Learning to export: Export growth and the destination decision of firms

Journal of International Economics 87 (2012) 89–97

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Learning to export: Export growth and the destination decision of firms☆ Katherine N. Schmeiser ⁎ Mount Holyoke College, Department of Economics, United States

a r t i c l e

i n f o

Article history: Received 21 December 2010 Received in revised form 26 September 2011 Accepted 13 November 2011 Available online 19 November 2011 JEL Classification: F12 F13 F14 L10

a b s t r a c t I find evidence that the geographic expansion of firm exports occurs slowly over time and that a large share of export growth is due to incumbent exporters entering new destinations. New exporters enter large countries and destinations with characteristics similar to their domestic market. Less similar, distant or less developed countries are entered by firms already exporting to other destinations. I formulate a dynamic general equilibrium model to test if these patterns are due to firms learning how to export (as other recent empirical findings have suggested) or other factors considered in the literature. In this model, heterogeneous firms experience learning in the form of market entry costs that depend on export history. Using Russian firm level data, I find that learning plays a significant role in explaining the observed entry patterns, which standard trade models cannot account for. © 2011 Elsevier B.V. All rights reserved.

Keywords: International trade Monopolistic competition Exports Learning

1. Introduction Recent evidence suggests that firms' export participation decisions are dynamic and driven by accumulation of export experience. This paper provides supporting evidence that the geographic expansion of firm exports occurs slowly, with the majority of firms initially entering just one destination, then gradually entering more. Additionally, it shows the composition of entry destinations differs between new exporters and continuing exporters, initially exporting to close and similar countries and eventually servicing more diverse countries. This slow and deliberate expansion is a significant contributor to exports, with 17% of total exports due to exporters entering new (to them) destinations. Theories that don't account for dynamic firm choice and geographic diffusion of exports cannot replicate this exporter behavior and hence mistakingly estimate the benefits of trade liberalizations. To amend this, I propose a model where firms learn how to export in the setting of a Melitz (2003) model of trade with monopolistically ☆ I would like to thank the participants of the Trade and Development Workshop at the University of Minnesota, my advisor Timothy Kehoe, and two anonymous referees for their invaluable comments. I would also like to thank Cristina Arellano, Thomas Holmes, Fabrizio Perri, Terry Roe, Steven Schmeiser, Yelena Tuzova and the participants at the Federal Reserve Bank of Minneapolis bag lunch for comments and suggestions. ⁎ Corresponding author at: Department of Economics, Mount Holyoke College, 115 Skinner Hall, 50 College Street, South Hadley, MA 01075, United States. E-mail address: [email protected]. 0022-1996/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jinteco.2011.11.006

competitive firms. The learning mechanism (modeled as learning by doing) is in the form of cost savings in exporting through accumulated export experience. This is due in part to firms familiarizing themselves with the bureaucratic costs associated with exporting, marketing and distribution costs Lawless (2009), and language costs. I call this class of learning “learning to export” in order to differentiate it from other forms discussed in the literature. For example, “learning by exporting,” stemming from the work of Clerides et al. (1998), examines whether high productivity firms export due to self selection or increased productivity through exporting. Learning through spillovers from other exporting firms (Koenig, 2009; Cassey and Schmeiser, 2010) addresses the issue of destination choice through agglomeration. Learning about product appeal and salability of firm goods in foreign markets is analyzed by Eaton et al. (2008). Here firms go through a “search and learning” process where they are uncertain about product appeal (though they gain information through research and behaviors of rival firms) until they export to a given market. The model in this paper includes a world with non-symmetric export destinations and heterogenous firms making export location and quantity decisions. In addition to an intensive margin of exports (where a firm exports more of their good to existing destinations) and an extensive margin (where new firms enter the export market), the model allows for an extensive margin where current exporters enter new locations. That firms make dynamic entry decisions due to learning changes the characteristics of exporter types we would otherwise expect to

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observe, altering the predictions of trade liberalizations. For example, smaller, less productive firms have greater incentive to initially enter the export market. The ability to decrease future export costs and later enter profitable countries drives their entry. In the long run, firms may export to more destinations and, in the short run, to fewer than otherwise predicted. Models with learning can then help formulate policies which target the specific destinations as well as the appropriate types of firms which can lead to the most future growth. The dynamic decision of firms to export has been clearly documented over the past few years. Eaton et al. (2007) analyze Colombian firm level export data over the period 1996–2005. The authors find that cohorts of firms entering the export market either soon exit or expand foreign sales by entering additional destinations. They find that certain foreign markets can serve as “testing grounds” for new exporters. Neighboring markets act as stepping stones for other Latin American markets, and once firms successfully penetrate neighboring and other Latin American locations, they are more likely to reach larger OECD markets, but not vice versa. Ruhl and Willis (2008) further document that new exporters begin exporting small amounts and, conditional on not exiting, gradually increase their exports over time. Similarly, Lawless (2009) uses a panel of Irish firms to analyze the dynamics of firm exporting and looks at exports along a hierarchy of countries (though finds that the data weakly supports this). The author finds that firms entering additional markets are entering less popular markets than those already served. When entry does occur, it occurs gradually with firms rarely adding more than four markets in a given year. Alvarez et al. (2008), using Chilean firm data, finds that firms' experience from exporting a product to a market increases the probability of the firm exporting the same product to a new market. This paper contributes to the empirical literature and additionally provides a theoretical analysis of the dynamic firm choice when firms learn to export. I suggest a computational approach for dealing with many non-symmetric countries and finally show that, compared to alternative mechanisms, learning to export can better explain the patterns of entry observed in the data. Section 2 describes the Russian firm level data, Section 3 describes the model and shows the importance of including firm dynamic choice in capturing multiple aspects of gradual geographic expansion. Section 4 describes my computational strategy and simulation results — comparing the results of learning to export with other specifications, while Section 5 concludes.

2. Data This section summarizes the exporting activities of manufacturing firms reported in Russian External Economic Activities (REEA, 2004) for the years 2003 and 2004. The REEA data is shipment level customs data and provides date of shipment, value of goods shipped, weight of shipment, and destination country. Goods are classified according to the Commodity Classification for Foreign Economic Activities (CC-FEA). To check for consistency of the data at the aggregate level, I compare the firm level data to the 4 digit Standard International Trade Classification (SITC) bilateral trade data, reported by the United Nations (UN, 2009). 1 While both the REEA and UN report free on board (FOB) exports, each have different sources of data. Unlike the REEA, the UN collects reports by enterprises and records them according to the SITC. While the two reports do not aggregate identically, in 2003, firm reported total exports from the REEA account for

1

I convert all values to 2000 USD using the producer price index for finished goods.

Table 1 Firms' transition probabilities between # of destinations. Ending # Starting # 0 1 2 3–5 6–9 10 +

0 – 0.52 0.31 0.19 0.17 0.26

1 0.80 0.38 0.28 0.14 0.07 0.06

2 0.12 0.07 0.24 0.19 0.04 0.03

3–5 0.07 0.03 0.15 0.38 0.27 0.07

6–9 0.01 0.00 0.02 0.09 0.34 0.10

10 + 0.01 0.00 0.00 0.01 0.12 0.47

88% of UN exports. Additionally export patterns in both sets of data are consistent (Cassey and Schmeiser, 2011). Throughout this section I compare firm exports in 2003 to those in 2004 to calculate the two extensive margins of trade of interest (firms entering the export market and exporters servicing additional destinations). In Schmeiser (2009) I test the sensitivity of this by aggregating firm shipments into monthly reports, both to overcome seasonal effects and take advantage of the rich shipment data, and find the results from the yearly aggregated data were consistent. A firm is considered an entrant if it exported in 2004 but not 2003. A firm is considered a continuing entrant if it exported in both 2003 and 2004 but exported to new (to them) destinations in 2004. 2.1. Firm transitions between number of destinations Table 1 shows a firm's transitions between numbers of export destinations in 2003 and 2004. From the table, we see that conditional on a firm increasing destinations, it does so gradually. For firms just entering the export market, 80% of them will only export to one destination. Firms who initially only exported to one destination, conditional on expansion, will likely only add one additional destination, and so on. Eaton et al. (2007) perform a similar analysis using Colombian manufacturing data. They find an even stronger trend of firms slowly increasing the number of destinations and a much lower ‘exit’ rate as represented by the lower diagonal (this is likely due to them looking at a longer time span). 2.2. Popularity of entry destinations Table 2 shows the ranking (by number of entrants) of the top 15 destinations that entrants and continuing entrants enter. We see that firms initially entering the export market typically enter markets which are either very large (the US and China), very similar to Russia (former Soviet Union countries), or bordering nations. When exporters enter new destinations, they generally enter further, more diverse (culturally) destinations. The fact that continuing exporters Table 2 Export destination by popularity. Rank

Entrants

Continuing entrants

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

China Netherlands Ukraine* Iran* Kazakhstan* Turkey* Cyprus* United States* Finland* Sweden* Korea, South Italy Taiwan* Hungary* India*

Switzerland* United Kingdom* Germany* Korea, South Netherlands Iraq* Denmark* Greece* Belgium* Pakistan* Italy Ethiopia* Spain* China Japan*

Note: An asterisk denotes a country that is unique to that group of firms.

K.N. Schmeiser / Journal of International Economics 87 (2012) 89–97 Table 3 Firms' transition probabilities between type of destinations.

FSU

OECD

EU

FSU, OECD

FSU, EU

OECD, EU

All

0.48 0.54 0.48 0.23 0.23 0.29 0.25

0.54 0.46 0.02 0.03 0.20 0.20 0.01 0.06

0.10 0.00 0.30 0.01 0.15 0.00 0.09 0.03

0.25 0.01 0.03 0.41 0.01 0.06 0.14 0.03

0.01 0.01 0.04 0.00 0.25 0.01 0.01 0.04

0.06 0.04 0.00 0.03 0.04 0.42 0.01 0.11

0.03 0.00 0.06 0.03 0.02 0.01 0.38 0.05

0.01 0.00 0.01 0.01 0.10 0.06 0.06 0.43

are entering further and less similar destinations may be a clue that exporters observe increasing productivity due to their export experience, enabling them to enter these destinations. However the literature does not conclusively support this, with Clerides et al. (1998) and others showing that exporters do not experience increased productivity (this is further explored later). Hence, other than exogenous factors that increase firm productivity, a story that explains the differing compositions is that firms are learning how to export (in Section 4 I will explore these different mechanisms). We further see evidence of destination ‘type’ hierarchy by looking at the transition matrix in Table 3. I consider the following market groups: Former Soviet Union (FSU) and bordering countries, OECD countries, and finally European Union and other close (less than 2000 miles) countries. We see that non-exporters are most likely to enter Former Soviet Union and bordering countries (54%), then EU and other close countries (25%) and OECD countries (10%). Rarely do firms initially enter multiple groups of countries. Once firms service the FSU, some may increase their market to the EU and eventually all three markets. 2.3. Contribution of entrant types Table 4 shows the contribution of entrants, exiters (firms who export in 2003 but not 2004), continuing entrants, and continuing exiters (firms who continuously export but exit a subset of destinations) to total exports. We see that, at the firm level, 21% of 2004 exports are from exporter entrants and 17% are from firms which exit. Additionally, 17% of exports consist of exporters entering new (to them) destinations and 20% of exports are due to firms exiting some destinations. Thus, a significant amount of exports are by firms typically not modeled. As a side note, at the aggregate (4 digit SITC level) these margins are generally 0, except for continuing entry which is positive at 3%. This is merely due to aggregation. This issue, along with interpreting the sum of the margins, is discussed further in Cassey and Schmeiser (2011). The bottom portion of Table 4 shows within destination contributions. For example on average, 22% of a destination's imports from Table 4 Contribution of entrant type.

Firm Level, 26,780 firms

Entrants

Exiters

Continuing entrants

Continuing exiters

entrant exports2004 total exports2004

exiter exports2003 total exports2003

inc:entrant exports2004 total exports2004

inc:exiter exports2003 total exports2003

0.21

0.17

0.17

0.20

Summary of destination specific contributions Average 0.22 0.23 Median 0.16 0.16 Max 1 0 Min 0 −1 Std. dev. 0.24 0.24

0.4 0.3 0.2 0.1 0 -0.1 -0.2

Entry Exit Cont. Entry

-0.3 Ka za kh s U tan kr ai ne C hi G n er a m an Li th y U uan zb i ek a is ta n La tv Es ia to ni a U Fin ni la te nd d St at es

None FSU OECD EU FSU, OECD FSU EU OECD, EU All

None

% of Total Exports

0.5

Ending

Starting

91

Fig. 1. Decomposition for the ten most popular destinations (according to number of firms exporting) over 2003 and 2004. Share of total Russian exports to each destination is: Kazakhstan (4.8%), Ukraine (6.2%), China (10.8%), Germany (4.3%), Lithuania (0.9%), Uzbekistan (1.1%), Latvia (0.7%), Estonia (1.2%), Finland (3.0%), United States (7.0%).

Russia are due to new Russian exporters, 23% of their imports are lost due to firms exiting the export market, 32% of their imports are due to Russian exporters now serving their destination and 29% is due to exporters dropping them as a destination. This shows again the importance of continuing entrants, this time within each potential market. Fig. 1 shows three of these margins for the ten most popular export destinations. Note that in comparing the values of continuing entry in Fig. 1 to the averages in Table 4, we see that the most popular destinations gain little from continuing entry. More generally, Cassey and Schmeiser (2011) find that on average, about 10% of total exports from developing countries are from entry of continually traded products to new countries, whereas in developed countries it is only 3% (this translates to 28% and 6% of continuous exports for developing and developed countries respectively). Additionally, they show that while the continuing exit margin may be high, continuing exporters leaving any given country produces nominal losses to that firm's total exports, which the model in Section 3 supports. The paper additionally finds that size of a country (measured by gdp) and trade openness (trade/gdp) negatively and significantly decrease the value of exports from continuing products exporting to new destination. This promotes the idea that as countries mature firms will have already spread geographically throughout the world and have less ability to obtain growth through this margin. If the sectoral data, due to aggregation, underestimates the value of continuing entry, this is strong motivation to understand the continuing entrant margin. In summary, I show evidence that 1) firms slowly expand the number of destinations they enter, 2) destination preferences are specific and differ between types of firms (entrants enter destinations that are either large or similar to the domestic market, whereas exporters entering additional destinations enter less similar, further destinations), and 3) the value of exports from continuing exporters entering new destinations is both large and understated at the product level. In the next section, I construct a dynamic general equilibrium model which will test whether learning through accumulation of export knowledge can explain these patterns, (and do so better than alternative model specifications). 3. Model

0.32 0.19 1 0 0.31

0.29 0.20 1 0 0.28

Consider a world with a Home country and N Foreign destinations. Each Foreign country n is identified by GDP (Yn), bilateral iceberg transportation cost (τn), and initial fixed entry cost (Fn). Each country consumes a continuum of goods. Foreign countries each produce one good for consumption and export which is distinct

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from all other goods in the world. In the Home country there is a measure of domestic firms, each producing differentiated goods. 3.1. Consumer's problem Consumers in the home country consume a combination of domestic goods and goods imported from N Foreign countries. The inter-temporal C.E.S. utility function for consumers in the Home country is given by: uH ¼

  t β log C t;H

∞ X t¼0

where consumption and a consumers budget constraint are given by N X ρ α H ∫ω ct;H ðωÞρ dω þ ð1−α H Þ c t;H;n

C t;H ¼

∫ω pt;H ðωÞct;H ðωÞdω þ

N X

!1 ρ

ð1Þ

n¼1

pt;H;n ct;H;n ≤ Y t;H :

n¼1

αH is the weight placed on consumption of Home goods (home bias), ρ ∈ (0,1), and ct,H(ω), ct,H,n are consumption of the home variety ω and foreign variety n. Consumers in Foreign country n consume a combination of their domestically produced good ct,n and imported goods from the Home country ct,n(ω). The inter-temporal utility function for a Foreign consumer in n is given by: un ¼

∞ X

  β log C t;n ; t

3.2. Firm's problem Firms are heterogeneous in productivity φ, fixed entry Fn(φ) (defined in Section 3) and continuation costs fn(φ). Additionally, there is a bilateral iceberg transportation cost τn and exogenous firm death rate δ. It − 1(φ) is the set of countries a firm producing good ω has exported to as of t-1. A firm's state in period t st = {I,P} consists of previous export destinations and the aggregate price in each country, P = {Pt,n}t,n. In each period t, a firm receives profits from each market it participates in:   xt;H ðst ; φÞ þ fH ; φ   xt;i ðst ; φÞ πt;i ðst ; φÞ ¼ pt;i ðst ; φÞxt;i ðst ; φÞ−qH τi þ f i ; φ

π t;H ðst ; φÞ ¼ pt;H ðst ; φÞxt;H ðst ; φÞ−qH

where qH is the per-unit production cost of Home firms. A firm's per-period profits are the sum of the profits less entry costs to new markets: πt ðst ; φÞ ¼ πt;H ðst ; φÞ þ

^I X n¼1

πt;n ðst ; φÞ−

^I X

Iq F ðs ; φÞ t;H i t

n¼1

where Ī = 1 only in the entry period, 0 otherwise. Market clearing conditions hold for each t: xt,n(ω) = ct,n(ω) and static firm maximization gives per-period prices for Home consumers: pt;H ðst ; φÞ ¼

s:t:

ð6Þ

qH ; φρ

t¼0

C t;n

 1 ρ ¼ α n c ρt;n þ ð1−α n Þ∫ω ∈ Ω ct;n ðωÞρ dω

ð2Þ

and for Foreign consumers of Home goods:

t;n

qH τ: φρ i

pt;n ct;n þ ∫ω ∈ Ω pt;n ðωÞct;n ðωÞdω ≤ Y t;n

pt;i ðst ; φÞ ¼

where Ωt,n is the set of Home goods available for consumption in country n in period t. First order necessary conditions yield per-period demand functions for Home goods, by Home and Foreign consumers respectively:

Foreign firms produce with a fixed proportions technology. This guarantees that the relative prices of Foreign goods are fixed in equilibrium. The relative price is determined by balanced trade.

t;n

ct;H ðωÞ ¼ Y t;H ct;n ðωÞ ¼ Y t;n

3.3. Learning

! 1 pt;H ðωÞ ρ−1 α H P ρt;H

pt;n ðωÞ

!

ð3Þ

1 ρ−1

ð1−α n ÞP ρt;n

Per period demand for foreign goods is given by:

ct;H;n ¼ Y t;H

ct;n ¼ Y t;n

pt;H ð jÞ

!

1 ρ−1

ð1−α H ÞP ρt;H pt;n

!

ð4Þ

1 ρ−1

α n P ρt;n

where the aggregate price index in country n in each period t is given by:

P t;H ¼

1

ð1−α H Þ 1−ρ

N X n¼1

P t;n ¼

ρ

! ρ−1 1

ρ

ρ

ρ−1 pt;H;j þ α H1−ρ ∫ω pt;H ðωÞ ρ−1 dω

 1  ρ−1 ρ ρ ρ 1 ρ−1 α n1−ρ pt;n þ ð1−α n Þ 1−ρ ∫ω ∈ Ω pt;n ðωÞ ρ−1 dω t;n

ð5Þ

The dynamics in this model are driven by the learning mechanism embedded in each firm's entry costs. This produces firm-destination specific entry costs consistent with Das et al. (2007). Specifically, learning determines the initial cost of entering each foreign market through cost savings on bilateral fixed entry costs. Let I be a vector of zeros and ones, where I = 1 if the firm previously exported to destination n. Additionally, let ^I be the total number of destinations (^I ¼ sumðIÞ) the firm has exported to. The cost of entering destination n is defined to be: −λ^I

F t;n ð⋅Þ ¼ F 0;n e

ð7Þ

Notice that this model nests the Melitz (2003) model, providing equivalent results when λ = 0. The intuition behind the learning function is consistent with that in the trade literature (Bangs J. M., 2000; Alvarez et al., 2008; Lawless, 2009). As a firm exports to more locations, they gain experience in setting up foreign facilities or hire employees who speak new languages, giving them a fixed cost advantage of entering new and similar destinations. Firms also learn about foreign preferences (advertising in different areas of the world), bureaucracy and best shipping methods. The particular function that I use is similar to the learning functions used in the managerial literature. Wright (1963) was the first

K.N. Schmeiser / Journal of International Economics 87 (2012) 89–97

empirical paper that identified learning in the production process, followed by Majd and Pindyck (1989) and many others. Argote et al. (1998) shows that learning is based on lagged production and that the shape of the learning curve is logarithmic. Reinterpreting production as number of destinations serviced, my cost function in 7 reflects destinations entered as a measure of export production and costs decrease exponentially. When λ = 1, the accumulated stock of export knowledge is equal to the lagged number of destinations so learning occurs very quickly, providing incentive to spread entry over just a few periods. λ = 0 is when no learning is present, firm decisions are static, and firms have no incentive to enter more than their initial set of destinations. When λ ∈ (0,1) there is opportunity for more dynamic decisions as firms slowly decrease entry costs. 3.4. Aggregate variables The aggregate price for the Home and Foreign countries are given by:

1

P t;H ¼

ð1−α H Þ 1−ρ 

P t;n ¼

N X n¼1

ρ

1

! ρ−1

ρ

1

ρ

1

ρ−1 α n1−ρ pt;n þ ð1−α n Þ 1−ρ ∫ω ∈ Ω pt;n ðωÞ ρ−1 dω t;n

Proposition 1. Consider a simple world where all potential export destinations are symmetric, with potential per-period profit π. Then,   π−F a learning parameter for Eq. (7) that satisfies λ > −1 is A log βF such that firms initially enter only a subset A of the possible N destinations. Additionally, if λ = 0 firms will immediately enter all profitable destinations. This λ satisfies the following condition:

 ρ−1 ρ

:

A

     π π π −λA − AF þ β N −Fe −F >N 1−β 1−β 1−β

such that it is more profitable to enter A destinations and receive the benefits of entry and lowering future fixed entry costs then it is to enter everywhere initially and benefit from higher initial period profits.

X t;n ¼ ∫ω ∈ Ω pt;n ðωÞxt;n ðωÞdω t;n

Proposition 2. In a model with learning, it is possible for a firm to enter an unprofitable destination in order to decrease fixed entry costs for a highly profitable destination. This will occur when fixed entry costs into the profitable destination are ‘very’ high.

and total exports are N X

This model produces two new endogenous implications that are important in matching the data. First, firms have incentive to ‘slowly’ enter destinations. This may have several meanings: as a firm learns, previously unprofitable destinations become profitable; additionally firms may enter only a subset of profitable destinations, delaying entrance in order to gain fixed cost benefits. This will be further analyzed in Section 4. Second, for specific sets of parameters, firms may enter destinations with negative per-period profits in order to benefit from the future reduction in fixed entry costs. This also allows for gradual geographic spread as well as firm-destination exit with little export value loss.

;

Aggregate exports to each country n are given by:

Xt ¼

3.6. Firm destination decisions

ρ

ρ

ρ−1 pt;H;n þ α H1−ρ ∫ω pt;H ðωÞ ρ−1 dω

93

X t;n :

1

3.5. Dynamic equilibrium Conditional on a firm exporting to a destination, the firm's maximization problem is static. Solving for maximized profits, we can then rewrite the maximized value function as a choice over the set of destinations I′ a firm exports to: n πHmax ðs; φÞ þ V ðs; φÞ ¼ max ′ I

∑

i¼1∈I ¼IþI ′

h io þ β ð1−δÞEδn ðV ðs′ ; φÞÞ

Consider a simplified world with two destinations with profits π1 b 0 and π2 > 0. Then as long as the fixed entry cost into the 2nd destination is so high that F2 > π1 − π2 − F1, a λ exists such that firms enter the 1st destination, gain the experience of exporting, then drop out of the 1st destination and continue exporting only to the 2nd. In other words,  π1 −F 1 þ β

π2 π −λ −F 2 e > 2 −F 2 1−β 1−β

 max  πi ðs; φÞ−qhIF i ðs; φÞ ð8Þ

where Ī = 1 only in the entry period, 0 otherwise. In each period, there is δ chance of death in the overall export market, and δn chance of death in each destination. Definition 1. A dynamic equilibrium in this model is value functions V(I,P,φ) for each type of firm ω = (I,φ), decision rule functions I′(I,P,φ), aggregate prices P, and distribution of firms such that for learning as defined in Eq. (7), the functions satisfy: (a) Home and Foreign consumer maximization problems given in Eqs. (1) and (2); (b) the firms' maximization problem given in Eqs. (6) and (8); (c) and the consistency of aggregate and individual decisions, given by aggregate prices in Eq. (5).

This occurs when λ > log



π1 −F 1 −ðπ2 −F 2 Þ βF 2



> 0 and F2 > π2 + 1 −

(π1 − F1). It is easy to see that if there are two destinations, say the United Kingdom and Kazakhstan, the parameter restrictions may be easily met. It is possible that export profits in Kazakhstan might be negative (or very low) and export profits in the United Kingdom quite large. However entry barriers to Kazakhstan would be quite low and barriers to the United Kingdom might be initially prohibitive — even with large potential profits. This is a scenario in which firms might find it profitable in the long run to enter Kazakhstan, observe one period of negative profits, learn how to export and decrease their fixed entry costs, then enter the United Kingdom (and exit Kazakhstan with little export loss). While this is not the focus of the paper, the fact that the model can account for the large amount of exit (and small loss to export value, as shown in Cassey and Schmeiser, 2011) supports the learning to export mechanism.

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K.N. Schmeiser / Journal of International Economics 87 (2012) 89–97

4. Computation In this section I consider three variations of the above model to show why learning is important in enabling this class of model to better match patterns in the data. In the first model, firms do not learn (λ = 0); the second model is the learning to export model presented above (λ > 0); the third model examines competing theories that affect firm productivity, specifically a learning by doing model where firm productivity increases with production. The third variant will here on be referred to as a learning by producing model (to distinguish it from the learning to export model) and learning will be modeled as e − γ(⋅) where γ > 0. (Note that the learning by producing model has qualitative similarities to models in which firm productivity exogenously increases over time, and is robust to definition of ‘production’. For comparison reasons, I consider production in number of destination, versus quantity produced. The results hold for the standard learning by doing in production quantity.) For each model variation a cutoff can be found for the lowest productivity firm with history I entering destination k by solving for ^ k such that the value of entering the destination the productivity φ is zero:    31−ρ 2  ρ ^ Lk −βð1−δÞV I′ ; P; φ ^k qH τ k 4qH f k þ F k s; φ 5 ^k ¼   φ : ð9Þ ρ 1 1 e−γI P k ρ1−ρ −ρ1−ρ ð1−α k Þρ Y k Note that γ > 0 only in models with productivity growth and the firm's fixed cost is different than the bilateral fixed costs only when λ > 0 (otherwise the fixed entry cost is independent of firm history and equals Fk). The continuation value is positive only when λ, γ > 0, albeit the function differs for each learning scenario. With no learning, the set of firms exporting to each destination will remain constant over time. However, as productivity grows over time (either exogenously or through learning by producing), firms will find it profitable to export to additional destinations. With learning, the cutoff productivity depends on the export history of individual firms as well as the potential future benefits. All things equal, a model with learning might induce low productivity firms to export who otherwise would not have and firms to export to more destinations over time. Clerides et al. (1998) find that high productivity firms sort into becoming exporters — not that firms gain efficiency through exporting. This suggests that a learning by producing story is empirically less supported than a learning to export story (although these results are not robust across studies). Nevertheless, in the next few sections I explore why the learning to export mechanism is generally more able to capture these new patterns found in the data. 4.1. Simulation In this section I present simulation results that allow us to gauge the benefit of incorporating learning to export into our models. That there are many countries and that countries are nonsymmetric is important when analyzing trends of firm export growth such as presented in this paper. In my sample of Russian exports there are 97 potential destinations. Computationally it is not feasible to have so much non-symmetry in this dynamic setting. I reduce the number of non-symmetric destinations firms can export to by breaking destinations into four groups: Former Soviet Union and bordering countries; OECD countries (excluding the EU and China), the European Union (excluding FSU and neighboring countries) and other close countries (distance between major cities of less than 2000 miles); and finally the rest of the world (ROW). 2 Table 9 2 Other ‘logical’ country groupings have been tested to see how sensitive these results are to specific classifications (by size, distance, borders, trade agreements, etc.) and the results are robust.

in the Appendix provides the complete listing of countries in each category. In order to allow for some level of non-symmetry and unique country destinations, I average the GDP and transportation costs of countries within a group and then assign those values as well as an initial bilateral fixed cost to each country. Hence, each destination within a group will have the same country properties (GDP, transport and initial bilateral fixed costs), however firms will still observe that there are multiple destinations within each group (albeit with identical properties). A firm then, may decide to export to only half of the Former Soviet Union countries even though observable profits in each country would be identical. Define Ij,t − 1(ω) to be the set of countries in group j a firm producing good ω has previously exported to. A firm's own state variables are the export locations the firm has exported to within each group: I = {I1,I2,I3,I4}, and the aggregate state variables for the aggregate price in each country, P = {Pj}j. I will be solving for a stationary equilibrium, in which Pt,i = Pi for every period t. Define st = {I1,I2,I3,I4,P}. Rewriting the value function in Eq. 8, we have: ( V ðs; φÞ ¼ max

I ′1 ;I ′2 ;I ′3 ;I′4

′

πHmax ðs; φÞ þ

Ij h 4 X X j¼1 )

max

πi

i ðs; φÞ−qhI j F i ðs; φÞ ð10Þ

i¼1

þ β½ð1−δÞEðV ðs′ ; φÞÞ

where again, δ is the probability of death in the export market, and each period, there is δj probability of death, now from each group j. In the learning by producing model, the value function is V ′ ðs; φÞ ¼

n o h i I j′ maxI′ ;I′ ;I′ ;I′ π ′Hmax ðs; φÞ þ ∑4j¼1 ∑i¼1 π ′i max ðs; φÞ−qhI j F i þ β½ð1−δÞEðV ′ ðs′ ; φÞÞ 1 2 3 4

where firm profit π′ = (e − γI) ρ/(ρ − 1)π includes the learning function on productivity. I assume that firm productivities are distributed log-normal: g ðφÞ ¼

ðln φ−μ Þ2 1 − pffiffiffiffiffiffi e 2σ 2 φσ 2π

For the simulation exercise I incorporate as much of the firm and country level data as is available, presented in Table 5. Recall pt;i ðst ; φÞ ¼

qt;H −1 φ τ ρ |{z} |{z}i

:

Firm Destination

To estimate transportation costs, I regress per-unit price (measured as value/weight of a shipment) on destination and firm effects.

Table 5 Parameter values. Variable

Description

Source

ρ =.5 ⇒ σ = 2 β =.96 α =.98 δ =.216 δn τ YH = 306 Yn g = 8.1% fn = Fn/3.9 qH = 1 Fn μ σ

Elasticity of substitution Time discount Home bias Exporter death rate Death rate from n Transportation cost Russian GDP (Billions USD) GDP in n Productivity growth Fixed continuation cost Per unit production cost Entry cost to destination n Mean of log-normal dist. of φ Std. deviation

(Gibson, 2006) (Gibson, 2006) – value lost/exports2003 value lostn/exports2003, Table 6 Table 6 NY.GDP.MKTP.KD ( (WDI, 2009)) NY.GDP.MKTP.KD ( (WDI, 2009)) Firm export growth by dest. (Alessandria and Choi, 2007) Normalized To match ranking in Table 3 From dist. of total exports From dist. of total exports

K.N. Schmeiser / Journal of International Economics 87 (2012) 89–97 Table 6 Country groupings.

OECD FSU/Neigh. EU/Close ROW

14 Avg.

Avg.

Avg. GDP

2003 exports

Dist.

τ

(Bill. USD)

(Mill. USD)

4682 1833 1252 4208

1.37 1 5.21 2.01

1911 182 107 76

453 476 172 30

Number of Export Destination

Destination

95

2*δn

0.108 0.216 0.163 0.282

+OECD, ROW

12 10 8 +EU

6 4

FSU

2 0 0

The destination coefficient allows heterogeneity in fixed costs to come from border effects, language, trade union, and distance effects. Since shipments are reported FOB, the model says that if costs of transportation (speaking in a broad term where costs incorporate distance, etc.) were the same across destinations then controlling for each firm the difference in value/weight across destinations should be the same. In fact, there is large variation in the value/weight of firms across destinations. With transportation costs to the FSU and other close countries normalized to 1, key values of the country groups are presented in Table 6. Distance is measured between capital cities of each country and the exogenous death rate from each group is determined by value lost of exiting firms in the data. For a stationary equilibrium, my computational algorithm amounts to the following iterative procedure: (1) Guess the aggregate price in each country. (2) Using value function iteration, solve the firm's problem, given aggregate prices, to find the optimal decision rules I′j(I1,I2,I3,I4,P,φ). (3) Use firms' decision rules to simulate the behavior of 2000 firms over 100 periods. (4) Use the stationary region of the simulated data to calculate the resulting aggregate price in each country. (5) If this gives an aggregate price close enough to the one guessed initially, stop, otherwise update the guess on price and go back to 2. Simulations are run for a sample of countries in each country grouping. Table 7 shows the transition matrix for firms in the learning to export model (with learning parameter λ = .5) versus a model without learning. We see that a model of learning can capture the gradual entrance of firms in export markets. (Note that the learning by production model in either specification can also capture gradual entry.) Additionally, Fig. 2 shows the destination path of a low productivity exporter in the learning to export model. Because the initial country groupings and fixed costs are chosen to match firm export paths from the data, we are not surprised to see that the firm initially enters Former Soviet Union destinations, then spreads out to EU, OECD, and

Table 7 Firms' transition probabilities between # of destinations. Learning λ = .5 Ending state Starting state 0 1 2 3–5 6–9 10 +

0 0.22 0.22 0.22 0.22 0.22 0.22

1 0 0 0 0 0 0

2 0 0 0 0 0 0

3–5 0.78 0.20 0 0 0 0

6–9 0 0.59 0.78 0.47 0.01 0

10 + 0 0.00 0.00 0.31 0.77 0.78

No learning

1

2

t

3

4

5

Fig. 2. Transitions for a low productivity firm, λ = .5.

the rest of the world. However, we do see again that firms in the learning to export model gradually spread geographically. Table 8 shows that (taking averages over 50 simulations) both learning models are able to capture growth by continuing entrants. While this is not a calibrated example, we can see that the learning models enable us to capture the importance of continuing exporters entering new destinations. (Note, some of the growth is capturing the randomness of exporter death and re-entry from δi. Removing group specific death causes the contribution in the no learning model to be 0.) While learning allows this class of model to generate gradual firm entry and learning parameters can certainly be found to capture the significance of continuing entry, there are a couple of key differences between the learning to export and learning by production (or other models that cause increased productivity) mechanisms. As mentioned above, much of the literature suggests that being an exporter does not in and of itself promote higher efficiency or productivity growth. This result, however, is not robust across country studies and even same-country studies provide varying results (see for example Hahn, 2005 and Hahn and Park, 2009). While Keller (2009) provides a nice summary of the literature, two studies in particular are relevant to this paper in their use of industry and destination. Luong (2010), by focusing on the automobile industry in China, is able to use firm-destination data and finds no evidence of increased domestic productivity growth. Alternatively, De Loecker (2007) uses firm-industry-destination entry data on Slovenian firms and finds increased productivity growth for exporters, particularly in certain industries and to high income destination countries (for example due to higher quality concerns). While the literature does not conclusively support increased productivity growth for exporters, destination and product composition clarify this for Russian firms. Firms begin exporting to Former Soviet Union countries and the majority of their destinations are not high income. Additionally, of the top 10 SITC 4 digit products exported to the U.S. in 2003 (according to the U.N.), eight are metal products (lead by Aluminum and aluminum alloys, unwrought), one is wood based (densified wood and reconstituted wood) and only the tenth good is mechanically advanced (reaction engines, for which the export value is only 4% of the top exported product). None of these products belong to the list provided by De Loecker of 6 products experiencing ‘immediate impact’ in learning by exporting and in fact, ‘basic metals’ appears in the ‘no evidence’ columns.

Table 8 Contribution of entry types.

Ending state Starting state 0 1 2 3–5 6–9 10 +

0 1 0.22 0.22 0.22 0.22 0.22

1 0 0.78 0 0 0 0

2 0 0 0.78 0 0 0

3–5 0 0 0 0.78 0 0

6–9 0 0 0 0 0.78 0

10 + 0 0 0 0 0 0.78

Data

Data Model: No learning Model: Learning by exporting Model: Learning by producing

Entrants

Continuing entrants

entrant exportsyear b total exportsyear b

inc:entrant exportsyear total exportsyear b

0.21 0.22 0.22 0.22

0.17 0.22 0.76 0.87

b

96

K.N. Schmeiser / Journal of International Economics 87 (2012) 89–97

In the model, this is analyzed through firms' at home production. Fig. 3(a) shows that in the learning to export model, a firm's home production remains constant whereas a firm who learns through producing for the export market experiences growth at home as well (this can be generalized to say that there is no increase in productivity for exporters in the domestic market). Another key feature that the learning to export mechanism captures is that firms may withhold entry into profitable destinations in order to gain learning advantages (Proposition 1). Fig. 3(b) shows that for a group of 10 identical and profitable countries, we can find values of learning λ such that firms enter only a subset of profitable destinations, withholding positive period profits in order to gain from decreased fixed costs. There is, however, no value of γ such that a firm who learns by producing will withhold entry into a profitable country (it is easy to prove this for all values of γ, similar to Proposition 1). Finally, Eaton et al. (2007) discuss countries which act as testing grounds. While Proposition 1 and Fig. 3(b) address one aspect of this, another benefit of the learning to export model is that it can be used to address the large amounts of firm-destination exit observed in the data (Proposition 2). That is, firms may choose to initially enter an unprofitable destination instead of a profitable one in order to learn how to export. The firm will then exit that destination and enter the profitable destination in the next period, taking advantage of the decreased fixed cost. This scenario is plausible when relatively minor losses may lead to knowledge gains to enter a profitable, yet costly destination. It is trivial, however, to show that a learning by production model cannot replicate this pattern. For firms learning to export, the value of the learning parameter λ will have obvious implications as to the speed of convergence and level of exports/number of destinations served by a firm. As λ approaches 0 or 1, convergence speeds up. For λ = 0, no learning occurs and firms will enter all profitable destinations immediately. With λ = 1, firms will initially enter very few countries, learn all they can, then enter all remaining profitable countries the next period. Fig. 3(b) shows for increasing λ firms initially enter fewer destinations. The real λ in a given country will likely depend on the level of development, trade openness, and even product characteristics, as indicated by (Cassey and Schmeiser, 2011).

5. Conclusion This paper confirms previous findings that the geographic expansion of firm exports is gradual. Additionally, it shows that destination preferences are specific and differ between types of firms (entrants enter destinations that are either large or similar to the domestic market, whereas exporters entering additional destinations enter less similar and further destinations), and that the value of exports from exporters entering new destinations is both large and understated at the product level. I show that a model where firms learn how to export can better account for the trends observed in the data than alternative explanations. Learning provides incentive for firms to gradually enter destinations. This may be through delayed entry into profitable destinations in order to benefit from future reductions in entry costs or through entry costs becoming manageable. Additionally, learning to export can capture the low export loss from continuing firms entering unprofitable countries (shown by Cassey and Schmeiser, 2011) as firms are willing to take small losses in order to learn about exporting. The dynamic entry decisions of firms alters the predictions of exporter characteristics. For example, in addition to the above features, less productive firms have greater incentive to initially enter the export market, knowing they can learn how to more profitably enter future destinations. Low productivity exporters may be able to penetrate further geographically than a non learning model would account for while higher productive firms may initially export to fewer destinations than expected. Generally, the composition of exporters changes over time. This new composition of firms changes expected outcomes of trade liberalizations. For example, policies that target further liberalizations with Former Soviet Union and bordering countries would help low productivity firms enter not only those countries, but future more diverse (and potentially more profitable) countries later on. Policies targeting countries further on the entry ranking list would have less of an effect on export growth through the learning mechanism and growth would occur mostly within the targeted destination.

Appendix A

Home Production

a) Changes in Home Production 100 90 80 70 60 50 40 30 20 10 0

Learning by Producing

Neighbors and FSU: Azerbaijan, Armenia, Kazakhstan, Kyrgyzstan, Mongolia, Norway, Tajikistan, Ukraine, Uzbekistan, China OECD (minus China and EU): Australia, Canada, France, Germany, Italy, Japan, United Kingdom, United States, Chile, Iceland, Mexico, New Zealand

Learning to Export

1

2

3

4

5

t

12

Initial Number of Countries Entered

Table 9 Description of 4 destination groups.

Learning by Producing

10 8 6 4

Learning to Export

2 0 0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Learning Parameter Fig. 3. Learning to export versus learning by producing.

1

EU or close: Albania, Austria, Bulgaria, Croatia, Cyprus, Denmark, Estonia, Finland, Greece, Hungary, Iran, Iraq, Ireland, Israel, Jordan, Lebanon, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, Turkmenistan Rest of world: Afghanistan, Algeria, Angola, Argentina, Bahrain, Bangladesh, Brazil, Cambodia, Cameroon, Sri Lanka, Colombia, Cuba, Ecuador, Equatorial Guinea, Ethiopia, Eritrea, Djibouti, Ghana, Guinea, India, Indonesia, Kenya, Liberia, Malaysia, Mauritania, Morocco, Oman, Namibia, Nepal, Nigeria, Pakistan, Panama, Peru, Philippines, Saudi Arabia, Senegal, Singapore, South Africa, Sudan, Thailand, United Arab Emirates, Tunisia, Egypt, Venezuela, Yemen

K.N. Schmeiser / Journal of International Economics 87 (2012) 89–97

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