Learning and Information Transmission within Multinational Corporations
Cheng Chen,
Chang Sun and
Hongyong Zhang
No 8477, CESifo Working Paper Series from CESifo
Abstract:
We propose that multinational firms learn about their profitability in a particular market by observing their performance in nearby markets. We first develop a model of firm expectations formation with noisy signals from multiple markets and derive predictions on expectations formation and market entries. Using a dataset of Japanese multinational corporations that includes sales expectations of each affiliate, we provide evidence supporting the model’s predictions. We find that a positive signal about demand inferred from nearby markets raises the probability of entry into a new market, or raises the firm’s sales expectation in an existing (focal) market. The latter effect is stronger when (1) the firm is less experienced in the focal market (2) the signals from the focal market are noisier and (3) the firm is more experienced in markets where signals are extracted.
Keywords: multinational production; learning; expectations formation; information transmission (search for similar items in EconPapers)
JEL-codes: D83 F10 F20 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-bec
References: Add references at CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
https://www.cesifo.org/DocDL/cesifo1_wp8477.pdf (application/pdf)
Related works:
Journal Article: Learning and information transmission within multinational corporations (2022)
Working Paper: Learning and Information Transmission within Multinational Corporations (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_8477
Access Statistics for this paper
More papers in CESifo Working Paper Series from CESifo Contact information at EDIRC.
Bibliographic data for series maintained by Klaus Wohlrabe ().