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.

THANK YOU!

lugovoy@iet.ru

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