Determinants of Economic Growth of Russian Regions
O. LugovoyThe Institute for the Economy in Transition
In collaboration with:I. Mazayev , D. Fomchenko, V. Dashkeyev (IET)E. Polyakov (The World Bank)
The 2007 Meeting of The AAG, April 19 2007, 3457, San Francisco, CA
A. HechtWilfrid Laurier University
Addressed questions:
• What are the fundamental causes of regional disparities?
• Why do growth rates differ across Russian regions?
• Is there an income convergence?
Distribution of per capita income in 2004.
Legend
29019 - 4521647696 - 6162162679 - 9508396749 - 120564
130038 - 172770265130 - 265131573898 - 573898N o D ata
Annual per capita grp growth rate (average 1998 - 2004),%
Legend
-1.0 - 2.02.0 - 2 .82.8 - 3 .83.8 - 4 .8
4.8 - 5 .95.9 - 7 .27.2 - 8 .3N o D ata
I. Differences in Income and Growth Rates
‘Deep’ Determinants
D. Rodrik’s proximate and deep determinants
Source: D. Rodrik, ‘Search of Prosperity’, 2003, p. 5, Figure 1.3
“Fundamental Causes” in income differences
(according to D. Acemoglu, S. Johnson, J. Robinson, 2005)
• Economic institutions
• Geography
• Culture
“Proximate” determinants (endogenous)
• Capital deepening (K)• Human capital accumulation (L, H)• Productivity (A)• Natural resources (R)
Benchmark production function:
Y = A·K ·(H·L)1- ·(1+R)y = a+r+·k+(1- )·h
“Deep” determinants of regional growth
• Physical geography: temperature, permafrost, distribution of natural resource deposits
• Economic geography (Infrastructure and trade) access to seaports, railway infrastructure, innovation diffusion, agglomeration)
• Institutions (indexes of corruption and trust, financial ratings and proxy variables)
Simultaneous equations model
y = y(k, h, r)dy = y(dk, m, h, r)m = m(y, Geo, Infr, Inst, …)dk = k(y, Geo, Infr, Inst, …)
Geo – GeographyInfr – InfrastructureInst - Institutions
Econometric techniques
• Panel data set: 77 regions, 8 years (1997-2004)
• Mundlak specification for ‘between’ and ‘within’ estimates
• 3SLS & FIML for estimation of simultaneous equation model (SEM)
Mundlak specification for the one-factor lineal regression model
Mundlak specification:
ititit xaay 10
itiitit xaxaay *110
itiiitit xxxay *110
or
within(Fixed Effect)
between(BE)
D. Rodrik’s proximate and deep determinants
Source: D. Rodrik, ‘Search of Prosperity’, p. 5, Figure 1.3
Regions with higher income (GRP p.c.) are characterized by:
• Relatively higher level of per capita investment;• Higher share of raw materials in industrial output
(including fuel, ferrous, non-ferrous and timber industries);
• Higher share of economically active population; • Higher share of the employed in economically active
population;• Increase in share of the employed in economically
active population;• Higher number of postgraduate students (per 10 th.);• Higher agglomeration (population in the largest city).
The faster growing regions are characterized by:
• Relatively higher level of per capita investment;
• Increase in per capita investment;
• Increase in the share of raw materials’ industries in industrial output;
• Availability of a seaport;
• Lower per capita output level (conditional convergence).
The larger migrants’ balance is observed at:
• Relatively wealthy regions (per capita output, mean over the period);
• Regions with the smaller population growth rates during the soviet period (early developed regions or “home” regions);
• Regions with relatively warmer climate (average January temperature);
• Regions with more developed infrastructure (railway passengers);
• Regions with higher agglomeration (population in the largest city).
• Unemployment level (official data) tends to be statistically insignificant.
The larger per capita investment is observed in:
• Regions with higher per capita output, (mean over the period);
• Regions with relatively warmer climate (average January temperature) on the one side, and regions with permafrost on the other (probably those with field development of natural resources and/or higher costs of investment);
• Regions with higher per capita fuel industry output;• Regions with increase in per capita fuel industry output;• Regions with more developed infrastructure (per capita
phones in1995);• Regions with higher per capita investment during the
period 1997-2004 are characterized with less corruption figures concerning small-scale business in 2005.
The larger per capita investment is observed in:
• “Better” programs of regional development (CEFIR est.)
• Less legislative risk.
• S&P rating (dummy variable).
Breaking down Economic Growth of Russian Regions (% of variation)
Unexplained Residual
39%
Dynamics Factors
13%
Institutions24%
Economic Geography
19%
Physical Geography
5%
Growth explained by economic geography (seaports, mineral resource-intensive production, infrastructure)
R egion is not presentin the m odel
Growth rate, %
-0 .0176 - -0.0089-0.0089 - -0.0051-0.0051 - -0.0028-0.0028 - 0.00000.0000 - 0 .00200.0020 - 0 .00560.0056 - 0 .01360.0136 - 0 .0273
Basic conclusions
• Proximate determinants matter for income level and growth
• Geography matters for proximate factors accumulation: cause to migration flows and capital accumulation
• Infrastructure, trade, agglomeration matter for physical and human capital accumulation.
• There are some statistical evidence of relationship between some institutions measures and ‘proximate’ determinants but the problem of endogenity remains.
N et M igration R ate (1998-2004 average)
Legend
-30 - -10-10 - -3-3 - -1-1 - 1
1 - 33 - 1010 - 30N o D ata
Population grow th betw een 1926 and 1989 census, %
Legend
0 - 100100 - 200200 - 400400 - 700
700 - 15001500 - 50005000 - 5815N o D ata
R egions w ith seaports
Postgraduate students per 10 000 inhabitants
Legend
0 - 33 - 44 - 66 - 8
8 - 1221 - 2230 - 37N o D ata
Physical geography (January means and permafrost margins)
January mean tem p.,celsius degrees
below -30-30 - -25-25 - -20-20 - -15-15 - -10-10 - -5-5 - 0above 0
Perm afrost spread m argins
con tinuous
pa rt ia l R eg ion is no t presen t in the m ode l
Economic Geography – Transport infrastructure (Railway traffic intensity)
nonebe low 1 .251.25 - 1.51.5 - 1 .751.75 - 2.0above 2.0
R eg ion is not present in the m odel
R ailroads
Railw ay passangers' departures per 1 inhabitant
Geography – Communication infrastructure (Stationery phones penetration)
Stationery phonesper 1000 inhabitants
R egion is not presen tin the m ode l
46 - 8181 - 9494 - 113113 - 128128 - 147147 - 185185 - 354
