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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
Local sourcing and production efficency
Emmanuel Dhyne1,2 Cédric Duprez11National Bank of Belgium 2UMONS
2nd ESCB Cluster 2 annual Conference - Paris - 7-9 November 2018
DISCLAIMER: The views expressed in this paper are those of the authors and do notnecessarily reflect the views of the National Bank of Belgium. The results presentedcomply with the confidentiality restrictions associated with the data sources used.
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Introduction
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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
Why does local sourcing matter ?
Figure 1 – TFP Premia and domestic sourcing
Note : To construct the blue line, we regress the TFP on cumulative dummies for the minimum number ofdomestic suppliers from which the firm sources, and sector controls. To construct the red line, we regress the TFPon cumulative dummies for the minimum number of domestic suppliers from which the firm sources, cumulativedummies for the minimum number of foreign countries from which the firm sources, and sector controls.
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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
Why does local sourcing matter ?
Figure 1 – TFP Premia and domestic sourcing
Note : To construct the blue line, we regress the TFP on cumulative dummies for the minimum number ofdomestic suppliers from which the firm sources, and sector controls. To construct the red line, we regress the TFPon cumulative dummies for the minimum number of domestic suppliers from which the firm sources, cumulativedummies for the minimum number of foreign countries from which the firm sources, and sector controls.
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Outsourcing has become standard practices in businessmanagement
• Due to reduction in trade costs and ICT development, firms’frontiers are blurred.
• Focus on core competencies / Outsourcing of non core to improveefficiency.
• Examples :
• Many Central Banks
• While in the late 90’s many were still doing (almost) everythingin-house, they now outsources many non-core tasks (f.i. cleaning,catering, HR tasks, security, IT support, technical support, ....) andeven some of their historical core activities.
• Most universities• Have also started to outsource non core activities such as security,
cleaning service, technical maintenance and catering.
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What do we do ?
• Are there trade frictions to domestic sourcing in a single market ?
• How do firms select their local suppliers ?
• To what extent do trade frictions shape the productivitydistribution ?
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Why Belgium is an interesting case to look at ?
• Small country : longest distance between two cities = 270km
• High density of firms : more than 700,000 firms
• Good transportation infrastructure
• 155,000 km of roads• 3,500 km of railways• 2,000 km of waterways
• No natural obstacles (mountain, lake, desert)
• ... but two main national languages (three in total)
• Very open economy, but still more than 80% of Belgian trade inintermediaries are domestic.
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Related literature
• Global value chains : Antràs et al. (2012), Koopman et al. (2014),Timmer et al. (2012)
• International sourcing : Antràs et al. (2017), Bøler et al. (2015),Halpern et al. (2015), Goldberg et al. (2010), Amiti and Konings(2007)
• Sectoral linkages : Acemoglu et al. (2012), Oberfield (2013)
• Firm-to-firm linkages : Atalay et al. (2011) for large US firms,Bernard et al. (2015) for Japan
• Other papers based on NBB B2B datasets : Magerman et al.(2016), Bernard et al. (2018), Duprez and Magerman (2018),Kikkawa et al. (2018), Tintelnot et al. (2018), ...
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The NBB B2B transaction dataset
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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
Firm to firm linkages in Belgium
• The NBB B2B dataset (described in Dhyne, Magerman, Rubinova,2015) based on the compulsory annual declarations of VATregistered customers to the Belgian Tax administration
• For each VAT, list of annual sales by VAT registered customers• Reporting threshold of a transaction : 250 EUR per year• 166 millions of transactions over the 2002-2012 period• Similar to an annual input-output matrix at the firm level (700K x
700K)
• Can be merged with other firm level datasets to get a large set ofboth the buyers and the sellers characteristics (location, sector,production variables, input consumption, employment, capital stock,international trade, domestic and foreign financial participations, ...)
• Provides a complete description of the organization of the Belgianproduction network in a given year
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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
The geography of the Belgian production network (1)
Figure 2 – Around 700,000 Belgian firms in 2011...
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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
The geography of the Belgian production network (2)
Figure 3 – ... and the 16,000,000 transactions between them
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The sourcing strategy of Belgian firms...
Table 1 – Sourcing strategy (in 2012)
p10 p25 p50 p75 p90 Average
# of domestic suppliers 1 4 9 23 47 21.2% of importers - - - - - 5.1# of source countries 1 1 2 4 9 3.8
Note : Based on 797,596 sourcing firms observed in 2012, including self-employed.
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... and its impact on productivity
Table 2 – Production function controlling for sourcing strategy - 2002-2012
Manufacturing Services
lit 0.644∗∗∗ 0.733∗∗∗
kit 0.091∗∗∗ 0.090∗∗∗
dom1,it −0.045∗∗∗ −0.035∗∗∗dom2,it −0.025∗∗∗ −0.095∗∗∗dom3,it 0.097∗∗∗ 0.084∗∗∗
dom4,it −0.003 −0.016∗∗∗forit 0.007∗∗∗ 0.034∗∗∗
Year dummies Yes YesSectoral dummies Yes YesDistrict dummies Yes YesAdditional controls Yes YesObservations 151,142 300,104
Note : dom1,it to dom4,it are the inverse hyperbolic sine of the number of domestic suppliers active in respectivelythe manufacturing sector (NACE Rev2 10 to 33), the wholesale and retail trade sector (NACE Rev2 45 to 47), theservice sector (NACE Rev2 55 to 82) and in other branches, forit represents the inverse hyperbolic sine of thenumber of countries from which firm i imports in period t. District dummies indicate in which Belgian district theheadquarter of firm i is located. Production functions are estimated using the Wooldridge LP approach.Significance levels : *** p < 0.01, ** p < 0.05, * p < 0.10
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Trade under micro-gravity
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Belgium is not a village
Figure 4 – Distribution of the distances between customers and suppliers
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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
Pecking order of source sectors
• Source sectors are ranked by the number of sourcing firms
• Retail and network industries excluded
Table 3 – Compliance with the pecking order of source sectors
Actual data CounterfactualAt the transaction level :Sectors 30.1% 17.1 %Sectors x Provinces 26.5% 13.5%Sectors x Districts 16.8% 8.2%Sourcing sector-specific at the transaction level :Sectors 33.1% 19.6 %
Note : Each cell represents the fraction of transactions that follow exactly the pecking order of NACE Rev.2 2digit source sectors, crossed or not with sourcing sectors, in the data and in the counter-factual exercise of randomselection of source sectors. Bootstraps based on 1,000 replications of both measures show that they aresignificantly different from each other at the 1% level.
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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
Most important source sectors
Table 4 – Sectors ranked by the number of sourcing firms (excluding retail andnetwork industries)
NACE Rev2.1 Legal and accounting2 Specialized construction activities3 Office administrative, office support and other business support activities4 Financial service activities, except insurance and pension funding5 Rental and leasing activities6 Activities of head offices, management consultancy activities7 Computer programming, consultancy and related activities8 Services to building and landscape activities9 Land transport and transport via pipelines10 Manufacture of fabricated metal products, except machinery and equipment
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A model of local sourcing
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A model of local sourcing
• Based on AFT (2017) multi-country model, we derive a model oflocal sourcing with endogenous firm’s boundary
• We relax the usual assumption of two types of firms (upstream firmsthat produce intermediates and downstream firms that produce finalgoods)
• Instead, we consider a model of trade in tasks where all firms sell tofinal demand and can provide tasks to other firms (consistent withthe data)
• Advantage of trade in tasks instead of trade in goods : avoidcomplex fixed-point issues in price-setting
• Main implications :• More productive firms have more suppliers• More productive and neighboring firms are more likely to be selected
as a supplier
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Consumers and producers
• Consumer preferences are CES. Demand for variety ω is
q(ω) = Ap (ω)−σ
• Monopolistic competition : each firm owns a blueprint to produce asingle variety ω
• Production requires the assembly of a unit continuum of tasks
• Marginal cost of firm i producing ω is
ci =
(∫ 10
zi (t)1−ρ dt
)1/(1−ρ)where zi (t) is the price of the individual task t paid by firm i .
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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
In-house production
• All firms can potentially produce all tasks in-house...
• Firm i draws its unit labor requirements to perform task t, ai (t),from a Frechet distribution (EK, 2002)
Pr [ai (t) > a] = e−φi aθ
where φi represents the ability to manage tasks (cf. Bloom et al.,2017, Syverson, 2011)
• ... or they can concentrate on their core competencies (good drawsin Frechet) and outsource the other tasks
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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
Trade in tasks
• Buyer has full bargaining power : tasks are bought at their marginalcosts (Tintelnot et al., 2017, Antràs et al., 2017)
• Outsourcing entails both variable (τij) and fixed (fij) trade costs
• The price paid by firm i for task t is given by
zi (t) = minj∈Ji{ai (t) , τijaj (t)}
where Ji is the set of suppliers for which i has paid the fixed cost fij
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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
Optimal sourcing strategy
• From Frechet distribution, the shares of tasks done in house orsourced from any supplier j ∈ Ji are given by
Xii =φi
φi + Θi, Xij =
φj (τij)−θ
φi + Θi
where Θi =∑
k∈Ji φk (τik)−θ is the sourcing strategy of firm i
(AFT, 2017)
• Taking advantage of constant mark-ups and the Frechet distribution,firm’s i marginal cost and profit are given by
ci = γ (φi + Θi )−1/θ
πi = (φi + Θi )σ−1θ Bi −
∑k
fik
where γ =[Γ(θ+1−ρθ
)]1/(ρ−1)and Bi = γ
σ−1
σ
(σσ−1
)1−σA
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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
Empirical predictions
• Using a First-order Taylor approximation, given the current sourcingstrategy Θi of firm i , the net gain of adding supplier j is given by
∆ij (Θi ) =σ − 1θ
Bi (φi + Θi )σ−1−θθ φjτ
−θij − fij
• Proposition 1 : More productive firms have more suppliers( d∆ijdφi > 0) whenever σ − 1 > θ (consumer demand is elastic andefficiency draws are heterogeneous)
• Proposition 2 : More productive and neighboring firms are morelikely to be selected as a supplier ( d∆ijdφj > 0 and
d∆ijτij
< 0)
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Explaining the choice of supplying partners
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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
A Probit equation of sourcing strategy• Main sample :
• all firms for which we observe positive employment, the location andfor which we can estimate TFP at the NACE 2 digit level.
• all observed transactions between 2002 and 2012 (42,454,703) and arandom selection of non-transactions involving those firms
• We add a random selection of 100 potential sourcing transactions foreach firm in our sample ⇒ 100,647,709 observations weighted totake into account the true "matching" probability (≈ 0.04%)
• Two subsamples• Restricts to the subset of transactions between manufacturing firms
(2,491,060 potential transactions, 1,646,796 ones)• Restricts to the subset of transactions for which the suppliers are
service firms only (22,547,915 potential transactions, 8,377,937 ones)
• Transaction characteristics : distance, language, financial linkage
• Supplier and customer characteristics : Employment, TFP (for thebuyer we consider its TFP in t-1 purged from its sourcing decisions)and additional controls (sector of activity, international trade status,multiple establishments, ownership status, district)
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Baseline regression
Table 5 – Sourcing choice of firm i : I (Salesijt > 0)
All suppliers Manufacturing suppliers only Service suppliers onlyEst. coef. Avg. elast Est. coef. Avg. elast. Est. coef. Avg. elast.
distij −0.231∗∗∗ −0.915∗∗∗ −0.183∗∗∗ −0.661∗∗∗ −0.241∗∗∗ −0.966∗∗∗
6= Langij −0.239∗∗∗ −0.613∗∗∗ −0.206∗∗∗ −0.526∗∗∗ −0.317∗∗∗ −0.722∗∗∗
t̃fpi,t−1 0.068∗∗∗ 0.270∗∗∗ 0.045∗∗∗ 0.163∗∗∗ 0.057∗∗∗ 0.230∗∗∗
tfpjt 0.106∗∗∗ 0.418∗∗∗ 0.124∗∗∗ 0.446∗∗∗ 0.125∗∗∗ 0.499∗∗∗
li,t−1 0.109∗∗∗ 0.430∗∗∗ 0.124∗∗∗ 0.447∗∗∗ 0.124∗∗∗ 0.495∗∗∗
ljt 0.117∗∗∗ 0.462∗∗∗ 0.051∗∗∗ 0.183∗∗∗ 0.135∗∗∗ 0.539∗∗∗
Participationijt 1.472∗∗∗ 142.2∗∗∗ 1.654∗∗∗ 135.54∗∗∗ 1.985∗∗∗ 603.3∗∗∗
Year dummies Yes Yes Yesi and j district dummies Yes Yes Yesi and j sector dummies Yes Yes YesAdditional controls Yes Yes YesObservations 100, 647, 709 2, 491, 060 22, 547, 915
Note : Sample of all B2B transactions observed in Belgium, completed by 100 potential transactions for eachbuyer, for the 2003-2012 period. I (salesijt > 0) is a binary variable that indicates whether firm i sources inputs fromfirm j at time t. distij is the log "as the crow fly" distance. 6= Langij is a binary variable indicating that firms i and j
do not share a common language. t̃fp and tfp are respectively the log total factor productivity (estimated at theNACE 2-digit level using the Wooldridge-LP estimator) purged or not from the sourcing strategy followed by thefirm. l is the log number of employees, in FTE. Participationijt is a dummy variable indicating that i or j owns atleast 50% of the capital of the other firm. Standard errors of the estimated coefficients are clustered at thesourcing firm level. Significance levels : *** p < 0.01, ** p < 0.05, * p < 0.1.
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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
Trade frictions at the sector level• For each NACE 2 digit sourcing sector, estimation of a Logit withboth buyers and sellers FE, on all potential links between all buyersand sellers in 2012.
Figure 5 – Estimation by NACE 2 digit sourcing sector, Logit with buyer andseller FE
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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
A gravity equation of firm-to-firm trade
• Sample : same samples as Probit, using similar weighting strategy totake into account the under-representation of 0’s in our sample.
• Use the Eaton-Kortum Tobit approach to model the size ofindividual transactions with the same set of explanatory variables
• Transaction specific variables : distance, common language, financialparticipation
• Supplier or customer specific variables : total employment, TFP,sector of activity, international trade status, multiple establishments,ownership status, ZIP code
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Introduction Data Gravity Model Results Productivity and trade frictions Conclusion
Baseline regression
Table 6 – Amount supplied by firm j to firm i : ln (Salesijt)
(Salesit )
All suppliers Manufacturing suppliers Services suppliersonly only
distij −1.560∗∗∗ −1.354∗∗∗ −1.527∗∗∗
6= Langij −1.632∗∗∗ −1.525∗∗∗ −2.008∗∗∗
t̃fpi,t−1 −0.181∗∗∗ −0.218∗∗∗ −0.229∗∗∗
tfpjt 0.821∗∗∗ 1.078∗∗∗ 0.915∗∗∗
li,t−1 0.082∗∗∗ 0.239∗∗∗ 0.144∗∗∗
ljt 0.696∗∗∗ 0.325∗∗∗ 0.729∗∗∗
Participationijt 11.137∗∗∗ 13.415∗∗∗ 13.262∗∗∗
Year dummies Yesi and j sector dummies Yesi and j Zip code dummies YesAdditional controls YesObservations 100, 469, 837 2, 487, 637 22, 504, 589
Note : Sample of all B2B transactions observed in Belgium, completed by 100 potential transactions for eachbuyer, for the 2003-2012 period. Salesijt is the amount of inputs bought by firm i from firm j at time t. Salesit is theamount of total sales of firm i at time t. distij is the log "as the crow fly" distance. 6= Langij is a binary variable
indicating that firms i and j do not share a common language. t̃fp and tfp are respectively the log total factorproductivity (estimated at the NACE 2-digit level using the Wooldridge-LP estimator) purged or not from thesourcing strategy followed by the firm. l is the log number of employees, in FTE. Participationijt is a dummy variableindicating that i or j owns at least 50% of the capital of the other firm. Standard errors of the estimatedcoefficients are clustered at the sourcing firm level. Significance levels : *** p < 0.01, ** p < 0.05, * p < 0.1.
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The impact of trade frictions on productivity
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Indicator of Connectivity
• For each ZIP k , we calculate the average distance w.r.t. all firms inthe network and the fraction of firms that do not share the samelanguage as firms located in k .
Distk =1∑j nj
∑j
(njDistkj
(β2β16= Langkj + (1− 6= Langkj)
))
• This variable indicates how easy it is for a firm located in Zip k toconnect to any other firms or service providers in Belgium, accordingto its location.
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Connectivity and productivity
Table 7 – Production function augmented with connectivity index
All suppliers Service providersonly
lit 0.663∗∗∗ 0.663∗∗∗
kit 0.089∗∗∗ 0.089∗∗∗
overlinedistkt −0.049∗∗∗ −0.043∗∗∗
dom1,it 0.010∗∗∗ 0.010∗∗∗
dom2,it −0.091∗∗∗ −0.091∗∗∗
dom3,it 0.104∗∗∗ 0.104∗∗∗
dom4,it −0.008∗∗∗ −0.008∗∗∗
forit 0.036∗∗∗ 0.036∗∗∗
Year dummies Yes YesSectoral dummies Yes YesDistrict dummies Yes YesAdditional controls Yes YesObservations 1,159,461 1,159,030
Note : dom1,it to dom4,it are the inverse hyperbolic sine of the number of domestic suppliers active in respectivelythe manufacturing sector (NACE Rev2 10 to 33), the wholesale and retail trade sector (NACE Rev2 45 to 47), theservice sector (NACE Rev2 55 to 82) and in other branches, forit represents the inverse hyperbolic sine of thenumber of countries from which firm i imports in period t. dist is the log of the connectivity variable. Districtdummies indicate in which Belgian district the headquarter of firm i is located. Production functions are estimatedusing the Wooldridge LP approach. Significance levels : *** p < 0.01, ** p < 0.05, * p < 0.10
• Firms located in the least connected area might suffer from a productivity handicap of10.7% w.r.t. to firms located in the best connected area
• Removing cultural barriers to trade could generate TFP gains of around 2.5% for firmslocated in Flanders and of aroubnd 4.5% for firms located in Wallonia.
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Concluding remarks
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Main take away
• Domestic trade frictions are non negligible, even in a dense/singlemarket as Belgium
• Domestic sourcing seem to matter for economic performances
• Local trade frictions shape the firm level economic performances
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Local sourcing and production efficency Emmanuel Dhyne, Cédric Duprez
IntroductionDataGravityModelResultsProductivity and trade frictionsConclusion