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Summary findings

Exports respond unpredictably to a change in real
exchange rates, suggests evidence from the 1980s.

Recent theoretical work (Paul Krugman and Richard
Baldwin) explains this as a consequence of the sunk costs
associatedwith breaking into foreign markets. Sunk costs
include the cost of packaging, upgrading product quality,
establishing marketing channels, and accumulating
information on demand sources.

Roberts and Tybout use micro panel data to estimate a
dynamic discrete-choice model of participation in export
markets, a model derived from the Krugman-Baldwin
sunk-cost hyster --is framework.

Applyingthe model to data on manufacturing plants in
Colombia (1981489), they test for the presence of sunk
entry costs and quantify the importance of those costs in
explaining export patterns. The econometric results
reject the hypothesis that sunic costs are zero.

The results, which control for both observed and
unobserved sources of plant heterogeneity, indicate that
prior export market experience has a substantial effect
on the probability of exporting, but its effect depreciates
fairly quickly. The reentry costs of plants that have been
out of the export market for a year are substantially
lower than the costs of a first-time exporter. After a year
out of the export market, however, the reentry costs are
not significantly different from the entry costs.

Plant characteristics are also associated with export
behavior: Large old plants owned by corporations are
more likely to export than other plants.

Variations in plant-level cost and demand conditions
have much less effect on the profitability of exporting
than variations in macroeconomic conditions and sunk
costs do. It appears especially difficult to break into
fereign markets during periods of world recession.

This paper -a product of the International Trade Division, International Economics Department-is part of a larger effort in
the department to describe the micro foundations of export supply response. The study was fundedby the Bank's Research Support
Budget under the research project "Micro-Foundationsof Successful Export Promotion" (RPO 679-20). Copies of this paper are
available free from the World Bank, 1818 H StreetNW, Washington, DC 20433. Please contactJermifer Ngaine, room R2-052,
extension 37959 (37 pages). March 199S.

dae)opt bsumAn *beaitnof th sern is =to et the fid& ow quk*, ecx if the presatiom are Amssm fbefypolisbed.7he
papersam7ythexammof deaSch2osnd sboud6 be dandckedoccodigly.7lOc0sq _drrtt= candcndsi re the
autbors'oumanbuldonot be akds o theWorldBaxk, its Fxeav Board of Dirasors or any of its cmbr outrk&s

Thc Poly Rcscb lrorkg PaperS by tPelicyRs ofc wr pDogriem to Cent e 5 e of se about

Produccd by the Policy Rescarch Dissemination Ccnter

An Empirical Model of Sunk Costs and the Decision to Export

Mark J. Roberts
Pennsylvania State University


James R. Tybout
Georgetown University

We would like to thank David Card, Terri Devine, Avinash Dixit, Chris Flinn, Zvi Griliches, James
Hanna, James Heckman, Ariel Pakes, David Ribar, Dan Westbrook and an anonymous refree for helpfil
discussions or comments. Support for this paper was provided by the Research Administion
Depatmn of The World Bank (RPO 679-20).


The responsiveness of exports to changes in the incentive structure has long interested policy
makers. But the empirical literature has provided little guidance on when or how exporters will respond
to new incentive structures. Research in this area has produced a variety of supply elasticity estinates
that vary dramatically across countries and time periods, and few hints on how to reconcile the diverse

Paul Krugman and Richard Baldwin haverecently argued that the emirical literature fails because
there are sunk costs associated with breaking into foreign markets -including upgrading product quality,
packaging, and the establishment of marketing channels. Hence the currcnt-period export supply function
depends upon the number and type of producers that were exporting in previous periods. Further, startup
costs mean that transitory policy changes or macro shocks can lead to permanent changes in market
structure, and thus that trade flows may not be reversed when a stimlus is removed. That is, sunk entry
or exit costs produce "hysteresis' in trade flows. Finally, wlhen future market conditions are uncertain,
sum,. costs make pattrns of entry and exit dependent upon the stochastic processes that govern variables
like dhe exchange rate.

Taking the Baldwin/Krugman perspective as a point of departure, this paper develops an
econometric model of a plant's decision to export. The model is fit u mwiro data for a large group of
anufacturing plants in Colcmbia from 1981-1989, and used to direcdy examine the determinants of a

plant's export decision for consistency with the theory.

The econometric results reject the hypothesis that sunk costs are zero. They also reveal that the
re-entry costs of plants that have been out of the export market for a year are substantially less than the
costs of a first-time exporter. Beyond a one year absence, however, the re-entry costs are not
significantly different than those faced by a new exporter. This is consistent with the view that an
important source of sunk entry costs for Colombian exporters is the need to accmulate information on
demand sources, information that is likely to depreciate upon exit from the market.

While the results indicate that sunk costs are a significant source of export market persistence,
both observed and unobserved plant characteristics also contibute to an individual plant's export
behavior. For example, plants that are large, old, and owned by corporations are all more likely to

A number of policy implications emerge. For example, it appears especially difficult to break
into foreign markets during periods of world recession. Also, although only 25 percent of the plants in
the Colombian panel exported during the sample period, the results imply that sufficiently favorable
macro conditions and/or reductions m sunk costs could make exporting profitable for a much larger
proportion of plants. Finaly, and most generaly, the estimates imply that countries undertaldng export
promotion policies should distinguish measures aimed at exanding the export volume of exisitag
exporters from policies aimed at promoting the entry of new exporters.

I. Introduction
Why is it that in some countries and time periods, a given trade and exchange rate regime
supports large scale production for foreign markets, while in other countries or time periods, the same
policies appear to induce a minimal export response? Put differently, whyare cstimates of export
supply equationsso sensitive to the time period or country under study?

In a recent series of papers Richard Baldwin, Paul Krugman, and Avinash Dixit have
proposed an answer.' Thay begin from the assumptionthat non-exporters must incur a sunk entry
cost in order to enter foreign markets. This mnakes the current-period export supply function
dependent upon the number and type of producers that were exporting in previous periods. Further,
it means that transitory policy changes or macro shockscan lead to permanent changes in market
structure, and thus that trade flows may not be reversed when a stimulus is removed. That is, sunk
entry or exit costs produce 'hysteresis" in trade flows. Finally, when future market conditionsare
uncertain, sunk costs make patterns of entry and exit depeodent upon the stochastic processes that
govern variablessuch as the exchange rate. Under plausible assumptions, greater uncertainty makes
trade flows less responsive to changes in these variables. None of these implicationsof sunk costs is
captured in standard empirical export supply functions, and all could contribute to the "instability" of
empirical relationships.2

To date, attepts to empirically validate the sunk-cost hysteresis framework have focussed on

'See, in particular, Dixit, 1989a and 1989b; Baldwin, 1988 and 1989; Baldwin and Krugman, 1989; Krugman, 1989).

2In their review of empirical studies of price and income elasticities for traded goods, Goldstein and Khan (1985, pp.
1087-1092)report a very wide range of estimates for the supply elasticity of total exports from developed countries. They
conclude that 'excluding the United States, the supply-price elsticity for the total exports of a representative industrial
countly appears to be in the range of one to four. The supply elasticity for U.S. exports is probably considerably higher
than that, perhaps even reaching ten to twelve.' They also discuss some evidence indicating that the response of export
supply to price changes is slower than demand-side adjustments. They speculate dh this may refict start-up costs
associatedwith export production or greater unceUity assocated with sHling abroad.


asymmetries in the response of trade flows to exchange rate appreciation versus depreciation.3 A

limitation of this approach is that data on the volume of trade flows, even for very disaggregated

commodities, cannot distinguish the entry and exit of exporters from the supply response of

continuing exporters. With the exception of Campa (1993), the foreign market entry and exit

patterns, which are the focus of the theory, have not been exanined for consistency with the sunk-

cost hysteresis model.'

In this paper we develop an empirical test of the sunk-cost hysteresis model that directly

examines entry and exit patterns in plant-level panel data. We develop and estimate a dynamic

discrete choice model of the decision to export when sunk entry or exit costs are present. In essence,

this model predicts exporting status in the current period as a function of plant characteristics,

previous exporting status and a serially correlated disturbance. It not only permits us to formally test

for the presencc of sunk costs (using coefficients on lagged exporting status), it allows us to

summarize the effects of time, individual producer characteristics, and prior exporting experience on

the probability of participating in the export market. The data we use describe the export patterns of

I The empirical evidence derived from trade flow data has produced no clear consensus. Basedon aggregate U.S. data,
Baldwin (1988) concludes ftat the substantial appreciation of the U.S. dollar during the early 1980's resulted in a structural
shift in U.S. impon pricing equations. This is consisteant with sunk-cost hysteresis. In contrast, Gagnon (1987) finds that
trade has been more responsive to relative prices in the more uncertain post-Bretton Woods era, a result inconsistent with
some versions of the hysteresis model. Using time-series data for U.S manufacturing industries. Feinberg (1992) fmds that
exports became more dispersed across destination markets as the dollar depreciated, suggesting that there was firm entry
into new country markets. The effect was weaker in industries where distribution networks, and thus presumably sunk entry
costs, are more important. Parsley and Wei (1993) focus on bilateral U.S.-Canada and U.S.-Japan trade flows fbr very
disaggregated commodities. They find that both the past history of U.S. exchange rate changes and measures of exchange-
rate volatility had no significant effect on trade flows. Both findings ar inconsistentwith the hysteresis model.

' Campa (1993) examines the number of foreign firms that made direct investnents in the 61 U.S. wholesale trade
industries over the 1981-87 period. He finds that exchange-rate uncertainty, which is proxied by the stndard deviation of
the monthly rate of growth of the exchange rate, is negatively cormlated with the number of firms investing in the U.S..
He also reports that an industry's sunk costs, which arc proxied by the advertising-sales ratio and ratio of fixed assets to net
worth of firms in the industry, is negatively correlated with foreign-firm entry. Both findings are consistent with the
hysteresis model. Although not in a trade context, related work by Bresnahan and Reiss (1991, 1994) shows how data on
net entry into a market can be used to make inferences about the ratio of sunk entry and exit costs to avenge profitability.
Their technique exploits the asymmetric response of the number of producers to population (demand) changes across
diffierent geographic markets. However, as they acknowledge, persistance in behavior due to permanent cross-producer
differences in profitability can create the appearance of sunk costs in their model.


Colombian manufacturing plants in four major exporting industries over the period 1981-1989, a nine-
year span characterized by substantial changes in aggregate demand and real exchange rates.

The empirical results strongly reject the hypothesis that sunk costs are zero. This implies that
prior export market experience significantly affects the current decision to export. Further, although
recent experience in foreign markets is extremely important, its effect depreciates fairly quickly over
time. A plant that exported in the prior year is up to 40 percentage points more likely to export in
the current year han an otherwise comparable plant that has never exported. But by the time a plant
has been out of the export market for two years its probability of exporting differs little from that of a
plantthat has never exported.

Severalpolicy implications emerge. First, although only 25 percent of the plants in our panel
exported during the sample period, our results imply that sufficiently favorable macro conditions
ador reductions in sunk costs could make exporting profitable for a much larger proportion of
plants. Second,our estimates imply that countries undertaldngexport promotion policiesshould
distinguishmeasures aimed at expanding the export volume of exisitng exporters from policies aimed
at promoting the entry of new exporters.

In the next section of the paper we summarize the theoretical sunk-cost model. The third
section provides an overview of the patterns of export participation among Colombian man uri
plants between 1981 and 1989. The fourth section develops an econometric model of the export
decision,and the fifth section presents our results. We briefly summarize and draw conclusions in
the sixth section. Readers uniterested in methodological issues may wish to skip section E and
readers uninterested in econometric problems may wish to skim section 1".

A Theoretical Model of Entry and Exit with Sunk Costs
As reviewed in Krugman (1989), sunk costs affect the export supply function for several

reasons. First, and most obviously, once the sunk costs of entering a market have been met, a
producerwill remain in that market as long as operating costs are covered. This implies that changes
in policy, exchange rates, or prices in foreign nmarkets can permanently alter market structure and
thusobserved export behavior. For example, devaluations that induce entry into the export market
may permanently increase the flow of exports, even if the currency subsequently appreciates.
Second, even if current conditions appear favorable to exporting, they may not induce entry into the
export narket if they are regarded as transitory. In this case, the expected future stream of operating
profits may not cover the sunk costs of entering foreign narkets. Thus large devaluations may induce
littleresponse from potential exporters if they are perceived as transitory. Finally, as formally
demonstratedby Dixit (1989a), the combination of sunk costs and uncertainty about future market
conditionscan create an option value to waiting. Dixit's simulation resdts suggest that even small
amouts of uncertity can significantly magnify the degree of persistence in a producer's expordng

To motivate our empirical work, we begin by reviewing the theoretical models that generate
these results (see footnote 1 for references).For each period t, le, the ti plant's expected gross
profits when exporting differ from its expected gross profits when not exporting by the amount
ir,(o,sJ. Here p, is a vector of market-level forcingvariables that the plant takes as exogenous (e.g.,
the exchange rate), and s,,is a vector of state variables specific to the plant (e.g., capital stocks and
geographiclocation). Once in the market, plants are assumed to freely adjust export levels in
response to current market conditions (Baldwin,1989). Thus the functionT#,,Sd represents the
increment to expected profits associated with exporting in year t, assuming that the profit-maximizing
levelof exports is always chosen.

These profits are gross because they have not been adjusted for the sunk costs of foreign
mariet entry or exit. Assume that if the i* plant last exported in year t-j (j 2 2) it faces a re-entry


cost of F , so upon resuning exports in year t it earns r1(plsd -Fl . Similarly,If the plant had
never exported previously, it faces an entry cost of P57andearns ir1(p,,sJ-P7 in its first year
exporting. Finally, a plant that exported in periodt-1 earns r,(p,,sd)during period t by continuingto
export and -X, if it exits. As in Dixit (1989a), these sunk costs represent the direct monetarycosts of
entryand exit. The j superscriptgeneralizes previous models to allow sunk re-entrycosts to depend
on the length of absence from the market. This could reflect the increasing irrelevanceof the
knowledge and experiencegained in earlier years, or the increasing cost of updatingold export
products. The i subscript allows sunkcoststo vary acrossplants with differencesin size, location,


previouscxperience,and other plantcharacteristics.

To collapsetheseearningspcwsibilitiesinto a single expression,define the indicatorvariable
Yi to take a value of 1 if the plant is exportingin period t, and 0 otherwise. Also,let the exporting
history of the plant through period t be givenby Yki, = (Y,,I j=O...J 3, whereJ1 is the age of the
plant. Tbenperiodt exporting profitsare:

Rsd2i;') = r p -F°(IY) -S (Fi-F ?)I,-ff]-X,Y,,I(1-Y1Y)

= (Y,where P, f (I *) .Y This last expression summarizesthe plant's most recent

exportingexperience: , = 1I whenthe plant's most recent exportng experienceoccurredj years,

earlier and 0 otherwise.
In period t. managersare assumed to choose the infinite sequence of values11,= (Yk,+,I i
2 0) thatmaximizesthe expected presentvalueof payoffs. In period t, this maximizedpayoff is:

5To keep ie notation ttable we have not added a tiwe bscrit to entry and exit costs. Inbe emp section, we
will st whether hy vary over ime, as would be expected if there are changes in credit market condions or trade policis
that affect accessto frig markets.


j of

max E, 6-1R Q>


where6 is the one-period discount rate and expectations are conditioned on the plant-specific
informationset, 0 1, Using Bellman's equation, plant i's current exporting status can be represented
as the Y,, value that satisfies:

vt,(Od = 1 ,( )e.) + 5E,{V...(Q,, 1


where E, denotes expected values conditioned on the information set 0,,. From the right-hand side of
this expression, it follows that the iuhplantwill be in the export market during period t if:

IAStsfi} + 5[E1(V,(fl,. 1) 1Y=1) -E,(VZ.1(O.1) I4=0)]2

F ° -(F0+.Xi) Y,,- + (Fill-F, ) Y,


where -fl;: + X,)is the sum of sunk entry costs for a plant that never exvported andexit costs for
current exporters, sometimes referred to as the "hysteresis band" (Dixit, 1989a).

Equation(1) provides the participation condition that will be estimated in section V. It has
severalempirical implications we will pursue. First, if there are no sunk costs, the participation
condition collapses to ri(p,si) > 0. Hence one can test the sunk-cost hysteresis framework by asking
whether,given a plant's current gross profits, its exporting history helps explain its current exporting
status. Second, if sunk costs do matter, equation (1) implies that they appear directly in each plant's
participationcondition as coefficients on binary variables that describe its exporting history.6 Hence
the magnitude of sunk costs and the rate at which past experience depreciates can be identified.

6The influence of sunk costs aLso comes through the expected value term on the left-hand side, but since this is a nonlinear
expression. coefficients on die indiaor variables are idtified.


Finally,this equation indicates that realizations on the variables p, and s3 , influence export decisions
through their effect on w(p,,sdand their effect on the expected future value of becoming an exporter
now. Th1islatter effect implies, for example, that exchange rate movements that managers consider
transitory will generally have less effect than equivalent movements that are viewed as long-term
regime shifts.

m. The Pattern of Export Participation in Colombia
Before discussing estimation issues and results, it is useful to introduce our data base, review
the export enviromnent in Colombia, and provide some aggregate evidence on the pattern of export
maiket participation during the 1980s.

The Data: The analysis in this paper is based on annual plant-level data collected as part of
the Colombian manufacturing census for the years 1981-1989. This census, which covers all plants
with 10 or more employees, provides information on each plant's geographic location, industry, age,
ownership structure, capital stocks, investment flows, expenditure on labor and materials, value of
output sold in the domestic market, and value of output exported. We have matched the individual
plant observations across years to form a panel.7 The data are particularly well-suited to analyzing
export market participation because they allow us to observe transitions of individual plants into and
out of the export market and to control for some important observable plant characteristics that are
likely to affect the export decision.

The Policy Regime and Export Particpation Rate: Table1 illustrates the basic paterns

7Thecensus data and matching process are discussed in greak, detail in Roberts (1994).


over the sample period for the 19 major exporting industries In the Colombian manufacturing sector.'
In general tenns, the macro environment in Colombia was not conducive to profitable exporting of
manufacturedgoods in the early 1980's. Responding to illegal exports, foreign capital inflows, and a
boom in the coffce market, the Colombian peso appreciated steadily between the mid-1970's and
1983. As shown in Table 1, this pattern was reversed aftr" 1983, with the currmncy losing
approximatelyone-half of its value by lY89. This partly reflected central bank currency market
interventionsto ease competitive pressures on tradeable goods producers.

The time-series pattern of manufactured exports largely mirrors this movement in the
exchange rate. The real value of manufactured exports from the nineteen major exporting industries
declined slightly from 109.6 (billion 1985 pesos) in 1981 to 95.9 in 1984, for an average annual
growth rate of -4.45 percent. F-rom 1984 to 1989 the pattern reversed and the quantity of exports
grew at an annual average rate of 21.1 percent."

Commercial policy sheltered import-competing producers throughout the sample period.
Substantial tariff barriers were reduced slightly after 1984, but quantitative restrictions on the imports
of products that competed with domestic industries were maintained. In addition to turning the terms
of trade against exporters, these polices made it more difficult to import raw materials or capital
goods that may have been necessary to increase the quality of manufactured products.'"

9 The nineteen industries and their SITC codes are: food processing (311/312), textiles (321), clothing (322). leathr
products(323/324).paper(341), printing (342). chemicals (351/352), plastic (356). glass (362). non-metal products (369).
iron and steel (371). metal products (381), nuchinery(382/383), transportation equipment (384), and miscellaneous
nianuficuring(390). These industries account forover 96 percent of Colombia's manufacturing sector exports and 85
percent of manufacturing output in each sample year.

' The real value of exports is measured as the peso value of exports deflated by an export price index from the IMF
InternationalFinancial Statistics. This measure will overstate the dependence of the physical v.;:ume of exports on the
exchange rate because of valuation effects.

"0 The long-term promection of the domestic market from import competition appears to have alo contributed to the low
product quality and low productivity that have made it difficult for Colombian exporters to compese in the intentional
market (World Bank, [992).


Nonetheless, the bias toward import-competing activities was partly offset by export subsidies, which
increased relative to the value of exports by approximately 50 percent between 1983 and 1984, and
thereafter declined." The incentives to export created by export policy therefore were counter to
those created by exchange rate movements.

The net effect of these changes on the number of exporting plants and the proportion of plants
that exported is summarized in the last two rows of Table 1. Again, the time-series pattem largely
reflects the movement in the exchange rate. Through 1984, there was net exit from the export
market, and a decline in the proportion of plants exporting. After that year there was net entry and a
steady increase in the participation rate. There is, however, some evidence of asymmetry in the
magnitude of the response. The modest 6 percent real appreciation between 1981 and 1983 was
accompanied by a decline in the export participation rate from .129 tc .113, but a much larger (45
percent) depreciation between 1984 and 1989 served only to increase the participation rate to .135.
From this short time series it appears that it took both a substantial and persistent devaluation to
induceentry into the export market. This is consistent with the conjecture that potential exporters
faced substantial start-up costs.

Survey evidence further suppons this hypothesis.'" First, to sell in developed country
markets, Colombian producers wera often required to invest in product quality upgrading. Second,
therewas little exporting infrastucture in the form of trading companies or distribution agents. These
companies typically provide transportation, customs, and shipping services, as well as information on

" This reduction in export subsidies reflected a reduction in two government programs used to promote exports. The
first program rebates customs duties paid on imported materials and capital equipment for plams that export. In 1980 41
percent of the value of exports came from plants that received rebates. This rose to 62 percent in 1984 and fell to 53
percent in 1986. The second program provides direct subsidies to exporters based on the value of their exports. The
average rate of subsidy increased from 7.6 percent of the value of exports in 1981 to 15.0percent in 1985 and tben fell to

8.7percent in 1986.
I2 lThe discussion in this section is based on World Bank (1992), which summarizes and interprets interviews with the
managers of several hundred Colombian plants.


prices, potential buyers, and product standards or requirements in other countries. The absence of
thesemiddlemen probably discouraged potential exporters, both by increasing the information costs
they faced, and by increasing the degree of uncertainty concerning foreign market conditions.
Apparently,however, the lack of a well-developed trading services sector did not affect all producers
equally. Exporters able to deal in large volumes or to ship to large markets were relatively less
constrained by the absence of trading intermediaries because they were able to sell directly to final
buyers. This finding suggests that sunk costs rose less than proportionately with export volume.

Exportmarket entry was also inhibited by institutional factorsthat affected expected profits.
Notably, a survey of Colombian fnancial institutions revealed that none were willing to lend money
against export orders or letters of credit from purchasers' banks. Lenders attributed this unusually
conservative practice to their inability to judge whether the potential borrowers could seriously
compete abroad. It mainly hurt first-time exporters and existing one-product, one-country exporters
attempting to enter new country or product markets.

Finally,as emphasized by Dixit (1989a), regime uncertainty may have induced producers to
delay entry into the export market, even after substantial devaluation. In the World Bank survey,
producers cited uncertainty about the permanence of the change in trade and exchange rate regimes as
incentivesto delay or forego entry into the export market. Theirmain concern was apparently that
lobbyistsin favor of protecting domestic industries would be able to reverse the trend toward trade

In summary, during the sample period many Colombian nufacturs viewed the export
market as more risky and less profitable than the domestic market. The lack of a trading services
sector,access to financing, and low product quality all appear to have constrained export marL:t
participation by raising the costs of entry or increasing the uncertainty of the profitability of


Entry and Exit in the Export Market: The analytical model reviewed in Section II implies
that this combinationof sunk costs and uncertainty should induce persistence in producers' exporting
stawus.That is, those who have already incurred the sunk start-up costs should be relatively likely to
export in the current period. Some preliminary evidence on this prediction is provided by transition
rates into and out of the export market, which are summarized in Table 2. Each row describes a
transition from the exporting status in column I to the status in column 2. The entries in the table are
the proportion of plants in each of the period t categoriesthat choose each of the two possible
categories in year t+1.A3 The top panel applies to the 19 major manufacturingindustries, and the
bottom panel applies to the 650 plants in the four major exporting industries -food, textiles, paper,
and chemicals -that will be used to estimate the econometric model in the next section.

The top row of each panel indicates that, of the plants that did not export in year t, more han
95 percent of them did not export in year t +1. For the plants initially in the export market, the
proportion of manufacturing plants that remain in the market from one year to the next varies from 83
percent to 91 percent over tine, and the proportion of plants in our four-industry subsamplethat
remains in the market varies from 85 percent to 95 percent. Clearly, there is substantial persistence
in the plant-level pattems of export market participation. Nonetheless, only 36 percent of the plants
in the subsample that exported at some time remained exporters for the whole sample period, and
among plants that did change exporting status, 60 percent did so more than once.14

Persistence in exporting status might be caused by sunk costs, as the hysteresis models
suggest. Alternatively, it might be caused by underlying plant heterogeneity: persistent cross-plant

'3 The data correspond to the group of plants dtat were io operation in each year 1981-1989. Thereare 2369 plants; in
this group and they represem approximately40 percent of thenumberof piano in operadon in any year. On average, 18.1
percent of these pla participated in the export market in any single year and they accounted fbr62.3percent of thetotal
numberof exporters of the value of manufactured

and 61.6 percent exports.
'' The appendix summarizes the patems of multiple switches into and out of the export market and the ability of the
empiricalmodel to explain these patrns.

differences in the payoff from exporting, rf(*), would explain why some plants are always in the
export market and others are always out. Similarly, the fact that many exporters enter or exit the
market multiple times can also be interpreted several ways: it could mean that sunk costs are small,
or it could reflect lingering benefits from having exported recently. In the next section we develop an
econometric framework that can discriminate among these competing explanations.


IV. An Empirical Model of Export Market Participation
The Estinating Equation: Our empirical model of a plant's exporting decision begins with
the participation condition given by equation (1). Define

r5 =p,,sd) + [E,(V,,(Q,,.,)IYi,=l) -E,(Vf,(Q,,..)I Yj,=0)]

as the latent variable representing the expected increment to gross future profits for plant i if it
exports in period t. Export market participation is then summarized by the dynamic discrete choice

_) YiI if ir,; -Fi° + (Fe +X)Y + E (F,` -FiJ)Y,rj > 0
0 otherwise

There are two ways we might proceed to estimate equation (2). First, we could develop a
structural representation of the participation condition by making specific assumptions about the form
of the profit function and the processes that generate s, andp," Alternatively, we could forego
identificationof structnral parameters, and approximate ir', -F' as a reduced-form expression in
exogenous plant and market characteristics that are observable to producers in period t. The
advantageof the first approach is that, in principle, it allows identification of the parameters of the
profit function (inter alia) and provides a complete description of the dynamic process. Its main
disadvantage is that very restrictive parameterizations are required to make structural estimation
feasible. This problem is particularly acute in our model because the dependence of sunk entry costs
upon the length of time out of the export market implies a participation series that is a t-order


This equation is similar to thoseused to study labor market participation and employment (Heckman, 1981a, 1981b).
"Eckstein and Wolpin (1959) and Rust (1993) summarize de literature on esiimating strucmral dynamic models of
discrete choice.

Markov process. Because of this difficulty, and because we do not need a structural model to assess
the role of sunk costs or to investigate the sensitivity of decisions to s,,and p, we pursue the reduced-
form approach.

To parameterize the reduced-form model, we assume that variation in ir;, -F: arises from
three different sources: time-specificeffects that reflect industry or macro-level changes in export
conditions(A.i), observable differences in plant characteristics (Zi,), and noise (es,):

(3) x,,-F = , + , + e,
The term jt, is an annual time effect reflecting temporal variations in export profitability and start-up
costs that are common to all plants. These timne effects pick up the influence of credit narket
conditions, exchange rates, trade policy conditions, and other time-varying factors captured by p, in
the analytical model. The vector 4, controls for factors represented by s,, and F, in the analytical
model: exogenous plant-specific determinants of current operating profits and start-up costs. It
includes a constant, a set of industry dumnmies defined at the three-digit SITC level, a dumnny variable
to control for the ownership structure of the plant (proprietorship and partnership versus corporation),
and a set of two locational dunmiies to distingish the Bogota and Medillin/Cali regions from all
others.'" The vector Z, also includes several continuous variables lagged one period and measured
in logarithms: the ratio of foreign to domestic prices for output, the wage rate, capital stock, and
plant age." Relative prices and wages affect the atractiveness of domestic versus foreign markets.
Capital stock and age proxy for efficiency: in addition to scale effects, studies of industrial evolution

' The base group for comparison is a plant in the food industry that is not a corporation and that is located in the

1 FPoreignprices are constructed using unit values of exports at the four-digit ISIC level; domestic prices were obtamied
at the same level from die Central Bank of Colombia.


suggest that efficient producers are more likely to survive and grow. *

Additionalrestrictions on sunk entry and exit costs are needed to identify the model. Let -y;j
=F -F' (j =2,...,J ) and 'y/ = -y = F: + X, (j 2 J4-l), implying that experience is
completely depreciated if it was acquired more than J years ago. (The problem of choosing J will be
discussed later). Further, let -Y/ =y j, implying that cross-plant variation in sunk costs is negligible
among producers who acquired their most recent exporting experience at die same time. Then
substituting(3) into (2), we obtain our basic estimating equation:


I if ° 5 /I, +
jZ, + yajy E 1+Y

Y { j-2
0 otherwise,
Properties of the disturbance term -will be discussed in the following subsection.

As noted earlier, the participation decision does not depend upon exporting history if sunk
costs are zero. Hence we can test the null hypothesis that sunk costs are unimportant in the export
decision by testing whether & and y 's are jointly equal to zero. If they are significant, we can use
them to make inferences about the rate at which export market experience decays. Using interaction
terms between the lagged participation variables and plant characteristics or macro variables, the
model can also be generalized to allow the sunk cost parameters -V's to vary with changes in these
variables. Finally, we can use equation (4) to study the importance of temporal (s, ) and cross-plant
(J4) variation in net expected profits from exporting (r;, -F,). In particular, we can impute
probabilities of entry or exit in response to a given shift in exogenous variables for plants with
different characteristics.

Econometric Issues: To isolate the inmportance of sunk costs, it is critical that we control for


all other sources of persistence in exporting status. Muchi of this task is accomplished by including
the vector of observable plant characteristics Z,, in equation (4). However, it is very likely that some
characteristics, such as managerial expertise or output quality, will remain unobserved and their
presence will induce serial correlation in the error term, e,,. If we use an estimator that ignores this
serial correlation, the model will incorrectly attribute its effect on exporting status to past participation
and thus overstate the importance of sunk costs.'9

Following Heckman (1981a, 1981b) we allow for serial correlation by assuming that e; is the
sum of a permanent. plant-specificcomponent and a white-noise component: eit a, + (it. Here ai
represents unobservable plant-level differences in managerial efficiency, foreign contacts, and other
factors that induce persistent plant-specific differences in the returns from exporting. We normalize
var(c4f)= 1, and assume that cov(aj,a,) = 0 v i*k, cov(Z,e,) = cov(aj,wjf) = 0 v i, t, and
cov(caj,,kso.j)= 0 v j+0. This specification implies that cov(ejt,e.,j) = var( 4.) = a2, so the parameter

o. is both the covariance between different time periods for a single plant, and the fraction of the
variance of e*, that arises from the permanent component in the error. Assuming that a and w are
each normally-distributed random variables, equation (4) can be estimated as a random-effects probit
There remains an additional problem. We observe a plant's export status in years 1 through
T, and our lag structure reaches back J periods, so equation (4) can be used to model the export

19 This is the problem of -spurious state dependence" discussed in the empirical literature on labor market
participation. See. for example, Heckman (1981a).

20 We do not control for the a, by using plant-specific dummy variables because of the "incidental parameters problem"
discussed in Neyman and Scon (1948), Chamberlin (1980), and Heckman (1981c). For a given number of time periods, the
number of a, values grows in direct proportion to the sample size, making consistent estimation as n-eo more difficult.
Under these conditions, a standard logit or probit estimator using plt-specific dummy variables will not yield consistent
slope coefficients. If the time dimension of the panel is small andlor the model is dynamic. the bias can be substantial. See
Hsiao(1986. pp. 159-161). Wright and Douglas (1975) and Heckman (1981c), for discussion of the magnitude of the bias.
In particular, Heckman (198ic) finds that the bias in slope coefficients from a dynamicprobit with unobservable effcs is
"disturbinglylarge" (p. 180) when T = S.


decisionin years J+I throughT. But Y,., and Y,,_Jvalues corresponding to these first J years camot

be treated as exogenous determinantsof Y4 (J+1 < t 5 21) because each depends on a,. Heckman
(1981c) suggests dealing with this "initial conditio'is" problem by using an approximate representation
for Yi, when t < J and allowing the disturbances in the first J periods to be corre!ated with the
disturbances in every other period. Specifically, suppose that expected profits in the export market
during periods I through J can berepresentedwith the equation:

(S) i ;, -I = X Zi, + soj, t =l, . ..J
where7; is a vector of pre-sample information from periods prior to t on the fh plant, A is a
conformable parameter vector and the random variable jp, is assumed to be normally distributed with

var(,o;,)= I, cov(so,,w,,)=O.2 Critically, we also let (i be correlated with the persistent plant

component of the error term f 2: cov(fp.,a,) = p. (Note that p can also be interpreted as the fraction
of variation due to a; in the first J periods.) Then the correlation of lagged dependent variables with
a, in years 1 through J can be recognized in the likelihood function by representing the Y1, process

using equation (5) for periods t=l,...,J, and equation (4) for periods t=J+I,...,T. This specification

adds the nuisance parameter vector X to the model as well as the parameter p. Although equation (5)
is an imperfect representationof the process generating the data, simulation evidence suggests that
Heckinan's procedure performs reasonably well.'

To construct the likelihood function for observations in periods 1 through T define:

21 In the empirical work we include all of the plant characteristics in Z7described above as explanatory variabls in the
initial-conditions equation (5). Also included are two-year lagged values of the plant's wages, capital stck, and export

2 An alternative solution to this problem is prevented because of the presec of time-varying exogenous variables in
the model. These make it impossible to solve the model for the steady-state probabilities of the pre-sample Ye realizations as
fncmtions of data and estimable parameters.


(6) b = 4u, + i4 + yY,,, +E-yii + ,ad], :J+1, ... T


(7) a,, = -(AZ, + t2,), t=...J
where , = (o, I (1 -Cr))Ifl and 42= (p(1-p))". The likelihoodfunctionfor a panel of n plants
can now be written as:

(8) L(A, ,, . FT, l, 9y . i p
UI ' { II 'Ia (2Y,-I)] II -[b" (2Y-1)]) 4(a) doe.

Here ii(-) is the standard normal cumulative distribution function and x(a)is the normal density
functionfor the unobserved plant effects.

To summarize, the model of export market participation consists of equations (4) and (5) and
is estimated with maximum likelihood using the likelihood function in equation (8).2 The
specificationof the error terms allows for serial correlation arisingfrom plant-specific differencesin
the profitability of exporting and for the initial conditions problem. Both cross-sectional and temporal
variationin the data are used to identify the coefficients. The former is due mainly to cross-plant
differences in industry, location,business type, age, capital stock, the relative price of foreign to
domesticoutput,and wages. The latter is due largely to economy-wide fluctuationsin macro
conditions, and to unobserved plant-specificshocks ((i and c), which combine with changes in 4,to
inducetemporal variation in Y1,. Note that, even if there were no variation in 4,, the transitory

'3 The integralin equaton (8) is approximated using the Hermite integrationformula discussed by Butler and Moffit
(1982). The results reportedbelow used seven evaluation points and were virualy unchanged from a five-point evaluation.


shocks ypk and w, would suffice to identify the coefficients on Y,. and ?,.

Specification Tests: If the empirical model provides a good approximation to the process
that generates Y,, it should fit the observed export market participation patterns up to a plant-specific
time-invariantcomponent plus serially-uncorrelated noise. Further, these disturbances should be
orthogonal to each other and to the vector Z. As discussed by Andrews (1988) and Rust (1992),
specificationtests can be used to test these assumptions on the disturbance term jointly with our

assumptionson the role 4, Yj,, and l

Specifically, using the estimated parameters of equations (4) and (5), we will repeatedly
combine actual series on the strictly exogenous variables (Zi,) with random draws on oil, i,and cal,to
generate Yk trajectories, plant by plant. If the model is valid, these simulated participation patterns
for the years J+1Ithrough Twill differ from the actual ones only because of the random realizations
on act,.p,and wi. Thus each possible sequence of zeros and ones should occur with approximately
the same frequency in both the simulated and actual data. Andrews' (1988) chi-square statistic
provides a metric for comparing the two sets of frequencies.2'

V. Econometric Results on Export Prticipation
In this section we report parameter estimates of equation (4). For all of the results reported
here, we focus on four major exporting industres during the period 1981 through 1989. These
industries are food products, textiles, paper products, and chemicals.25 We limit our sample to the

24 Further details of the test are provided in the Appendix.

5 We wish to limit the analysis to those industries in which Colombia appears able to comnpete in iternational markets.
The industries were chosen because they account for a substantial percentage of total manufactured exports and have a
relatively high proportion of plants participating in the export market. These four industries account for 58.6 percent of the
value of manufactured exports in 1984 and 59.1 percent in 1987. rTe percentage of plants in our sample that export


650plants in these industries that were in operation in each sample year.?6 This sample is not

representativeof the population of manufacturing plants, however, it is appropriate for examining the

effects of sunk costs on established producersY The final data set consists of 9 annual

observations,covering the years 1981-1989, for each of 650 plants; a total of 5850 observations. The

observations for 1981-1983 are treated as the three pre-sample years and are *sed to control for the

initial-conditionsproblemusing equation (5). The observations for 1984-1989 are used to estimate

the role of sunk costs using equation (4).

The parameter estimates for equation (4) are reported in Table 3 for several model

specifications.The most general model, reported in the first column, includes three lags of past

participation(Y,s,, pi2 2,ij-3 ) as well as interaction terms involving Y,,.,and year dunuy variables.

Trhese interaction terms allow the sunk costs of a new exporter to vary over time with market

conditions.8 The remaining three columns in Table 3 report results for models that restrict the

averages8.45 percent per year in the food industry, 19.7 in textiles. 15.4 in paper products, and 45.3 in chemicals.

2 The main difference between the continuing group of plants we analyze and the plants that exit producion over the
period is that the latter group has a lower rate of entry into the export market, averaging 1.9 percent per year. and a lower
degree of persistence once in, averaging .795. The differences, however, do not alter the general conclusionthat transition
rates, particularly for plants that do not export, are low and persistence is high. When examining the plants that entered
productionover the period. a pattern of export market transitions very similar to the plants we analyze is observed,
particularlyafter the plants have been in operation for a few years. The main implication of these patterns for our analysis
is that focusing solely on the group of continuing plants does not distort the patterns of export market transitions present in
the manufacturing sector. It is this pattern of export market transitions that is important in estimating the econometric

n A more general framework would treat each plant as making simultaneous decisions to enter or exit production and
enter or exit the export market. In this case, each plant could be viewed as choosing among four alternatives: do not
produce,produce only for the domestic market, produce only for the export market, or produce for both. This approach is
unnecessarilycomplicatedfor modeling the export decision in Colombia because there are no pro-ducersthat sell only in the
exportmarket. In addition, very few plants enter production and the export market at the same time. As a result, focusing
on the exporting behavior of parnts that are already in operation, as we do, provides a reasonable startng point for
analyzing the export determinantsin Colombian manufacturing.

2 We also estimated models that allowed sunk costs to vary with observable plant characteristics by including
interactionsbetween lagged participation and plant size, business ype, and industry. None of these additional interactions
were ever statistically significam and we do not report them here. We also generalized the pattern of time variation in sunk
costs by interacting year dummies and the two period lag in participation. The additional coefficients from this specification
were also insignificanL


coefficientson past participation. The second column restricts the interaction termswith Y..,to equal
zero, implying sunk costs do not vary over time. The '.ird column restricts the coefficents
on k, 2 and kft-3 to equal zero, implying that the sunk costs of entry are the same for all non-
exporting plants regardless of whether they had ever been exporters. The fourth column combines the
previous two sets of restrictions. The final column restricts past participation to have no effect on
current participation and is consistent with no sunk entry or exit costs.

Specification tests are reported at the bottom of each column in Table 3.29These chi-square
statisticshave 5 degrees of freedom, and provide an omnibus test of whether the model's
parameterizationanddistributional assumptions are valid. The test does not reject the specification in
either column 1 or 2 -both these models include three lagged values of past export participation.
However, the models in columns 3, 4, and 5, whichall restrict past participation to have at most one
lag, are all rejected by the specification test. This implies that Y, does not follow a first-order
Markov process. Given that model I performs very well by this metric, we make it the focus of our
discussion below.

Sunk Cost Parameters: Consider first the coefficientson Y,,,, ?2, Yd 3. a Yk.,
interacted with time dummies. Together, these parameters isolate the importance of sunk costs.
Using a likelihood ratio test to compare model1 and model 5, we findthat they are jointly significant
witha x2(8) statistic of 206.06. This finding supports the basis premise of the hysteresis literature that
there are substantial sunk costs involved in entering or exiing the export market.

TThere are 2' = 64 possible time paths for Y,, over the 1984-1989period, some of which are quite rare. Accordingly,
to improve the power of our test, we group these according to initial expordng staus and the number of swiches in export
staus over tine, arriving at six types of trajectories. The categories of plant-specific Y, trajectories are: all ones, aU zeros,
begin as a one and switch once, begin as a one and make multiple switches, begin as a zero and switcb once, and begin as a
zero and make multiple switches. The actual and predicted frequencies for these six trajectories are reported in the


Lookingat individual coefficients,we find that lagged export participation Y11, hlasa strong
positiveeffecton the probability of exporting, as expect,;d. Further, sunk entry costs vary
significantlyover time, as coefficients on the interaction terms between Y,., and the time dummies
imply.? The sign pattern on the coefficients implies that the sunk costs fell from 1984 to 1985 for
the typical plant, and rose steadily thereafter. One interpretation is that it is easier to break into an
expanding world market than a shrinking one: 1985 was a year of relatively robust global expansion
and overvaluation in the United States, while the late 1980s were a period of relatively slow growth
in the United States.

As shown in equation (4), the coefficients on PI2 and P,3 measurethe sunk costs of a new

exporterminus the sunk costs of a plant that last exported two or three years earlier, respectively.
They indicate that experience two years ago doessignificantly reduce sunk costs, relative to the costs
of a plant that has never exported. That is, the benefits of past export market participation do not
depreciate fully upon exit. On the other hand, the coefficent on t-3 is not significantly different

than zero, implying that plants that last exported three years earlier face re-entry costs roughly as
largeas those faced by a plant entering the market for the first time. Hence our choice of a three
year lag structure appears to capture all of the relevant history.

Expected Profits from Exporting: The remaining coefficients in Table 3 summarize the
influence of year effects and plant characteristics on the expected profitability of exporting, net of
sunk entry costs (ir, -Pi,). (Recall that this expression gives the net return from exporting for a
plant with no prior foreign market experience.)

The time dummies indicate there is variation over time, but only the 1987 and 1989

30 A lirelihoodratio testfor the joint signifrcanceof these interaction tens is rejected at the .05 significance level with
a 2(5) staistic of 14.54.


coefficientsare significantly different than zero.3" Net of entry costs, the expected future profits
from exporting were highest in 1986. This conforms to the relatively rapid entry rate between 1986
and 1987 (Table 2), and may have reflected producer anticipation of the coming years of currency
depreciation. The incentives to begin exporting soon dissipate, however, and by 1989 expected net
profits reach their in-sample low. Again, this is consistent with the falling entry rates in Table 2.

Interestingly, the decline in net expected profits appears to be due to rising sunk costs rather
than falling expectations of gross operating profits. Relative to the base year, sunk entry costs were

1.08 higher in 1989, while expected profits net of sunk costs were only .847 lower. In fact, if exit
costs (Xi) were zero, producers already exporting were doing better in 1989 than in any other year.
One interpretation of this pattern is that the real exchange rate, which was favorable to exporters in
1989, determines expected operating profits for incumbents, while the strength of the world
economy, which was ebbing in 1989, determines the ease with which new exportms can break in.
Net export profitability also varies systematically with observable plant characteristics.
Notably we find that increases in plant size (measured by the plant's capital -l), increases in age,
and corporate ownership all increase the probability of exporting. The plant size result may reflect
scale economy-based exporting, as in Krugman (1984).32 Alternatively, since efficient plants tend to
grow relative to others, capital stock may simply be serving as a proxy for productivity. The age
coefficient may also pick up cost differences among producers. If market forces select out inefficient
producers then older plants will tend to be more competitive in world markets, either because of cost
advantages that cannot be imitated by rivals or because they have had time to move down a learming

3' The hypothesis that the time dummies are jointly equal to zero is rejected with a likelihood ratio tesL The test
statistic is 23.48 versus a x2(5) critical value of 15.1 at the .01 significance level.

2 Combined with our fmding that sunk costs do not rise with size, it is consistent with the well-known positive
correlation between plant size and export participation. See Caves (1989) and Berry (1992) for a review of the evidence
relating size and propensity to export.


curve,33 Even if the annual pay-offfrom exporting were the same for young and old plants, the
youngones would perceiveless return to breaking into the market because they are less I ikely to

Location matters as well, presumably because of transport costs. Bogota, the base category
in the model, is land-locked in the Andes mountain range, and plants there are among the least likely
to produce for foreign markets. Cali and Medellin are also inland, but less mountainousand closer to
thecoast. Nonetheless, we estimate that these cities are as unlikely to serve as a base for exporters as
Bogota. Perhaps their locational advantage is offset by their lack of Bogota's agglomeration
economies.Finally, the port cities of Cartagena and Baranquilla are most likely to host exporting

Interestingly,neither wage rates nor export prices relative to domestic output prices are
significant determinants of exporting behavior. This shouldnotbe interpreted to mean that prices
don't matter; time dummies have already controlled for general movements in relative prices, and the
plant-specificprice variables therefore reflect across-plantdeviationsfrom average trends that can
resultfrom local market conditions, measurementerror, and differences in mput or output quality.
Althoughwe would expect increases in the export price to increase export market participation,we
have no strong priors on whether changes in the cost of labor should make exporting more or less
attractive than servicing the domestic market.

UnobservedPlat Heterogeneityand Noise: The final sources of vaniaon in export status
are unobserved error components: persistant plant heterogeneity, a;, and transitory noise, w. As
shown in Table 3, .336 of the total unobserved variation is due to persistent heterogeneity, andthis

I A decline in the probability of failure as a plant ages has been found by Roberts (1994) for Colombia andby Tybout
(1994) for Chile. Liu and Tybout (1994) also find tht failing plants in Colombia are systematically less productive than
survivingplants. Both patterns have been found in data from the U.S.. (see Evans (1987). Dunne. Roberts. and Samuelson
(1989). and Bailey. Hulten and Campbell (1992)).


fractionis significantlydifferentthanzero. Oncethiserror componentis controlledfor, however,
our specificationtests indicatethat remainingunobservedvariationis seriallyuncorrelatedand
orthogonalto the set of explanatoryvariables.

In the pre-sampleyears,the fractionof variationdue to unobservedplanteffectsis much
higher(.928). This is simplybecausethe laggedparticipationvariablesare notused to predictY,,
during 1981-1983,and their effectisshiftedto the disturbance. Ananalogouseffectis presentin
model5, whichleavesoutlaggedparticipationvariablesfor all years. There,ai accountsfor .817of
totalunexplainedvariation. Model5 alsodemonstratesthat laggedparticipationis stronglycorrelated
withthe vectorZ;,. Notethat withoutY,,, and , e remaiing variablesassumea larger

role in predictingexportingstatus. Allof theseresultsconfirmthatexportmarketparticipation
equationswithoutdynamicsare seriouslymis-specified.
Sunk Costs, Heterogeneity,and Export Probabilies: Table4 quantifiesthe effectsof
observableplantcharacteristics,unobservedheterogeneity,andpast participationon currentexport
marketparticipation. It is basedon estimatesof the unrestrictedmodelreportedin column1 of Table

3. The threepanelsallowthe observabledeterminantsof exportprofitability(age,capitalstock,
industryetc.)to vary. Plantsat the25th, 50th, and75thpercentileof 3Z,are compared. Within
eachpanelplantsare distinguishedby whetherthey neverexported(Y,,= Y,,2 = P 3 = 0), last

exportedthreeyears earlier (Y, =
== = 0; 1r3 = 1), last exportedtwoyears ago (Y,,

= f-4. = 0; = 1), or exportedlastyear (Y., = 1; Y,,2 = Fr3 = 0). The rows ofthe

tablesummarizethe effectof unobservedheterogeneityby allowingthe normally-distnbuted
pmanent plantcomponentaeto vary from-2 to 2.

Exporthistorymattersfor plantsthat haveeitherabove-averageiZa or above-averagea,
values. Thus,althoughonly25 percentof our sampleeverexported,roughly75 percentof theplants
mightwellremainexportersif they weregivensomeforeignmarketexperience. (Theprobabilities


that they would do so range from 14 percent to nearly 100 percent.) Expected profits from exporting
for the remaining plants are so low that if they were somehow given export market experience it
would not be sufficient to make them continue in the export market.

Table 4 also demonstrates how quickly experience depreciates. The difference in export
probabilities between otherwise comparable plants that exported last year and those that last exported
two years ago is substantial, ranging from .30 to .40. However, the difference between plants that
last exported two years ago and plants that last exported three years ago is substantially smaller,
ranging from .10 to .15, and plants that last exported three years ago are not much more likely to do
so than those that never did.

Finally, inferences about the effects of changing macro conditions can also be drawn from

Table 4.3 For example, relative to 1984. the less favorable macro conditions that prevailed in 1989
shiftedthe probabilities that non-exporters would begin exporting approximately to those in the row
directy above.

Overall, the results reported in Tables 3 and 4 reveal that the export participationdecisionis
affected by time-period or macro conditions, observable plant cost or demand variables, unobserved
time-invariantplant heterogeneity, and -importantlyfor the sunk-cost hysteresis models -prior
export market experience. The latter has a particularly substantial effect on the probability a plant
exports and this is consistent with the plant facing significant entry costs in the export market.

VL Conclusions
In an attempt to explain the asymmetric patterns of export and import adjustment resulting
from exchange rate movements, a recent group of papers develops theoretical models that rely on

34 Sine a one-unit change in a corrsponds to a .71 shift in bk (by equation 6 and the deflinion of . anying that
shifts bj by .71 corresponds to a one-row upward movement in Table 4.


sunkcosts of entry to produce hysteresis in trade flows. Beginning with the assumption that entry

into foreign markets requires exporters to incur some sunk costs, these models demonstrate that

temporary changes in market variables, such as exchange rates, can result in permanent changes in
market structure and exports because of the entry and exit of exporters. While the hypothesized

responsein these models is clearly a micro process of entry and exit, empirical tests to date have
relied on aggregate or sectoral data on trade flows and prices.

In this paper we develop an econometric model of a plant's decision to export and use it to
test one of the key assumptions underlying the theoretical models of sunk cost hysteresis. We utilize
micro data for a large group of manufacturing plants in Colombia from 1981-1989 and directly
examinethe determinants of a plant's export decision for consistency with the theory. The
implicationof the sunk cost models that we test is that past participation in the export market will
have a significant effect on the probability of exporting in the current period. Equivalently, the
presence of sunk entry or exit costs will lead to true state dependence in the export decision.

The econometric results, which control for both observed and unobserved sources of plant
heterogeneity,indicate that prior export participation has a significant effect on the probability a plant
exports. Equivalently, we reject the hypothesis that sunk costs are zero. The empirical results also
reveal that the re-entry costs of plants that have been out of the export market for a year are
substantiallyless than the costs of a first-time exporter. Beyond a one year absence, however, the
re-entry costs are not significantly different than those ficed by a new exporter. This is consistent
with the view that an important source of sunk entry costs for Colombian exporters is the need to
accumulate information on demand sources, information that is likely to depreciate upon exit from the

While the results indicate that sunk costs are a significant source of export market persistence,
both observed and unobserved plant characteristics also contribute to an individual plant's export


behavior. Plants that are large, old, and owned by corporations are all more likely to export.
Variation in unobserved sources of difference in profitability can lead to as much as a 30 percentage
point difference in the probability of exporting for a plant with no prior experience.

This combination of plant heterogeneity andsunk costs implies that the response of aggregate
or sectoral exports to changes in policy or the macro enviromnent will likely be idiosyncratic with
respect to country and time period. The magnitude of the supply response will depend upon the
number and type of plants already participating in the export market, the stability or pennanence of
the policy regime, the magnitude of temporary shocks, and the sunk costs of entering a new market.
The latter, in turn, is likely to vary with the degree of information producers have about foreign
markets, the type of market they are likely to enter, the type of product being exported, and the
policy regimne. Given the number of idiosyncratic forces at work, it is not surprising that standard
empirical export supply functions have exhibited marked instability across countries and time.

Finally,our findings suggest that countries undertaking export promotion policies should
distinguishmeasuresaimed at expanding the export volume of exisiting exporters from policies aimed
at promoting the entry of new exporters. The latter include acions directed at reducing entry costs
and uncertainty, such as providing information about potential markets, developing exporting
infrastructure,or providing a stable macro and policy environment. If entering the export market is a
more significant hurdle for firms than expanding their output once in the market, these entry
promotion policies may be more effective at expanding exports than direct subsidies based on the
value of exports.



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Table 1
Colombian Manufactured Exports 1981-1989
(nineteenthree-digitSrTC industries)
1981 1982 1983 1984 1985 1986 1987 1988 1989
Real Effective
Exchange Rate Index
Real ValueofExports
Export Subsidy Rate
Number of Exporting
Proportionof Plants
that Export
.129 .128 .113 .107 .117 .122 .119 .124 .135

Table 2
Plant Transition Rates in the Export Market 1982-1989

Year t Year t+1 1982--1983-1984-1985-1986-1987-1988-Average
Status Status 1983 1984 1985 1986 1987 1988 1989 1982-1989
(Nineteen three-digit manufacturing industries)
No Exports No Exports .974 .971 .957 .963 .973 .972 .958 .967
Exports .026 .029 .043 .037 .026 .028 .042 .033
Exports No Exports .168 .135 .131 .108 .158 .086 .107 .128
Exports .832 .865 .869 .892 .842 .914 .893 .872
(Four major exporting industries)
No Exports No Exports .971 .969 .972 .960 .983 .972 .985 .973
Exports .029 .031 .028 .040 .017 .028 .015 .027
Exports No Exports .108 .101 .152 .124 .149 .085 .054 .110
Exports .892 .899 .848 .876 .851 .915 .946 .890

Table 3
Dynamic probit model of export participation

(standard errors in parenthesis)
Explawtory variable Model I Model 2 Model 3 Model 4 Model S
Intercept -8.312(1 .456) -8.3931(1.426) *9.2850(1.433) -9.381(l.458) -16.954e(l.710)
r,., 1.933*(.261) 2.170*(.152) 1.707-(.245) 1.8650(.124)
Y,.,0(1985Dummy) -.106 (.313) -.170 (.315)
Y*.,1(1986Dunumy) -.009 (.318) *.051(.321)
Y1.*(1987 Dunumy) .185 (.324) .202 (.327)
Y,..0(1988 Dummy) .261(.328) .296 (.334)
Y1.A(1989Dummy) 1.082*(.368) 1.112*(.371)
Y,-2 .732*(.195) .777*(.193)
y.-3 .347 (.248) .340 (.24)
1985Dununy -.155 (.201) -.215 (.161) -.130 (.197) -.221 (.163) -.276 (.178)
1986Dummy .004 (.189) .018 (.159) .026 (.187) .019 (.161) -.068 (.179)
1987 Dummy -.469*(.220) -.398*(.169) -.498*(.218) -.436-(.171) -.476(.1I89)
1988Dummy -.248 (.202) -.149 (.170) -.286 (.201) -.205 (.174) -.3300(.193)
1989 Dummy -.947*(.253) -.378'(.178) -.902*(.249) -.458*(.182) -.520*(.198)
ln(Wage,.) .216 (.147) .231 (.145) .256 (.149) .236 (.150) .4700(.173)
ln(ExponpriceM,) -.062 (.062) -.059 (.061) -.064 (.062) -.084 (.067) .0140(.083)
1naK,.) .247*(.045) .239*(.043) .291 (.041) .2880(.043) .5370(.052)
Agel., .337(.11S) .330*(.114) .379*(.115) .427*(.116) .7010(.169)
Corporation .368+(.158) .333*(.155) .365*(.154) .4500(.161) .396 (.229)
TextilesInd. Dununy 1.018'(.198) .999*(.194) 1.124-(.179) 1.221(. 196) 2.392*(.259)
Paper Ind. Dummy .251 (.183) .254 (.180) .250 (.189) .237 (.190) .940*(.273)
Chemicals Ind. .5320(.179) .503'(.176) .875*(.187) .628*(.181) 2.996*(.285)
Cali/Medellin -.099 (.143) -.099 (.139) -.060 (.140) -.123 (.143) .5470(.190)
Other region .378*(.139) .371*(.136) .409*(.136) .456*(.141) 1.4300(.204)
Var(a) .336*(.076) .314*(.076) .416*(.053) .446*(.059) .817*(.016)
Cov(qP.a) .928*(.013) .92280(.013) .9170(.013) .926*(.012) .7790(.025)
In(L) -845.80 -853.07 -854.09 -860.86 -948.83
Specification et 5.121 5.519 12.065* 9.S330* SS.112*

Reject null hypothesis at the .05 significance level.
** Reject null hypothesis at Ihe .10 signficance level.

Table 4
Predictedprobability of exporting

25thpercedti of A5Z.O 501hpercenileof 17,. 75thpercetileofOZ.,
= 0 0 0 0 0 0
00 0 0 0
YI3=0 0 1 0 0 0 1 0 0 0 1 0
-2 .000 .000 .000 .006 .000 .000 .001 .024 .001 .002 .006 .096
-1 .000 .000 .001 .037 .001 .002 .007 .104 .006 .015 .037 .277
0 .001 .004 .011 .141 .007 .016 .040 .291 .035 .071 .140 .547
1 .011 .026 .OS9 .358 .038 .077 .149 .564 .135 .225 .356 .797
2 .056 .108 .197 .636 .144 .238 .371 .808 .348 .482 .633 .938

Appendix: SpecificationTests

Andrews'(1988) specificationtest compares realized (Y,, Z,,) trajectories in our sample to

expected trajectories based on the estimated model. To construct his X2 statistic, we first partition the

possible(Ye, Z,,) trajectories into a limited number of cells. In our case we have 2' -64 possible

trajectories for Y&,someof which are very unusual. To avoid cells that are nearly empty we distinguish

six types of trajectories: Y,, = 0 V t; Yft = 0 initially, but switches onceduring the sanple period; f,

= 0 intially and switches at least twice during the sample period: Y,,= I v t; Y,, I initially, but

switchesonce during the sample period; and Y.= 0 initially and switchesat least twice during the sample

period. Letting the vector indicator function TI(Y I,Y1,+2, ... YUT maP Y sequencesinto these 6

cells, the observed frequencies of the different trajectories in our sample is the vector


P. )=-,E(Y,,, * ) . Elements of this vector are reported in Table A 1. I below.
Table Al.l: Observed versus Predicted Frequences of Y,,Trajectories
(based on Column1, Table 3)
Trajectory type Observed Expected Frequencies
always a non-exporter .761 .743
begin as a non-exporter, switch once .046 .051
begin as a non-exporter, switch at .045 .052
least twice
always an exporter .098 .089
begin as an exporter, switch once .017 .028
begin as an exporter, switch at least .03 .037

Next, to generate model-based expected values for each of these cells, we use estimated parameter
values from Table 3 in conjunction with the observed 4, trajectories and random draws on a5, P,,and
w,,to repeatedly simulate Y', sequences, plant by plant. (The reported tests are based on 200 simulations
per plant.) Distributions for each of these random variables are based on the assumptions described in
section III. Averaging over all of the outcomes for the rhplant, we get the probabilities it will fall in

each cellunder the null hypothesis that our specification is correct: Q(1,Zu,Z1J.1 **Z,7 JI,y,Ca,P) Finally,
averaging these probabilities over all plants, we obtain the expected sample-wide frequencies of
each cell in the partition:

Q.(I) = 1E Q(Z, Z ZU.1 *ZT I p, y, or p)

The expected frequencies generated by the model in column 1 of Table 3 are reported in the
second column of Table A1.1. The test statistic is calculated as a quadratic form in the difference
between the two columns, X2 1)= (P -Q. Q) ; where the weighting matrix W is given

by equation (15) in Andrews' appendix.

Policy Research Working Paper Serles


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