1.) Distance Data from:
Berry, H., M. Guillen, N. Zhou, 2010. An Institutional Approach to Cross National Distance, Journal of International Business Studies, 41(9): 1460-80.
Abstract: Cross-national distance is a key concept in the field of management. Previous research has conceptualized and measured cross-national differences mostly in terms of dyadic cultural distance, and has used the Euclidean approach to measuring it. In contrast, our goal is to disaggregate the construct of distance by proposing a set of multidimensional measures, including economic, financial, political, administrative, cultural, demographic, knowledge, and global connectedness as well as geographic distance. We ground our analysis and choice of empirical dimensions on institutional theories of national business, governance, and innovation systems. In order to overcome the methodological limitations of the Euclidean approach, we calculate dyadic distances using the Mahalanobis method, which is scale invariant and takes into consideration the variance–covariance matrix. We empirically analyze four different foreign expansion choices of US companies to illustrate the importance of disaggregating the distance construct and the usefulness of our distance calculations, which we make freely available to managers and scholars
Berry, H., M. Guillen, N. Zhou, 2010. An Institutional Approach to Cross National Distance, Journal of International Business Studies, 41(9): 1460-80.
Abstract: Cross-national distance is a key concept in the field of management. Previous research has conceptualized and measured cross-national differences mostly in terms of dyadic cultural distance, and has used the Euclidean approach to measuring it. In contrast, our goal is to disaggregate the construct of distance by proposing a set of multidimensional measures, including economic, financial, political, administrative, cultural, demographic, knowledge, and global connectedness as well as geographic distance. We ground our analysis and choice of empirical dimensions on institutional theories of national business, governance, and innovation systems. In order to overcome the methodological limitations of the Euclidean approach, we calculate dyadic distances using the Mahalanobis method, which is scale invariant and takes into consideration the variance–covariance matrix. We empirically analyze four different foreign expansion choices of US companies to illustrate the importance of disaggregating the distance construct and the usefulness of our distance calculations, which we make freely available to managers and scholars
Companion Document that explains updates to original dataset:
| bgzdistancecompanionupdateaugust2023.docx |
Link to 2023 Updated Stata Pooled Distance File (must have Stata to open the file):
www.dropbox.com/s/l9yljyzkwbo00zw/StataAllDistancesPooled2020Update.dta?dl=0
www.dropbox.com/s/l9yljyzkwbo00zw/StataAllDistancesPooled2020Update.dta?dl=0
2.) Global Product Integration Index (GPII) Data from:
Currently available in articles in advance/early view:
Berry, H., Kaul, A. 2024. What drives global product integration? An empirical update. J Int Bus Stud 55: 252-269.
Abstract:
In this paper, we revisit the industry-level drivers of global product integration, i.e., cross-border product flows within multinationals. While traditional explanations for these flows have focused more on benefits from R&D intensity and scale at the parent level, we examine a more comprehensive set of factors, incorporating recent theoretical advances as well as the changing nature of global competition, and examining both the level and direction of integration. Using comprehensive data from the Bureau of Economic Analysis (BEA) over the 1999-2019 period, we argue and show that while factors that increase the returns to aggregation or decrease the returns to adaptation tend to raise the overall level of global product integration, factors that increase the returns to arbitrage have a directional impact, raising flows from foreign affiliates. Finally, we make available in this paper our industry-level data on intra-firm product integration, which we call the Global Product Integration Index (GPII), thus offering a valuable resource for future research.
Excel File with GPII Data:
Currently available in articles in advance/early view:
Berry, H., Kaul, A. 2024. What drives global product integration? An empirical update. J Int Bus Stud 55: 252-269.
Abstract:
In this paper, we revisit the industry-level drivers of global product integration, i.e., cross-border product flows within multinationals. While traditional explanations for these flows have focused more on benefits from R&D intensity and scale at the parent level, we examine a more comprehensive set of factors, incorporating recent theoretical advances as well as the changing nature of global competition, and examining both the level and direction of integration. Using comprehensive data from the Bureau of Economic Analysis (BEA) over the 1999-2019 period, we argue and show that while factors that increase the returns to aggregation or decrease the returns to adaptation tend to raise the overall level of global product integration, factors that increase the returns to arbitrage have a directional impact, raising flows from foreign affiliates. Finally, we make available in this paper our industry-level data on intra-firm product integration, which we call the Global Product Integration Index (GPII), thus offering a valuable resource for future research.
Excel File with GPII Data:
| globalproductintegrationindex82-19berrykauljibs2023.xlsx |
Heading photo taken while in Lake Como, Italy.
