Unemployment rate, taking into accounttwo indicators: a) official unemployment rate (evaluation of “totalunemployment” by sample polls according to the ILO methods is carried out onlyon the regional level, what considerably biased the indicator downward. In somedepressive towns, for instance, Yuzha in Ivanovo oblast, total unemployment ismeasured at about 40 per cent) and b) the total share of non-workingpopulation, in which authors include alongside with traditional groups ofpensioners, children, and housewives a fuzzy group of “shadow businesspersons”and even commuter traders making note that this increases the value of thisindicator for satellite towns, especially located near the capital.
Average annual industrial slump in 1991through 1996 (having no other indicators of general economic dynamics). Theauthors note that the contribution of this indicator is differentiateddepending on the share of “industry” in the total number of employed, what madethis indicator “semi-floating.” As an independent indicator it was used intowns where over 40 per cent of labor force are employed by industry. There areabout 300 such towns (about 1/3 of the total), for instance, Miass in Ural,Naberezhnye Chelny, etc., even some regional capitals – Tula, Izhevsk, Ulyanovsk. Fortowns with the share of those employed by industries at 20 to 40 per cent thesignificance of the factor was reduced twofold, for non-industrial towns whereindustry employs less than 20 per cent of labor force and where prevailservices, trade, transport, tourism, science, administration, banks it was nottaken into account. Only in about 50 out of total 940 towns (mostly in smallcenters, towns of Ural and Siberia prevailed at the top of the list) industrygrew.
Ratio between gross wages of working population (in stead of traditional indicator of personal incomes) andaverage regional subsistence level. The latter also varies across towns, but the data are notavailable and this indicator is better than nominal wage not adjusted forprices at all, what would result in only northern and eastern towns takingupper part of the list. Due to the introduction of subsistence level thesetowns were supplemented by a rather large number of industrial centers locatedin European Russia: Almetyevsk, Tolyatti, Kirishi, Cherepovets, Nizhnekamsk,etc.; both capitals were ranked somewhere near the 200th position. The database lacksinformation about wage arrears, therefore, the ranking hardly reflects the realsituation.
Consumption level (goods and services)measured as the ratio between per capita volume of retail trade and thesubstance level (Why See above). Moscow, some regional centers or richindustrial and resort towns (even small – close to Tolyatti are Anapa,Pyatigorsk, Gelendzhik, Minvody, i.e. towns where this sphere significantlydepends on tourist demand) lead the ranking.
Per capita capital investment,unfortunately the data is available for 1996 only. This parameter significantlyvaries (from tens of millions of pre-denominated Rubles in Vuktyl, Chudovo,Maloyaroslavets, Norilsk, or Mirny to laughable amounts in other towns. In thisregard regional capitals are somewhere close to the average investment levelacross all towns (Rub. 2 mil.), although the difference in this case is alsoapparent: Rub. 5 million in Moscow as compared to Rub. 1.4 million in St.Petersburg.
Convenience of housing is the average ofseveral indicators (availability of sewer system, running water, telephoneline). This parameter has higher values in capitals and their suburbs, someresorts, towns specialized in science, R&D, nuclear related activities(Obninsk, Pushchin, Novovoronezh, Sosnovy Bor) and, generally, in new towns. Inthe lower part of the list are mostly positioned small neglected towns locatedin remote parts of Russian provinces or economically underdeveloped ethnicalregions.
Environment was evaluated by thesingle parameter: the amount of pollution in the atmosphere per 1 ha of townterritory. The contrastsare apparent. The leader in terms of pollution is Norilsk. However, thecontribution of this factor to the total was reduced twofold, taking into account the fact thatenvironmental problemsare outside the list of most urgent concerns (nourishment, etc.)
Pair correlations between seven indicatorsturned out to be of small significance, usually at 0.1 – 0.3. The degree of correlationbetween investment and wages and between wages and per capita consumption levelacross all towns are somewhat more significant (0.4). The authors encountered afew instances of strange combination of high and low evaluations of “related”indicators, what made them to doubt the quality of the data. However, theauthors could not adjust the data basing on expert evaluation (too little dataon too many towns) and preferred to truncate the most striking and suspiciousextremums. Notwithstanding these instances, a certain logic of ranking mayusually be traced.
The finalevaluation of town prosperity was obtained by rankinginitial indices within the 10 point interval and calculating their arithmeticmean. It was hardly feasible to apply more refined methods taking into accountgaps in and apparent rounding of the data. The authors had known beforehandthat this would result in averaging-out and smoothing things over, however, theseries did not contract too much: results vary within the interval from 2 to 9points. Correlation analysis revealed that levels of consumption, wages,investment, and unemployment (precisely in this order) affected the resultsmost significantly.
12. A typology of MoscowOblast’sregions*
In order to determine the typologicalspecifics of the functional structure of towns located within the Moscow oblastthey were >
However, these data are insufficientlyrepresentative (due to lack of information about certain industries thequantitative data were approximated, while groups of industries were toogeneral). Therefore, the authors regarded the results with caution andelaborated them basing on expert evaluations before presenting the typology inTable 5. Arithmetic means of initial indicators were calculated for each taxon.
The following features characterize thetypes of towns we have singled out. Moscow is set in a separate type. The cityis a unique center not only in the region, but in the country on the whole. Itscharacteristic feature is the function of the capital reflected in thestructure of its economy and a higher (about five times above theregion’s average)share of employed in administration. Two other most important functions– industrial andscientific-educational – are represented in almost equal proportion. The share of employedin industry is about two times below the regional average, while employment inscience and education is two times above that level. The share of employed inconstruction, transport, trade, public health care in the Moscow structure isconsiderable (at the average or somewhat above the average values).
The second rather clearly identified typecomprise scientific centers (Dubna, Pushchino, Troitsk), where practically halfof labor force is employed in science and R&D. There was also registered ahigher share of employed in trade and public catering.
The third group of towns was conventionallydefined as “satellite towns.” They are located in the close environs of Moscowand have similar features. At the same time, subgroups included into this typediffer considerably. Subgroup A represent scientific and industrial centers. Inthis group the share of employed in science is considerably above the regionalaverage; however, it is below the level observed in Moscow and scientificcenters. The share of employed in industry is above the regional average acrosspractically all towns belonging to this subgroup. The same applies to publichealth care. The common feature of industrial and scientific towns and othersatellite towns is a higher share of employed in public utilities, whatreflects their function of “bedroom” towns with considerable amounts ofresidential housing construction.
Subgroup B comprises the most typicalsuburban centers among satellite states. Here the share of employed in industryis below the regional average; however, as in case of Moscow, it does notevidence the underdevelopment of industrial sphere.
These towns are most polyfunctional amongcenters belonging to this group: employment in science and education is abovethe regional average, a considerable share of employed in construction,transport, trade, and public health care, housing and public utilities,administration.
Subgroup C comprises towns with mostconsiderable localization of industrial function among satellite towns. Thissubgroup includes mostly medium and large centers of different industrialspecialization; however, the feature they have in common is the share ofemployment in industry above the regional average. The employment in housing,public utilities, and administration is also somewhat above the average.
The last group (D) comprises small centersin Moscow environs having an especially significant localization of industrialfunctions (monofunctionality) among satellite towns. A specific feature ofLobnya is the development of transport functions. Many towns have developedfunctions of science, education, public health care, developed housing andpublic utilities.
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