trade liberalisation and poverty: what have we learned in fifteen years?

Trade Liberalisation and Poverty: What have we learned

in fifteen years?

L Alan WintersProfessor of Economics, University of Sussex

CEPR, IZA, GDN

Trade Liberalisation and Poverty: What have we I learned

in fifteen years?

L Alan WintersProfessor of Economics, University of Sussex

CEPR, IZA, GDN

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What is the issue?

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Conclusion from 1999

Trade Liberalisation

• generally stimulates growth• and through it poverty alleviationBUT• it creates losers• some of whom may be, or may become, poor

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Therefore Public Policy should:

• proceed with liberalisation,• predict the poverty impacts,• possibly pre-empt them, and• protect the poor with general anti-poverty

policies

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6

Trade and Growth: Levels vs. Changes

• Income– Higher vs. growing more rapidly– Permanent vs. transitory growth effects

Y”

Event

Y

Y’

time

Log

(y)

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Empirical challenges

1. Defining and measuring openness Binary (Sachs/Warner); (X=M)/GDP Averages – weights, Anderson-Neary

2. Establishing causation Liberalisation → growth or vice versa?

3. Separating openness from other policies the attribution problem

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What have we learned (I) Case studies

• No closed economy has developed post WW2– Nineteenth century and 1930s low relevance

• Five common features of successful growers: – Macro stability– High savings and investment– Use markets to allocate resources– Committed, credible, capable government– Fully exploited world economy

Growth Commission (2008)

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What have we learned? (II) Instruments

• Frankel and Romer (AER, 1999) gravity– Many followers; not always successful

• Noguer and Siscart (2005, JIE)– More countries in gravity instruments → clarity

• But see Bazzi and Clemens (AEJ-MAC, 2013)– Strong critique of weak instruments– Country size is treacherous - little time variance– Be serious about the exclusion restriction– This includes GMM and system GMM too!

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Exclusion

• Deaton (2010, JEL) – instrumentation requires a narrative

• Bazzi and Clemens– Many studies take the form:

growth = f(x, W); x=g(Z)– If you estimate: growth = h(y, W*); y=m(Z)– You strictly have to reject every one of them!

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Other instruments

• Feyrer (2009, NBER WP) – Time varying instrument for trade– Importance of difference in sea distance and air

distance becomes more significant as air travel cheapens

• Romalis (2007, NBER WP) – US tariffs (also time varying), but

• Commodity structure; • Relates trade policy level to output growth

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What have we learned (III) time series

• Mixed results• Gries, Kraft & Meierrieks (2009, WD)

– GMM– Individual African economies– Granger-Hsaio causation

• Wacziarg & Welch (2008, WBER)– 13 country statistical case studies

• Kneller, Morgan & Kanchanahatakij (‘08, WE)– Panel of event studies

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What have we learned (IV) Conditions

• Heterogeneity of country studies• E.g. Chang, Kaltani & Loayza (2009, JDE)

and Bolaky and Freund (JDE, 2008)– Interactions of openness with

Property Rights

Financial Depth

Lab. market flexibility

Rule of law Education Firm entryGovernance Telecoms Firm exit

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Income, Openness and Regulation(Bolaky-Freund; JDE 2008)

Less regulated half More regulated half

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• Weaker effects of liberalisation in poor countries• Benefits for low-income Africa uncertain• Linear interaction – where is it identified from

in the sample space? • Conditions are highly correlated, tested 1-by-1

– Is it just catching Africa in 1980s/1990s– Is Africa different in 2000s/2010s?

Property Rights

Financial Depth

Lab. market flexibility

Rule of law Education Firm entryGovernance Telecoms Firm exit

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What have we learned (V) Productivity

• Selectivity• Imported inputs (Amiti & Koenings (2007,

AER) and AER P&P 2009 • Learning by Exporting (Fernandes & Isgut,

2005, WB; Blalock & Gertler, 2004, JDE)• Learning to Export (Iacovone, 2009, WB)

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What does this mean for policy?

• Vast majority of growth policy is not trade policy; try to make trade policy simple and unobtrusive

• Treat as decision, not a hypothesis test

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Policy-Relevant Growth Econometricsg = Xα + Zγ + βt

X ‘maintained’ variables; Z ‘optional’ variablest is tariff level

• Hypothesis testing H0 : β=0 against H1 : β≠0

• Trade policy ‘affects’ growth if t-statistic on β > 2• Issues: (a) uncertainty about β

(specification as well as sampling ), (b) relative size of g and βt

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Decision Approach

• Have to decide• Balance of evidence and priors• Costs of different errors• Cost of uncertainty

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Distribution of growth incrementsPercent change in growth over liberalisation

02

468

1012

-2 -1 0 1 2 3 4 5 6 7More

percent p.a.

num

ber

Kneller et al (2008)Sample of 47 events

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Cost of error

• ‘We know of no credible evidence … that suggests that trade restrictions are systematically associated with higher growth rates’

Rodriguez and Rodrik (2001, p.317)

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Growth and Poverty

• Large literature – not dealt with here• One influential study

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Shares in Long-run Poverty Reduction (Kraay, JDE 2006, cross-section)

0% 20% 40% 60% 80% 100%

FGT 0

FGT 1

FGT 2

Watts

Share of poverty reduction

growth sensitivity distribution

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The Microeconomics/Distribution

• Conclusion from 1999 • Trade Liberalisation

– creates losers– some of whom may be, or may become, poor

• Much is uncertain, but not all

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Trade Policy and Poverty – Causal Connections

Trading Domain

Tradables

Pass through, competition

National Taxes, regulation, distributors, procurement

Regional

Distribution, taxes, regulation, co-ops

Co-operatives, technology, random shocks

Subsis- tence

World Prices and Quantities

Border Price

Wholesale Price

Tariffs, QRs

Retail Price

Tariff Revenue

Welfare

)

Exchange Rate

elderly

Household Welfare

Prices, Wages Endowments,

Profits, Other Income

elderly

young

males

females

Enterprises

Profits Wages Employment

Tariff Revenue

Taxes

Spending

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Conceptual Framework

Winters World Economy (2002) 25

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Two approaches• CGE Modelling supplemented with modules

or satellite accounts for personal incomes– Hertel, Tarr, Cockburn– Clear, quantitative, perfect attribution, necessary

ex ante– But not strongly grounded in data and outcomes

• Frontier is ex post studies– Based on actual outcomes, messy, difficult

• Start with a hybrid to point up the issues

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• Uses the framework on Mercosur– Welfare = f(prices, incomes); – SOE– traded prices changed by tariffs etc– Non-traded prices and wages = f(traded prices)

• Estimates based on theory, then simulates

Porto (JIE, 2006)

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…and sons

• Nicita (JDE, 2009) • Mexico

– Expenditure main effect

• Marchand (JDE, 2012)• India

– Consumption dominates

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Empirical Use of the Framework

• Identity, definition; LHS unobservable• Observable equivalent Δ(real consumption)

Δrcj = ….. β (Σi wji dlnτi )+ uj

– Second order effects – Omitted variables – Parameter heterogeneity – Errors of observation

Δrcj = ….. β (exposure) + uj

• Partial studies: pass-through; wage effects, ε25th June 2013 30

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Topalova (AEJ-AE, 2010)

• India, 1990s reforms• Real consumption or poverty rate by region (77)

and district (450)• Regressed on employment-weighted tariffs and

fixed effects; hence,– Import-based– D-i-D – only relative effects

• Greater exposure → worse poverty outcomes

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Refinements

• Standard robustness tests• Agricultural wages/returns main channel• Harm is associated with lack of mobility –

regional and sectoral = big issue for the poor – i.e alternatives to agriculture weakened

• Inflexible labour laws are a key factor• Export exposure → liberalisation reduces

poverty (Topalova, 2007)

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Off-shoots

• Hasan et al (REcStat, 2007) state level data and add data on NTBs: reverse result

• Krishna et al (NBER, 2010): – reverse result for ‘leading’ regions; strong in laggards

• Castilho (WD, 2012) Brazil:– using same exposure variable, same result – using exports → lib. helps; imports → lib. hurts

• McCaig (JIE, 2011) Vietnam– Exposure to US tariff cuts reduces poverty

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Wages – skills gap• Large literature – generally suggests

liberalisation increases the skills premium• Amiti and Cameron (JIE, 2012): Indonesia

– Unskilled abundant– Firm-level data – estimate within-firm effects;– Tariffs on inputs and outputs– Tariff cuts for outputs – insignificant effects– Tariff cuts for inputs – closes skills gap– Stolper-Samuelson type of effect?

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Conclusion from 1999 2013

Trade Liberalisation

• generally stimulates growth• and through it poverty alleviation

• Generally more secure, but maybe with a few more caveats for low income countries

Conclusion from 1999 2013

• BUT, it creates losers• some of whom may be, or may become, poor

• Conclusion re-inforced • High-quality empirics – more precise and

focussed, but all partial in one way or another• Role of segmentation strongly evident

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Therefore Public Policy should:

• proceed with liberalisation,• predict the poverty impacts,• possibly pre-empt them, especially by

increasing mobility and flexibility, and• protect the poor with general anti-poverty

policies25th June 2013 37

25th June 2013 Antwerp 38

Thank you

• Do border price shocks get transmitted to poor households?

• Are markets created or destroyed?• How well do households respond?• Do the spillovers benefit the poor?• Does trade liberalisation increase

vulnerability?

Winters, McCulloch and McKay JEL (2004)

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Households and Markets

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Wages and Employment• Does liberalisation raise wages or

employment?• Is transitional unemployment concentrated on

the poor?

Government Revenue and Spending• Does liberalisation actually cut government

revenue?• Do falling tariff revenues hurt the poor?

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