headquarters intangible capital and fdi paniagua... · 2018. 10. 24. · headquarters intangible...

Headquarters intangible capital and FDI

S. Gil-Pareja R. Llorca-Vivero J. Paniagua1

University of Valencia

ETSG 2018 Warsaw

[email protected], Llorca, Paniagua (UV) HQ intangible capital and FDI ETSG 2018 Warsaw 1 / 20

Motivation Example

Valencia’s Fiesta

Gil, Llorca, Paniagua (UV) HQ intangible capital and FDI ETSG 2018 Warsaw 2 / 20

Motivation Example

Valencia’s Fiesta

Motivation Example

Valencia’s Fiesta

Motivation Example

Valencia’s Fiesta

Outline

1 MotivationExampleContributionsStylized facts on Reinvestment

2 The modelSetup & Domestic productionForeign productionEmpirical equations

3 Empirical StrategyEmpirics

4 ResultsCountry-level resultsSector-level resultsRobustness & Endogeneity

5 Conclusions

Motivation Contributions

This paper

1 develops a model that captures formally the mechanisms that explainHQ’s capital effect on physical capital and labor

we incorporate sector and country-specific spillovers that stem fromHQ intangible capital:Sectors with higher endowment of intangible capital have higher levelsof bilateral flows of foreign capital and jobs

2 uses different measures of HQ intangible capital to estimate thespillovers in a panel of greenfield FDI data for 200 countries and 40sectors during 2003-2016 with structural gravity

We find that those sectors in country pairs with a higher level of HQintangible capital exhibit higher levels of FDI (in both margins)Bonus: jobs, financial constraints

Motivation Contributions

This paper

1 develops a model that captures formally the mechanisms that explainHQ’s capital effect on physical capital and labor

we incorporate sector and country-specific spillovers that stem fromHQ intangible capital:Sectors with higher endowment of intangible capital have higher levelsof bilateral flows of foreign capital and jobs

2 uses different measures of HQ intangible capital to estimate thespillovers in a panel of greenfield FDI data for 200 countries and 40sectors during 2003-2016 with structural gravity

We find that those sectors in country pairs with a higher level of HQintangible capital exhibit higher levels of FDI (in both margins)Bonus: jobs, financial constraints

Motivation Stylized facts

Fact 1: HQ intangible capital has increased1

2003 2006 2009 2012 2015Years

Investment (USD) Projects Jobs

(a) Greenfield FDI

2003 2006 2009 2012 2015Years

Investment in HQ Number of HQ

(b) Foreign Headquaters

Fact 2: HQ intangible capital and FDI are correlated.−

−5 0 5 10Size of HQ (log)

(c) HQ intangible capital intensity−

0 1 2 3 4Number of HQ (log)

(d) Foreign HQ number

Fact 3: Sectoral and country variation.

Table: Sectoral and country variation in HQ intangible capital

(a) Secotrs

mean sd min max

HQ intangible capital (m USD) 3,859.23 4,672.38 75.25 20,789.57HQ intangible capital number 145.74 230.37 3 1337

(b) Countries

mean sd min max

HQ intangible capital (m USD) 812.28 3,023.58 0 29,714.98HQ intangible capital number 30.88 118.97 0 1276

The model Setup & Domestic production

Our world is populated by J countries with consumers with f astandard Cobb-Douglas preferences.In order to satisfy the demand, individual firms combine labor andcapital in the production of goods. We depart from the standardapproach by considering two types of capital: physical and intangiblecapital (management, finance or marketing)

We use a nested CES production function allowing in a first levelsubstitution between physical and intangible HQ intangible capital,which are combined with labor to produce final goods.

xjsz = θηss Ll [K kHh]1−l , (1)

To model spillovers from intangible capital, we assume that ηs(h) is astrictly concave function that increases with h.

The model Foreign production

The setup

We allow foreign firms to enter a foreign market and produce goods forthe domestic market. These firms produce goods with the blueprintsfrom their home country i. They must adapt the physical capital tolocal specifications and translate the blueprints for local workers.

Therefore, foreign firms face transfer costs of intangible capital (e.g.,Knowledge as in Keller & Yeaple, 2013 AER) with a marginal costs of

ωij (α)≡ α(τ1/ηsij H+ τijK +wjL) (2)

An the firm’s problem:

maxπijs =max{(vsYj)

P(1−σ)/σ

θηss Ll [K kHh]1−l −ωij(α)− f }. (3)

The setup

P(1−σ)/σ

The setup

P(1−σ)/σ

In equilibrium

K ∗ij =

(vsYj)1/σ

P(1−σ)/σ

(k− lk)θ ηss

ατ1−ψ−lk+k+1/ηs ·(h−lh)ij

(k−lkl wj

)l (k−lkh−hl

)h−lh 1

1−ψ

L∗ij =

(vsYj)1/σ

P(1−σ)/σ

lθ ηss

ατlk−k1+1/ηs ·(hl−h)ij w1−k+lk−h+hl

k−lk)lk−k ( l

h−lh)hl−h

11−ψ

The model Empirical equations

Assuming Pareto-distributed productivities, aggregating over firms, weobtain log-linear transformations of country-pair-sector equations for FDIand jobs:

ln ˜FDIijs = θ0+ni + sj +λs +ηs

1−ψlnθs −

ψ−1− lk+k

1−ψlnτij −

(h− lh)

ηs(1−ψ)lnτij +ωijs ,

ln L̃ijs = θ0+ni + sj +λs +ηs

1−ψlnθs −

lk−k

1−ψlnτij +

(h− lh)

ηs(1−ψ)lnτij +ωijs .

1 HQ intangible capital has a positive effect on the levels of foreigncapital and jobs.

2 HQ intangible capital reduces the negative effect of transaction costsfor physical capital

3 HQ intangible capital reduces the positive effect of transaction costsfor foreign labor

1 Transaction costs for foreign labor is ambiguous: depend onintangibility!

Empirical Strategy Empirics

EstimationWe use the the Pseudo-Poisson Maximum likelihood (PPML) estimatorproposed by Silva and Tenreyro (2006) using Larch’s et al. (2017)procedure:

FDIijst =exp

β1FTAijt +β2BITijt +β3HQijst+

β4HQijst ×BCit +β5HQijst ×BCjt +β6HQijst × lnDij+

λit +λit +λij +λs

+eijst ,

DataFDIMarkets: firm level greenfield investments

200 countries, 26 sectors, during the period 2003-2016Intensive and extensive marginsForeign Jobs

FDIijst =exp

+eijst ,

FDIijst =exp

+eijst ,

Results Country-level results

(1) (2) (3)FDI Flows Projects Jobs

BIT 0.238∗ 0.122 0.260(0.14) (0.07) (0.18)

FTA 0.184∗ 0.053 -0.074(0.10) (0.04) (0.08)

HQdummy 0.155∗∗∗ 0.170∗∗∗ 0.188∗∗∗

(0.04) (0.01) (0.03)

Observations 78,527 78,527 60,834R2 0.77 0.96 0.90

Home*Year FE Yes Yes YesHost*Year FE Yes Yes YesCountry-pair FE Yes Yes Yes

Robust standard errors in parentheses, clustered by country pair∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Results Country-level results

(1) (2) (3) (4) (5) (6)FDI Flows Projects Jobs FDI Flows Projects Jobs

mainBIT 0.247∗ 0.124∗ 0.262 0.249∗ 0.123∗ 0.263

(0.14) (0.07) (0.18) (0.14) (0.07) (0.18)

FTA 0.186∗ 0.054 -0.073 0.185∗ 0.054 -0.074(0.10) (0.04) (0.08) (0.10) (0.04) (0.08)

HQijst 0.184∗∗∗ 0.175∗∗∗ 0.194∗∗∗ 0.785∗∗∗ 0.167 0.461∗

(0.04) (0.01) (0.03) (0.27) (0.10) (0.25)

HQijst ×BCit -0.328∗∗∗ -0.048 -0.080 -0.329∗∗∗ -0.048 -0.080(0.10) (0.03) (0.08) (0.10) (0.03) (0.08)

HQijst ×BCjt 0.100 -0.002 0.029 0.069 -0.002 0.018(0.13) (0.05) (0.16) (0.13) (0.05) (0.17)

HQijst × lnDij -0.072 0.001 -0.032(0.09) (0.01) (0.03)

Observations 78527 78527 60834 78527 78527 60834R2 0.769 0.964 0.892 0.770 0.964 0.892Home*Year FE Yes Yes Yes Yes Yes YesHost*Year FE Yes Yes Yes Yes Yes YesCountry-pair FE Yes Yes Yes Yes Yes Yes

Standard errors in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Results Sector-level results

(1) (2) (3)FDI Flows Project Jobs

BIT -0.042 0.067 0.158(0.18) (0.07) (0.20)

FTA 0.372∗∗∗ 0.080∗∗ 0.367∗∗∗

(0.09) (0.04) (0.11)

HQdummy 0.109∗∗∗ 0.159∗∗∗ 0.175∗∗∗

(0.03) (0.01) (0.03)

Observations 78942 78955 67923R2 0.970 0.938 0.937

Home*Year FE Yes Yes YesHost*Year FE Yes Yes YesCountry-pair FE Yes Yes Yes

Sector FE Yes Yes Yes

(1) (2) (3)FDI Flows Project Jobs

BIT -0.038 0.069 0.171(0.18) (0.07) (0.20)

FTA 0.372∗∗∗ 0.080∗∗ 0.373∗∗∗

(0.09) (0.04) (0.11)

HQijst 0.137 ∗∗∗ 0.102 0.883∗∗∗

(0.03) (0.11) (0.30)

HQijst ×BCit -0.049 -0.071∗∗ -0.132(0.09) (0.04) (0.09)

HQijst ×BCjt 0.234∗ 0.084∗ 0.406∗∗

(0.14) (0.04) (0.18)

HQijst × lnDij 0.004 0.007 -0.081∗∗

(0.04) (0.01) (0.03)

Observations 78042 78056 67251R2 0.970 0.938 0.937Home*Year FE Yes Yes YesHost*Year FE Yes Yes YesCountry-pair FE Yes Yes YesSector FE Yes Yes Yes

Sector HQ dummy Sector HQ dummyAerospace 0.950*** Financial 0.239***Alternative Energy -0.571* Food & Tobacco 0.163Automotive Components -0.029 Industrial Machinery 0.238**Automotive OEM 0.093 Medical Devices 0.196Beverages 11.489 Metals -0.054Biotechnology -1.977 Paper -0.283Business Machines 0.252 Plastics 0.496*Chemicals -0.508*** Rubber 0.785Coal -0.116 Semiconductors 0.943**Communications 0.421*** Software 0.252***Consumer Electronics 0.561** Textiles 1.473***Electronic Components 0.610*** Transportation -0.320**Notes: Control variables included but not reportedAll estimates include a full set con country*year and country-pair FE∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Results Robustness & Endogeneity

(1) (2) (3) (4) (5) (6)FDI Flows Project Jobs FDI Flows Project Jobs

BIT 0.437 -0.247 -1.252 0.256 -0.053 -0.065(0.52) (0.11) (1.27) (0.52) (0.11) (3.60)

FTA -0.543 1.207∗∗∗ -0.014 0.157 1.141∗∗∗ -0.042(0.77) (0.15) (1.33) (0.39) (0.16) (1.51)

lnHQCapex 0.319∗∗∗ 0.171∗∗∗ 0.357∗∗∗

(0.05) (0.02) (0.05)

lnHQextensive 0.098 0.253∗∗∗ 0.422∗∗∗

(0.13) (0.05) (0.10)

Observations 4367 4367 4335 4367 4367 4335R2 1.000 0.999 1.000 1.000 0.999 1.000Home*Year FE Yes Yes Yes Yes Yes YesHost*Year FE Yes Yes Yes Yes Yes YesCountry-pair FE Yes Yes Yes Yes Yes YesSector FE Yes Yes Yes Yes Yes Yes

Results Robustness & Endogeneity

(1) (2) (3) (4)HQ (volume) FDI Flows Project Jobs

FTA -0.693∗∗∗ 0.447∗∗∗ 0.107∗∗∗ 0.424∗∗∗

(0.22) (0.09) (0.04) (0.11)

BIT 0.062 -0.056 0.050 0.168(0.21) (0.18) (0.07) (0.20)

Reinvestment 0.022∗∗∗

(0.00)

Reinvestment (hat) 0.158∗∗∗ 0.069∗∗∗ 0.138∗∗∗

(0.03) (0.02) (0.03)

Observations 4367 78942 78955 67923R2 0.993 0.970 0.937 0.937

Standard errors in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Conclusions

The (in)tangible value of the paper

1 We tackled the effects of intangible capital in foreign production, morespecifically the HQ spillover effect of intangible capital on capital andlabor

We constructed a model that guided us into a empirical analysis of thiseffect.

2 We found that HQ intangible capital increases levels and reducestransaction costs of FDI intensive and extensive margins.

3 Regarding jobs, intangible capital has two offsetting effects on foreignlabor:

1 it increases its level,2 while intensifying the negative effect of distance costs.

4 Policy: Be aware that the spillovers from intensively intangible firmsmight impede this goal if transaction costs are high enough.

Conclusions

headquarters intangible capital and fdi paniagua... · 2018. 10. 24. · headquarters intangible...

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