managerial incentives and the organization of chinese

Managerial Incentives and the Organization of Chinese Processing Trade

Huiya Chena and Deborah L. Swenson*b

aPrice Waterhouse Coopers bUniversity of California, Davis and NBER

Abstract

Chinese processing trade has grown considerably as firms adopted a wide array of organizational forms to have their products assembled in China for export. To understand the organization of processing trade we modify Grossman and Helpman’s (2004) model of managerial incentives to account for the economic costs associated with firms’ input control decisions in China. We examine Chinese processing trade between 1992 and 2003 to test the model’s predictions. As predicted by the model, we find that firm productivity is related to processing choices. In addition, the organization of processing trade is found to match tariff levels at the product level.

JEL Classifications: F14, F23, L22

Keywords: Firm heterogeneity, multinational firms, boundaries of the firm

1. Introduction Processing trade plays a pivotal role in China’s trade activities and is distinguished

by a number of interesting characteristics. Notably, while the largest share of processing trade is conducted by the foreign affiliates of multinational firms in China, a unique aspect of Chinese processing trade is the coexistence of multiple forms of organization.1 In particular, in addition to the processing trade operations conducted by foreign subsidiaries, outsourcing also occurs under two additional organizational forms: the

* Corresponding author. Mailing address: One Shields Avenue, Department of Economics, University of California, Davis, CA 95616 USA. Fax: (530) 752-9382. Email: [email protected].

1 The World Investment Report (2004) by the United Nations Conference on Trade and Development (UNCTAD) documents that China’s Foreign Direct Investment (FDI) inflows amounted to 36 percent of Chinese Gross Domestic Product (GDP) in 2003. The trade data from the Customs General Administration of the People’s Republic of China reveals that in 2003 nearly 80 percent of Chinese processing exports were from foreign affiliates of multinational firms.

Huiya Chen and Deborah L. Swenson Asia-Pacific Journal of Accounting & Economics 17 (2010) 235–252

236

Pure-Assembly (PA) regime and the Import-and-Assembly (IA) regime.2 Under the PA regime, final goods producers are in charge of supplying inputs to Chinese assembly factories. In contrast, IA regime assemblers maintain control over input purchases.

Recent works in international trade highlight how economic factors influence the structure of international commerce. For example, Melitz (2003) and Helpman et al. (2004) show how firm heterogeneity affects firm’s modes of serving overseas customers, while Grossman and Helpman (2004), Antràs and Helpman (2004) and Feenstra and Hanson (2005) show how incentive issues influence organizational form. Since China has integrated rapidly with the world economy in recent years, Chinese trade provides an ideal setting for the study of economic factors and organizational choice. China is particularly interesting, due to the importance of processing trade.

To analyze how economic conditions affect organizational choices we build on Grossman and Helpman’s (2004) managerial incentives model of organizational decisions. Their work provides insights into the case where a final goods producer is unable to fully observe managerial effort by subsidiary managers and subsidiary manager shirking reduces product quality, and thus the probability of success. However, in contrast with their assumption that factory owners maintain control over input purchase decisions, we modify their model to include salient features of Chinese processing trade. Differences in the structure and compensation that are associated with the different forms processing trade, suggest that heterogeneous firms will sort into different organizational forms based on productivity. In particular, our framework suggests that of those firms who are sufficiently productive to produce, the highest productivity firms should engage in PA outsourcing, followed in descending order by firms engaged in FDI outsourcing, and IA outsourcing. The model also suggests that changes in tariff or trade costs will influence the ideal method of organization.

To test our predictions, we use Chinese data on processing trade. The empirical analysis confirms the model’s predictions regarding firm heterogeneity and the relative prevalence of outsourcing and FDI for different commodity groups. First, productivity influences firm organizational choices: multinational firms with intermediate productivity prefer vertical integration through FDI, while the remaining firms engage in global production through outsourcing. We also examine the sensitivity of organizational choice to changes of tariff and transportation costs.

This paper makes two contributions to the literature. First, by modifying Grossman and Helpman’s (2004) model of organizational choice to include decisions regarding the delegation of input control, we provide an additional explanation for the influence of economic factors on firm organizational decisions. These effects are complementary to the mechanisms identified by Feenstra and Hanson (2005).3 In addition, it provides

2 Feenstra and Hanson (2005) develop this taxonomy and provide a detailed comparison of the two processing trade regimes.

3 This work also complements that of Ma (2006) who applies a modified version of Markusen (2002) to Chinese trade data. Similarly, our paper is related to Feenstra and Spencer (2005) who examine the mode adopted by firms in their sourcing of intermediate inputs: contractual, pure external transactions or via their own foreign affiliates.


237

further empirical evidence that industry characteristics and the economic environment influence the relative prevalence of organizational forms.4

2. The Model

Our model demonstrates how industry characteristics, including intra-commodity group productivity dispersion and trade costs, influence a final good producer’s choice of organizational form. While the model applies generally to the production choices for final goods producers in high wage countries conducting international processing trade in a low wage host locations, our model concentrates on a US final good producer’s choice between vertical integration and outsourcing under the assumption that the firm will locate its assembly operations in China. Thus, the producer decides whether to do its own assembly by engaging in FDI or to outsource assembly tasks to a Chinese partner.

As in Grossman and Helpman (2004), the final good producer searches for components. Each unit of final good requires one unit of input and the assembly operation’s success depends on the procurement effort of the manager. Since the final good producer is unable to fully observe the manager’s effort, the final good producer provides a bonus to the manager to ensure adequate procurement effort. For this reason bonuses are contingent on the revenue generated by sales of the final product.

2.1 Chinese Processing

Foreign firms engaged in contractual outsourcing may choose either the PA regime or the IA regime. Under the PA regime, a US final good producer exerts the effort to locate suitable inputs for its component assembler in China. The inputs belong to the US firm, and the US firm pays the assembly factory a processing fee for the output it produces. By contrast, for products processed under the IA regime, the Chinese contract assembler has authority to search for and import intermediate inputs. The imported inputs belong to the Chinese assembly operation, and the post-processed output can be sold to any final good producer. Because of the weaker connection between component producers and final good producers under the IA regime, the magnitude of technology transfers between the final good producers and the component producers in China is likely to be smaller under the IA regime than it is under the PA regime.

2.2 The Firm Problem

A final producer’s choice of organizational form depends on its productivity. As in Melitz (2003) each final producer receives a productivity draw after it pays a fee to enter the industry. The productivity draws enable producers to predict their revenue and expected profits based on the organizational form they select.

4 Antras (2003) examines how capital intensity and R&D intensity are related to the fraction of firms engaged in vertical integration through FDI, while Helpman, Melitz and Yeaple (2004) show how other industry characteristics, such as productivity dispersion and trade frictions, affect organizational choices.


238

Each type of organizational form faces a distinct set of organizational costs. While some costs are determined by the nature of the activity, other costs such as bonuses are set to elicit effort e from the manager in China. To ensure an entrepreneur’s participation in the processing activity, a final good producer has to make a non-negative fixed upfront payment U in addition to the bonus. Likewise, if the firm uses FDI for its assembly operations it must pay a non-negative wage w to the subsidiary manager who coordinates the processing activity. The final good producer in the US maximizes expected profit by choosing the organizational form and compensation scheme that elicit the preferred effort level from its assembly subsidiary or outsourcing partner in China.

To examine the relative merits of these strategies we note our assumptions about the nature of production and then analyze the threshold revenues which define the productivity boundaries between these alternative forms of organizational.

Assumption 1: As in Grossman and Helpman (2004), we assume the probability of a successful final sale is a piecewise linear concave function of the Chinese processing manager’s effort. Figure 1 illustrates the connection between a project’s probability of success and manager effort. Zero effort leads to a tiny probability, h0, of success. An intermediate effort, e1, induces a medium probability of success, h1 = h(e1). Full effort, E, guarantees success, and h(E) equals one. Since full effort, E, is normalized to one, the intermediate effort level e1, lies between zero and one. There are diminishing returns to effort, as the marginal productivity of effort along the upper segment between medium and full effort is less than the marginal productivity along the first segment.

Figure 1: Managerial Effort and the Probability of Project Success

Effort

Probabilityof Success

e1

1

h(e1)

h0

E = 1

Given the parameters of the compensation scheme chosen by the foreign firm the Chinese manager chooses his effort to maximize his utility. The final good producer


239

induces intermediate effort by paying φ1 = φ(e1) = (e1 – 0)/(h1 – h0) in bonus.5 Alternatively, a bonus φE = φ(E) = (1 – e1)/(1 – h1) motivates the manager to exert full effort. In the case of foreign investment, the subsidiary’s manager exerts full effort E on observable tasks. However, the subsidiary manager only exerts an intermediate level of effort when performing tasks that are unobservable by the foreign firm.

Assumption 2: Compensation is structured such that entrepreneurs engaged in IA outsourcing exert medium effort e1 on every task, while entrepreneurs operating under the PA regime are paid the full bonus and exert full effort E on every task

Assumption 3: Final good producers select upfront payments or wages such that the participation constraints for FDI or IA outsourcing are binding. This implies that the Chinese manager is indifferent about working with the different outsourcing producers.

2.3 Organizational Choice

After receiving a productivity draw, final producers forecast the revenue for each form of operation. Naturally, both revenue and profit increase with the productivity draw. A final good producer who receives a very low productivity draw will not produce. If the productivity draw is slightly higher, the final good producer enters the market through the IA regime, outsourcing from low cost overseas processing factories. For a higher productivity draw, the foreign firm chooses to operate its own foreign subsidiary, since improved supervision under FDI yields higher profit for this final good producer despite the higher fixed wage costs paid to the subsidiary manager. Finally, since firms with the highest productivity draws experience the greatest returns to effort, they choose PA outsourcing structuring compensation to elicit full managerial effort.

5 For a given bonus level, the participation constraints imply that managers will select effort such that φ(e)h'(e) = 1 , where h'(e) denotes the marginal productivity of effort. For effort level increases between zero to e1,

h '(e)h1 h0

e1 0––= . Thus

––

= =(e) 1h' (e)

e1 0h1 h0

.


240

Figure 2: Organizational Choices for Fragmented Production in China

OPA (PAoutsourcing)

Profit of a U.S. final goodproducer

2R1R Revenue (Productivity)

3R

Exit Outsource(Import &Assembly)

Invest Abroad(FDI)

Outsource(Pure-Assembly)

V (profit underFDI)

OIA (IAoutsourcing)

Figure 2 illustrates the connection between outsourcing organization and expected profits. The solid line shows the expected profit for a final good producer who chooses to outsource its production components. For productivity draws to the left of the kink, a firm pays the intermediate level of bonus and engages in outsourcing under the IA regime (OIA), while it switches to the full effort bonus and implements the PA regime (OPA) for productivity draws beyond the kink. Consequently, the profit functions for the lower and the upper part of the outsourcing curve are:

∏OIA = h1R – (S + e1 + C)(1 + τ) and OPA R

1 e1

1 h1

C– ––

–=∏ . (1)

In these expressions, R represents the final good producer’s total revenue, which is determined by its productivity draw. Trade costs (τ) include both tariffs and transport costs for the processed output. The Chinese manager’s effort determines the probability of success, h(e). The input cost C appears in the second term of ∏OIA since processing plants in the IA regime purchase and retain control over inputs used in production. The Chinese entrepreneur’s compensation is pinned down by his opportunity cost S.

The second expression ∏OPA, has three components. Since the PA contract is set to attain full effort, and therefore guaranteed project success, revenues are R. The second


241

term is the bonus paid to the PA manager, and the last term C, represents the cost of components procured by the foreign firm for the Chinese assembly operation.

The dashed line in Figure 2 displays expected profits for final good producers engaged in FDI and operate their own subsidiary. Expected profit for vertical FDI (v) is:

∏V = (δ + (1 – δ) h1) R – (S + δ + (1 – δ)e1 + C)(1 + τ). (2)

As before, project value is based on the firm’s productivity draw, and therefore, expected revenue R. When firms perform FDI, a fraction δ of the manager’s tasks are observable while effort on remaining tasks is unobservable.Since we assume the subsidiary manager exerts full effort on observable tasks and medium effort on the unobservable tasks they are paid a wage premium [δ + (1 – δ)e1] based on their effort. This managerial effort also implies that the project’s success probability is [δ + (1 – δ)h1].

Based on the expressions for expected profit, we solve for the threshold revenue levels which form the boundaries between each organizational form. We also analyze the effect of changes in trade costs on each of these threshold levels.6 Outsourcing under the IA regime allows a final good producer to break even with the revenue level, R1, where

++

+=R1

(S e1 C)h1

(1 ). (3)

Since the minimum revenue level R1 increases with trade costs τ, declining trade costs enable firms that would not have entered the market in the past to enter and profitably engage in outsourcing under the IA regime.

Although it is more costly to create a foreign subsidiary, subsidiaries enable firms to improve their monitoring of employees in China, and thus increase the probability of firm success. Weighing the gains due to a higher probability of success against the increased fixed costs of operating a subsidiary generates the threshold revenue, R2, at which firms are indifferent between outsourcing under the IA regime or engaging in FDI.

––

= +(1 )R2

1 e1

1 h1

(4)

Finally, at the productivity threshold, R3,

– – –

– ––

=+ + +

+R3

1 e1

1 h1

C (S (1 )e1)(1 )

1 ( (1 )h1 ) (5)

the benefit of having a higher probability of success under the PA regime offsets the cost of paying the large bonus which elicits full effort from the Chinese partner.

6 An appendix of derivations is available from the authors on request.


242

When trade costs decline, equations (4) and (5) show that the incentives to operate foreign subsidiaries increases, as firms at near the previous thresholds R2 and R3 choose FDI over outsourcing under the IA or PA regimes, respectively. These changes in threshold decisions are represented in Figure 3.

Figure 3: Effects of Falling Trade Costs on the Decision Thresholds

τ decreases τ decreases τ decreases

Exit Outsource(Import &Assembly)

Invest Abroad (FDI) Outsource(Pure Assembly)

R1 R2 R3

Following the literature we model industries as populated by final producers who produce unique varieties of the final good and engage in monopolistic competition. Prior to entry, we assume final goods producers receive a productivity draw. These assumptions allow us to calculate the ratio of outsourcing to FDI implied by the theoretical model and the shape parameter k for the Pareto distribution for each industry. Two testable hypotheses follow. First, the prevalence of PA outsourcing should be greater in high productivity industries, while IA outsourcing is more prevalent in lower productivity industries. Second, our model framework predicts that declines in trade costs will encourage the expansion of outsourcing through foreign investment.

3. Data

To test the implications of our model, our analysis uses data on Chinese processing exports to the United States. The data, which record commodity trade under the Harmonized System (HS), enable us to distinguish between processing activities conducted by foreign invested firms, as opposed to outsourcing. As in Helpman, Melitz and Yeaple (2004) we analyze trade shares; in this context, we test how economic factors affected the organization of processing trade. Our dependent variables – the share of processing trade conducted by foreign-invested enterprises, and the shares of processing trade performed under the IA outsourcing or PA outsourcing regimes – are constructed from the commodity level trade data. The trade share of foreign-invested enterprises is based on data for 1992 to 2003, since the reporting of data by HS classification began in 1992, while the trade shares for the two outsourcing regimes are constructed for 1997 to 2003, since the reporting of data by processing regime began in 1997.

We calculate productivity heterogeneity for each commodity group since firm heterogeneity, as an indicator of industry productivity, is a key factor in our model.


243

Ideally we would like to use the productivity dispersion for US final good producers. However, since no concordance maps U.S. industries to overseas processing trade at the HS8 industry level we instead use data on Chinese export transactions.Under the assumption that each export transaction represents the activity of a single firm in the market, the dispersion measure is calculated as the standard deviation of the logarithm of each firm’s sale within a commodity group.7 As noted by Helpman, Melitz and Yeaple (2004), since high productivity firms have higher shipments, intra-commodity sales dispersion provides insight into productivity level of the group.8

Trade costs are constructed from US import data on commodity-specific tariff and transportation costs. We calculate the value-weighted average of the ad-valorem tariffs and freight rates at the HS4 or HS6 digit level from the original HS10 trade data.9

Figure 4 demonstrates the variation across industries and across time in organizational form, juxtaposed against the tariff and transportation cost changes we exploit for identification. For example, Figure 4 illustrates the contrast between changes in export shares and tariff rates for the low productivity textile and footwear industries, as compared with the changes in the high productivity office machinery and electric machinery industries. In support of our basic hypothesis, Figure 4 indicates that the high productivity industries had a higher export share of outsourcing under the PA regime than the lower productivity industries did. In addition, the time series developments shown in Figure 4 provide support for our second hypothesis, since they indicate that the share of FDI processing trade increased as tariff rates fell. However, while industry correlations match our hypotheses, we need econometric evidence to test how industry characteristics and trade liberalization influenced the organization of Chinese processing trade.

7 Since the Chinese export transactions record HS8 product, the exporter’s organizational form, the ownership of imported inputs, the location of the exporter up to the city-district level, the final destination, and any entrepôt ports that the product goes through, Feenstra and Hanson (2005) note the data are close to firm-level observations.

8 Following Grossman and Helpman (2004), Antràs and Helpman (2004), and Helpman et al. (2004) we assume firm productivity is Pareto distributed. As in Helpman, et al. (2004), we use information on sales dispersion within industries to determine which industries are higher or lower productivity.

9 Trade costs for Chinese exports to the U.S. up to 2001 are calculated using Feenstra et al. (2002) US export data. Costs for 2002 and 2003 calculated using CDs-ROM of “U.S. Import of Merchandise” compiled by the US Department of Commerce.


244

Figure 4: Tariff Rates and Export Shares by Organizational Type

(1997-2003)Man-made Staple Fibers

0

10

20

30

40

50

60

70

1997 1998 1999 2000 2001 2002 2003Year

Export Shares

0

2

4

6

8

10

12

14

16

Tariff Rate %

Outsourcing_PA Outsourcing_IA FDI Tariff Rate


Footware

0

10

20

30

40

50

60

70

80

1997 1998 1999 2000 2001 2002 2003

Year

Export Shares

8.7

8.75

8.8

8.85

8.9

8.95

9

9.05

Tariff Rate %


245

Figure 4: Tariff Rates and Export Shares by Organizational Type (continued)

1997 1998 1999 2000 2001 2002 2003


Year

Office Machines

0

20

40

60

80

100

120

Export Shares

00.20.40.60.811.21.41.61.82

Tariff Rate %

1997 1998 1999 2000 2001 2002 2003


Year

Electric Machinery

0

10

20

30

40

50

60

70

80

Export Shares

0

0.5

1

1.5

2

2.5

3

Tariff Rate %


246

4. Empirical Results

In this section we use econometric tests to decide whether managerial incentives shape firm organizational form, and further, to compare the economic importance of managerial incentives versus other economic factors in these decisions. We begin by studying the relative prevalence of processing trade conducted by outsourcing versus foreign investment between 1992 to 2003. For each of our regressions reported in Table 1, the dependent variable is the ratio of outsourcing sales to FDI sales for processing trade products grouped to the HS4 or HS6 industry level of aggregation. Since we observe decisions over time, we can test the importance of industry dispersion, which reflects the underlying industry productivity, as well as the time varying changes in trade costs.

Whether we use standard least squares or Tobit analysis, we find that firm organization decisions match our predictions, namely, firm organizational decisions are based on firms sorting into organizational form based on firm productivity, with outsourcing being most common in lower productivity industries, and foreign investment more common in higher productivity industries. However, in the case of trade costs, our results are mixed. The hypothesis is that lower trade costs will cause firms to reduce their reliance on outsourcing, as they shift towards processing trade conducted by foreign affiliates. In Table 1, this implies that the coefficients on the freight costs and tariff costs should all be positive. We find that tariff costs are consistent with the predicted effects: the data reveal that there was a shift towards the use of foreign investment in sectors that experienced declines in US import tariffs. In contrast, the results based on freight costs of shipment are mixed - while most of the HS6 level regressions support our hypothesis, the HS4 regressions do not. Nonetheless, the conflicting evidence on trade costs is less surprising if one examines the general trends in transportation and tariff costs for the time period. As Figure 5 illustrates, tariffs declined broadly over the time interval of estimation, while transportation costs oscillated. Due to the fixed costs of setting up foreign affiliates or locating contractual partners for outsourcing, processing trade organization decisions are likely to be long- lived and unresponsive to the short-run fluctuations such as those of transportation costs. In contrast, since tariff reductions are negotiated, predictable, and permanent, it makes sense that firms would incorporate tariff incentives in their organizational decisions.

Next, to place our results in the context of the broader literature, we augment our estimating framework to encompass a broader set of economic variables included in Feenstra and Hanson’s work on Chinese processing trade. For these regressions, we use multinomial logit estimation to evaluate the probability of selecting FDI or PA regime outsourcing, compared with IA regime outsourcing. As argued earlier, we predict that only the lowest productivity firms will prefer IA regime outsourcing. For this reason, we predict that the coefficient on dispersion (and therefore productivity) should be positive in all regressions. In the case of trade costs, higher trade costs favor outsourcing over foreign direct investment. Thus, we predict that trade costs will have a negative effect on foreign investment and a positive effect on PA outsourcing. Since we are now utilizing the distinction between the two forms of outsourcing, the analysis now begins in 1997.


247

Figure 5: Tariff and Freight Rates for China's Exports to the US (1992-2003)

3

3.5

4

4.5

5

5.5

6

6.5

7

7.5

8

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003Year

Percent

Tariff rate Freight rate

Table 1: Ratio of Outsourcing to Vertical Integration Estimates for 1992-2003

Dependent Variable: ln(relative sales of outsourcing to FDI)HS4 Level HS6 Level

RE Least sq RE Tobit RE Least sq RE Tobit

(1) (2) (3) (4) (5) (6) (7) (8)Ln(Freight) -0.157 -0.083 0.061 -0.099 0.048 0.096 0.151 0.22

[0.069]b [0.068] [0.276] [0.198] [0.041] [0.040]b [0.136] [0.131]c

Ln(Tariff) 0.103 0.036 0.321 0.005 0.077 0.016 0.288 0.059[0.010]a [0.011]a [0.050]a [0.034] [0.007]a [0.008]b [0.030]a [0.027]b

Dispersion -0.627 -0.677 -4.512 -2.536 -1.004 -1.014 -2.913 -2.802[0.254]b [0.254]a [1.511]a [0.562]a [0.142]a [0.140]a [0.494]a [0.441]a

Time Trend -0.135 -0.519 -0.121 -0.57[0.007]a [0.030]a [0.005]a [0.022]a

Constant 1.406 2.313 12.656 4.971 2.56 3.275 12.78 15.577[0.586]b [0.007]a [2.927]a [0.030]a [0.326]a [0.005]a [1.140]a [0.022]a

Observations 5968 5968 8534 8534 14034 14034 23480 23480Overall R-sq / LL 0.035 0.061 -24856 -24703 0.038 0.055 -62866 -62545Uncensored Obs 5968 5968 14034 14034Left-censored Obs 1159 1159 4052 4052Right-censored Obs 1407 1407 5394 5394

Standard errors in brackets with a ,b and c respectively denoting significance at the 1%, 5% and 10% levels.Coefficients for HS2 dummy variables in the least sq regressions not shown


248

Table 2 reports the estimates on firm organization based on multinomial logit regressions. As predicted, for industries with higher intra-commodity group dispersion more final good producers choose to engage in FDI. The trade cost results also meet our the predictions, as we generally find a negative association between FDI and trade costs, and a positive association between trade costs and the use of PA regime outsourcing. As was the case in Table 1, the tariff coefficients all meet the predictions, while the coefficients for freight costs are mixed.

We include a time trend in our specification to account for changes in firm ability to conduct outsourcing activities. For example, over time, technology developments may increase the fraction of monitorable tasks grew over time, and therefore the share of outsourcing that is best done by FDI. Consistent with this idea, the data reveal a negative time trend for the ratio of outsourcing to FDI. These results hold both for a linear panel, a non-linear Tobit regression, and for dispersion measured at different HS levels. Since the average tariff rate fell steadily between 1992 and 2003, it is worth noting that the time trend may also capture effects due to tariff liberalization.

Table 2 displays another robustness check on the effect of dispersion which is based on Logit regressions. Treating each observation in the data set as an exporting factory, we calculate the intra-commodity group dispersion at the HS4 level. From the Multinomial Logit estimates in Table 2, higher dispersion within a commodity group increases the log-odds between FDI and IA outsourcing as well as the log-odds between PA outsourcing and IA outsourcing. Based on the specification in column (3) of Table 2, a one standard deviation increase in dispersion raises the log-odds between foreign subsidiary exports and outsourcing exports by 0.261.


249

Table 2: Logit Estimates of Managerial Incentives Model (1997-2003)

Dependent Variable: Choice of Organizational From Multinominal Logit Binary Logit

FDI PA FDI PA FDI Outsourcing Outsourcing

(1) (2) (3) (4)Freight -0.553 -2.301 -1.107 -1.629 0.498 -0.353

[0.173]a [0.231]a [0.182]a [0.279]a [0.146]a [0.163]b

Tariff -4.077 3.02 -0.606 1.784 -5.495 -1.435[0.196]a [0.207]a [0.238]b [0.270]a [0.168]a [0.197]a

Dispersion 1.199 0.497 0.741 0.543 0.945 0.468[0.036]a [0.045]a [0.044]a [0.054]a [0.028]a [0.035]a

Time Trend 0.067 0.085 0.082 0.088 0.025 0.04[0.005]a [0.006]a [0.005]a [0.006]a [0.004]a [0.004]a

Ln(Population) 0.067 0.072 0.038 0.066 0.039 0.011[0.015]a [0.020]a [0.015]b [0.020]a [0.013]a [0.013]

North Coast 0.984 0.28 1.013 0.243 0.93 0.972[0.069]a [0.110]b [0.069]a [0.111]b [0.064]a [0.063]a

Beijing Area 1.088 0.679 1.094 0.68 0.916 0.926[0.048]a [0.071]a [0.048]a [0.071]a [0.045]a [0.045]a

Shanghai Area 0.711 0.41 0.732 0.372 0.623 0.651[0.044]a [0.066]a [0.045]a [0.066]a [0.042]a [0.042]a

South Coast 1.311 2.232 1.191 2.271 0.35 0.232[0.043]a [0.063]a [0.044]a [0.064]a [0.040]a [0.041]a

Constant/Dummies -3.429 -3.427 HS dummies -2.705 HS dummies[0.159]a [0.213]a [0.136]a

Observations 85406 85406 85406 85406Percent Predicted 55.27 57.60 58.76 61.50Log Likelihood -79970.496 -77839.675 -56711.022 -54883.902Comparision Group: IA Outsourcing Outsourcing

Robust standard errors in brackets with a ,b and c respectively denoting significance at the 1%, 5% and 10% levels. Coefficients for HS-section dummy variables not shown

The estimates in Table 2 support the model’s predictions that the log-odds between FDI and IA outsourcing decreases with tariff, and the log-odds between PA outsourcing and IA outsourcing increases with tariff. A one standard deviation tariff decrease raises the log-odds between subsidiary and outsourcing exports by 0.264.

Our use of contemporaneous transportation costs may explain why the transportation costs coefficients contrast with the model’s predictions on organizational form. As Figure 5 shows, freight rates were highly cyclical. Thus, it is possible that final producers made decisions on average transportation costs rather than current freight rates.


250

The last variables of note are geographic dummy variables for Chinese coastal areas, each of which has positive effects on the log-odds between FDI and outsourcing.10

To interpret the economic implications of our estimates, Wooldridge’s (2003) method which computes how marginal changes in the underlying variables influence organizational probabilities. For example, results in column (2) of Table 2 imply that in 2003 the more developed Southern coast raised a machinery producer’s probability of PA outsourcing by 0.27, or 27 percent. It also caused a 0.24 decline in the probability of FDI, and a 0.03 decline in the probability of IA outsourcing in Southern coast areas. For machinery producers located in the Southern coast in 2003, a one standard deviation increase in dispersion among machinery producers is associated with a 0.12 reduction in the probability of PA outsourcing, and a 0.128 increase in the probability of FDI. Finally, a one standard deviation increase in export tariff leads to a 0.0092 increase in the probability of PA outsourcing, and a 0.0091 decline in the probability of choosing FDI. Taken together, the economic magnitudes indicate that the effects of firm heterogeneity exert effects that large relative to tariff changes, and almost comparable to the known attractiveness associated with the advantageous Southern coast location.

To further evaluate the implied economic effects, we also do calculations based on the Logit estimates for FDI versus outsourcing shown in column (4) of Table 2. Coastal regions have better telecommunication and internet services, which facilitate a multinational’s monitoring of its processing factory. We again choose machinery producers located in the Southern coast in 2003 as an example, when we calculate the effect of increasing the dispersion measure increases by one standard deviation. In 2003, a machinery producer in the Southern coast had a 0.032 higher probability of choosing FDI over outsourcing. The predicted effect of increasing the dispersion measure is a 0.0165 increase in the FDI probability. Finally, a one standard deviation increase in tariff rate reduces the FDI probability by 0.0033.

10 Following Feenstra and Hanson (2005), the north coastal region consists of Heilongjiang, Jilin, and Liaoning. The Beijing area includes Beijing, Tianjin, Hebei, and Shandong. The Shanghai area includes Shanghai, Jiangsu, and Zhejing. The south coastal provinces consist of Fujian, Guangdong, and Hainan Island.


251

Table 3: Partial Effects based on Table 2 Logit Estimates

Dependent Variable: Choice of Organizational From Multinominal Logit Binary Logit

FDI PA FDI PA FDI Outsourcing Outsourcing

(1) (2) (3) (4)Freight 0.148 -0.321 -0.074 -0.138 0.123 -0.088

[0.036]a [0.033]a [0.042]c [0.042]a [0.036]a [0.040]b

Tariff -1.370 1.013 -0.364 0.370 -1.362 -0.356[0.041]a [0.031]a [0.048]b [0.037]a [0.042]a [0.049]a

Dispersion 0.232 -0.064 0.116 0.001 0.234 0.116[0.007]a [0.006]a [0.008]a [0.007] [0.007]a [0.009]a

Time Trend 0.006 0.006 0.009 0.005 0.006 0.010[0.001]a [0.001]a [0.001]a [0.001]a [0.001]a [0.001]a

Ln(Population) 0.007 0.004 0.001 0.006 0.010 0.003[0.003]b [0.003] [0.003] [0.003]b [0.003]a [0.003]

North Coast 0.184 -0.075 0.193 -0.081 0.208 0.217[0.014]a [0.013]b [0.013]a [0.012]a [0.012]a [0.012]a

Beijing Area 0.162 -0.030 0.164 -0.030 0.211 0.213[0.011]a [0.011]a [0.011]a [0.010]a [0.009]a [0.009]a

Shanghai Area 0.119 -0.021 0.129 -0.029 0.151 0.157[0.011]a [0.010]b [0.011]a [0.010]a [0.010]a [0.010]a

South Coast 0.060 0.207 0.031 0.224 0.087 0.058[0.011]a [0.009]a [0.011]a [0.009]a [0.010]a [0.010]a

Industry Dummies No HS1 No HS1at the 1%, 5% and 10% levels. Partical Effects for HS1 dummy not shown

5. Conclusions

We investigate how the interplay between managerial incentives, heterogeneous firm productivity and trade costs shape firm organizational choices. To structure the analysis, institutional features of Chinese processing trade regimes are incorporated in Grossman and Helpman’s (2004) managerial incentives model. The modified model suggests that the lowest and highest productivity final good producers will choose outsourcing, while producers with intermediate productivity levels will choose FDI for their processing activities. In addition, the model predicts that decreases in trade costs will increase the share of processing trade conducted by foreign invested firms.

Our empirical work turns to data on Chinese processing exports to the US to test these ideas. The results, which are robust to changes in aggregation and changes in the empirical specification, support the predictions of the model. First, the results show that firms sort into organization form based on productivity: moving from low to high, the order is IA outsourcing, FDI, and PA outsourcing. Second, reduced tariff costs are found to increase the share of processing trade conducted via FDI operations, although the estimated magnitude of the tariff effect is much smaller than the effects due to heterogeneity and productivity sorting.


252

References

Antràs, Pol., 2003, “Firms, Contracts, and Trade Structure”, Quarterly Journal of Economics 118(4), 1375-1418.

Antràs, Pol. and Elhanan Helpman, 2004, “Global Sourcing”, Journal of Political Economy 112(3), 552-580.

Feenstra, Robert C., Haiyan Deng, Alyson C. Ma and Shunli Yao, 2004, “Chinese and Hong Kong International Trade Data”, Center for International Data, University of California, Davis Manuscript.

Feenstra, Robert C. and Gordon H. Hanson, 2005, “Ownership and Control in Outsourcing to China: Estimating the Property-Rights Theory of the Firm”, Quarterly Journal of Economics 120(2), 729-761.

Feenstra, Robert C. and Barbara J. Spencer, 2005, “Contractual Versus Generic Outsourcing: The Role of Proximity,” NBER Working Paper No. 11885.

Grossman, Gene M. and Elhanan Helpman, 2004, “Managerial Incentives and the International Organization of Production”, Journal of International Economics, 63(2), 237-262.

Helpman, Elhanan, Marc, J. Melitz, and Stephen R. Yeaple, 2004, “Export versus FDI with Heterogeneous Firms”, American Economic Review 94(1), 300-316.

International Trade Center UNCTAD/WTO (1995) “Survey of China’s Foreign Trade: An Analysis of China’s Export and Import Data at the Enterprise Level.” Geneva: ITC.

Ma, Alyson C., 2006, “On the choice of in-house production versus outsourcing by multinationals”, Journal of International Trade and Economic Development, 15(2), 231-254.

Markusen, James R., 2002, Multinational Firms and the Theory of International Trade. Cambridge, MA: MIT Press.

Melitz, Marc J., 2003, “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity”, Econometrica, 71(6), 1695-1725.

Wooldridge, Jeffrey M., 2002, Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press.

managerial incentives and the organization of chinese

Documents