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Information and Communication Technology and Firm Geographic Expansion. (2023). Jiang, Xian.
In: CESifo Working Paper Series.
RePEc:ces:ceswps:_10452.

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  1. (2021) documents that the rising US industry concentration is driven by top firms opening more establishments across locations. Therefore, understanding the role of ICT in the geographic expansion of multi-unit firms is important for studying the aggregate implications of ICT growth as well as policies that improve ICT access. A.4 ICT Adoption and Firm Geographic Expansion: Robustness I show that the relationship between a firm’s ICT adoption and its geographic expansion is robust to defining a firm by FIRMID. Table A.11 column (1) shows the baseline results of Equation (1), where a firm is defined by a FIRMID–industry pair and focuses on firms and their establishments in the manufacturing sector.
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  4. Additional Tables Table A.8: Establishment Summary Statistics of the 1999 Computer Network Use Supplement N Mean S.D. ICT Adoption Internet 35,000 0.743 0.437 Intranet 35,000 0.406 0.491 Electronic data interchange (EDI) 35,000 0.229 0.420 Extranet 35,000 0.061 0.239 Local area network (LAN) 35,000 0.687 0.464 Communication Within Firm 35,000 0.401 0.490 Customers 35,000 0.271 0.445 Suppliers 35,000 0.476 0.499 Notes: This table shows summary statistics of establishments in the Computer Network Use Supplement (CNUS) to the 1999 Annual Survey of Manufactures, including the establishment’s ICT adoption and communication patterns. Each variable regarding ICT adoption is an indicator set to one if the establishment is connected to a type of network. Variables regarding communication are indicators set to one if the establishment communicates with other company units, external customers, and external suppliers, respectively.
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  24. Column (1) uses the LBD–CNUS matched sample from 1987 to 2007. Columns (2)–(4) use the 1999 subsample of the matched sample. Standard errors are clustered at the firm level. Standard errors using the instrumental variable approach in column (4) are calculated by 1,000 bootstrapped samples. The regressions are weighted by the weights provided in the 1999 Annual Survey of Manufactures. Significance levels: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
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  25. Columns (2)–(4) use the CNUS sample and estimate a regression of the form: Yi,99 = α + β1Intraneti,99 + β2EDIi,99 + β3Extraneti,99 + β4Interneti,99 + β5LANi,99 + Xi,99γ + εi,99, where the dependent variables are indicators set to one if any establishment of the firm provides information to other company units, external customers, and external suppliers. Xi,99 is a vector of firm characteristics in 1999, including the logarithm of capital to labor ratio, logarithm of employment, skill mix measured by the ratio of nonproduction workers to production workers, firm age fixed effect, state fixed effect, and industry fixed effect at the four-digit NAICS level. Significance levels: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
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  32. Figure 1: Number of Establishments per Firm for Multi-Unit Firms from 1977 to 2012 6 7 8 9 10 11 Average Number of Establishments per MU Firm 1977 1982 1987 1992 1997 2002 2007 2012 Year Notes: This figure plots the average number of establishments per firm for multi-unit firms in all sectors during 1977 and 2012, using the Longitudinal Business Database (LBD). Each firm is defined by a FIRMID–industry pair, where industry is at the six-digit NAICS level. Multi-unit firms are firms that operate more than one establishment in a certain industry. See Section 2 for details on data sources and construction of variables.
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  33. Figure 4: Map of NSFNET in 1992 Notes: This figure shows the NSFNET backbones and its node sites in 1992. The circles represent the exterior nodes at the following cities: Princeton (NJ), San Diego (CA), Champaign (IL), Ithaca (NY), Pittsburgh (PA), Boulder (CO), Salt Lake City (UT), Palo Alto (CA), Seattle (WA), Lincoln (NE), Houston (TX), Ann Arbor (MI), College Park (MD), Atlanta (GA), Argonne (IL) and Cambridge (MA). The black lines represent traffic flows on the network.
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  78. Table 5: First-Step Estimation Results Establishment Employment Share All Years Year 1999 OLS OLS OLS IV (1) (2) (3) (4) Log(Miles) -0.172∗∗∗ -0.293∗∗ -0.652∗∗ -1.202∗∗∗ (0.047) (0.137) (0.199) (0.387) Log(Miles) × Intranet 0.394∗∗ 0.997∗∗ (0.167) (0.401) N 59500 3100 3100 3100 F-stat 15.27 Market Access Y Y Y Y HQ-Set-ICT FE Y Y Y Y Establishment Location FE Y Y Y Industry FE Y Y Y Establishment Location-Year FE Y Industry-Year FE Y Notes: This table presents the first-step estimation results. The dependent variable is the scaled within-firm employment shares of establishments. Log(Miles) is the distance between the firm’s headquarters and the establishment.
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  79. The second margin is the total number of multi-unit firms, as shown in Panel D. The number of multi-unit firms in the manufacturing sector increased before the late-1990s but has been decreasing since then, consistent with the overall trend of the total number of manufacturing establishments documented in Fort et al. (2018). This decline in the number of multi-unit firms offsets that increase in the number of establishments per firm, leading to an overall decline in the number of establishments. Finally, the increasing trend in Panel A is not solely due to selection. Firms that continue to operate in an industry throughout the period have been increasing their number of establishments, reflecting the firm expansion across locations. These multi-unit firms are large firms, accounting for a significant share of employment and output. Additionally, Rossi-Hansberg et al.
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  80. Tintelnot, F. (2017). Global production with export platforms. The Quarterly Journal of Economics 132(1), 157–209.

  81. To reconcile these two phenomena, we need to consider two additional margins. The first is the firm’s industry scope, as explained in the previous appendix section. Panel B plots the average number of industries per FIRMID, which decreased over time.64 Combined with the firm’s within-industry geographic expansion, Panel C shows that the overall number of manufacturing establishments belonging to the same FIRMID first declined in the 1980s, driven by the decreasing number of industries, and then rose from the late 1990s, driven by the increasing number of establishments within industries, i.e., geographic expansion.
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  28. Multi-plant operation and headquarters separation: Evidence from Japanese plant-level panel data. (2016). Tomiura, Eiichi ; Okubo, Toshihiro.
    In: Japan and the World Economy.
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  29. Spatial fragmentation of industries by functions. (2015). Cutrini, Eleonora ; Bode, Eckhardt ; Bade, Franz-Josef.
    In: The Annals of Regional Science.
    RePEc:spr:anresc:v:54:y:2015:i:1:p:215-250.

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  30. Neighborhoods to nations via social interactions. (2015). Ioannides, Yannis.
    In: Economic Modelling.
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  31. Inter- and intra-firm linkages: Evidence from microgeographic location patterns. (2015). Behrens, Kristian ; Sharunova, Vera .
    In: CEPR Discussion Papers.
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  32. Growing through cities in developing countries. (2014). Duranton, Gilles.
    In: Policy Research Working Paper Series.
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  33. Neighborhoods to Nations via Social Interactions. (2014). Ioannides, Yannis.
    In: Discussion Papers Series, Department of Economics, Tufts University.
    RePEc:tuf:tuftec:0803.

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  34. How has information technology use shaped the geography of economic activity?. (2013). Forman, Chris .
    In: Chapters.
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  35. Urban Functional Specialisation and the Interplay between Firm’s Communication Costs. (2013). Gori, G. F..
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  36. The spatial organization of multinational firms. (2012). Defever, Fabrice.
    In: Canadian Journal of Economics/Revue canadienne d'économique.
    RePEc:wly:canjec:v:45:y:2012:i:2:p:672-697.

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  37. Spatial fragmentation of industries by functions. (2012). Cutrini, Eleonora ; Bode, Eckhardt ; Bade, Franz-Josef.
    In: Working Papers.
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  38. What Drives Skill-biased Regional Employment Growth in West Germany?. (2012). Cordes, Alexander .
    In: Chapters.
    RePEc:elg:eechap:14606_11.

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  39. Inshoring: The geographic fragmentation of production and inequality. (2012). Liao, Wen-Chi.
    In: Journal of Urban Economics.
    RePEc:eee:juecon:v:72:y:2012:i:1:p:1-16.

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  40. Agglomerative Forces and Cluster Shapes. (2012). Kominers, Scott ; Kerr, William.
    In: Working Papers.
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  41. Urban agglomeration and CEO compensation. (2012). John, Kose ; HASAN, IFTEKHAR ; Francis, Bill ; Waisman, Maya .
    In: Research Discussion Papers.
    RePEc:bof:bofrdp:2012_017.

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  42. Who’s got the aces up his sleeve? Functional specialization of cities and entrepreneurship. (2011). Heblich, Stephan ; Falck, Oliver ; Audretsch, David.
    In: The Annals of Regional Science.
    RePEc:spr:anresc:v:46:y:2011:i:3:p:621-636.

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  44. Agglomerative Forces and Cluster Shapes. (2010). Kominers, Scott ; Kerr, William.
    In: NBER Working Papers.
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  45. Soft Information and Investment: Evidence from Plant-Level Data. (2010). Giroud, Xavier.
    In: Working Papers.
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  46. Soft Information and Investment: Evidence from Plant-Level Data. (2010). Giroud, Xavier.
    In: Working Papers.
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  47. Mobility of Corporate Headquarter Functions: A Literature Review. (2009). Deschryvere, Matthias .
    In: Discussion Papers.
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  48. Agglomeration and cross-border infrastructure. (2008). Puga, Diego.
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  49. The agglomeration of headquarters. (2008). Henderson, J. Vernon ; Davis, James.
    In: Regional Science and Urban Economics.
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