Regional Savings in the Context of Aggregate Transfer Accounts and Territorial Patterns of the Age Structure

RESEARCH ARTICLE Copyright Nazarova AG. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Regional Savings in the Context ofAggregateTransfer Accounts andTerritorialPatterns oftheAgeStructure Anzhela G. Nazarova 1 1 National Research University Higher School of Economics (HSE University), Moscow, 109074, Russia Received 12 September 2024 ♦ Accepted 21 January 2025 ♦ Published 1 October 2025 Citation: Nazarova AG (2025) Regional Savings in the Context of Aggregate Transfer Accounts and Territorial Patterns of the Age Structure. Population and Economics 9(3):129-147. https://doi.org/10.3897/popecon.9.e136949 Abstract The article examines the applied use of aggregate estimates of national transfer accounts (NTA) asan additional aspect ofregional macroeconomic analysis. The NTA essence isthe construction ofage-specific, orintergenerational, distribution ofresources inannual terms. However, constructing aggregate estimates (across all ages for aneconomy orregion) isvaluable for macroeconomic analytics onits own. The NTA construction isa relatively new tool, but itis already demanded worldwide for interdisciplinary data analysis atthe intersection ofeconomics, demography, and statistics. This study analyzes and explains retrospective changes inhow regions make savings looking atthese changes from anadditional prospective. Its starting point was anassumption about aconnection between structural shifts inregional profiles oflife cycle balance sheet results (NTA key parameters) and changes inthe proportions ofregions’ disposable income distribution. The article describes the calculation methodology and the first results obtained ina 10-year retrospective study covering 2011-2020. Experimental calculations for Russia are based onthe international UNNational Transfer Accounts Manual adapted tothe Russian statistical data. Three qualitatively different groups ofregions have been identified. The first group ismarked bya steady surplus inthe analyzed indicators, the second one– bya steady deficit, while the third one– bya mixed picture. Experimental calculations confirm that regarding the third group ofregions, trends inaggregate estimates ofregional savings and inlife cycle balance results have changed from adeficit toa surplus. These changes indirectly indicate positive shifts inthe quality oflife ofthe population, meaning that they are indirectly indicative ofpositive shifts insocial sustainability ofthe Russian regions. Keywords regional economic life cycle result, net saving, aggregate transfer accounts, national accounts, surplus, deficit JEL codes: E16, E21, J11, O11 Population and Economics 9(3): 129–147 DOI 10.3897/popecon.9.e136949 A.G. Nazarova: Regional Savings in the Context ofAggregateTransfer Accounts andTerritorialPatterns of the Age Structure 130 Introduction The distribution ofdisposable income between consumption and savings isa key reproductive proportion that enables growth and creates development potential for the economy atboth national and regional levels. From the point ofview ofnational accounting1, saving shows the amount offinancial resources available toan economy after part ofincome has been used for current (final) consumption. Inthis regard, estimating and analysing the characteristics ofsavings are among crucial research tasks. Byretrospectively assessing and analysing values ofnet savings (SNA, NTA)2 and life cycle result (NTA) across regions, wecan make implicit conclusions about changes inthe quality oflife ofthe population byregion asone ofthe aspects oftheir social sustainability. Almost all UN countries use national accounting (Lee et al. 2022). Some states such asCanada actively use subnational accounts for regional analysis (Tatarinov 2005). National transfer accounts are part ofthe SNA satellite framework inthe system ofnational accounting, asthey target adefinite kind ofanalysis– economic and demographic (UN 2013). According tothe System ofNational Accounts, 2008(UN etal. 2009), satellite accounts manifest SNA flexibility. The complete process ofconstructing national transfer accounts consists oftwo stages. First, estimates relating tothe economy asa whole are calculated and balanced (aggregate accounts are constructed) atthe macro level. Second, the aggregate estimates showing the total for all ages are distributed byage profile, i.e. anidentical set oftransfer accounts isconstructed for age cohorts. The approach toconstructing NTA was theoretically grounded onthe life cycle theory ofFranco Modigliani and Richard Brumberg (Modigliani and Brumberg 1954) and its development byA.Ando and F.Modigliani (Ando and Modigliani 1963). The NTA framework was set inearly 90s bydemographer R. Lee and macroeconomic theorist A.Mason. Itis based onthe economic life cycle concept. The NTA principles and country analysis results obtained aspart ofthe National Transfer Accounts international research project can befound innumerous publications byeconomists (Lee 2003; Lee etal. 2008; Lee and Mason 2009, 2010a, 2010b, 2011; Lee 2012; Mason etal. 2006; Mason and Lee 2013; Lee 2016; Mason atal. 2022), and bycountry experts (e.g. D’Albis and Moosa 2015; Zannella 2017), and others. The Russian NTA system is constructed by the National Research University Higher School ofEconomics (HSE University). Two institutes collaborate within HSE University– the Vishnevsky Institute ofDemography dealing with the distribution offlows across ages and generations, and the Centre ofDevelopment Institute constructing NTA aggregate estimates. The international methodology for constructing national transfer accounts became atheoretical basis for NTA-related experimental calculations inRussia. Respective publications were translated into Russian under HSE University’s umbrella in2022(UN 2022) and adapted tothe peculiarities ofthe Russian statistics. A full range ofNTA calculations (construction ofaggregate estimates and intergenerational distribution) was first performed for Russia for the year of2013(Denisenko and Kozlov 2019). Now HSE University works onthe construction oftransfer accounts 1 System ofNational Accounts (SNA) and National Transfer Accounts (NTA) consistent with the SNA (UN 2013; UN2022). 2 Excluding consumption offixed capital (Rosstat 2021a). Population and Economics 9(3): 129–147 131 onan ongoing basis. Experimental calculation ofaggregate estimates ofthe life cycle result account across regions balanced with the calculations for the economy asa whole isan addition toand extension ofthe work ofthe Centre ofDevelopment Institute onthe construction ofNTA aggregate estimates for Russia (covering the period from 2011to2020). Results ofregional life cycles: economic content, methodologicalapproach toassessment, and key conclusions The result ofthe economic life cycle isthe central value inthe NTA system. Itshows the ratio between the volume ofindividual consumption and the population’s labour income. Its value can beboth insurplus (the sign “-” according tothe NTA logic) and indeficit (the sign “+”) inthe age profile, asconsumption and labour income growth rates differ significantly across age groups (Wang and Mason 2007). Interms ofaggregate estimates calculated using national accounts statistics (SNA) for all ages, the life cycle result suggests that working-age generations inthe surplus stage ofthe life cycle “sponsor” neighbouring age groups whose life cycle result isin deficit, thus restoring their resource balance (Lee and Ogawa 2011). Ifthe economy shows asustainable estimated deficit ofthe life cycle atthe aggregate level, itmeans that the growth ofthe working-age population’s total surplus does not offset the increase inthe non-working-age population’s total deficit over the same period (Mason and Lee 2013). The analysis ofeconomic relationships using NTA can becompared toan iceberg. Atits tip, there isan aggregate value ofthe life cycle deficit orsurplus and its financing through the resource channels ofgovernments and the private sector. Amacroeconomic view onresource flows inthe NTA context ispresented inmore detail in(Lee and Mason 2010b; Lee 2014; Lee 2016). Going tothe level below, wemove tothe analysis ofsavings. Inaddition tothe view through the prism ofSNA, transfer accounts offer macro-level understanding ofthe financial background of the saving process. The life cycle balance surplus is methodologically manifested inthe growth ofincome from savings (the value ofa country’s orregion’s net savings). The deficit result (balance) of the life cycle essentially represents the volume of“excessive” consumption and indicates the region’s need for additional financing, which is covered by the current transfer support and income from operations related to the redistribution ofassets. Methodologically, alife cycle deficit isfinanced from net current transfers (a balance between the received and transferred ones). The saving process isinfluenced bythe emerging (or already sustainable) pattern oflife cycle deficit coverage (Lee 2012; Rosero-Bixby 2011; Lee and Mason 2011; Solé etal. 2020). The findings from foreign studies carried out bycountry research teams during the implementation ofthe NTA international project showed aninteresting pattern. Saving was less active incountries where the life cycle deficit was mainly covered bypublic (government) transfers compared toregions (countries) where the source was predominantly private financing (Lee and Mason 2010; Mason and Lee 2006). Considering the character ofthe information base and regional estimates inthe SNA, formulas 1-4represent the methodological approach tocalculating main elements ofthe account ofthe Russian regions’ life cycle result (in aggregate form). Regional peculiarities ofthe calculation are taken into account informula 3. A.G. Nazarova: Regional Savings in the Context ofAggregateTransfer Accounts andTerritorialPatterns of the Age Structure 132 The result ofa region’s life cycle was calculated asfollows: LCDi orLCSi = Ci– Linc, (1) where i– constituent entity ofthe Russian Federation (region); Ci– volume ofconsumption inthe region (in terms ofthe NTA methodology); Linc– labour income inthe region; LCDi orLCSi– life cycle result: deficit orsurplus. We estimate the value ofthe region’s consumption expenditures using the transfer accounts methodology based onthe NTA macro controls formula for consumption: Ci = Cifact + FLi + Cicol– Nipr, (2) where i– constituent entity ofthe Russian Federation (region); Ci– volume ofconsumption inthe region (in terms ofthe NTA methodology); Cifact– actual final consumption ofhouseholds (HHs) inthe territory ofthe Russian Federation’s constituent entities, total (SNA, p. 41). Its volume includes HHs’ expenses (sector’s own expenses onthe payment basis and social transfers inthe natural form extended toHHs bythe General Government (GG) and Non-Profit Institutions Serving Households (NPISH) sectors)3; Cicol– consumption ofcollective services bythe GGsector (estimate for the region); FLi– elements ofactual final consumption ofHHs calculated atthe federal level for the economy asa whole (estimate for the region); Nipr– taxes onproducts (collected atthe regional level)4. A number ofadjustments was required toestimate the region’s total consumer expenditures following the NTA methodology, taking into account the methodological peculiarities ofkeeping regional statistics onnational accounts (SNA). The hypotheses used inthe calculation were asfollows. First, the value “Elements ofactual final HHs consumption calculated atthe federal level for the economy asa whole” (as part ofthe value “Actual final consumption ofHHs ofthe Russian Federation”) was distributed across constituent entities ofthe Russian Federation byexpertize. The average per capita level ofthis category ofexpenditures was used aswell asthe average annual population ofconstituent entities. Information onthe region’s population istaken either directly from the annual statistical handbook Regions ofRussia. Social and Economic Indicators orcalculated based onthe SNA data (as aquotient ofthe actual final consumption ofHHs inthe region divided bythe amount ofactual consumption per capita). The final values ofthe average annual population ofthe Russian Federation are also included inthe Population ofthe Russian Federation bySex and Age information and analytical materials ofFederal State Statistics Service (Rosstat). Second, the total volume ofregional final consumption expenditures needed tobe recalculated into basic prices (for “cost comparability” ofthe region’s consumption indicators and GRP). This isdue tothe fact that inline with the SNA methodology, gross regional product 3 The consumption ofnon-market goods and services ismeant. GGand NPISH sectors buy part ofgoods and services toprovide them tothe population for free. The volume ofsocial transfers inthe natural form consists ofthe GGsector’s expenditures onconsumption ofindividual goods and services, and the NPISH sector’s social transfers inthe natural form. 4 Characteristics ofthe information base are described inmore detail in(Nazarova 2022). Population and Economics 9(3): 129–147 133 (GRP) isestimated byRosstat incurrent basic prices, which include the prices ofproduction ina given industry and the amount ofsubsidies onproducts, but exclude taxes onproducts. Meanwhile, regional SNA statistics include regional consumption volumes incurrent market prices. Following the NTA methodology, the volume oftaxes onproducts estimated for the regions bydirect counting were removed from the estimates ofconsumer expenditures incurrent market prices relying onthe statistics ofthe Federal Tax Service5 and the Federal Treasury ofthe Russian Federation6. The algorithm for calculating the regional volume oftaxes onproducts (in current prices and onan accrued basis, see formula2) isbased onthe official statistical methodology for calculating the value “Net taxes onproducts” (Rosstat 2021b) and considers the regional peculiarities ofdata collection. For example, statistics ontransactions with the rest ofthe world (foreign-trade and transfer flows) are not collected for regions, socustoms duties cannot bedistributed across regions and are not considered. Inaddition, the GRP calculation does not include value added created asa result ofmulti-regional activities (services offinancial intermediaries) and taxes onvalue added. The methodological approach todirectly counting the volume oftaxes onproducts isreflected informula 3. NNvatNvatA SY iprivi iii i N =++++ () = ∑, 1 (3) where i– constituent entity ofthe Russian Federation (region); Nipr– taxes onproducts (collected atthe regional level); Nvati– value-added tax ongoods (works, services) sold inthe territory ofthe Russian Federation taking into account the change (increase/decrease) inthe tax arrears inthe current year; Nvatvi– value-added tax ongoods (works, services) imported into the territory ofthe Russian Federation; Ai– excise taxes onexcisable goods (products) produced inthe territory ofthe Russian Federation taking into account the change (increase/decrease) inthe tax arrears inthe current year; Si– government fee for using the names “Russia”, “Russian Federation” and words and collocations derived from them inthe names oflegal entities; Yi– disposal fee. The calculations showed that, on average, the estimate of the total volume of taxes onproducts “from below” (based onthe regional breakdown bytype oftaxes onproducts) covered about 97% ofthe total amount published byRosstat aspart ofthe consolidated national accounts statistics. As the regional breakdown ofstatistics onthe distribution ofsubsidies onproducts isnot published, this value was not considered inthe framework ofthe expert assessment. Their total volume isnot significant inthe economy asa whole, sothe hypothesis was accepted that itwould not much impact the results obtained without taking them into account. 5 https://www.nalog.gov.ru 6 https://www.roskazna.gov.ru A.G. Nazarova: Regional Savings in the Context ofAggregateTransfer Accounts andTerritorialPatterns of the Age Structure 134 Labour income ofa constituent entity ofthe Russian Federation iscalculated applying the following formula: LE SE in ci i i N =+ () = ∑, 1 (4) where i– constituent entity ofthe Russian Federation (region); Linc– labour income inthe region; Ei– remuneration ofemployees inthe region adjusted for the estimate ofhidden income; SEi– income ofthe self-employed (from self-employment) inthe region. The calculation for 2011-2020was made based onexperimental construction ofthe life cycle result account (which isthe first ofthe NTA accounts) for regions. The comparative analysis ofthe results showed astrong dispersion ofregional estimates deviating from the average level inthe economy. The balance result varied from asignificant surplus (-) todeficit (+), comparable with the volume ofGRP insome regions. The variation inestimates was characteristic both ofdifferent federal districts and territories within one federal district. The regional dispersion ofresults was affected bydifferences: • in the sectoral structure ofgross value added (GVA) production inthe regions; • in the mechanisms ofdistribution and redistribution ofterritorial resources; • in the accessibility ofrent incomes for constituent entities ofthe Russian Federation. A sustainable surplus result of the life cycle has been achieved by the following three qualitatively different categories ofregions: • raw material regions with asignificant resource base and large production facilities based onthe extraction ofraw materials for export, and strong specialization inthe areas with highest wages; • the most socially and economically developed cities with federal status (having the status ofan individual region), the so-called financial and economic centres, where the increased demand for goods and services and the location oflarge companies’ central offices have become determinants ofthe overall level oflabour income; • regions where the extraction ofmineral resources iscombined with the availability ofhigh-tech manufacturing industries (chemistry, petrochemistry, and oil refining) orwhere there are export-oriented manufacturing enterprises (production facilities based onraw material processing). Most regions from this group donot receive subsidies toequalize fiscal capacity, orthe share ofsubsidies from the federal budget does not exceed 10% oftheir own revenues. A sustainable deficit result ofthe life cycle has been shown byregions where the population’s age profile was characterized bya large share ofthe younger (0-15years) orolder (65+) age groups. This age profile had a“constraining” effect onthe life cycle result, aspeople inyounger and older ages objectively experience agap between consumption and labour income levels. Data on the life cycle result in the context of these types of regions are presented intables 1, 2, 5inthe next section ofthe article, inparallel with the regional values ofnet savings. Population and Economics 9(3): 129–147 135 Regional income from savings: economic content, methodologyforcalculation and analysis The volume ofincome from savings can beestimated for the region based onthe SNA methodology only asa whole, without singling out institutional units, while the level ofthe economy makes itpossible tocalculate savings both asa whole and for corporations (non-financial and financial), HHs, NPISHs, the GG, and the Rest ofthe World (ROW) sectors. This isdue tothe fact that statistics oninstitutional units are not collected for GRP, unlike GDP. However, ifwe draw aparallel with the economy, the population (the HHsector) accounts for 1/5to1/3ofthe total gross savings (Nazarova 2024). The estimated volume of net savings can be either positive or negative, meaning its surplus (+) or deficit (–), respectively. Regions with asurplus ofsavings have their own potential investment resource, the use of which for capital expenditures will contribute to the modernization of production (cost reduction) and consequently leading to an increase ingross value added created inthe region. Anegative value ofsavings (deficit) shows the amount ofadditional financing required for the region tocover necessary current expenditures7. The region receives external resources tocompensate for the lack ofits own funds (through various financing channels). Methodologically, savings were calculated atthe regional level intwo stages. At the first stage, estimates ofincome saved inthe regions were estimated ona gross basis, inaccordance with the SNA methodology. Under the SNA framework, the savings value iscalculated asa remainder– asa difference between the amount ofdisposable income and its use for final consumption (for current needs). Disposable income, inits turn, isa sum ofrevenues derived bythe economy and sectors from production activity and asa result ofthe reallocation ofcash revenues and current transfers inthe form oftaxes, benefits, and other social payments. Inthe regional context, gross savings were estimated asa difference between GRP regional estimates and total final consumption expenditures ofthe regions: Sigr = GRPi– Cisna, (5) where i– constituent entity ofthe Russian Federation (region); Sigr– gross saving volume inthe region (SNA methodology); GRPi– GRP volume inthe region; Cisna– expenditures onthe region’s final consumption, total (SNA methodology). The second step ofthe calculation was atransition from estimating savings ona gross basis toa net basis (as transfer accounts methodologically use the term “net savings”): Sinet = Sigr– Cicapform, (6) where i– constituent entity ofthe Russian Federation (region); Sinet– net savings volume inthe region (SNA methodology); Sigr– gross savings volume inthe region (SNA methodology); Cicapform– volume offixed-capital consumption inthe region (SNA methodology). 7 For example, federal budget subsidies granted tothe regions toequalize their fiscal capacity. A.G. Nazarova: Regional Savings in the Context ofAggregateTransfer Accounts andTerritorialPatterns of the Age Structure 136 A methodological difference between gross and net savings isthat the first estimate takes into account consumption offixed capital (CFC), while the second one disregards it. The expert assessment ofregional savings ona net basis required the estimated distribution ofthe CFC value across constituent entities ofthe Russian Federation, since official statistics disclose its volumes for the economy asa whole inthe context ofinstitutional units and activity types (industries). The hypothesis isbased onthe SNA methodological thesis that CFC isa decrease inthe current value offixed assets owned and used bythe producer during the reporting period asa result ofphysical wear and tear, obsolescence ornormal accidental damage that can beforeseen and insured against (Rosstat 2021a). Year-end regional information onthe size offixed assets inthe economy atfull accounting value (considering revaluation) isavailable inthe statistical handbook Regions ofRussia. Social and Economic Indicators (a set ofbasic characteristics ofRussia’s constituent entities). The CFC volume for the economy asa whole published byRosstat aspart ofthe SNA statistics was distributed across regions inaccordance with the estimated structural weights ofthe region’s fixed assets inthe total value ofthe economy’s fixed assets. This approach islogical, since consumption offixed capital (in fact, its depreciation) isamong inherent characteristics offixed assets. At the stage when calculations ofsavings on a net basis are verified, the aggregate volume ofregional estimates obtained was balanced with the volume ofnet savings inthe economy asa whole (taken from Rosstat’s regular statistical handbook National Accounts ofRussia). Further, the aggregated regional life cycle results calculated for the full range ofRussia’s constituent entities were analysed together with the characteristics ofincome saved bythe regions and their demographic situation, asit directly affects both the life cycle result and the level ofincome saved inthe region. As aresult ofthe analysis, three qualitatively different groups ofregions were identified: • regions with asustainable surplus ofnet savings; • regions with asustainable deficit ofnet savings; • regions with amixed pattern ofnet savings. Stably positive net savings have been shown by14regions ofthe Russian Federation over adecade (2011-2020). We’d like tonote that the Tyumen region entered this group infull, with Khanty-Mansi autonomous areas– Yugra and Yamalo-Nenets autonomous area, and the Tyumen region without autonomous areas, while the Arkhangelsk region was only partially included. The Nenets autonomous area has astably high surplus ofnet savings, while the Arkhangelsk region without the Nenets autonomous area showed amixed picture ofnet savings. Intwo more regions ofthe Russian Federation, there were near-zero orinsignificant negative savings ina single year, while there was asignificant surplus during the rest ofthe period. The surplus pattern ofsavings ischaracteristic ofthe territories where the life cycle result was asteady surplus oran insignificant deficit, while the age profiles ofsuch groups were quite close tothe average distribution inthe economy. Almost all regions included inthis category donot receive equalization grants, ortheir share does not exceed 10% ofthese regions’ own revenues. Only the Chukotka autonomous area and the Magadan region remain highly subsidized interms ofper capita subsidies (iMonitoring). The regions from this group share similar features: • astrong resource base, industries based onthe extraction ofraw materials for export, i.e. there isspecialization inareas with high wages; Population and Economics 9(3): 129–147 137 • cities which can becalled financial and economic centres with relatively high labour income; • a combination of mineral resource extraction and large-scale gas and oil refining, metallurgy, chemistry and petrochemistry production facilities; • high-tech manufacturing industries and non-resource export enterprises. The final distribution ofregions with asustainable surplus ofnet savings (and economic life cycle) ispresented intable1. Stably negative net savings (their deficit) was shown by45regions. Positive savings took place infour other regions inone single year, against the background ofa significant deficit throughout the rest ofthe period. The regions included inthe “savings anti-rating” have anumber offeatures incommon: • their set coincides with asample ofregions consistently indeficit interms ofeconomic life cycle; • all regions are highly subsidized. Asignificant share oftheir revenues istransfers from the federal budget. Subsidies range from 20% toover 40% ofthese regions’ own revenues, according tothe Russian Ministry ofFinance and Accounts Chamber ofthe Russian Federation (2022); • for the most part, these are either the “youngest” orthe “oldest” regions, where the population’s share inthe older (65+) oryounger (0-15years) age cohorts inthe total population issignificantly higher than the economy average. Summarized estimated numbers for this group are presented intable2. A mixed pattern of net savings (alternating periods of deficit and surplus) was demonstrated by 18 regions. In terms of financial flows, this is a situation when aparticular region acted alternately asa borrower ofresources which itlacks (when there isa deficit ofown savings) and alender (when net savings are insurplus) inrelation toother regions. For this group ofregions, averaged annual estimates were calculated for two 5-year periods– 2011-2015and 2016-2020(see table3). The thermogram table shows that since the turn of2015-2016, many ofthem have moved into the area ofsustainable savings surplus. The number ofregions showing the surplus of net savings and life cycle has almost doubled. This qualitative shift isconsistent with the effect ofthe two main factors over time: On the one hand, since 2016, the official methodology for calculating GRP and consumer spending has been adjusted atthe regional and national levels. Changes affected the building ofregional gross output volumes and GVA ofeconomic activity types in2016-2019. Aspart ofthe GRP, the CFC value was determined for sectors based onthe current market value offixed capital. The cost ofhousing services produced and consumed byhome owners was estimated under the calculation methodology for the Russian Federation. Inaddition, physical volume indices for 2019were estimated across economic activity types included in “B”, “C”, “D”, and “E” sectors of OKVED-2 (Russian National Classifier of Types ofEconomic Activity– 2) based onindustrial production indices recalculated taking into account the new base year (2018, previously– 2010). This topic iscovered inmore detail in(Nazarova 2024). The impact ofthe updated methodology was expressed inthree aspects: • anincrease inthe GRP nominal volume; • changes inthe sectoral GRP structure; • changes inthe ratings ofregions according tothe GRP indicators. A.G. Nazarova: Regional Savings in the Context ofAggregateTransfer Accounts andTerritorialPatterns of the Age Structure 144 Conclusions The analysis ofstructural shifts inthe proportions ofdisposable income inthe Russian regions has identified their direct correlation with changes inthe regional life cycle results. The first experimental results help make an opinion about the emerging and ongoing shifts insocial sustainability ofthe Russian regions. Over a decade, the ratio ofregions with anet savings surplus toa net savings deficit increased from 0.27(18:65) inlate Table 5. Differences inlife cycle results and net savings byconventional category ofconstituent entities ofthe Russian Federation Categories ofregions andtheirnumber Net savings, (+) surplus, (-)deficit (average for 2011-2020, % ofGRP) Life cycle result, (-) surplus, (+)deficit (average for 20112020, % ofGRP) 1. RAW MATERIAL (“EXTRACTING”) REGIONS Surplus from 70.3% ofGRP to13.5% ofGRP From surplus of-33.5% ofGRP to deficit of17.8% ofGRP 2. FINANCIAL AND ECONOMIC CENTRES Surplus from 35.4% ofGRP to23.3% ofGRP Surplus from -5.7% ofGRP to-1.1% ofGRP 3. REGIONS RELYING ONHIGHTECH MANUFACTURING Surplus from 30.2% ofGRP to11.5% ofGRP Deficit from 4.0% ofGRP to26.9% ofGRP 4. OTHER REGIONS (age profiles close tothe average inthe economy) Deficit from -69.7% ofGRP to-108% GRP Deficit from 92.2% ofGRP to23.0% ofGRP 5. “OLD” REGIONS Deficit from -84.4% ofGRP to-10.0% ofGRP Deficit from 100.9% ofGRP to30.6% ofGRP 6. “YOUNG” REGIONS Deficit from -147.9% ofGRP to-53.7% ofGRP Deficit from 168.4% ofGRP to60.3% ofGRP Source: the author’s calculations. Table 4. Gross value added (GVA) ofthe economy inbasic prices and gross regional product (GRP) byconstituent entities ofthe Russian Federation 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 GRP byconstituent entities ofthe RF, total (GVA incurrent basic prices), Rbtrln 45.4 49.9 54.1 59.2 65.7 74.1 79.7 90.2 95.1 94.4 GVA inmarket prices (inGDP), Rbtrln 52.1 59.0 63.9 68.7 74.6 77.1 82.9 92.8 98.5 97.0 Share ofGDP distributed between regions, % 87.1 84.6 84.7 86.1 88.1 96.1 96.2 97.2 96.5 97.3 For the period onaverage 86.1 96.7 Sources: the author’s calculations based onRosstat’s data. Population and Economics 9(3): 129–147 145 2011to0.73(36:49) inearly 20218. Itindirectly indicates apositive macroeconomic shift insocial sustainability ofthe Russian regions. The focus ofanalysis proposed inthe study provides abasis for indirect conclusions about shifts inthe quality oflife ofthe population. 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Ob utverzhdenii Ofitsial’noi statisticheskoi metodologii rascheta potrebleniya osnovnogo kapitala [On approval of the Official Statistical Methodology for the Calculation ofConsumption ofFixed Capital]. Order ofthe Federal State Statistics Service (Rosstat) No21dated 22.01.2021. https://legalacts.ru/doc/prikaz-rosstata-ot-22012021-n-21-ob-utverzhdeniiofitsialnoi/?ysclid=lprinj9mrl587431545[Accessed on16.08.2024]. Rosstat (2021b) Ob utverzhdenii Ofitsial’noi statisticheskoi metodologii rascheta pokazatelya «Chistye nalogi naprodukty» [On approval ofthe Official Statistical Methodology for Calculating the Value ofNet Taxes onProducts]. Order ofthe Federal State Statistics Service (Rosstat) dated 17.12.2021 No 926. https://rosstat.gov.ru/storage/mediabank/met926_17122021.pdf [Accessed on02.08.2024]. National Accounts ofRussia. Catalogue ofpublications. Statistical editions / Rosstat. URL: https:// rosstat.gov.ru/folder/210/document/13221 Regional Development. Bulletin ofthe Accounts Chamber ofthe Russian Federation. No6(295). Chamber of the Russian Federation (2022). URL: https://www.sptulobl.ru/law/Bul-6-2022. pdf?ysclid=lzx6p212l1434629340 Acknowledgements This research article was prepared within the framework of“Social Policy for Sustainable Development and Inclusive Economic Growth” Strategic Project, which ispart ofHigher School ofEconomics’ development program under the “Priority 2030” academic leadership initiative. The “Priority 2030” initiative isrun byRussia’s Ministry ofScience and Higher Education aspart ofNational Project “Science and Universities”. The conclusions are also confirmed bythe results ofthe joint research project “Regional Transfer (Intergenerational) Accounts” carried out by the National Research University Higher School ofEconomics (Moscow) and Ufa University ofScience and Technology (Ufa). Information about the author Nazarova Anzhela Georgievna – PhD (Economics), Head Expert, HSE Centre of Development Institute, National Research University Higher School of Economics (HSE University). Moscow, 109074, Russia. E-mail: anazaro[email protected], anzh[email protected]