# Figure B.1: Optimal ownership as a function of investment interrelatedness. Figure C.1: Marginal effects at low interrelatedness

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1 Online Appendix for: Lileeva, A. and J. Van Biesebroeck. Outsourcing when Investments are Specific and Interrelated, Journal of the European Economic Association Appendix A: Proofs Proof of Proposition 2 Proof of Proposition 3 Appendix B: Comparative statics in ρ Figure B.1: Optimal ownership as a function of investment interrelatedness Appendix C: Flexible specification Discussion of estimating equation (C.1) Figure C.1: Marginal effects at low interrelatedness Figure C.2: Marginal effects for different technology intensities Figure C.3: Marginal effects for varying levels of specificity Appendix D: Tables with additional results Table D.1: Correlations between different investment intensities Table D.2: Assessing the proxy for interrelatedness Table D.3: Coefficient estimates for flexible specification 1

2 Appendix A: Proofs Proof of Proposition 2 Because the problem is symmetric for buyers and suppliers, we only need to discuss the trade-off between backward vertical integration and outsourcing. We focus on the case of α S = 0 which is the most advantageous for the (buyer) integrated organizational form. Ex ante transfers with efficient Nash bargaining ex post guarantees that the chosen ownership structure will maximizes the joint surplus W z = α B x z B + α S x z S + ρx z Bx z S 1 2 (xz B) (xz S) 2, z {BI, O}. This expression is evaluated at the equilibrium investments in (11) or (12), which in the case of α S = 0 simplify to: x BI B x O B = = 1 λ ρx BI B 2 = α B D BI and x BI S 1+θ 2 α B D O and x O S = 1 2 ρxo B, with D BI = 1 (1 λ 2 )ρ 2 / and D O = 1 ρ 2 /. The optimal choice of organizational form is characterized by comparative statics in W = W BI W O. In particular, its dependence on α B is given by W = x BI B + (α B + ρx BI S x BI α B B ) xbi B α B + (ρx BI B x BI S ) xbi S + α B = x O B (α B + ρx O S x O B) xo B (ρx O B x O S ) xo S α B α B α [ B (D BI ) ( ) ] 1 λ 2 2 ρ 2 α [ B (1+θ)(3 θ) (D O ) 2 + 3(1+θ)2 16 ρ 2] Taking the limit for ρ 1, i.e. maximum interrelatedness, we find that W [ ] ρ 1 = α 5 2λ+λ 2 B (1+θ)(15 θ) α ((3+λ 2 )/2) 2 9 B < 0 if λ > λ(θ), hence BI will never be optimal if λ > λ(θ). Equating the expression in square brackets to zero and fully differentiating yields λ/ θ < 0. The thresholds rises with specificity (lower θ), but it attains a maximum at λ(0) =

3 Proof of Proposition 3 The comparative static of the welfare differential W = W BI W O with the interrelatedness parameter is given by W = (α S + ρx BI B x BI S ) xbi S + (α B + ρx BI S x BI B ) xbi B + xbi B x BI S (α S + ρx O B x O S ) xo S (α B + ρx O S x O B) xo B x O Bx O S. We now determine the sign of this expression at a low level of interrelatedness, i.e. for ρ 0. The partial derivatives of the optimal investments in equations (11) and (12) are: x BI B = x O B [ ] 1 λ 2 α S + 2ρα B + 1 λ2 ρ 2 α S and xbi S (1 1 λ2 ρ 2 ) 2 = = 1+θ [α S + ρα B + 1 ρ2 α S ] (1 1 ρ2 ) 2 and xo S = [ ] 1 λ 2 α B + 1 λ2 2 ρα S + 1 λ2 ρ 2 α B ) 1+θ (1 1 λ2 ρ 2 ) 2 ] [α B + ρα S + 1 ρ2 α B (1 1 ρ2 ) 2, which for ρ 0 simplify to x BI B x O B = 1 λ2 ρ 0 α S and xbi S = 1 λ ρ 0 = 1+θ ρ 0 α S and xo S 2 α B ρ 0 = 1+θ α B. The optimal investments themselves simplify to x BI B ρ 0 = α B and x BI S ρ 0 = 1 λ 2 α S x O B ρ 0 = 1+θ 2 α B and x O S ρ 0 = 1+θ 2 α S. Substituting all these expressions in the partial derivative of W, we find that W ρ 0 = 1 α Bα S [(1 λ)(3 + λ) 2(1 + θ)] > 0 if λ < λ(θ), hence for ρ < ρ and λ < λ, greater interrelatedness increases the probability of integration. Equating the expression in brackets to zero and fully differentiating yields λ/ θ = 1/(1+λ) < 0. The thresholds falls with reduced specificity (higher θ), and it attains a minimum over the relevant range, λ [0, 1] and θ [0, λ], at λ(λ) =

4 If instead we take the limit for ρ 2 0, the expressions simplify enough to establish that the λ threshold for comparative static to be positive is raised. It now becomes W ρ 2 0 = W ρ 0 [ (1 λ)(3 + λ) + αbρ 2 [ 1 λ + αsρ f(λ, θ) ρ 0 = W 1 + λ (1 + 1 λ 2 2 (1 + θ)( + 2θ) ] 32 + ( 1 λ ) 2 ) 2 (1 + θ)( + 2θ) ] 32 with f(λ, θ) > 0 for all λ [0, λ(θ)]. This relaxes the λ > λ constraint for which the marginal effect of ρ is positive. Hence, λ/ > 0.

5 Appendix B: Comparative statics in ρ It is impossible to derive comparative statics of the equilibrium that hold over the full range of other parameter values. We can, however, simulate and graph how the optimal ownership pattern varies with some parameters, holding the others constant. Figure C.1 illustrates how ownership depends on the extent of interrelatedness and the relative investment intensities for λ = 0 and θ = 0.5. It would look similar as long as λ is sufficiently low relative to θ. At the horizontal axis, for ρ = 0, moving left to right corresponds to a horizontal line in Figure 1 as well. Outsourcing is the optimal ownership for sufficiently similar buyer and seller investment intensities and integration towards the edges. For positive levels of interrelatedness, moving up along the vertical axis, the region where outsourcing is optimal shrinks at first, consistent with Proposition 3. The inwardsloping borders indicate that for investment intensities that are not too asymmetric, a small increase in interrelatedness makes integration more likely. This corresponds to the horizontally shaded area in the middle of Figure 2. For the particular parameter values chosen, there is an area of intermediate levels of interrelatedness where outsourcing is never optimal. Eventually, the prediction of Proposition 2 comes through: for very high interrelatedness, outsourcing will be optimal even if investment intensities are highly asymmetric. For highly asymmetric investment intensities, only this positive effect of interrelatedness on outsourcing will ever come into play. It corresponds to the even shaded areas at the edges of Figure 2. Figure B.1: Optimal ownership structure as a function of investment interrelatedness interrelatedness (ρ ) Outsourcing Proposition 2 (high externality) Supplier Integration Outsourcing Buyer Integration Relative importance of buyer investment ( ) to supplier investment ( ) Note: The figure holds constant the following two parameters: λ =0 and θ =0.5 5 Proposition 3 (low externality)

6 Appendix C: Flexible specification As described in Section 5.3, we generalize the estimating equation (3) to evaluate the marginal impact of investments on the outsourcing probability in a flexible way. estimate the following general specification, still using a Probit model: We OUT S fj = 2 2 k βfj kl (XB i ) k (Xj S ) l + Controls fj + ε fj k=0 l=0 with β kl fj = β kl 0 + β kl mpmulti-plant f + β kl ir INT ER ij + β kl cscost-share fj +β kl sp1rauch j + β kl sp2f raction fj + β kl sp3(log(buyers i /Suppliers j )) 2. (C.1) This equation contains five uninteracted technology intensity terms two linear, two quadratic, and one interaction term with a further 30 terms interacting the five technology terms with the six variables in the second and third line of equation (D.1). The first two interaction variables are a multi-plant indicator and the input similarity, the measure of investment interrelatedness used previously. The third interaction term is the share of input j in the buyer s total costs. AAGZ found theoretically and empirically that the predictions of the PRT model are enhanced for high input shares. The last three variables are proxies for the likelihood that the relationship between buyer f (with core output i) and the supply industry j involves specific investments. As discussed in the data section, the technology intensity variables are imperfect proxies for specific investments. The PRT predictions are only expected to hold in situations where investments are truly relationship-specific. The last three variables in the linear combinations are intended as proxies for the level of specificity and indicators of whether the PRT should apply. Rauch j is a dummy variable that indicates whether input j is a differentiated product. A successful supply relationship for differentiated products is more likely to entail specific investments. Our SCG commodity classification is equivalent to the 6-digit HS classification, which maps straightforwardly into the SITC classification of Rauch (1999). Products that are traded on a listed exchange or whose prices are quoted in trade publications are assigned a value of zero. A second proxy is the fraction of total Canadian production of input j that is represented by the demand of buyer f. If this is large, outsourcing can be a risky strategy as finding alternative sources of supply (or demand) can be hard difficult in the short run. The loss in surplus when a relationship breaks down and a firm has to 6

7 turn to its outside option is related in a similar way to the relative number of buyers and suppliers in the industry. We calculated this third proxy for investment specificity at the most-detailed commodity level. Using the parameter estimates of equation (D.1), which are reported in Table B.3 in the Appendix, we can calculate the marginal effects of buyer and supplier technology intensity on the likelihood of outsourcing. Ai and Norton (2003) have illustrated the complications in calculating marginal effects for variables that enter interacted with other variables in a nonlinear Probit model. In general, these will be a function of all other variables in the model and to make the presentation transparent we have to hold the other variables constant at some value. To facilitate the presentation, we present the results graphically. On the vertical axis of Figure in the paper and the figures in this Appendix is the derivative of the cumulative normal density function that takes the righthand side of equation (D.1) as argument. This expression can be evaluated at different values of choice for the variables in the model. First, in Figure D.1, we evaluate the marginal effects at a low level of interrelatedness (one standard deviation below the mean) and average values for all other variables, in particular the specificity proxies. The multiplant dummy is evaluated at the sample mean in the graphs on the left, but set to one in the graphs on the right. These last graphs correspond to the four panels in Figure in the paper. On the horizontal axis, is the log difference in buyer minus supplier investment intensity log(x B /X S ). In the situation without interrelatedness externality (ρ = 0), it is the empirical counterpart of the log(α B /α S ) measure on the horizontal axes of Figures 1 3, which played a crucial role in Proposition 1. It is expressed in numbers of standard deviations from the respective sample means. A value of one indicates that the buyer intensity is one standard deviation higher relative to the average buyer intensity than the supplier intensity is relative to its own average. This can mean, for example, that the buyer is one standard deviation above the mean, while the supplier has average intensity. The same x-value also represents the situations with average intensity for the buyer and the technology intensity one standard deviation below the average for suppliers, etc. We use frequency weights from the sample to construct a weighted average for transactions with the same x-value. The interpretation of the patterns in Figure D.1 is as follows. If the variable on the 7

8 Figure C.1: Marginal effect of technology intensity on outsourcing at low interrelatedness (all firms) (only multi-plant firms) Skill Skill Innovation Innovation R&D Technology use Capital R&D Technology use Capital X-axis: Buyer minus Supplier intensity Buyer investments Supplier investments Notes: The lines are the predicted marginal effects of the respective investments on the outsourcing probability, obtained by differentiating the cdf of the normal distribution that takes the right-hand side of equation (13) as argument. All variables are evaluated at their sample average, except for "Input similarity" which is set to one standard deviation below its mean. The multiplant dummy is evaluated at the sample mean in the graphs on the left, but set to one in the graphs on the right. On the horizontal axis is the relative buyer to supplier investment intensity, expressed in numbers of standard deviations. For example, a value of two indicates that buyer investments (X B ) are two standard deviations higher than supplier investments (X S ), both relative to their own mean. This could mean that X B is at one (S.D.) above and X S at one below, or X B two above and X S at the mean, or a number of other combinations. 8

9 horizontal axis is high, buyer investments dominate and the relevant margin in the optimal ownership choice will be between buyer integration and outsourcing. A marginal increase in the importance of buyer investment makes integration more attractive as it gives him optimal incentives. The theory thus predicts that on the right side of each graph buyer integration is the relevant form of integration and that higher buyer investments will have a negative effect on the outsourcing probability in such a situation. At the opposite side of the x-spectrum, supplier investments dominate and a similar increase in buyer intensity is now predicted to have opposite effects. It will boost the attractiveness of outsourcing relatively to supplier integration. In sum, the marginal impact of buyer investments (dashed lines) should be to reduce the probability of outsourcing where these investments dominate, at high x-values, but to make outsourcing more likely at low x-values. Results for supplier investments (solid lines) should be opposite on both sides of the spectrum. These are the predictions of Proposition 1. The marginal effects are calculated from the extremely flexible specification in equation (D.1). Point estimates are reported in the Appendix and marginal effects are plotted in Figure D.1. Results for the full sample, in the graphs on the left, illustrate that the PRT predictions are strongly supported in situations with high relative buyer intensity (high x-values) for four out of five technology proxies. At high relative supplier intensity (low x-values), the results are still strong for the skill intensity measure of investment intensity, but the impact of technology on ownership is weak for the other measures. The argument in AAGZ, that forward integration is rare in manufacturing, could limited the explanatory power in the sample at the supplier integration versus outsourcing margin. The results are even more supportive if the sample is limited to multiplant firms, see the graphs on the right in Figure D.1. As can be seen from the vertical scales, the magnitudes of the marginal effects tend to be estimated two to four times higher for multiplant firms. For the innovation and R&D measures, the PRT predictions are now also supported in high relative supplier intensity situations (low x-values). For capital intensity, the results are still in line with predictions for buyer investment: it weakly decreases outsourcing on the right and noticeably increases outsourcing on the left. The impact of supplier investments, however, follows the same downward-sloping pattern, which is opposite of the PRT predictions. 9

10 As mentioned earlier, the five technological intensity measures are ordered by increasing ease with which investments can be appropriated in the case of a breakdown of the relationship (the λ parameter). At one extreme, the human capital in the skill intensity measure is likely to be accompanied with investments in tacit knowledge that are nearly impossible to appropriate. At the other extreme, physical capital investments of the subsidiary should be relatively easy to appropriate, i.e. the reduction in surplus should be modest if the owner fires the subsidiary. The effects for different investment proxies are plotted in two separate graphs in Figure D.2, in the case of low interrelatedness and differentiated products (high specificity). On the left are the two measures that are expected to be most difficult to appropriate: skill and innovation. On the right are the other three measures, where λ is expected to be higher. Figure C.2: Marginal effects of different technology intensities on outsourcing for low interrelatedness and high specificity hard to appropriate easy to appropriate X-axis: Buyer minus Supplier intensity / Buyer / Supplier: skill intensity / Buyer / Supplier: R&D intensity / Buyer / Supplier: innovation / Buyer / Supplier: technology / Buyer / Supplier: capital intensity Notes: The lines are constructed as in Figure, but now marginal effects for different technology intensities are shown on the same scale, which is 20 times higher in the left graph. All variables are evaluated at the sample mean, except for the Multi-plant and the Rauch dummies which are set to one, and the Input similarity variable which is evaluated at one standard deviation below the mean. 10

11 The effects are an order of magnitude larger on the left; the vertical axis for these two variables was rescaled appropriately. The signs of the marginal effects also accord better with the theory in the left panel of Figure D.2. For the three measures plotted in the right panel, some marginal effects are indistinguishable from zero. The theory predicts that at the extreme (for λ equal to one), integration becomes highly unattractive as subsidiaries stop investing altogether. For most investments in the right panel, the positive effect on the outsourcing probability at one side of the x-spectrum dominates the negative effect at the other side of the x-spectrum. This is consistent with integration being particularly damaging for the subsidiary s incentives if λ is high. Finally, we illustrate in Figure D.3 how the marginal effects of technology intensity on the outsourcing probability vary with investment specificity. Recall that the PRT predictions are only relevant if investments are at least somewhat specific for θ equal to one firms will always choose outsourcing. We limit attention to transactions for multiplant firms and for low interrelatedness, as the results should be more pronounced on this subsample. For the same reason, we plot the marginal effects for the innovation and skill intensity measures. Results are similar but less distinctive for R&D and technology use. There are three sets of lines for suppliers and producers in Figure D.3, corresponding to different values for the specificity proxies in equation (D.1). For the short-dashed lines labeled low specificity, the differentiated goods dummy (Rauch) and the fraction of Canadian production that the input demand of this transaction represents (F raction) are both set to zero. The imbalance in number of buyers and suppliers (log 2 (Buyers/Suppliers)) is evaluated at its average value. The results indicate very clearly that the intuitive marginal effects discussed earlier are driven entirely by transactions that are most specific. The effects are negligible in low specificity situations. For the other two sets of lines, Rauch is set to one and F raction at one standard deviation above the mean, representing high specificity situations. The long-dashed lines further evaluate log 2 (Buyers/Suppliers) at one standard deviation below the mean, indicating similar numbers of buyers and suppliers, while it is one standard deviation above the mean for the solid lines. In the innovation graph on the right, effects are very pronounced at high values for Rauch and F raction, but the number of buyers to suppliers ratio is not very important. For investments in skill, however, asymmetries in technology intensity have a much 11

13 buyers to sellers influences other aspects of the transaction as well, e.g. bargaining power or supply assurances, making technology a less important factor in predicting ownership. References Ai, Chunrong and Edward C. Norton (2003). Interaction Terms in Logit and Probit Models. Economics Letters, 80, Rauch, James E. (1999). Networks versus Markets in International Trade. Journal of International Economics, 8,

14 Appendix D: Tables with additional results Table D.1: Correlations between different investment intensities Skill Innovation R&D Technology Capital use Skill 1 Innovation R&D Technology use Capital Note: Partial correlation statistics between the investment intensity measures for buyer industries

15 Table D.2 Assessing the proxy for interrelatedness correlation of adoption with average of "same input" firms "other" firms Design and Engineering Computer aided design and/or engineering 0.288*** *** CAD output used to control manufacturing machines 0.102*** *** Digital representation of CAD output for procurement 0.111*** *** Fabrication and Assembly Flexible manufacturing systems 0.12*** *** (Computer) Numerically controlled machine 0.287*** *** Material working laser 0.073** *** Pick and place robots 0.158*** *** Other robots 0.061* *** Automated Material Handling Automated storage and retrieval systems *** Automated guided vehicle systems *** Inspection and Communication Automated inspection/testing of incoming material 0.112*** *** Automated inspection/testing of final product 0.180*** *** Local area network for technical data 0.208*** *** Local area network for factory use 0.079** *** Computer network linking plant to suppliers/customers 0.080** *** Programmable controller 0.21*** *** Computers used for control on factory floor 0.172*** *** Manufacturing Information Systems Material requirement planning 0.228*** *** Manufacturing resource planning 0.115*** *** Integration and Control Computer integrated manufacturing 0.085** *** Supervisory control and data acquisition 0.086** -0.21*** Artificial intelligence and/or expert systems 0.07** *** Note: correlation between the vector of firm-level adoption decisions and the average adoption frequency by other firms that share the same core input ("same input" firms) and the average adoption frequency of allother firms.

16 Table D.3: Coefficient estimates for flexible specification Dependent variable is firm-commodity outsourcing indicator X = Skill Innovation R&D Tech. use Capital (1) (2) (3) () (5) Size -0.07*** *** *** *** *** (0.016) (0.017) (0.017) (0.019) (0.017) Age 0.102*** 0.127*** 0.130*** 0.115*** 0.113*** (0.03) (0.035) (0.033) (0.03) (0.03) Non-production workers (0.13) (0.10) (0.139) (0.1) (0.17) Productivity (0.030) (0.030) (0.030) (0.029) (0.028) Complexity (0.116) (0.119) (0.115) (0.111) (0.122) X B ** (5.55) (1.561) (1.71) (0.271) (0.195) X S *** (5.60) (1.815) (1.6) (0.307) (0.239) X B x X S *** (10.18) (2.055) (1.61) (0.066) (0.03) (X B ) (6.751) (1.821) (1.798) (0.05) (0.028) (X S ) *** (7.352) (2.273) (1.626) (0.061) (0.032) Input similarity *** *** *** -2.70*** -2.92*** (1.536) (0.592) (0.79) (0.735) (0.585) (X B ) x Input similarity (8.292) (2.798) (2.651) (0.06) (0.317) (X S ) x Input similarity * *** (8.01) (2.819) (2.275) (0.8) (0.352) (X B x X S ) x Input similarity *** *** ** *** *** (19.86) (3.572) (3.293) (0.096) (0.03) (X B ) 2 x Input similarity 1.97*** ** 0.102** (13.00) (3.032) (2.71) (0.077) (0.09) (X S ) 2 x Input similarity 35.10*** (13.137) (3.95) (2.720) (0.100) (0.06) Cost share *** ** *** *** (1.309) (0.322) (0.362) (0.269) (0.520) (X B ) x Cost share * 0.65** (5.073) (1.569) (2.35) (0.21) (0.231) (X S ) x Cost share (6.290) (2.271) (1.872) (0.239) (0.175) (X B x X S ) x Cost share -2.89*** -5.5** *** ** (11.565) (2.51) (3.1) (0.053) (0.026) (X B ) 2 x Cost share *** ** (6.957) (2.367) (3.361) (0.051) (0.028) (X S ) 2 x Cost share * *** 0.077* 0.057** (9.790) (2.10) (2.395) (0.06) (0.023) Multiplant dummy -0.93** -0.8*** *** * -0.23* (0.87) (0.15) (0.223) (0.156) (0.255) (X B ) x Multiplant ** (2.613) (0.83) (0.91) (0.12) (0.120) (X S ) x Multiplant ** (2.872) (0.816) (0.779) (0.130) (0.101) (X B x X S ) x Multiplant 17.95***.296** *** (6.866) (1.783) (1.26) (0.03) (0.02) (X B ) 2 x Multiplant *** * -3.50*** *** (.083) (1.860) (1.253) (0.03) (0.019) (X S ) 2 x Multiplant -8.88* ** -0.05** (5.365) (0.972) (0.929) (0.031) (0.019) Rauch dummy (0.839) (0.266) (0.322) (0.268) (0.388) (X B ) x Rauch *** ** (2.818) (0.959) (0.776) (0.151) (0.107) (X S ) x Rauch (3.636) (1.72) (1.28) (0.201) (0.162) (X B x X S ) x Rauch (7.038) (1.618) (1.223) (0.036) (0.019) (X B ) 2 x Rauch (.765) (1.09) (0.886) (0.028) (0.01) (X S ) 2 x Rauch (5.679) (1.896) (1.19) (0.07) (0.022) log (# prod. / # suppl.) 0.181** 0.070*** 0.072* 0.066** (rel. #) (0.10) (0.028) (0.039) (0.032) (0.031) (X B ) x (rel. #) (0.35) (0.108) (0.152) (0.022) (0.013) (X S ) x (rel. #) *** 0.3** 0.069*** 0.026** (0.57) (0.11) (0.11) (0.02) (0.012) (X B x X S ) x (rel. #) (0.535) (0.169) (0.195) (0.00) (0.002) (X B ) 2 x (rel. #) (0.2) (0.130) (0.150) (0.00) (0.001) (X S ) 2 x (rel. #) *** * * ** (0.556) (0.152) (0.178) (0.006) (0.002) Constant 3.351** 3.191*** 3.958*** 3.02*** 3.508*** (1.790) (0.600) (0.595) (0.710) (0.530) Observations log likelihood Notes: Coefficient estimates from Probit estimations pooling two years (1988 and 1996), standard errors (clustered at the most detailed industry level) in brackets. Results in each column use different measures of investment intensity (indicated at the top). ***Significant at the 1% level, **5%, *10%.

17 Appendix D: Tables with additional results Table D.1: Correlations between different investment intensities Skill Innovation R&D Technology Capital use Skill 1 Innovation R&D Technology use Capital Note: Partial correlation statistics between the investment intensity measures for buyer industries

18 Table D.2 Assessing the proxy for interrelatedness correlation of adoption with average of "same input" firms "other" firms Design and Engineering Computer aided design and/or engineering 0.288*** *** CAD output used to control manufacturing machines 0.102*** *** Digital representation of CAD output for procurement 0.111*** *** Fabrication and Assembly Flexible manufacturing systems 0.12*** *** (Computer) Numerically controlled machine 0.287*** *** Material working laser 0.073** *** Pick and place robots 0.158*** *** Other robots 0.061* *** Automated Material Handling Automated storage and retrieval systems *** Automated guided vehicle systems *** Inspection and Communication Automated inspection/testing of incoming material 0.112*** *** Automated inspection/testing of final product 0.180*** *** Local area network for technical data 0.208*** *** Local area network for factory use 0.079** *** Computer network linking plant to suppliers/customers 0.080** *** Programmable controller 0.21*** *** Computers used for control on factory floor 0.172*** *** Manufacturing Information Systems Material requirement planning 0.228*** *** Manufacturing resource planning 0.115*** *** Integration and Control Computer integrated manufacturing 0.085** *** Supervisory control and data acquisition 0.086** -0.21*** Artificial intelligence and/or expert systems 0.07** *** Note: correlation between the vector of firm-level adoption decisions and the average adoption frequency by other firms that share the same core input ("same input" firms) and the average adoption frequency of allother firms.

19 Table D.3: Coefficient estimates for flexible specification Dependent variable is firm-commodity outsourcing indicator X = Skill Innovation R&D Tech. use Capital (1) (2) (3) () (5) Size -0.07*** *** *** *** *** (0.016) (0.017) (0.017) (0.019) (0.017) Age 0.102*** 0.127*** 0.130*** 0.115*** 0.113*** (0.03) (0.035) (0.033) (0.03) (0.03) Non-production workers (0.13) (0.10) (0.139) (0.1) (0.17) Productivity (0.030) (0.030) (0.030) (0.029) (0.028) Complexity (0.116) (0.119) (0.115) (0.111) (0.122) X B ** (5.55) (1.561) (1.71) (0.271) (0.195) X S *** (5.60) (1.815) (1.6) (0.307) (0.239) X B x X S *** (10.18) (2.055) (1.61) (0.066) (0.03) (X B ) (6.751) (1.821) (1.798) (0.05) (0.028) (X S ) *** (7.352) (2.273) (1.626) (0.061) (0.032) Input similarity *** *** *** -2.70*** -2.92*** (1.536) (0.592) (0.79) (0.735) (0.585) (X B ) x Input similarity (8.292) (2.798) (2.651) (0.06) (0.317) (X S ) x Input similarity * *** (8.01) (2.819) (2.275) (0.8) (0.352) (X B x X S ) x Input similarity *** *** ** *** *** (19.86) (3.572) (3.293) (0.096) (0.03) (X B ) 2 x Input similarity 1.97*** ** 0.102** (13.00) (3.032) (2.71) (0.077) (0.09) (X S ) 2 x Input similarity 35.10*** (13.137) (3.95) (2.720) (0.100) (0.06) Cost share *** ** *** *** (1.309) (0.322) (0.362) (0.269) (0.520) (X B ) x Cost share * 0.65** (5.073) (1.569) (2.35) (0.21) (0.231) (X S ) x Cost share (6.290) (2.271) (1.872) (0.239) (0.175) (X B x X S ) x Cost share -2.89*** -5.5** *** ** (11.565) (2.51) (3.1) (0.053) (0.026) (X B ) 2 x Cost share *** ** (6.957) (2.367) (3.361) (0.051) (0.028) (X S ) 2 x Cost share * *** 0.077* 0.057** (9.790) (2.10) (2.395) (0.06) (0.023) Multiplant dummy -0.93** -0.8*** *** * -0.23* (0.87) (0.15) (0.223) (0.156) (0.255) (X B ) x Multiplant ** (2.613) (0.83) (0.91) (0.12) (0.120) (X S ) x Multiplant ** (2.872) (0.816) (0.779) (0.130) (0.101) (X B x X S ) x Multiplant 17.95***.296** *** (6.866) (1.783) (1.26) (0.03) (0.02) (X B ) 2 x Multiplant *** * -3.50*** *** (.083) (1.860) (1.253) (0.03) (0.019) (X S ) 2 x Multiplant -8.88* ** -0.05** (5.365) (0.972) (0.929) (0.031) (0.019) Rauch dummy (0.839) (0.266) (0.322) (0.268) (0.388) (X B ) x Rauch *** ** (2.818) (0.959) (0.776) (0.151) (0.107) (X S ) x Rauch (3.636) (1.72) (1.28) (0.201) (0.162) (X B x X S ) x Rauch (7.038) (1.618) (1.223) (0.036) (0.019) (X B ) 2 x Rauch (.765) (1.09) (0.886) (0.028) (0.01) (X S ) 2 x Rauch (5.679) (1.896) (1.19) (0.07) (0.022) log (# prod. / # suppl.) 0.181** 0.070*** 0.072* 0.066** (rel. #) (0.10) (0.028) (0.039) (0.032) (0.031) (X B ) x (rel. #) (0.35) (0.108) (0.152) (0.022) (0.013) (X S ) x (rel. #) *** 0.3** 0.069*** 0.026** (0.57) (0.11) (0.11) (0.02) (0.012) (X B x X S ) x (rel. #) (0.535) (0.169) (0.195) (0.00) (0.002) (X B ) 2 x (rel. #) (0.2) (0.130) (0.150) (0.00) (0.001) (X S ) 2 x (rel. #) *** * * ** (0.556) (0.152) (0.178) (0.006) (0.002) Constant 3.351** 3.191*** 3.958*** 3.02*** 3.508*** (1.790) (0.600) (0.595) (0.710) (0.530) Observations log likelihood Notes: Coefficient estimates from Probit estimations pooling two years (1988 and 1996), standard errors (clustered at the most detailed industry level) in brackets. Results in each column use different measures of investment intensity (indicated at the top). ***Significant at the 1% level, **5%, *10%.

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