Learning from Siblings within Multinational Firms · * This research is conducted as a part of...

DPRIETI Discussion Paper Series 19-E-053

Learning from Siblings within Multinational Firms

CHEN, ChengClemson University

SUN, ChangUniversity of Hong Kong

ZHANG, HongyongRIETI

The Research Institute of Economy, Trade and Industryhttps://www.rieti.go.jp/en/

https://www.rieti.go.jp/en/index.html

RIETI Discussion Paper Series 19-E-053

July 2019

Learning from Siblings within Multinational Firms*

CHEN Cheng SUN Chang ZHANG Hongyong†

Abstract

Using a unique dataset of Japanese multinational corporations (MNCs), we provide

evidence that MNCs learn about potential profitability in the destination market by observing

the performance of their affiliates (henceforth “siblings”) in other nearby markets.

Specifically, good historical performance of siblings in nearby markets raises the probability

of foreign direct investment (FDI) entries into the destination market. For a market where

the MNC has established an affiliate, good sales performance of nearby siblings raises the

sales expectations of affiliates established in that market. To explain these facts, we provide

a simple model of a multinational firm learning about its profitability in multiple markets.

The model further predicts that nearby siblings' historical performance has a larger impact

on the established affiliate's expectations when the affiliate is less experienced and/or its own

signals are noisier. We confirm both predictions in our data.

Keywords: multinational production, learning, expectation formation, information spillovers

JEL classification: F1; F2; D83

The RIETI Discussion Papers Series aims at widely disseminating research results in the form of

professional papers, with the goal of stimulating lively discussion. The views expressed in the papers are

solely those of the author(s), and neither represent those of the organization(s) to which the author(s)

belong(s) nor the Research Institute of Economy, Trade and Industry.

* This research is conducted as a part of project “Studies on the Impact of Uncertainty and Structural Change in

Overseas Markets on Japanese Firms” undertaken at the Research Institute of Economy, Trade and Industry

(RIETI). This study utilizes the data of the questionnaire information based on “the Basic Survey on Overseas

Business Activities” which is conducted by the Ministry of Economy, Trade and Industry (METI). The authors

are grateful for helpful comments and suggestions by Taiji Furusawa, Jung Hur, Toshiyuki Matsuura, Masayuki

Morikawa, Eduardo Morales, Hiromichi Ozeki, Eiichi Tomiura, Sumiko Urabe, Daniel Xu, Makoto Yano and

Discussion Paper seminar participants at RIETI. Financial support from HKGRF (project codes: 17500618,

17507916, 27502318), JSPS KAKENHI (grant numbers: 17H02531, 17H02554) and RIETI is greatly

appreciated. † Chen: Clemson University, [email protected]. Sun: the University of Hong Kong, [email protected].

Zhang: RIETI, [email protected].

1 Introduction

Firms face substantial uncertainty when doing business abroad, and this is particularly true

for multinational corporations (MNCs). MNCs have to make entry and production decisions

in a foreign market, even when they have limited information about consumers’ tastes and

their own productivity in the destination market. Given the existence of large sunk costs of

doing foreign direct investment (FDI) and multinational production (MP), MNCs’ entry and

production decisions can be costly when the information is imperfect. Naturally, MNCs have

incentives to learn about their demand and supply conditions in the destination market via

various sources of information. Not surprisingly, trade economists began to pay attention to

this phenomenon after firm-level FDI data became available. Previous studies have found that

MNCs obtain information about new markets by exporting before entry (Conconi et al. (2016),

Chen et al. (2018)). In this paper, we show that they also learn about new (destination) markets

by observing and utilizing performance information of their affiliates in other nearby countries,

which we refer to as “learning from siblings”.

Using a 20-year panel dataset of Japanese MNCs, we provide two sets of motivating evidence

for learning from siblings within MNCs. First, we show that good historical (sales) performance

of affiliates in markets within the same continent (referred as “nearby siblings”) raises the prob-

ability of FDI entry into a new market in the continent. In contrast, good historical (sales)

performance of affiliates outside the continent (referred as “remote siblings”) has a weak and

statistically insignificant impact on FDI entry. Second, we study the formation of sales expec-

tations after the MNC has entered the destination market and find a similar pattern: Good

historical performance of nearby siblings raises sales expectation next year while the historical

performance of remote siblings has no impact on this.

To explain these findings, we build a simple model of MNCs’ learning about their demand

(and/or supply) conditions in multiple markets. As information about firm-level demand con-

ditions in the destination market is imperfect,1 a MNC has to form an expectation for these

conditions both before and after entering that market. Before entering the foreign market,

the MNC learns its demand conditions imperfectly from historical performance of its affiliates

in nearby markets, as demand/supply shocks to the same MNC are assumed to be correlated

across nearby markets.2 After observing historical performance of affiliates in nearby markets,

the MNC decides whether to enter the destination market. Naturally, when historical perfor-

1Although we use demand conditions throughout the paper, the interpretation can be either demand or supplyconditions.

2E.g., Consumers’ tastes for Honda cars are probably highly correlated between Germany and Denmark orbetween Thailand and Malaysia.

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mance of nearby siblings are better, the MNC forms a higher expectation for its (would-be)

demand conditions in the destination market and is more likely to enter that market. However,

as the correlation of demand conditions is assumed to be zero between the potential destination

and remote markets, the model predicts that the MNC does not take into account historical

performance of remote siblings when making the market entry decision.

After the MNC enters the foreign market, its affiliate in the destination market updates the

expectation for its demand conditions over the life cycle. Different from previous papers in the

literature, we allow the affiliate to learn both from its own performance (i.e., average past sales)

and from performance of its nearby siblings. The model predicts that the affiliate utilizes both

sources of information (i.e., signals) to form the expectation for future sales, as both signals are

informative for the affiliate’s demand conditions in the destination market. Importantly, the

model also yields a life-cycle prediction that the weight the affiliate puts on its past performance

(and on its nearby siblings’ past performance) increases (and decreases) with the affiliate’s age,

when the affiliate forms the expectation. The intuition hinges on the time-varying precision of

the two signals. Specifically, although the (absolute) precision of both types of signals increases

with the affiliate’s age (thanks to the increasing number of both types of signals over time),

the precision of the signals from nearby siblings increases slower over time due to the imperfect

correlation of demand conditions across nearby markets. As a result, the relative precision

of the affiliate’s own signals increases over the affiliate’s life cycle, which leads to its higher

weight in the formula of expectation formation over time. Conversely, as the relative precision

of nearby siblings’ signals decreases over time, its weight in the expectation formation formula

shrinks over time.3 Finally, the model predicts that nearby siblings’ past performance (and the

affiliate’s own past performance) plays a bigger (and smaller) role in the expectation formation

process, when the affiliate is located in a market with more volatile time-varying idiosyncratic

shocks. The is because a more volatile environment in the destination market implies less precise

signals obtained from that market, and thus the affiliate depends its expectation more on signals

received from nearby markets.

We provide further evidence consistent with the above predictions. First, we interact the

affiliate’s age with its own historical performance and with nearby siblings’ historical perfor-

mance in our expectation-formation regressions. We find the coefficient of the former to be

positive and that of the latter to be negative. Second, we construct a model-consistent measure

of country-level volatility of time-varying idiosyncratic shocks and interact this measure with

the two historical performance measures. We find that a higher volatility of time-varying id-

3In fact, if we assumed a perfect correlation of demand conditions across nearby markets, the weights of bothsignals would increase with the affiliate’s age.

2

iosyncratic shocks (in the destination market) makes the impact of the affiliate’s own historical

performance (on the sales expectation) smaller while makes the impact of the nearby siblings’

past performance (on the sales expectation) larger. Both patterns are hard to rationalize with

other potential explanations (e.g., MNC-region-specific demand shocks) but are consistent with

our learning model.

Our paper contributes to three strands of literature. First, our paper builds on the idea

that firms learn about one market by operating in other markets. Such learning behavior was

first studied in the context of exporting. For example, previous studies show that firms tend to

export to markets that are similar to their prior destination markets, such as Eaton et al. (2008),

Lawless (2009, 2013), Albornoz et al. (2012), Chaney (2014), Defever et al. (2015), and Morales

et al. (2017). However, we know little about how MNCs learn about their profitability in foreign

markets. One mechanism that has been recognized is learning from exporting (Conconi et al.

(2016), Deseatnicov and Kucheryavyy (2017)). In addition, Chen et al. (2018) used information

on multinational affiliates’ expectation for future sales to show that they are better at predicting

their future sales in a particular market, if their parent firm had previous export experience

there. In the current paper, we show that MNCs can also learn about their profitability in a

given market by producing and selling in nearby market(s).

Second, our paper contributes to the literature that studies MNCs’ location choices. Given

the existence of high (fixed) entry and operation costs of doing MP, MNCs tend to set up

production in one country to serve nearby markets (see Tintelnot (2017)). This implies that

it is less likely for a MNC to set up production facilities in a market, if it is already present

in a nearby market. In contrast, if producing in one market helps reduce the entry costs into

neighboring markets (Morales et al. (2017)) via reducing information frictions, one would expect

the opposite pattern. In a recent paper, Garetto et al. (2016) provided suggestive evidence that

the presence of a MNC in a nearby country does not increase or reduce the probability of

entry in another country. Our exercise is different from Garetto et al. (2016), as we do not

study correlations of market entries and profitability (within a MNC) across nearby markets.

Instead, we examine the impact of past performance in nearby markets on FDI entry into the

destination market, conditioning on the MNC’s presence in nearby markets. Importantly, our

evidence suggests that MNCs learn about their profitability by observing performance of existing

affiliates. However, it does not necessarily imply that the entry probability into a destination

market increases because of the presence of the MNC’s affiliates in nearby markets.4

Finally, our paper is related to a growing literature on exporter and multinational firm

4If the MNC has the equal probability (i.e., 50%) of having good and bad sales performance in nearby markets,(roughly speaking) the presence of its affiliates in nearby markets has no impact on its entry probability into thedestination market.

3

dynamics. Although learning about own profitability is far from capturing all stylized facts

about exporter dynamics (Das et al. (2007), Ruhl and Willis (2016), Fitzgerald et al. (2016)),

it seems to be successful at capturing several key features of the data, such as the quick decline

of exit rates over the exporter’s life cycle.5 On the other hand, studies on the dynamics of

MNCs is scant. Recent papers such as Garetto et al. (2016), Gumpert et al. (2016), Bilir and

Morales (2018) and Chen et al. (2018) started to look at MNC dynamics or the joint dynamics

of exporting firms and MNCs. However, these papers either assume multinational affiliates in

different countries have no linkages with each other except for sharing some common technology

(from the headquarters), or use models where there is one single foreign market. The evidence

we provide in this paper suggests that information transmission within MNCs may create an

additional linkage between affiliates, and is potentially important for modeling multinational

firm dynamics.

The paper is organized as follows. In the next section, we discuss our data and present

two empirical results that we see as motivating evidence for learning within MNCs. Section 3

outlines a simple learning model, from which we rationalize the patterns in Section 2 and derive

additional theoretical predictions. We test these predictions in Section 4 and conclude in Section

5.

2 Data and Motivating Evidence

2.1 Data and Variables

The dataset we use is a parent-affiliate matched 20-year panel dataset of Japanese MNCs. It is

called the Basic Survey on Overseas Business Activities (BSOBA, Kaigai Jigyo Katsudo Kihon

Chosa) prepared by the Ministry of Economy, Trade and Industry (METI) of the Japanese gov-

ernment. This survey is mandatory and conducted annually via self-declaration survey forms

(one for the parent firm and another one for each foreign affiliate) sent to the parent firm at the

beginning of each fiscal year. The survey form for parent firms includes variables concerning the

parents’ sales, employment, industry classification, etc, and the survey for the foreign affiliates

collects information on their sales, employment, country and industry information, etc. Based

on the annual survey, we constructed a panel dataset of parent-affiliate pairs from 1995 to 2014

which includes both manufacturing and non-manufacturing firms. Each parent-affiliate pair is

traced throughout the period using an identification code. Compared to other standard multi-

national datasets such as the US BEA survey, our data is unique in that it contains information

5For example, see Akhmetova and Mitaritonna (2013); Aeberhardt et al. (2014); Fernandes and Tang (2014);Timoshenko (2015a,b); Cebreros (2016); Berman et al. (2017).

4

on affiliate-level expectations. Specifically, affiliates of Japanese MNCs are asked to report their

forecasted sales for next year to the Japanese government (in the survey form). This enables us

to provide evidence for learning that directly uses affiliate-level expectations.

We define markets at the country-industry level. Throughout this paper, industries are

broadly defined - we classify each affiliate as in one of the six industries: agriculture, mineral

and construction; food, textile and wood products; chemical and petroleum products; steel, iron

and metal products; production of machinery; service industries.6 For a (potential) market of

a Japanese MNC, we define “nearby” and “remote” markets by first grouping all countries into

seven geographic regions: North America, Latin America, Asia, Middle East, Europe, Oceania

and Africa. A nearby market is a country-industry cell that satisfy two conditions: (1) the

country is in the same region as the market under study and (2) the two markets are in the

same industry. Similarly, a “remote” market is in the same industry but located in another

region. “Nearby” and “remote” siblings are existing affiliates of the same MNC in nearby and

remote markets, respectively.

We distinguish nearby siblings from remote siblings since we think past experience in markets

within the same region contains more information (for a MNC’s demand conditions in a specific

market inside the region) than past experience in more distant countries outside the region. For

example, Toyota Spain can learn the demand of Toyota cars in Spain from historical sales of

Toyota’s cars in other European countries, as European consumers are likely to have similar

tastes and preferences for cars with the same features, concepts or even company brand names.

To the contrary, Asian or North American consumers may have different tastes and preferences

from European consumers, thus the historical sales there may not be informative about Toyota’s

demand conditions in Spain. Empirical evidence in the next subsection suggests that this is

indeed the case. In Section 4, we provide further evidence that correlation of affiliates’ time-

invariant demand is higher within a region than between regions.

We focus on horizontal FDI by defining an entry only when the firm first sets up an affiliate

with high local sales shares. Because local sales shares of affiliates decline as they grow older

(Garetto et al. (2016)), we try to be conservative and use each affiliate’s average local-sales-to-

total-sales ratio (over its life cycle) to determine whether an affiliate is “vertical” or “horizontal”.

In our baseline regressions, we define affiliates to be “horizontal” only when this ratio is above

85%. We define entry when a parent firm sets up its first horizontal affiliate in the destination

market. We perform robustness checks using alternative thresholds.

Following Chen et al. (2018), we focus on firm learning about their idiosyncratic demand/supply

6Our data provide a finer industry definition, with 29 industries in total. However, since multinational firmsare likely multi-product firms spanning multiple industries, we want to capture learning at a broader level. Wetherefore try to use a broader industry definition than that available.

5

conditions in the destination market. We therefore tease out aggregate components in affiliates’

performance by regressing the affiliate’s log local sales on country-year and industry-year fixed

effects. Suppose we denote the log local sales of affiliate i in year t as rit, then we run the

following regression:

rit = δkt + δst + rit,

where δ denotes the estimated fixed effects and k and s denote country and industry of the

affiliate, respectively. We use the residual from this regression (denoted as rit) as a measure of

exceptional performance of the affiliate. Similarly, we project the parent firms’ domestic sales on

parent-industry-year fixed effects and use the residual parent sales as a control for productivity

shocks that may be common across all affiliates of the same parent firm.

Finally, we define two key regressors in our empirical analyses. According to a typical learning

model where a firm gradually discovers its productivity or demand parameter (i.e., Jovanovic

(1982)), the firm infers the unknown parameter using the entire history of signals. Therefore,

we construct the cumulative average of past performance of existing affiliates as follows:

rnearbypskt ≡

1N(τ ≤ t, i ∈ I(p, s, k))

∑

τ≤t,i∈I(p,s,k)

riτ , (1)

rremotepskt ≡

1N(τ ≤ t, i ∈ I(p, s, k)c)

∑

τ≤t,i∈I(p,s,k)c

riτ ,

where I(p, s, k) denotes the set of parent p’s affiliates in industry s and in other countries that

are in the same region as country k. The set I(p, s, k)c include affiliates of the same parent firm

in other regions and in industry k. The N(·) function calculates the total number of signals

observed before time t. Since the samples in subsections 2.2 and 2.3 are different, we delay our

discussion about the summary statistics of these two key variables until the next subsection.

2.2 Fact 1: Good Sibling Performance Raises Entry Probability

In this subsection, we study how historical performance of MNCs’ existing affiliates affects the

probability of entering new markets. First, we transform the affiliate-year-level dataset into a

parent-destination-year level dataset, where “destination” is a country-industry cell. In principle,

each parent firm can enter a particular destination in any year. We keep the destination-year

combinations in which the parent firm has not established any affiliates yet and study the

probability of setting up horizontal MP there in the next year.

Table 1 shows the number of observations and next years’ entries by each year in the sample

we use our baseline regressions. There are on average around 54,500 observations (parent-

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destination combinations) in each year, and around 59 of them will conduct horizontal FDI

next year. Therefore, the average entry rate is around 0.11%. Note that there are 5826 more

horizontal FDI entries between 1996 and 2014, but we have to exclude them since we do not have

information on their siblings’ performance. This could happen when (1) there exist siblings in

similar and remote markets, but data on past sales are missing or (2) the parent has not entered

any of the similar or remote markets yet.

When we examine the data by region, we see that Asia and Europe are the two regions

with the most number of observations and entries. This is understandable since (1) Asia and

Europe are two main destination markets for Japanese multinationals (Spinelli et al. (2018))

and (2) the number of countries in these two regions are relatively large. However, given the

rising importance of China as a production location and a market for sales, the rate of entry in

Asia is at 0.23%, much higher than the other regions.

Table 1: Number of observations and entries by year

(1) (2) (3) (4)Year # of obs. # of next year’s entry rate # of next year’s

entries (included) % entries (excluded)

1995 32822 82 0.25 4341996 37673 86 0.23 3621997 38478 55 0.14 2381998 41801 52 0.12 1931999 49330 48 0.10 2632000 48238 58 0.12 3102001 44931 65 0.14 4262002 52720 64 0.12 4242003 54441 63 0.12 4072004 57343 60 0.10 3802005 59834 62 0.10 3832006 60076 47 0.08 2992007 59745 55 0.09 2722008 62306 37 0.06 1622009 65204 49 0.08 2532010 63671 86 0.14 3512011 64984 87 0.13 3932012 69995 41 0.06 2202013 72770 23 0.03 56Total 1036362 1120 0.11 5826

Notes: Column 1 shows the number of observations by year in our baseline regression.Column 2 shows the number of next year’s entries for observations included in Column 1,while Column 3 calculates the entry rates (Column 2/Column 1). Finally, Column 4 showsthe number of next year’s entries for observations not included due to missing independentvariables (siblings’ and parents’ cumulative performance).

We are now ready to introduce our econometric specification. In particular, we run the

following regression:

Pr(Enterpsk,t+1 = 1) = β1rnearbypskt + β2rremote

pskt + β3rpt + δskt + δp + εpk,t+1, (2)

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Table 2: Number of observations and entries by region

(1) (2) (3) (4)Region # of obs. # of next year’s entry rate # of next year’s

entries (included) % entries (excluded)

Africa 17182 5 0.03 47Asia 351155 806 0.23 4334Europe 456005 227 0.05 415Middle East 5170 5 0.10 38North America 70728 21 0.03 729Oceania 34146 10 0.03 94South America 101976 46 0.05 169Total 1036362 1120 0.11 5826

Notes: Column 1 shows the number of observations by region in our baseline regression.Column 2 shows the number of next year’s entries for observations included in Column 1,while Column 3 calculates the entry rates (Column 2/Column 1). Finally, Column 4 showsthe number of next year’s entries for observations not included due to missing independentvariables (siblings’ and parents’ cumulative performance).

where the dependent variable is a binary variable indicating whether a parent firm p enters

destination country k in year t+ 1. The independent variables are the “experience” of “nearby

siblings”, the “experience” of “remote siblings” and the exceptional performance of the parent

firm in year t, which have been defined in the previous subsection. We also control for various

fixed effects in our regressions, such as the (affiliate) industry-country-year fixed effects and the

parent firm fixed effects (headquarter/HQ fixed effects).

Before we show the regression results, we present summary statistics of the regressors and

related variables in Table 3. The sample is constructed at the parent-firm-destination-year level,

and only observations with at least one nearby sibling and one remote sibling are included. This

is the same sample as in Table 1 and Table 2. The median observation has one nearby sibling

and three remote siblings, and the average number of siblings (2.50 and 6.40) is larger than

the median, which suggests that the distribution is right-skewed. Though many multinational

firms enter new destinations during our sample period, they probably have established foreign

operations in developed regions long time ago (e.g., North America and Europe). This is re-

flected by the average age of the nearby and remote siblings, with medians of 15.26 and 15.79,

respectively. Finally, there is substantial variability in the cumulative average residual sales.

For example, the 75th percentile of nearby siblings’ experience is 207 log points higher than

the 25th percentile, which translates into an average of 692% difference in past sales. The three

regressors (nearby siblings’ experience, remote siblings’ experience and residual parent sales) are

also far from being perfectly correlated. The correlation coefficients are between 0.38 and 0.45.

Table 4 shows the results of the estimation equation 2. In Column 1, we estimate equation 2

and control for country-year fixed effects but not headquarter fixed effects. Both nearby siblings’

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Table 3: Summary Statistics of Siblings and Parents

Obs. mean std. dev. 25 pct. median 75 pct.

Number of nearby siblings 1036362 2.500 3.291 1 1 3Average age of nearby siblings 1036362 15.26 9.414 8.500 13.50 20Cumulative experience of nearby siblings 1036362 -0.328 1.653 -1.288 -0.239 0.782Number of remote siblings 1036362 6.403 9.987 2 3 7Average age of remote siblings 1036362 15.79 7.693 10.50 14.80 20Cumulative experience of remote siblings 1036362 -0.0619 1.443 -0.906 0.0296 0.890Residual sales of parents 1036362 -0.171 1.745 -1.293 -0.0763 1.043

Notes: Nearby siblings refer to affiliates of the same parent firm in the same region. Remote siblings refer toaffiliates of the same parent firm in other regions. We calculate the cumulative average residual sales followingthe definition described earlier.

experience and the parent firms’ performance raises the probability of FDI entry next period.

A one-standard deviation change in nearby siblings’ experience raises the entry probability by

1.65 × 0.0115% = 0.019% which is around 17% of the average entry probability (0.11%). In

contrast, experience of remote siblings does not have a significant impact on the probability

of FDI entry. We view this as suggestive evidence that firms learn about their demand or

productivity in potential markets using information from nearby markets, but not from remote

markets. As firm-specific demand and productivity conditions are more likely to be correlated

across markets within the same region (i.e., nearby markers), the parent firm can have more

informed decisions of market entry by exploiting the information value of operating in nearby

markets. In Column 2, we further control for headquarters fixed effects, and the main difference is

that the parent firms’ performance becomes insignificant, which suggests the over-time variation

in parent firms’ performance does not predict FDI entry. Finally, we show in Column 3 that the

results are robust when we drop the parent firms’ performance but control for headquarters-year

fixed effects.

Alternatively, we follow Conconi et al. (2016) to model the hazard ratio of firm p that enters

country k and industry s between time t and t+ 1 using the Cox regression model:

hpsk(t|X) = hj(t) exp(β1r

nearbypskt + β2rremote

pskt + β3rpt

), (3)

where hpsk(t) is the hazard ratio for strata j, and the terms in the exponential function are

defined in the same way as in equation (2). The key assumption of this model is that the

regressors shift the hazard function hs(t) proportionally. The hazard functions within each

strata do not need to be estimated, and they are allowed to be different. We specify strata at

different levels to check the robustness of the results.

Table 5 shows the results from the Cox regression models, which are qualitatively similar

to the linear probability model. When we set the strata at country or country-year level, both

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Table 4: The impact of siblings’ experience on next period entry

Dep. Var: 1(Enterspk,t+1)× 100 (1) (2) (3)

Nearby Siblings’ Experience 0.0115a 0.0143a 0.0168a

(0.00278) (0.00337) (0.00380)Remote Siblings’ Experience 0.00241 0.00396 0.00807

(0.00390) (0.00527) (0.00601)Parents’ Performance 0.00773a -0.000425

(0.00297) (0.00751)Country-Ind-Year FE Yes Yes YesHQ FE No Yes NoHQ-Year FE No No Yes

N 1036362 1036362 1063970R2 0.025 0.029 0.047# of HQ 1982 1982 1989# of HQ-Destination 131038 131038 131840# of Entries 1120 1120 1147

Notes: Dependent variable is an indicator variable indicating whether the headquartersenters a particular destination next year. Standard errors are clustered at headquarters(HQ) level. Significance levels: a: 0.01, b: 0.05, c: 0.10.

nearby siblings’ experience and parent firm performance have a positive impact on the hazard

of FDI entry. According to the estimates in Column 1, a one-standard deviation increase in

nearby siblings’ experience raises the hazard ratio by e1.65×0.127 − 1 = 23%. Note that since the

subject of the survival analysis is at the headquarters-destination-year level, we cannot specify

the strata at a level finer than headquarters-destination. In Column 3, we set the strata at

the headquarters level. It turns out that the standard error of the parent firm performance is

no longer estimated, probably due to collinearity between this variable and survival time fixed

effects. We therefore dropped this variable in the table. However, the coefficients of nearby and

remote siblings’ experience are similar irrespective of controlling the parent firm’s performance

or not. The results are also robust when we set the strata at the headquarters-year level.

2.3 Fact 2: Good Sibling Performance Raises Self Expectation

In this subsection, we use our measure of affiliates’ expected sales to study how siblings’ past

performance affect the formation of expectations. Specifically, the regression equation we run is

as follows:

logEt(Ri,t+1) = β1rit + β2rnearbypskt + β3rremote

pskt + β4rpt + δkt + δst + δp + εi,t+1, (4)

where we examine how affiliate’s own experience and its siblings’ experience affect its expected

sales next year. The affiliate’s own experience, rit, is defined as the cumulative average residual

log sales of itself. The siblings’ experience variables are defined as in equation (1), and we control

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Table 5: The impact of siblings’ experience on next period entry (survival analysis)

(1) (2) (3) (4)

Nearby Siblings’ Experience 0.127a 0.113a 0.228a 0.225a

(0.0271) (0.0291) (0.0355) (0.0520)Remote Siblings’ Experience 0.0315 0.0140 -0.0501 -0.0520

(0.0277) (0.0290) (0.0491) (0.0687)Parents’ Performance 0.0637b 0.0616b

(0.0293) (0.0282)

N 982682 982682 1005227 1005227# of HQ 1905 1905 1911 1911# of HQ-Destination 124112 124112 124625 124625# of Entries 1140 1140 1168 1168Log likelihood -6121.5 -3876.1 -5058.3 -4531.0Strata Country-Industry Country-Industry-Year HQ HQ-Year

Notes: Results of Cox regression models. Standard errors are clustered at headquarter(HQ) level. Significance levels: a: 0.01, b: 0.05, c: 0.10.

for parent firm performance, parent firm and country-year fixed effects as in the estimation

equation (2). Since this regression is at affiliate-year level, we can also control for (affiliate)

industry-year fixed effects δst to tease out industry-wide shocks.

Table 6: The impact of siblings’ experience on expected sales next year

Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)

Self Experience 0.837a 0.838a 0.824a 0.825a

(0.00840) (0.00860) (0.0101) (0.0106)Nearby Siblings’ Experience 0.0207b 0.0224b 0.0302a 0.0297a

(0.00934) (0.0100) (0.0102) (0.0111)Remote Siblings’ Experience 0.0124 0.0113 0.0159 0.00663


(0.0151) (0.0185)Country-Year FE Yes Yes No NoIndustry-Year FE Yes Yes No NoCountry-Industry-Year FE No No Yes YesHQ FE Yes No Yes NoHQ-Year FE No Yes No Yes

N 40669 38977 37608 35789R2 0.864 0.882 0.879 0.898# of HQ 1119 1012 1094 988# of affiliates 9298 9067 8750 8493Strata Country-Industry Country-Industry-Year HQ HQ-Year

Notes: Dependent variable is log of expected sales next year. Standard errors are clusteredat headquarter (HQ) level. Significance levels: a: 0.01, b: 0.05, c: 0.10.

The results from the basic regression are reported in Column 1 of Table 6. Note that the self

experience is a key determinant of future sales expectation, with a precisely estimated coefficient

of 0.837. Nearby siblings’ experience also positively affects the expectation. If average past sales

of all nearby siblings increase by one log point, the affiliate’s expected sales increase by 0.02

11

log points. In contrast, remote siblings’ experience does not seem to have a significant impact,

which is consistent with the evidence we present for FDI entries in last subsection. In Columns

2 - 4, we experiment with different sets of fixed effects and the results are robust.

2.4 Robustness Checks

Our baseline regressions do not exclude tax havens such as Hong Kong and Singapore, because

these are “large” tax havens which affiliates may have major operations. Moreover, coun-

tries/regions such as Singapore and Hong Kong are usually seen as important step stones to

enter other markets of Asia. Thus, we want to keep them in our preferred specifications. In

the appendix, we replicate all the previous regressions by excluding the tax havens which are

defined as the same as in Gravelle (2009).

We also experiment with alternative definitions of horizontal FDI. In our baseline sample,

we count an entrant as a horizontal affiliate, if the average share of its local sales (in total sales)

over the years is above 85%. We use stricter thresholds such as 90% and 95% in the appendix.

This slightly reduces our sample size, but the results remain largely unchanged.

3 Model

In this section, we present a partial equilibrium model of firm learning which features both

self-discovery (i.e., Jovanovic (1982); Arkolakis et al. (2017)) and learning from neighbors (i.e.,

Fernandes and Tang (2014)) to explain the documented empirical facts. As our empirical findings

are at the micro-level, we study the learning problem of a single firm and emphasize age dynamics

of firm-level expectations. As information concerning firm-level demand conditions is imperfect,

the MNC has to form an expectation for these conditions in the destination market both before

and after the market entry. Before entering the foreign market, the MNC learns its demand

conditions in the destination market imperfectly from performance of its affiliates in nearby

markets. After observing the performance of nearby siblings, the MNC decides whether to

enter the destination market and is more likely to enter , when its nearby affiliates have better

historical sales performance.

The key feature of our model rests on the expectation formation after market entry. If the

MNC enters the foreign market, its affiliate in that market updates its expectation for demand

conditions over the life cycle. Different from previous papers in the literature (e.g., Fernandes

and Tang (2014), Timoshenko (2015b), Berman et al. (2017)), we allow the affiliate to learn its

demand conditions both from its own performance (i.e., average past sales) and from performance

of its nearby siblings.

12

The model first predicts that the affiliate utilizes both its average past sales and its nearby

siblings’ average past sales to form its sales expectation, as both signals are informative for

the affiliate’s demand conditions in the destination market. Next, the key prediction of the

model is that the importance of self-discovery (and learning from nearby siblings) increases

(and decreases) with the affiliate’s age, as the weight the affiliate puts on its own past sales

(and on its nearby siblings’ past sales) increases (and decreases) with the affiliate’s age in the

expectation formation process. The intuition for the second prediction hinges on the time-

varying precision of the two types of signals. Specifically, although the (absolute) precision of

both signals increases with the affiliate’s age, the precision of the signals from nearby siblings

increases slower with the age due to the imperfect correlation of demand conditions across

markets. As a result, the affiliate increases the weight that is put on its own past performance

over time when forming the expectation. Conversely, as the relative precision of nearby siblings’

signals decreases over time, its weight in the formula of expectation formation shrinks over the

affiliate’s life cycle. Finally, the model predicts that nearby siblings’ past performance (and the

affiliate’s own past performance) plays a bigger (and smaller) role in the expectation formation

process, when the affiliate is located in a market with more volatile time-varying idiosyncratic

demand/productivity shocks. The is because a more volatile environment in the destination

market implies less precise signals obtained from that market, and thus the affiliate depends its

expectation more on signals received from nearby markets.

3.1 Setup

We study a partial equilibrium model with a single MNC. Suppose there are three foreign

countries (countries 1,2,3) in the world, and countries 1 and 2 are in the same region (e.g.,

Asia, North America, Europe) with the remaining country in another region. Assume that

consumers in all foreign markets (i.e., countries) have CES preferences, and the MNC has to

decide whether to enter market 1, given that it has already set up affiliates in markets 2 and 3.

As foreign consumers’ preferences are CES, the MNC’s demand function in market j is

qt(ω) = Ateajt(ω)pt(ω)−σ, (5)

where t denotes the time; ω is the variety produced; σ is the elasticity of substitution. Parameter

At is the aggregate market demand shifter, and ajt is the firm-specific demand in market j. For

each market j, the MNC faces demand uncertainty which comes from the demand shifter ajt (ω).

We assume that ajt (ω) is the sum of a time-invariant market-specific demand draw θj (ω) and

13

a transitory shock εjt (ω):

ajt (ω) = θj (ω) + εjt(ω), εjt(ω)i.i.d.∼ N

(0, σ2

ε

). (6)

The MNC understands that θj (ω) is drawn from a normal distribution N(θ, σ2

θ

), and the

independent and identically distributed (i.i.d.) transitory shock, εjt (ω), is drawn from another

normal distribution N(0, σ2

ε

). On the supply side, we assume that firms are homogeneous in

labor productivity. In order to produce q units of output, the firm has to employ the same

amount of workers and pay each worker at the wage rate of wt.

The fundamental assumption of the model is that the MNC does not know the value of θj(ω)

and therefore has to form an expectation for it in order to make an entry decision into the foreign

market. After entry, the MNC’s affiliate in market j also updates its belief for θj(ω) over time.

Naturally, sources of information the MNC utilizes to form its expectation are key predictions

of the model and related to how demand shocks are correlated across different markets.

We introduce interdependence of demand shocks across markets as follows. The variance-

covariance matrix of the MNC’s demand draws in this three-country world is denoted as

σ2θ σ2

12 σ213

σ212 σ2

θ σ223

σ213 σ2

23 σ2θ

.

As countries 1 and 2 are in the same region, we assume that the correlation coefficient between

θ1(ω) and θ2(ω) is positive and bigger than that between θ1(ω) (and θ2(ω)) and θ3(ω):

Assumption 1

ρ12 ≡σ2

12

σ2θ

> ρ13 ≡σ2

13

σ2θ

= ρ23 ≡σ2

23

σ2θ

≥ 0,

where σ2ij is the covariance of the demand draws in markets i and j where i 6= j. For simplicity,

we assume that

Assumption 2

ρ13 = ρ23 = 0.

We adopt these two assumption in what follows. In Appendix A, we show that the correlation of

time-invariant demand tends to be larger between countries within a region than that between

14

countries across regions. We adopt Assumption 2 to simplify the analysis, and we will discuss

to what extent our model’s empirical predictions depend on this assumption.

The timing of the model is stated as follows. After a MNC enters a market j, its affiliate

in that market makes the output choice after observing the demand shifter, ajt (ω), in period t.

As a result, the realized sales are

Rjt (ω) = Ateajt(ω)

(wtβ

)1−σ, (7)

where β ≡ σ−1σ is the inverse of the mark-up. It is clear from the above equation that the

logarithm of realized sales is the sum of ajt of a term that only consists of aggregate variables.7

Therefore, the log residual sales we have constructed (after teasing out the components of log

sales that are affected by country-year and industry-year factors) equal ajt. At the end of each

period, affiliate in market j forms an expectation for its sales next period by utilizing information

on historical (log) residual sales in its own market and in its nearby siblings’ markets. Finally,

at the end of each period, the MNC utilizes information on historical (log) residual sales in the

nearby siblings’ markets when deciding whether to enter a new destination market.

3.2 Expectation Formation before Market Entry

How does a MNC use various sources of information to form its expectation before entering

the market? In short, the model predicts that the MNC only uses sales information of its

affiliate in the same region (i.e., economy 2) to forecast its “would-be” demand in market 1.

The rationale is that a MNC’s demand conditions across markets within the same region are

correlated. Therefore, nearby siblings’ historical sales performance has information value, when

the MNC forecasts its demand in the market that it is going to enter. Naturally, when the

forecast is higher than a certain threshold, the MNC chooses to enter market 1.

The specific question we have in mind is how a MNC forms its forecast for θ1(ω) after observ-

ing a2(ω) and a3(ω), where a2(ω) and a3(ω) are average past (log) residual sales of the MNC’s

affiliates in countries 2 and 3 respectively. For the affiliate itself, the number of observations it

has observed by year t is just its age. For its siblings (in the nearby or remote region), we treat

the number of affiliate-year pairs of the same parent firm that show up in our dataset (in the

nearby or remote region) by year t as the total number of signals the affiliate receives (from the

nearby or remote siblings) by year t. Table 3 shows that the average age of nearby siblings is

roughly the same as that of remote siblings, and the average number of remote siblings is larger

than that of nearby siblings. Thus, we assume that there are more realizations of signals for

7Specifically, this term equals log(At)− (σ − 1)[log(wt)− log(β)].

15

a3(ω) (denoted by t3) than for a2(ω) (denoted by t2). Moreover, Table 3 also indicates that the

average age of both nearby and remote siblings is quite high (i.e., fifteen), which implies large

enough t2 and t3. Therefore, although the evidence suggest that t3 > t2, it also indicates that

both t3 and t2 are quite large (above thirty). In total, we consider the case in which the number

of signals received from both the nearby and remote regions is sufficiently large in the model:

Assumption 3 The number of signals the affiliate receives (from the nearby or remote siblings)

in a given year is sufficiently large. I.e., t2 →∞ and t3 →∞.

We solve the affiliate’s optimal forecasting problem before it enters the destination market.

As θ1, θ2 and θ3 are jointly distributed normal, the distribution of θ1(ω) conditioning on a2(ω)

and a3(ω) is still normal with the mean of

θ +

[ρ12σ

2θ , ρ13σ

2θ

]

∆

[σ2θ + σ2

εt3, −ρ23σ

2θ

−ρ23σ2θ , σ2

θ + σ2εt2

][a2(ω)− θ

a3(ω)− θ

]

,

where

∆ ≡(σ2θ +

σ2ε

t3

)(σ2θ +

σ2ε

t2

)−(ρ23σ

2θ

)2

and t2 and t3 are sufficiently large. We denote the signal-to-noisy ratio as

λ ≡σ2θ

σ2ε

.

Therefore, the updating rule can be simplified to

θ +σ4ε

∆

[ρ12λ

2 + ρ12λt3− ρ13ρ23λ

2, ρ13λ2 + ρ13λ

t2− ρ12ρ23λ

2][a2(ω)− θ

a3(ω)− θ

]

,

which leads to the rule of forming the expectation (before entry) as

E0a1(ω) = β0θ + β2a2(ω) + β3a3(ω), (8)

where

β2 ≡σ4ε

∆

(ρ12λ

2 +ρ12λ

t3− ρ13ρ23λ

2),

β3 ≡σ4ε

∆

(ρ13λ

2 +ρ13λ

t2− ρ12ρ23λ

2),

and

β0 = 1− (β2 + β3).

16

The following proposition discusses how the MNC incorporates information from markets 2 and

3 when forming its expectation for its (would-be) demand in market 1.

Proposition 1 Under Assumption 1 and 2, the MNC uses average past residual sales in market

2 to form its expectation for future sales. As a result, when average past residual sales of nearby

siblings in market 2 are higher, the MNC forms a better expectation for its (would-be) demand

in market 1. Consequently, the MNC is more likely to enter that market.

Proof. As we assume that ρ12 > ρ13 = ρ23 = 0, β2 is strictly positive and β3 is zero. What

matters for the entry decision of the MNC is whether the expected demand is market 1 exceeds

a certain threshold. Therefore, when the average past sales of nearby siblings are higher, the

MNC is more likely to enter market 1. QED.

Proposition 1 rationalizes the empirical finding presented in Section 2.2. That is, when the

average realized sales of the MNC’s affiliates in nearby markets are higher, the MNC is more

likely to enter the destination market.

It is worth discussing how the results would change if we allow ρ12 > ρ13 = ρ23 > 0. Under

this alternative assumption, both β2 and β3 are positive according to the above formula. When

demand shocks are correlated between any two destination markets, information from all the

other markets should be used when the MNC contemplates entering market 1. However, when

ρ12 is sufficiently larger than ρ13 and ρ23, β2 is guaranteed to be bigger than β3. The fact that

we are getting an almost zero coefficient for β3 in our estimation suggests that ρ13 and ρ23 are

likely to be sufficiently small.

3.3 Expectation Formation after Market Entry

After the MNC enters market 1, its affiliate in that market continues to update the belief for

θ1(ω). As we have shown in last subsection, information provided by the affiliate in market 3

has little value for the affiliate in market 1, as the correlation of demand conditions between the

two markets is extremely low. As a result, the affiliate in market 1 updates its belief by utilizing

a series of signals of a1i(ω) and a2i(ω) (i ∈ {1, 2..., t − 1}). The question we want to study in

this subsection is how important the two types of signals are for the expectation formation in

market 1. The angle from which we view this question is the affiliate’s age, as this is the key

dimension of life-cycle learning models (e.g., Timoshenko (2015b), Arkolakis et al. (2017), Chen

et al. (2018)).

Let us use t1 to denote the age of affiliate in market 1 and use a1(ω) to denote average past

(log) residual sales of the affiliate in market 1 by the end of age t1. As θ1, θ2 and θ3 are jointly

distributed normal, the distribution of θ1(ω) conditioning on a1(ω) and a2(ω) is still normal

17

with the mean of

θ +

[σ2θ , ρ12σ

2θ

]

Γ

[σ2θ + σ2

εt2, −ρ12σ

2θ

−ρ12σ2θ , σ2

θ + σ2εt1

][a1(ω)− θ

a2(ω)− θ

]

,

where

Γ ≡(σ2θ +

σ2ε

t1

)(σ2θ +

σ2ε

t2

)−(ρ12σ

2θ

)2

and t2 is sufficiently large. The above expression can be simplified to

θ +1

(λ+ 1

t1

)(λ+ 1

t2

)−(ρ12λ

)2

[(1− ρ2

12)λ2 + λt2, ρ12λ

t1

][a1(ω)− θ

a2(ω)− θ

]

.

The weights put on a1(ω) and a2(ω) are

β1 ≡λ(λ+ 1

t2

)−(ρ12λ

)2

(λ+ 1

t1

)(λ+ 1

t2

)−(ρ12λ

)2 (9)

and

β2 ≡ρ12λt1(

λ+ 1t1

)(λ+ 1

t2

)−(ρ12λ

)2 (10)

respectively. The following proposition characterizes the age-dependent forecasting rule of the

affiliate.

Proposition 2 Under Assumption 1 and 2, the MNC’s affiliate in market 1 uses average past

residual sales in markets 1 and 2 to form its expectation for future sales. Under Assumption

1-3, the weight it puts on self signals (i.e., average past residual sales in market 1) increases

with the affiliate’s age, while the weight it puts on signals from nearby siblings (i.e., average past

residual sales in market 2) decreases with the affiliate’s age.

Proof. The first part of this proposition is true, as both β1 and β2 are bigger than zero. The

second part is true, as β1 increases with t1, when t2 is sufficiently large (i.e., approaching infinity)

implied by Assumption 3. For the final part of this proposition, note that β2 can be simplified

to

β2 ≡ρ12λ

t1(1− ρ212)λ2 + λ

under Assumption 3 as t2 goes to infinity. As 1 > ρ12, the weight the affiliate puts on nearby

signals, β2, decreases with the affiliate’s age, t1. QED

18

Why do we have the diverging age-profiles for the two weights that show up in the formula of

expectation formation? At the first glance, we note that the precision of both signals increases

with the affiliate’s age. Therefore, both weights should increase over time, which is true when

ρ12 = 1 (i.e., when the two signals are perfectly correlated).8 However, when the demand shocks

are not perfectly correlated, there is a competition between the two signals when time elapses.

Specifically, the affiliate’s own signals are more precise than nearby siblings’ signals at each

given point of time. When time elapses, this difference increases. This is why the weight that

is put on signals sent from nearby siblings goes down with the affiliate’s age. Next, as both the

absolute precision and the relative precision (compared to the signals from nearby siblings) of

the affiliate’s own signals increase over time, the weight that is put on the affiliate’s own signals

always increases with its age. Finally, the weight that is put on the prior belief always decreases

with the affiliate’s age, as its absolute precision is time-invariant. In total, Proposition 2 yields

predictions that are consistent with empirical findings presented in Section 2.3.

Finally, we discuss how country-level volatility of time-varying idiosyncratic shocks influences

the affiliate’s optimal forecasting rule. When calculating the volatility measure in Section 4.2,

we use the standard deviation of residual sales growth and forecast errors of old enough Japanese

affiliates (i.e., older than ten years). Thus, this measure mainly reflects the variance of firm-

level noises (i.e., time-varying idiosyncratic shocks) in our model. Therefore, we discuss how an

increase in the variance of firm-level noises, σ2ε , affects the affiliate’s forecasting rule after entry

in the next proposition.

Proposition 3 Under Assumption 1 and 2, the MNC’s affiliate in market 1 uses average past

residual sales in markets 1 and 2 to form its expectation for future sales. Next, the weight it

puts on its own average past residual sales decreases with the standard deviation of residual sales

growth and forecast error of old enough affiliates in market 1, while the weight it puts on past

residual sales of nearby siblings increases with this volatility.

Proof. As we study how an increase in the variance of ε in country 1 affect the forecasting rule,

we have to differentiate σ2ε,1 (in country 1) from σ2

ε,2 (in country 2) in Equation (9) and (10).

As a result, the two equations are modified to

β1 ≡λ1

(λ2 + 1

t2

)− ρ2

12λ1λ2(λ1 + 1

t1

)(λ2 + 1

t2

)− ρ2

12λ1λ2

= 1−

1t1

(λ2 + 1

t2

)

(λ1 + 1

t1

)(λ2 + 1

t2

)− ρ2

12λ1λ2

(11)

8To be more precise, this statement requires that λ t1t2

does not increase faster than the decrease in 1t2

when

the firm becomes older. This is true when t1t2

is kept constant as the firm ages.

19

and

β2 ≡ρ12λ2

t1(λ1 + 1

t1

)(λ2 + 1

t2

)− ρ2

12λ1λ2

, (12)

where λ1 ≡σ2θ

σ2ε,1

and λ2 ≡σ2θ

σ2ε,2

are the signal-to-noise ratios in country 1 and country 2 respec-

tively.

The first part of this proposition is true, as both β1 and β2 above are bigger than zero. For

the second part of the proposition, we increase σ2ε,1 (i.e., the variance of residual growth and

forecast error of old enough Japanese affiliates in market 1) which leads to a smaller λ1. As a

result, β1 (and β2) decreases (and increases) with σ2ε,1 respectively. QED

The intuition for the above proposition is straightforward. When the affiliate’s own signals

becomes less precise, it is going to depend its forecast more on the nearby siblings’ signals and

less on its own signals, conditioning other things’ being equal.

4 Testing Model Predictions

In this section, we tests the model predictions (Propositions 2 and 3) using our affiliates’ sales

expectation data.

4.1 Siblings’ Performance Matters More when the Affiliate is Younger

A key challenge to identifying learning is the possibility of headquarters-industry-region level

shocks to the multinational firm’s profitability, which can potentially explain all our findings

so far. In columns 2 to 4 of Table 7, we present our the first central piece of evidence that

supports the learning model. We break down the sample into affiliates with different ages, as

age is related to the key predictions of the standard life-cycle learning model (Jovanovic (1982)).

We find that the impact of nearby siblings’ experience is higher for younger affiliates while the

impact of self experience is higher for older affiliates. When affiliates are less than three years

old, the coefficient of nearby siblings’ experience is three times of that in Column 1, while the

coefficient of self experience is much lower. When the affiliates are older than seven, the siblings’

experience barely plays a role in its expectation formation. This pattern cannot be explained by

headquarters-industry-region level shocks. A natural explanation is that when affiliates become

older, they accumulate more information (i.e., signals) about their profitability and rely more

on their own experience (instead of their siblings’ experience).

To confirm the increasing (declining) impact of self (nearby siblings’) experience on the

expectation formation, we interact the historical performance with affiliate age in Table 8. Since

20

Table 7: The impact of siblings’ experience on expected sales next year (by age group)

Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)Sample: all ages 1 ≤ age ≤ 3 4 ≤ age ≤ 6 age ≥ 7


(0.00840) (0.0171) (0.0159) (0.00713)Nearby Siblings’ Experience 0.0207b 0.0627b 0.0468b 0.0103


(0.0129) (0.0374) (0.0273) (0.0141)Parents’ Performance 0.0368b 0.0353 0.0854a 0.0397b

(0.0151) (0.0420) (0.0250) (0.0162)Country-Year FE Yes Yes Yes YesIndustry-year FE Yes Yes Yes YesHQ FE Yes Yes Yes Yes

N 40669 3975 5316 30203R2 0.864 0.852 0.890 0.888# of HQ 1119 562 642 967# of affiliates 9298 2419 2865 6916

Notes: Dependent variable is log of expected sales next year. Standard errors are clusteredat headquarter (HQ) level. Significance levels: a: 0.01, b: 0.05, c: 0.10. Note that thenumber of observations in columns 2-4 does not add up to that in column 1 because wehave excluded the singletons (observations whose variation is completely absorbed by fixedeffects) when calculating thses numbers, and the set of singletons varies across specifica-tions.

some affiliates are quite old in our data, we create two age measures to capture the nonlinear

effects of age: the log of affiliates’ age and affiliates’ age capped at 10. We further control the

direct impact of age on expected sales using a complete set of age fixed effects. Taking the

estimates in Column 2 as an example, we find that one additional year of age raises the impact

of self experience by 0.033 and reduces the impact of nearby siblings’ experience by 0.018. In

Columns 3 and 4, we replace parent firm’s (i.e., headquarters’) performance and headquarters

fixed effects by headquarters-year fixed effects, and the results are very similar.

21

Table 8: The impact of siblings’ experience (interacted with age) on expected sales next year

Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)


(0.0172) (0.0173) (0.0173) (0.0172)× log(age) 0.109a 0.110a

(0.00684) (0.00705)×max{age, 10} 0.0330a 0.0335a

(0.00190) (0.00191)Nearby Siblings’ Experience 0.149a 0.160a 0.154a 0.167a

(0.0205) (0.0211) (0.0230) (0.0240)× log(age) -0.0561a -0.0576a

(0.00810) (0.00897)×max{age, 10} -0.0177a -0.0184a

(0.00239) (0.00270)Remote Siblings’ Experience 0.0133 0.0143 0.0122 0.0135

(0.0126) (0.0125) (0.0161) (0.0163)Parents’ Performance 0.0374a 0.0401a

(0.0143) (0.0136)Country-Year FE Yes Yes Yes YesIndustry-year FE Yes Yes Yes YesHQ FE Yes Yes No NoHQ-Year FE No No Yes YesAge FE Yes Yes Yes Yes

N 40598 40598 39549 39549R2 0.873 0.873 0.890 0.891


22

4.2 Siblings’ Performance Matters More for Expectation Formation in Mar-

kets with Noisier Signals

In this subsection, we explore how the dependence of the affiliate’s expectation on its nearby

siblings’ performance varies with aggregate volatility and present our second piece of evidence

that supports the learning model. We will show that nearby siblings’ past performance (or

the affiliate’s own past performance) plays a bigger (or smaller) role in the expectation forma-

tion process, when the affiliate is in a more risky market. This finding is a key prediction of

the learning model (e.g., Jovanovic (1982)), as the aggregate volatility of firm growth in the

destination market is related to the precision of signals that the affiliate receives from its own

past performance. Naturally, the learning model would predict that the affiliate is going to

use less information from itself and more information from its nearby siblings when forming its

expectation for future sales.9

To test this prediction, we first construct a measure of σε as in our model. If the only

firm-level uncertainty comes from ε, we can simply use the standard deviation of the residual

sales growth rates of affiliates in each country as a proxy for σε.10 However, in our learning

model, the firm-level uncertainty also comes from the fact that firms do not know about their

time-invariant demand θ ex ante. This force, however, is only relevant when firms are young.

Evidence in Table 7 suggests that firms almost learn the value of θ at around age 7. We therefore

use the variance of the residual sales growth rates of affiliates above age 7 as a proxy for σε.

Another measure we use is the standard deviation of the forecast errors (the difference between

the realized sales next year and the expected sales) for affiliates above age 7, since the major

source of forecast errors for these affiliates should be ε rather than θ.11

We perform the following regression to examine the impact of σε:

logEt(Ri,t+1) = β1rit + β2rnearbypskt + β3rremote

pskt + β4rpt

β5rit × σε,k + β6rnearbypskt × σε,k + δst + δkt + δp + εi,t+1. (13)

Our new estimation equation is equation (4) with the addition of two new terms: the interaction

term between country-level volatility and the affiliate’s own experience, and the interaction term

between country-level volatility and its nearby siblings’ experience defined in equation (1). The

9Remember that we define nearby siblings based on regions. Therefore, if one economy has a higher level ofaggregate volatility, it means that all the other economies (in the region) combined have a relatively lower averagelevel of aggregate volatility.

10Ideally, one would want to calculate a proxy σε at the country-industry level because it is our definition of“markets”. However, this causes more measurement errors in σε since we have fewer observations in each cell.We decide to aggregate sales growth rates at the country level instead.

11Chen et al. (2018) use this intuition to calibrate σε in a quantitative learning model.

23

country-level volatility measure, σε,k, is defined as the standard deviation of log residual sales

growth of Japanese affiliates in country k. To ensure precision of this measure, we only include

countries that have at least 20 observations.

The regression results are presented in Table 9. In columns 1 and 2, we approximate σε,kusing residual sales growth rates and forecast errors, respectively. The results in the first two

columns show that β5 is negative while β6 is positive, which confirm our hypothesis. In columns

3 and 4, we replace parents’ performance and parent fixed effects with parent-year fixed effects,

and the results are robust. We also tried to use a measure of residual sales growth and residual

forecast errors by removing country-year and industry-year fixed effects before calculating the

standard deviation, and the results are barely changed.

Table 9: The impact of siblings’ experience (interacted with uncertainty) on expected sales nextyear

Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)


(0.0705) (0.0731) (0.0713) (0.0713)× SD(sales growth) -1.101a -1.063a

(0.156) (0.157)× SD(forecast error) -0.938a -0.953a

(0.265) (0.256)Nearby Siblings’ Experience -0.210a -0.234a -0.247a -0.241a

(0.0792) (0.0724) (0.0828) (0.0792)× SD(sales growth) 0.536a 0.628a

(0.180) (0.189)× SD(forecast error) 0.957a 0.993a




N 36479 36388 35149 35056R2 0.868 0.867 0.885 0.884


There is a worry that the reason why Japanese affiliates utilize information from itself and

from nearby siblings differently is that they have different age distributions in different economies

(i.e., the composition effect). Specifically, if Japanese affiliates in more risky economies are

younger on average, our second empirical finding implies that they are going to use information

from nearby siblings (and from itself) more (less) when forming the expectation. This is con-

sistent with the empirical patterns presented in Table 9, even if the actual precision of various

24

signals were not affecting the firms’ expectation formation process. In order to alleviate this po-

tential concern, we include interaction terms between the historical performance (of the affiliate

and of its nearby siblings) and the affiliate’s age (log age or an age dummy variable capped at

ten) into regression equation (13). We further control the direct impact of the affiliate’s age on

expected sales using a complete set of age fixed effects. The regression results are presented in

Table 10. As the table shows, the inclusion of age-related variables does not change the sign and

the significance level of our estimates of β5 and β6, although it reduces the quantitative impact

of country-year level volatility on the firm’s expectation formation process.

Table 10: The impact of siblings’ experience (interacted with uncertainty and age) on expectedsales next year

Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)



(0.142) (0.145)× SD(forecast error) -0.499b -0.500b

(0.216) (0.211)× log(age) 0.106a 0.111a 0.105a 0.111a

(0.00685) (0.00679) (0.00694) (0.00691)Nearby Siblings’ Experience 0.0657 -0.0129 0.0210 -0.0243

(0.0771) (0.0741) (0.0820) (0.0819)× SD(sales growth) 0.185 0.289c

(0.166) (0.174)× SD(forecast error) 0.601b 0.653b

(0.250) (0.273)× log(age) -0.0544a -0.0556a -0.0535a -0.0557a




N 36479 36388 35149 35056R2 0.873 0.872 0.889 0.889


Finally, one may ask whether σθ affects the coefficients differently than σε, as predicted by

Proposition 3. This is difficult because the standard deviation of residual sales growth rates or

forecast errors when firms are young depends on both σθ and σε, which are hard to separately

identify. Theoretically, the cross-sectional statistics contain the most information about σθ when

we focus on age one affiliates. However, the number of observations of residual sales growth or

25

forecast errors is greatly reduced when we focus on this subset of firms, so σθ is likely to be noisily

measured. Nevertheless, we calculate the standard deviation of residual sales growth for these

firms in each country σθ,k, and add the interaction terms between historical performance and σθ,kin regression (13). Table 11 shows the horserace regression results. The new interaction terms

rit× σθ,k and rnearbypskt × σθ,k turn out to be small and insignificantly different from zero, probably

because σθ,k is not precisely measured. However, the coefficients before the old interaction terms

rit× σε,k and rnearbypskt × σε,k are similar to those in Table 9, which make us more confident about

the previous results.

Table 11: The impact of siblings’ experience on expected sales next year - interacted with proxiesof σθ and σε

Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)Uncertainty measures based on sales growth residual growth


(0.0786) (0.166) (0.0752) (0.180)× proxy of σθ -0.118 -0.0715 -0.110 -0.0717

(0.101) (0.0987) (0.0970) (0.0937)× proxy of σε -0.865b -1.656a

(0.417) (0.462)Nearby Siblings’ Experience 0.00433 -0.0988 0.0138 -0.191

(0.0901) (0.178) (0.0839) (0.205)× proxy of σθ 0.0233 0.00330 0.0111 -0.00674

(0.111) (0.109) (0.104) (0.103)× proxy of σε 0.324 0.632


(0.0118) (0.0118) (0.0118) (0.0118)Parents’ Performance 0.0406a 0.0409a 0.0406a 0.0413a

(0.0144) (0.0144) (0.0144) (0.0143)Country-Year FE Yes Yes Yes YesIndustry-year FE Yes Yes Yes YesHQ FE Yes Yes Yes YesAge FE Yes Yes Yes Yes

N 33185 33185 33185 33185R2 0.868 0.868 0.868 0.869


26

5 Conclusion

In this paper, we use a unique dataset of Japanese MNCs to provide evidence that MNCs

learn about profitability in the destination market by observing performance of their affiliates

in nearby markets. Specifically, good historical performance of siblings in nearby markets raises

the probability of the entry of foreign direct investment (FDI) into the destination market.

In addition, in the market where the MNC has entered, good sales performance of siblings in

nearby markets also raises the expectation for future sales held by the MNC’s affiliate(s) in

that market. In contrast, good historical performance of siblings in remote markets does not

affect the entry rates and expected sales forecasted by affiliates in a certain destination market.

Importantly, such an impact declines over the affiliate’s life cycle, while self discovery becomes

more important when the affiliate becomes older. Finally, we show that nearby siblings’ past

performance (and the affiliate’s own past performance) plays a bigger (and smaller) role in

the expectation formation process, when the affiliate is located in a market with more volatile

time-varying idiosyncratic demand/productivity shocks. We view these findings as evidence

for information transmission and spillovers within firms. The simple model we provide here

rationalizes all the empirical findings and can be a good starting point for studying multinational

dynamics.

We believe there are at least three fruitful avenues for future research. First, constructing a

structural model is useful for us to estimate structural parameters of the model (e.g., correlations

of demand shocks, variance of various shocks) and conduct counterfactual exercises. Second,

incorporating information transmission within firms into a quantitative MP framework (e.g.,

Helpman et al. (2004) and Ramondo and Rodrıguez-Clare (2013)) helps us quantify the role

information transmission within firms plays in determining entry and production patterns of

MP. We leave these potentially interesting topics to future research. Finally, the current paper

does not consider information spillover across firms, which may also influence firms’ activities

abroad and may have strong policy implications (Fernandes and Tang (2014), Hamilton (2018)).

27

A Empirical Appendix

A.1 Correlation in θ

Here we compare the within-region and cross-region correlation of the time-invariant demand

θ. To obtain measures of such correlations, we first try to extract model-consistent measures

of θ from the data. According to the model, firms that are old enough have almost learned

the value of θ and the variability in its sales is only caused by ε. Therefore, if we average over

a large number of realized (log) sales, we can obtain a proxy for θ. We perform this exercise

for each parent-firm-market, only taking observations when the affiliate is above 7 years old (7

years old included). We then obtain a parent-firm-market level dataset. We pair each market

that a parent firm has entered with all the other markets it has entered, and then calculate the

cross-sectional correlation. This correlation can be calculated for two markets within the same

region, or two markets in different regions. In row 1 of Table A1, we show the within-region

and cross-region correlations calculated in this way. The within-region correlation is around 0.4,

higher than the cross-region correlation.

One worry of this calculation is that the proxy for θ is contaminated by other factors, such

as aggregate shocks and MNC-level global shocks that are not market-specific. To address these

issue, we compute two alternative proxies for θ. First, we take country-year and industry-year

fixed effects out of log sales, so that the residual e1(sales) is arguably idiosyncratic demand. We

then calculate the average within a parent-firm-market for affiliate-year observations where the

affiliate is more than 7 years old. Second, we use a different residual e2(sales) which we obtained

by regression log sales on log parent firm domestic sales as well as the fixed effects. This further

removes the MNC-level global shocks that are not firm-specific. We use this measure to construct

a third proxy for θ. The correlation of θ, within and across region, are shown in row 2 and 3 of

Table A1. These correlation are smaller than that in row 1, but the within-region correlation is

always larger than the cross-region correlation, and the difference is around 0.1.

28

Table A1: Correlation of Demand Within and Between Regions for Affiliates above Age 7

Demand Measure Corr. within Region Corr. between Regions

log(sales) 0.407 0.302(11542) (26002)

e1(sales) 0.362 0.264(11484) (25781)

e2(sales) 0.313 0.216(11381) (25547)

Notes: Each observation is a HQ-country-country pair (two different countries). For eachHQ-country cell, we take average of sales for all affiliates above age 10. When the demandmeasure is log(sales), we simply use the log of local sales for each affiliate. When thedemand measure is e1(sales), we regress log local sales on country-year and industry-yearfixed effects, and use the residual as a measure of firm idiosyncratic demand. When thedemand measure is e2(sales), we further control parent sales in Japan beyond the fixedeffects to obtain the residual sales. All correlation coefficients are significant at 1%.

29

A.2 Robustness to Excluding Tax Haven Affiliates

Here we exclude tax havens. Note that we haven’t run the specification with headquarters-

country-industry level entries and headquarters-industry level siblings.

Table A2: Sibling Signals on Sales Expectations

Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)


(0.00880) (0.00890) (0.0107) (0.0111)Nearby Siblings’ Experience 0.0260a 0.0303a 0.0348a 0.0396a






30

Table A3: Sibling Signals on Sales Expectations - interacted with age

Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)


(0.0176) (0.0179) (0.0175) (0.0178)× log(age) 0.113a 0.113a

(0.00676) (0.00682)×max{age, 10} 0.0333a 0.0339a


(0.0217) (0.0221) (0.0242) (0.0253)× log(age) -0.0580a -0.0585a

(0.00858) (0.00952)×max{age, 10} -0.0180a -0.0183a




N 35138 35138 34271 34271R2 0.872 0.872 0.890 0.890


31


Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)




(0.267) (0.257)Nearby Siblings’ Experience -0.0961 -0.230a -0.116 -0.235a

(0.0794) (0.0701) (0.0859) (0.0767)× SD(sales growth) 0.331 0.395c





N 35106 35040 34237 34171R2 0.867 0.867 0.885 0.885


32

A.3 Robustness to Stricter Definitions of Horizontal Siblings

If we define horizontal entries by setting the horizontal sales share at 90% instead of 85%.

The results are very similar. One major difference is that for the linear probability model of

entry, remote siblings’ experience becomes significant when controlling for headquarters-year

fixed effects. This is not true when we use the Cox regression model.


Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)


(0.00854) (0.00873) (0.0104) (0.0108)Nearby Siblings’ Experience 0.0182c 0.0207b 0.0276a 0.0279b






33


Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)


(0.0171) (0.0176) (0.0171) (0.0175)× log(age) 0.111a 0.111a

(0.00658) (0.00666)×max{age, 10} 0.0334a 0.0337a


(0.0212) (0.0220) (0.0239) (0.0252)× log(age) -0.0579a -0.0600a

(0.00828) (0.00925)×max{age, 10} -0.0181a -0.0189a


(0.0127) (0.0126) (0.0166) (0.0169)Parents’ Performance 0.0337b 0.0365a


N 38254 38254 37070 37070R2 0.875 0.875 0.892 0.892


34

Table A7: Sibling Signals on Sales Expectations - interacted with uncertainty

Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)




(0.269) (0.262)Nearby Siblings’ Experience -0.208a -0.226a -0.247a -0.239a






N 34351 34260 32909 32816R2 0.870 0.868 0.886 0.885


35

Table A8: Sibling Signals on Sales Expectations - interacted with uncertainty and age

Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)




(0.217) (0.215)× log(age) 0.107a 0.112a 0.106a 0.111a

(0.00687) (0.00681) (0.00696) (0.00692)Nearby Siblings’ Experience 0.0776 0.000694 0.0322 -0.0154

(0.0779) (0.0737) (0.0819) (0.0822)× SD(sales growth) 0.164 0.271


(0.247) (0.271)× log(age) -0.0567a -0.0576a -0.0560a -0.0579a




N 34351 34260 32909 32816R2 0.874 0.874 0.891 0.890


36

The results are largely similar if we set the threshold share at 95%.


Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)


(0.00901) (0.00912) (0.0111) (0.0116)Nearby Siblings’ Experience 0.0203b 0.0210b 0.0260b 0.0240b

(0.00998) (0.0106) (0.0109) (0.0122)Remote Siblings’ Experience 0.00721 0.00488 0.0101 -0.00324





37


Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)


(0.0176) (0.0181) (0.0174) (0.0179)× log(age) 0.112a 0.111a

(0.00686) (0.00688)×max{age, 10} 0.0334a 0.0336a


(0.0219) (0.0227) (0.0243) (0.0257)× log(age) -0.0603a -0.0621a

(0.00855) (0.00942)×max{age, 10} -0.0184a -0.0190a


(0.0129) (0.0128) (0.0168) (0.0170)Parents’ Performance 0.0348b 0.0377a


N 34922 34922 33638 33638R2 0.875 0.875 0.893 0.893


38

Table A11: Sibling Signals on Sales Expectations - interacted with uncertainty

Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)




(0.276) (0.271)Nearby Siblings’ Experience -0.197b -0.216a -0.241a -0.237a






N 31294 31204 29799 29707R2 0.869 0.868 0.887 0.886


39

Table A12: Sibling Signals on Sales Expectations - interacted with uncertainty and age

Dep. Var: logEt(Ri,t+1) (1) (2) (3) (4)




(0.218) (0.214)× log(age) 0.107a 0.113a 0.106a 0.112a

(0.00721) (0.00713) (0.00727) (0.00719)Nearby Siblings’ Experience 0.0980 0.0129 0.0438 -0.0147

(0.0779) (0.0763) (0.0819) (0.0846)× SD(sales growth) 0.131 0.253


(0.250) (0.271)× log(age) -0.0588a -0.0594a -0.0579a -0.0595a




N 31294 31204 29799 29707R2 0.874 0.874 0.891 0.891


40

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