 Republic of Sakha (Ya2.03 2.87 0.45 2.59 2.59 2.kutia) Republic North Ossetia 5.42 6.21 2.34 2.34 6.(Alania) Republic Tatarstan 2.74 2.80 4.31 1.49 4.31 2.Republic of Tyva 5.49 5.79 0.09 0.09 5.Republic Khakassia 0.60 2.23 1.72 4.01 4.01 1.Rostov Oblast 5.88 6.78 2.45 0.03 0.03 6.Ryazan Oblast 2.58 3.03 3.27 0.63 0.63 3.Samara Oblast 2.17 2.18 2.53 0.70 2.53 2.Saratov Oblast 3.84 4.80 0.81 0.22 0.22 4.Sakhalin Oblast 2.00 4.38 1.38 3.01 3.01 4.Sverdlovsk Oblast 2.00 1.66 2.45 0.16 0.16 2.Smolensk Oblast 3.75 4.68 5.25 3.75 5.Stavropol Krai 2.62 3.09 5.09 3.96 2.62 5.Tambov Oblast 5.91 7.25 2.72 1.30 1.30 7.Tver Oblast 1.55 2.09 0.36 1.86 0.36 2.Tomsk Oblast 3.17 2.99 3.80 0.57 0.57 3.Tula Oblast 1.47 1.36 0.54 0.26 0.54 1.Tyumen Oblast 3.16 1.27 1.32 2.49 2.49 3.Udmurtia Republic 1.69 1.42 2.00 2.04 2.04 2.Ulyanovsk Oblast 2.16 3.51 4.79 0.19 0.19 4.Khabarovsk Krai 4.15 4.65 5.03 0.98 0.98 5.Chelyabinsk Oblast 0.55 2.32 1.88 1.37 1.88 2.Chita Oblast 0.95 1.95 5.13 0.99 0.95 5.Chuvash Republic 0.45 0.74 4.96 1.21 0.74 4.Chukotka autonomous 5.54 5.65 9.51 9.51 5.okrug Yaroslavl Oblast 4.28 3.40 7.16 3.09 3.09 7.The assessments of the remainder growth rates made by different methods display a great deal of diversity. The minimum and maximum values of the assessments have opposite signs for rather a great number of regions. For 27 regions (Amur, Arkhangelsk, Astrakhan, Belgorod, Vladimir oblasts, St. Petersburg, among others) both margins of the assessment of the remainder growth rates are positive, while those for Magadan oblast and Republic of Ingoushetia are negative. In 55 regions (from 79 concerned) the lower assessment margin was found by means of the dual assessment method basing on GRP deflator and price index for investment goods. The upper margin of the assessment of the remainder growth rate in more than half of the regions was found by means of direct method with account of changes in the capacity loading rate.

Fig. 3.15 presents diagrams of dispersion of the respective TFP assessments with GRP growth rates.

Similarly, the analysis of the correlation between GRP growth rates and those of remainder between 1997 and 2002 leads to different results. The assessing of the remainder by the direct method exposes the existence of an explicit linear correlation between the GRP growth rates and those of the remainder, which testifies to the fact that a major part of growth rates are not determined by the basic factors. The assessment of the remainder by the dual method fails to identify such a correlation, while the analysis of growth rates for the periods 199798 and 19992002 leads to analogous results (see Annex 2, Figs. 2A-8 and 2A-9).

3.4. Labor Productivity in the Industrial Sector The above assessments demonstrated that decomposition of growth of value indicators, such as GRP, in regional terms allows only rough and often confusing (under employment of different methods) assessment of growth in productivity. Let us note that while explaining value indicators on the nationwide level (GDP, sectoral GVA), in the course of decomposition of growth one also noted rather a great unexplained remainder (some 70%). While on the one hand this result evidences great measure errors, it questions prerequisites that underlie the decomposition, on the other.

The strongest prerequisite is likely to be the assumption of factor prices being equal to their marginal products and meeting the condition of long-term equilibrium, which appears doubtful in the case of Russia. Indeed, as demonstrated by Hsieh (2002), this prerequisite is likely to appear stronger for developing economies than for developed ones, which manifests itself in the difference between assessments of the direct and dual methods of decomposition of growth.

The IET papers (Entov, Lugovoy at al. (2003)) also demonstrated that once completed, a decomposition of growth on the basis of physical indicators provides a smaller remainder, even providing aggregated inputs are employed. By contrast to physical values, price indicators are more volatile and dependent on rapidly changing prices revenues can experience dramatic changes under a constant physical volume of output.

Unfortunately, employment of the physical volume indicators is impossible on the level of a nation or regions economy as a whole.

The physical volumes indices can be computed relatively accurately just for a few individual industries that produce a homogenous product whose quality grows over time. The index of physical volume of industrial output (IPI) can form some approximation of such an index.

The IPI dynamics by regions and Russia as a whole are given on Fig. 3.16 (see also Annex 2, Map 4). One can note that the turning point of the trend to decline in industrial output in Russia occurred in 1998 on average, while the regional dynamics vary by regions.

While some regions saw the turn of the trend in 1995, some others as late as in 2000. However, the decline at the beginning of the period in question and rise at its end is noted in all the regions. Let us also note that in 2003 it was only a few regions (Arkhangelsk, Belgorod, Leningrad, Tomsk oblasts and Nenetsky AO) that reached and surpassed the 1990 level of output, while the averaged volume of output across Russia accounted for less than 70% of its 1990 level.

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Fig. 3.16. The Dynamics of the Industrial Output Index by Regions of the Russian Federation in 19902003 (1990=100) The conduct of decomposition of growth in industrial output, again, necessitates the respective assessments of labor and capital inputs. Given a complete absence of acceptable statistics by fixed assets in regions, it is possible to assess just labor productivity (the output to the employees in an industry ratio). The respective assessments are given in Fig. 3.17 (see also Annex 2, Map 5).

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Fig. 3.17. The Dynamics of the labor Productivity Index in the Industrial Sector by Regions of Russian Federation in 19902003 (1990=100).

As shown by computations (see Annex 2, Table 2A-17), the turn of the trend in labor productivity in the industrial sector from decline to growth occurred in 1994 on average, or in 4 years earlier than the change in the output trend, while in some regions labor productivity had a positive trend through the whole period of question.

Importantly, labor productivity in the industrial sector exceeded the 1990 level in a major part of regions.

These results match sector-level computations and those made on the level of the economy as a whole (Entov, Lugovoy at al.

(2003)). The trend to rise in productivity began taking shape much earlier than growth in output.

As landmarks for a further analysis that should form a second step of the research into sources of growth we can suggest a statistical examination of the impact a broad array of factors have on the growth in output, basic inputs and productivity.

Preliminary comparisons between the received assessment of labor productivity in the industrial sector and some factors show that the labor productivity growth rate in regions is positively correlated with the volume of foreign investment and negatively with financial aid granted to the regions.

Whereas capital forms one of the basic factors that affect labor productivity, a positive correlation between productivity and investment once again proves that labor productivity growth rate was greater in regions with relatively greater foreign investment.

A negative correlation between the federal financial aid and labor productivity can be attributed both to the effect the financial aid has on productivity and vice versa the aid was allocated primarily to depressive regions with a smaller productivity growth rate, which requires an additional examination.

Annex Table 2A-Decomposition of Growth in GVA in Industry (MPL=0,7, MPK=0,3) 199298 199903 1992GVA 11.75 6.43 4.59 I. Factor inputs 8.28 3.25 3.63 I.1 Labor 5.69 1.31 2.82 Employment 4.44 0.02 2. Worked time 1.25 1.33 0.I.2 Capital 2.58 1.94 0.81 Inventories 0.69 0.06 0. Fixed assets 0.06 0.08 0. Loading rate 1.95 1.92 0. II. TFP 3.47 3.18 0.96 Price factor 2.25 1.26 1. Remainder 1.22 4.44 0.Table 2A-Decomposition of Growth in GDP 199398 199903 1993GDP 7.30 6.56 1.3 I. Factor inputs 3.46 1.79 0.96 I.1 Labor 1.46 0.06 0.56 Employment 0.84 0 0. Worked time 0.62 0.06 0.I.2 Capital 2 1.73 0.4 Fixed assets 0.05 0.41 0. Loading rate 1.95 1.32 0. II. TFP 3.835 4.77 0.34 Table 2A-Results of Computations of GRP Growth Indices by Regions on the Basis of Different Deflators Variant 2: Com- Variant 3: comVariant1: Index putation on the Variation (as % putation on the Variation (as % Regions of physical basis of GDP of variant 1) basis of regions of variant 1) volume of GRP deflator CPI 1 2 3 4 5 Russian Federation 123.95 111.61 9.95 106.33 14.Altay Krai 106.13 82.48 22.29 84.51 20.Amour Oblast 102.19 85.18 16.64 98.38 3.Arkhangel' Oblast 128.99 105.92 17.89 112.98 12.Astrakhan Oblast 152.63 125.86 17.54 127.08 16.Belgorod Oblast 135.95 100.59 26.01 97.81 28.Bryansk Oblast 116.49 83.78 28.08 79.85 31.Vladimir Oblast 121.46 95.11 21.70 98.85 18.Volgograd Oblast 117.19 88.85 24.19 85.93 26.Vologda Oblast 121.52 98.85 18.65 112.44 7.Voronezh Oblast 114.88 100.13 12.84 99.68 13.the city of Moscow 149.39 193.77 29.71 141.81 5.the city of Saint134.77 126.96 5.80 116.79 13.Petersburg Jewish Autonomous 95.98 102.79 7.10 108.77 13.Oblast Ivanovo Oblast 101.51 78.65 22.52 79.28 21.Irkutsk Oblast 89.40 74.41 16.77 81.12 9.Kabardino-Balkar 153.18 121.43 20.73 120.81 21.Republic Kaliningrad Oblast 120.40 125.55 4.28 122.24 1.Kaluga Oblast 107.60 95.42 11.33 98.25 8.Kamchatka Oblast 82.06 74.07 9.74 70.78 13.Karachay-Cherkessya 129.91 96.73 25.54 91.23 29.Republic Kemerovo Oblast 111.02 70.31 36.67 81.14 26.Kirov Oblast 100.61 81.77 18.72 84.21 16.Kostroma Oblast 112.15 90.98 18.88 91.99 17.KrasnodarKrai 122.57 117.50 4.14 115.56 5.Krasnoyarsk Krai 114.53 94.15 17.79 103.17 9.Kurgan Oblast 108.46 88.00 18.87 78.93 27.1 2 3 4 5 Kursk Oblast 123.34 83.38 32.40 74.25 39.Leningrad Oblast 147.24 130.75 11.20 129.74 11.Lipetsk Oblast 115.49 108.14 6.36 108.93 5.Magadan Oblast 83.76 87.80 4.82 96.96 15.Moscow Oblast 133.30 121.98 8.49 115.87 13.Murmansk Oblast 110.76 94.74 14.46 81.12 26.Nizhny Novgorod 127.06 105.93 16.63 88.77 30.Oblast Novgorod Oblast 123.93 103.51 16.47 108.49 12.Novosibirsk Oblast 125.35 93.41 25.48 104.89 16.Omsk Oblast 111.19 72.97 34.38 78.67 29.Orenburg Oblast 115.67 91.32 21.05 93.03 19.Orel Oblast 143.95 118.94 17.38 130.22 9.Penza Oblast 114.13 90.17 20.99 84.24 26.Perm Oblast 126.11 102.31 18.87 89.45 29.Primorsky Krai 102.41 92.55 9.63 101.06 1.Pskov Oblast 107.72 97.62 9.38 100.63 6.Republic Adygeya 99.68 79.59 20.15 74.22 25.Altay Republic 113.68 115.49 1.60 111.15 2.Republic Bashkor112.65 88.51 21.43 87.26 22.tostan Republic Buryatia 123.23 94.37 23.42 100.32 18.Republic of Dagestan 143.52 161.45 12.49 167.29 16.Republic of Ingou96.56 104.00 7.70 81.08 16.shetia Republic of Kalmykia 146.27 234.97 60.64 222.33 52.Republic Karelia 116.03 106.31 8.38 113.33 2.Republic Komi 108.10 103.73 4.04 118.64 9.Republic Mary-El 97.78 92.82 5.08 83.79 14.Republic Mordovia 130.13 73.26 43.70 60.64 53.Republic of Sakha 107.07 96.73 9.66 125.14 16.(Yakutia) Republic North 150.73 120.89 19.80 123.95 17.Ossetia (Alania) Republic Tatarstan 122.53 104.01 15.11 93.29 23.Republic of Tyva 123.73 104.13 15.84 127.29 2.Republic Khakassia 98.30 89.23 9.23 80.62 17.Rostov Oblast 144.30 107.47 25.52 90.82 37.

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