Gianmarco Ottaviano From Micro to Macro · 2019. 5. 23. · From Micro to Macro: Firm Heterogeneity...

From Micro to Macro:Firm Heterogeneity and Export

Competitiveness (*)

PIIE Conference9 November 2017

Gianmarco OttavianoProfessor at the London School of Economics and University Bocconi Milan, Department of Economics

Filippo di MauroCompNet Chairman, Visiting Professor at the National University of Singapore, Department of Strategy and Policy

(*) Many thanks to Bernardo Mottironi and Emanuele Forlani for their skilful research assistance

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www.ecb.europa.eu ©

OverviewFocus on exports:• Exports allow countries to obtain from abroad the things

they need (“means rather than ends”)Research question:• Is the distribution of firm productivity associated with

sectoral export performance across countries?• If so, which features of the firm productivity distribution

correspond to better sectoral export performance?Research scope:• Only correlation, not causation• Unique harmonized cross-country data from the

Competitiveness Research Network (CompNet; www.comp-net.org)

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What is CompNet: The Network• Research network founded in 2012 to foster the debate

on competitiveness also leveraging disaggregated trade, firm-level and input-output data

• Aims at providing a robust theoretical and empirical link between drivers of competitiveness and macroeconomic performance

• Partner Institutions: European Bank of Reconstruction and Development (EBRD); European Central Bank (ECB); European Commission (EC); European Investment Bank (EIB); Halle Institute for Economic Research (IWH); Tinbergen Institute (TI).

• Data providers: National Statistical Institutes, EU Central banks and research institutes

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CompNet: The Firm-Level Dataset

• Unique in terms of its coverage and cross-dimensional analysis potential as it links, for example, trade or the financial status of firms with their productivity.

• Variables on employment, trade, productivity, mark-ups, financial constraints and more

• Definitions and computation are harmonized across countries• Built at firm level, indicators are then aggregated to sectoral level

(NACE 2-digit) in order to preserve confidentiality• Provides moments of the distributions for those variables. Joint

distributions are also available. This allows to analyze, for instance, whether more productive firms grow faster or whether firms with higher mark-ups are less financially constrained

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CompNet Dataset: Moments of the firm productivity distribution by country

Source: Our calculation from CompNet (4th Vintage)

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CompNet Dataset: Export and Productivity (Level by Country)

Source: Our calculation from CompNet.

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Belgium, coeff: 0.29*** Croatia, coeff: 0.13*** Czech Rep., coeff: 0.36*** Denmark, coeff: 0.17***

Estonia, coeff: 0.23*** Finland, coeff: 0.29*** France, coeff: 0.19*** Italy, coeff: 0.33***

Latvia, coeff: 0.32*** Poland, coeff: 0.31*** Slovakia, coeff: 0.25***

median_logExport Fitted values

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Graphs by country

Each point is a sector-year observationExport levels (median) vs TFP decile (median), by country

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www.ecb.europa.eu © Source: Our calculation from CompNet.

CompNet Dataset: Export and Productivity (Growth by Country)

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Belgium, coeff: 0.009*** Croatia, coeff: 0.008*** Czech Rep., coeff: 0.016*** Denmark, coeff: 0.008***

Estonia, coeff: 0.031*** Finland, coeff: 0.015*** France, coeff: 0.012*** Italy, coeff: 0.013***

Latvia, coeff: 0.012 Poland, coeff: 0.020*** Slovakia, coeff: 0.021***

median_growthExport Fitted values

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Graphs by country

Each point is a sector-year observationExport growth (median) vs TFP decile, by country

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CompNet Dataset: Sectors

Manufacturing sector N. Manufacturing sector N.

Food products 10 Basic metals 24

Textiles 13 Fabricated metal products 25

Wearing apparel 14 Computer, electronic and optical products 26

Leather and related products 15 Electrical equipment 27

Wood, cork, straw and plaiting 16 Machinery and equipment 28

Paper products 17 Motor vehicles, trailers and semitrailers 29

Reproduction of media 18 Transport equipment 30

Chemical products 20 Furniture 31

Rubber and plastics 22 Other manufacturing 32

Non-metallic mineral products 23 Repair and installation of machinery 33

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CompNet Dataset: Export and Productivity (Level by Sector)

Source: Our calculation from CompNet.

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Sector 10, coeff: 0.27*** Sector 13, coeff: 0.24*** Sector 14, coeff: 0.21*** Sector 15, coeff: 0.22*** Sector 16, coeff: 0.23***




median_logExport Fitted values

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Graphs by sector

Each point is a country-year observationExport levels (median) vs TFP decile (median), by sector

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www.ecb.europa.eu © Source: Our calculation from CompNet.

CompNet Dataset: Export and Productivity (Growth by Sector)

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Sector 17, coeff: 0.009*** Sector 18, coeff: 0.002 Sector 20, coeff: 0.009*** Sector 22, coeff: 0.011*** Sector 23, coeff: 0.011***


Sector 29, coeff: 0.011*** Sector 30, coeff: 0.010*** Sector 31, coeff: 0.019*** Sector 32, coeff: 0.015*** Sector 33, coeff: 0.010**

median_growthExport Fitted values

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Graphs by sector

Each point is a country-year observationExport growth (median) vs TFP decile, by sector

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“It Takes More than a Moment”

• These graphs reveal positive correlation of export (level and growth) with productivity level across countries and sectors

• They also reveal noticeable heterogeneity in firm productivity distributions across countries and sectors

• Our specific focus is on how the moments of the firm levelproductivity distribution affect export performance across countries within sectors

• Traditional “macro story”: average (first moment) of firm productivity determines sectoral productivity and this in turn determines exports

• New “micro story”: higher moments of the firm productivity distribution matter too (Barba Navaretti et al, 2017)

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Export Selection (Melitz, 2003)

• Due to fixed export costs, firms have to be productive enough in order to export

• This means that a “fat right tail” of very productive firms matters above and beyond average firm productivity

Firm productivity

Firm density

“export cut-off”

ExportersNon-Exporters

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Testing the Aggregate Implications of Export Selection• This simple story is less straightforward to test that it

seems (Barba et al, 2017)• Several confounding factors as trade flows are

determined by origin, destination and origin-destination characteristics

• To filter these factors out we adopt a modern “gravity approach”

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Gravity and Export Competitiveness• A “gravity regression” explains export flows from an

origin country to a destination country as a function of bilateral distance-related trade frictions and country characteristics

• Country characteristics are captured by “exporter fixed effects” and “importer fixed effects”

• In particular, the exporter fixed effect subsumes all factors affecting the ability of the origin country to export (“export competitiveness”) after controlling for all confounding factors that are not origin specific

• We therefore study the relation between this “export competitiveness” and the moments of the productivity distribution of firms in the origin country

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First Step: Gravity Regression• Estimate gravity by sector (balanced) using Poisson Estimator.

• Baseline: includes all bilateral export flows from OECD countries (i) to destinationcountries (d) for several manufacturing sectors (s) from 2001 to 2012 (t).

• We include both CompNet and other countries (such as USA or China).• We estimate the following Eq. (1) for each sector s (sectoral index left implicit)

𝑬𝒙𝒑𝒐𝒓𝒕𝒊,𝒅,𝒕 = 𝐞𝐱𝐩[𝜶𝒊,𝒕 + 𝜷𝒅,𝒕 − 𝜸𝑫𝒊,𝒅] + 𝜺𝒐,𝒅,𝒕

• 𝜶𝒊,𝒕 ∶ origin*year fixed effects -> “Export competitiveness index”• 𝜷𝒅,𝒕: destination * year fixed effects• 𝑫𝒊,𝒅: is a vector of trade cost variables (distance, common border, etc…)

Fixed effects 𝜶𝒊,𝒕measure the competitiveness of the sampled countries as suppliers,netting out importer-specific and country-pair-specific characteristics.We consider fixed effects 𝜶𝒊,𝒕 from CompNet countries.

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First Step: ResultsSector Food Beverages Textile Wearing Leather Wood Paper Printing Chemic. Pharmac.

IsicCode Rev4 22 23 24 25 26 27 28 29 30 31

Log(Distance) -1.088*** -1.144*** -.9079*** -.9808*** -.7257*** -.9655*** -.6455*** -.7744*** -.3031*** -.5814***

(.0237) (.0285) (.0271) (.0247) (.0264) (.0258) (.0167) (.0378) (.0536) (.033)

C.Border .4184*** .5712*** .5283*** .4531*** .2363*** .3166*** .3647*** .4648*** .732*** .4805***

(.0338) (.0288) (.0499) (.0348) (.055) (.0394) (.0328) (.0403) (.0687) (.043)

C,Language .1888*** .3191*** .1454** .4669*** .2817*** .36*** .2671*** .1049** .2531*** .2442***

(.0378) (.0305) (.0691) (.0321) (.0442) (.041) (.0357) (.0421) (.0423) (.0462)

Colony .0748* 0.0142 .3927*** .0722*** -.1619*** -0.0605 .0712*** -.5851*** -.0718* -.1192*

(.0428) (.0344) (.0512) (.0243) (.0367) (.0398) (.0264) (.0717) (.0405) (.0651)

RTA .4674*** -.0773* .2953*** .3458*** .3833*** .2556*** .434*** 1.058*** .1315* .4993***

(.0456) (.0454) (.0542) (.0414) (.039) (.0381) (.0376) (.0497) (.0749) (.0529)

Obs 29400 29400 29400 29400 29400 29400 29400 29400 29400 29400

R2 0.911 0.912 0.767 0.916 0.929 0.917 0.911 0.941 0.863 0.900

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First Step: Results (Cont.)

Sector Plastics Non-Metallic

Basic Metals

FabricatedMetals Computer Electrical

Equip Machinery Motor Veh TransportEquip

Furnitureand othermanuf.

IsicCodeRev4 22 23 24 25 26 27 28 29 30 31/32

Log(Distance) -1.088*** -1.144*** -.9079*** -.9808*** -.7257*** -.9655*** -.6455*** -.7744*** -.3031*** -.5814***

(.0237) (.0285) (.0271) (.0247) (.0264) (.0258) (.0167) (.0378) (.0536) (.033)

C.Border .4184*** .5712*** .5283*** .4531*** .2363*** .3166*** .3647*** .4648*** .732*** .4805***

(.0338) (.0288) (.0499) (.0348) (.055) (.0394) (.0328) (.0403) (.0687) (.043)

C,Language .1888*** .3191*** .1454** .4669*** .2817*** .36*** .2671*** .1049** .2531*** .2442***

(.0378) (.0305) (.0691) (.0321) (.0442) (.041) (.0357) (.0421) (.0423) (.0462)

Colony .0748* 0.0142 .3927*** .0722*** -.1619*** -0.0605 .0712*** -.5851*** -.0718* -.1192*

(.0428) (.0344) (.0512) (.0243) (.0367) (.0398) (.0264) (.0717) (.0405) (.0651)

RTA .4674*** -.0773* .2953*** .3458*** .3833*** .2556*** .434*** 1.058*** .1315* .4993***

(.0456) (.0454) (.0542) (.0414) (.039) (.0381) (.0376) (.0497) (.0749) (.0529)

Obs 29400 29400 29400 29400 29400 29400 29400 29400 29400 29400

R2 0.911 0.912 0.767 0.916 0.929 0.917 0.911 0.941 0.863 0.900

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First Step: Export Competitiveness

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Country Mean SD IQR Min Max Obs

Austria 13.16 1.903 1.57 7.563 15.33 215Belgium 13.1 2.238 2.923 7.207 16.4 231Croatia 10.37 2.14 2.141 3.992 13.42 213Estonia 10.85 2.462 2.98 3.471 13.9 227Finland 12.3 2.618 3.959 5.696 15.91 238France 14.1 2.144 2.606 8.022 16.78 240Germany 14.83 2.262 3.71 9.51 17.87 240Hungary 12.15 2.562 3.309 5.946 15.46 200Italy 14.36 2.324 2.764 8.853 17.52 241Lithuania 10.82 2.395 2.941 3.543 13.85 200Poland 12.85 2.585 3.873 6.001 16.18 168Portugal 12.39 2.766 3.01 5.757 15.76 140Romania 11.74 2.783 2.574 2.741 15.59 200Slovakia 11.65 2.552 2.84 4.84 14.97 218Slovenia 11.13 2.323 2.265 4.228 14.15 225Spain 13.68 2.43 3.108 7.735 16.98 240

Total 12.5 2.742 3.584 2.741 17.87 3472Unit of observation: Origin year by sector

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First Step: Export Competitiveness (Cont.)

Comments

1. TheG7Europeaneconomies(Germany,France,Italy)showthehighestvaluesofexportcompetitiveness:“size”and“centrality”matter.

2. Exportcompetitivenessispositivelycorrelatedwithsectoraltradebalance(indexofcorrelationequalto0.46).

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First Stage: Gravity RegressionExport competitiveness index and trade balance

Source: authors elaborations on OECD trade data. ,Each dot is defined at country-sector-year level. The y-axis reports the trade balance defined as 9:;<=>;

9:;?=>;. The X-axis reports the fixed effects computed from the gravity model. Red line is the linear

interpolation.

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Second Step: Moments

HigherMoments(Asymmetry)

Foreachcountry-sector-yeartriple,wemeasuretheasymmetryofdistributionusing

parametric(Skewness index– thirdmoment)andnonparametric(Pearson'ssecond

skewness coefficient)asymmetryindices

𝑃𝑒𝑎𝑟𝑠.=,F,G =>9HIJ,K,L<>9M=HIJ,K,L

FG.M9N.J,K,L,

and the skewness index reported in the CompNet database (third moment).

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Second Step: Descriptive StatisticsDescriptive Statistics: Total Factor Productivity (Levels)

Country TFP(Mean) TFP(Pers.) TFP(Skew) TFP(P90/P10) TFP(P80/P20)AUSTRIA 2.634 0.194 1.656 2.355 1.778BELGIUM 2.240 0.253 1.598 2.917 1.992CROATIA 1.944 0.249 1.242 3.870 2.446ESTONIA 2.207 0.188 0.809 3.589 2.342FINLAND 2.623 0.216 1.418 2.656 1.853FRANCE 2.668 0.201 1.447 2.666 1.876GERMANY 1.825 0.238 1.795 3.795 2.399HUNGARY 1.527 0.276 1.846 5.609 2.912ITALY 2.147 0.232 1.680 3.017 2.032LITHUANIA 1.772 0.250 1.270 5.118 2.936POLAND 1.668 0.231 1.758 4.652 2.664PORTUGAL 1.893 0.233 1.922 3.664 2.333ROMANIA 1.398 0.288 2.093 5.787 3.159SLOVAKIA 1.589 0.240 1.835 4.863 2.695SLOVENIA 2.278 0.216 1.180 3.382 2.132SPAIN 1.923 0.266 2.055 3.490 2.222Total 2.015 0.238 1.595 3.862 2.364

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Second Step: Specification

• We estimate the following Eq.2:

𝑪𝒐𝒎𝒑. 𝑰𝒏𝒅𝒊𝒔𝒕 = 𝒂𝟎 + 𝒂𝟏𝑳𝒐𝒈𝑻𝑭𝑷 𝑴𝒆𝒂𝒏 𝒊,𝒔,𝒕<𝟏 + 𝒂𝟐𝑳𝒐𝒈𝑭𝒊𝒓𝒎𝒔𝒊,𝒔,𝒕<𝟏 +

𝒂𝟑𝑳𝒐𝒈𝑾𝒂𝒈𝒆𝒊,𝒔,𝒕<𝟏 + 𝒂𝟒𝑨𝒔𝒚𝒎𝒊,𝒔,𝒕<𝟏 + 𝑪𝒊 + 𝑺𝒔 + 𝑻𝒕 + 𝒖𝒊,𝒔,𝒕

• The dependent variable is the exporter fixed effect from the first stagegravity regression (“Export competitiveness”)

• Asym refers to the asymmetry of the firm productivity distribution inorigin country i.

• Explanatory variables are lagged one period to reduce concerns aboutreverse causality.

• We look at different combinations of country, sector and year fixed effects(C, S, and T)

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Second Step: Total Factor Productivity

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TheavailabledataallowustocomputeonlytwocrosscountrycomparablestatisticsonTFP:meanandasymmetry

(1) (2) (3) (4) (5) (6) (7) (8) (9)

LogTFP(Mean)t-1 .0224** .0281*** .0276*** .0245** .0297*** .0287*** .0233** .0292*** .0282***(.0096) (.0095) (.0101) (.0095) (.0095) (.0098) (.0095) (.0099) (.0099)

TFP(Skew)t-1 .0829*** .0775*** .0854***(.0149) (.0147) (.0144)

TFP(Pers)t-1 .7477*** .7188*** .6819***(.1301) (.1412) (.1268)

Log Firmst-1 .9136*** .8867*** .9195*** .9188*** .8936*** .9199*** .9273*** .8992*** .9323***(.0219) (.0223) (.0226) (.0229) (.0234) (.0236) (.0231) (.0233) (.0228)

Log Waget-1 -.4563*** -.4341*** -.4735*** -.4508*** -.4289*** -.4654*** -.5796*** -.5579*** -.602***(.0445) (.0433) (.0487) (.0449) (.0437) (.0494) (.0477) (.0433) (.0532)

Cons. 7.74*** 7.753*** 7.492*** 7.588*** 7.624*** 7.375*** 7.93*** 7.972*** 7.655***(.1985) (.1947) (.2192) (.2651) (.2599) (.293) (.1643) (.1516) (.1798)

ObS 2643 2643 2540 2643 2643 2540 2643 2643 2540R2 0.9658 0.9664 0.9653 0.968 0.9684 0.9674 0.9683 0.9689 0.9679Country F.E. Yes Yes Yes Yes Yes Yes No No NoSector F.E. Yes Yes Yes No No No Yes Yes YesYear F.E. Yes Yes Yes No No No No No NoSector X Year F.E. No No No Yes Yes Yes No No NoCountry X Year F.E. No No No No No No Yes Yes Yes

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Second Step: Aggregate Effects

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Increaseofonestandarddeviationin:

%Δ CountryCompetitiveness

(col.5)

%Δ CountryCompetitiveness

(col.6)AverageProductivity 4,2% 4,07%DispersionIndex 6,9% 7,04%

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What Policy Implications?

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• They depend on the drivers of the asymmetry and the dispersion shown by the productivity distributions

• Productivity distribution may depart from the efficient one if there is “misallocation” of resources at the firm-level

• Therefore, we measure allocative efficiency and check how it correlates with the asymmetry of the productivity distribution of producers within sector across countries

• In case of a robust correlation at granular levels, we may argue that reallocation polices can increase growth, not only by their direct effect on aggregate productivity, but also by strengthening the export channel

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Misallocation

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• We define “misallocation” as in Olley and Pakes (1996):• “The decomposition splits the weighted average of firm

productivity in two components: the unweightedindustry mean and the covariance between productivity and size.”

• “The latter captures allocative efficiency since it reflects the extent to which firms with higher than average productivity have a greater market share in terms of size.”

• We regress the asymmetry measures of the productivity distribution on “allocative efficiency” in the sense of O-P

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Asymmetry and Allocative efficiency

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Skewness, coeff: 2.52* Pears Index, coeff: 0.79***

P80/P20, coeff: 8.55*** P90/P10, coeff: 17.32***

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Asymmetry and Allocative efficiency (Cont.)

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Skew Skew Pears Pears P80/P20 P80/P20 P90/P10 P90/P10OP 2.52* 0.39 0.79*** 0.44* 8.55*** 3.58*** 17.32*** 4.18*

Constant yes yes yes yes yes yes yes yesCountry FE no yes no yes no yes no yes

Year FE no yes no yes no yes no yes

• Regressing the asymmetry measures (Skewness, Pears index and percentiles ratios) on the O-P covariance term), without and with fixed effects for country and year, confirm the positive correlationbetween asymmetry and allocative efficiency:

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Conclusion

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• Higher moments of the firm productivity distribution are an important determinant of the country’s ability to export, over and above the average productivity

• In particular, a fatter “right tail” is associated with higher ability to export

• This asymmetry, in turn, is also positively associated with higher allocative efficiency

• In the context of this Conference’s question, slower productivity will not necessarily need to be associated with slower export, provided allocative efficiency improves

• Detailed microeconomic information on how firms perform and how resources are distributed among them is therefore crucial also for fact-based macroeconomic policies. We have the data

Gianmarco Ottaviano From Micro to Macro · 2019. 5. 23. · From Micro to Macro: Firm Heterogeneity...

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