the effect of political leaders ’ educational and professional background on trade liberalization...

The Effect of Political Leaders’ Educational andProfessional Background on Trade Liberalization

Evidence from Tariff Rates, 1988-2005

Marek Hlaváč, MPP

Harvard University Political Economy and Government

[email protected]

Bratislava Economic MeetingJune 8th, 2012

Marek Hlavac(Harvard) Leader Background & Trade Liberalization June 8, 2012 1 / 18

Overview

I examine the effect of political leaders’ educational and professionalbackgrounds on trade liberalization, as measured by the level of tariff ratesimposed on imported goods and services. I find that, during the timeperiod from 1988 until 2005, countries with university-educated chiefgovernment executives imposed lower tariffs than countries whose leadersdid not have a university education. Leaders’ educational background ineconomics is associated with greater reductions in tariff rates than abackground in law. A professional background in the military or as a unionleader is associated with higher tariff rates. These results suggest that theeducational and professional backgrounds of government leaders can haveimportant effects on trade policy.


Section 1: Hypotheses and Theoretical Background

Three Hypotheses



Three Hypotheses

Hypothesis 1: A government leader’s university-level educationalqualification will be associated with a reduction in the level of tariffrates, compared to the baseline of not having a university education.



Three Hypotheses


Hypothesis 2: A government leader’s educational background ineconomics will be associated with a lower level of tariff rates than aneducational background in law.



Three Hypotheses



Hypothesis 3: A government leader’s professional background inscientific economics will be associated with a lower level of tariff ratesthan a professional background in law.



Hypothesis 1




Hypothesis 1


Stolper-Samuelson Theorem (1941): gain/loss from trade based onfactor abundance (e.g., Mayda, 2005; Mayda and Rodrik, 2005)



Hypothesis 1



Reevaluation by Hainmueller and Hiscox (2006)



Hypothesis 1



Reevaluation by Hainmueller and Hiscox (2006)

Lack of labor market pressures for government leaders: Ideationalchannel more likely



Hypotheses 2 and 3





Hypotheses 2 and 3



Economics: survey experiment by Hiscox (2006); consensus aboutefficiency benefits of trade liberalization; experiments about the effectof economics education on attitudes and behavior



Hypotheses 2 and 3



Economics: survey experiment by Hiscox (2006); consensus aboutefficiency benefits of trade liberalization; experiments about the effectof economics education on attitudes and behavior

Law: This needs work. So far, Murphy, Shleifer and Vishny (1991) onrent-seeking and growth. Looking for survey data about lawyers ’attitudes towards trade, regulation in general.


Section 2: Data and Empirical Strategy

Dependent Variable: Tariff Rates

Most Favored Nation (MFN) vs. Applied Rates

Simple vs. Import Share-Weighted

Product Coverage: All, Manufacturing, Primary



Independent Variable of Interest: Leader’s Background

set of dummies from Dreher et al. (2008)

Education: 7 categories

Profession: 11 categories



Control Variables

Level of Economic Development: real GDP per capita

Rate of Economic Growth: lagged real GDP growth

Political Regime: democracy dummy based on Polity IV

Government Idelogy: omitted due to data availability: suggestions?



List of Countries Included in the Sample

Algeria China IndiaArgentina Colombia IrelandAustralia Costa Rica Israel

Austria Czech Republic ItalyBangladesh Denmark JapanBelgium Ecuador Kenya

Mexico Portugal SwitzerlandMoldova Romania SyriaNetherlands Russian Federation Tanzania

New Zealand Saudi Arabia ThailandNicaragua Singapore TogoNorway Slovakia Tunisia

Bolivia Egypt Lebanon Panama Slovenia TurkeyBrazil Finland Madagascar Paraguay South Africa United KingdomBulgaria France Malaysia Peru Spain United StatesCanada Germany Mali Philippines Sri Lanka UruguayChile Greece Mauritius Poland Sweden Venezuela



Summary Statistics

Variable Source Unit Observations Mean Standard DeviationGDP per capita WDI (2011) constant2005 inter- 1,181 13.817 11.207

national dollars, PPP,thousands

GDP growth ratet−1 WDI (2011) percent, annual 1,169 3.168 4.531Democracy Polity IV (2011) dummy (0/1) 1,152 0.759 0.428

Education Dreher et al.(2008) dummy (0/1)— Unknown 1,174 0.054 0.225— Not University 1,174 0.193 0.395— Economics 1,171 0.180 0.385— Law 1,174 0.252 0.434— Politics 1,174 0.062 0.242— Natural Science 1,174 0.035 0.184— Other University 1,174 0.213 0.410

Profession Dreher et al.(2008) dummy (0/1)— Unknown/None 1,174 0.013 0.112— Entrepreneur 1,174 0.018 0.133— White Collar 1,174 0.120 0.325— Blue Collar 1,174 0.017 0.129— Union Executive 1,174 0.027 0.163— Science (Economics) 1,174 0.043 0.202— Science (Other) 1,172 0.074 0.262— Law 1,174 0.112 0.316— Military 1,174 0.152 0.360— Politician 1,174 0.342 0.474— Other 1,174 0.072 0.258

Note: One observation represents a country-year.



Empirical Model

Ordinary Least Squares (OLS) with the following specification:

TariffRatei ,t = αControlsi,t + βBackgroundi,t + γYeart + ϵi ,t , (1)

where Controlsi ,t is a vector of control variables, Backgroundi ,t is a vectorof education or professional background dummies, and ϵ is a well-behavedstochastic error term. The subscripts i and t index countries and years,respectively.

In all specifications, a set of year dummies (Yeart ) is included to accountfor changes to tariff rate levels that affect all countries in a given year:attitudes towards governance, multilateral trade negotiation rounds?


Section 3: Estimation Results

Estimation Results: All Products, Education

Dependent Variable: Tariff Rate, All ProductsMost Favored Nation, Most Favored Nation, Applied, Applied,

simple weighted simple weightedGDP per capita −0.790∗∗∗ −0.527∗∗∗ −0.953∗∗∗ −0.610∗∗∗

(0.132) (0.107) (0.127) (0.108)(GDP per capita)2 0.013∗∗∗ −0.008∗∗∗ 0.016∗∗∗ 0.010∗∗∗

(0.003) (0.002) (0.003) (0.002)GDP growth ratet−1 0.160∗ 0.102 0.193∗∗ 0.136

(0.094) (0.088) (0.092) (0.089)Democracy −4.451∗∗∗ −2.861∗∗∗ −4.284∗∗∗ −3.365∗∗∗

(1.128) (1.013) (1.008) (1.015)Education— Economics −3.172∗∗∗ −5.219∗∗ −2.915∗∗∗ −4.965∗

(0.685) (2.593) (0.638) (2.597)— Politics −2.285∗∗∗ −4.243∗ −2.233∗∗∗ −3.985

(0.639) (2.460) (0.586) (2.463)— Law −0.893 −2.862 −0.137 −2.723

(0.709) (2.435) (0.686) (2.438)— Natural Science −2.485∗∗∗ −4.764∗ −2.376∗∗∗ −4.549

(0.768) (2.752) (0.803) (2.769)— Other University −3.197∗∗∗ −4.297∗ −2.509∗∗∗ −3.930∗

(1.003) (2.204) (0.917) (2.209)Constant 23.154∗∗∗ 19.291∗∗∗ 22.430∗∗∗ 18.997∗∗∗

(1.697) (1.857) (1.504) (1.861)Year effects Yes Yes Yes YesObservations 698 698 698 698Adjusted R-squared 0.385 0.118 0.456 0.134Notes: Statistically significant at the ***1, **5, *10 percent level.

Heteroskedasticity-robust standard errors are in parentheses.



Estimation Results: All Products, Profession

Dependent Variable: Tariff Rate, All ProductsMost Favored Nation, Most Favored Nation, Applied, Applied,


(0.160) (0.118) (0.152) (0.119)(GDP per capita)2 0.016∗∗∗ 0.011∗∗∗ 0.019∗∗∗ 0.013∗∗∗

(0.004) (0.003) (0.003) (0.003)GDP growth ratet−1 0.180∗∗ 0.110 0.210∗∗ 0.145

(0.091) (0.088) (0.088) (0.089)Democracy −3.189∗∗∗ −2.647∗∗∗ −2.966∗∗∗ −3.035∗∗∗

(1.116) (0.824) (1.025) (0.824)Profession— Entrepreneur 1.079 1.051 1.245 1.401

(1.132) (1.006) (1.406) (1.097)— White Collar 0.216 0.288 0.459 0.399

(0.712) (0.710) (0.648) (0.690)— Blue Collar 2.981∗ 3.364∗∗ 2.213 1.977

(1.624) (1.607) (1.687) (1.511)— Union Executive 3.709∗∗∗ 2.138∗∗ 3.677∗∗∗ 2.191∗∗

(0.863) (1.008) (0.871) (1.009)— Science (Economics) −1.033 −0.483 −1.426∗∗ -0.243

(0.791) (0.877) (0.760) (0.880)— Science (Other) −0.619 0.339 −0.394 0.612

(0.732) (0.855) (0.799) (0.880)— Law 1.439∗∗ 1.982∗∗∗ 2.456∗∗∗ 1.796∗∗∗

(0.661) (0.642) (0.668) (0.637)— Military 5.358∗∗∗ 4.541∗∗∗ 5.001∗∗∗ 4.887∗∗∗

(1.561) (1.423) (1.376) (1.419)— Politician 3.025∗∗∗ 3.231∗∗ 3.058∗∗∗ 3.529∗∗

(0.733) (1.392) (0.696) (1.389)



Estimation Results: Manufactured Products, Education

Dependent Variable: Tariff Rate, Manufactured ProductsMost Favored Nation, Most Favored Nation, Applied, Applied,


(0.138) (0.114) (0.130) (0.116)(GDP per capita)2 0.013∗∗∗ 0.009∗∗∗ 0.017∗∗∗ 0.011∗∗∗

(0.003) (0.003) (0.003) (0.003)GDP growth ratet−1 0.160 0.115 0.205∗∗ 0.154∗

(0.100) (0.087) (0.097) (0.089)Democracy −4.620∗∗∗ −3.767∗∗∗ −4.425∗∗∗ −4.237∗∗∗

(1.179) (0.959) (1.036) (0.966)Education— Economics −2.794∗∗∗ −2.410∗∗∗ −2.726∗∗∗ −2.161∗∗∗

(0.698) (0.537) (0.656) (0.546)— Politics −2.108∗∗∗ −1.676∗∗∗ −2.160∗∗∗ −1.473∗∗∗

(0.659) (0.543) (0.608) (0.551)— Law −0.084 0.133 0.340 0.253

(0.748) (0.564) (0.713) (0.576)— Natural Science −2.191∗∗∗ −1.528∗∗ −2.182∗∗∗ −1.355∗

(0.784) (0.700) (0.842) (0.757)— Other University −2.507∗∗ −1.879∗∗ −2.134∗∗ −1.516∗

(1.087) (0.867) (0.958) (0.881)Constant 22.853∗∗∗ 18.749∗∗∗ 22.259∗∗∗ 18.495∗∗∗





Estimation Results: Manufactured Products, Profession

Dependent Variable: Tariff Rate, Manufactured ProductsMost Favored Nation, Most Favored Nation, Applied, Applied,


(0.167) (0.146) (0.156) (0.147)(GDP per capita)2 0.017∗∗∗ 0.013∗∗∗ 0.020∗∗∗ 0.015∗∗∗

(0.004) (0.003) (0.004) (0.003)GDP growth ratet−1 0.178∗ 0.135 0.221∗∗ 0.174∗∗

(0.097) (0.084) (0.092) (0.085)Democracy −3.186∗∗∗ −2.478∗∗∗ −3.015∗∗∗ −2.848∗∗∗

(1.172) (0.956) (1.057) (0.961)Profession— Entrepreneur 1.449 1.974∗ 1.499 2.273∗∗

(1.205) (1.064) (1.495) (1.158)— White Collar 0.756 1.056∗ 0.820 1.102∗

(0.658) (0.585) (0.647) (0.582)— Blue Collar 3.311∗∗ 3.292∗∗ 2.471 1.963

(1.659) (1.369) (1.768) (1.314)— Union Executive 4.215∗∗∗ 3.729∗∗∗ 3.765∗∗∗ 3.753∗∗∗

(0.802) (0.666) (0.805) (0.682)— Science (Economics) −0.833 −0.453 −1.324∗ −0.253

(0.763) (0.708) (0.773) (0.720)— Science (Other) −0.223 0.693 −0.201 0.894

(0.758) (0.734) (0.838) (0.776)— Law 2.541∗∗∗ 3.196∗∗∗ 3.091∗∗∗ 2.949∗∗∗

(0.656) (0.597) (0.690) (0.617)— Military 5.497∗∗∗ 5.216∗∗∗ 5.060∗∗∗ 5.457∗∗∗

(1.667) (1.400) (1.426) (1.397)— Politician 3.487∗∗∗ 3.061∗∗∗ 3.295∗∗∗ 3.300∗∗∗

(0.708) (0.610) (0.701) (0.615)



Estimation Results: Primary Products, Education

Dependent Variable: Tariff Rate, Primary ProductsMost Favored Nation, Most Favored Nation, Applied, Applied,

simple weighted simple weighted

GDP per capita −0.631∗∗∗ −0.415∗∗∗ −0.801∗∗∗ −0.475∗∗∗(0.152) (0.148) (0.129) (0.147)

(GDP per capita)2 0.012∗∗∗ 0.009∗∗∗ 0.015∗∗∗ 0.011∗∗∗

(0.004) (0.003) (0.003) (0.003)GDP growth ratet−1 0.157∗ 0.032 0.136∗ 0.060

(0.085) (0.118) (0.079) (0.118)Democracy −3.895∗∗∗ −0.648 −4.194∗∗∗ −1.321

(1.313) (1.615) (1.356) (1.606)Education— Economics −4.459∗∗∗ −10.162∗ −4.202∗∗∗ −9.798∗

(0.958) (5.825) (0.951) (5.826)— Politics −2.886∗∗∗ −9.002 −2.745∗∗∗ −8.574

(0.872) (5.548) (0.810) (5.546)— Law −3.674∗∗∗ −9.016∗ −3.265∗∗∗ −8.710

(0.900) (5.448) (0.897) (5.447)— Natural Science −3.383∗∗ −10.985∗ −3.571∗∗∗ −10.483∗

(1.317) (6.153) (1.077) (6.159)— Other University −5.578∗∗∗ −8.910∗ −5.008∗∗∗ −8.474∗

(1.161) (4.775) (1.229) (4.776)Constant 24.222∗∗∗ 20.459∗∗∗ 24.644∗∗∗ 20.069∗∗∗





Estimation Results: Primary Products, Profession

Dependent Variable: Tariff Rate, Primary ProductsMost Favored Nation, Most Favored Nation, Applied, Applied,


(0.165) (0.130) (0.142) (0.129)(GDP per capita)2 0.012∗∗∗ 0.009∗∗∗ 0.016∗∗∗ 0.011∗∗∗

(0.004) (0.003) (0.003) (0.003)GDP growth ratet−1 0.184∗∗ 0.030 0.158∗∗ 0.059

(0.083) (0.128) (0.076) (0.128)Democracy −3.208∗∗ −2.601∗∗∗ −3.448∗∗∗ −3.053∗∗∗

(1.266) (0.931) (1.312) (0.928)Profession— Entrepreneur −0.120 −3.166 −0.401 −2.614

(1.446) (2.191) (1.268) (2.180)— White Collar −1.539 −3.584∗ −1.670 −3.235∗

(1.317) (1.894) (1.021) (1.815)— Blue Collar 1.936 2.425 0.777 0.283

(1.760) (3.035) (1.502) (2.790)— Union Executive 2.050 −2.016 3.011 −1.891

(1.534) (3.218) (1.965) (3.165)— Science (Economics) −1.651 −2.524 −1.872∗ −2.180

(1.225) (1.981) (0.993) (1.933)— Science (Other) −1.910∗ −2.583 −1.574∗ −2.116

(1.146) (1.952) (0.946) (1.914)— Law −2.313∗∗ −3.170∗ −1.260 −3.159∗∗

(1.119) (1.683) (0.893) (1.598)— Military 4.986∗∗∗ 0.817 5.375∗∗∗ 1.446

(1.772) (2.435) (1.785) (2.397)— Politician 1.489 0.560 1.517 1.002

(1.155) (3.237) (0.942) (3.200)


Conclusion

Conclusion

Product CoverageHypothesis All Manufactured Primary

1: university-level education leads to lower tariff rates yes yes yes2: economics education associated with lower tariff rates than law educationyes yes no3: economics profession associated with lower tariff rates than law professionyes yes no

Avenues for future research:

What is special about primary products?

Other outcome variables: bound rates, binding coverage, etc.


the effect of political leaders ’ educational and professional background on trade liberalization...

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