1. INFLUENCES OF EXPORT SURVIVAL IN KENYAN FIRMS
MAJUNE KRAIDO SOCRATES
P.O. BOX 10743-00100,
NAIROBI, KENYA.
+254712400386/+254780400386
mkraidosocrates@gmail.com/mksocrates@yahoo.com
M.A. ECONOMICS (UNIVERSITY OF NAIROBI), B. ECONOMICS AND STATISTICS
(UNIVERSITY OF NAIROBI)
APRIL, 2016

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ABSTRACT
This study adds to the growing literature of export survival among firms. Export survival has
mainly been studied at country-level due to the availability of product-level data. However, a firm-
level review gives better insight since it uses data from the unit of analysis, firms. Ideally, product-
level data only aggregates these firm-level datasets. Hence, with the availability of recently created
Exporter Dynamics Database by the World Bank, this study proposes to examine the determinants
of export survival among Kenyan exporters. The key objectives will be to: make market
distinctions for Kenyan exports (large and small markets); establish the influence of the type of
employment on survival (skilled and unskilled employees); establish the influence of production
chains on survival; and establish the influence non-reciprocal preferential trade agreements
(African Growth and Opportunities Act) on survival. Synonymous to recent survival studies, a
discrete-time model will be applied over the continuous-time model of Cox (1972). The former is
econometrically superior because it accounts for unobserved heterogeneity, it deals with tied
durations and it does not impose the assumption of proportional hazards.

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CHAPTER ONE
1.0 INTRODUCTION
This study adds to the growing literature on export survival at firm level. Export survival entails
the magnitude of an existing trade relationship or the intensive margin1
. Its opposite involves
increasing export destinations and product diversification, referred to as extensive margin. Export
survival is critical for reducing failure rates of firms in the infant years of a trade relationship, in
deepening existing relationships and enhancing export growth in the long-run. Hence,
understanding determinants of export survival is a significant policy concern especially for
liberalized economies like Kenya.
In recent times, literature focusing on export durations has emerged; the main insight is that trade
is short-lived. Starting with the seminal paper of Besedeš and Prusa (2006), the median survival
period of US imports is between two and four years. This has been revised to one year by Hess
and Persson (2012). Brief durations have also been affirmed by other country and product-level
studies such as Brenton et al. (2010), Fuguzza et al. (2011), Brenton et al. (2012), Carrère et al.
(2014) and Corcoles et al. (2015). Firm-level studies have recently been popularized due to the
availability of data2
. For instance, Spanish and Indian firms export for a median period of two and
three years respectively (Esteve-Pérez et al., 2013; Pradhan et al., 2016). Other firm-level studies that
confirm brief trade durations include: Bekes et al. (2012), Stirbat et al. (2013), Wagner et al. (2013), Fu et
al. (2014), Fuguzza et al. (2014), Görg et al. (2014), Boehe et al (2015), D’Amato et al. (2015), Gullstrand
et al. (2015), Inui et al. (2016), Lejour (2015), Martuscelli et al. (2015), Straume et al. (2015).
Whereas export survival studies are on the rise, their prevalence is still low in Africa. To the best
of my knowledge, only Brenton et al. (2012) and Kamuganga (2012) have exclusively studied
export survival in African countries3
using product-level data. Firm level attempts include
Mohammed (2011) in Ghana, Banda et al. (2013) in Zambia and Cadot et al. (2013) in Malawi,
Mali, Senegal and Tanzania. Furthermore, country-specific studies have only been conducted in
Nigeria (Arawomo, 2015) and Kenya (Kinuthia 2014; Majune, 2015).
1
It is henceforth a proxy of the market share of an exporting firm.
2
Most studies use customs data (Fernandes et al., 2015)
3
However, many studies use a huge dataset with some African countries i.e. (Besedeš et al., 2006; Brenton et al.,
2010; Fuguzza et al., 2011; Hess et al., 2012 and; Carrère et al., 2014).

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This gap in literature is what this study seeks to fill. The first goal of this study is formulate stylized
facts of export survival in Kenya using firm data. So far, no such study exists4
. A firm-level review
gives a better analysis since it uses data from the unit of analysis, firms. Ideally, product-level data
only aggregates these firm-level datasets. Therefore, the newly developed Exporter Dynamics
Database (henceforth EDD) is proposed for use. This dataset consists of firm-level records inter
alia entry, exit and survival from 70 countries with 56 of them as developing including Kenya5
(Fernandes et al., 2015).
The second goal is to identify the influences of export duration in Kenya. It is eminent that
developing countries have the lowest survival rates yet long survival is the main reason developed
countries export more (Brenton et al., 2012; Fernandes et al., 2015)6
. For instance, the average
survival rate of Kenyan exporters between 2006 and 2008 was 8% lower than that of all developing
countries in the sample, 35% and 43% respectively7
(Fernandes et al., 2015). Basing on previous
studies, several explanations can be raised. That is: firm-specific characteristics (size, age, product
type, volume of trade, productivity, ownership, number of employees, number of partners,
production network, and financial access); and market-specific characteristics (distance, sunk
costs, fixed costs, size, language, concentration of exporters, regulations, institutions and trade
agreements). Nevertheless, these factors are unclear and remain country/region specific. This
study, in line with very current literature, focuses on market distinctions (large and small
markets)8
; type of employees (skilled and unskilled employees)9
; production chain10
; and non-
reciprocal preferential agreements (African Growth and Opportunities Act, henceforth AGOA)11
.
The third goal is purely econometric. This study, similar to recent studies, seeks to adopt a discrete-
time model as opposed to the continues-time model of Cox (1972). The former model is preferred
4
Only Kinuthia (2014) and Majune (2015) have studied export survival in Kenya but they used country-level data.
5
The dataset runs from 2003 to 2013. Further description can be obtained from Fernandes et al., (2015).
6
Developing countries have both high entry and exit rates. In contrast, developed countries have low entry and low
exit tendencies.
7
Majune (2015) establish that the survival rate of Kenyan exports beyond the first year is 36%. This is 20%
according to Kinuthia (2014).
8
Studied by (Bekes et al., 2012; Gullstrand et al., 2012; Esteve-Perez et al., 2013; Straume et al., 2015),
9
Studied by Luong et al. (2014).
10
Studied by Mohammed (2011), Stirbat et al. (2013), Fuguzza et al. (2014), Corcoles et al. (2015) and Martuscelli
et al. (2015).
11
Studied by Fuguzza et al. (2014), Majune (2015).

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over the latter model due to the following reasons: it accounts for unobserved heterogeneity, it
deals with tied durations, it neglects the proportional hazard assumption which is dubious, and it
is available in most statistical software like STATA12
. The following studies have so far applied
the discrete-time model at firm-level; Mohammed (2011), Görg et al. (2012), Cadot et al. (2013),
Esteve-Perez et al. (2013), Stirbat et al. (2013), Wagner (2013), Fu et al. (2014), Fuguzza et al.
(2014), Corcoles et al. (2015), D’Amato et al. (2015), Gullstrand et al. (2015), Martuscelli et al.
(2015), Straume et al. (2015), Inui et al. (2016) and Pradhan et al. (2016). A few of these studies
are discussed in Table 1:
Table 1: A select firm-level studies
Topic and author(s) Data source and years
covered
Model(s) used Essential findings
Mohammed, A.A.
(2011).Determinants of Export
Survival: The Case of
Ghanaian
Manufacturers.
Ghana Manufacturing
Enterprise Survey. 1991-1998.
Complementary log-log (c
log-log) model and Logistic
model.
Survival is boosted by time of
exporting, age, size and export
intensity. Final products
reduce survival.
Esteve-Perez, S., Pallardo, V.,
and Requena, F. (2013). The
duration of trade: Spanish
firms and export partners.
CAMARAS-SABI. 1997 –
2006.
Complementary log-log
model.
Survival varies with
destination i.e. low-risk and
high-risk partners. In general,
survival in low-risk partners is
driven by size, age and
efficiency.
Fugazza, M., and McLaren, A.
(2014). Market access, export
performance and survival:
Evidence from Peruvian firms.
Monthly Peruvian exports
flow data. January 2001 –
December 2009.
Random effects probit model
and Complementary log-log.
Survival is increased by
destination and product
diversification, being part of a
production chain.
Gullstrand, J. and Persson, M.
(2015). How to combine high
sunk costs of exporting and
low export survival?
Statistics Sweden (Swedish
food chain). 1997- 2007.
Random effects logit model
and random effects probit
model.
Longer survival in core
markets than peripheral
markets.
Martuscelli, A., and Varela, G.
(2015). Survival is for the
fittest: Export survival
patterns in Georgia.
Georgian Customs and
Georgia National Statistical
Office. 2006 – 2012.
Probit model. Survival is boosted by
production efficiency,
networks, trade agreements
and product diversification.
Only 25% of exports survive
past the first year.
12
Refer to Hess and Persson (2012) for detailed explanation. However, common discrete time models are probit,
logit and Complementary log-log.

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REFERENCES
Arawomo, D.F. (2015). Survival of Nigeria's Exports in Her Biggest Markets: Continuous and
Discrete Time Estimations. Economics Bulletin, 35(3), 1700-1708.
Banda, B., and Simumba, J. (2013). The Birth, Death and Survival of Exports in Zambia, 1999-
2011 (No. 12). ZIPAR Working Paper.
Békés, G., and Muraközy, B. (2012). Temporary trade and heterogeneous firms. Journal of
International Economics, 87(2), 232-246.
Besedeš, T. and Prusa, T. J. (2006). Ins, Outs, and the Duration of Trade. The Canadian Journal
of Economics, 39(1), 266-295.
Brenton, P., Cadot, O., and Pierola, M. D. (2012). Pathways to African Export Sustainability. 1818
H Street NW, Washington, DC 20433, US: World Bank Publications.
Brenton, P., Saborowski, C., and von Uexkull, E. (2010). What explains the low survival rate of
developing country export flows? World Bank Economic Review, 24(3), 474–499.
Cadot, O., Iacovone, L., Pierola, M. D., and Rauch, F. (2013). Success and failure of African
exporters. Journal of Development Economics, 10 (1), 284-296.
Carrère, C., and Strauss-Kahn, V. (2014). Developing Countries Exports Survival in the OECD:
Does Experience Matter? FERDI, Working Paper 100.
Córcoles, D., Díaz‐Mora, C., and Gandoy, R. (2015). Export Survival in Global Production
Chains. The World Economy. Doi: 10.1111/twec.12249.
D'Amato, L., and Sangiácomo, M. (2015). Survival in Export Markets: Does Foreign Financing
matter? Retrieved from http://hdl.handle.net/123456789/53174.
Fernandes, A. M., Freund, C., and Pierola, M. D. (2015). Exporter behavior, country size and stage
of development: evidence from the exporter dynamics database. World Bank Policy
Research Working Paper, (7452).
Fu, D., and Wu, Y. (2014). Export survival pattern and its determinants: an empirical study of
Chinese manufacturing firms. Asian‐Pacific Economic Literature, 28(1), 161-177.
Fugazza, M. and McLaren, A. (2014). Market Access, Export Performance and Survival: Evidence
from Peruvian Firms. Review of International Economics, 22(3), 599–624
Fugazza, M., and Molina, A.C. (2011). On the Determinants of Exports Survival. Working Paper
no.311 (Geneva: FREIT).

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Görg, H., and Spaliara, M. E. (2012). Financial health, exports, and firm survival: Evidence from
UK and French firms (No. 2012-49). Scottish Institute for Research in Economics (SIRE).
Gullstrand, J., and Persson, M. (2015). How to combine high sunk costs of exporting and low
export survival. Review of World Economics, 151(1), 23-51.
Hess, W., and Persson, M. (2012). The duration of trade revisited. Empirical Economics, 43(3),
1083-1107.
Hess, W., and Persson, M. (2011). Exploring the duration of EU imports. Review of World
Economics, 147(4), 665-692.
Inui, T., Ito, K., and Miyakawa, D. (2016). Export Experience, Product Differentiation, and Firm
Survival in Export Markets. Japanese Economic Review, doi: 10.1111/jere.12083.
Kamuganga, D. N. (2012). Does intra-Africa regional trade cooperation enhance Africa's export
survival? (No. 16/2012). Graduate Institute of International and Development Studies
Working Paper.
Kinuthia, B.K. (2014). Export Duration and Determinants of Exports Survival in Kenya. Trade
Discourse in Kenya: Some Topical Issues Volume 2, Nairobi, Kenya.
Luong, T. A., and Chen, W. C. (2014). Labor Heterogeneity and the Margins of Exports. Retrieved
from http://eiit.org/WorkingPapers/Papers/TradePatterns/FREIT504.pdf.
Martuscelli, A., and Varela, G. J. (2015). Survival is for the fittest: export survival patterns in
Georgia. World Bank Policy Research Working Paper, (7161).
Mohammed, A.A. (2011). Determinants of Export Survival: The case of Ghanaian Manufacturers
Working Paper, UNU-MERIT.
Pradhan, J. P., and Das, K. (2016). Regional Profile of Export Duration of Firms. In Manufacturing
Exports from Indian States (107-129). Springer India.
Stirbat, L., Record, R., and Nghardsaysone, K. (2013). Determinants of export survival in the Lao
PDR. World Bank Policy Research Working Paper 6301.
Straume, H. M. (2015). Four essays in international trade: Trade flows in food products (PhD
Thesis, University of Bergen, Bergen, Norway). Retrieved from
http://hdl.handle.net/1956/10850.