Productivity Di erences Within and Between Countries · Empirical Strategy Decompose inequality...

Productivity Differences Within and Between Countries

Melissa Dell

MIT

OECD Lagging Regions Meeting, June 2010

Dell (MIT) Productivity Differences OECD Lagging Regions 1 / 21

Introduction and Conceptual Framework

Motivation

Income differences across geographic space are both large andpersistent.

This talk provides a comparative perspective from the Americas, inorder to:

Introduce a context for guaging the generalizability of patterns thatemerge in the OECDHighlight methodologiesOffer additional insight into why regional inequalities arise and persist


Introduction and Conceptual Framework

Outline for Presentation

1 Quantify the magnitude of income differences between regions withincountries, relative to the magnitude of income differences acrosscountries.

2 Briefly discusses why differences arise and persist

GeographyPublic infrastructureExample of detailed quantitative study on persistence


Inequality Patterns in the Americas

Examining Inequality in the Americas: Data

Population censuses and household expenditure surveys for 18countries in the Western Hemisphere.

11 countries with income data geo-referenced to municipalities(municipality mean population between 26,000 and 108,000)7 additional countries with data geo-referenced to bigger regions.

Results both for labor income and household expenditure.

Construct comparable measures across surveys for both.

Results both with regionally-deflated incomes and nationally-deflatedincomes.



Incomes in Central AmericaFigure A2: Labor incomes in Mexico and Central America

Mean Labor Income (PPP $)

<4,000

4,000 - 7,000

7,000 - 10,000

10,000 - 15,000

>15,000



Incomes in South AmericaFigure A3: Labor incomes in South America

Mean Labor Income (PPP $)

<4,000

4,000 - 7,000

7,000 - 10,000

10,000 - 15,000

>15,000



Empirical Strategy

Decompose inequality within the Western Hemisphere intobetween-country, between-municipality and within-municipalitydifferences.

Decompose labor income into predicted (from a Mincer regression)and residual components.

Relevant for understanding the role of human capital vs. residualfactorsResidual factors similar to “technology” in cross-country models(physical capital mobile within national boundaries).

Use additively decomposable measures of inequality (from the GeneralEntropy class).

Theil Index and Mean Log Deviation Index



Results: Summary

Large within-country differences.

Between-municipality differences about half to a quarter ofbetween-country differences with the United States included.Without the United States, about twice between-country.

Large between-country predicted income differences

Human capital factors explain about half of the between-country andbetween-municipality differences.

Factors related to differences in productive efficiency (i.e. localinstitutions, public goods) appear important for regional variation.



Results: Between vs. Within

Income Inequality (Theil Index)Between Within Between

Cntry. Cntry. Mun.sample (pop. weights) (1) (2) (3)

Mun. (actual) 0.250 0.544 0.058Mun. (equal) 0.285 0.622 0.088No US (equal) 0.048 0.706 0.114

All (actual) 0.253 0.542 0.054All (equal) 0.235 0.619 0.061No US/CA (equal) 0.071 0.726 0.081



Results: Predicted vs. Residual

Predicted Labor Income Residual Labor IncomeBtwn Btwn Within Btwn Btwn WithinCntry Mun/Reg Mun/Reg Cntry Mun/Reg Mun/Reg

(1) (2) (3) (4) (5) (6)

Ref. to Mun.Mun. (actual) 0.170 0.015 0.131 0.033 0.040 0.389Mun. (equal) 0.166 0.040 0.142 0.041 0.053 0.404No US (equal) 0.031 0.053 0.157 0.040 0.057 0.421

All (actual) 0.163 0.014 0.130 0.040 0.037 0.392All (equal) 0.158 0.026 0.140 0.045 0.043 0.433No US/CA (equal) 0.081 0.035 0.158 0.042 0.050 0.467


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Climate

Appendix C: Maps

Bolivia - Temperature

Average Annual Temperature

26 C

4 C

State Boundaries

Bolivia - Labor Income

Median Income (PPP $)

<1,500

1,500 - 3,000

3,000 - 4,500

4,500 - 6,000

>6,000

State Boundaries

Brazil - Temperature

Mean Annual Temperature

28 C

14 C

State Boundaries

Brazil - Labor Income

Median Income (PPP $)

<4,500

4,500 - 5,500

5,500 - 6,500

6,500 - 8,000

>8,000

State Boundaries


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Climate

Use high-resolution data on long-run climate averages to examine therelationship between climate and economic prosperity.

Dependent Variable is:Log per capita GDP

(PWT) Log labor income

(1) (2) (3) (4) (5)

Temperature -0.085* -0.089 -0.085** -0.012*** -0.019**(0.017) (0.072) (0.004) (0.004) (0.009)

Precipitation 0.000 0.019 -0.003** 0.000 0.002(0.015) (0.047) (0.001) (0.001) (0.001)

Elevation, slope, coast no no yes yes yesCountry F.E. no no no yes yesState F.E. no no no no yesR-squared 0.23 0.21 0.61 0.82 0.88Number of clusters 260 260 260Number of observations 134 12 7684 7684 7684


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Road Infrastructure

Theil Index Income RegressionsBetween WithinCountry Country Baseline Controls

(1) (2) (3) (4)

Brazil 1.049 -0.022 -0.019(0.004) (0.003)

Mexico 0.379 -0.124 -0.096(0.011) (0.010)

Panama 0.756 -0.157 -0.138(0.026) (0.025)

United States 0.795 -0.080 -0.076(0.026) (0.021)

Venezuela 0.747 -0.017 0.010(0.006) (0.006)

All (actual) 0.439 0.815All (equal) 0.286 0.656No U.S. (equal) 0.249 0.655


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A Historical Example

Now we will discuss a study examining the fundamental determinantsof sub-national income differences in Andean Peru

These income differences are large

To understand their origins, we must briefly discuss institutions incolonial Peru


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The Mining Mita

The mining mita was instituted by the Spanish government in 1573and abolished in 1812

It required over 200 indigenous communities in Peru and Bolivia tosend one seventh of their adult male population to work in the Potosısilver and Huancavelica mercury mines

The Potosı mines, discovered in 1545, provided the largest deposits ofsilver in the Spanish Empire

The mita assigned 14,181 conscripts from southern Peru and Boliviato the Potosı mines and 3,280 conscripts from central and southernPeru to the state-owned Huancavelica mines (Bakewell, 1984, p. 83)


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The mita’s extent

!

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Study BoundaryMita Boundary5000 m

0 m

Potosi

Huancavelica

Uyuni Salt Flat


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Results

The mita’s long run effects lower household consumption by around 25%in subjected districts today and increases malnutrition in children byaround six percentage points.

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Land tenure

Large negative effect on the concentration of haciendas - few largelandowners in mita districtsImpact persisted through the 20th century

Public goods

Mita districts less integrated into road networksHistorically lower levels of education

Markets and subsistence

Residents of mita districts are substantially more likely to besubsistence farmers


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Interpretation

My hypothesis: The long-term presence of large landowners innon-mita districts provided a stable land tenure system thatencouraged public goods provision.

Why is public goods provision higher outside the mita catchment?1 Large landowners controlled a larger percentage of the productive

factors (land and labor)

2 Property rights less secure in mita districtsIncentives to protect peasant rights to land disappeared when the mitawas abolishedDe facto communal land tenure, numerous land confiscations,widespread livestock rustling and banditry (Flores Galindo, 1987;Jacobsen, 1993; Tamayo Herrera, 1982)

3 Landowners possessed the political connections required to securepublic goods

Roads twist to pass through as many haciendas as possible (Stein,1980)


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ResultsFigure 2

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<5.506.206.907.608.30>9.00

(a) Consumption (2001)

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<0.100.180.260.340.42>0.50

(b) Stunting (2005)

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0.005.0010.0015.0020.00>25.00

(c) Haciendas (1689)

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0.000.260.520.781.04>1.30

(d) Haciendas (1845)


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ResultsFigure 2 (cont.)

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0.000.180.360.540.72>0.90

(e) Haciendas (1940)

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<0.010.040.070.090.12>0.15

(f) Education (1876)

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<5.0018.0031.0044.0057.00>70.00

(g) Road Density (2006)

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●● 50 Obs.

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●● 10706 Obs.

●● 16034 Obs.

0.000.100.200.300.40>0.50

(h) Ag. Market Participation (1994)


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Summary

Subnational income differences are large, even relative tocross-country income differences

When U.S. excluded, about twice as large as cross-country differences

Evidence that both geography and institutional arrangements play arole


Productivity Di erences Within and Between Countries · Empirical Strategy Decompose inequality...

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