Business Dynamics and Productivity Growthwith an Application to Taiwanese Electronics Firms
September 30H 2009
Loretta Fung,a Alice Nakamura,b and Masao Nakamurac
Abstract
Empirical progress on understanding the consequences of various sorts of business dynamics -- whether
from a technological change, job creation and destruction, or firm governance and management
perspective -- requires reliable ways of characterizing business dynamics and of measuring the impacts ofvarious sorts of business dynamics on business and industry and national productivity growth. We review
the literature on characterizing and on measuring the impacts of business dynamics on productivity
growth. We introduce the business dynamic status categories used in virtually all of the studies of others:classifications of continuing, entering and exiting. This classification can be implemented with just two
periods of data. We also propose an alternative dynamic classification for businesses utilizing three
periods of data. For the empirical portion of this paper, like many others, we adopt the basic approach of
Foster, Haltiwanger and Krizan. We implement this decomposition using data for Taiwanese electronics
firms. We first use the conventional dynamic status classification approach for firms and then use oursuggested new FNN approach. We demonstrate the value of the FNN classifications.
1.

Introduction“A wave of innovation across a broad range of technologies, combined with considerable
deregulation and a further lowering of barriers to trade, fostered a pronounced expansionof competition and creative destruction.”
Alan Greenspan, Speech on Economic Volatility, 2002.

Technological advances are widely viewed as an engine of economic growth. Moreover, newbusinesses are viewed by many as playing a key role in realizing the economic growth potential of newtechnologies. The presumed interrelationships of new technologies, new businesses and economic growth
are one main reason for intense interest in the dynamic birth, growth and death cycles of businesses.1
Schumpeter (1942, p. 83) coined the term, “creative destruction,” which he described as follows:“The fundamental impulse that keeps the capital engine in motion comes from the newconsumers’ goods, the new methods of production and transportation, the new markets....
[The process] incessantly revolutionizes from within.... This process of Creative
Destruction is the essential fact of capitalism.
Others refer to this same sort of process as the natural selection mechanism (NSM) of economic
Darwinism (e.g., Nishimura, Nakajima and Kiyota 2005).
aDepartment of Economics, National Tsing Hua University. E-mail: phfung@mx.nthu.edu.tw
School of Business, University of Alberta. E-mail: Alice.Nakamura@ualberta.cacUniversity of British Columbia. E-mail: masao.nakamura@sauder.ubc.ca
1
For example, an MIT commission report suggests that establishments at the top of the productivity distribution reston their laurels and lose their competitive advantage. See Caves and Barton (1990).
b

1

Employment issues are a second reason for intense interest in firm dynamics. Growing businesses
create jobs. It is also true, of course, that growing businesses often take market share from otherbusinesses, leaving the employees of those others businesses with lower earnings or unemployed.
However, in countries open to trade, consumers can abandon domestic producers for the products offoreign competitors. Thus many of those who care deeply about maintaining and improving domestic
employment opportunities nevertheless feel strongly that established businesses should not be shieldedfrom the competition resulting from new entrants and the growth of other established businesses.
In an influential paper in the macroeconomics literature, Caballero and Hammour (1994) statethat industries undergoing continuous creative destruction can accommodate variations in demand in two
ways: “they can vary either the rate at which production units with new techniques are created or the rate
at which units with outdated techniques are destroyed.” However, “industries” are not the decision
makers for businesses. Businesses are created and developed and closed by the choices of managers andinvestors in their interrelated efforts to foresee, shape and respond to changing product demand and input
supply conditions. Some of those who believe that business management and governance matter strive to
extract yet another sort of lesson from research on business dynamics. They are interested in trying tounderstand how firms can be governed and managed over time so they will continue to be successful.
Empirical progress on understanding the consequences of various sorts of business dynamics -whether from a technological change, job creation and destruction, or firm governance and management
perspective -- requires reliable ways of characterizing business dynamics and of measuring the impacts ofvarious sorts of business dynamics on business and industry and national productivity growth. It is theliterature on characterizing and on measuring the impacts of business dynamics on productivity growth towhich we turn our attention in section 2.
In the case of information technology (IT) goods such as computers, there has been an ongoingproliferation of new goods, and IT products have been improving in terms of functionality and droppingin price faster than other consumer and producer goods. As Hayashi and Nomura (2005) explain, thesituation with IT goods raises problems for as well for the measurement of the output and input
aggregates that are needed for measuring productivity at all levels of aggregation and for macroeconomic
models. We gloss over these problems in this study and focus instead on the definition of the firm
dynamics states: an issue the must be faced no matter how productivity is measured. In section 3 weintroduce the business dynamic status categories that are used in virtually all of the studies reviewed in
section 3: classifications of continuing, entering and exiting businesses that can be implemented with justtwo periods of data.
As the literature review in section 2 indicates, a number of different ways have been proposed forfactoring productivity growth into components associated with the productivity growth of these2businesses, including shifts in input utilization and market share among continuing businesses, and withbusiness births and deaths. The productivity decomposition used in the empirical part of this study is
introduced in section 4. In section 5, we propose an alternative dynamic classification for businesses
utilizing three periods of data. In section 6, we use both the conventional and our new FNN businessdynamic status classifications in analyzing data for Taiwanese electronics firms. We demonstrate thevalue of the FNN classifications. Section 7 concludes.

2.

Lessons from the Literature
The empirical study of business dynamics got its start with the development of data sets that

permit the tracking over time of business establishments or firms. Here we review some of the milestone
studies, with special attention to the business dynamics status groups that are defined and findings that
relate to these definitions.

2.1

Pioneering Studies
Baldwin and Gorecki (1991) use establishment based data for Canada, together with individual

establishment and firm identifiers,2 for the 1970s and early 1980s. In this study, the appearance of a new
identifier defines a birth. The disappearance of an identifier from the universe of all establishment
identifiers defines an establishment death. BG focus on the size of entrants at birth and their subsequenttransition paths. Distinctions are made between greenfield (i.e., completely new) and merger entries.
Establishments under common ownership are linked together into aggregates that can be thoughtof as virtual firms. BG note that exit rates, calculated at, say, the individual four-digit industry level, arenot be the same as for the manufacturing sector as a whole, since an establishment may leave one
manufacturing industry for some other industry, which BG term a switch or a transfer. Entry rates
defined for the manufacturing sector as a whole will not include establishment switches that take place
between industries. The list of dynamic states defined and constructed in this study includes births, deaths,
continuing, transfers, acquisitions, or divestitures.
Bresnahan and Raff (1991) (BR) use U.S. panel data for a period of great economic change to try
to gain further insight into how establishment dynamics affect changes in productivity. In the UnitedStates during the Great Depression, half the establishments, and more than half the firms, in the motor
vehicle industry closed between the 1929 peak and the 1933 trough.3 By exploiting the panel feature of
their data for auto industry establishments, BR are able to explore differences among closing, continuing,

2

These identifiers stay with business units over their lifetime.
Okazaki (2008) documents the adjustments in another time and place of great economic upheaval: the textile
industry over the years of 1894-1924 in Japan.
33and newly started establishments: differences that BR find are large and systematic. They find thatclosures accounted for almost one-third of the decline in industry employment. Labor productivity
appears also to have fallen at continuing establishments. The following upturn was largely accomplishedby expansions of existing establishments and firms.
Baily, Hulten and Campbell (1992) (BHC) use data for 1972-1988 from the Longitudinal
Research Database (LRD) developed by the U.S. Census Bureau. For any given year t, BHC note thatsome of the establishments operating in t will also have been operating in the prior period. Theseestablishments are designated the “stayers” by BHC. Some of the establishments operating in t will haveentered between the previous and current time periods. Some establishments operating in the prior period
are no longer operating in t. They exited the industry. BHC decompose industry productivity growth into
the contributions of stayers, entrants, and exits. They also try to ascertain how much of overall growth isfrom the establishments with particular sorts of productivity growth patterns, such as those that managed
to stay on top in terms of productivity performance for prolonged periods of time.
BHC identify a positive contribution to growth from increasing output shares among higher
productivity establishments and decreasing output shares among lower productivity establishments. This
dynamic shifts an industry toward a higher productivity average. Old establishments are found to be only
slightly less productive than are more recent ones. BHC find evidence consistent with the notion of wellmanaged establishments that are able to stay on top for long periods. Establishments that are part ofhigher productivity firms also are found to have higher contemporaneous productivity. Establishments infirms with more rapid productivity growth grow more rapidly too.
Entry and exit are reported to play a relatively small role in industry productivity growth overtime. BHC find that the majority of the entrants and exits are very small establishments, and a typicalentering establishment -- like the typical exiting establishment -- is found to have productivity that is well
below average. The smaller and less productive entering establishments are found to have quite highprobabilities of exiting quickly.
Griliches and Regev (1995) (GR) examine panel data for firms for the years of 1979-1988
assembled by the Israeli Central Bureau of Statistics. For the three subperiods of 1979-82, 1982-85, and1985-88, GR estimate the productivity impacts of continuing firms (those present at both the beginningand the end of the period), those that ‘closed’ (exited), and those that ‘opened’ (entered) by the end of theperiod. They ask how much of the growth in average productivity occurs within firms and how much is
the result of the mobility of resources between them, both as a result of exit and entry and also as aconsequence of the differential growth of surviving firms. GR find too that firms that can be seen fromthe data to exit in the future have lower productivity performance years prior to when they exit.

4

Nevertheless, GR conclude that most of the changes in aggregate productivity come from changes
within firms rather than from different exit and entry rates and various weight shifts. Griliches and Regev(1995, p. 201) write:“In spite of the large amount of turnover and churning in firms and jobs, most of the
productivity growth occurred within firms. Productivity growth in industry as a whole did
not come primarily from the exit of failing firms or from the faster growth of moreproductive firms. What happened within firms was decisive and that is what needs
attention....”
Haltiwanger (1997) uses data from the U.S. Longitudinal Research Database (LRD). Haltiwanger
presents decomposition results for labor productivity based on both gross output and value-added
measures. He notes that the measurement of labor productivity (particularly on a gross output basis) is
less fraught with measurement problems than measures of total factor productivity (TFP). Haltiwanger
finds that quite similar results are generated when using output weights as share weights in thedecomposition, but with establishment level productivity measured in terms of labor productivity. An
employment weighted decomposition of labor productivity yields roughly similar results too.
Haltiwanger reports that the shift in output towards establishments that are also increasing
productivity is a major factor in accounting for the average industry productivity change, but that net
entry plays an important supporting role as well.
Foster, Haltiwanger and Krizan (2000/2001) (FHK) use U.S. Census of Services establishmentlevel data for 1987 and 1992. They find that the impact of net entry is disproportionate since entering
establishments tend to displace less productive exiting establishments, even after controlling for overall
average growth in productivity. FHK also find that the gap between the productivity of entering andexiting establishments increases as the horizon over which the changes are measured increases.

2.2

Important Post 2000 Studies4
Nishimura, Nakajima, Kiyota, Minetaki, Tamai, and Kurokawa (2004) (NNKMTK) and also

Nishimura, Nakajima and Kiyota (2005) (NNK) set out to investigate whether the natural selection
mechanism of economic Darwinism works in severe recessions. They use data from the Basic Survey ofJapanese Business Structure and Activities (BSJBSA) conducted by the Japanese Ministry of Economy,
Trade and Industry (METI). This survey covers all commercial firms with 50 employees or more andcapitalization of over 30 million yen that are at least partly engaged in mining, manufacturing, wholesale
and retail sales, and restaurant activities. Nishimura and his collaborators analyze the entry/exit behavior
of Japanese firms during the 1990s and find that relatively efficient firms exited while relatively
4

Another interesting alternative decomposition is presented by Diewert and Fox (2009). We do not include theirstudy in this section, however, because their only empirical results are for synthetic data.

5inefficient firms survived during the banking-crisis period of 1996–1997. They conclude that the natural
selection mechanism (NSM) malfunctioned during the severe 1996-1997 recession. They discuss various
reasons why this malfunction might have occurred.
One empirical finding of this study is that efficient firms in terms of TFP exited while inefficientones survived in the banking-crisis period of 1996-1997. Further, this phenomenon is observed mainly for
new entrants and contributes substantially to a fall in macro TFP after 1996. These facts strongly suggest
a malfunctioning of NSM in severe recessions.
They examine the dynamics of Japanese information service industries. They use the data from the Surveyof Selected Service Industries, Volume of Information Service Industries, conducted by the Ministry of
Economy, Trade and Industry (METI).
In these studies, firm entry is defined as the appearance of a new firm identification code or the
reappearance of an old code. Either temporary or permanent disappearance of an identification code isdefined as a firm exit. Continuing firms are defined as those with present identification codes for at least
two consecutive years. The author notes that without these procedures, it would be difficult to construct
TFP measures consistently for all firms. Firms that switch-in or switch-out for an industry are also
identified.
Nishimura and his collaborators note that in popular terminology, “exit” means the complete
closure of a business. However, according to this definition, “dormant” firms with no significant businessthat are still not closed are classified as continuing firms. The authors explain that in Japan there are asizable number of “dormant firms” existing simply for tax-shelter and/or other purposes, although it isvery difficult to determine the number of such dormant firms. In the economic analysis of productivity,
we are concerned not with dormant firms, but with “active” firms. In this respect, entry and exit numberspresented in previous studies, based on Company Statistics and the like, are probably misleading becauseof dormant firms. In contrast, in the BSJBSA, the criteria of including firms with 50 employees or more
and a capitalization in excess of 30 million yen mean that the included firms are likely to be “active.”
Fukao and Kwon (2005) (FK) also use firm-level panel data for 1994-2001 from the BSJBSA.
They decompose TFP growth in the manufacturing sector into a within-firm effect, a reallocation effect,
and an entry-exit effect. FK find that both the exit effect (excluding the switch-out effect) and the switchout effect for the manufacturing sector as a whole from 1996 to 2001 were negative and contributed to thedecline in TFP growth in the manufacturing sector. They find also that both the entry effect (excluding theswitch-in effect) and the switch-in effect were positive in almost all the industries. Moreover, the entry
effect (including the switch-in effect) was positive in both the upturn and the downturn periods. But
probably as a result of the low entry rate, the size of the entry effect was not large. FK find that theredistribution effect – that is, the between effect plus the covariance effect – was positive. And, they find6

that the within effect, i.e. the effect of TFP growth within staying firms, was the largest factor among all
the effects. Moreover, this effect changed pro-cyclically.
Brown and Earle (2008) (BE) assemble comparable annual panel data with long time series on of
manufacturing firms in six of these economies – Georgia, Hungary, Lithuania, Romania, Russia, andUkraine. They use these data to measure the contribution of inter-firm employment reallocation toaggregate productivity growth during the socialist and reform periods in six transition economies. BE findthat reallocation rates and productivity contributions are very low under socialism. After reforms, theyrise dramatically, and productivity contributions greatly exceed those observed in market economies.
Early in transition, faster reform is associated with larger contributions from reallocation, but later, and on
average over the whole transition, this relationship is reversed. Though reallocation rates are larger infaster reforming economies, higher productivity dispersion in slower reformers creates much higher
productivity gains for a given volume of reallocation. The results imply that reallocation should beviewed as necessary regular maintenance for a well-functioning economy, and particularly large
productivity contributions tend to reflect previous neglect more than current virtue.
BE argue that the conventional measures of the contributions of entry and net entry of previous
studies are difficult to interpret, since they are highly sensitive to the share of new entrants and trend
productivity growth. They address this problem by decomposing the standard entry term into
proportionate and disproportionate entry, and the latter is useful for evaluating entry’s contribution. The
results from the decomposition into proportionate and disproportionate entry show that entrants’productivity is actually lower on average than that of surviving incumbents in several of the countries,
particularly when using shorter decomposition periods. By calculating separate disproportionate entry
terms for each entry cohort within the decomposition period, they show that the terms for disproportionate
entry of all cohorts together are dragged down by the most recent cohorts.
Carreira and Teixeira (2008) (CT) examine data drawn from an annual business survey run by thePortuguese Statistical Office (INE). The sample of manufacturing firms comprises some 1,900 units,observed over a 10-year period (1991–2000, unbalanced panel). This paper decomposes the productivity
growth into four components—within, between, cross, and net entry, as suggested by Foster et al. (2001)
and Griliches and Regev (1995)—and discusses the cyclical pattern of internal versus external
restructuring. The results of the decomposition of industry productivity growth suggest that the share ofexternal restructuring is stronger in recession, while internal restructuring seems to be more dominant in
expansions. CT conclude that in Portugal the competitive pressures on inefficient firms are inadequate inexpansions.
Foster, Haltiwanger and Syverson (2008) (FHS) note that whereas it is true that businesses withhigher measured productivity levels tend to grow faster and are more likely to survive, nevertheless,7selection is actually on profitability rather than productivity. Productivity, of course, is only one of several
factors that determine profits. they note that a limitation of empirical research with business microdata isthat establishment-level prices are typically unobserved. Usually, establishment output must be measured
as revenue divided by a common industry-level deflator. Therefore within-industry price differences areembodied in output and productivity measures. The connection between productivity and survival
probability, reallocation, and industry dynamics may be overstated, and the impact of demand-side
influences on survival understated.
FHS investigate the nature of productivity growth in industries where they can observe quantities
and prices separately. They show that physical productivity is inversely correlated with price while
revenue productivity is positively correlated. FHS find that young producers charge lower prices thanincumbents. One implication of their findings is that the literature understates new producers’
productivity advantages and entry’s contribution to aggregate productivity growth. Their estimates alsosuggest that demand variations across producers are the dominant factor in determining survival.

3.

A Conventional Dynamic Status Classification of Businesses Using Two Periods of Data
In the studies of others, business dynamics status groups have usually been defined with respect

to the universe of businesses present in one or the other or both of two time periods. Letting St −1 denotethe set of all businesses present in t − 1 and letting S t denote the set of all businesses present in t, theuniverse of businesses present in t − 1 or t can be represented as(3-1)

U( t − 1, t ) = St −1 ∪ St .
Businesses present in both t − 1 and t are referred to as continuing (C). We denote the set of thesebusinesses by SC and their number by N C . Businesses present only in the second of the two periods arereferred to as entering (E). We denote the set of these businesses by S E and their number by N E .
Finally, we refer to businesses present in only the first of the two periods as exiting (X). The set of allthese businesses is denoted by S X and their number by N X . Note that among all of the businesses
present in t − 1 or t, it is only the businesses classified as continuing for which t minus t − 1 difference
variables can be defined. Note that the period t firms are thus all either classified as continuing or entering,and the period t − 1 firms are all either classified as continuing or exiting.
The conventional definitions and notations for the business dynamics status sets defined above
are shown for convenience in table 1.

8

Table 1. Conventional Definitions for Turnover Status Variables
Symbols for the sets and numbers of businesses Name for
SymbolDefinitionin U( t − 1, t ) by dynamics status
for status
statusof status
Continuing
Present in bothSC , N C
C
in t − 1 and tt − 1 and t
Present in t
SE , N E
Entering in t
Ebut not t − 1
Present in t − 1
SX , N X
Exiting in t − 1
X
but not in t
4.

Decomposition Specifics for This Study

4.1

Within Group, and Group and Business Unconditional, Share Parameters
For the purpose of defining performance measures for various groupings of the stated universe ofbusinesses and for the universe as a whole, it is common for researchers to define share parameters.
These could be shares of revenue, shares of costs, or some other sort of shares. Foster, Haltiwanger andSyverson (2008) assert that the choice of weight is “an open question”. The most common choices areeither output (or revenue) weight or employment weight.
Within group shares are the first type of share parameter we define. We denote the period t sharefor business i in group g relative to all businesses in group g by θ tig. Group shares are the second type ofshare parameter we define. We denote the period t share of all businesses in group g relative to allbusinesses in the given universe by θgt . Shares for individual businesses relative to the universe are thethird type of share parameter we define. We denote the share for business i relative to all businesses
present in the universe by θit .
If Sg denotes the set of all businesses in group g, then the following conditions must be satisfied
for the three types of share parameters:
N

(4-1)

∑ θit g = 1 , ∑ θgt = 1 ; and ∑ θit = 1.

i∈Sg

All g

i =1

Note too that the three sorts of share parameters defined here are related as follows for any choice ofvalues for i, g and t:(4-2)

θit g × θgt = θit .

9

We now let Π it denote some given period t firm performance level measure that can becomputed for every firm present in period t. Once Π it has been defined, now we can define a period t
performance level measure for the businesses in some set g as
(4-3)

Π gt ≡ ∑ θit g Π it for all g.
i∈Sgt

Also, we can define a performance level measure for the N businesses present in the universe byU( t − 1,1) by:(4-4)Π t ≡ ∑ θgt Π gt
All g

= ∑ θgt ( ∑ θit g Π it )

(4-5)

All g

i∈Sgt

N

= ∑ θit Π it .

(4-6)

i =1

4.2

The Productivity Decomposition Used in Our Empirical Analysis
We follow Foster, Haltiwanger and Krizan (2000/2001) (FHK), Foster, Haltiwanger andSyverson (2008), Carreira and Teixeira (2008) and others in adopting the FHK modification of thedecomposition proposed by Baily, Hulten, and Campbell (1992) (BHC). Continuing, entering and exiting
firms are compared to aggregate productivity in the initial year (t-1).

∆Π t = ∑ θit −1∆Π it + ∑ ∆θit (Π it −1 − Π t −1 ) + ∑ ∆θit ∆Π it
(4-1)

i∈SC

i∈SC

i∈SC

+ ∑ θit (Π it − Π t −1 ) − ∑ θit −1 (Π it −1 − Π t −1 )
i∈S E

i∈S X

where ∆x t = x t − x t −1 . The first component on the right hand side of equation (4-1), ∑ θit −1∆Π it ,i∈S C

represents productivity improvement of continuing firms (holding share constant at the initial level), and
it is known as the within firm effect. The second component,∑ ∆θit (Π it −1 − Π t −1 ) , represents thei∈SCchange of shares (holding productivity constant) and it is known as the between-firm effect. The third
component,∑ ∆θit ∆Π it , is the cross effect. The fourth and fifth components represent the contribution

i∈S C

10

of entry and exit to productivity growth. Here, the initial aggregate productivity ( Π t −1 ) is used as thereference point. Firm entry is considered contribute positively to productivity growth if the entrants are
more productive as compared to the initial average. Similarly, exits are considered contribute positively to
productivity growth if the exits are less productive as compared to the initial average.

5.

Rethinking the Conventional Dynamic Status Classifications for Companies
Notice that the conventional classification system (which is all that is possible with only two

periods of data) provides no way of distinguishing a key group of businesses: those that fail within one
period of entering. Even if the data periods are consecutive years, the percentage of businesses that enterone period and are already gone again by the next can easily be 20 percent or more even in good
economic times. In a recession, or when the data periods are multiple years wide, or multiple years apart,the percentage of businesses that enter one period and are already gone again by the next can easily
exceed half. Data permitting, it thus seems preferable to define business dynamic status states based on
three rather than two periods of data.
Consider the three periods of data denoted by t − 1 , t and t + 1 . Businesses present in t − 1 , t andalso t + 1 will be referred to as continuing survivors in period t ( CS t ). The set of these businesses istt
SCS
and the number of them in period t is denoted by N CS
. Businesses not present in t − 1 , but present

in t and t + 1 will be referred to as entering survivors in period t ( ES t ). The set of these businesses istt
SESand the number of them in period t is denoted by N ES
. Businesses not present in t − 1 , present in t,

and then absent again in t + 1 will be referred to as entering failures in period t ( EF t ). The set of thesettbusinesses is S EF
and the number of them in period t is denoted by N EF
. Finally, businesses present int − 1 and in t, but absent in t + 1 will be referred to as exiting survivors in period t ( XSt ). The set ofttthese businesses is S XS
and the number of them in period t is denoted by N XS
. Note that all of thetttbusinesses present in t (denoted by SSt ) fall into one, and at most one, of the sets SCS, SES, S EF
andt
SXS. That is, these sets are mutually exclusive and they are also exhaustive if the universe of interest is

taken to be all businesses in existence in period t. The details for our proposed FNN categorization are
shown, for convenience, in table 2.

11Symbolforstatus
CS t
ES t

EF t

XS t

Table 2. FNN Turnover Status Variables for Period t
Symbols for the sets andnumbersof businesses inU ( t − 1, t , t + 1)
Definitionby dynamic status
Name for statusof status
Present in t − 1 , tContinuingsurvivortt
SCS, N CSin t
and t + 1
Absent in t − 1 , butttEntering survivor in t
S ES
, N ES
present in t and t + 1
Absent in t − 1 , present in t,ttEntering failure in t
, N EFSEF
but absent again in t + 1
Present in t − 1 and tttExiting survivor in t
SXS
, N XSbut absent in t + 1

Table 3 shows all possible sequences of being present and absent for a business in periods

t = 0,1,2,3 and shows the results of applying first the conventional and then the HNN definitions
turnover status definitions with the various possible sequences for a business being present and absent
over four years for which data are available.

Table 3. The Conventional versus the FNN Definitions Applied
Conventional definition
FNN definition
based on two periods of based on three periods of
data
data
Status in t=2
based on data
Possible sequences
Status based
Status in t=1
forof present (P) and absent
on data forbased on data for
t=1, t=2 and(A)
t=1 and t=2
t=0, t=1 and t=2
t=3
Continuing (C) based on conventional 2-period dynamic status definitions
1

P 0 P1P 2 P 3

C

CS1

CS2

2

P 0 P1P 2 A 3

C

CS1

XS2

3

A 0 P1P 2 P 3

C

ES1

CS2

4

A 0 P1P 2 A 3

C

ES1

XS2

Entering (E) based on conventional 2-period dynamic status definitions
5

P 0 A1P 2 P 3

E

---

ES2

6

A 0 A1P 2 P 3

E

---

ES 2

7

P 0 A1P 2 A 3

E

---

EF 2

8

A 0 A1P 2 A 3

E

---

EF 2

12

Exiting (X) based on conventional 2-period dynamic status definitions
9

P 0 P1A 2 A 3

X

XS1

---

10

P 0 P1A 2 P 3

X

XS1

---

11

A 0 P1A 2 P 3

X

EF1

---

12

A 0 P1A 2 A 3

X

EF1

---

Not present in either of the two designated periods
13

P 0 A1A 2 P 3

--

---

---

14

P 0 A1A 2 A 3

--

---

---

15

A 0 A1A 2 P 3

---

---

---

16

A 0 A1A 2 A 3

---

---

---

6.

A Decomposition of Firm Dynamic Effects on Electronics Industry Productivity in Taiwan
The story of how Taiwan was transformed into a technically advanced, relatively wealthy newly-

industrialized economy has been much studied. From the early 1980s on, the Taiwanese government
began to focus on the strategic development of high-tech, high value-added and energy-efficientindustries. Semiconductors are the backbone of the Taiwanese electronics industry.
Almost all of Taiwan's semiconductor industry is located in the Hsinchu Science-Based Industry
Park, which also houses the country's leading computer establishments including Acer. Hsinchu wasdesigned and launched and nurtured by the Taiwanese government following the model of California's
Stanford Research Park. Hsinchu Park also includes a government research institute (ITRI), and two of
Taiwan's most significant technical universities (Chiaotung and Tsing Hua). The government-owned Park
offers firms that settle there attractive terms for setting up a business, as well as a range of taxation
benefits and allowances, low-interest loans; R&D matching funds; tax benefits (e.g., investment
allowances); and special exemptions from tariffs, commodity, and business taxes. Also, a firm that is
accepted as a resident of Hsinchu Park stands a much greater chance by that fact alone of raising bankloans for investment.
Taiwan now acts as the technological gateway between the United States and China. Production
networks composed of small and medium enterprises are an integral part of the Taiwan’s economic
development miracle. Sturgeon (2002) explains that the production system for the computer andtelecommunication industry is highly “modular.” Saxenian and Hsu (2001) explain that foundries arose
first in Taiwan, as key firms in that country decided that their best strategy for becoming internationallycompetitive in the industry was to focus on perfecting the process technology and let others do thedesigns. These firms were founded or run by Taiwanese nationals who had been trained in the UnitedStates or who had worked for U.S. semiconductor firms previously. By now, a thriving electronics

13

industry has been created in Taiwan. Mathews (1997) explains that by the mid-1990s, the industry hadreached a level of output that placed it behind only the United States, Japan, and Korea.

6.1

Data Description
In the empirical analysis, we use data on Taiwanese electronics firms. The firm-level data are

extracted from the Industry, Commerce, and Service Census for the Manufacturing Sector for the years of
1986, 1991, and 1996. This census is conducted every five years by the Directorate General of Budget,
Accounting, and Statistics, Executive Yuan, Taiwan. The comprehensive coverage of firms makes it
possible to trace firm turnover status by matching firm identification numbers over census years. The data
set includes information on the starting year, number of employees, wages and salaries, materials, energy,
fixed assets, subcontracting activity, revenue, domestic sales and exports. Firm productivity is constructedby using a multilateral index number approach with output being measured as revenue deflated by anindustry price index (see appendix A).
The micro data on electronics firms used for our empirical analysis is well-documented inprevious studies such as Aw (2002), Aw, Chen, and Roberts (2001), and Fung (2008). According to Fung(2008), the firm exit rate in the Taiwanese electronics industry was 43% between 1986 and 1991 and 30%
between 1991 and 1996. We choose to focus on the electronics industries because it is an industry with
rapid productivity growth, high rates of firm entry and exit, and quality variation in products.
This data set covers electronics firms in both the cross-sectional and time series dimensions, sothat we can identify firm turnover status in these three census years. In addition, the starting year
information makes it possible to further classify firms in operation in 1986 into firms that were in
operation in 1981 (the last census year before the 1986 census) and firms that entered after 1981.
Next, we apply the FNN definition to firms present in 1986 and 1991. Based on the definition, firms thatappeared in both 1986 and 1991 are the ones that were classified as continuing survivors and enteringsurvivors in 1986 and as continuing survivors and exiting survivors in 1991. The firms that were inpresence in 1986 but not in 1991 were the ones that were defined as exiting survivors and entrant failuresin 1986. The firms in presence in 1991 but not in 1986 were the ones that were defined as enteringsurvivors and entering failures in 1991.
Based on the available information, we are able to apply the FNN definition not only to 1991 but
to 1986. Table 4 summarizes the share of firms and employment of firms in different turnover statusbased on the FNN definition. To sum up, continuing firms are disproportionately large. In 1986,
continuing firms consisted of 26% of the firms and 56% of the total employment. The exiting continuing
firms are much smaller. They consisted of 25% of the firms but only 23% of employment. In addition, thesuccessful entrants are relatively larger as compared to the entrants that failed.

14

Table 4. Firm and Employment Shares of Firms in Different Turnover Status
CS86 XS86 ES86 EF86
Firms

0.26

0.25

0.20

0.29

Employment

0.56

0.23

0.10

0.10

91

91

91

EF91

CS

XS

ES

Firms

.023

0.09

0.39

0.29

Employment

0.52

0.09

0.27

0.11

The firm level observations must be formed into groups to carry out a FHK typeproductivity decomposition. Table 5 shows how these groups can be formed for the conventionaland for the FNN classification approaches.

Table 5. The FNN Dynamic Status Sets That Fall
within Each Conventional Dynamic Status GroupGroup
The sets defined using the FNN definitions that fall into each(g)of the designated conventional dynamic status groups
Continuing ( SC ) based on conventional2-period dynamic status definitions
1
1, 2
1
2

S1 ≡ SCS ∩ SCS

2

S12,2 ≡ S1CS ∩ S2XS

3

2
S13,2 ≡ S1ES ∩ SCS

4

S14,2 ≡ S1ES ∩ S2XSEntering ( SE ) based on conventional 2-period dynamic status definitions

5

S15,2 ≡ S2ES

6

S16,2 ≡ S2EF
Exiting ( SX ) based on conventional 2-period dynamic status definitions

7

S17,2 ≡ S1XS

8

S18,2 ≡ S1EF

Table 5 shows the four sets, created using the FNN definitions, that fall into the conventional
continuing category. The set S11,2 consists of the firms that were continuing going into period 1, and thatare continuing still following the second of the two designated periods for which the conventional groups
are defined. In addition, we have the S12,2 group of firms that will exit following the second of the two15periods. We also have the S13, 2 firms that just entered prior to the first of the two periods. And we have
the S14,2 group consisting of firms that just entered prior to the first period and will exit following thesecond period. From table 5, we see also that the conventional group of entering firms includes both the
FNN entering survivors that will continue on following the second period and the FNN entering failuresthat will exit following the second period. Finally, we see that the conventional group of exiting firms
includes both the FNN firms that were continuing coming into the two periods and the firms that justentered and will be gone again by period 2.
Of course, the added complexity of the FNN classification is only worthwhile if more usefulinferences result from using this classification. We now turn to the data and apply both the conventional
and the FNN classifications.

6.3

Empirical Results
We can further explore the difference between continuing survivors, exiting survivors, enteringsurvivors and entering failure by testing if their productivity indicators are different at the 5 percent
significance level. The results are reported in Table 6. Firms in operation in 1986 can be grouped into
conventional continuing firms -- firms that have been in operation since 1981 -- and firms that enteredafter 1981. The results in column 1 indicate that the conventional continuers are more productive
compared to the conventional entrants. These results confirm that the contribution of these firms to theaggregate productivity growth should be examined separately. We can further categorize conventional
continuers into continuing survivors (those survived to 1991) and exiting survivors (those stayed in 1986
but exited before 1991). Results in the first two rows of column 2 indicate that continuing survivors are
significantly more productive as compared to the exiting survivors. Similarly, the general entrants can beclassified into entering survivors and entering failures. Results in the bottom two rows of column 2
indicate that the entering survivors are significantly more productive as compared to the entering failures.
Similar patterns are found for the firms in operation in 1991.

16

Table 6. Productivity Differentials
Firms in Operation in 1986
Firms in Operation in 1991
TurnoverTurnoverTurnoverTurnoverStatusMean FNN
Mean Status
Mean FNN(1981-86)
lnTFP Definition
lnTFP (1986-91)
lnTFP Definition
(1)
(2)
(3)
Continuing
Continuing
Survivors-0.054
Survivors(CS)(CS)
Conventional
Conventional
Exiting
Exiting
generalgeneral-0.069Survivors-0.084
0.165
Survivors
continuing
continuing
(XS)
(XS)
firms
firms
Significantly
Significantly
Different at
Yes
Different at
5%?
5%?
Entering
Entering
Survivors-0.093
Survivors(ES)(ES)
Conventional
Conventional
generalEntering
generalEntering-0.108-0.1180.163
entering
Failure (EF)entering
Failure (EF)
firms
firms
Significantly
Significantly
Different at
Yes
Different at
5%?
5%?
Significantly
Significantly
Different at
Yes
Different at
No
5%?
5%?

Mean
lnTFP
(4)
0.180

0.127

Yes

0.175
0.147
Yes

Notes: The entrants between 1981 and 1986 are identified using the starting year information for firms that are in
operation in 1986. lnTFP can be negative because it is expressed as a deviation from the mean in the base year.

We can apply the new definition to the FHK type of productivity decomposition. The
classification of firms follows the last two columns of table 3. Take 1986 as t=1 and 1991 as t=2: two
census years that we have enough information to define firm turnover status using the FNN definition.
We can start with decomposing productivity growth using the two-period definition (continuing, C,
exiting, X, and entering firms) based on the data for years 1 and 2. The productivity growth between year
1 and 2 can be decomposed as:(6-1)

Π 2 − Π1 = ∑ θi2 (Π i2 − Π1) − ∑ θ1i (Π1i − Π1) + ∑ θi2 (Π i2 − Π1) − ∑ θ1i(Π1i − Π1)
i∈SC

i∈SC

i∈SE

i∈SX

Recall that when the two period definition is applied, the continuing firms were defined as the firms thatwere in presence in both years 1 and 2. Using the three period definition, the general continuing firms canbe further specified into four groups: (1) firms that were classified as continuing survivors in both year 1
and 2; (2) firms that were classified as continuing survivors in year 1 and exiting survivors in year 2; (3)firms that were classified as entering survivors in year 1 and continuing survivors in year 2; and (4) firmsthat were classified as entering survivors in year 1 and exiting survivors in year 2. In addition, the17entering firms can be represented as two groups and the exiting firms as another two groups. In all, for the
FNN classification, there are the eight groups summarized in table 5. By applying this more detailed
definition, the first two elements on the right hand side of the equation above can be specified into:
8

(6-2)

Π 2 − Π1 = ∑ [ ∑ θi2 (Π i2 − Π1) − ∑ θ1i (Π1i − Π1)] .
g =1 i∈S1g, 2i∈S1g, 2

Further, following the FHK method, the growth of each group of firms that were observed in both periods(that is, for groups g = 1,…,4 ) can be further decomposed into the within-firm, between firm and crossfirm components:(6-3)∑ θi2 (Π i2 − Π1 ) − ∑ θ1i(Π1i − Π1) = ∑ θ1i∆Π i2 + ∑ θi2 (Π1i − Π1 ) + ∑ ∆θi2 ∆Π i2 ,

i∈Sg

i∈Sg

i∈Sg

i∈Sg

i∈Sg

where ∆x 2 = x 2 − x1 , and the Sg groups are defined as in table 5..
Table 7 summarizes results of productivity decomposition using the FHK approach with either
the usual two period definition of entry, exit and continuing firms (equation number) and the FNN
definition. The first row of Table 7 reports the results of productivity decomposition using the usual two
period definition.

As discussed in Section 4.2, when the FHK method is applied, the growth ofcontinuing firms can be further decomposed into within-firm, between-firm and cross-firm components.
The results indicate that the productivity growth of the Taiwanese electronic industry was primarily
driven by the within-firm growth among continuing firms while the net entry of firms (i.e. exits being
replaced by entrants) played a secondary but significant role.
The second to fourth rows of Table 7 report the results of further decomposition of the within,between and cross firm components into the contribution of firms in different turnover status using the
FNN definition. The results indicate that among the continuing firms, their contribution to productivity
growth is uneven. The within-firm growth is primarily attributable to the growth of firms that wereclassified as continuing survivors in both periods (i.e., firms that have been in operation in all the census
years observed).55

Using the two-period definition, there were 2,721 continuing firms, out of which 1,182 firms were classified ascontinuing survivors in both periods 1 and 2 and they account for 70% of the within firm growth.

18

Table 7. Productivity Decomposition using FHK Approach, 1986-1991
Total
Growth(1)Within(2)+

Between
(3)+

Cross
(4)+

Entrant
(5)+

Exit
(6)

17.00%

1.03%-2.26%

9.71%

0.11%

2
S12,2 ≡ S1CS ∩ SCS

11.76%

0.87%-2.39%

S12,2 ≡ S1CS ∩ S2XS

2.17%

0.10%

-0.82%

2
S13,2 ≡ S1ES ∩ SCS

2.05%

0.14%

0.79%

S14,2 ≡ S1ES ∩ S2XS

1.02%

-0.08%

0.16%=

25.59%Net Entry(7) =(5)+(6)
9.82%

Firms present both
periods (1986 and1991)

Firms present onlyin period 2 (1991)

S15,2 ≡ S2ES

6.93%

S16,2 ≡ S2EF

2.78%

Firms present onlyin period 1 (1986)

S17,2 ≡ S1XS

-0.24%

S18,2 ≡ S1EF

0.36%

The sixth and seventh rows further decomposes the contribution of entry to the growth of enteringsurvivors and the growth of entering failures. The results show that the entry of firms that eventuallysurvived have a much greater contribution to the aggregate productivity growth as compared to theentrants that eventually failed. The last two rows further decompose the contribution of exits to
productivity growth into the contribution of entering failures (in period 1) and exiting survivors.
Interestingly, the entering failure contributes positively to productivity growth (i.e., they are less19

productive as compared to the average productivity in period 1) while the exiting survivors contributenegatively.
Table 8. Definition of Firm Turnover Status (A: Absent; P: Present)

CS86
XS86
ES86
EF86
CS91
XS91
ES91
EF91
7.

t = 0 (1981)
P
P
A
A

t = 1 (1986)
P
P
P
P
P
P
A
A

t = 2 (1991)
P
A
P
A
P
P
P
P

t = 3 (1996)

P
A
P
A

Concluding Remarks
Kui (2007) explains that the main countries involved in the Asian production networks are theUnited States, Japan, Korea, Taiwan, Hong Kong and Singapore. Taiwanese firms specialize in personal
computer electronics and are said to be relatively open. Their adjustments to market conditions andtechnological shifts are claimed by analysts such as Dujarric and Hagiu (2009) to be more agile than their
Japanese counterparts. In Japan, Korea and Taiwan, there is government support for small and medium
sized enterprises in the IT and electronics industry. The SMEs in Taiwan have achieved a broad range ofcapabilities during 1990s and turned Taiwan into a world-class supply source for a variety of electronic
hardware products. Also, the Taiwanese semiconductor industry is now the third largest in the world, after
North America and Japan. There is a great deal of interest in this industry within and outside of Taiwan.
Empirical progress on understanding the consequences of various sorts of business dynamics -whether from a technological change, job creation and destruction, or firm governance and management
perspective -- requires reliable ways of characterizing business dynamics and of measuring the impacts ofvarious sorts of business dynamics on business and industry and national productivity growth. We review
the literature on characterizing and on measuring the impacts of business dynamics on productivity
growth. We introduce the business dynamic status categories used in virtually all of the studies of others:classifications of continuing, entering and exiting. This classification can be implemented with just two
periods of data. We also propose an alternative dynamic classification for businesses utilizing three
periods of data. For the empirical portion of this paper, like many others, we adopt the basic approach of
Foster, Haltiwanger and Krizan (2000/2001). We implement this decomposition using data for Taiwaneseelectronics firms. We first use the conventional dynamic status classification approach for firms and thenuse our suggested new FNN approach. We demonstrate the value of the FNN classifications.

20

Appendix A.

The Total Factor Productivity Measure Utilized

The total factor productivity (TFP) is constructed using a multilateral index number approachdeveloped by Caves, Christensen and Diewert (1982) and extended by Good, Nadiri and Sickles (1997).
It was used in empirical studies such as Aw, Chen and Roberts (2001) and Fung (2008). The formula canbe specified as:tln TFPit = (ln Yit − ln Y t ) + ∑ (ln Yτ − ln Yτ −1 ) − ∑ (1/ 2)(δ ijt + δ jt )(ln xijt − ln x jt )
τ =1

(A-1)

j

t

−∑∑ (1/ 2)(δ jτ + δ jτ −1 )(ln x jτ − ln x jτ −1 ), j = L, K , M , E ,τ =1jwhere i indexes businesses and j inputs. Yit is the output of a firm which is measured by three alternative
methods that will be discussed below. xijt denotes input j in production, the inputs are: labor (L), capital(K), materials (M) and energy (E). Input shares (δijt) are the shares of the firm's total expenditure
attributable to input j. Assume perfect competition and constant returns to scale, a firm's input sharesshould sum up to one; therefore, the share of capital can be calculated as the residual share. ln Y , ln x j ,δ j denote the average of the logarithms of output and input, and input shares. Using this method, both theoutput and the input are expressed as a deviation from the mean in the base year (1986 in this paper).
Conceptually, the output and input of a firm are compared to a fictitious firm (with output and input at themeans) in the base year. This TFP measure captures both the difference between firm i and the fictitious
firm in year t and the movements of means over the years.

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