Location choices across the value chain: How activity and capability influence co-location. Appendix

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1 Location choices across the value chain: How activity and capability influence co-location. Appendix Juan Alcácer Stern School of Business New York University 40West 4 th Tisch Hall 7-10 New York NY (212) jalcacer@stern.nyu.edu

2 This appendix complements the paper "Location choices across the value chain: How activity and capability influence co-location". It consists of three sections. Section 1 provides a description of the data used in the paper, including sources, sampling, and steps followed to build the dataset, as well as basic summary statistics. Section 2 introduces a set of additional statistical analyses that explores the stability of the main results of the paper vis-a-vis changes in measurements, samples and methods. Finally, Section 3 discusses how generalizable the results are to other industries. 1. Data description 1.1. Sources I identified all firms that produced or sold cellular phone handsets worldwide in the year 2000, based on information collected by two market research companies in telecommunications: the Gartner Group and the Strategis Group. The Report on Cellular Phones and PHS by the Communications Industry Association of Japan and the directory of the Korea Electronic Industries Cooperative complemented the information for Japanese and Korean companies. This process yielded 54 firms whose combined production represented 98% of worldwide demand in 1999 (The Strategis Group 1999). The three sources used to gather location data were: (1) companies official web sites and annual reports which provided basic location information for plants, R&D and sales subsidiaries; (2) extensive searches of news archives using the following databases: Business & Industry database by FirstSearch, ABI/INFORM Global and Proquest Databases by Proquest, and Lexis-Nexis Academic Universe; and (3) reports from investment banks and research firms 1 through the Research Bank Web by Investext. Only subsidiaries directly involved in production, R&D or sales of cellular phone handsets were considered. For example, if Sony had a sales subsidiary in Thailand that did not sell cellular phone handsets, the subsidiary would not be included in the dataset. At the country and region levels, I defined a subsidiary as the physical presence of a firm performing an activity - production, R&D or sales - in a country, regardless of the number of actual sites that perform the activity. In the case of manufacturing, I focused the analysis on production subsidiaries rather than on individual plants. For example, if Sony had 1

3 two plants in the US, it would be recorded as having one production facility in the American market. All plants and R&D facilities were considered for analysis at the cluster level Data description Table A offers descriptive statistics for the firms in this sample. European companies are grouped into one region since all source countries belonged to the European Union (EU) for the whole period studied ( ) and therefore had similar telecommunication legislation. Scandinavian countries are grouped apart because they experienced an earlier development of wireless technology associated with more open telecommunication policies. The Chinese group was defined in terms of culture and includes firms from Taiwan and Hong Kong. Japan, South Korea, and USA are regions on their own because of market size, cultural differences and the number of firms involved. The Australian and South American groups are formed by just one firm each, and although they are used in the analysis, they are omitted in tables and graphs for exposition reasons. Japan and South Korea accounted for 31% and 20% of the firms active in the industry in the year 2000, followed in a distant third place by the US. Asia accounted for 64% of the firms, followed by Europe with 22%. In terms of in-house production and outsourcing, 51 firms had at least one production facility and three firms did not have plants, selling exclusively products manufactured by OEM partners. Approximately 24% of the firms manufactured their own products; 39% combined different levels of inhouse production with outsourcing. A third of the firms sampled were OEMs unassociated with any brand name. Although the number of new entrants increased starting in the early 1990s, entries were evenly distributed across periods. As for technology, two thirds of the firms were engaged in either GSM or CDMA, the two most common digital wireless standards. Firm size, measured as total assets and sales, ranged from smaller producers in Scandinavia and South Korea to large and well-known firms such as Ericsson, Nokia, Motorola, Panasonic, and Sony. To explore how time to enter in an industry affects location decisions, I classified firms in four cohorts ( , , , and ) following Gerrard (1998). The first period, from the late 70 s to 1985, was characterized by diverse wireless standards, strong investment in basic R&D and 2

4 low levels of market development. During the second period, from 1985 to 1990, regional standards emerged and mass production started. The period between 1990 and 1995 was characterized by strong growth in demand, a proliferation of producers, and the opening of international markets. The big boom in cell phones occurred in this period when the European Union adopted a unique standard, the Japanese government allowed the sale of cell handsets and liberalized the market, and American service providers increased sales across the country. The fourth period, from 1995 to 2000, was marked by demand growth in developing country markets, price competition, and technological sophistication. Accordingly, around 60% of the firms in my sample entered after 1990, with entries equally distributed across the first two cohorts. Table B shows summary statistics for subsidiaries. The firms in the sample had 103 plants, 100 R&D facilities, and 559 sales subsidiaries active in the year An average company in the sample had roughly two plants, two R&D facilities, and ten sales subsidiaries. Note that there is a wide variance in these figures, with the maximum number of subsidiaries for production, R&D, and sales being eight, eleven, and forty-four. Table B also provides a geographic profile of subsidiaries based on the year 2000 data. Japan, China, and South Korea had the most plants, while the most popular R&D lab locations were Japan, the US, South Korea, and the UK. The top five markets in terms of numbers of subscribers, USA, China, Japan, UK, and Germany, attracted 23% of all sales subsidiaries. Note that with the exception of Austria and Norway, all the R&D labs were located in countries housing production facilities Within- firm co-location index Co-location indices can provide also information about the overall degree of firm dispersion. Table C shows co-location levels across activities within the same firm at the country level. On average, 3 out of 4 plants were in countries where the firm also located R&D facilities and 9 out of 10 plants were in countries with sale subsidiaries. Similarly, R&D facilities were located in countries where plants or sales subsidiaries were located (0.68 and 0.89 co-location indices respectively). Approximately 40% of sale subsidiaries were in countries where the firm also located either production or R&D facilities, suggesting that sales subsidiaries are more loosely connected to other activities in the value chain. 3

5 2. Robustness Checks Before settling on these results, I conducted several alternate tests using different samples, variable definitions, and estimation techniques. First, a number of the firms in the sample did not significantly contribute to global output. These firms could bias empirical results since they normally owned just one plant in their home country, did not represent a credible threat to other players in the industry, and were often overlooked by competitors when deciding new locations. I estimated equation (2) for a subsample 2 consisting of firms that had at least one plant overseas. Most companies that dropped from the original sample correspond to Chinese, Korean, and Japanese producers, many were OEMs. Results using this subsample are similar in sign and significance. Second, the operationalization of differences in firm capabilities in equation (2) looks only at relative position of firms in the dyad. Thus, the effect of the differences between tiers is the same regardless of the two tiers involved. For example, according to the coefficient of tier AB for production in Table 3, the effect of differences in firm capabilities on co-location would be the same (5%) whether two firms, A and B, belong to tiers 1 and 2 or 3 and 4. In order to release this assumption, I introduced three alternative specifications for differences in firm capabilities (variable tier AB ). First, I introduced two dummy variables, tier A>B and tier A<B, that indicate if the focal firm in the dyad is more capable than the reference firm, regardless of the size of the difference. Second, I used a set of four dummy variables indicating whether the focal and reference firms were leaders (belonging to the first tier) or laggards (belonging to any other tier). Results obtained in both cases are similar to those in Table 3 in meaning, sign, and significance level. Third, I estimated equation (2) introducing sixteen dummy variables indicating all combinations of tiers for focal and reference firms. Although results revealed differences across tiers involved, they were consistent with those in Table 3. Third, since my dependent variable is bounded between zero and one, I estimated equation (2) using QAP based on Tobit estimation instead of OLS estimation. The results differ in magnitude but are similar 4

6 in sign and significance. Additionally, I estimated equation (2) using OLS with results similar in sign and significance to those presented in section 6.3. Fourth, to explore whether activity co-location is driven by specific capabilities instead of firm-wide capabilities, I measured a firm s strength in one particular activity production, R&D or sales 3 and reestimated equation (2) with these activity-specific capabilities. Activity-specific capabilities are highly correlated with each other 4 and also with firm-wide capabilities 5, suggesting that firms in the sample are similarly capable across activities. Given these high levels of collinearity, it is not surprising that the results using activity-specific capabilities are similar in sign and magnitude to those obtained using firmwide capabilities. Although the R-squares are higher when activity-specific capabilities are used, the increase is marginal suggesting that using activity-specific capabilities does not offer more explanatory power. Fifth, I estimated the basic models (at country level and firm-wide capabilities) using a 5-year lag in capability with similar results to those obtained using 2-year lags. A longer lag period minimizes endogeneity concerns related to firm capabilities and location choices. Finally, I also used total revenue instead of total assets to measure firm size with similar results. Additional specifications including the number of subsidiaries for focal and reference firms produced similar results in sign with lower significance levels. 3. Generalizing from the wireless industry Before these findings can be extended to other industries, several characteristics of the wireless industry that may impact co-location must be discussed. Most notably, the wireless industry experienced phenomenal growth between 1990 and 2000: the number of handsets sold increased almost ten-fold, from 42.9 million units in 1990 to 416 million units in Demand was so intense that it frequently outpaced supply. We would expect that excess demand weakened competitive pressures, allowing marginal firms to survive longer and increase co-location levels. On the other hand, the industry experienced deep structural changes during the same period that could mute the effect of excess demand. The introduction of digital technologies, and GSM in particular, sparked a period of significant structural change. New 5

7 dominant players like Nokia and Ericsson emerged as part of a wave on entries and exits, which continued through We would expect these structural changes to intensify competition and accelerate the exit of weak firms, lowering co-location levels. Other industry factors meriting discussion include technological innovation and government intervention. Technology has played a key role in the wireless industry since its origin. Emphasis on technology may increase the benefit of knowledge spillovers, thus amplifying the benefits of geographic clustering in R&D (Audretsch and Feldman 1996). This trend could have been even stronger in the period leading to the year 2000 since a new technology, known as the third generation (3G) standard, was under development. Pools of firms created to develop and promote competing 3G standards - could have chosen to locate close to each other, increasing co-location in R&D. Governments may also have influenced location decisions by impacting firms competitive positions. For example, the South Korean government identified the wireless industry as strategic, and used subsidies to encourage entry and international expansion of local firms. Subsidies may have reduced the effects of competition, increasing Korean firms ability to co-locate. Fortunately, many high-tech industries share traits with the wireless industry. For example, the structural changes in the wireless industry are consistent with a pattern of industry evolution in which waves of radical innovation create periods of substantial entry and exit, giving rise to new dominant players. This pattern, identified by Abernathy and Utterback (1978), is prevalent in high-tech endeavors, making my findings relevant to a large number of industries. 6

8 Table A: Sample descriptive statistics Table B: Sample descriptive statistics for subsidiaries Firm % Firm % Firms Subs Mean x firm Std. Dev Min Max bution by country of origin Distribution by type Number of subsidiaries China 7 13% In-house production 13 24% Production Japan 17 31% OEM 17 31% R&D South Korea 11 20% In-house and OEM 21 39% Sales Europe* 8 15% No Production 3 6% Scandinavia** 4 7% Geographic distribution of subsidiaries USA 5 9% Country Prod. % R&D % Sales % Rest of the World*** 2 4% Japan 15 15% 19 19% 27 5% Total 54 China 14 14% 6 6% 26 5% South Korea 13 13% 12 12% 20 4% bution by entry time Distribution by technology^ USA 7 7% 18 18% 32 6% Before % GSM 36 67% Brazil 7 7% 1 1% 8 1% (1985,1990] 10 19% CDMA 33 61% France 6 6% 4 4% 15 3% (1990,1995] 19 35% TDMA 15 28% UK 5 5% 12 12% 22 4% (1995,2000] 15 28% PHS/PDC 17 31% Germany 5 5% 5 5% 19 3% Malaysia 4 4% % bution by total assets (US$ billions) Distribution by total sales (US$ billions) Mexico 4 4% % (0.0, 0.5] 15 28% (0.0, 0.5] 15 28% Hong Kong 2 2% % (0.5, 3.0] 13 24% [0.5, 3.0] 12 22% Finland 2 2% 3 3% 8 1% (3.0, 20.0] 13 24% (3.0, 20.0] 12 22% Taiwan 2 2% 3 3% 16 3% (20.0, 78.0] 13 24% (20.0, 78.0] 15 28% Australia 2 2% 2 2% 14 3% Singapore 2 2% 2 2% 13 2% Sweeden 2 2% 2 2% 12 2% Spain 2 2% 1 1% 15 3% nce, Italy, Germany, Netherlands and the UK Czech Republic 1 1% % nmark, Finland and Sweden India 1 1% % ustralia and Brazil Indonesia 1 1% % al for percentage does not add to 100 since a company can have more than 1 technology Ireland 1 1% % Philippines 1 1% % Denmark 1 1% 4 4% 7 1% Italy 1 1% 2 2% 14 3% Canada 1 1% 1 1% 13 2% Hungary 1 1% 1 1% 7 1% Austria % 7 1% Norway % 8 1% 7

9 Table C: Co-location index, descriptive statistics Production R&D Sales Obs Mean Std. Dev. Min Max Obs Mean Std. Dev. Min Max Obs Mean Std. Dev. Min Max Co-location index, across actvities within firm Production R&D Sales

10 Table D: Description of variables used as weights for random assignment of subsidiaries Activity Variable used for random assignment Description Source R&D Cellular phone subscribers No. of subscribers per country International Telecommunications Union (ITU) R&D personnel engaged in R&D activities Gross domestic expenditure on R&D by all source of funds United Nations Educational, Scientific and Cultural Organization (UNESCO) Expenditure in R&D Personnel engaged in R&D for all category of personnel United Nations Educational, Scientific and Cultural Organization (UNESCO) Patent stock, all technologies, WIPO Number of patents granted by country. Country determined by assignee country World International Patent Organization (WIPO) 1999 Patent stock, all technologies, INPADOC Number of patents granted by country. Country determined by assignee country INPADOC Patent stock, telecommunications, first inventor, INPADOC Number of patents granted by country. Country determined by country of first inventor INPADOC Patent stock, telecommunications, all inventors, INPADOC Number of patents granted by country. Country determined by country of all inventors INPADOC Patent stock (families), telecommunications, all inventors, INPADOC Number of patents granted by country. Country determined by country of all inventors. INPADOC Unit of analysis is a family of patents, not a patent Production Cellular phone subscribers No. of subscribers per country International Telecommunications Union (ITU) Employment telecommunication equipment Number of employees in 4-digit SIC code 3320 (Telecommunications equipment) 1995-United Nations Industrial Development Organization manufacturing 1999 (UNIDO) Industrial Statistics Databases Output of telecommunication equipment Output in US$ corresponding to 4-digit SIC code 3320 (Telecommunications UNIDO Industrial Statistics Databases equipment) Exports of telecommunication equipment Exports in US$ corresponding to 4-digit SIC code 3320 (Telecommunications UNIDO Industrial Statistics Databases equipment) Sales Cellular phone subscribers No. of subscribers per country International Telecommunications Union (ITU) Projected demand of handsets Projected shipments of cellular phone handset per country, World cellular phone database, The Strategis Group Projected cellular phone subscribers Projected number of subscribers per country, World cellular phone database, The Strategis Group Notes: * Values are averaged within the periods specified. The assignment procedure was repeated using only values for 1999 and for periods , (when data were available) with similar results * In the case of INPADOC, the allocation of patents to countries was performed by looking at (1) country of assignee, (2) country of first inventor (3) country of all inventors as robustness checks * The use of family patents instead of patents as unit of analysis is to avoid double counting of the same patent filed in different countries and varying propensity to patent across countries * In the case of WIPO, only country of assignee was available * Projected demand of cellular phones and subscribers came from 1999 reports and reflect projections made at that time for the periods specified 9

11 1 Dresdner Bank, Credit Suisse First Boston, Merrill Lynch and Espicom Business Intelligence 2 This sub-sample includes 25 firms and 600 observations. It is roughly 25% of the original sample. 3 The sales-specific capability is the equally weighted average of the brand and distribution categories; production-specific capability is defined by the value in the manufacturing category. Unfortunately, none of the Gartner ranking categories measures unequivocally R&D-specific capabilities (the product dimension includes some variables pertinent to R&D but also include production related measurements). Therefore, I used firm-specific patent stock data from INPADOC, which takes information from over 71 world patent signatories, to identify R&Dspecific capabilities 4 Correlation (production, R&D) = 0.93, correlation (production, sales) = 0.84, correlation (R&D, sales) = Correlation values are 0.88 for production and sales, and 0.75 for R&D. 10