Determinants of International R&D Outsourcing: The Role of Trade 

María García-Vega and Elena Huergo* 

Dpto. de Fundamentos del Análisis Económico I. Universidad Complutense.  

GRIPICO (Group for research in Productivity, Innovation and Competition). Universidad Complutense. 

 

 

Abstract 

 
This paper investigates the determinants of international R&D outsourcing, in particular the role of 
trade. We construct a monopolistic competition model with heterogeneous firms where outsourcing 
increases a firm’s fixed transaction as well as its productivity. Financial constraints affect the decision 
to outsource R&D more to non-exporters than to exporters. In contrast, exporters are more sensitive to 
a lack of information because they have higher losses when there is technology leakage. We test these 
predictions using a panel database of Spanish companies. The results highlight the relevance of 
information in competitive markets, and the role of trade to induce companies to engage in other 
globalization strategies. 
 
 
JEL Classification: F2, L24, O3. 

Keywords: R&D outsourcing, foreign locations, exporters, financial constraints, technology leakage. 

                                                 

* Corresponding author: TBA. Dpto. Fundamentos del Análisis Económico I. Facultad CC. Económicas y Empresariales. 
Universidad Complutense de Madrid. Campus de Somosaguas. 28223 Madrid. Spain. Emails: María García-Vega: 
mlgarcia@ccee.ucm.es;  Elena Huergo: ehuergo@ccee.ucm.es 

+ The authors are grateful for detailed comments from Christian Volpe, Luis Miguel de Castro, Lourdes Moreno, and the 
audiences at X Jornadas de Economía Internacional (Madrid), the ETSG 2007 Conference (Athens), the ESF/COST Workshop 
on “Science and Technology Research in a Knowledge-based Economy” (Leuven), the XXXII Simposio del Análisis 
Económico (Granada), and Workshop on “International Economics” (Zaragoza). All errors are ours. This research has been 
partially funded by the CICYT project SEJ2004-02525/ECON. Elena Huergo also acknowledges financial support from the 
CDTI (Centro para el Desarrollo Tecnológico Industrial). 
* Corresponding author: Dpto. Fundamentos del Análisis Económico I. Facultad CC. Económicas y Empresariales. 
Universidad Complutense de Madrid. Campus de Somosaguas. 28223 Madrid. Spain. Phone: +34 913942408. Fax: +34 
913942561. E-mail: ehuergo@ccee.ucm.es 



 1

1. Introduction 

In highly competitive industries, innovation is essential for the survival of the firms. Companies have 

incentives to buy technological services from commercial providers, customers, universities or even competitors 

located around the world (Baumol, 2001). For example, in the pharmaceutical industry, a large part of the 

clinical tests are done by external companies in Israel, Taiwan or South Korea; Boeing subcontracts the design of 

some components from companies located all around the world; manufacturers of semiconductors work closely 

with suppliers from other countries to jointly develop new products (The Economist Intelligent Unit, 2007).  

Despite the growing importance of international R&D outsourcing, little is known about its 

determinants. In this paper, we study the determinants to international R&D outsourcing, in particular we 

analyse how trade can affect the decision to outsource R&D internationally. We develop a theoretical model and 

provide empirical evidence. 

Some previous studies have empirically analysed the complementarities between international R&D 

outsourcing and internal R&D (e.g., Arvanitis and Hollenstein, 2006, and Braga and Willmore, 1991, Veugelers 

and Cassiman, 1999). An important message from this literature is that international R&D outsourcing does not 

seem to substitute domestic R&D, since firms need absorptive capacities to outsource R&D1. These papers, 

however, do not look at the role of trade, which is our main research question. Specifically, we analyse to what 

extent trade favours international R&D outsourcing, and whether some of the determinants to outsource R&D 

abroad differ between exporters and non-exporters. We focus on the factors that firms report as the most 

important ones for their decision to outsource R&D: intellectual property loss, and financial constrains (The 

Economist Intelligent Unit, 2007, Lewin and Couto, 2006, R&D magazine, 2007). We have three main sets of 

theoretical and empirical findings. First, we find that international R&D outsourcing is more likely for exporters 

than for non-exporters. Second, we show that financial constraints affect only non-exporters. Third, we find that 

exporters are more sensitive than non-exporters to a lack of information that helps to monitor technological 

leakage. 

                                                 

1 Absorptive capacities typically refer to internal R&D investments or other firm characteristics that enable companies to 
use external technologies (Cohen and Levithal, 1990). Also the literature that analyses international outsourcing of 
intermediate goods has emphasized potential complementarities between outsourcing and R&D (Glass and Saggi, 2001, 
Marjit and Mukherjee, forthcoming). 



 2

In order to explain the link between trade and international R&D outsourcing, we propose a model of 

monopolistic competition with heterogeneous firms. In the model, companies can outsource internationally, 

domestically, or not at all. Outsourcing increases the firms’ productivity and fixed transaction costs. We assume 

that there is a cash-in-advance constraint at the beginning of the period, which deters the possibility to outsource 

unless a bank finances the transaction costs. Under borrowing constraints, the bank extends finance to a firm if 

the company can offer its operating profits to the creditor.  

The model predicts that exporters tend to outsource more abroad than non-exporters for the following 

reasons: First, there is a scale effect associated with exports. As exporters are selling to a larger market, 

technological international outsourcing is relatively less costly for exporters. Secondly, exporters have higher 

operating profits than non-exporters. By trading, a firm decreases its financial constraints, which allows the 

purchase of technology abroad. However, a company becomes reluctant to outsource R&D internationally when 

there is technology leakage. As in Lai et al (2005), we formalize this concept by assuming that a firm 

experiences a reduction in its demand when there are hold-up problems. This leads to exporters facing larger 

potential losses than non-exporters under technology leakage. The model predicts that an exporter becomes more 

dependent on information than a non-exporter if technology leakage can be partly monitored with information 

about the technology or about the market characteristics.  

We provide empirical evidence by using data on approximately 7000 Spanish companies for the years 

2004 and 2005. We find that being an exporter increases the probability of outsourcing R&D abroad by more 

than one half. We show that financial constraints are obstacles to outsourcing internationally for non-exporters, 

but that this is not the case for exporters. By contrast, only exporters find that the lack of information on markets 

or on technology is an obstacle to outsourcing abroad. This result is consistent with the existence of hold-up 

problems associated with R&D outsourcing as shown by Baccara (2007) and Lai et al. (2005). 

Our paper is related to the existence literature as follows. Our model builds on recent contributions by 

Antràs and Helpman (2004) and Helpman et al. (2004). In both models, the firm decides whether to outsource 

intermediate goods depending on the firm’s productivity, among other reasons. In our paper, we also emphasize 

the importance of firm selection with regard to R&D outsourcing. Our paper is also part of the literature of 

financial market frictions and firms’ decisions (Cooley and Quadrini, 2001). For example, Manova (2006) shows 



 3

that credit constraints determine trade patterns. In Chaney (2005) and Manova (2006) heterogeneous companies 

face credit constraints to finance their costs of trade. But unlike these papers, we study how financial constraints 

can affect the decision to outsource R&D.  

This paper is also related to the growing literature on international outsourcing (e.g., Feenstra and 

Hanson, 1999, Grossman and Helpman, 2002), but we analyse outsourcing of technological services instead of 

manufactured goods. Our paper is also related to the literature that analyses international diffusion of knowledge 

among countries (e.g., Eaton and Kortum, 1996, or Keller, 2004, among others). Our contribution is that we 

analyse a specific channel of international knowledge transfers - the acquisition of technological services among 

firms.  

The structure of the paper is as follows. In Section 2, we present a theoretical model for the determinants 

of R&D outsourcing. In this section, we first specify the basic model in which firms cannot export. Then, we 

extend the model into an open economy framework. We study the determinants of R&D outsourcing with 

financial constraints. Finally, we consider the case of leakage of technology. Section 3 describes the data, sets 

out the empirical methodology, and presents the empirical results of the estimations. Section 4 concludes. 

 

2. The model  

Our model builds on the Antràs and Helpman (2004) and Helpman et al. (2004) models of monopolistic 

competition with heterogenous firms. We analyse an industry with a measure M of active firms. We assume that 

the economy has two sectors: one is characterized by a numeraire good, and the other by differentiated products. 

The preferences of a representative consumer are given by the following utility function: 

0= +U x Q ,      (1) 

where 0x  is the consumption of the numeraire good, and Q  is an index of consumption of the differentiated 

products. As in Dixit and Stiglitz (1977), Q  reflects the consumer’s taste for varieties, which can be written as: 

1/

0

α

α⎛ ⎞
= ⎜ ⎟
⎝ ⎠
∫
M

jQ q dj and 0 1α< < , 

where jq  is the quantity of variety j  of the differentiated product demanded by the consumer. The elasticity of 



 4

substitution among varieties is 1 (1 )σ α= − . In this set up, the aggregate demand for any of the varieties of the 

differentiated product is given by  

1/( 1)

( )
α−

= j
j

p
y p E

P
,      (2) 

where E is the aggregate expenditure or the market size, jp  is the price of the good, and /( 1)

0

α α−= ∫
M

zP p dz  is the 

weighted aggregate price index.   

 As in Melitz (2003), we assume that firms are heterogeneous. Companies have different levels of 

productivity, denoted by 1ϕ > , associated with traditional inputs (labor, physical capital, materials…), and internal 

R&D resources that provide absorptive capacities. We consider that a firm’s efficiency is drawn from a Pareto 

distribution with support [1, )ϕ ∈ ∞ , and shape ψ . More productive firms have lower marginal costs than their less 

productive counterparts.   

 After knowing their efficiency level, firms can outsource part of their innovative activities either to 

companies in their country or to firms in another country. In both cases, their marginal cost decreases. The cost of 

producing one unit of output is equal to 1 (  )ϕ ic , with 1,  ic ≥ for 0, ,i D F= . The constant ic  is a multiplicative 

increase of productivity due to outsourcing domestically if =i D , or to outsourcing abroad if =i F , respectively. If 

firms do not outsource (in this case 0=i ), there is no productivity increase (and thus 0 1=c ). This multiplicative 

form ensures that more productive firms benefit relatively more from outsourcing than less productive companies. 

Our justification for this specification is that firms with higher efficiency have higher absorptive capacity than less 

efficient firms. The firm’s productivity increases if the firm outsources, which in the model implies that 

0 1F Dc c cζ= > = , with 0ζ > . The parameter ζ  is a way of modelling the degree to which the productivity 

increase is different if the firm outsources abroad instead of domestically. What is the interpretation of ζ ? If 1ζ > , 

outsourcing abroad is more productive than domestic outsourcing. For some industries it is a question of the 

complexity of technology. For example, aerospace companies buy some pieces from very specialized foreign 

providers. In this line, Hagedoorn (1993), Brusoni et al. (2001), and Cesaroni (2004) show that technology sources 

in some fields provided by suppliers from other countries are the best option for the firm to keep up to date. The 



 5

reason is that international outsourcing offers the possibility to buy the best available technology. The size of ζ  

might also depend on the firms’ focus on the market. Market proximity can be especially important for international 

companies that face competition. For example, manufactures of food and industrial ingredients buy research studies 

on dietary habits from external companies located in foreign countries. In this way, the company can better meet the 

requirements of their overseas customers.  

 We model the cost of outsourcing as a transaction fixed cost denoted by if , for ,i D F=  (e.g., Feenstra et 

al., 2004). If the firm does not outsource, it bears a fixed cost 0f . R&D outsourcing requires close collaboration 

between the research teams. Therefore, international outsourcing implies higher communication, coordination, and 

organizational costs than domestic outsourcing. In the model, this assumption implies that 0 0> > ≥F Df f f , as in 

Antràs and Helpman (2004).  

 Each firm faces a cash-in-advance constraint: at the beginning of every period, it requires a bank to finance 

the outsourcing investment and the fixed cost. Both the bank and the borrower are risk-neutral. We assume that only 

one-period debt contracts are signed with the bank (Cooley and Quadrini, 2001). As in Melitz (2003), we assume 

that a constant exogenous shock can force companies to exit the market2. If the firm defaults, the bank liquidates the 

company, and the firm immediately exits the industry. The bank perfectly observes a firm’s characteristics. It makes 

a take-it-or-leave-it offer to the firm, and issues funds at an interest rate r . As in Cooley and Quadrini (2001), the 

bank chooses the interest rate such that the expected repayment from the loan is equal to the repayment of a riskless 

loan, as in the following equation:  

0(1 ) (1 )  (1 )λ λ+ = + + −i ir f r f C ,  with 0(1 ) ,ir f C+ >  for 0, ,i D F= .  (3) 

The left-hand side of equation (3) gives the return of the loan at the riskless interest rate 0r . The right-hand side of 

the equation says that with probability λ , the firm can repay its debts, and with probability 1 λ− , it goes bankrupt. 

In the case of bankruptcy, the bank gets the collateral denoted by C . 

 A firm’s objective consists of maximizing profits subject to the constraints given by equations (2) and (3), 

                                                 

2 For the purpose of our model, we do not need that the probability of bankruptcy depends on firm’s characteristics. The 
model still predicts that large and more productive firms are less financially constrained than small and less productive 
companies. 



 6

where profits are given by3 

( )( , )  ( ) (1 ) (1 )
 

ϕ λ λ
ϕ

⎡ ⎤
= − + + + −⎢ ⎥

⎣ ⎦
i i

i

y pB c p y p r f C
c

, for 0, ,i D F= . 

On the right-hand side, the first term reflects the firm’s income, and other three terms are the variable costs, and 

the return of the loan at the riskless interest rate as expressed in equation (3). This leads to the standard pricing 

rule ( , ) 1 (   )ϕ α ϕ=i ip c c . We call ( , )ϕΠ ic  the maximum profit of a firm of type ϕ  that chooses outsourcing 

of type i  after applying this pricing rule.  

We assume that the company with the lowest level of efficiency that decides not to outsource obtains 

zero profit, i.e., 0(1, ) 0cΠ =  (Zero Profit Condition). To make the analysis interesting, we consider that 1ζ > ; 

this rules out the trivial case where firms always prefer not to outsource R&D abroad. We show in Appendix 1 

that, under the previous assumptions, there exist two unique efficiency thresholds. The first threshold refers to 

the efficiency such that companies are indifferent with respect to either outsourcing domestically or not 

outsourcing, and the second one to outsourcing either domestically or abroad. They are denoted by ϕD  and ϕF , 

respectively, and they are calculated as the efficiency levels for which maximum profits of the decisions are 

equalized: 0( , ) ( , )D D Dc cϕ ϕΠ = Π , and ( , ) ( , )ϕ ϕΠ = ΠF D F Fc c . We obtain a partition of firms similar to the one 

in Antràs and Helpman (2004), in the component-intensive sector, where only highly efficient firms outsource 

abroad. As it can be seen in Figure 1, if a firm’s efficiency is ϕ ϕ≥ F , the company prefers to outsource 

internationally; if [ , )ϕ ϕ ϕ∈ D F , the firm outsources in the domestic market; and finally, if ϕ ϕ< D , the company 

chooses not to outsource4.  

INSERT FIGURE 1 

Both thresholds, specified in Appendix 1, depend negatively on market size E . This reflects the simple 

idea that in larger markets fixed transaction costs are a lower proportion of the firm’s income than in smaller 

markets. Moreover, the thresholds depend negatively on the aggregate price in the industry and the number of 

                                                 

3 In what follows, we omit the sub-index j to simplify the expressions. 
4 In this simplified model without financial constraints, all firms that choose the same type of outsourcing have to pay the 
same interest rate independently of their efficiency. However, the implications of the model do not change if either the 
probability of bankruptcy or the collateral depends on the company’s efficiency. In that case, the interest rate would depend 
negatively on the firm’s efficiency.  



 7

companies in the market. We also show in Appendix 1 that Fϕ  decreases with ζ . In other words, if foreign 

outsourcing is more productive than domestic outsourcing, there are more companies that decide to outsource 

abroad.  

2.1 Open economy model 

 We now assume that there are two identical countries that trade the varieties of Y. Trade for continuous 

exporters involves only one type of cost: there is a variable per unit cost of the good that is transported5. This 

variable cost takes the form of an iceberg cost, so that for one unit of a good to arrive at the final destination, 

1>τ  units of the good need to be shipped. While a firm’s prices in the domestic market are the same as before, 

i( , ) 1 ( )ϕ α ϕ=ip c c , exporters set prices in the foreign market taking into account the transport cost, as in the 

following expression ( , ) ( )ϕ τ α ϕ=x i ip c c .  

 Under these assumptions, we want to answer the following question: Is the probability of outsourcing 

higher for exporters than for non-exporters? In our framework, this is equivalent to determining whether the 

efficiency threshold to outsource is lower for exporters than for non-exporters. 

 The total maximum profits of a non-exporter with efficiency ϕ  that chooses outsourcing of type i are 

equal to the maximum profits in the domestic market. They are given by 

0
(  )  (1 )( , ) (1 )

( )

ρϕ αϕ
φ

−
Π = − +i

nx i i
c Ec r f

P
, where /(1 ) 0ρ α α= − > , and ( )φP  is the weighted aggregate price 

index in the domestic economy with trade. This price is a function of , , , ,( , , , )φ ϕ ϕ ϕ ϕ= D x D nx F x F nx , which is the 

efficiency threshold to outsource domestically for exporters and non-exporters (denoted by ,ϕD x  and ,ϕD nx , 

respectively), and the efficiency threshold to outsource abroad for exporters and non-exporters (denoted by ,ϕF x  

and ,ϕF nx , respectively). Since both countries are identical, the aggregate price indices are equal in the domestic 

and in the foreign market. 

                                                 

5 We also assume that there is a sunk cost to start exporting. In the empirical analysis, we study the decision to outsource for 
continuous exporters. For this reason, we assume in the model that exporters have already paid the sunk cost of entry into 
the foreign market. The sunk trade cost implies that only some firms can export, and that there exists a unique efficiency 
threshold such that firms with efficiency lower than the threshold do not export, while firms with efficiency higher than the 
threshold decide to export (Melitz, 2003). 



 8

 The maximum profit of an exporter with efficiency ϕ  that chooses outsourcing of type i is equal to the 

sum of the profits in the domestic and in the foreign market, as expressed in the following equation; on the right-

hand side, the first term is the income minus the variable cost to sell in the domestic market, the second term 

reflects the income minus the variable cost to sell in the foreign market, and the third term is the return of the 

loan at the riskless interest rate: 

1/( 1)

0
(  )  (1 ) (  )  ( )( , ) (1 )

( ) ( )

ρ ρ αϕ α ϕ τ α τϕ
φ φ

−− −
Π = + − +i i

x i i
c E c Ec r f

P P
. 

We can express the maximum profit of an exporter in terms of the profits of a firm (with the same 

characteristics) that does not export as:  

1/( 1)(  )  ( )( , ) ( , )
( )

ρ αϕ τ α τ
ϕ ϕ

φ

−−
Π = Π + i

x i nx i
c Ec c

P
. 

 We show in Appendix 1 that if 1ζ > , some firms outsource R&D abroad, and that if 1ζ ≤ , firms do not 

outsource R&D abroad. Furthermore, if 1ζ > , the threshold efficiency levels for outsourcing are larger for non-

exporters than for exporters, i.e., , ,ϕ ϕ<i x i nx  for ,=i D F . The difference between the minimum thresholds to 

outsource depends on the differences between the profits of exporters and non-exporters. It implies that, before 

making the decision to outsource, exporters need to be less efficient than firms with similar characteristics that 

operate only in the domestic market (although, ex post exporters become more efficient due to outsourcing). The 

probability of outsourcing internationally is equal to the probability of having efficiency that is higher than the 

efficiency threshold. This result leads to the following proposition. 

 
Proposition 1: If 1ζ > , the probability of outsourcing R&D abroad is higher for exporters than for non-

exporters.   

 
 Exporters have on average higher revenues than non-exporters before outsourcing, consequently fixed 

transaction costs associated with outsourcing become relatively lower in terms of the total revenue. We consider 

the general case in which transaction costs of international R&D outsourcing are the same for exporters than for 

non-exporters. If we assume a more specific framework where transaction costs are lower for exporters than 

non-exporters, the results of the model would be reinforced. 



 9

2.2 Open economy model with financial constraints 

There is much evidence showing that financial constraints have important implications for firm decisions 

(e.g., Clementi and Hopenhayn, 2006). These constraints can influence a firm’s purchase of technology. 

Moreover, there are some empirical studies that find that continuous exporters tend to be financially healthier 

than non-exporters (Greenaway et al., 2007). In this section, we introduce financial constraints in the model, 

distinguishing their effect for exporters and non-exporters.  

We assume that banks only extend finance to the firm if the company can offer its operating profits 

0 ( , )ϕΠk ic  to the creditor, i.e., 0(1 ) ( , )i k ir f cϕ+ ≤ Π , with =k nx  for non-exporters, and =k x  for exporters 

(e.g., Manova, 2006), for ,=i D F . A company obtains financial credit if its efficiency level is as large as the 

threshold ,ϕi k . This threshold is calculated by solving for the efficiency level such that operating profits are 

equal to the repayment of the loan plus the interest, for both exporters and non-exporters, respectively,  

0 0
,

(1 ) (1 )( , ) λϕ
λ

+ − −
Π = i

k i k i
r f Cc , for ,=i D F .    (4) 

We show in Appendix 1 that operating profits are larger for exporters than for non-exporters. Therefore, the 

efficiency threshold to obtain financial resources to outsource is lower for exporters than for non-exporters, i.e., 

, ,ϕ ϕ<i x i nx , for ,=i D F . As it can be seen in Figure 2, and as we show in Appendix 1, the impact of financial 

constraints on the decision to outsource is more important for non-exporters than for exporters. The reason is that 

the increase in the efficiency threshold needed to outsource when there are financial constraints (compared with 

the situation without financial frictions) is larger for non-exporters than for exporters: , , , ,ϕ ϕ ϕ ϕ− < −i x i x i nx i nx , 

or, equivalently, , , , ,( ) ( ) 0ϕ ϕ ϕ ϕ− − − >i nx i nx i x i x , for ,=i D F . Furthermore, this differential increases as 

financial constraints increase. It implies that the decrease in the probability of outsourcing is higher for non-

exporters than for exporters. These results can be expressed as the following proposition. 

 

Proposition 2: The negative effect of financial constraints on the probability of outsourcing R&D abroad is 

higher for non-exporters than for exporters. 

 

 Trade increases operating profits. Our model shows that this fact leads to a decrease in the financial 



 10

constraints of continuous exporters. Having lower financial constraints, exporters can buy technology more 

easily than non-exporters.  

INSERT FIGURE 2 

2.3 Open economy model with technology leakage 

 A key characteristic of R&D outsourcing, and, particularly, outsourcing abroad, is that it induces some 

risks in a situation of imperfect contracts, hold-up problems, and cultural differences (Ornelas and Turner, 2008, 

Lai et al., 2005, Baccara, 2007). Companies can be reluctant to outsource because they can be exposed to 

subcontractors, especially in countries with poor intellectual property rights. The next step of our analysis is to 

look at the impact of technology leakage in an open economy framework. In order to simplify the model, we 

assume that there are no financial constraints in the economy. As in Lai et al. (2005), we consider that firms 

experience a reduction in their demand when there is technology leakage. That is,  

1/( 1)

1/( 1)

      if there is no leakage
( , ) ,  where (0,1)

    if there is leakage    

j

j
j

p
E

Py p
p

E
P

α

α
δ δ

δ

−

−

⎧
⎪⎪= ∈⎨
⎪
⎪⎩

. 

The variable δ  reflects the importance of technology leakage. We assume that it depends on the capacity to 

monitor a subcontractor. Furthermore, we consider that companies can monitor better if they have some 

knowledge of the technological characteristics of the innovation or the market. These assumptions guarantee 

that, from the point of view of the company, information reduces the potential hold-up problem. In this very 

simple framework, the pricing rule does not change with respect to the base case. Prices in the domestic market 

are equal to ( , ) 1 ( )ϕ α ϕ=i ip c c , and in the foreign market exporters set prices equal to ( , ) ( )ϕ τ α ϕ=x i ip c c . It 

implies that maximum profits, in case of outsourcing, are given by 

0
(  )  (1 )( , , ) (1 )

( )

ρδ ϕ αϕ δ
φ

−
Π = − +i

nx i i
c Ec r f

P
, and 

1/( 1)(  )  ( )( , , ) ( , , )
( )

ρ αδ ϕ τ α τϕ δ ϕ δ
φ

−−
Π = Π + i

x i nx i
c Ec c

P
, for 

,=i D F , for non-exporters and exporters, respectively. Under the previous simplified assumptions, the negative 

effect of leakage on profits is more severe for exporters than for non-exporters since 

( , , ) ( , , ) 0ϕ δ ϕ δ
δ δ

∂Π ∂Π
− >

∂ ∂
x i nx ic c

. If there is more leakage (δ  decreases), the reduction in profits is higher for 



 11

exporters than for non-exporters. If profits decrease, the minimum efficiency threshold needed to outsource 

increases. This effect is more relevant for exporters than for exporters. Thus, information that helps to monitor 

subcontractors is relatively more valuable for exporters than for non-exporters. We summarize this result in the 

following proposition. 

 

Proposition 3:  When companies decide to outsource internationally, the lack of information is an obstacle that 

is relatively more important for exporters than for non-exporters. 

 In summary, our model relates trade to international R&D outsourcing. It predicts that: 

(i) When a firm exports, it becomes more likely to outsource (Proposition 1).  

(ii) Trade has two indirect effects on the decision to outsource technology abroad.  

 Trade makes exporters less financially constrained than non-exporters. This effect facilitates 

international R&D outsourcing for exporters. The theory predicts that financial constraints decrease the 

probability of outsourcing internationally mostly for non-exporters (Proposition 2).  

 In contrast, the model shows that exporters have larger losses than non-exporters in case of technology 

leakage. When there is a lack of information that helps to monitor subcontractors, exporters are more 

exposed to the potential danger of international outsourcing than non-exporters. This effect tends to 

decrease international R&D outsourcing for exporters. As a result, the model predicts a negative 

relationship between a lack of information and international R&D outsourcing for exporters, and a weaker 

relationship for non-exporters (Proposition 3).  

 In the next section, we investigate these predictions empirically. 

 

3. The empirical evidence  

3.1. The data and the main variables 

 The data set that we use comes from a survey of innovating Spanish firms (Panel de Innovación 

Tecnológica, PITEC). It is a panel database constructed from two ongoing statistics: the Technological 

innovation survey, and the Statistics about R&D activities, carried out in a coordinated way since 2002 by the 



 12

Spanish National Institute of Statistics6. In the survey, each company provides information on some of its 

economic data, such as sales or number of employees. The firm also answers several questions about its 

innovating activities. Although 2003 is the first year of the panel, in this paper, we only use the years 2004 and 

2005 for reasons of comparability7. In order to avoid endogeneity problems, some variables related to exporting 

activity are included with a one-period lag. The empirical analysis is conducted for the year 2005, for which we 

have information from 7,205 innovating firms, that is, firms that perform R&D internally and/or buy R&D 

services from other companies or institutions. We consider that a firm is outsourcing R&D if the R&D provider 

is outside of the company. In the survey, there are 2,759 firms that outsource R&D, 447 of which are located 

abroad. 

 The main interest of our analysis consists of testing the predictions developed in the previous section: to 

what extent exports increase international R&D outsourcing (proposition 1), and whether exporters and non-

exporters are influenced differently by financial constraints (proposition 2), and by information problems 

(proposition 3) at the moment of international R&D outsourcing. Our data provides detailed information on these 

variables at the firm level. Each firm indicates its purchases of R&D services from other companies or 

institutions at foreign locations. This variable is our measure of R&D outsourcing, and it is defined in the survey 

as:  

“Acquisitions of R&D services outside the firm from foreign providers through contracts, informal 

agreements, etc… Funds to finance other companies, research associations ,etc… that do not directly 

imply purchases of R&D services are excluded”. 

 

                                                 

6 The database is constructed on the basis of the annual Spanish responses to the Community Innovation Survey (CIS). This 
survey is specifically designed to analyze R&D and other innovating activities following the recommendations of the OSLO 
Manual on performing innovation surveys (see OECD and Eurostat, 1997). The survey is targeted to industrial companies 
whose main economic activity corresponds to sections C, D, and E of NACE 93, except non-industrial companies because 
of the imprecision of methodological marking in the international context by other branches of activity. The questions we 
quote bellow are the English version from the CIS questionnaire. These questions are the exact equivalent of the Spanish 
questionnaire.  
7 Initially, the panel was assembled with two samples of firms surveyed on the basis of a census: firms with 200 or more 
employees and firms with internal R&D expenditures in 2003. In 2004, the panel was enlarged with two new sets of firms 
employing fewer than 200 employees: firms with external R&D expenditure but without internal R&D expenditure, and 
non-innovative firms. The data after 2005 are  not available yet. 



 13

Note that this variable also excludes the acquisition of software, royalties, or investments in foreign R&D 

capacity8. 

Companies report their exports. We define a dummy variable for being an exporter, and a variable for 

export intensity, constructed as the ratio of exports over total employment.  

Companies answer questions about the factors that prevent them to innovate. We construct a variable 

that measures a firm’s financial constraints with the information that is specified in the following question in the 

database. The companies are asked:  

“During the three years 2003 to 2005, how important were the following factors for hampering your 

innovation activities? Lack of funds within your enterprise or group, lack of finance from sources 

outside your enterprise, and innovation costs too high.” 

For each of the factors, a company can answer that the importance of the factor was high, intermediate, or low, 

or that the factor was not relevant. We assign a number that varies from zero (not experienced) to three (high 

importance) for each answer. Then, we calculate the average importance of the cost factors. This is our variable 

lack of finance. We expect that financial constraints affect non-exporters but not exporters, or at least that the 

latter are influenced to a lesser extent.  

We construct a variable that quantifies the value of the information for the company. In the survey, the 

companies are asked: 

“During the three years 2003 to 2005, how important were the following factors for hampering your 

innovation activities? Lack of information on technology, and lack of information on markets” 

Again, for each of the factors, companies can answer that the importance of the factor was high, intermediate or 

low, or that the factor was not experienced. We calculate the variable lack of information in the same way as the 

variable lack of finance. This variable is our measure of the capacity of a firm to monitor its subcontractors. We 

consider that information is a relevant asset for the firm because it helps to avoid technology leakage. Therefore, 

access to knowledge stimulates international R&D outsourcing. We expect that the variable lack of information 

is negatively correlated with international R&D outsourcing for exporters, and that it has less influence for non-

exporters than for exporters.  

                                                 

8 R&D services are defined in the survey as: “Creative work to increase the volume of knowledge and to create new or 
improved products and processes (including the development of software)”. 



 14

3.2. Descriptive analysis 

 During 2005, around half of the continuous innovating firms were actively engaged in external R&D 

expenditures9. However, only a small percentage of those companies (9.4%) bought R&D abroad (see Figure 3). 

The database distinguishes the firms’ R&D providers. These can be firms belonging to the same company, other 

companies, public administrations, universities, non-profit institutions, and other international organizations. We 

consider R&D outsourcing if the provider is outside the enterprise group. As it can be seen in Figure 4, among 

firms that buy technology abroad, nearly 70% outsource. The definitions of the other variables are documented 

in Appendix 2. 

INSERT FIGURES 3 AND 4 

 Proposition 1 states that the probability of outsourcing R&D abroad is higher for exporters than for non-

exporters. This is a relationship that is strongly supported by the data. Our first empirical observation is depicted 

in Figure 5. There, we show that there are almost twice as many exporters as non-exporters that outsource 

abroad.  

INSERT FIGURE 5 

 In Table 1, we show some descriptive statistics of the variables of our empirical specification. The 

frequencies of the qualitative variables are quite similar between the sample of innovating firms and the sub-sample 

of innovating firms that outsource R&D, with the exception of the variables related with exports. The exporting 

intensity is higher for companies with international R&D outsourcing than for companies with national outsourcing 

or for firms with only internal R&D expenditures. Again, this pattern illustrates that international outsourcing and 

export activity are strongly positively related.  

INSERT TABLE 1 

 Figure 6 displays our second empirical observation. We compare the importance of the lack of finance for 

the firm by exporting status. Among the companies that do not outsource internationally, non-exporters are more 

concerned about their lack of financial resources than exporters. However, this obstacle is less relevant for non-

exporters than for exporters once firms outsource abroad. This pattern is consistent with our Proposition 2. This 

                                                                                                                                                                        

 
9 We call a firm with positive innovation expenditures both in 2004 and 2005 a “continuous innovator” 



 15

feature suggests that non-exporters have to face fewer financial constraints than exporters in order to outsource 

abroad. 

 We depict our third empirical observation in Figure 7. We compare the importance of the lack of 

information for the firm by its exporting status. For companies that do not outsource internationally, we can see 

that exporters are more influenced by the lack of information than non-exporters. Once companies outsource 

internationally, exporters are less concerned about the lack of information than non-exporters. We interpret this 

variation as reflecting that exporters need to accumulate more information than non-exporters in order to 

outsource, which is consistent with Proposition 3.  

INSERT FIGURES 6 AND 7 

 In summary, the descriptive analysis supports the propositions. In the next section, we test our predictions 

econometrically by controlling for potential covariates.  

3.3. The empirical specification  

 We now turn to examining the determinants of international R&D outsourcing at the firm level. We 

simultaneously estimate two equations by maximum likelihood. The first one describes a firm’s decision to 

contract technological activities abroad (selection equation). The second equation refers to the intensity of the 

R&D expenditure abroad. The selection equation is given by: 

1 if 0
0 if 0.

F

F

w
rd

w
ϕ ϕ α ε
ϕ ϕ α ε

′− = + >⎧
= ⎨ ′− = + ≤⎩

   (5) 

 The intensity equation, conditional on the firm reporting international R&D outsourcing, is expressed as in 

the following specification: 

* if 1
0 if 0.
r z e rd

r
rd

β′⎧ = + =
= ⎨

=⎩
       (6) 

 In equation (5), rd  is the observed binary endogenous variable equal to one if a given firm reports 

international R&D outsourcing, and zero for non-R&D reporters. The vector w  reflects factors that influence this 

decision. In equation (6), the variable r stands for international R&D outsourcing expenditures, and the vector z  

represents its determinants. We denote the error terms as e  and ε , and we assume that they are distributed as a 

normal bivariate with zero mean, variances 2 1εσ =  and 2σ e , and correlation coefficient ερe .   



 16

 Vectors w  and z  include, among others, the variables lack of finance and lack of information, and the 

indicators of the exporting activity. Notice that R&D outsourcing abroad can stimulate exports, given that 

outsourcing can allow access to a wider technological network and the accommodation of the firm’s products to 

the specificities of foreign markets. This can generate a potential endogeneity problem. In order to avoid this 

problem, we include the indicators of the exporting activity lagged one period. We also incorporate each of the 

lack of finance and lack of information variables multiplied by the being an exporter dummy variable (interacted 

variables) in the regression. We denote these variables as lack of finance: exporters, lack of finance: non-

exporters, lack of information: exporters, and lack of information: non-exporters, respectively. 

 In the theoretical model, each firm has an ex ante efficiency ϕ  associated with the productivity of 

traditional inputs and R&D resources. Recall that we assume that firms with ex ante higher efficiency have also 

higher absorptive capacity. Therefore, we include in the regressions a group of variables that reflect internal 

capabilities such as economies of scale coming from specialization, and firms’ specific advantages. We include 

the variables: R&D employment, a binary variable for companies with continuous R&D engagement, and firm 

size. Under the existence of economies of scale, we expect that firm size affects outsourcing positively10. 

Additionally, we include two dummy variables denoting whether the firm is part of a multinational, and whether 

it has applied for patents. An important result in the literature refers to the fact that multinationals produce with 

intensive technological techniques (e.g., Girma and Görg, 2004). Being a multinational can imply having 

management and organization advantages or superior knowledge capital. The same effect happens if the 

company has applied for a patent. We also include two dummies reflecting whether the firm belongs to a high-

tech manufacturing industry, or to the high-tech services sector. Finally, we include a set of geographical 

dummies. The reason for including these variables is that firms can learn from the outsourcing strategy 

implemented by geographically close companies, given the importance of agglomeration effects to induce 

spillovers. The list of high-tech activities and the definition of the geographical variables can be found in 

Appendix 2.  

                                                 

10 For example, Chang and Robin (2006) show that firm size is a key variable for explaining technology imports in 
Taiwanese firms. 



 17

 In addition, if a firm’s ex ante efficiency is higher than a certain threshold Fϕ , the firm finds outsourcing 

abroad profitable. In the model, this threshold depends on the lack of finance, the lack of information, export 

activity, and relative gains in productivity between the different types of outsourcing (the parameter ζ ), among 

other factors. Following these considerations, we also include in our specification variables that could make 

foreign outsourcing more productive than domestic outsourcing. We consider that market focused companies can 

obtain more productivity gains if they outsource abroad than less market focused firms. Companies report the 

importance of institutional sources of information, internal sources of information, and market sources of 

information in order to innovate. These variables can measure how market-focused the companies are11. We 

consider that companies that obtain information from universities, other public research centres or internal 

sources are less market-focused than companies that prefer market sources of information. Therefore, we expect 

that these two groups of firms obtain fewer gains from outsourcing than companies that obtain many innovations 

from the market.  

 We have estimated both equations with the same set of explanatory variables ( z w= ), with two 

exceptions. We expect the continuous R&D engagement to be relevant for the outsourcing decision, but we assume 

that it has no effect on outsourcing intensity. The model predicts that the decision to outsource abroad instead of 

domestically depends on both costs differentials and the decision to maintain regular internal R&D activities 

(through being more productive). However, we consider that the intensity of outsourcing abroad mostly depends on 

costs differentials. The other exception refers to the export activity that in the selection equation is introduced 

through the dummy variable being an exporter, while in the intensity equation it is introduced in terms of export 

intensity. We expect that among continuous exporters, those with a higher presence in international markets have 

more operating profits, and therefore more incentives to outsource more abroad.  

3.4. The results 

                                                 

11 See the detailed construction of the variables in Appendix 2. 



 18

 In order to get a first approximation of the determinants of international R&D outsourcing, we estimate the 

probability of outsourcing from domestic and/or from foreign locations through a probit model without interacted 

variables12. The results are presented in Table 2.  

INSERT TABLE 2 

 In these preliminary estimations, we cannot detect any impact of lack of finance or lack of information on 

the determinants to outsource. We find that firms’ internal R&D capabilities, proxied by continuous R&D 

engagement and R&D employment, increase the probability of outsourcing R&D. The rest of the explanatory 

variables show the expected effects: export activity, being a multinational, the patents application, and the 

maintenance of technological cooperation increase the propensity to buy R&D.   

 Next, we turn to the analysis of international R&D outsourcing. We estimate equations (6) and (7) using a 

Generalized Tobit model without including the interacted variables. The first column of Table 3 exhibits the 

coefficients of the probit model for the decision to outsource R&D internationally. The second one corresponds to 

the intensity, taking the selection term into account. The correlation term ερe  is statistically different from zero, 

pointing out the need to estimate a selection model for the observed intensity.   

INSERT TABLE 3 

Being an exporter increases the probability of outsourcing R&D abroad by 5 percentage points. The 

estimated coefficients in column (2) show that export intensity yields a significant effect in the outsourcing 

expenditures. To place this result in perspective, the probability to outsource abroad of an average company with 

external R&D expenditures is 9.4%, which implies that exporting increases the probability to outsource 

internationally by more than one half. This result is consistent with our Proposition 1, and our first empirical 

observation, supporting the hypothesis of complementarities between both types of internationalisation strategies. 

Our theory predicts that financial constraints and information are major determinants for international R&D 

outsourcing. The estimations show that lack of information reduces the probability of outsourcing by approximately 

2 percentage points, but it has a negligible influence on the quantity outsourced abroad. By contrast, we find that the 

                                                 

12 The interacted variables are the variables lack of finance and lack of information multiplied by the dummy variable being 
an exporter . 



 19

lack of finance has a negative impact on the quantity that is outsourced abroad, while having no influence on the 

outsourcing decision.  

 Conditional on reporting external R&D expenditures, most variables have the expected positive impact on 

the propensity to contract R&D abroad. Larger firms are more likely to outsource than smaller companies, although 

the relationship is non-linear. Firms that consider market sources of information as crucial are keener to outsource 

internationally. However, companies that find institutional sources of information important give priority to 

domestic locations. This is what we would expect if they were less market-focused.  

 The previous estimations have the limitation that we cannot distinguish whether the impacts of financial 

constraints and information problems differ between exporters and non-exporters. We address this issue by 

including the interacted variables in the specification. The results of the estimations are reported in Table 4. We 

find strong confirmation for our Proposition 3. In columns (1) and (2), we show that for exporters a lack of 

information on technology or on markets decreases the probability of outsourcing R&D abroad by approximately 3 

percentage points, while for non-exporters the impact of this variable is negligible.  

 We also find some support for Proposition 2. The lack of finance variable has a negative effect on the 

outsourcing expenditure only for non-exporters but it has no effect on the decision to outsource, either for exporters 

or for non-exporters. Plausibly, smaller firms tend to be more financially constrained than large companies13 . 

Therefore, firm size can be negatively correlated with lack of finance. This simultaneity can induce a bias in the 

estimated coefficient for non-exporters. To avoid this simultaneity problem, columns (3) and (4) present the 

regressions excluding the variable size in the estimations. The results are statistically significant: lack of finance 

affects the decision to outsource R&D abroad but only for non-exporters, as Proposition 2 states. Financial 

constraints lead to a decrease in international R&D outsourcing by non-exporters of almost 2 percentage points, 

and it has no effect on exporters. This finding suggests that exporters outsource abroad more than non-exporters 

due to lower financial constraints, in line with our Proposition 2, and not only because they have advantages in 

finding suppliers abroad.  

INSERT TABLE 4 

                                                 

13 For example Carpenter and Petersen (2002) show that the growth of most small firms is constrained by internal finance. 



 20

 These results are consistent with our theoretical model and with the empirical observations. However, 

international outsourcing can stimulate exporting activity. We have addressed this potential endogeneity problem 

using the lags of the being an exporter and export intensity variables in the estimations. As a robustness test, we 

now examine the determinants of international R&D outsourcing only for exporters14. As Table 5 shows the 

magnitudes of the estimated coefficients increase compared to those calculated in Table 4. The evidence suggests 

that financial constraints do not prevent exporters from outsourcing; however, exporters seem more sensitive to the 

lack of information than non-exporters.  

INSERT TABLE 5 
 

4. Conclusions  

In this paper, we study the determinants of international R&D outsourcing by analysing the role of trade, 

financial constraints, and information. We develop a model of monopolistic competition with heterogeneous 

firms. The model shows that the probability of outsourcing R&D abroad is higher for exporters than for non-

exporters. Furthermore, financial constraints decrease the probability of outsourcing internationally. This effect 

is more important for non-exporters than for exporters. The reason is that, as firms become more global, they can 

obtain more revenue. By contrast, exporters have major losses if there is technology leakage. It makes them 

more sensitive to obtaining information that helps to monitor their outsourced R&D.  

The empirical results are consistent with the predictions of the model. The probability to outsource 

abroad of an average company with external R&D expenditures is 9.4%. Being an exporter increases this 

probability by around 5.5 percentage points. Financial constraints reduce the probability of outsourcing abroad 

for non-exporters, by 2 percentage points. For exporters, we find no relationship between outsourcing abroad and 

financial constraints. The lack of information on technology or on markets reduces the probability of outsourcing 

abroad only for exporters, by approximately 3 percentage points. Our empirical results also reflect that more 

R&D-intensive companies tend to buy more R&D abroad.  

International R&D outsourcing is becoming a rapidly growing source of technological flows that can 

decrease productivity differences across countries. The type of public policies to promote international 

                                                 

14 The same model has been estimated for the sub-sample of non-exporters. The results are less robust due to the small 
number of non-exporters that outsource in foreign locations. They are available from the authors upon request. 



 21

acquisitions of R&D, and therefore to enhance the country’s technological advantage, can differ depending on 

the internationalization of the firm. Stronger intellectual property rights can induce exporters to outsource R&D 

internationally because it can reduce the hold-up problem. However, innovative non-exporters can require soft 

loans, grants, or investments from specialized financial organizations, which in turns should make them less 

financially constraint. 

 An aspect that remains to be studied is the consequences of international R&D outsourcing for the domestic 

industry. Relative to public policy, we consider this to be an important issue to be analysed in future research. 

 

References 

Antràs, P, and E. Helpman (2004), “Global Sourcing”, Journal of Political Economy 112, pp. 552-580. 
Arvanitis, S. and H. Hollenstein (2006), “Determinants of Swiss firms’ R&D activities at foreign locations. An 

empirical analysis based on firm-level data”, Swiss Federal Institute of Technology Zurich Working 
Paper No. 127. 

Baccara, M. (2007), “Outsourcing, Information Leakage and Consulting Firms”, RAND Journal of Economics 
38(1), pp. 269-289. 

Baumol, W. (2001), “When is inter-firm coordination beneficial? The case of innovation”, International Journal 
of Industrial Organization 19, pp. 727–737.  

Braga, H. and L. Willmore (1991), “Technological imports and technological effort: An analysis of their 
determinants in Brazilian firms”, Journal of Industrial Economics 39(4), pp. 421-432. 

Brusoni, S., Prencipe, A., and K. Pavitt (2001), “Knowledge specialization, organizational coupling, and the 
boundaries of the firm:  why do firms know more than they make?”, Administrative Science Quarterly, 
46(4), pp. 597-621. 

Carpenter, R., and B. Petersen (2002), “Is the Growth of Small Firms Constrained by Internal Finance?” The 
Review of Economics and Statistics, Vol. 84, No. 2, pp. 298-309 

Cesaroni, F. (2004), “Technological outsourcing and product diversification: do markets for technology affect 
firms’ strategies?”, Research Policy 33(10), pp. 1547-1564. 

Chaney, T. (2005), “Liquidity constrained exporters”, Mimeo. 
Chang, C. and S. Robin (2006), “Doing R&D and/or importing technologies: The critical importance of firm size 

in Taiwan’s manufacturing industries”, Review of Industrial Organization 29, pp. 253-278. 
Clementi, G and H. Hopenhayn (2006), “A theory of financing constraints and firm dynamics”, Quarterly 

Journal of Economics, 121(1), pp. 229-265. 
Cohen, W and D. Levithal (1990), “Absorptive capacity: A new perspective on learning and innovation” 

Administrative Science Quarterly, 35, pp. 128-152. 
Cooley, T. and V. Quadrini (2001), “Financial markets and firm dynamics”, American Economic Review 95(5), 

pp. 1286-1310.  
Dixit, A. and J. Stiglitz (1977), “Monopolistic competition and optimum product diversity”, American Economic 

Review 67(3), pp. 297-308. 
Eaton, J., and S. Kortum (1996), “Trade in ideas: Patenting and productivity in the OECD”, Journal of 

International Economics 40:3-4, pp. 251-278. 
Feenstra, R. and G. Hanson (1999) “The impact of outsourcing and high-technology capital on wages: estimates 

for the United States 1979 – 1990,” Quarterly Journal of Economics 114, pp. 907–40.  
Glass, A. and K. Saggi (2001) “Innovation and wage effects of international outsourcing,”, European Economic 

Review 45 pp. 67–86. 
Girma, S. and H. Görg (2004), “Outsourcing, foreign ownership and productivity: Evidence from UK 

establishment level data”, Review of International Economics 12(5), pp. 817-832. 
Greenaway, D., Guariglia, A., and R. Kneller, (2007), “Financial factors and exporting”, Journal of International 



 22

Economics 73(2), pp. 377-395. 
Grossman, G. and E. Helpman (2002), “Integration versus outsourcing in industry equilibrium,” Quarterly 

Journal of Economics 117, pp. 58–119. 
Hagedoorn, J. (2002), “Inter-firm R&D partnerships: an overview of major trends and patterns since 1960”, 

Research Policy 31, pp. 477-492. 
Helpman, E., Melitz, M., and S. Yeaple (2004), “Export versus FDI with heterogeneous firms”, American 

Economic Review 94(1), pp. 300-316.  
INE (2006), Encuesta sobre innovación tecnológica en las empresas. Years 2004 and 2005. 
Keller, W. (2004), “"International Technology Diffusion”, Journal of Economic Literature, 42(3), pp. 752-782 
Lai, E., Riezman, R. and P. Wang (2005), “Outsourcing of innovation” available at SSRN: 

http://ssrn.com/abstract=935003.  
Lewin, A. and V. Couto (2006). Next Generation Offshoring. The Globalization of Innovation. Duke C.I.B.E.R. 
Manova, K. (2006), “Credit constraints, heterogeneous firms, and international trade” available at SSRN: 

http://ssrn.com/abstract=952552.  
Marjit, S. and A. Mukherjee (forthcoming), “International Outsourcing and R&D: Long run Implications for 

Consumers”, Review of International Economics. 
Melitz, M. (2003), “The impact of intra-industry reallocations and aggregate industry productivity”, 

Econometrica 71(6), pp. 1695-1726. 
OECD and Eurostat (1997), Oslo-Manual, Proposed Guidelines for Collecting and Interpreting Technological 

Innovation Data, Paris. 
Ornelas, E. and J. Turner (2008), “Trade liberalization, outsourcing, and the hold-up problem”, Journal of 

International Economics 74, pp. 225-241. 
R&D magazine, June 2007. 
The Economist Intelligent Unit (2007), “Sharing the Idea. The emergence of global innovation networks”, The 

Economist. 
Veugelers, R. and B. Cassiman (1999), “Make and buy in innovation strategies: evidence from Belgian 

manufacturing firms”, Research Policy 28, pp. 63-80. 



 23

Appendix 1:  Proofs 

Proof of the existence of Dϕ  and Fϕ  in autarky without financial constraints. 

We want to show that in the closed economy without financial constraints, there exist two unique efficiency levels denoted 

as Dϕ , and Fϕ , such that 0( , ) ( , )D D Dc cϕ ϕΠ = Π , and ( , ) ( , )F D F Fc cϕ ϕΠ = Π . Furthermore,  

0 0

,  then   ( ,1) ( , ) ( , )
for ,  then   ( , ) ( , ),  and ( ,1) ( , )

,  then   ( , ) ( , ),  and ( , ) ( , )

F D F

D F F D D

D F D

c c
c c c
c c c c

ϕ ϕ ϕ ϕ ϕ
ϕ ϕ ϕ ϕ ϕ ϕ ϕ
ϕ ϕ ϕ ϕ ϕ ϕ

> Π < Π < Π⎧
⎪ < < Π < Π Π < Π⎨
⎪ < Π < Π Π < Π⎩

 

If there are two unique efficiency thresholds such that D Fϕ ϕ< , the price index can be expressed as 

( 1) ( 1) ( 1)

/( 1)

00 1

1 1 1( ) ( ) ( )
  

D F

D F

M

z
D F

p dz M d d d
c c c

α α α
α α α

ϕ ϕ
α α

ϕ ϕ

µ ϕ ϕ µ ϕ ϕ µ ϕ ϕ
αϕ αϕ αϕ

− − −
∞

−

⎡ ⎤
⎢ ⎥⎛ ⎞ ⎛ ⎞ ⎛ ⎞

= + +⎢ ⎜ ⎟ ⎥⎜ ⎟ ⎜ ⎟
⎝ ⎠ ⎝ ⎠⎝ ⎠⎢ ⎥

⎣ ⎦

∫ ∫ ∫ ∫  

where 1( ) ψµ ϕ ψ ϕ += , since we consider that a firm’s efficiency is drawn from a Pareto distribution with support [1, )ϕ ∈ ∞  

and shape ψ . After some arithmetic calculations, we obtain that /( 1)

0

   
M

z
Mp dz

ρ
α α α ψ ξ

ψ ρ
− =

−∫ , where (1 ) 0ρ α α= − >  and 

0( ) ( ) 1D D F D Fc c c cρ ρ ρ ψ ρ ρ ρ ψξ ϕ ϕ− −= − + − + . Additionally, we have assumed that ψ ρ>  to ensure that the integral converges.  

 

The profits of a firm with efficiency ϕ  are equal to: 

0 0
( , ) (  ) (1 )( )( , ) ( , ) ( , ) (1 ) (1 )

  
i i

i i i i i
i

y c cEc p c y c r f r f
c M

ρϕ ϕα ψ ρϕ ϕ ϕ
ϕ ψ ξ

− −
Π = − − + = − + , for 0, , .i D F=  

Now, if there exists a unique Dϕ  such that 0( , ) ( , )D D Dc cϕ ϕΠ = Π , it would imply that  

0
0 0 0

(  ) (  ) (1 )( )  (1 )( )(1 ) (1 )
  

D D D
D

c cE Er f r f
M M

ρ ρϕ ϕα ψ ρ α ψ ρ
ψ ξ ψ ξ

− − − −
− + = − + , which is equivalent to:  

0
0 0

( ) (1 )( )(1 )( )
 

D D
D

c cEr f f
M

ρ ρ ρϕα ψ ρ
ψ ξ

−− −
+ − = .     (A.1) 

And, if there exists a unique Fϕ  such that ( , ) ( , )F D F Fc cϕ ϕΠ = Π , it would imply that: 

0 0
(  ) (  ) (1 )( )  (1 )( )(1 ) (1 )

  
F D F F

D F
c cE Er f r f

M M

ρ ρϕ ϕα ψ ρ α ψ ρ
ψ ξ ψ ξ

− − − −
− + = − + , which is equivalent to: 

0
( ) (1 )( )(1 )( )

 
F F D

F D
c cEr f f

M

ρ ρ ρϕα ψ ρ
ψ ξ

−− −
+ − = .     (A.2) 

Dividing equation (A.2) by (A.1), we obtain that  

0

0

( )( )
( )( )

DF D
D F

F DD

f fc c
f fc c

ρ ρ
ρ ρ

ρ ρϕ ϕ
−−

=
−−

.     (A.3) 

Equation (A.3) implies that for any Dϕ , there exists a unique Fϕ . The partitioning of firms by outsourcing type ( F Dϕ ϕ> ) 

will occur if the relative transaction costs are higher than the relative increase in productivity due to outsourcing, i.e., 

00

F D F D

DD

c c f f
f fc c

ρ ρ

ρ ρ

− −
<

−−
. Furthermore, in the equilibrium some firms do not outsource (that is the case when 1Dϕ > ), as we 



 24

observe in the data, if the relative transaction cost between outsourcing domestically and not outsourcing is high: 

0( / )D Df f cρ> . The reason is as follows: the zero profit condition, i.e., 0(1, 1) 0cΠ = = , implies that 

0 0
 (1 )( ) 1 (1 )

 
E r f

M
α ψ ρ
ψ ξ

− −
= + ⇔  

0
0 0

 (1 )( ) 1 ( ) ( )
 (1 ) D D F D F

E c c c c
M r f

ρ ρ ρ ψ ρ ρ ρ ψα ψ ρ ϕ ϕ
ψ

− −− −
⇔ − = − + −

+
.    (A.4) 

Furthermore, we know that 0
 (1 )( )(1, ) (1 )

 
D

D D
cEc r f

M

ρα ψ ρ
ψ ξ

− −
Π = − + . Substituting the zero profit condition in the previous 

equation, we obtain that 0 0 0(1, ) (1 ) (1 )D D Dc r f c r fρΠ = + − + , which implies that if 0( / )D Df f cρ> , then (1, ) 0DcΠ < . This is 

enough to show that 1Dϕ > . Note that if F Dc c≤  and F Df f> , firms would not have incentives to outsource abroad. In this 

case, firms would only outsource domestically.   

 

Substituting equation (A.3) into (A.4), and after some arithmetic calculations, we find that there is a unique threshold 

efficiency value Fϕ  given by 

1

1 1 1
0 0 0 0

( ) (1 )( ) 1
 (1 ) ( ) [( ) ( ) ( ) ( ) ]

F D
F

F D D D F D F D

f fE
M r f c c f f c c c c f f

ψ ρ
ρ ψ

ρ ρ ψ ρ ψ ρ ρ ρ ψ ρ ρ ρ ψ ρ ψ ρ

α ψ ρϕ
ψ

−
−

− − −

⎡ ⎤ −− −
= −⎢ ⎥+ − − − + − −⎣ ⎦

 

which ends the proof. 

 

Note that if the market size increases ( E ), the number of firms ( M ) decreases, or the riskless interest rate ( 0r ) decreases, 

the threshold values Fϕ , and Dϕ  decrease since ψ ρ> . Moreover, since F Dc cζ= , the threshold value Fϕ  increases if ζ  

increases. 

 

Proof that the threshold efficiency for outsourcing (both domestically and abroad) is larger for non-exporters than for 

exporters. 

Let ,D nxϕ  be the threshold efficiency levels for non-exporters such that outsourcing domestically or not outsourcing imply 

the same maximum profits for the company. It satisfies the following expression, , 0 ,( , ) ( , )nx D nx nx D nx Dc cϕ ϕΠ = Π . Moreover, 

for ,D nxϕ ϕ< , non-exporters prefer not to outsource, i.e., 0( , ) ( , )nx nx Dc cϕ ϕΠ > Π , and for ,D nxϕ ϕ> , non-exporters prefer to 

outsource domestically, i.e., 0( , ) ( , )nx nx Dc cϕ ϕΠ < Π . Similarly, for exporters we know that for ,D xϕ ϕ< , exporters do not 

outsource, i.e., 0( , ) ( , )x x Dc cϕ ϕΠ > Π , and for ,D xϕ ϕ> , exporters prefer to outsource domestically, i.e., 

0( , ) ( , )x x Dc cϕ ϕΠ < Π . The threshold efficiency level such that outsourcing domestically or not outsourcing imply the same 

maximum profits for exporters satisfies: , 0 ,( , ) ( , )x D x x D x Dc cϕ ϕΠ = Π . This is equivalent to the following equation, 

1/( 1) 1/( 1)
, 0 ,

, 0 ,

(  )  ( ) ( )  ( )
( , ) ( , )

( ) ( )
D x D x D

nx D x nx D x D

c E c E
c c

P P

ρ α ρ αϕ τ α τ ϕ τ α τ
ϕ ϕ

φ φ

− −− −
Π + = Π + ⇔  

1/( 1)
,

, 0 , 0

( )  ( )
( , ) ( , ) ( )

( )
D x

nx D x nx D x D D

E
c c c c

P

ρ α
ρ ρϕ τ α τ

ϕ ϕ
φ

−−
⇔ Π −Π = − , 



 25

which is always positive since 1τ α> > . In summary, first of all, we know that if , ,D x D nxϕ ϕ< , then 

, 0 ,( , ) ( , )nx D x nx D x Dc cϕ ϕΠ > Π , and that if , ,D x D nxϕ ϕ> , then , 0 ,( , ) ( , )nx D x nx D x Dc cϕ ϕΠ > Π . Secondly, we have just obtained 

that , 0 ,( , ) ( , ) 0nx D x nx D x Dc cϕ ϕΠ −Π > . Therefore, , ,D x D nxϕ ϕ< . It follows immediately with the same argument that 

, ,F x F nxϕ ϕ<  if F Dc c> . Hence, threshold efficiency for outsourcing both domestically and abroad is larger for non-

exporters than for exporters if 1ζ > . Q.E.D. 

 

Proof that the efficiency threshold to obtain financial resources for outsourcing is lower for exporters than for non-

exporters. 

Operating profits of an exporter with efficiency ϕ  that decides to outsource can be expressed in terms of its profits if it does 

not export as: 
1/( 1)

0 0 (  )  ( )
( , ) ( , )

( )
i

x i nx i
c E

c c
P

ρ αϕ τ α τ
ϕ ϕ

φ

−−
Π = Π +  for ,i D F=  where , , , ,( ) ( , , , )D nx D x F x F nxP Pφ ϕ ϕ ϕ ϕ=  is the 

aggregate weighted price. Therefore, equation (4) can be written as 
1/( 1)

,0 0
,

(  )  ( ) (1 ) (1 )
( , )

( )
i x i i

nx i x i

c E r f C
c

P

ρ αϕ τ α τ λ
ϕ

φ λ

−− + − −
Π + = . Or equivalently as: 

1/( 1)
,0 0 0

,

(  )  ( )(1 ) (1 ) (1 ) (1 )
( , )

( )
i x ii i

nx i x i

c Er f C r f C
c

P

ρ αϕ τ α τλ λ
ϕ

λ φ λ

−−+ − − + − −
Π = − < , 

which implies that , ,i x i nxϕ ϕ< , for ,i D F= . 

 

Proof that the increase in the efficiency threshold needed to outsource when there are financial constraints is larger for 

non-exporters than for exporters: , , , ,i x i x i nx i nxϕ ϕ ϕ ϕ− < −  for ,i D F= . 

We present the proof for .i D=  The analysis is the same for international outsourcing, i.e., for i F= . We know that the 

efficiency threshold to outsource domestically without financial constraints solves the following equation: 

, 0 ,( , ) ( , )nx D nx D nx Dc cϕ ϕΠ = Π . Therefore, we obtain that , 0 0
0

( )(1 )( )
( ) (1 )D nx D

D

Pr f f
c c E

ρ
ρ ρ

φϕ
α

= + −
− −

, where ( )P φ  represents 

the aggregate price under free trade without financial constraints. The minimum efficiency level to outsource domestically 

with financial constraints is given by 0 0
,

(1 ) (1 )
( , ) D

nx D nx D
r f C

c
λ

ϕ
λ

+ − −
Π = . Consequently, 

0
,

(1 ) (1 ) ( )
(1 )

D
D nx

D

r f C P
c E

ρ
ρ

λ φϕ
λ α

+ − −
=

−
 where ( )P φ  is the aggregate price under free trade with financial constraints. In a 

similar way, we obtain the values for ,D x
ρϕ , and ,D nx

ρϕ . In particular, we get: 

, 0 0 1 ( 1)
0

( )(1 )( )
( )[(1 ) ( ) ]D x D

D

Pr f f
c c E

ρ
ρ ρ α

φϕ
α τ α τ −= + −

− − + −
, and 0

, 1 ( 1)

(1 ) (1 ) ( )
[(1 ) ( ) ]

D
D nx

D

r f C P
c E

ρ
ρ α

λ φϕ
λ α τ α τ −

+ − −
=

− + −
. 

Now, we substitute the previous values and calculate 
1

1 ( 1)
, ,

, ,

( )1
(1 )

D nx D nx

D x D x

α ρϕ ϕ τ α τ
ϕ ϕ α

−− ⎡ ⎤−
= +⎢ ⎥− −⎣ ⎦

, which is larger than one since 

0ρ > , and 
1 ( 1)( ) 0

(1 )

ατ α τ
α

−−
>

−
, which ends the proof. 



 26

Appendix 2: Definitions of variables 
 

Continuous R&D engagement: It is a dummy variable that takes the value one if the enterprise reports 
continuous R&D engagement in intramural R&D activities during the period 2003-2005.  
 
Cooperation: It is a dummy variable that takes the value one if the enterprise had some co-operative 
arrangements on innovation during the period 2003-2005. 
 
Exporter (t-1): It is a dummy variable that takes the value one if the enterprise exported in 2004. 
 
Export intensity (t-1): Exports per employee in 2004 (in logarithms). 
 
Geographical regions: 

Basque Country: It is a dummy variable that takes the value one if the firm is located in the Basque Country. 
Catalonia: It is a dummy variable that takes the value one if the firm is located in Catalonia. 
Madrid: It is a dummy variable that takes the value one if the firm is located in Madrid. 

 
Lack of finance: In the database, the companies are asked: “During the three years 2003 to 2005, how 
important were the following factors for hampering your innovation activities? Lack of funds within your 
enterprise or group, lack of finance from sources outside your enterprise, and innovation costs too high” 

For each of the factors, the company can answer that the importance of the factor was high, intermediate, or low, 
or that the factor was not experienced. We assign a number that varies from zero (not experienced) to three (high 
importance) for each answer. Then, we calculate the average importance of the cost factors.  This is our variable 
lack of finance.   
 
Lack of information: In the database, the companies are asked: “During the three years 2003 to 2005, how 
important were the following factors for hampering your innovation activities? Lack of information on 
technology, and lack of information on markets”  
 
For each of the factors, the company can answer that the importance of the factor was high, intermediate, or low, 
or that the factor was not experienced. We assign a number that varies from zero (not experienced) to three (high 
importance) for each answer. Then, we calculate the average of the importance of the knowledge factors. This is 
our lack of information variable.   
 
Patent: It is a dummy variable that takes the value one if the firm applied for patents during the period 2003-
2005. 
 
Proxies for market proximity: In the database, the companies are asked: “In the period 2003-2005, what was 
the importance of each of the following sources of information in order to innovate?” For each of the sources, 
the company can answer that the importance of the source was high, intermediate, or low.  

Internal sources of information: It is a dummy variable that takes the value one if information from internal 
sources (within the company or the firm’s group) were of high importance. It takes the value zero otherwise. 
Institutional sources of information: It is a dummy variable that takes the value one if information from 
universities or other higher education or research institutes were of high importance. It takes the value zero 
otherwise. 
Market sources of information: It is a dummy variable that takes the value one if information from 
suppliers, customers, competitors, scientific media or professional associations were of high importance. It 
takes the value zero otherwise. 

 
R&D employment: R&D employment over total employment in 2005.  
 
R&D expenditure at foreign locations: In the database, the companies are asked about the expenditures in 
2005 corresponding to “acquisitions of R&D services outside the firm from foreign providers through contracts, 
informal agreements…”. (This variable is included in logarithms). 
 
Size: Number of employees in 2005. 
 



 27

Table A1 
Classification of high and mid-high technology sectors 

 
NACE-Rev.1 Sectors  

 High and mid-high technology manufacturing sectors 
24 Chemicals and chemical products 
29 Machinery and equipment n.e.c. 
30 Office machinery and computers 
31 Electrical machinery and apparatus n.e.c. 
32 Radio, television and communication equipment…  
33 Medical, precision and optical instruments… 
34 Motor vehicles, trailers and semi-trailers 
35 Other transport equipment 

 High technology services 
64 Post and telecommunications 
72 Computer and related activities 
73 Research and development 

 



 28

Figure 1 
Efficiency thresholds to outsource domestically and abroad in autarky 

 
 
 

 
( , )icϕΠ  

 
 
           
 
          ( , )FcϕΠ  
 
            
          ( , )DcϕΠ  
 
 
          0( , )cϕΠ  
 

 
 
         Dϕ         Fϕ    ϕ  
 
 
 
 
 
  

 
Note:  The function ( , )icϕΠ  with , ,i 0 D F=  is the profit function for a firm with efficiency ϕ  that decides not to 

outsource ( i 0= ), to outsource domestically ( i D= ), or to outsource abroad ( i F= ). 



 29

Figure 2 
Efficiency thresholds to outsource R&D internationally for exporters and non-exporters  

with financial constraints. 
 
 

 
( , )nx icϕΠ  

    ( , )x FcϕΠ             
( , )x icϕΠ                    0 ( , )x FcϕΠ            ( , )x DcϕΠ   

 
         ( , )nx FcϕΠ  
 
                            0 ( , )nx FcϕΠ  
                  ( , )nx DcϕΠ  
           s 
 
           
 
 
 
 
  
 
         ,F xϕ         ,F xϕ      ,F nxϕ                            ,F nxϕ        
 
 
 
 
 
  

 
 
Note: The function ( , )nx DcϕΠ  is the profit function of a non-exporter when it outsources domestically, ( , )nx FcϕΠ  is the 

profit function for a non-exporter when it outsources abroad, ( , )x DcϕΠ  is the profit function of an exporter when it 

outsources domestically, ( , )x FcϕΠ  is the profit function for an exporter when it outsources abroad, 0
nxΠ  and 0

xΠ  are 
operating profits for non-exporters and exporters, respectively, and s  is the firm’s financial constraint to outsource abroad, 
i.e., 0[(1 ) (1 ) ] /Fs r f Cλ λ= + − − . 
 
 
 
 
 
 
 
 
 



 30

 
Figure 3 

External R&D expenditure by location in 2004 and 2005 
(Percentage of firms in the sample of continuous innovators) 

53.3% 52.2%

37.6% 38.4%

6.3% 6.7% 2.7%2.8%

0%

20%

40%

60%

80%

100%

2004 2005

Domestic and foreign external R&D
Only foreign external R&D
Only domestic external R&D
Without external R&D expenditure

 
Figure 4 

External R&D expenditure at foreign locations by provider in 2005 
(Percentage of firms) 

Own group and 
other firms

6%

Only other 
institutions

14%

Other firms and 
institutions

56%

Only own group
24%

 
Note: Other institutions include public administration, universities, non-profitable institutions and other international 
organizations.  



 31

Figure 5 
R&D outsourcing by exporting activity in 2005.  

(Percentage of firms in the sample of firms with external R&D expenditures) 

8.6%14.2%

19.2%

33.9%

0%

10%

20%

30%

40%

50%

Exporters Non-exporters

Firms without international R&D
outsourcing

 

Figure 6    
Lack of finance by exporting activity in 2005 

(Mean value in the sample of firms with external R&D expenditures) 

1.77
1.79

1.72

1.82

1.65
1.68
1.71
1.74
1.77
1.8

1.83

Exporters Non-exporters

Firms with international outsourcing
Firms without international outsourcing

 

Figure 7  
Lack of information by exporting activity in 2005 

(Mean value in the sample of firms with external R&D expenditures) 

1.34

1.28

1.17
1.2

1.05
1.1

1.15
1.2

1.25
1.3

1.35
1.4

Exporters Non-exporters

Firms with international outsourcing
Firms without international outsourcing

 



 32

 
Table 1 

Descriptive Statistics 
 

 
Firms with R&D 

expenditure 
Firms with external 
R&D expenditure 

Firms with 
international 
outsourcing 

  Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
R&D expenditure at foreign locations (in log.) 0.53 2.35 1.28 3.52 10.56 2.08 
Exporter 0.57 0.49 0.63 0.48 0.74 0.44 
Exporter (t-1) 0.37 0.48 0.45 0.50 0.59 0.49 
Export intensity (in log.) 5.70 5.11 6.32 5.09 7.64 4.85 
Lack of finance 1.80 0.89 1.80 0.88 1.75 0.88 
Lack of information 1.27 0.78 1.30 0.77 1.18 0.73 
Proxies for market proximity       
  Internal sources of information 0.62 0.48 0.64 0.48 0.69 0.46 
  Institutional sources of information 0.18 0.38 0.26 0.44 0.26 0.44 
  Market sources of information 0.53 0.50 0.58 0.49 0.69 0.46 
Absorptive capacity       
  Continuous R&D engagement 0.69 0.46 0.70 0.46 0.84 0.37 
  R&D employment (% over total employment) 23.14 28.41 24.64 30.14 27.10 30.57 
  Multinational: subsidiary 0.13 0.34 0.16 0.37 0.22 0.41 
  Multinational: parent company 0.07 0.25 0.09 0.29 0.11 0.31 
  Patent 0.17 0.38 0.22 0.41 0.33 0.47 
  Technological cooperation 0.39 0.49 0.55 0.50 0.59 0.49 
  Size (number of employees) 241.8 1554.8 336.7 2050.8 353.1 930.8 
  Geographical regions       
    Basque country 0.13 0.34 0.14 0.35 0.12 0.33 
    Catalonia 0.26 0.44 0.22 0.42 0.32 0.46 
    Madrid 0.14 0.35 0.13 0.33 0.17 0.38 

 



 33

Table 2 
Determinants of R&D outsourcing (both nationally and internationally) 

Probit model 
 

 All firms Manufacturing Services 
 dy/dx Std. E. dy/dx Std. E. dy/dx Std. E. 
Exporter (t-1) 0.056 *** 0.015 0.047 *** 0.018 0.055 * 0.030 
Export intensity (t-1) 0.006 *** 0.001 0.006 *** 0.002 0.006 ** 0.003 
Lack of finance -0.006   0.007 -0.005   0.010 -0.007   0.012 
Lack of information 0.009   0.008 0.009   0.011 0.014   0.014 
Proxies for market proximity        
  Internal sources of information 0.018   0.013 0.032 * 0.016 -0.001   0.021 
  Institutional sources of information 0.161 *** 0.017 0.205 *** 0.023 0.106 *** 0.025 
  Market sources of information 0.026 ** 0.012 0.017   0.016 0.046 ** 0.020 
Absorptive capacity        
  Continuous R&D engagement 0.065 *** 0.014 0.075 *** 0.018 0.028   0.023 
  R&D employment 0.085 *** 0.024 0.162 *** 0.047 0.096 *** 0.029 
  Multinational: subsidiary  0.069 *** 0.019 0.053 ** 0.024 0.074 ** 0.033 
  Multinational: parent company 0.081 *** 0.024 0.054 * 0.032 0.124 *** 0.039 
  Patent 0.077 *** 0.016 0.082 *** 0.020 0.056 * 0.030 
  Technological cooperation 0.238 *** 0.013 0.235 *** 0.017 0.226 *** 0.020 
  Size 2.3E-05 ** 9.1E-06 1.7E-04 *** 3.8E-05 1.2E-05  1.1E-05
  Size squared  -3.0E-10  2.2E-10 -2.0E-08 *** 4.7E-09 -4.4E-11  2.4E-10
  High and mid-tech manufacturing 0.007   0.014 -0.008   0.016    
  High-tech services -0.079 *** 0.019   -0.046 ** 0.021 

Pseudo R2 0.11 0.12 0.10 
Log likelihood -4,282.94 -2,609.11 -1,519.04 
Number of observations 7,205 4,378 2,602 

 
 
Note: Std. E.: Estimated standard error. All regressions include the constant and dummies for geographical regions. Marginal 
effects (dy/dx) are computed at sample means. For dummy variables. the marginal effect corresponds to the discrete change 
from 0 to 1. * Significant at 10%. ** significant at 5%. *** significant at 1%. 



 34

Table 3 
Determinants of international R&D outsourcing 

Generalized Tobit model 
 

All firms  
(1) 

Locating R&D abroad 
(2) 

Foreign R&D expenditure
 Coefficient Std. E. Coefficient Std. E. 
Exporter (t-1) 0.302 *** 0.068    
Export intensity (t-1)    0.060 ** 0.026 
Lack of finance -0.002   0.040 -0.259 ** 0.127 
Lack of information -0.127 *** 0.044 -0.266   0.167 
Proxies for market proximity     
  Internal sources of information 0.031   0.068 -0.252   0.218 
  Institutional sources of information -0.163 ** 0.072 0.096   0.287 
  Market sources of information 0.241 *** 0.067 0.632 ** 0.248 
Absorptive capacity     
  Continuous R&D engagement 0.146   0.089    
  R&D employment 0.350 *** 0.111 0.928   0.894 
  Multinational: subsidiary  0.212 *** 0.080 0.725 ** 0.289 
  Patent 0.235 *** 0.070 0.640 ** 0.290 
  Size 0.0003 *** 0.9E-04 0.001 *** 0.0004 
  Size squared  -3.0E-08 ** 1.2E-08 -1.2E-07   7.4E-08 
  High and mid-tech manufacturing 0.164 ** 0.070 0.850 *** 0.253 
  High-tech services 0.166   0.102 0.502   0.408 
Selection term   0.677 ** 0.179 
 dy/dx  Std. E. dy/dx  Std. E. 
Exporter (t-1) 0.054 *** 0.013    
Export intensity (t-1)    0.060 ** 0.026 
Lack of finance 0.000   0.007 -0.256 ** 0.117 
Lack of information -0.022 *** 0.008 -0.095   0.137 
Proxies for market proximity     
  Internal sources of information 0.005   0.012 -0.294   0.198 
  Institutional sources of information -0.027 ** 0.011 0.314   0.241 
  Market sources of information 0.041 *** 0.011 0.309   0.218 
Absorptive capacity     
  Continuous R&D engagement 0.024 * 0.014    
  R&D employment 0.061 *** 0.019 0.459   0.837 
  Multinational: subsidiary  0.040 ** 0.017 0.444 * 0.235 
  Patent 0.044 *** 0.014 0.327   0.242 
Log likelihood:  -1,734.11 
Number of observations 2,759 359 

 
Note: Std. E.: Estimated standard error. All regressions include the constant and dummies for geographical regions. Apart 
from coefficients. marginal effects (dy/dx) are reported at sample means for the probability of locating R&D abroad and for 
the expected value of R&D expenditures at foreign locations (in log.) conditional on locating R&D abroad. * Significant at 
10%. ** significant at 5%. *** significant at 1%. 



 35

Table 4  
Determinants of international R&D outsourcing 

Generalized Tobit model   
 

 (1)  
Locating R&D abroad 

(2)  
Foreign R&D expenditure 

(3)  
Locating R&D abroad 

(4)  
Foreign R&D expenditure 

 Coefficient  Std. E. Coefficient Std. E. Coefficient Std. E. Coefficient Std. E. 
Exporter (t-1) 0.257 *** 0.076    0.295 *** 0.075    
Export intensity (t-1)    0.049 * 0.029    0.075 ** 0.029 
Lack of finance: exporters 0.039   0.045 -0.151   0.138 0.031   0.045 -0.156   0.149 
Lack of finance: non-exporters -0.089   0.059 -0.537 ** 0.248 -0.096 * 0.058 -0.610 ** 0.256 
Lack of information: exporters -0.159 *** 0.053 -0.345 ** 0.172 -0.165 *** 0.052 -0.459 ** 0.183 
Lack of information: non-exporters -0.060   0.072 -0.037   0.336 -0.057   0.071 -0.048   0.340 
Proxies for market proximity          
  Internal sources of information 0.034   0.068 -0.224   0.213 0.042   0.067 -0.148   0.232 
  Institutional sources of information -0.164 ** 0.072 0.074   0.278 -0.158 ** 0.072 0.107   0.275 
  Market sources of information 0.232 *** 0.067 0.596 ** 0.248 0.240 *** 0.067 0.775 *** 0.264 
Absorptive capacity          
  Continuous R&D engagement 0.149   0.091   0.138   0.088    
  R&D employment 0.375 *** 0.113 1.007   0.864 0.293 *** 0.110 0.451   0.788 
  Multinational: subsidiary  0.213 *** 0.080 0.748 ** 0.295 0.227 *** 0.080 0.883 *** 0.306 
  Patent 0.238 *** 0.071 0.654 ** 0.288 0.262 *** 0.070 0.816 *** 0.298 
  Size 2.9E-04 *** 8.5E-05 0.001 *** 0.0004      
  Size squared  -3.0E-08 *** 1.2E-08 -1.2E-07 *  7.4E-08      
  High and mid-tech manufacturing 0.159 ** 0.070 0.846 *** 0.248 0.153 ** 0.070 0.835 *** 0.260 
  High-tech services 0.177 * 0.104 0.517   0.411 0.176 * 0.102 0.661   0.415 
Selection term   0.677 ** 0.176   0.767 *** 0.116 
 dy/dx  Std. E. dy/dx  Std. E. dy/dx  Std. E. dy/dx  Std. E. 
Exporter (t-1) 0.045 *** 0.014    0.058 *** 0.015    
Export intensity (t-1)    0.049 * 0.029    0.075 ** 0.029 
Lack of finance: exporters 0.007   0.008 -0.204   0.127 0.006   0.009 -0.209   0.133 
Lack of finance: non-exporters -0.015   0.010 -0.418 * 0.245 -0.019 * 0.011 -0.448 * 0.245 
Lack of information: exporters -0.027 *** 0.009 -0.132   0.138 -0.032 *** 0.010 -0.180   0.143 
Lack of information: non-exporters -0.010   0.012 0.043   0.320 -0.011   0.014 0.048   0.321 
Proxies for market proximity          
  Internal sources of information 0.006   0.011 -0.269   0.194 0.008   0.013 -0.219   0.203 
  Institutional sources of information -0.027 ** 0.011 0.295   0.235 -0.029 ** 0.013 0.375   0.231 
  Market sources of information 0.039 *** 0.011 0.284   0.212 0.045 *** 0.012 0.369 * 0.218 
Absorptive capacity          
  Continuous R&D engagement 0.024 * 0.014   0.025 * 0.015    
  R&D employment 0.065 *** 0.019 0.504   0.813 0.057 *** 0.021 -0.044   0.763 
  Multinational: subsidiary  0.040 ** 0.017 0.464 * 0.246 0.048 *** 0.018 0.504 ** 0.248 
  Patent 0.044 *** 0.014 0.338   0.239 0.055 *** 0.016 0.378   0.243 
Log likelihood  -1,731.38  -1,746.40 
Number of observations 2,759 359 2,759 359 

See notes from Table 3. 



 36

Table 5 
Determinants of international R&D outsourcing: Exporters 

Generalized Tobit model 
 

Exporters  
(1) 

Locating R&D abroad 
(2) 

Foreign R&D expenditure
 Coefficient Std. E. Coefficient Std. E. 
Exporter (t-1) 0.195 ** 0.195    
Export intensity (t-1)    0.068 ** 0.028 
Lack of finance 0.036   0.036 -0.112   0.157 
Lack of information -0.145 *** -0.145 -0.364 ** 0.177 
Proxies for market proximity     
  Internal sources of information 0.039   0.039 -0.276   0.277 
  Institutional sources of information -0.094   -0.094 0.175   0.292 
  Market sources of information 0.185 ** 0.185 0.735 *** 0.280 
Absorptive capacity     
  Continuous R&D engagement 0.294 *** 0.294    
  R&D employment 0.448 *** 0.448 1.897 ** 0.734 
  Multinational: subsidiary  0.089   0.089 0.582 * 0.336 
  Patent 0.151 * 0.151 0.586 ** 0.287 
  Size 0.0004 *** 9.7E-05 0.002 *** 0.0004 
  Size squared  -3.5E-08 *** 1.2E-08 -1.5E-07 ** 6.8E-08
  High and mid-tech manufacturing 0.135 * 0.135 0.853 *** 0.274 
  High-tech services 0.169   0.169 0.633   0.545 
Selection term   0.866 ** 0.956 
 dy/dx  Std. E. dy/dx  Std. E. 
Exporter (t-1) 0.041 ** 0.017    
Export intensity (t-1)    0.068 ** 0.028 
Lack of finance 0.008   0.011 -0.179   0.132 
Lack of information -0.032 *** 0.012 -0.088   0.139 
Proxies for market proximity     
  Internal sources of information 0.008   0.017 -0.351   0.233 
  Institutional sources of information -0.020   0.018 0.354   0.241 
  Market sources of information 0.039 ** 0.017 0.382 * 0.228 
Absorptive capacity     
  Continuous R&D engagement 0.058 *** 0.018    
  R&D employment 0.098 *** 0.035 1.044 * 0.605 
  Multinational: subsidiary  0.020   0.023 0.414   0.275 
  Patent 0.034 * 0.019 0.301   0.249 
Log likelihood:  -1,218.66 
Number of observations 1,752 265 

Note: Std. E.: Estimated standard error. All regressions include the constant and dummies for geographical regions. 
Apart from coefficients. marginal effects (dy/dx) are reported at sample means for the probability of locating R&D 
abroad and for the expected value of R&D expenditures at foreign locations (in log.) conditional on locating R&D 
abroad. * Significant at 10%. ** significant at 5%. *** significant at 1%.