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Trade Liberalisation and Poverty: What have we learned in fifteen years?. L Alan Winters Professor of Economics, University of Sussex CEPR, IZA, GDN. Trade Liberalisation and Poverty: What have we I learned in fifteen years?. L Alan Winters Professor of Economics, University of Sussex - PowerPoint PPT Presentation
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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
Antwerp
What is the issue?
25th June 2013 3
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
Antwerp
Therefore Public Policy should:
• proceed with liberalisation,• predict the poverty impacts,• possibly pre-empt them, and• protect the poor with general anti-poverty
policies
25th June 2013 5
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
25th June 2013 Antwerp 8
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)
25th June 2013 Antwerp 9
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
25th June 2013 Antwerp 13
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
14
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
25th June 2013 Antwerp 16
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)
25th June 2013 Antwerp 17
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
25th June 2013 Antwerp 18
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
25th June 2013 Antwerp 19
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
25th June 2013 Antwerp 21
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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23
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
23
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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
25th June 2013 24
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
25
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
25th June 2013 29
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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
25th June 2013 31
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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)
25th June 2013 32
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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
25th June 2013 33
Antwerp
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?
25th June 2013 34
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)
39
Households and Markets
39
40
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?
40
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