DP2009/XX
Exporting and performance: The impact ofdestination characteristics on learning effects
DRAFT
Prepared for presentation at CAED 2009

Richard Fabling and Lynda Sanderson

October 2009

JEL classification: D21, D24, F10

www.rbnz.govt.nz/research/discusspapers/

Discussion Paper Series

ISSN 1177-7567

DP2009/XX
Exporting and performance: The impact of destination
characteristics on learning effects
DRAFT
Prepared for presentation at CAED 2009∗
Richard Fabling and Lynda Sanderson†
Abstract
We examine the effect of export market entry on firm performance, including productivity. Our novel contribution to the literature is the treatmentof export status as an incremental process, in which firms may export toone or more markets and each of these markets provides additional potential
for learning to occur. Using propensity-score matching techniques on the
population of New Zealand manufacturing exporters, we test whether entry
into additional markets imparts new performance-enhancing knowledge. Focussing on new markets provides several benefits. Most importantly, since
we match on firms’ export history as well as their current characteristics,
we reduce the problem of selection on unobservables (such as managerial
preferences) which would confound a causal interpretation. This formulation
provides a more stringent test of performance improvements from entry, as
well as a much larger number of entry events on which to test our model.
Finally, by splitting our sample according to characteristics of destination
countries (specifically, per capita income), we are able to test whether potential performance improvements from exporting are dependent on the foreign
market involved.

∗

The views expressed in this paper are those of the authors and do not necessarily reflect
the views of the Reserve Bank of New Zealand. The authors wish to thank Statistics
New Zealand for access to the data and Dave Maré for valuable discussions on theimplementation of matching estimators.
†
Richard Fabling: RBNZ (richard.fabling@rbnz.govt.nz); Motu Economic and Public
Policy Research. Lynda Sanderson: RBNZ (lynda.sanderson@rbnz.govt.nz); University of Waikato.
ISSN 1177-7567 c Reserve Bank of New ZealandDisclaimer
This research uses data that was accessed while the authors were on secondment to Statistics New Zealand in accordance with security and confidentiality provisions of the Statistics Act 1975. Only people authorised by the Act
are allowed to see data about a particular business or organisation. The results of this work have been confidentialised to protect individual businesses
from identification. The analysis and interpretation of these results were undertaken while the authors were at the Reserve Bank of New Zealand. The
opinions, findings, recommendations and conclusions expressed in this report
are those of the authors. Statistics New Zealand, the Reserve Bank of New
Zealand, Motu and the University of Waikato take no responsibility for any
omissions or errors in the information contained here.
The results are based in part on tax data supplied by Inland Revenue to Statistics New Zealand under the Tax Administration Act 1994. This tax data
must be used only for statistical purposes, and no individual information is
published or disclosed in any other form, or provided back to Inland Revenue for administrative or regulatory purposes. Any person who had accessto the unit-record data has certified that they have been shown, have readand have understood section 81 of the Tax Administration Act 1994, which
relates to privacy and confidentiality. Any discussion of data limitations or
weaknesses is not related to the data’s ability to support Inland Revenue’s
core operational requirements.
Statistics New Zealand protocols were applied to the data sourced from the
New Zealand Customs Service. Any discussion of data limitations is not
related to the data’s ability to support that agency’s core operational requirements.

1

Introduction

That exporters outperform domestically focused firms has become somethingof an established fact in the empirical trade literature. Exporters have beenfound to be larger, more productive and to pay higher wages than their domestically focused counterparts. Moreover, this “exporter premium” is found
to exist before firms begin exporting, suggesting that it can be largely explained as self-selection of productive firms into export markets (eg, Bernard
and Jensen 1999; Greenaway and Kneller 2004; Van Biesebroeck 2005; Greenaway and Kneller 2007; Wagner 2007).
In contrast, the jury remains out on what (if any) additional benefits exporting imparts on firms. Theoretical models suggest three broad channels
through which exposure to offshore markets in general, and exporting inparticular, may lead firms to improve their productivity: forced efficiency
gains due to increased competition;1 improved access to new knowledge andtechnologies through greater contact with offshore suppliers, customers andcompetitors; and higher profits, economies of scale and greater incentives todevelop specialised products for larger markets. Despite the variety of possible channels through which it may occur, the notion of firms improving theirproductivity performance through exporting has been generally referred to as“learning-by-exporting” (LBE) and we follow that convention in this paper.
Reviews of the LBE literature suggest that while many studies find evidencein support of “learning”, almost an equal number fail to find any such effect(Greenaway and Kneller 2007; Wagner 2007). Comparison of these papers
is complicated by differences in methodology, data availability, explanatory
variables controlled for and the wide range of countries which have beenstudied. However, a broad view of the literature, in consideration of the hypothesized channels through which exporting might impact on productivity,
suggests a number of possible explanations for conflicting results.
In this paper we examine two options: (1) that the finding of LBE depends
on the relative sophistication of the exporting and destination countries; and(2) that methodological differences explain much of the variation in overall
1This channel is hard to reconcile with standard assumptions of profit maximising firms,
but fits within the literature on X-inefficiencies. In practice, almost all empirical considerations of the effect of exporting on productivity use revenue-based estimates of
productivity, which conflate changes in the prices received by firms with changes in theefficiency of their production processes. An increase in competition might therefore
be observed as a fall in productivity, due to reduced profit margins even if the firm ismaking improvements in their underlying efficiency.

2

results. In order to address these questions, we utilise administrative data on
the population of New Zealand manufacturing exporters linked to detailed
firm-level data on exports by destination. To address the methodologicalconcerns noted above, we test a range of approaches, from a simple regression model to our preferred approach of using propensity score matching with
controls for firms’ past export history. We document the extent of the exporter premium among New Zealand firms and test for observable “learning”
in both the full population of exporters and in sub-populations determinedby the degree of sophistication of the destination country, as proxied by percapita income.
While much of the literature on the firm-level consequences of exporting has
focused on identifying productivity improvements, we follow the early workof Bernard and Jensen (1999) in expanding the set of variables of interest
to include employment and the capital-labour ratio as well as labour productivity and multi-factor productivity (MFP). Hence we address not only
the question of whether entry into export markets leads to firm-level productivity improvements, but also whether exporting impacts on aggregate
productivity or income, through the reallocation of resources towards firms
which were already productive or through increasing the returns to labour.
Finally, this paper provides the first detailed study of the learning-by-exporting
hypothesis using New Zealand data. On theoretical grounds, New Zealandappears to be a prime candidate for observing both a strong self-selection
effect and productivity improvements due to exporting. The vast geographic
distances between New Zealand and potential export markets (particularly
the developed markets of Europe and North America) impose relatively highcosts on exporters compared to many of the countries which have been studied previously, and hence may lead to more pronounced self-selection. Meanwhile, all three hypothetical channels for productivity improvements throughexporting may be relevant for New Zealand firms. While New Zealand is open
in the sense of having relatively low barriers to foreign trade and investment,
domestic market size and distance from other major markets are likely tohave reduced the degree of effective competition in the domestic market. It
is also likely that New Zealand firms reach the limit of domestic expansion
possibilities at an earlier stage than firms in larger markets, thus enhancing the probability that exporting will be important for expansion, if not
for productivity performance. Finally, New Zealand’s relatively low level ofproductivity compared to other advanced economies and low observed investment in research and development (R&D) suggest that New Zealand firms
may have plenty to learn from competitors, suppliers and customers offshore.

3Our results suggest that when standard methodologies are used New Zealandfirms do exhibit some gains from exporting, at least for employment, labourproductivity and the capital-labour ratio. However, when we introduce ourmost stringent matching methodology much of this effect disappears. In thenext section we outline the existing empirical literature on exporting andproductivity and review methodological options for identifying LBE effects.
Section 3 describes the data and our empirical approach. Section 4 gives theresults, while section 5 summarises and discusses areas for further work.

2

Literature review

Over the past 15 years, since the publication of seminal works by Bernard
and Jensen (1995) and Roberts and Tybout (1997), research on the determinants and consequences of firm-level export performance has flourished.
Studies of the exporting-productivity relationship in particular have been
completed for over 30 countries, from Austria to Zimbabwe.2 A wide rangeof studies have focused on identifying the proximate determinants of exporting ability, from innovative ability (eg, Roper and Love 2002) to government
support (eg, Görg et al 2008) and demonstration effects from other local
firms (Greenaway et al 2004; Greenaway and Kneller 2008). Other studies
have considered whether firm-specific characteristics interact with exporting
to determine the existence and extent of productivity benefits from exporting.3 Finally, methodological developments have allowed for a reassessmentof a number of early results, using more sophisticated techniques (eg, Wagner
2002; Girma et al 2004). This review focusses on three areas in which we hopeto add to the existing understanding: the inclusion of multiple measures offirm performance; incorporation of destination country characteristics; andmethodological issues in the selection of appropriate control groups.

2.1

Exporting, productivity and reallocation

Because most LBE studies only consider productivity growth within the firm,
it is often difficult to evaluate potential benefits to the aggregate economy
2Austria is one of 14 countries included in a cross-country comparative study by ISGEP(International Study Group on Exports and Productivity) (2008). Zimbabwe is one ofnine sub-Saharan African nations studied by Van Biesebroeck (2005).
3
For example, Baldwin and Gu (2003) consider differences between foreign- anddomestically-controlled, and between young and older plants, while Fryges and Wagner(2008) focus on export intensity.

4

through resource reallocation. However, a number of studies provide convincing evidence that reallocation from less productive domestically-focussed
firms towards more productive export-oriented firms is a significant sourceof aggregate productivity growth.
Bernard and Jensen (2004) compare the performance of exporters and nonexporters in the US on a number of dimensions and find that, while exporting
does not lead to productivity improvements in firms, it does have a significant effect on firm growth in employment and sales (both domestic andforeign). They find that over 40 percent of TFP growth in the US manufacturing sector can be attributed to the impact of exporting on resource
reallocation. Baldwin and Gu (2003) find that continuing and new exporters
together account for almost all aggregate productivity growth in Canadianmanufacturing, through a combination of intra-firm improvements in productivity and inter-firm reallocations towards more productive firms. Meanwhile,
Pavcnik (2002) considers the impact of trade liberalisation in Chile between
1979 and 1986. Characterising industries according to their trade orientation (export-oriented, import-competing, or non-traded goods sector) andcomparing firm-level performance between sectors and over time, Pavcnik(2002) suggests that reallocation of resources within the economy accounted
for around two-thirds of aggregate growth in Chilean manufacturing.
These results, alongside similar findings from Falvey et al (2004), Hanssonand Lundin (2004) and others, imply that even if there is no firm-level productivity benefit from exporting, aggregate productivity may well be enhanced through resource reallocation and the expansion of already productive
export-oriented firms. Such impacts are likely to be particularly important
for New Zealand, where the small domestic market is likely to limit growth
opportunities.

2.2

Heterogeneous destination markets

Given the three key channels through which exporting is expected to impact
upon firm performance, it seems that many of these benefits are more likely
for exports to large, highly developed destinations. First, the competitive
disciplines imposed upon exporting firms are likely to be more severe whenexporting to large, developed markets, as these markets can be expected tohave a significant number of local suppliers already and may also attract a
broader range of suppliers from abroad. At the same time, more sophisticated consumers are likely to place greater demands on exporters in terms of
product quality and timeliness. Second, opportunities to learn from offshore5contacts will be more beneficial the greater the degree of sophistication of
those contacts.4 Finally, in imperfectly competitive markets, firms may beable to charge higher prices to consumers in wealthy countries, leading tohigher observed value-added with no change in the underlying efficiency of
the firm.5
If learning relies on the destination country having superior economic performance to the exporting country, we would expect to find that LBE is morecommonly observed in less developed countries (LDCs). Such evidence canbe garnered “by feel” – the unsystematic approach of reading many studies
and looking for general patterns in the results – or through statistically robust methods. One paper which takes the latter approach is that of Martins
and Yang (2009). These authors perform a meta-analysis of 218 estimatesdrawn from 32 studies on the productivity impacts of exporting. They investigate the impact of both methodological and contextual differences in thestudies performed in explaining which studies are most likely to find LBE
effects. The methodological issues they consider include whether the study
uses a matching approach to the evaluation of impacts, whether the measureof productivity used is TFP or labour productivity, and whether the impacts
are estimated for the year of entry or longer term.6 Contextual issues are limited to the sample size (number of firm-year observations), the years coveredby the data, and the development level of the source country.7
Of the 218 estimates in the paper, 55 percent used data from developed countries and 41 percent used propensity score matching techniques. The authors
perform meta-regressions using four different weighting systems, based on the
rankings of the journals in which the studies are published. They provide
two specifications – their standard results and one controlling for the standard error of the initial estimate. Over the eight resulting specifications of
the model, only one result came through consistently in all specifications:firms in LDCs are more likely to experience a stronger impact of LBE thanthose in developed countries. While this is far from conclusive evidence that
destination country characteristics matter, it is consistent with a model in
4

Providing that the exporting firm is itself sophisticated enough to benefit from thesecontacts. See Sanderson (2004) on the role of “absorbative capacity” in determining
firms’ ability to benefit from international engagement.
5
Fabling, Joyce, and Sanderson (2009) find some indications of pricing-to-market basedon destination GDP per capita. The extent to which charging higher prices to foreign
markets affects observed productivity will depend on the degree to which these gains
are offset by higher marginal costs (eg, transportation and insurance).
6
Methodological issues are discussed further in Section 2.3, below.
7
Development level is a binary variable based on UN definition of a developed economy,
but the authors note that their results are robust to alternative definitions.

6which firms are more likely to learn from exporting if their exports put themin contact with firms or consumers in countries more developed than theirown.
There are very few papers which have directly addressed the question of destination market characteristics, perhaps due to a lack of detailed destination
data for many countries. However, four recent papers have attempted toanswer this question.
Trofimenko (2008) provides perhaps the most comprehensive treatment ofheterogeneity in learning responses to exporting, allowing for the relationshipto depend not only on the destination of exports, but also on the relative (exante) productivity of the firm and the level of sophistication of the industry(based on the share of highly skilled employment). Her results suggest thatexporting does impart a productivity benefit to firms and that more productive firms gain an additional benefit from exporting to advanced economies.
However, her ability to provide conclusive answers on the impacts of destination characteristics is limited by a lack of firm-specific data on export
destinations. In the absence of firm-level destination data, Trofimenko links
industry-year aggregate shares of exports by destination to firm-level productivity and performance data, which includes an indicator of whether the firm
exported in a given year. She thus tests whether LBE effects are stronger forexporting firms in industries which export to high-income countries, rather
than for firms exporting to high-income countries.
Two papers using Slovenian data (Damijan et al 2004 and De Loecker 2007)
also consider the relationship between productivity growth and the characteristics of export destinations. Both papers find that exporting has a positiveeffect on productivity growth, but that this impact is limited to firms exporting to high-income countries.8 The two papers differ in their estimatesof the timing of such effects – Damijan et al suggest that the productivity
boost from exporting is strong but short-lived, observed only in the first andsecond years of exporting, while De Loecker finds ongoing productivity gains
as much as five years out from export entry.
It is worth noting, however, that the economic environment in Slovenia overthe period covered by these studies was somewhat exceptional. Since gaining
8

Damijan et al (2004) compare three possible destination groups: countries of theformer Yugoslavia, OECD countries, and all others. De Loecker (2007) instead divides
export destinations on regional boundaries, classifying North America, Western andSouthern Europe as high income regions. Some noise may be introduced by this lattermethod. For example, under De Loecker’s definition Japan would be counted as a
low-income export destination.

7

independence from Yugoslavia in 1991, Slovenia has undergone a substantial
programme of privatisation and trade liberalisation. Between 1994 and 2000,
the value of manufacturing exports increased by 42 percent and the numberof exporting firms quadrupled (De Loecker 2007). Given the specific circumstances faced by Slovenian firms over this period, it is not surprisingthat exports to developed countries may have provided rapid access to technologies and management styles not easily available domestically,9 nor thatexports to former-Yugoslav countries (which up until the 1989 would nothave even counted as exports) did not provide such benefits.10
The question therefore arises as to whether the results of Damijan et al(2004) and De Loecker (2007) can be realistically assumed to apply for firms
in open, developed economies. Evidence from Pisu (2008) suggests that theanswer is no. Using data on Belgian manufacturing firms between 1998 and2005, Pisu finds that while initial examination suggests that firms which
enter export markets experience productivity gains relative to those thatremain domestically focussed and that this relationship is stronger amongfirms that export to high-income destinations, these results are not robust to
more formal empirical tests. Specifically, when matching methods are applied
to determine a suitable control group of non-exporting firms, all significant
LBE effects disappear and the positive relationship between exporting andproductivity is shown to be due entirely to self-selection.

2.3

Methodological issues

One of the key methodological questions in empirical investigations of causal
relationships is the identification of a suitable control group. The goal of any
evaluation is to compare observed outcomes with the counterfactual outcome– in our case, to compare the productivity growth of firms which entered
new export markets with the productivity growth that same firm would haveexperienced had they not entered. Clearly, a direct comparison is impossible,
as we can only ever observe a firm in one state or the other.
9

Damijan and Majcen (2003) also note that Slovenia received relatively low levels of
inward FDI over the 1990s and that FDI does not seem to have had the strong effecton growth that it did in other transition economies.
10
Weak product market competition and less demanding consumers in the former Yugoslav countries, especially combined with free-trade agreements between Slovenia andthree of the four former Yugoslav countries imply that entry barriers to these countries
are low (Damijan 2001), while at the same time providing little scope for learning-byexporting.

8If selection into exporting were random, a simple comparison of exportingand non-exporting firms would provide an appropriate test of the impacts ofexporting. However, it has been widely shown that selection into exporting
is non-random: exporting firms have superior performance prior to entry.
A simple comparison of productivity outcomes for exporting firms relative
to non-exporting firms would therefore pick up not only differences due toexporting, but also pre-existing differences in productivity levels and growth
rates between exporters and non-exporters. One of the more contentious
questions in quantifying LBE effects has been the appropriate implementation of controls for non-random selection. That is, robust evaluation of thecausal relationship between exporting and productivity requires the identification of a plausible counterfactual.
Although authors have approached the question in various ways, two core
methods can be identified in the literature to date. Most early papers (andmany more recent ones) build on the approach introduced by Bernard andJensen (1999), using a series of panel regressions (with or without controls
for unobserved firm fixed effects) of the formln(P RODit ) = α + β(EXP ORTit ) + γ(Zit−1 ) + it

(1)

or,
∆ ln(P RODit ) = α + β1 (ST ARTit ) + β2 (ST OPit ) + β3 (CON T IN U Eit )+γ(Zit−1 ) + it(2)where EXPORT, START, STOP and CONTINUE are dummies representing
the export status of the firm and the excluded category is non-exporters.
Hence, the estimated impact of exporting is the difference in the productivity
growth rate of firms which have recently entered relative to those which do notexport, beyond that which can be explained by differences in a set of control
variables. The control variables used differ across studies, with most studies
including at least industry and year dummies, and often some measure of firmsize. Some papers then allow for the impact of exporting to differ depending
on firm characteristics (eg, Baldwin and Gu 2003; Fryges and Wagner 2008).
Others also allow for export intensity (the share of exports in total sales)
to matter (eg, Hansson and Lundin 2004; Fryges and Wagner 2008). Moresophisticated regression frameworks have also been applied, including the9use of instrumental variables and system-GMM (eg, Baldwin and Gu 2003;
Van Biesebroeck 2005).
Critics of the standard regression approach argue that a comparison of allexporting firms with all non-exporting firms gives a flawed picture of the
returns to exporting. In particular, these papers suggest that a matched firmmodel provides a more robust control for the differences between exportingand non-exporting firms. These methodologies draw heavily on the literature
on programme evaluation (eg, Smith 2004; Imbens and Wooldridge 2008) andconsider export entry as a “treatment”.
Although the exact details of matching estimators differ, there is a standard
two-step procedure which is common across all models. The first step involves determining a suitable control group of firms which look similar to thetreatment group ex ante but which do not receive treatment. Firms which
are not “similar” to the treated firms are then discarded or down-weighted.11
The most common approach is to match firms based on the probability of receiving treatment conditional on pre-treatment characteristics – the “propensity score”.12 Rosenbaum and Rubin (1983) prove that as long as there areno (unobserved) characteristics which are associated with both the potential
outcome and the probability of treatment (“unconfoundedness”) and suitable
control cases can be found for each treated case (“overlap”), conditioning on
the propensity score is sufficient to remove all the bias associated with differences in pre-treatment characteristics between the treated and untreatedgroups. Thus, all systematic differences in outcomes between the treated andcontrols are attributable to the treatment. Once propensity scores have been
calculated, a number of possible matching techniques may be applied, differing with respect to the number of control firms matched to each treated firmand vice versa and the requirements for determining how similar two firms
must be to be considered a valid control.13 The second step is a comparisonof the outcome variables of interest between the two groups some time after
treatment.
A key question in matching models then is whether the observable differences
between firms are sufficient to control for self-selection bias or whether there
11

Some treated firms may also be discarded if no suitable match can be found. As such,
matching models have the additional advantage of forcing researchers to confront theissue of “common support”, and hence to consider the extent to which their findings
can be generalised to the entire population.
12
Alternatives include matching firms based on the underlying observable characteristics(eg, firm size, industry, foreign ownership) or on a combination of propensity score andother characteristics (known as Mahalanobis matching).
13
See Caliendo and Kopeinig (2008) for a practical discussion of matching methods.

10

is instead some unobserved factor which determines both the probability oftreatment and the later outcomes. For the purposes of our work on exportingand productivity, one possible such factor might be managerial incentives andability. Managers with a strong focus on growth might be expected to beboth more likely to enter into export markets and more likely to exhibit
higher productivity growth in future periods, regardless of export market
entry.
A range of options to help control for unobservable, time-invariant differences
between treated and untreated firms have been developed. One option is toimplement a difference-in-difference (DID) matching estimator, as suggested
by Heckman et al (1998). This method has been implemented in a numberof studies of exporting and productivity (eg, Girma et al 2004; Alvarez andLópez 2005; De Loecker 2007). While a standard matching model simply
compares the ex-post performance of new exporters with that of matched
non-entrants, the DID estimate instead compares the change in performance
between the two groups in the period following market entry. Further alternatives include using a regression-adjusted or bias-corrected matching estimator, including relevant covariates in the second stage outcome regressions
to capture any remaining observable differences between matched pairs. Finally, Imbens (2004) suggests weighting on propensity scores to obtain abalanced sample of treated and untreated firms.
The empirical evidence suggests that matching models may provide a more
stringent test of the LBE hypothesis. Studies using matching methods areless likely to find significant LBE effects than those using panel methods(Greenaway and Kneller 2007; Martins and Yang 2009). However, it is also
possible that the inability to find significant effects of exporting is due to the
reduction in sample size when using matching methods.14
Other methodological issues discussed in the literature include the timingof any potential gains from exporting. As well as identifying whether future
exporters already showed superior performance levels to non-exporters in the
period prior to export market entry (the self-selection hypothesis) a numberof papers have considered whether future exporters see a boost in productivity growth in the years leading up to market entry (the “learning-to-export”
14

For example, if only 3 percent of firms commence exporting over the study period andeach is matched to a single control firm, 94 percent of all observations are discarded.

11

hypothesis) (eg, Alvarez and López 2005).15 Similarly, many authors distinguish between the contemporaneous impacts in the year of export market
entry and longer term effects (eg, Greenaway and Kneller 2008). Timing aspects of the export-productivity relationship are important as they provide
some insight into the channels through which exporting may be acting. As
efficiency gains due to learning or competitive pressures are likely to takesome time to realise, instantaneous productivity boosts may reflect insteadthe benefits of improved capacity utilisation. In contrast, longer term ongoing growth benefits are more likely to imply an evolutionary learning process.
In this paper we focus on the productivity differential between entrants andnon-entrants two years after the entry decision – a choice driven by sample
size considerations. Future versions of the paper will investigate timing issues
more fully.

3

Data and empirical strategy

The key goal of this paper is to identify whether there are observable benefits attributable to exporting among New Zealand firms and whether the
existence and/or magnitude of such benefits depends upon the developmentlevel of the firm’s export destinations.
To address this question we use data from the prototype Longitudinal Business Database (LBD). The LBD is based on Statistics New Zealand’s Longitudinal Business Frame and draws together administrative and survey dataon all economically significant enterprises (firms) in the New Zealand economy. Firm performance measures used in this paper are derived from Inland
Revenue Department and Annual Enterprise Survey data,16 while detailed
export activity records are obtained from shipment level data from the New
Zealand Customs Service, probabilistically matched to firms on the Longitudinal Business Frame. A full list of the variables used and their definitions
is included in Appendix A.
15

Productivity improvements in the years prior to export market entry are often explainedin the literature as firms actively gearing up to enter foreign markets. In some cases
they may also reflect active involvement by offshore potential customers (particularly
for firms from developing countries). Questions of causality remain an issue here,
however, as an unexpected positive productivity shock may also push firms into export
markets. Finally, it is possible that a strategy of actively moving towards export
markets may lead instead to a fall in productivity in the years prior to entry if firms
are investing in capital equipment or R&D which will not be fully utilised until they
expand into offshore markets. See eg, Bellone et al (2008).
16
Following the method of Fabling and Grimes (forthcoming).

12

We restrict attention to employing, ever-manufacturing firms. In some cases,
observed exports have been reallocated from non-manufacturing enterprises
to manufacturing enterprises within the same parent-subsidiary group.17 For
the remainder of this paper, we use the term firm to refer to both individual
enterprises and groups of manufacturers within a parent-subsidiary relationship. We further restrict to those firm-year observations for which we havea full set of outcome variables: MFP, labour productivity, employment andthe capital-labour ratio. This reduces the population from 175,449 to 116,529
observations, or 31,083 firms over seven years (2000-2006) for cross-sectional
statistics.
Our preferred methodological approach is to implement propensity scorematching based on firm characteristics and to compare outcomes between
entrants and non-entrants two years following market entry. In order to implement this approach we need to be able to follow firms over a four year
period: the “control year” (t = 0) provides the ex ante performance metrics
on which we match; the “treatment year” (t = 1) is the year in which weeither observe an export market entry (treatment) or not (control); and thesecond year following treatment (t = 3) is the point at which we compare
outcomes between the treated and non-treated groups. When the matching involves export history variables these are additionally calculated over
−4 ≤ t ≤ 0.
Throughout the analysis we use data from only those firms which can provide
performance data at t = 0 and t = 3. Matching on lagged performance andconsidering two-year forward outcomes further restricts the treatment years
we consider to 2001-2005. Table 1 shows the incidence of missing financial
performance data in the treatment and outcome years relative to the control
year.18 This attrition forces us to qualify our conclusions regarding the quality of match between treatment and control firms. Specifically, table 2 shows
marginal coefficients from a probit regression of MFP attrition at t = 3 forthe population that has MFP at t = 0. New and continuing exporters are observed to have a lower attrition rate than either non-exporters or firms whichcease exporting (negative coefficients on transition01 and transition11). If
this is due in part to the effect of treatment, we may underestimate causal
effects (provided some sub-optimal matches have occurred where the ex ante
best matched control firm is precluded because it cannot provide outcomedata).
17

The methodology and rationale for this allocation is documented in Fabling and Sanderson (forthcoming).
18
As there are a number of possible reasons for firms to have missing performance data
we cannot be certain that this attrition is due entirely to firm closures.

13

Table 1
Proportion of missing MFP data conditional on MFP at t = 0
Treatment year (t=1)
First year outcome (t=2)
Second year outcome (t=3)

Observations
0.215
0.295
0.365

Firms
0.188
0.268
0.334

Table 2
Probit regression for absence of MFP data at t = 3
Marginal
p-valueeffectsrc01 (Northland)
0.020
0.108
rc02 (Auckland)
0.019*** 0.006
rc03 (Waikato)
0.019**
0.034
rc04 (Bay of Plenty)
0.035*** 0.000
rc05 (Gisborne)-0.0290.241
rc06 (Hawke’s Bay)
-0.0100.416
rc07 (Taranaki)
0.002
0.877
rc08 (Manawatu-Wanganui) -0.014
0.198
rc09 (Wellington)-0.001
0.868
rc12 (West Coast)
0.062**
0.021
rc13 (Canterbury)-0.025*** 0.001
rc14 (Otago)-0.024**0.042
rc15 (Southland)
0.003
0.863
rc16 (Tasman)-0.0150.444
rc17 (Nelson)
0.009
0.608
rc18 (Marlborough)-0.034*0.058
MFP-0.049***0.000
ltotemp-0.071***0.000
klratio-0.019***0.000
FDI
0.052***
0.003
transition01-0.059*** 0.000
transition10
0.071*** 0.000
transition11-0.048*** 0.000N82,392
Pseudo R2
0.040
Robust p-values in brackets (clustered on groups).
Significant at: * 10%; ** 5%; *** 1%
Regression includes a full set of (unreported) industryyear dummies. TransitionXY is a dummy equal
to 1 if export status is X at t = 0 and Y at t = 1.
Transition00 is the omitted category.

14

Several alternative measures of entry are examined. The simplest definition
uses a single year of export history, and defines the treatment variable as “thefirm exports (to any destination) in period t = 1, and did not export (to any
destination) in t = 0”. This definition of “year-on-year” entry has been usedby a number of authors (eg, Aw et al 2000), but does not, in our view, provide
a suitable distinction given the variety of observed export histories. However,it does provide the opportunity to control specifically for those differences,giving some insight into the importance of controlling for the unobserved
differences which help to determine a firm’s export propensity.
In our preferred specifications we base our measures of export market entry
on five years of historical data, with a firm counted as an entrant only if they
have not been observed to export over the preceding five years. We thenconsider two alternative forms of entry – “first-time” entry into exportingand “incumbent” entry into new markets by incumbent or past exporters.
The former provides the cleanest definition of entry into exporting, withentrants defined as firms which export in period t = 1, and did not export(to any destination)in the five years from t = −4 to t = −0.
Meanwhile, the latter definition allows for a more stringent test of the learningby-exporting hypothesis in its purest form. Matching incumbent exporters
with other incumbent exporters gives us a much better match quality, reducing the chance that unobserved differences between our treatment andcontrol groups will bias our results. For example, the move into exporting
can represent a significant increase in risk for New Zealand firms and many
firms may have no interest in making that leap, even if their observable characteristics suggest they should have the ability to export.19 If we have alreadyobserved a firm to export at some point in the past, we can be more confident
that they have at least some desire to export. Entry into new markets canthen be used as an indicator of first access to the technologies and consumer
demands associated with a different destination.
By distinguishing between new markets according to their per capita income
level, we are able to separately consider the effect of entry into a high-income
country as opposed to entry into exporting more generally. Relatively moresophisticated export markets should imply greater opportunities for learning.
We therefore distinguish between entry into high- and low-income markets,
19

According to the Business Operations Survey 2007, even among manufacturing firms,
over half do not earn any overseas income. Of those firms, 21% cite prohibitive costs or
barriers to exporting. 54% note that the New Zealand market is sufficient, while 59%
state that their requirement for physical proximity to customers prevents them fromentering offshore markets (Statistics New Zealand 2008).

15

Table 3
Share of exporters and entrants by year and by industry, alternative definitions of entry
Year
2000
2001
2002
2003
2004
2005
2006
Total

Current
exporters
0.131
0.135
0.141
0.147
0.152
0.151
0.154
0.14.5First-timeentrants
0.015
0.014
0.016
0.018
0.018
0.016
0.014
0.016

Year-on-year
entrants
0.024
0.024
0.026
0.029
0.028
0.027
0.025
0.026

Incumbent entry
into new markets
0.473
0.494
0.474
0.490
0.512
0.504
0.484
0.491

Year
C21
C22
C23
C24
C25
C26
C27
C28
C29
Total

Current
exporters
0.238
0.164
0.091
0.102
0.379
0.098
0.109
0.157
0.094
0.145

First-timeentrants
0.019
0.015
0.009
0.017
0.023
0.017
0.014
0.017
0.016
0.016

Year-on-year
entrants
0.028
0.026
0.014
0.027
0.044
0.026
0.023
0.031
0.024
0.026

Incumbent entry
into new markets
0.562
0.462
0.432
0.442
0.503
0.546
0.480
0.510
0.426
0.491

where high-income is defined as countries having GDP per capita greaterthan USD17,000. This cut-off point roughly reflects the per capita incomeof New Zealand in our dataset and also splits the sample of New Zealandexports approximately equally between high- and low-income destinations.20
At the same time, as expansion into new markets is a more common event
than first-time entry into exporting, we also increase the size of our sample in the matching model, making it more likely that we will be able toidentify significant differences in performance if they do exist. If the potential performance benefits from exporting are due to learning from contact
with competitors, suppliers or consumers in offshore markets we would expect that entry into additional offshore markets should provide new learning
opportunities.
Table 3 summarises the prevalence of entry in each year and industry, according to each definition. This table shows a general increase in the proportionof manufacturers exporting over time from 13 to 15 percent (as found by Fabling and Sanderson (2008)). First-time entry is a rare event, with less than
two percent of firms entering in any given year. In contrast, incumbent en20

GDP per capita data comes from United Nations Statistics (GDP) and the US Census
Bureau (population).

16

Table 4
Hysteresis in exporting
export year count
0
1
2
3
4
5

Exporting at t=1
No
Yes
0.796
0.016
0.032
0.015
0.012
0.014
0.007
0.015
0.005
0.015
0.004
0.070

Proportion(Yes/(Yes+No))
0.020
0.320
0.534
0.689
0.759
0.951

try is not rare, though Fabling and Sanderson (forthcoming) show that mostfirms only trade to one country at a time. Clearly, the year-on-year entry
measure provides an inaccurate picture of the number of true entrants, asalmost 40 percent of year-on-year entrants have in fact exported previously(table 3).
Table 4 tabulates export status in the treatment year with status in theprevious five years. As expected, participation rates at t = 1 rise rapidly
with past export participation. In particular, firms which have at least some
export experience are an order of magnitude more likely to export at t = 1
than those with no export history. This creates difficulties for the analysisof learning effects, as re-entrants may differ from first-time entrants in boththeir ex ante characteristics and their response to exporting (eg, re-entry
may provide fewer learning opportunities than first-time entry). However,the use of year-on-year entry allows us to directly examine the impact ofusing export history variables as part of the matching model.
We therefore compare, in turn, outcomes of the following control and treatment groups, working our way from the simple entry definitions most commonly used in the literature to our more sophisticated matching method:
Entry type

Population

Treatment(1)First-time

Entry into exporting(2)

First-time(3)

Year-on-year(4)

Year-on-year(5)

Incumbent

Firms which have not exported in
the past five years
Firms which have not exported in
the past five years
Firms which did not export in thecontrol year
Firms which did not export in thecontrol year
Prior exporters(6)

Incumbent

Prior exporters

Control forexport history
N

Entry into exporting to a high-income
country(Re-)entry into exportingN(Re-)entry into exportingY

Entry into a new export destination

Y

Entry into a new high-income export
destination

Y

In each case we estimate the average treatment effect on the treated (ATT)from a matching model, in which the probability of treatment is estimated17

N

separately for each two digit industry based on initial firm characteristics.
The performance characteristics used in specifications 1-3 are MFP, log employment, the capital-labour ratio and a foreign ownership dummy, plus afull set of year and region dummies. In specifications 4-6 we also add a fullset of export history variables, as defined in Appendix A. The estimatedprobability of treatment (or propensity score) is then used to match eachtreated firm to a single control firm within the same industry, using nearest neighbour matching and restricting to the common support.21 We thenpool observations across all industries to compare industry-year demeaned
outcomes between the treated and control groups, adding additional controls for industry.22 Standard errors are calculated through boot-strapping
across both the first stage propensity score estimation and the second stage
estimation of the treatment effect. The boot-strapped sample is drawn independently from the treated and untreated groups to maintain the same
matched sample size across repetitions.
For comparison, we also report the results of a simple regression, in whichfirm outcomes (in year t = 3) are regressed on pre-treatment outcomes (t = 0)
and a dummy variable indicating treatment, as well as a full set of industry
and year dummies (Zi,t=0 ):OU T COM Et=3 = α(OU T COM Et=0 )+β(T REAT M EN Tt=1 )+γ(Zi,t=0 )+t=3 .
(3)

4

Results

4.1

Exporters are different in cross-section

To set the scene, we first provide a visual comparison of the four performancevariables across three groups of firms – current exporters, past exporters andnon-exporters. Figure 1 shows kernel densities of the performance metrics
with all industries pooled together. Industry-year averages are removed fromall performance metrics to allow comparability and to enable matching to bepooled across years (within an industry). Like studies in other countries, we
21

We use psmatch2 (Leuven and Sianesi 2003) to generate propensity scores and test
the balancing of the matched sample.
22
Industry ATTs were also produced but the estimated effects were seldom significantly
different from zero, perhaps due to the small sample available for each industry.

18

find that current New Zealand exporters are larger, more capital-intensive
and have higher labour productivity than past exporters (ie, their distributions sit to the right), who in turn perform better on these three measuresthan non-exporters.23 In contrast, however, we see little difference in the
MFP levels of the three groups.
In contrast to other papers considering differences across destination markets, we see little to suggest that high-income destinations present a greater
challenge for prospective exporters. Figure 2 compares the distribution of observed performance measures across three groups: exporters to low-income
countries only; exporters to high-income countries only; and exporters whichtarget both low- and high-income countries. We see little to distinguish between exporters to low- and high-income countries. Rather, the substantive
differences are between firms which export to only one group of countries andthose which export to both. Given the relative concentration of firms which
only export to a single destination (shown in Fabling and Sanderson forthcoming), it seems likely that this distinction is due more to the comparisonof single-market exporters and multiple-market exporters, rather than theperformance requirements to export to both high- and low-income countries
specifically.
To quantify the relationship between performance and export history we perform simple regressions which control for additional firm characteristics that
may impact on performance. Table 5 presents the results of these regressions. Columns A,C,E and G present results estimated over all firms, whileother columns show results for the sub-sample of firms with some past export
experience. Regional Council dummies are included to control for regional
differences in, eg, infrastructure, agglomeration or land quality which may
affect both firm performance and the probability of exporting.24
These estimates suggest that there are indeed differences between exporters
and non-exporters on a range of performance dimensions. Firms which haveexported more frequently over the prior five years (export year count), thosewhich have exported more recently (inv export time) and those which havebeen increasing their share of exports (d export share incum xbar) performbetter across most of our measures than those with less export experience.
Meanwhile, firms which export a larger share of their sales on average overthe five year period perform consistently less well.
23

Employment comparisons are affected by the prevalence of very small firms among
non-exporters, in particular working proprietor only firms.
24
See Maré (2008) and Fabling, Grimes, and Sanderson (forthcoming) for New Zealandevidence of agglomeration and localised export learning effects respectively.

19

Figure 1
Kernel density of performance by export status

Figure 2
Kernel density of exporter performance by destination income20Table 5
Cross-sectional performance and export experience
MFP
LP
(A)
(B)
(C)
(D)
All
Exports
All
Exportsrc01 (Northland)-0.179***-0.058-0.099***-0.009[0.000][0.203][0.000][0.895]rc02 (Auckland)
0.115***
0.086***
0.247***
0.214***[0.000][0.000][0.000][0.000]rc03 (Waikato)-0.061***-0.055**0.037**
0.037[0.000][0.047][0.034][0.285]rc04 (Bay of Plenty)-0.042***-0.0480.045**
0.047[0.008][0.133][0.020][0.227]rc05 (Gisborne)-0.111***-0.306*** -0.022-0.248***[0.009][0.000][0.664][0.003]rc06 (Hawke’s Bay)
-0.028-0.0390.064***
0.059[0.160][0.343][0.009][0.243]rc07 (Taranaki)-0.022
0.019
0.034
0.076
[0.360][0.685][0.251][0.284]rc08 (Manawatu-0.086***-0.058**-0.020-0.004
Wanganui)[0.000][0.044][0.341][0.910]rc09 (Wellington)
0.024
0.028
0.122***
0.122***[0.107][0.267][0.000][0.000]rc12 (West Coast)-0.076*-0.098*0.014-0.040[0.060][0.090][0.755][0.538]
rc13 (Canterbury)-0.050***-0.086***
0.037**
0.009[0.000][0.000][0.011][0.690]rc14 (Otago)-0.055***-0.0500.026
0.008[0.005][0.183][0.273][0.849]rc15 (Southland)
0.034-0.0180.099***
0.031[0.154][0.660][0.001][0.574]
rc16 (Tasman)-0.206***-0.094*-0.150***0.003[0.000][0.092][0.001][0.968]rc17 (Nelson)-0.059*-0.024-0.023-0.078[0.057][0.608][0.538][0.153]rc18 (Marlborough)-0.106***0.008-0.0230.003[0.002][0.892][0.565][0.961]fdi
0.086***
0.041
0.452***
0.339***[0.000][0.122][0.000][0.000]0.008
0.004
0.082***
0.060***
export year count[0.207][0.571][0.000][0.000]inv export time
0.114***
0.077**
0.247***
0.089***[0.000][0.010][0.000][0.007]-0.003*-0.004***
0.003
0.002
n countries 5yr[0.067][0.001][0.120][0.418]cty entry-0.0020.001
0.002
0.008*[0.666][0.784][0.675][0.051]cty exit-0.005-0.001-0.0050.003[0.327][0.821][0.331][0.626]
avg export share-0.184***-0.217*** -0.548***-0.633***[0.002][0.000][0.000][0.000]d exports incum xbar
0.062***
0.051**
0.128***
0.118***[0.006][0.024][0.000][0.000]d exports entry xbar
0.032
-0.0030.097*** -0.013[0.116][0.909][0.000][0.607]d exports exit xbar
-0.048**0.001-0.202*** -0.002[0.016][0.972][0.000][0.938]Constant-0.0050.282*** -0.221***0.299***[0.847][0.000][0.000][0.000]N
116,529
21,915116,529
21,915
R2
0.017
0.039
0.089
0.136
Robust p-values in brackets (clustered on groups).
* significant at 10%; ** significant at 5%; *** significant at 1% level.
21
Regressions include a full set of (unreported) industry-year
dummies.

klratio(E)(F)
All
Exports
0.084***
0.052[0.007][0.496]0.160***
0.190***[0.000][0.000]0.091***
0.058[0.000][0.161]0.057**
0.037[0.017][0.422]0.107*
0.146[0.078][0.251]0.086***
0.146**[0.005][0.022]0.013
0.060[0.731][0.498]-0.016-0.033[0.521][0.501]
0.098***
0.077**[0.000][0.031]0.103*
0.018[0.053][0.853]0.012
0.015[0.482][0.601]0.011-0.012[0.685][0.819]-0.078**-0.161*[0.043][0.086]0.039
0.114[0.449][0.327]0.026-0.086[0.562][0.256]
0.092*
0.018[0.070][0.859]0.553***
0.479***[0.000][0.000]0.066***
0.044***[0.000][0.000]0.180***
0.034[0.000][0.382]0.005**
0.006**[0.025][0.018]-0.0020.002[0.768][0.771]-0.007-0.003[0.334][0.708]-0.224**-0.269***[0.016][0.004]0.041
0.039[0.194][0.216]
0.097***
0.014[0.000][0.639]-0.229*** -0.058*[0.000][0.065]-0.238***0.097[0.000][0.123]
116,529
21,9150.055
0.096

ltotemp(G)(H)
All
Exports
0.248***
0.158[0.000][0.152]0.365***
0.314***[0.000][0.000]0.322***
0.376***[0.000][0.000]0.322***
0.393***[0.000][0.000]0.255***
0.000[0.001][1.000]0.282***
0.209**[0.000][0.014]0.257***
0.167[0.000][0.107]0.386***
0.376***[0.000][0.000]0.309***
0.323***[0.000][0.000]0.272***
0.323**[0.000][0.036]0.435***
0.485***[0.000][0.000]0.400***
0.343***[0.000][0.000]0.497***
0.611***[0.000][0.000]0.173***
0.185[0.001][0.193]0.139***-0.081[0.009][0.527]0.205***-0.151[0.000][0.147]0.828***
0.603***[0.000][0.000]0.260***
0.209***[0.000][0.000]0.375***-0.014[0.000][0.772]0.023***
0.024***[0.000][0.000]0.018**
0.028***[0.048][0.001]
0.011
0.025*[0.438][0.061]-1.613***-1.755***[0.000][0.000]0.254***
0.276***[0.000][0.000]0.144***-0.084**[0.000][0.032]-0.354***0.130***[0.000][0.003]-0.713***-0.197**[0.000][0.024]
116,529
21,915
0.367
0.483

4.2

Self-selection or Learning-by-Exporting?

In this section, we begin by reporting the probit models on which the matching is conducted. We then present ATT results based on that matching.
Table 6 contains the critical propensity score (probit) models on which thematching is performed.25 The four reported models conform to the four
potential treatments (first-time, year-on-year with and without export history controls, and incumbent). If selection into treatment is non-random,
as previous studies have shown, these models must adequately capture thesystematic differences between entrants and non-entrants in order to validate the estimated ATT. The results confirm our ex ante expectations: firms
which are larger, more productive and more highly capital intensive are more
likely to commence exporting (Column A), and also more likely to expand
into new markets (Column D).
There is little difference between the results in Columns A (first-time entrants) and B (year-on-year entrants), with higher coefficients for the latter
regression reflecting the higher overall probability of observed entry whenre-entrants are included. Comparison of Columns B and C provides justification for the inclusion of export history variables in the propensity scoreregression and reinforces our concerns about the legitimacy of the year-onyear treatment variable. The impact of including these variables is to mildlyreduce the magnitude of the coefficients on most of the firm characteristics,
while increasing the explanatory power of the model from a Pseudo R2 of
0.105 to 0.188. That is, the model including detailed export history variables
provides a substantially better quality match between control and treatmentgroups than that using ex ante performance characteristics alone.
At this point, it is important to note that within each of our industry-specificpropensity score matching models there were occasional variables which did
not balance.26 That is, our match on the propensity score has left us withdifferences in the distribution of some covariates between the control andtreatment groups. In principle, a high quality match requires that after conditioning on the propensity score, additional conditioning on the firm-level
25

These results are presented for pooled industries with industry dummies. Table 6 istherefore a summary version of the propensity score calculations used in the causal
analysis in which we estimate propensity scores separately for each industry and matchwithin industries.
26
Inability to balance was more pronounced in some industries than others, with C28(Machinery and Equipment Manufacturing) in particular being unbalanced on a number of outcome variables.

22

Table 6
Probit regressions of export market entry
rc01 (Northland)
rc02 (Auckland)
rc03 (Waikato)
rc04 (Bay of Plenty)
rc05 (Gisborne)
rc06 (Hawke’s Bay)rc07 (Taranaki)
rc08 (Manawatu-Wanganui)rc09 (Wellington)
rc12 (West Coast)
rc13 (Canterbury)
rc14 (Otago)
rc15 (Southland)
rc16 (Tasman)rc17 (Nelson)
rc18 (Marlborough)MFPltotempklratio
FDI
export year countinv export timen countries 5yr
cty entry
cty exit
avg export shared exports incum xbar
d exports entry xbar
d exports exit xbar

first-time-0.001[0.457]0.004***[0.001]-0.003**[0.026]-0.002[0.184]-0.007**[0.022]-0.002[0.293]-0.003[0.238]-0.005***[0.004]-0.003**[0.023]0.005[0.232]-0.001[0.565]-0.002[0.199]-0.006***[0.003]0.000[0.970]0.001[0.657]-0.005**[0.037]0.003***[0.000]0.009***[0.000]0.005***[0.000]0.003[0.380]

year-on-year-0.004[0.111]0.007***[0.000]-0.005***[0.007]-0.003[0.206]-0.011***[0.006]-0.004*[0.084]-0.005*[0.057]-0.007***[0.001]-0.003*[0.063]0.001[0.789]-0.001[0.467]-0.004*[0.090]-0.007**[0.022]-0.003[0.493]0.003[0.407]-0.006*[0.082]0.005***[0.000]0.015***[0.000]0.008***[0.000]0.003[0.554]

year-on-year-0.002[0.252]0.005***[0.000]-0.003**[0.017]-0.002[0.197]-0.009**[0.012]-0.004*[0.057]-0.004[0.106]-0.006***[0.000]-0.003**[0.035]0.003[0.481]-0.001[0.270]-0.004**[0.042]-0.007***[0.005]0.000[0.917]0.003[0.430]-0.005*[0.081]0.004***[0.000]0.010***[0.000]0.005***[0.000]0.002[0.484]0.003*[0.051]0.111***[0.000]-0.002[0.132]0.005*[0.053]0.002[0.130]0.014***[0.007]0.010[0.107]-0.014***[0.004]-0.004[0.111]
99,852
3,066
0.188

N
94,611
99,852N (LHS = 1)
1,860
3,066
23 0.105
Pseudo R2
0.078
Robust p-values in brackets (clustered on groups).
* significant at 10%; ** significant at 5%; *** significant at 1% level.
Regressions include a full set of (unreported) industry-year dummies.

incumbent-0.035[0.134]0.006[0.452]0.011[0.390]-0.020[0.175]-0.076*[0.087]0.029[0.138]-0.035[0.128]-0.042**[0.014]0.013[0.263]-0.024[0.532]-0.006[0.507]-0.004[0.804]-0.035[0.151]-0.017[0.593]0.057**[0.037]0.033[0.203]0.019***[0.001]
0.046***[0.000]0.014***[0.001]0.004[0.789]0.021***[0.000]0.209***[0.000]0.009***[0.000]0.032***[0.000]-0.005[0.166]0.007[0.788]0.101***[0.000]0.055***[0.001]-0.021[0.268]21,9157,353
0.181

covariates should not provide any extra information about the treatment decision. In general, the variables that we were unable to balance are likely
to be fairly marginal in the differentiation between firms. Many of the unbalanced results were on regional dummies, suggesting that the issue wasmainly due to small numbers of firms in some areas. However, there werealso some industries and definitions of entry in which output variables were
unbalanced. In particular, the capital-labour ratio is unbalanced in several
specifications, implying that higher capital intensity is a key factor differentiating exporters from non-exporters. Balancing was also more difficult inthe model of incumbent new entry, probably due to the high share of treatedfirms on this measure of entry.27
Having convinced ourselves that we can at least partially explain treatmentusing t = 0 variables, we examine the causal effects of exporting on performance. Table 7 presents two sets of results, the naı̈ve regression model of
Equation 3 and the results using matched firms. To test the robustness ofthe estimated ATT we reestimate the model dropping industries which failto balance on one or more outcome variables (changed statistical significancein this test is represented by † and ‡ in table 7).
In all cases, the naı̈ve (ie, biased) regressions suggest that after controllingfor initial performance, firms which enter exporting perform significantly better on all four metrics than those which do not. Estimated coefficients on
the treatment effect for new exporters range between 0.135 and 0.229 forlabour productivity, the capital-labour ratio and total employment and arealways highly significant. In contrast, the effect appears to be much lower
and less significant when the outcome in question is MFP, in keeping withthe observed lack of difference between exporters and non-exporters shownin Figure 1, and smaller coefficients in the earlier cross-sectional regressions(Table 5). The estimated impact of entry into new export markets by incumbent exporters is weaker but still significant across labour productivity,
the capital-labour ratio and employment. Thus, it would appear that there
is potentially a positive causal relationship between both new entry into exporting and expansion of export markets and firm performance, at least interms of employment, capital deepening and labour productivity.
Turning to the results of the matching model, we see that the more robust
estimates based on matched firms suggest a weaker – and in many cases insignificant – relationship between export entry or expansion and firm performance. Following the standard methodology of comparing first-time entrants
27

In any given year between 27 percent and 50 percent of all observations of incumbent
exporters showed entry into new markets.

24with matched non-exporters (the top row of table 7) shows a positive causal
relationship between exporting and three of the four performance variables.
Two years after entry, new exporters exhibit a premium of around eight percent in labour productivity and the capital-labour ratio, and a 16 percent
employment premium relative to non-exporters, but are not significantly different from non-exporters with respect to MFP. When we restrict the treatment definition to include only first-time entries in which the destination is ahigh-income country, coefficients become weaker and lose significance. This
may reflect the smaller sample available (774 treated firms under the highincome definition, compared to 990 firms for all first-time entry). However,
we continue to see a positive impact of treatment on employment.

25

26

Matched firms
Treatment
MFPLP
klratio
ltotempN
rate
First-time entry
0.026
0.087**
0.089*
0.161***
41,2860.024[0.478][0.041][0.055][0.001]First-time entry
0.025
0.067
0.054
0.125**†
41,2860.019(High Income only)[0.549][0.101][0.309][0.012]Year-on-Year entry
0.056**
0.092***
0.054*†
0.114***
43,6890.037(no history)[0.027][0.001][0.096][0.002]Year-on-Year entry
0.032
0.050
0.052
0.014
43,6890.037(history)[0.293][0.139][0.187][0.778]
Incumbent Entry-0.020*0.084***
0.065***
0.108***
0.010‡-0.000-0.059*†0.062
11,0430.367[0.098][0.000][0.000][0.000][0.695][0.996][0.057][0.136]
Incumbent Entry-0.0110.060***
0.056***
0.099***-0.014-0.009-0.0260.082*
11,0430.215(High Income)[0.418][0.000][0.000][0.000][0.569][0.745][0.416][0.086]Regression populations are matched industry subsamples using psmatch2 (Leuven and Sianesi 2003).
All regressions include (unreported) industry dummies.
* significant at 10%; ** significant at 5%; *** significant at 1% level. P-values reported in brackets.
Standard errors bootstrapped (100 reps) for matched firm model.
Robustness checks were performed by dropping those industries which did not balance on one or more outcome variable. †(‡) indicatesestimates that become insignificant (significant) when those industries were dropped.

MFP
0.044**[0.024]0.043*[0.051]0.040**[0.041]

Naı̈ve regression approachLPklratio
ltotemp0.177***
0.135***
0.218***[0.000][0.000][0.000]0.183***
0.144***
0.229***[0.000][0.000][0.000]0.172***
0.138***
0.211***[0.000][0.000][0.000][As above]

Table 7
Causal Models of Exporting and Firm Performance

However, the first-time entry definition is subject to the critique that they
are not able to control for unobserved differences between exporters and nonexporters, as discussed above. Turning to the year-on-year definition, we findthat without controls for export history entry has a positive and significant
impact on all four performance variables. However, when we control for pastexport history, none of the effects is significant at the 10 percent level.
Comparison of first-time and year-on-year results requires an understandingof population differences. Using five years of export history, we select new
first-time entrants (treated firms) from a population which includes only firms
which have not exported at all over the past five years. With the year-on-year
definition, sporadic and exited exporters enter into both the treated groupand the pool of potential controls. If firms which have exited exporting arealready on a downward performance trajectory (as has been suggested byeg, Girma et al 2003), these firms can be expected to see a continued fall in
performance relative to the control year, pushing up the observed difference
between the control and treatment groups as we match new exporters andre-exporters to failing past exporters. When we introduce controls for pasthistory into the matching model, we reduce the potential for new exporters
to be matched with past exporters and this observed impact disappears.28
Finally, we consider entry into new destination countries by incumbent exporters. Here, we match on a full set of firm and export history variables.
The results suggest no impact of new market entry on employment, labour
productivity and multifactor productivity, and a mildly negative effect on
the capital-labour ratio. The capital-labour ratio is the outcome variable onwhich we have most trouble achieving a balanced sample of matched firms,
and the negative result is not robust to the exclusion of industries which do
not balance. In turn, the estimated impact of MFP becomes mildly significant (and positive) when we exclude those industries which do not balance.
The failure to balance is likely due to the high share of treated firms under
this definition (on average, each year around 37 percent of all incumbent
exporters entered a new market) reducing the pool of potential control firms.
Restricting the treatment variable to entry into high income markets reduces
the balancing problem somewhat (averaged across years, only 21 percent offirms enter a high income country). Two years after entry, firms entering
new high-income countries exhibit an employment premium of around eightpercent relative to matched incumbent exporters which did not enter a new
28

There are also arguments for underestimation of the causal effect. For example, reentering exporters may have already gained the performance premium (if this is aone-off effect). However, the empirical results suggest the upward bias dominates.

27

market, though this coefficient is quite imprecisely estimated.

5

Conclusion

Our results suggest that common models of the impacts of exporting on firm
performance may be upwardly biased due to a lack of appropriate controls
for differences between entrants and non-entrants. In particular, propensity
score matching can be significantly improved by including export history
variables. When this more stringent control is applied almost all evidence ofperformance premia due to exporting disappears.
We find no evidence to suggest that exporting to high-income countries has astronger effect on firm performance than exporting to low-income countries.
While naı̈ve regression estimates suggest slightly higher point estimates when
considering only high-income entries, matching negates this finding.
Further, we find that the positive impacts of entry into exporting are due toan increase in employment and capital investment, rather than an improvement in the efficiency with which firms utilise inputs. This suggests thatscale and reallocation effects may be of more importance to aggregate productivity benefits from exporting, rather than efficiency improvements at thefirm-level. However, it must be noted that increases in capital intensity may
in part reflect learning from offshore, if exporting helps firms to identify newtechnology in which to invest. Capital-intensity is a key difference between
exporting and non-exporting firms, so much so that it creates difficulties inbalancing the sample. This finding has implications for the interpretationof our work, where alternative matching methods may be necessary, andfor other published papers, which focus on labour productivity and do notcontrol for capital use.
A number of other issues remain unaddressed in this paper. These include
methodological issues common to all analyses and others which are specificto the question at hand. The more general matters include ignoring endogeneity of inputs when measuring productivity. This issue may be material
because simple OLS estimates of production functions bias capital coefficients
down and exporters have higher capital-labour ratios. Possible solutions tothis problem have been proposed by Olley and Pakes (1996), Levinsohn andPetrin (2003) and Ackerberg et al (2006) but have not yet been implemented
using New Zealand data.
There remain issues around the continuity of firm identifiers in the LBD.
28

Firms which experience changes in ownership or legal structure are often
allocated a new firm identifier by Statistics New Zealand. Such changes
imply some mismeasurement of export histories and that some firms falsely
appear to exit.29 Our method of allocating exports within parent-subsidiarygroups reduces this problem, but does not completely eliminate it.
More specific to this analysis are issues around the channels through whichfirms are believed to benefit from exporting. In this paper we consider only
entry into new destination markets, but it is possible that market entry may
interact with other characteristics of the firm, including the type of products
traded, in determining the impact of exporting on performance. For example,
it may be that learning only provides a benefit for exporters of sufficiently
sophisticated products. Future work will explore in more detail differences
across both markets and products. With respect to possible scale effects fromexporting, it is also worth noting that most New Zealand exporters export
only a very small proportion of their sales (Fabling et al 2008). If scaleeffects are indeed a major factor in firms’ ability to improve their performancethrough exporting, these benefits may be restricted to a small subset of the
population.
Further work is also needed to identify the dynamic effects on performance.
In both the matching and the naı̈ve regression models we can make no conclusions regarding the timing or dynamic aspects of the performance improvements based on the results outlined above. For example, our results
show an employment premium of 16 percent in the second year after entry.
However, we cannot tell whether this is due to a sustained higher growthrate of 8 percent per annum or a one-off jump of 16 percent when the firm
starts exporting. Clearly such a distinction is important to understanding
the long-term effects of LBE. Further analysis of timing may help explain
the mechanisms through which performance improvements occur (eg, capacity utilisation versus access to technology).
Finally, (like other researchers before us) we have ignored the possibilityof multiple (endogenous) treatments. That is, we have shown in previous
papers that firms which are already in an export market are more likely to
expand into new ones (Fabling et al forthcoming) and that firms with priorexperience are more likely to survive when they enter markets (Fabling and
Sanderson forthcoming). Further work is required to identify the relative
impact of sequential entries.

29

See Fabling and Sanderson forthcoming and Fabling and Grimes forthcoming for how
this affects export history variables and firm attrition respectively.

29

References
Ackerberg, D, K Caves, and G Frazer (2006), “Structural identificationof production functions,” UCLA Department of Economics, mimeo.
Alvarez, R and R López (2005), “Exporting and performance: Evidence
from Chilean plants,” Canadian Journal of Economics, 38(4), 1384–
1400.
Aw, B, S Chung, and M Roberts (2000), “Productivity and the decision
to export: Micro evidence from Taiwan and South Korea,” WorldBank Economic Review, 14(1), 65–90.
Baldwin, R and W Gu (2003), “Export market participation and productivity performance in Canadian manufacturing,” Canadian Journalof Economics, 36(3), 634–657.
Bellone, F, P Musso, L Nesta, and M Quere (2008), “The U-shaped
productivity dynamics of French exporters,” Review of World Economics (Weltwirtschaftliches Archiv), 144(4), 636–659.
Bernard, A and J Jensen (1995), “Exporters, jobs and wages in US
manufacturing: 1976-1987,” Brookings Papers on Economic Activity,
Microeconomics, 67–119.
Bernard, A and J Jensen (1999), “Exceptional exporter performance:
cause, effect, or both?” Journal of International Economics, 47(1),1–25.
Bernard, A and J Jensen (2004), “Exporting and productivity in theUS,” Oxford Review of Economic Policy, 20(3), 343–357.
Caliendo, M and S Kopeinig (2008), “Some practical guidance for the
implementation of propensity score matching,” Journal of Economic
Surveys, 22(1), 31–72.
Damijan, J (2001), “Slovenian investment activity in former Yugoslav
markets: Trade-promoting or efficiency seeking motivation?” Economic and Business Review, 3(3-4), 229–247.
Damijan, J and B Majcen (2003), “Trade policy in a small advanced
transition economy: Trade policy review of Slovenia, 2002,” The
World Economy, 26(9), 1369–1394.
Damijan, J, S Polanec, and J Prašnikar (2004), “Self-selection, export
market heterogeneity and productivity improvements: Firm level
evidence from Slovenia,” LICOS, K.U.Leuven, Discussion Papers,
148/2004.
De Loecker, J (2007), “Do exports generate higher productivity? Evidence from Slovenia,” Journal of International Economics, 73(1),69–98.
Fabling, R and A Grimes (forthcoming), “The “suite” smell of success:30Complementary personnel practices and firm performance,” Reserve
Bank of New Zealand, Discussion Papers.
Fabling, R, A Grimes, and L Sanderson (forthcoming), “Export market choices of New Zealand firms,” Reserve Bank of New Zealand,
Discussion Papers.
Fabling, R, A Grimes, L Sanderson, and P Stevens (2008), “Some riseby sin, and some by virtue fall: Firm dynamics, market structure
and performance,” Ministry of Economic Development, Occasional
Paper, 08/01.
Fabling, R, S Joyce, and L Sanderson (2009), “The evolution of export unit values: Some stylised facts,” paper presented at 50th New
Zealand Association of Economists Conference, Wellington, New
Zealand.
Fabling, R and L Sanderson (2008), “Firm level patterns in merchandise trade,” Ministry of Economic Development, Occasional Paper,
08/03.
Fabling, R and L Sanderson (forthcoming), “Entrepreneurship and aggregate merchandise trade growth in New Zealand,” Reserve Bankof New Zealand, Discussion paper, DP2009/09.
Falvey, R, D Greenaway, J Gullstrand, and Z Yu (2004), “Exports,
restructuring and industry productivity growth,” University of Nottingham, Research Paper, 2004/40.
Fryges, H and J Wagner (2008), “Exports and productivity growth:First evidence from a continuous treatment approach,” Review ofWorld Economics (Weltwirtschaftliches Archiv), 144(4), 695–722.
Girma, S, D Greenaway, and R Kneller (2003), “Export market exit
and performance dynamics: A causality analysis of matched firms,”
Economics Letters, 80(2), 181–187.
Girma, S, D Greenaway, and R Kneller (2004), “Does exporting increase
productivity? A microeconometric analysis of matched firms,” Review of International Economics, 12(5), 855–866.
Greenaway, D and R Kneller (2004), “Exporting and productivity in
the United Kingdom,” Oxford Review of Economic Policy, 20(3),358–371.
Greenaway, D and R Kneller (2007), “Firm heterogeneity, exporting andforeign direct investment,” Economic Journal, 117(517), F134–F161.
Greenaway, D and R Kneller (2008), “Exporting, productivity and agglomeration,” European Economic Review, 52(5), 919–939.
Greenaway, D, N Sousa, and K Wakelin (2004), “Do domestic firms
learn to export from multinationals?” European Journal of Political
Economy, 20(4), 1027–1043.
31

Görg, H, M Henry, and E Strobl (2008), “Grant support and exporting
activity,” The Review of Economics and Statistics, 90(1), 168–174.
Hansson, P and N N Lundin (2004), “Exports as an indicator on or
promoter of successful Swedish manufacturing firms in the 1990s,”
Review of World Economics, 140(3), 415–445.
Heckman, J, H Ichimura, J Smith, and P Todd (1998), “Characterizing
selection bias using experimental data,” Econometrica, 66(5), 1017–
1098.
Imbens, G and J Wooldridge (2008), “Recent developments in the econometrics of program evaluation,” National Bureau of Economic Research, Working Papers, 14251.
Imbens, G W (2004), “Nonparametric estimation of average treatment
effects under exogeneity: A review,” The Review of Economics andStatistics, 86(1), 4–29.
ISGEP (International Study Group on Exports and Productivity)(2008), “Understanding cross-country differences in export premia:
Comparable evidence for 14 countries,” Review of World Economics,
144(4), 596–635.
Leuven, E and B Sianesi (2003), “psmatch2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing,” Statistical Software
Components, Boston College Department of Economics.
Levinsohn, J and A Petrin (2003), “Estimating production functions using inputs to control for unobservables,” Review of Economic Studies,70, 317–341.
Maré, D (2008), “Labour productivity in Auckland firms,” Ministry of
Economic Development, Occasional Paper, 08/09.
Martins, P and Y Yang (2009), “The impact of exporting on firm productivity: A meta-analysis,” Review of World Economics, forthcoming.
Olley, G and A Pakes (1996), “The dynamics of productivity inthe telecommunications equipment industry,” Econometrica, 64(6),1263–1297.
Pavcnik, N (2002), “Trade liberalization, exit, and productivity improvement: Evidence from Chilean plants,” Review of Economic Studies,69(1), 245–276.
Pisu, M (2008), “Export destinations and learning-by-exporting: Evidence from Belgium,” National Bank of Belgium, Working Paper,
140.
Roberts, M and J Tybout (1997), “The decision to export in Colombia:
An empirical model of entry with sunk costs,” American Economic
Review, 87(4), 545–564.
32

Roper, S and J Love (2002), “Innovation and export performance: Evidence from the UK and German manufacturing plants,” Research
Policy, 31(7), 1087–1102.
Rosenbaum, P and D Rubin (1983), “The central role of the propensity
score in observational studies for causal effects,” Biometrika, 70, 41–
55.
Sanderson, L (2004), “Trade and networks: Mechanisms for productivity growth,” Ministry of Economic Development, Occasional Papers,
06/05.
Smith, J (2004), “Evaluating local economic development policies: Theory and practice,” in Evaluating local economic and employment development: how to assess what works among programmes and policies, eds S Arzeni, A Nolan, and G Wong, Organisation for Economic
Co-operation and Development.
Statistics New Zealand (2008), “Business Operations Survey: 2007, Hot
off the press,” Statistics New Zealand: Wellington.
Trofimenko, N (2008), “Learning by exporting: Does it matter where one
learns? Evidence from Columbian manufacturing firms,” Economic
Development and Cultural Change, 56, 871–894.
Van Biesebroeck, J (2005), “Exporting raises productivity in subSaharan African manufacturing firms,” Journal of International Economics, 67(2), 373–391.
Wagner, J (2002), “The causal effects of exports on firm size and laborproductivity: First evidence from a matching approach,” Economics
Letters, 77(2), 287–292.
Wagner, J (2007), “Exports and productivity: A survey of the evidence
from firm-level data,” The World Economy, 30(1), 60–82.

33

Table 8
Appendix A: Data definitions
Label

Description

SourceOUTCOME VARIABLES
All outcome variables are calculated following Fabling and Grimes (forthcoming) andhave industry-year averages removed
ltotemp
Log Total Employment (ln L) where L is working proprietors
LEED
plus average monthly employees
klratio
Capital-labour Ratio (ln K − ln L) where K is capital services AES, IR10, LEEDLP
Labour productivity (ln Y − ln L) where Y is value-added
AES, IR10, LEEDMFP
Multi-factor productivity  from the regression:
AES, IR10, LEEDln Y = α ln L + β ln K + ind year dummies + 
FIRM VARIABLES
FDI
Dummy=1 if firm is foreign-owned
LBF, IR4(LBF foreign ownership ≥25% or IR4 foreign control)
rc01–rc18
Regional Council dummies set to one if the firm has someLBF
employment in the area30
EXPORT HISTORY VARIABLES
Change variables are defined as differences across the two time periodst ∈ {−4, −3} and t ∈ {−1, 0}
export year countNumber of past five years in which the firm exported
Customs
inv export timeInverse of number of years since firm last exported
Customs(set to zero if firm hasn’t exported in past five years)n countries 5yr
Number of countries exported to over past 5 years
Customs
cty entry
Number of countries entered in past 5 years
Customs
cty exit
Number of countries exited in past 5 years
Customs
avg export share
Total exports over total sales in years of exporting
Customs, BAI
d exports incum xbar
Export value growth in countries that are exported to
Customs
at both the start and end of past five year period
relative to average export value across the period
d exports entry xbar
Export value growth in countries that are entered
Customs
relative to average export value across the period
d exports exit xbar
Export value growth in countries that are exited
Customs
relative to average export value across the period34