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Arnoldshain Seminar XIIOB-Universiteit Antwerpen, België

“Exports, Skills and Wages: The impact of destination in a middle

income country”

Adriana PeluffoInstituto de Economia, FCEA

UDELAR

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Outline1. Introduction2. Empirical Strategy

2.1. Data2.2. Methodology2.2.1. OLS2.2.2. IV-GMM

3. Results3.1. Descriptive Statistics3.2. Conditional correlations3.3. IV estimation

4. Concluding Remarks

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1. INTRODUCTION

Objective

Analyse the link between exports and its destination to high income countries on the demand for skilled labour.

MOTIVATION

► Exports are an important source of income, and exporting firms are superior in a number of features.

►Channel of transfer of technology: ↑ increases in productivity and income in LDCs.

►Impacts on employment, demand for skills, wages, and wage inequality.

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Expected Contribution

1. Gather empirical evidence of the effects of exports to high income countries on the demand for skilled labour, at the micro level, for a small middle income country.

2. Analyse associations as well as causal relations.

3. In the agenda: contribute to the debate on the role and the interplay of “where” and “what” we exports, and its impacts in a developing context.

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Background

--Great number of studies show the superiority of exporting firms: Bernard and Jensen (1999) for the US, Aw and Hwang (1995) for Taiwan; Bernard and Wagner (1997) for Germany; Aw et al. (2000) for South Korea; Kraay (1999) for China; Delgado et al. (2004b) for Spain; Girma et al. (2004a) for the United Kingdom; Álvarez and López (2005) for Chile, Isgut and Fernandes (2007) for Colombia, de Loecker (2007) for Slovenia. These works find that exporting firms are superior in several features: size, productivity, wages.

-These empirical studies gave rise to theoretical models trying to explain this stylised fact: Melitz (2003) and several extensions.

Extensions:

-Eaton et al. (2008): model in which firm performance and exports depends positively on distance.-Holmes & Stevens (2012): exporter wage premium depends positively on distance.-Matsuyama (2008) and Bustos (2011): exporting requires higher levels of technology and skills. What matters is exporting “per se”.-These models focus on the supply side: technology.-Verhoogen (2008): exporting causes quality upgrading which increases the demand for skills, rising skilled wages and wage inequality.. Focus on the supply side.

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-Brambilla et al. (2012): exports to higher income countries demand more skilled labour. Vertical differentiation and price affect demand between different markets (demand side); skilled intensive services are required in order to export (supply side).-Integrates supply and demand mechanisms.

-Analysis suggesting that “what you export matters” (Hausmann et al. 2005).

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Alternative explanations for higher demand for skills:

1) Rent sharing in models of fair wages (Egger & Kreickemeir, 2009, Amiti & Davis, 2008).

2) Efficiency wages: exporters to high income countries have more skilled labour force and pay higher wages in order to reduce turnover.

3) Scale economies attached to exporting to different destinations: the size of the market and the scale of the firm determine technology choice and larger firms choose more skill intensive technologies (Yeaple, 2005).

Empirical evidence

• Verhoogen (2008), Mexico• Bastos & Silva (2006), Portugal• Manova & Zhang (2012), China• Schmillen (2011), Germany• Gorg et al. (2011), Hungary• Brambilla et al. (2012), Argentina

Summing up: most evidence finds positive associations on exporting to high income countries and skills.

• For Uruguay: evidence that firms with international linkages demand more skilled labour than domestic oriented firms (Peluffo, 2012);

Barboni et al. (2013) firms exporting to HI destinations are more productive but learning process take places in similar countries.

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2. Empirical Implementation

2.1. Data2.2. OLS2.3. IV-GMM

2.1. DataTwo main data sources: a) at the firm level from Annual Economic

Surveys (Instituto Nacional de Estadisticas)b) Data on value and destination (Direccion

Nacional de Aduanas).

Complemented with aggregated data at the national level (Central Bank, IECON).

OECD classification of high and low income countries (2 definitions: only HI OECD and HI OECD and non-OECD).

Period: 1997-2006

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Descriptive Statistics, sample for 1997-2006Share of exports by economic blocs

Descriptive StatisticsFirm level data: Exports by destination (% of total

exports in value)

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Exports by destination (% of total exports in value)

0%

10%

20%

30%

40%

50%

60%

1997 1998 1999 2000 2001 2003 2004 2005 2006

Mercosur

NAFTA

Rest of America

European Union

Rest of the Word

Descriptive StatisticsMain features of domestic, exporting firms and exporters to HI countries, average for the period 1997-2006

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Exp HI countries

0 1 1Total employment 50 136 188 97Sales(a) 23.9 136 207 85.6Value added(b) 10.2 40.8 55.6 26.8Total factor productivity 52,339 78,529 89,374 66,889Average wages 76,052 108,643 108,149 93,738Export propensity 0.000 0.324 0.473 0.187Non production/Total workers 0.347 0.313 0.287 0.329P&T/Total workers 0.062 0.073 0.070 0.068

Exporting StatusTotal

2.2. Methodology2.2.1. OLS estimation

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Estimate the proportional differences in different measures of skills of exports and exports to high income countries.

Basic export premium in term of measures of skills, and the destination specific exporter premia.

i, j, t, stands for firm, industry and year respectively.Y : measures of skills proxied by p&t/po; non-prod/po, and

average wages.EXP : exports as a dummy variable, and also as export

intensity (i.e. exports/sales).HIGH_INC: share of exports to high income countries over

total exports.Dj and Dt: industry and time dummies.

Yit=β1EXPit+β2HIGHINCit+Xit' β3+Dj+Dt+ εit

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Explanatory VariablesExports: dummy equal 1 if the firm undertake

exporting activities and zero otherwise; Export intensity: share of exports in total sales by

the firm;Exports to high income countries: share of

exports to high income countries over total exports.

Two definitions of high income countries: a) OECD high income countries, b) OECD and Non-OECD high income countries.

Additional Variables: firm size (lnsales), TFP (ln tfp),

Dj, Dt.

Conditional associations: dependent: ln average wages

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(1) (2) (4) (7) (8)VARIABLES ln_avgwage ln_avgwage ln_avgwage ln_avgwage ln_avgwage

Export intensity -0.0571* -0.0455(0.0297) (0.0306)

Exporting firms 0.0284* 0.0413** 0.0410**(0.0169) (0.0171) (0.0171)

Exp Richer -0.0374 -0.0766** 0.0448(0.0337) (0.0334) (0.0693)

Exp*Richer -0.147*(0.0766)

Ln Sales 0.234*** 0.226*** 0.235*** 0.226*** 0.227***(0.00616) (0.00651) (0.00621) (0.00652) (0.00654)

Industry dummies Yes Yes Yes Yes Yes

Time dummies Yes Yes Yes Yes Yes

Constant 6.814*** 6.934*** 6.811*** 6.934*** 6.923***(0.112) (0.117) (0.112) (0.117) (0.117)

Conditional associations: dependent p&t/total workers

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(1) (2) (4) (7) (8)VARIABLES skill5b skill5b skill5b skill5b skill5b

Export intensity 0.0125*** 0.0147***(0.00376) (0.00435)

Exporting firms 0.00736*** 0.00848*** 0.00845***(0.00215) (0.00233) (0.00233)

Exporting to Richer -0.00745** -0.00626* 0.00160(0.00377) (0.00329) (0.00827)

Exp*Richer -0.00939(0.00843)

Ln Sales 0.00720*** 0.00689*** 0.00732*** 0.00693*** 0.00698***(0.000581) (0.000697) (0.000582) (0.000696) (0.000696)

Industry dummies Yes Yes Yes Yes Yes

Time dummies Yes Yes Yes Yes Yes

Constant -0.123*** -0.118*** -0.124*** -0.118*** -0.119***(0.0108) (0.0123) (0.0108) (0.0123) (0.0123)

Conditional associations: dependent non-production/total workers

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(1) (2) (4) (7) (8)VARIABLES skill1 skill1 skill1 skill1 skill1

Export intensity -0.145*** -0.148***(0.0112) (0.0098)

Exporting firms -0.0670*** -0.0621*** -0.0626***-0.0063 (0.00655) (0.00654)

Exporting to Richer -0.0048 -0.0292*** 0.0813***(0.0107) (0.0106) (0.0283)

Exp*Richer -0.132***(0.0296)

Ln Sales 0.0215*** 0.0223*** 0.0216*** 0.0224*** 0.0230***-0.00176 -0.0063 -0.00176 (0.00192) (0.00190)

Industry dummies Yes Yes Yes Yes Yes

Time dummies Yes Yes Yes Yes Yes

Constant -0.152*** -0.167*** -0.1526*** -0.167*** -0.174***-0.0317 -0.0337 (0.0317) (0.0336) (0.0334)

Summary results, OLS

  LN AVG_WAGES P&T/Total Emp. Non-Prod/Total Emp.

Export intensity Mixed + -

Export status + + -

Richer NS/- NS/- NS/-

Ln Sales + + +

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2.2.2 IV-GMM

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Endogenous variables: exporting status of the firm; export intensity of the firm (share of exports in total sales),share of exports to high income countries in total exports.

To construct instruments we follow Brambilla et al. (2012): use the exogenous variation in export intensity and destinations generated by the devaluation of major trading partners.

Growing literature that looks at changes in major trade partners as a source of identification: Revenga (1992); Bustos(2009); Park et al. (2010); Verhoogen (2008).

We also tried as instruments lagged values of the endogenous regressors but these were no good instruments.

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Instrument for the share of exports to high income

countries: interaction of a post-devaluation variable with the pre-devaluation share of the firm’s exports that were targeted to Mercosur´s partners.

We use 2 predevaluation years for the share of exports: 1997 and 1998. Shares of exports to the Mercosur in 1997 and 1998 precede the devaluation( ), so they measure exogenous exposure to the devaluation.

Rationale: following the devaluation, those firms that were most exposed to Mercosur’s markets adjusted by moving away from these markets and by exploring new markets in high income countries.

A positive correlation is to be expected between the scope to diversify exports and exports to high income countries.

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Two specifications for Post: as year dummies following the devaluation (from 1999 to 2006), so that the Instrumental variables are:

In this way the impact of the devaluation may vary over time

as firms adjust to the exchange rate shock.

The other specification tried is the interaction of with the regional exchange rate (weighted average bilateral exchange rate), thus our second instrument is:

𝐼𝑛𝑠𝑡_𝐻𝐼𝑖𝑡𝐻𝐼1,97 = 𝜙𝑡 ∗𝜆𝑖,97𝑀𝐸𝑅𝐶

𝐼𝑛𝑠𝑡_𝐻𝐼𝑖𝑡𝐻𝐼1,98 = 𝜙𝑡 ∗𝜆𝑖,98𝑀𝐸𝑅𝐶

To deal with the endogeneity of export intensity –ratio of exports to sales- we construct a measure of the average exchange rate faced by a given firm in international markets:

Where is the share of exports of firm i to country c on total sales in 1997 and 1998 (which is predetermined) and is the exchange rate of country c (to the Uruguayan peso).

Rationale: given the shares of exports to market c in the pre-devaluation period (1997 and 1998), a higher exchange rate would induce firm i to export more to this market –i.e. is more competitive in this market-. Thus, we expect that our instrument is positively correlated with the export share.

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We have at least two possible instruments for each endogenous variable, and we can over-identify the model to test the goodness of the instruments.

Evidence of serial correlation: Heteroskedastic and autocorrelation robust standard errors.

Endogenous variable InstrumentsShare of exports to HI countries Share of exports to Mercosur in 1997 and 1998

interacted by a Post devaluation variablePost devaluation var: time dummies, regional exchange rates

Export/ Sales Weighted average exchange rateweights: exports to each destination;

Export status Idem export intensityExport status lagged twice

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IV-GMM: ln average wages, Heteroskedastic and Autocorrelation robust se (HAC)

(1) (2) (3) (4) (5) (6) (7)VARIABLES ln_avgwage ln_avgwage ln_avgwage ln_avgwage ln_avgwage ln_avgwage ln_avgwage

Export intensity 0.933*** 0.969*** 1.297*** 0.236** 0.0441 -0.019 0.696***(0.139) (0.141) (0.146) (0.112) (0.0778) (0.0761) (0.158)

Richer -2.249*** -2.314*** -2.452*** -0.688** -0.295 -0.189 -1.427***(0.298) (0.306) (0.361) (0.269) (0.18) (0.175) (0.361)

Ln Sales 0.206***(0.0109)

Ln TFP 0.454***

(0.0372)Constant 11.29***

(0.0246)Observations 4,773 4,773 4,773 2,699 4,199 4,199 2,478

IV-GMM: ln average wages, Heteroskedastic and Autocorrelation robust se (HAC)

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(1) (2) (3) (4) (5) (6) (7) (8)VARIABLES ln_avgwage ln_avgwage ln_avgwage ln_avgwage ln_avgwage ln_avgwage ln_avgwage ln_avgwage

Exporting firms 0.405*** 0.417*** 0.518*** 0.116** 0.00381 -0.0524 0.292*** 0.505***(0.0488) (0.0483) (0.0416) (0.0576) (0.0444) (0.0441) (0.0515) (0.0482)

Richer -0.719*** -0.726*** -0.0777 -0.248 -0.222* -0.220* -0.373** -0.200(0.143) (0.143) (0.159) (0.16) (0.124) (0.121) (0.162) (0.157)

Ln Sales 0.196***(0.0131)

Ln TFP 0.462***

(0.0319)Constant 11.07***

(0.0309)

Observations 5,312 5,312 5,312 2,699 4,199 4,199 2,780 3,608

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IV-GMM: prof & tech/total workers, Heteroskedastic and Autocorrelation robust se (HAC)

VARIABLES Skill5 skill5 skill5 skill5 skill5 skill5 skill5

Export intensity 0.0151 0.0169 0.0250* 0.0359* 0.0386*** 0.0360*** 0.0492**(0.0139) (0.014) (0.0133) (0.019) (0.0135) (0.0134) (0.023)

Richer -0.0452 -0.0495* -0.0351 -0.0278 -0.038 -0.0324 -0.0657(0.029) (0.0294) (0.0285) (0.0446) (0.0298) (0.03) (0.0496)

Ln Sales -0.000117(0.00198)

Ln TFP 0.0194***

(0.00546)

Constant 0.0697***

(0.00234)

Observations 4,777 4,777 2,626 2,701 4,201 4,201 2,478

IV-GMM: prof & tech/total workers, Heteroskedastic and Autocorrelation robust se (HAC)

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(1) (2) (3) (4) (5) (6) (7)VARIABLES skill5 skill5 skill5 skill5 skill5 skill5 skill5

Exporting firms 0.00326 0.00426 0.0130** 0.0194* 0.0188** 0.0171** 0.0134(0.00635) (0.00628) (0.00581) (0.01) (0.00745) (0.00735) (0.00903)

Richer -0.0196 -0.0211 0.0306 0.0412 0.0319 0.0323 0.0151(0.0158) (0.0158) (0.0194) (0.0263) (0.0205) (0.0203) (0.0234)

Ln Sales -0.00243(0.00255)

Ln TFP 0.0196***

(0.00561)Constant 0.0683***

(0.00382)

Observations 5,318 5,318 5,318 2,701 4,201 4,201 2,781

IV-GMM: non-production workers/total employment, Heteroskedastic and Autocorrelation robust se (HAC)

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(1) (2) (3) (4) (5) (6) (7)VARIABLES skill1 skill1 skill1 skill1 skill1 skill1 skill1

Export intensity -0.156*** -0.152*** -0.136*** -0.195*** -0.211*** -0.219*** -0.0704(0.0336) (0.0342) (0.0378) (0.0434) (0.0312) (0.0351) (0.0472)

Richer -0.0141 -0.0229 0.0627 0.135 0.180*** 0.189** -0.104(0.073) (0.0745) (0.088) (0.102) (0.0679) (0.0773) (0.104)

Ln Sales 0.0154***(0.00458)

Ln TFP 0.0211*

(0.0119)Constant 0.356***

(0.00614)

Observations 4,777 4,777 4,777 2,701 4,201 4,201 2,478

IV-GMM: non-production workers/total employment, Heteroskedastic and Autocorrelation robust se (HAC)

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(1) (2) (3) (4) (5) (6) (7)VARIABLES skill1 skill1 skill1 skill1 skill1 skill1 skill1

Exporting firms -0.0788*** -0.0745*** -0.0593*** -0.122*** -0.131*** -0.139*** -0.0373*(0.0158) (0.0158) (0.0155) (0.0259) (0.0208) (0.0209) (0.0194)

Richer -0.256*** -0.259*** -0.170*** -0.220*** -0.194*** -0.193*** -0.212***(0.0409) (0.0411) (0.0467) (0.0612) (0.0489) (0.0489) (0.0539)

Ln Sales 0.0301***(0.00605)

Ln TFP 0.0245**

(0.0122)

Constant 0.399***

(0.0109)

Observations 5,318 5,318 5,318 2,701 4,201 4,201 2,781

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Summary IV-GMM

  LN AVG_WAGES P&T/Total Emp. Non-Prod/Total Emp.

Export intensity + + -

Export status + +-

Richer - MIXED -

Ln Sales + + +

Ln TFP + + +

Concluding Remarks

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Exports to high income countries do not translate in a higher demand in skills and wages for the Uruguayan case, while exports in general do.

Findings n line with the empirical results by Matsuyama (2007) and Bustos (2011) who argue that what matters is exporting “per se”, but opposite to the findings for Argentina (Brambilla et al., 2012) and Verhoogen (2008) for Mexico.

Exports to high income countries are mainly from sectors with low R&D intensity, and mostly “commodities” with (relative) low scope for differentiation. Then it follows that the productive structure and specialization of the country, characterised by sectors of low technological content, with low value added and low sophistication, can be at the heart of these results, or in Hausmann et al. (2005) words “what we export matters.”

In our research agenda is to analyse further the interaction between “where” and “what” to provide a sound explanation for these results.

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Agenda

Moreover, recently a new literature on export quality measured by unit values argues that there is important variance across countries in differences of quality within narrowly defined product categories.

In this regard, the dynamics of quality (measured by the growth of export unit values) potentially offers insights into the drivers of economic growth by acting as a proxy for the accumulation of underlying factors of production that yield high-quality goods and perhaps greater productivity (Maloney and Lederman, 2012).

This is other topic which deserves further attention (“how”) to provide a comprehensive explanation for these results.

Thank you!Dank U wel!

Muchas Gracias!