- (ii) Additional descriptive statistics for input-output linkages. Table 12 displays the ten naics 6-digit manufacturing industries with the shortest and with the longest input distances in 2005, respectively.
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- (iii) Minimum distance to the us. For each plant, we compute the great circle distance (in kilometers) to the nearest us land border crossing. Crossings allow for either trucks or trains or both. There are 118 such crossings (including crossings to Alaska). They are geocoded at the 6-digit postal code level using the pccfs. The set of land border crossings is stable over the 2001–2013 period of our analysis. A.4. Geographical controls and census data (i) Geographical specialization measures. We discuss in detail the construction of the specialization measures and additional results derived using those measures in Appendix B.
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- (iv) Ad valorem transport cost measures. This measure is provided by Statistics Canada and consists in the ratio of the price index in the transport sector and the price index of each 6-digit naics industry. The indices are normalized to 1 in 2007 and they span the period 2001–2013.
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- (iv) Additional descriptive statistics for intra-firm linkages. Table 15 summarizes the different internal distance measures by year. As can be seen from that table, employment and input-output weighted distance measures are smaller than ‘raw’ distance measures, thus suggesting that bigger and more strongly vertically linked plants are closer to each other (see also Figure 4 in the paper).47 The average distance across plants of multiunit firms hovers around 47Johnson and Noguera (2012) use similar distance-weighted measures to show that the international fragmentation of the value chain is geographically localized among nearby countries. iii Table 13: Summary and descriptive statistics for specialization measures (6-digit, strict, 5 kilometer). owncount j(i) own empl j(i) speccount j(i) spec empl j(i) Year Observations Mean Std dev. Mean Std dev. Mean Std dev. Mean Std dev.
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- (v) Share of non-homogeneous inputs. We use Nunn’s (2007) 4-digit naics classification for the share of non-homogeneous (differentiated or reference-priced) inputs of each industry. We interact this variable with other selected variables of interest.
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- 45Rosenthal and Strange (2003) are an exception. They look at the effects of affiliated vs unaffiliated plants on births and employment levels at new plants. Their results are, however, inconclusive. More recently, Brown and Rigby (2015) also look at larger, older, foreign-owned, and multiunit firms. other establishements in the same industry. The reason might be that large multiunit plants can draw on significant internal resources, thus making them more footloose with respect to their external environment.
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- Appendix C: Propensity score matching procedure To construct our counterfactual multiunit firms, we use 1:1 nearest neighbor propensity score matching to associate a ‘comparable’ standalone plant with each multiunit plant. We match each plant in each year-industry on the following variables: log employment, exporter dummy, log geographical controls constructed from the Census data (see Appendix A.4 for details), log plant-level geographical specialization measures (strict 6-digit count measures computed within a 5 kilometer radius; see Appendix B.1 for details), the of log number and the log average ubiquity of products of the plant (see Appendix A.5 for details), and the log relative input distances computed for N = 5. Table 10 summarizes the match variables and the balance of controls for 2001, and displays the T-stats for equality of means tests.
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- Figure 6: Spatial structure of Air Liquide Canada Incorporated in 2013.
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- has dramatically fallen in Canada over the years, both in terms of plant counts and in terms of employment totals (see Behrens, Bougna, and Brown, 2015; and Behrens and Bougna, 2015, for additional evidence for Canada). Observe further that, as expected, resource-based industries make up a fair share of the list, both for manufacturing and for non-manufacturing. The reason for this is that our measure of specialization is a topographic measure: industries that are overrepresented in thinly populated regions will rank among the most specialized ones. Note also that there is substantial variation in the specialization measures at the plant level. In other words, within narrowly defined 6-digit industries, plants in highly specialized areas coexist with plants in poorly specialized areas.
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- Our data span four different industrial classifications: naics1997, naics2002, naics2007, and naics2012. We have concorded those classifications to a stable set of 242 manufacturing industries and 864 industries in total. The manufacturing classification remained fairly stable over time. The largest changes have been in the construction sector (between 1997 and 2002) and in the internet-related publishing and database management (after 2002). We report results using our stable concordance, which allows for consistent use of industry fixed effects. It also allows to consistently assign the variables that are coded at the industry level.
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- Table 10: Balance of controls for the psm procedure (2001 sample, all plants).
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- The comparison of time-invariant plant identifiers allows us to associate a plant in year t with itself in year t + 1. This provides a refinement of the assignment of the firm identifier in case the establishment’s name has changed in a way that the preliminary data treatment could not accommodate. However, use of this ‘tool’ is limited to the 2003-2013 sample due to a structural change in the plant identifier design implemented by Scott’s.
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- They are available for 240 6-digit industries (238 in 2013), which explains why we lose a few observations when including them in our estimations.
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- This establishment-level database contains information on plants operating in Canada, with an extensive coverage of the manufacturing sector. Our data span the years 2001 to 2013, in two-year intervals. For every establishment, we have information on its primary 6-digit naics code and up to four secondary 6-digit naics codes; the opening year of the establishment; its employment; whether or not it is an exporter; whether or not it is a headoffice; its 6-digit postal code; up to ten products produced by the establishment; and the legal name of the entity to which it belongs. The latter is used to group plants into firms (see paragraph (ii) below). We geocode all plants by latitude and longitude using their 6-digit postal code centroids obtained from Statistics Canada’s Postal Code Conversion Files (pccf). See Appendix A.3 for details on the geocoding of the database.
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- Total Rural Urban weak Urban moderate Urban strong Urban cma Standalone 289,699 10,124 13,026 18,540 10,120 237,889 Multiunit 31,890 1,127 1,441 1,723 877 26,722 Share of multiunit plants 9.92% 10.02% 9.96% 8.50% 7.97% 10.10% (excluding 2001) (9.71%) (9.66%) (9.89%) Total 321,589 11,251 14,467 20,263 10,997 264,611 Notes: Breakdown of the distribution of plant types (standalone or multiunit) by ‘urban type’ dummies. In 2001, the classification is ‘census metropolitan area’ or ‘rural’, detailed urban types are only available starting in 2003.
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- We report results for d = 5 and for the strict 6-digit industry definitions. Results for other choices are similar. The top 10 industries are determined in decreasing order of their specialization measures. We report time averages across industries. iv 700–800 kilometers, depending on the year. Note that this figure is comparable to that reported in Aarland et al. (2007) for the us, where firms have an average distance between plants of about 635 kilometers – this is slightly less than in our case, but Canada has a more dispersed geography (especially in terms of the density distribution of the population) than the us. Table 15: Summary and descriptive statistics for internal distance measures of multiunit firms. All industries Year # firms Average # plants Average df Average d w
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- When taken together, our results provide indirect evidence for more vertical disintegration in more geographically specialized locations. This is consistent with previous findings by Holmes (1999) for the us and by Li and Lu (2009) for China. Disintegration seems to operate at a narrow industrial definition, at a small geographical scale, and when there are many small firms (as opposed to a few big ones). The latter result suggests that industrial organization vii is key to understanding the specialization-disintegration link, as previously suggested in the literature (e.g., Rosenthal and Strange, 2010; or Holmes and Stevens, 2014). See also Table 9, which provides results consistent with the ones reported in Table 17 above.
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