Essays On Russian Economic Geography: Measuring Spatial Inefficiency
Bibliographic record
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. Soviet 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 allocation 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 incurred — 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".