The Pennsylvania State University The Graduate School Department of Economics ESSAYS ON RUSSIAN ECONOMIC GEOGRAPHY: MEASURING SPATIAL INEFFICIENCY AThesisin Economics by Tatiana N. Mikhailova c 2004 Tatiana N. Mikhailova Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2004 The thesis of Tatiana Mikhailova has been reviewed and approved* by the following: Barry W. Ickes Professor of Economics Thesis Advisor Chair of Committee N. Edward Coulson Professor of Economics Eric Bond Professor of Economics Regina Smyth Assistant Professor of Political Science Robert Marshall Professor of Economics Head of the Department of Economics *Signatures are on file in the Graduate School. Abstract Compared with other transition countries Russia faces the burden of extreme cold. This is not, however, strictly a function of geography. Soviet location policy directly affected the average (population weighted) temperature of the Russian economy. So- viet policy moved industry and population from the western part of the country to the east, effectively making Russia even colder than it was in the pre-Soviet era. Movements to the east have the significant impact on aggregate temperature because the isotherms on the Eurasian continent resemble lines of longitude, not latitude. Thus, the Russian economy entering transition faces not only the usual burden of an inhospitable Russian climate, but also suffers from the extra disadvantage due to the legacy of Soviet location policy. In this thesis I estimate the cost to Russian economy of the inefficient spatial al- location of its productive resources. Spatial inefficiency can result not only in added production and distribution costs — when regional comparative advantages are not exploited and unnecessary transportation and communication expenditures are in- curred — but also the wrong allocation of labor brings inefficiency in consumption: there are extra costs associated with people living in unsuitable places. In the Russian context, “unsuitable” usually means “too far” and “too cold.” I focus on the “cost of cold,” or precisely, the cost of production being wrongly located in places with a climate too cold. The first essay is a counterfactual exercise. To obtain a benchmark of spatial efficiency, I construct an allocation of industry and population that would result in Russia in the absence of Soviet location policy. To design such an allocation, I impose Canadian behavior on Russian initial conditions. I estimate a spatial dynamic model on Canadian regional panel data in a multinomial logit framework. I then project the estimated relationship onto Russia. The result is a hypothetical allocation of population and industry, specific to Russia’s endowment and initial conditions, but free of any disadvantages stemming from Russian historical circumstances. This procedure, however, ignores the effect of WWII — a major exogenous shock to Russian economy with no precedent in Canada. We should expect that war would have an impact on industry allocation irrespective of economic or political system. To account for the possible effects of WWII I conduct a separate simulation exercise, taking into account the fact that the war was fought primarily in the west. The results of this exercise show that the eastern part of Russia is still significantly overdeveloped — in other words, WWII explains only a small part of the misallocation. iii I construct an index of Temperature Per Capita (TPC) to capture the effect of lo- cation on aggregate temperature. Unlike other temperature indicators widely used in empirical growth literature, TPC is obtained by aggregating the temperature readings not over the territory, but over the population distribution. Thus, it provides more informative measure of temperature-related comparative advantages or disadvantages of the economy, especially in short-run. I use the estimated allocation of population to construct the counterfactual TPC. The comparison with the actual TPC reveals that due to Soviet location policy Russia has become about 1.5◦C “colder.” In the second essay I estimate the cost of cold directly. The most profound con- sequences of cold are extra energy use, added construction costs, health effects and productivity loss. Using Russian regional data on energy use, health and production I estimate the elasticity of each of these factors with respect to temperature. I then use these elasticities together with the TPC indices of the actual and the projected allocation to estimate the extra burden of cold that resulted from the Soviet location policy. The results show strong and significant effect of cold on all the factors exam- ined. An increase in TPC of 1.5◦C would have improved aggregate health indicators: average infant mortality rate would have been 1.5% lower, country-wide aggregate mortality rate — at least 0.8% lower. The estimations for construction industry re- veal a 3.5% productivity loss in the actual allocation compared to the counterfactual. The most significant impact of cold is on energy consumption. Cross-sectional analysis reveals that consumption of various kinds of energy by manufacturing pro- ducers increases 2.5 to 4% when January temperature drops 1◦C. Thus, 1.5◦CTPC difference between the actual and counterfactual allocations translates into 3.5-6% industrial energy consumption increase country-wide. The similar results were ob- tained for the residential energy consumption: various estimates point on 6 to 9% total energy loss due to the misallocation of population. The cost of extra energy consumption and the loss of construction productivity amount to 1.2% to 2.1% of Russian GDP yearly. iv Contents List of Tables vii List of Figures viii Acknowledgments ix 1 Introduction 1 2 Where Russians Should Live 4 2.1Stylizedfacts............................... 4 2.2Theidea.................................. 8 2.3Thetheoreticalframework........................ 11 2.4Datadescription............................. 14 2.5Theprocedureandtheestimationresults................ 14 2.5.1 EstimatingtheCanadianpaneldynamicmodel........ 15 2.5.2 Estimationissues......................... 16 2.5.3 ProjectingCanadianbehaviorontotheRussiandata..... 21 2.5.4 AccountingforWWII...................... 24 2.5.5 Correction for the exogenous cross-regional fertility differences 27 2.5.6 Temperaturepercapitadynamics................ 30 2.5.7 Alternative criteria for model selection and robustness checks . 33 2.6Conclusions................................ 35 3 The Cost of the Cold 37 3.1TheRoleofClimate........................... 37 3.2Energy................................... 39 3.2.1 Energyuseinproducingsectors................. 40 3.2.2 Residentialenergyconsumption................. 42 3.2.3 Cost................................ 46 3.3Productivity................................ 49 3.3.1 AggregateProduction...................... 50 3.3.2 Construction........................... 53 3.4Health................................... 57 3.4.1 Mortalityandmorbidity..................... 57 v 3.4.2 Whatisthecostoftheexcessmortality?............ 61 3.5Conclusions................................ 63 Appendices A Details of dataset construction 65 A.1 Dependent variables: population and manufacturing employment . 65 A.2Regionalcharacteristics.......................... 67 B Algorithm for choosing the optimal model 70 CTables 72 Bibliography 86 vi List of Tables 2.1 The results of the Monte-Carlo simulations for the projected Siberian population:selectdistributionquantiles................. 25 2.2 Excess population in Siberia and Far East, according to alternative forecastmodels. ............................. 36 3.1Heatingdegree-daysforselectcitiesandRussiaasawhole....... 45 3.2 Savings of energy in counterfactual relative to actual allocation. 46 3.3Counterfactual“savings”ofenergy.................... 48 3.4Regionalproductionfunctionestimates(secondstage)......... 51 3.5 Regional production function estimates, GRP deflated by subsistence minimumindex(secondstage)...................... 53 3.6Relativepricesofconstructionoutput.................. 54 3.7 Construction industry production function IV estimation. First stage. 55 3.8 Construction industry production function IV estimation. Second stage. 56 3.9 Production function estimates. Test of the robustness to the weighting. 56 3.10 Aggregate morbidity rate as a function of temperature. Robustness to specification................................. 59 3.11 Aggregate mortality rate as a function of temperature. Robustness to specification................................. 59 3.12Standardizedmortalityrateasafunctionoftemperature........ 60 3.13Infantmortalityrateasafunctionoftemperature............ 62 A.1Regionalcharacteristics.......................... 68 C.1 Results of the restricted system estimation. Equations for population. 72 C.2 Results of restricted system estimations. Equations for industry. 73 C.3Projectedvsactualpopulation.Canada................. 74 C.4Projectedvsactualpopulation.Russia.................. 76 C.5 The industrial structure control series. ................. 77 C.6 Electricity in 1991. ............................ 78 C.7 Electricity in 1992. ............................ 79 C.8 Thermal energy in 1991. ......................... 80 C.9 Thermal energy in 1992. ......................... 81 C.10 Fuels in 1991. ............................ 82 C.11 Fuels in 1992. ............................ 83 vii List of Figures 2.1Isotherms:averageJanuaryairtemperature............... 5 2.2ChangeinTPCindexinRussia,CanadaandUSA..........