China economic review volume 33 issue 2015 doi 10 1016 j chieco 2015 01 005 seyoum, mebratu; wu, renshui; yang, li technology spillovers from chinese outward direct investment the case of ethi

Technology spillovers from Chinese outward direct investment:
The case of Ethiopia
Mebratu SEYOUM
a,1
,RenshuiWU
b,
⁎
, Li YANG
b,2
a
Department of International Economics and Business, School of Economics, Xiamen University, Xiamen 361005, Fujian, China
b
The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen 361005, Fujian, China
article info abstract
Article history:
Received 28 January 2014
Received in revised form 10 November 2014
Accepted 3 January 2015
Available online 9 January 2015
The present study uses firm survey data of 1033 manufa cturing firms operating in Ethiopia
in 2011 to examine the impact of Chinese outbound direct investment on the productivity of do-
mestic firms. Particularly, we attempt to answer two questions. Firstly, are Chinese-owned
(henceforth foreign) firms more productive than local ones? Secondly, does the pre senc e of
foreign firms generate technology spillovers on domestic firms operating in the same industry?
Our empirical results show that foreign firms are more productive and that their presence has
different spillover effects on the productivity of domestic firms. In particular, we find that domes-
tic firms with higher absorptive capacity experience positive spillovers, while those with low ab-
sorptive capacity witness negative spillover. We also find that small firms and non-exporting
firms benefit more from spillovers than do other types of domestic firms. In this study, instrumen-
tal variables are used to address the potential endogeneity between foreign firm presence and
domestic firm productivity.

© 2015 Elsevier Inc. All rights reserved.
JEL classification:
F21
D24
O33
L60
Keywords:
Absorptive capacity
China
Ethiopia
Outward direct investment
Spillovers
1. Introduction
Over the past decade, though insignificant in global terms,
3
China's outward direct investment (ODI) flows to Africa have increased
rapidly. The increase has generated interest and concern over the effects of China's ODI on developing Sub-Saharan host economies.
Some argue that Chinese ODI provides an alternative source of capital, technology and skills and that it has been instrumental in ful-
filling financial and technological gaps for Africa (Brautigam, 2009; Foster, Butterfield, Chuan, & Pushak, 2008). On the negative side,
some contend that the primary objective of Chinese ODI in Africa is to find resources, and markets for their products where it drives
African countries to resource-based economies and crowds-out local industries (Kaplinsky, 2008; Kaplinsky & Morris, 2009).
China's ODI in Africa has generated considerable attention for several reasons. One reason is the rapid pace at which China's ODI has
risen and expanded in Africa.
4
Second, small a nd medium private Chinese firms have recently become prom inent in vestors in African
manufacturing sector that there are uncertainties about the impact of their activities on the African economies where the investment
China Economic Review 33 (2015) 35–49
⁎ Corresponding author. Tel.: +86 18959215421.
E-mail addresses: (M. Seyoum), (R. Wu), (L. Yang).
1

Tel.: +86 13599515882.
2
Tel.: +1 703 473 7308.
3
The largest part of Chinese ODI goes to Asia, Latin America and Europe, respectively.
4
According to the Ministry of Commerce of China (MOC), China's ODI stock in Africa rose from 900 million dollars in 2003 to nearly 16 billion dollar in 2011.
/>1043-951X/© 2015 Elsevier Inc. All rights reserved.
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is made.
5
The third reason relates to the way in which the C hinese are perceived to invest; there is a belief that Chinese firms behave dif-
ferently from other firms, either because the Chinese state is often behind ODI o r because they have a different culture and institutional
structure (Buckley et al., 2 007).
The purpose of this study is to examine the impact of Chinese manufacturing ODI on the productivity of the Ethiopian manufactur-
ing sector. More specifically, we analyse two issues. The first issue is to examine whether foreign-owned firms exhibit higher levels of
productivity than domestic ones. Then, we investigate whether the productivity of domestic firms is correlated with the presence of
foreign firms in the same industry.
6
Identifying such effects would be consistent with the existence of intra-industry or horizontal
technology transfer spillovers. We further explore whether productivity gains stemming from horizontal spillovers vary with domes-
tic firms' characteristics.
The study focuses on Ethiopia for two reasons. First, Ethiopia has received a substantial amount of Chinese ODI in manufacturing,
and is ranked among the top four recipient countries in Africa.
7
And over the past few years, China has emerged as the largest source of
FDI in Ethiopia in terms of number of investment projects, with over 970 projects as of end-2012 (Ethiopian Investment Agency [EIA]
data).
Second, to the best of our kn owledge, no attempt has so far been made to systematically investigate the impact of Chinese

manufacturing FDI on developing Sub-Saharan host economies.
8
Using a more disaggregated dataset for Chinese FDI in manufactur-
ing, thi s empirical study is, to our knowledge, the first to present a detailed analysis of the impact of Chinese FDI on a host country in
Sub-Saharan economies.
The analysis is based on 1033 manufacturing firms operating in Ethiopia in 2011. The data come from the survey of large and
mediu m scale manufacturing industries conducted by the Ethiopian Central Statistical Agency (CSA), Ministry of Finance and
Economic Development (2012). The survey data covers firms in the formal manufacturing sector, which employ 10 persons and
more and use power driven machinery. The dataset contains detailed information on the basic information of the establishment, own-
ership structure, foreign equity participation, output, assets, employment, wages, input costs, location and other information.
The findings can be summarized as follows. We find that foreign-owned firms are significantly more productive than their local
counterparts, suggesting that there are direct benefits from Chinese FDI. With regard to spillover effects, we find that Chinese FDI
has different spillover effects on domestic firms dependent upon their characteristics. More specifically, our empirical results reveal
that: (i) domestic firms with high absorptive capacity (smaller technology gap with foreign firms) experience positive spillovers,
while those with low absorptive capacity witness negative spillovers; (ii) small firms and non-exporting firms benefit more from
spillovers than do other t ypes of domestic firms; and (iii) skilled labour of domestic firms does not enhance their capacity to attract
FDI spillovers.
The remainder of the paper is organized as follows. Section 2 presents a brief overview of Chinese ODI flows to Ethiopia. Section 3
explains the theoretical framework for the role of foreign ownership on the productivity and technology spillovers. Section 4 intro-
duces the econometric model, data and variable definitions. In Section 5 we present our regression results, while Section 6 concludes.
2. Overview of Chinese ODI flows to Ethiopia
China's ODI flows to the African continent have grown rapidly over the past decade and Ethiopia is a good example of this trend.
Fig. 1 shows the trends in China's ODI flows to Ethiopia from 2004 to 2010, using the Ministry of Commerce of China (MOC) 2010
Statistical Bulletin of China's Outward Foreign Direct Investment. China's ODI flows to Ethiopia rose from virtually zero in 2004,
reaching 24 million dollars in 2006 to a peak of 73.4 million dollars in 2009 before it had declined to 58.5 million dollars in 2010.
According to MOC data, the stock of ODI from China to Ethiopia in 2 010 was 368 million dollars.
The official statistics reported by MOC (Fig. 1 ) seem to understate the true investment volume of Chinese ODI in Ethiopia. Accord-
ing to the EIA figures, the accumulated stock of Chinese ODI in Ethiopia stood at nearly two billion dollars at the end of 2010.
9
As

shown in Table 1, the amount of annual ODI flows from China to Ethiopia has been increasing rapidly, albeit from a low base: from
181.71 million dollars between 2002 and 2004, to 414.29 million dollars during the period 2005–2007 and rose further to over one
billion dollars between 2008 and 2010. Similarly, the total number Chinese investment projects in Ethiopia reached 944 projects in
2010, a whopping 782.24% increase from the period 2002–2004. According to EIA data, out of the 944 investment projects, 632
projects (66%) are engaged in the manufacturing sector.
Fig. 2 looks at China's ODI flows as a share of total FDI inflows for the past decade. There is a visible trend exhibiting that Chinese
ODI has been growing very fast, and that it is taking over the principal position in new FDI attracted to the country. In terms of the
number of investment projects, China's contribution rose from 11% in the period of 2000–2005, reaching 29% in 2007 to a peak of
32% in 2008. During the period 2006–2011, China took the top place contributing 25% of the total FDI attracted to Ethiopia.
5
According to Shen (2013) estimates, small and medium private Chinese enterprises, predominantly concentrated in manufact uring and service industries,
accounted for 55% of all Chinese investment projects in Africa by the end of 2011.
6
In our case, sectors are defined at a more aggregate level, hence some intra-industry spillovers may, in reality, capture vertical spillovers (see Table 2).
7
According to Shen (2013) estimates, based on data from MOC and African host governments, Nigeria, South Africa, Zambia, Ethiopia and Ghana (in that order) are
the top five Chinese ODI recipient countries in Sub-Saharan Africa.
8
We use “ODI” and “FDI” interchangeably.
9
It isfair to argue that the EIA data captures Chinese ODI in Ethiopia more comprehensively and accurately than the official data reported by MOC, simply because the
EIA data covers all ODI projects, large or small, with the latter less likely to be captured or registered by MOC data.
36 M. Seyoum et al. / China Economic Review 33 (2015) 35–49
3. FDI and productivity spillovers: literature review
FDI is considered to influence the productivity and competitiveness of host-country economic activities for at least two quite
different reasons. First, multinational firms bring to their host-country superior productive assets such as technological know-how,
managerial and entrepreneurial skills, and marketing techniques. Second, there are spillovers of technology transfer from foreign
firms, which affect the productivity of domestic firms. Spillovers occur when foreign firms cannot fully internalize all quasi-rents as
a result of their productive activities.
3.1. Are foreign firms more productive than local ones?

The orthodox literature suggests that foreign firms undertake FDI to exploit firm-specific ownership advantages that arise from the
possession of intangible assets such as better access to advanced technology inputs and more efficient organization in production and
distribution. In addition, they tend to operate on a lower (production and distribution) cost curve than domestic firms, which allows
them to compute successfully with domestic firms who have intimate knowledge of local market conditions including customs,
consumer preferences, legal environment and business practices. If so, other things being equal, we expect foreign firms to be
more productive than domestic ones.
Consonant with the conventional literature, a number of studies conducted in Sub-Saharan African countries confirm that foreign
firms are more productive than domestic firms. A recent Africa investor survey conducted by the United Nations Industrial Develop-
ment Organization (UNIDO) in 2011 reveals that, on average for all surveyed countries, foreign firms are 11% more productive than
domestic ones.
10
For country-specific studies in Africa, Melese and Waldkirch (2011) use a sample of 6574 manufacturing firms for
the period 2002 to 2009 to show that fo reign-owned firms are more productive than domestically owned firms in Ethiopia . In
Kenya, Gachino (2013) employs firm survey data for 180 manufacturing firms, and finds similar productivity advantages in favour
foreign firms.
Thus, the first issue examined in this paper is whether Chinese-owned firms exhibit significantly higher levels of productivity than
domestic firms in Ethiopia. If so, the presence of Chinese firms can be expected to impact positively on the host-country, because their
higher productivity levels help productivity in their respective industries to shift upward, which is also reflected in the aggregate
productivity of the host country (Caves, 1996).
3.2. Are there any spillover gains from FDI?
There is a large literature on the existence and direction of spillovers of technology transfer from FDI. The existence of spillovers is
based on two assumptions. The first assumption lies on the fact that multinational firms have better access to advanced technology
and other advantages and have, th erefore, higher levels of productivity (Caves, 1996). The second is related to the fact that
technology-based assets transferred to the host economy have the characteristics of a public good (Romer, 1990). Dissemination
and appropriation of their qualities may take place in the form of unintentional transmission or intentional transfer from multination-
al to local firms through demonstration effects, worker mobility or direct linkage with local agents.
Furthermore, multinational firms may inject a higher level of competitive intensity in the host-country, which may produce addi-
tional spillover benefits. Foreign firms typically enter markets characterized by high entry barriers and consequently strong monop-
olistic distort ions. Their entry thus may reduce monopolistic distortions and raise the productivity of local agents by improving
resource allocations in the host country (Caves, 1996). On the other hand, negative effects may arise from competition if foreign

firms, which happen to produce at a lower marginal cost, gain market shares from local firms. Aitken and Harrison (1999) argue
that even if local firms benefit from technology spillovers, productivity of local firms may still decline (rise in average cost) if the
output demanded from them is reduced as foreign firms take a large share of the market. However, local firms may also respond to
foreign competition by making better use of existing resources or investing in new technologies in order to maintain their market
share and, therefore, drive a higher level of productivity on the process (Blomstrom & Kokko, 1998). Technical efficiency in the indus-
try is thus improved.
Spillovers of technology transfer from FDI also depend on the characteristics of domestic firms, which shape absorptive capacity to
internalize spillovers (Farole & Winkler, 2012). Domestic fir
ms' absorptive capacity is their ability to recognize, assimilate and apply
10
The survey was carried out in Burkina Faso, Burundi, Cameroon, Cape Verde, Côte d'Ivoire, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mali, Mozambique,
Niger, Nigeria, Rwanda, Senegal, Tanzania, Uganda, and Zambia.
Table 1
China's outward FDI flows to Ethiopia, 2002–2010.
Source: authors’ computations based on data from the Ethiopian Investment Agency (EIA).
Years Number of projects Value of projects (US$ millions)
2002–2004 107 181.71
2005–2007 598 414.29
2008–2010 239 1279.0
Total 944 1875
37M. Seyoum et al. / China Economic Review 33 (2015) 35–49
outside knowledge to commercial ends (Cohen & Levinthal, 1989). It is argued that where local firms are constrained by their limited
absorptive capacity, the expected productivity spillover benefits of FDI either do not show up or are negative (the latter case may
occur if the entry or presence of foreign firms shrinks local firms' market share). Conversely, the greater the local firms' capacity to
absorb new technology and processes, the more productivity spillover benefits. The factors that influence local firms' absorptive
capacity include (i) technology gap between foreign and local firms; (ii) local firm's size; (iii) exporting behaviour of domestic
firms; and (iv) share of skilled labour at the firm level.
The technology gap between foreign and local firms has been acknowledged as one of the most important moderating factors
for the realization o f FDI spillover potential ( Findlay, 1978; Wang & Blomstrom, 1992; Kokko, Tanzani, & Zejan, 1996,among
others). However, studies on the role of technology gap for spillover effects of FDI conflict. For instance, some argue that a

large technological gap between foreign and local firms should enhance positive spillovers, because the potential for improve-
ment is large (Sjoholm, 1999; Wang & Blomstrom, 1992). Others argue that domestic firms need some minimum amount of
technical capacity to be able to benefit from spillovers and thus smaller technology gap between foreign and domestic firm re-
sults in larger spillovers (Blomstrom, Globerman, & Kokko, 1999; Blomstrom & Kokko, 1998). While Bla lock and Gertler (2009)
suggest that if the technology gap between foreign and local firms is too large or too small productivity spillover benefits may
not be realized.
Despite many attempts by others (cf. Hale & Long, 2006; Kokko, 1994; Sjoholm, 1999) to examine the role of technological gap on
FDI spillovers, results are inconclusive. Kokko (1994) employs cross-section industry level data for Mexico to show that productivity
spillovers are smaller b ecause of high technology gap between foreign and local firms. Similarly, Kokko et al. (1996) extend Kokko's
(1994) analysis for Mexico to examine the role of technology gap on productivity spillovers, using cross-section firm-level data for
Uruguay. They find evidence supporting the notion that smaller technology gap between foreign and local firms enhances productiv-
ity spillovers. Hale and Long (2006) observe the same phenomenon in China. On the contrary, Sjoholm (1999), using cross-section
data for 8086 firms in Indonesia, shows that spillovers from FDI are found in sectors with a high degree of competition and less ad-
vanced technology. Putting it differently, he finds that the larger the technology gaps between domestic and foreign firms, the larger
the spillovers—it seems that larger technology gap leaves more ground for improvement.
An
other important characteristic that affects the extent and nature of FDI spillovers is firm size (Dimelis & Louri, 2004; Sinani &
Meyer, 2004). Larger domestic firms tend to have stronger capacity to compete with foreign firms and imitate their technology and
management practices (Crespo & Fontoura, 2007). Moreover, they are better positioned to spread fixed costs of R&D over a larger
sales base and hence are able to exploit economies of scale and scope in R&D activities (Cohen & Levinthal, 1989). They also pay better
wages and therefore find it easier to attract workers employed by foreign firms (Markusen & Trofimenko, 2007). On the other hand,
larger domestic firms may be competitive and hence operating at their maximum efficiency; therefore, the scope for technology
transfer from foreign firms could be limited (Dimelis & Louri, 2004). Others also suggest that smaller firms may benefitmorefrom
multinational firms if, for example, they are endowed with higher proportion of skilled labour (Sinani & Meyer, 2004); moreover,
they may operate at suboptimal efficiency level lacking the necessary technology and knowledge of productive assets and therefore
can enjoy higher spillover benefits from FDI presence (Dimelis & Louri, 2004).
Aitken and Harrison (1999) employ firm level panel data to assess the effects of foreign equity participation on the productivity of
domestic firms in Venezuela. They find significant negative spillovers from FDI for small enterprises only (less than 50 employees),
which they attributed to market-stealing effect. Likewise, the study by Boly, Coniglio, Prota, and Seric (2013) uses firm level data
for 19 Sub-Saharan economies to show that large and young firms enjoy more positive spillovers than do other type of firms. In con-

trast, based on a sample of 3742 manufacturing firms operating in 1997 in Greece, Dimelis and Louri (2004) find that productivity
Fig. 1. China's outward FDI flows to Ethiopia, 2004–2010. Source: MOFCOM, 2010 Statistical bulletin of China's outward foreign direct investment.
38 M. Seyoum et al. / China Economic Review 33 (2015) 35–49
spillovers accrue mostly for small local firms. Sinani and Meyer (2004) observe the same phenomenon in Estonia, especially for small
enterprise with a higher proportion of skilled labour.
Exporting behaviour has been linked to a domestic firm's capacity to absorb new technology and management practices. Two
opposing views exist in the literature on the role of exporting behaviour in capturing FDI spillovers. On the one hand, some argue
that domestic exporting firms tend to have a stronger capacity that places them in a better position to mitigate negative spillovers
of FDI—because they are generally characterized by highe r productivity, be it via self-selection process where more productive
firms become exporters or learning by-exporting (Melitz, 2003; Crespo & Fontoura, 2007).
On the other hand, some suggest that local exporting firms have exposure to additional channels through which they can learn
about advanced knowledge, skill and technology from their international connections and hence the potential for FDI-induced exter-
nalities is limited (Sinani & Meyer, 2004). In addition, exporting firms may already enjoy higher productivity and hence there may be
little knowledge spillover to be transferred to them f rom FDI. Besides, l ocal exp orting firms may not have the incentives to upgrade
their technology if they face lower competitive pressure from FDI (assuming that foreign firms do not export to the same market),
which lowers t he scope and magnitude of positive FDI spillovers.
In the bulk of existing empirical literature, studies find no clear evidence whether exporting behaviour enhances or reduces the
productivity spillovers from FDI. For instance, Barrios and Strobl (2002) employ firm level panel data for Spanish manufacturers
from the period 1990 to 1998 to show that the gains from FDI spillovers are larger for exporting firms. Girma, Gorg, and Pisu
(2008) observe the same phenomenon in the U.K. for intra-industry spillover. So do Schoors and Van der Tol (2002) in Hungary. In
contrast, several empirical studies find little or no productivity spillovers for exporting local firms (cf. Sinani & Meyer, 2004;
Blomström and Sjöholm, 1999, among others).
Adomesticfirm's capacity to absorb new technology and managerial practices is also linked to its share of skilled labour.
The economics literature posits that investment in skilled labour and R&D n ot only increases innovation, but also raises a
firm's ability to recognize, assimilate and apply outside knowledge to commercial ends (Cohen & Levinthal, 1989; Glass &
Saggi, 2002; Sinani & Meyer, 2004). Blalock and Gertler (2009) apply panel data of Indonesian manufacturers from 1988 to
1996 to argue that the share of skilled labour (the percentage of wor kers wit h collage degrees) considerably increases domes-
tic firms' productivity spillovers from FDI. However, for highly skilled countries such as the U.K., Girma and Wakelin (2007)
confirm such a finding for small enterprises only whereas Sinani and Meyer (2004) find that a larger share of skilled labour
enhances positive spillovers for large enterprises in Estonia. On the other hand, Cuyvers, Soeng, Plasmans, and Van den

Bulcke (2008) find that a firm's human capital intensity does not determine its ability to adopt foreign technology in
Cambodian manufacturing sector.
Therefore, as discussed above, the characteristics of the recipient firms are important mediating factors for technology spillover
potential to turn into actual technology spillovers. The absence or presence of such fi
rm-specific
characteristics may crucially influ-
ence observed spillovers from FDI and thus not taking them into account can bias empirical results. It is along these lines that in
this study, we focus on how spillovers differ according to the characteristics of domestic firms in addition to estimating the produc-
tivity increase (or decrease) of domestic firms in the same industry.
4. FDI and productivity spillovers: estimation strategy, data and variables
4.1. Estimation strategy
If the superior technology embodied in foreign-owned firms is diffused to local firms, productivity levels of local firms should
increase. To examine productivity spillovers fr om foreign-owned to locally owned firms, we follow an approach sim ilar to that
Fig. 2. The percentage of Chinese investment of total FDI. Source: Shen (2013), based on EIA.
39M. Seyoum et al. / China Economic Review 33 (2015) 35–49
taken by earlier literature and estimate an augmented Cobb–Douglas production function. Following Dimelis and Louri (2004),we
specify the following general form for the production function:
Y
i
¼ L
α
i
K
β
i
M
γ
i
e
X

λ
p
X
ip
þ λ
0
þ ε
i
i¼1;2;
…
;n; p¼1;2 ;
…
;P
ð1Þ
where Y
i
denotes the output of firm i, measured by gross sales; L
i
, K
i
, M
i
denote labour, fixed capital and intermediate material inputs
used in each firm i; α, β, γ are the elasticities of output with respect to capital, labour and intermediate material inputs, respectively.
X
ip
denotes the p-th control variable which is not explicitly included, for example, the one correlated with FDI; λ
o
is a constant param-
eter. Finally, the error t erm, ε

i
captures all other unobservable factors influencing output. Log-linearizing Eq. (1) yields the following
estimation equation:
lnY
i
¼ λ
0
þ αlnL
i
þ βlnK
i
þ γlnM
i
þ
X
p
p¼1
λ
p
X
ip
þ ε
i
ð2Þ
Since our main objective relates to labour productivity, we transform Eq. (2) to obtain its labour-intensive form.
ln
Y
i
.
L

i

¼ λ
0
þ βln
K
i
.
L
i

þ γln
M
i
.
L
i

þ α þ β þ γ−1ðÞlnL
i
þ
X
p
p¼1
λ
p
X
ip
þ ε
i

ð3Þ
Eq. (3) can be rewritten by adding several control variables, as follows:
lnLP
i
¼ α
0
þ α
1
lnKI
i
þ α
2
lnMI
i
þ α
3
lnL
i
þ α
4
SKILL
i
þ α
5
lnAGE
i
þ α
6
SCALE
i

þ α
7
CFOR
i
þ ε
i
α
7
CFOR
i
þ ε
i
: ð4Þ
LP stands for labour productivity, which is influenced by capital intensity (KI), material inputs intensity (MI), labour inputs (L),
skilled labour (SKILL), firm age (AGE), firm scale (SCALE) and foreign presence (CFOR). Appendix A describes the explanatory variables
adopted in the econometric investigation, which follow the theoretical and empirical literature discussed above. We expect coeffi-
cients for the independent variables to be positive and significant. A statistically significant positive coefficient correlated with
CFOR implies that foreign firms have higher levels of productivity than their locally owned counterparts.
To examine whether the presence of foreign firms affects the productivity of local firms in the same industry, we build on the
standard equation encountered in Eq. (4). The difference now is that the left-hand side concerns only local firms, rather than all,
firms:
lnLP
ij
¼ α
0
þ α
1
lnKI
ij
þ α

2
lnMI
ij
þ α
3
lnL
ij
þ α
4
SKILL
ij
þ α
5
lnAGE
ij
þ α
6
SCALE
ij
þα
7
FDI
j
þ ε
ij
ð5Þ
where subscript j stands for sector j; LP, KI, MI, L, SKILL AGE and SCALE are same as Eq. (4). FDI is the measure of foreign firms'
presence in sector j.
Table 2
Distribution of firms with foreign capital by industrial sectors in 2011.

Source: authors’ computations based on data from the Central Statistical Agency (CSA) of Ethiopia, Large and Medium Manufacturing and Electricity Industries Survey 2012.
Industrial sectors All fi rms Domestic Foreign
#% #%
1. Food, beverage and tobacco 199 182 91.5 17 8.5
2. Spinning, weaving and finishing of textiles 26 19 73.1 7 26.9
3. Leather and footwear 79 72 91.1 7 8.9
4. Paper, paper products and printing 96 90 93.7 6 6.3
5. Chemical and chemical products 57 43 75.4 14 24.6
6. Rubber and plastic products 92 81 88.0 11 12.0
7. Other non-metallic mineral products 156 146 93.6 10 6.4
8. Basic iron and steel 32 28 87.5 4 12.5
9. Fabricated metal products except machinery and equipment 85 77 90.5 8 9.5
10. Manufacturer of oven 4 3 75.0 1 25.0
11. Bodies for motor vehicles, trailers and semi-trailers 6 4 66.7 2 33.3
12. Wood and furniture 201 198 98.5 3 1.5
Total 1033 943 91.4 90 8.6
40 M. Seyoum et al. / China Economic Review 33 (2015) 35–49
Following the literature, we employ the share of foreign firms' output as percentage total output at the 4-digit sectorial level as our
measure of intra-industry FDI presence.
FDI
Y
j
¼
X
i∈ j
Y
for
i
X
i∈ j

Y
i
ð6Þ
where i ∈ j indicates a firm in a given sector, Y
i
is firm-level output in a given sector, and Y
i
for
is the output if the firm is foreign.
11
A
statistically significant positive coefficient correlated with FDI would be consistent with the existence of intra-industry or horizontal
technology spillovers.
To examine the effects of domestic firm's characteristics on technology spillovers, we will add four measures of absorptive capacity
namely technology gap (TG), firm size (SIZE), skilled labour force (SKILL) and exporting behaviour (EXP) and the interaction term
between these measures and our FDI presence as additional explanatory variables in Eq. (5).
TG is measured by the difference o f the average labour p roductivity (the ratio of total sales to total employment, weighted by firm
asset size) of foreign firms at the 4-digit industry level and the labour productivity of a local firm in the same industry, following
Kokko et al. (1996).
12
A negative value for the individual domestic firm indicates that the local firm is more productive than the average
foreign firms, while a p ositive value indicates that t he firm is less produc tive. We define a positive gap dummy that takes the value one
when TG is positive and zero otherwise. This dummy variable allows the isolation of local firms with low absorptive capacity. We interact
the positiv e gap dummy w ith F DI presence and expect it to have a n egative effect.
Following the literature, firm size is defined by the number of workers employed in a firm where firms with 50 or more employees
are considered as large and firms with less than 50 employees are categorized as small firms.
13
Accordingly, we define a SIZE dummy
that takes the value 1 if the firm has more than 50 employees and 0 otherwise. We interact the SIZE dummy with our FDI presence to
examine the effect of SIZE on technology transfer.

14
In a similar vein, we include an interaction term between the individual domestic
firm's share of high skilled-labour to its total workforce (SKILL) and our FDI presence to determine if SKILL has an effect on technology
spillovers.
We further explore if FDI spillovers differ between domestic exporters and non-exporters. A firm is considered to be an exporter if
its export sales are equal to or greater than 5% of its total sales. We consider this simple (and also most widely used) definition
adequate for the sake of identifying differences in firm characteristics between domestic exporters and non-exporters. Accordingly,
we spilt the sample into exporting and non-exporting firms and estimate Eq. (5) separately for each group.
11
A firm is considered foreign if the share of foreign ownership is equal to or greater than 10%.
12
As an alternative measurement, we measured TG as the difference of the (un-weighted) average productivity of foreign firms at the 4-digit industry level and that of
alocalfirm in the same industry. The results obtained following this approach were consistent to the ones reported.
13
The definition of large and small firms varies by country. The Central Statistical Agency of Ethiopia follows similar classification where firms are defined as: small
(between 10 and 19 employees), medium (between 19 to 49 employees) and large (greater than 50 employees).
14
An alternative approach is to split the entire sample into large and small firms and estimate Eq. (5) separately for each group. The results obtained following this
approach were consistent to the ones reported.
Table 3
Number and size, exporting behaviour and productivity of domestic and foreign firms.
Source: authors’ computations based on data from the Central Statistical Agency (CSA) of Ethiopia, Large and Medium Manufacturing and Electricity Industries Survey
2012.
Firm characteristics Number and size distribution of firms
All fi rms Domestic firms Foreign firms
#%#%#%
Small 710 69 674 71 36 40
Large 323 31 269 29 54 60
Total 1033 100 943 100 90 100
Exporting behaviour of firms

Exporting 68 7 54 6 14 16
Non-exporting 965 93 889 94 76 84
Total 1033 100 943 100 90 100
Productivity of firms
All firms Domestic firms Foreign firms
Y

L
(mean value
a
)
413.19 377.87 774.24
Small firm (b 50 employees).
a
In thousand birr.
41M. Seyoum et al. / China Economic Review 33 (2015) 35–49
Given the cross-sectional nature of our dataset, solving the problem of estimating technology spillovers from FDI when FDI is en-
dogenous is a major challenge. The major challenge arises from identifying the direction of causality between foreign output share and
domestic firm productivity. Foreign firms may enhance the productivity of domestic firms through technology and knowledge trans-
fers and spillover, but it may be also the case that they are attracted to certain industries that already exhibit higher productivity. In the
latter case, the estimated coefficient on foreign output share would be biased upward. On the contrary, if foreign firms are attracted to
or are mainly concentrated in industries that exhibit lower productivity, the estimated coefficient on foreign output share would be
biased downward. This endogeneity concern is addressed adequately by using instrumental variables two-stage least squares (IV
2SLS) approach, as explained in greater detail below.
4.2. Data and instruments
The data employed in this study is the annual firm survey data from the large and medium scale manufacturing industries
conducted in 2012 by the CSA, Ministry of Finance and Economic Development. This survey data was obtained directly from CSA.
The survey covers firms in the formal manufacturing sector, which employ 10 persons and more and use power driven machinery.
The survey data is well suited to examine spillover effects from FDI, because it contains information on variables that are commonly
applied in econometric estimation of firm level production functions. Particularly, the data includes financial information as well as a

wide range of indicators on firm characteristics such as foreign ownership, employment and skills, exporting behaviour and region
and sector of firms. Sectors are defined according to the International Standard Industrial Classification (ISIC Revision-3.1), but in
some cases are further aggregated.
The number of firms used in our econometric estimation is reduced to 1033 (out of 1937). The number of firms in the estimation is
reduced for the following reasons: i) we omit firms for which we cannot calculate key variables due to missing information; and ii) we
include only sectors that have foreign firms (seven sectors at the 4-digit level in the survey had no foreign presence). After these
considerations, the final data consists of 943 observations for domestic firms and 90 for foreign firms.
15
The sectoral distribution of firms with foreign capital in 2011 is presented in Table 2. The relative presence of foreign capital
and ownership is more visible, in terms of percentage shares, in bodies for motor vehicles, trailers and semi-trailers; spinning,
weaving and finishing of textiles; and chemical and chemical products. Ta bl e 2 indicates that there is difference in the sector
wise distribution among foreign firms implying the need to control for industry specific factors that influence firm level
productivity.
Table 3 below presents summary statistics on number and size distribution of firms, exporting behaviours of firms and
productivi ty by firm ownership. In terms of number and size distribution of firms, the majority of foreig n firms (60%) are
considered to be large firms (N 50 employees), while the majority of domestic firms(71%)arecategorizedassmallfirms
(b 50 employees). Furthermore, about 93% of the domestic firms report that they do not export some or all of their production.
Interestingly, and consiste nt with o ther survey findin g s (cf . Shen, 2013; UNIDO, 2 011; World Bank, 2012 ), Table 3 suggests that
the majority of Chinese investors in our survey data (about 84%) are essentially local market seekers, i.e., they do not export
some or all of their production. In terms of labour productivity, Chinese firms are on average 2 times more productive than
domestic firms.
Moreover, there is a definitive preference for independent market entry among Chinese firms. As reflected in Table 4
below, 89% of the Chinese firms in the survey prefer wholly or majority ownership (N 50%) as opposed to minority ownership.
Only 11% of t he surveyed firms are minority ownership with local partners. This trend is consistent with other survey findings
in Ethiopia (World Bank, 2012) and other African countries that show that Chinese fi rms tend to be wholly Chinese owned
(Shen, 2013).
To identify the causal relationship between FDI presence and domestic firm productivity, we instrument our measure of FDI pres-
ence using sectorial measure of sector targeting by the EIA. The study of Harding and Javorcik (2012) was the first to utilize informa-
tion on investment promotion efforts to attenuate endogeneity concerns. So do Farole and Winkler (2012) for a cross-sectional setting
analysis of FDI and spillovers. The choice for this instrument is based on two assumptions. First, this variable must be correlated with

15
To maintain confidentiality,the dataset that was provided to us has no firm identifiers. However, by following the information in the survey data on sector activity of
firms, location (region, city/town, district, house no.) of firms, and phone address of firms, one is able to identify and determine firms' ownership.
Table 4
Foreign firms by ownership type.
Source: authors’ computations based on data from the Central Statistical Agency (CSA) of Ethiopia, Large and Medium Manufacturing and Electricity Industries Survey
2012.
Ownership type
#%
Wholly (or majority) 80 89
Minority 10 11
Total 90 100
Note: Minority ownership (b 50).
42 M. Seyoum et al. / China Economic Review 33 (2015) 35–49
FDI presence. Second, it must be uncorrelated with domestic firm productivity. We believe that sector targeting by the EIA is well
suited as an instrument, as it is likely to meet both assumptions.
First, it is reasonable to assume that sector targeting by national investment promotion agencies (IPAs) is correlated with FDI
presence, because the primary objective of such policy tool is to identify and promote investment opportunities in host countries
to attract FDI (UNCTAD, 2001; UNIDO, 2011; Wells & Wint, 1990). Sector targeting is deliberated to be the best policy tool for invest-
ment promotion activities, because more intense efforts focused on a few priority sectors are likely to lead to greater FDI inflows than
less intense across-the-board efforts to pr omote FDI (Loewendahl, 2001; Proksch, 2004). Employing a difference-in-differences
approach, Harding and Javorcik (2011) show that targeting a particular sector by a national IPA leads to more than doubling of FDI
inflows into the sector, with significant time lag effects between these two. So do Bobonis and Shatz (2007) and Charlton and
Davis (2006).
Second, Harding and Javorcik (2012) show that the choice of sector targeting by IPAs is less likely to be driven by the quality of
domestic firms or industries in a host country, particularly in the context of developing countries like Ethiopia. Instead, IPAs choose
targeting a particular set of sectors in the hope and expec tation that greater FDI inflows into these sectors can make a positive
contribution to economic growth by generating jobs, bringing additional capital, and transferring new technologies and expertise
to host-countries.
In the past decade, under different national strateg y plans (Plan for Accelerated and Sustained Development to End Poverty

[PASDEP] and Growth and Transformation Plan [GTP]), the Government of Ethiopia and with the help from the World Bank and
other multilateral institutions has identified a particular set of priority sectors for investment aimed at attracting greater FDI inflows
into these sectors. Consequently, the Ethiopian government has identified leather and leather products, textile and garment, and agro-
processing industries as priority sectors to potential international investors. Also, und er the GTP, the list of priority sectors was
extended to include metal and engineering, and chemicals and pharmaceuticals.
The primary selection criterion for these sectors is to enhance their competitiveness internationally and increase domestic
value addition and sales in line with the country's latent comparative advantage (World Bank, 2011). For instance, the country
has a strong latent competitive advantage in prod ucing leather and leath er products due to the outstanding quality an d quant ity
of local leather and extremely low labour costs. Moreover, Ethiopia's large population (about 92 million), cheap and abundant
labour and a fast growing economy (ave raging 10.6% per year over the past decade) add to its attraction as a FDI host economy in
textile and garment, and food processing industries (World Bank, 2012). The reason for the inclusio n of metal and engineering,
and chemicals and pharmaceuti cals as priority area for investment is to reduce the country's heavy dependent on impor ts of
these products (World Bank, 2011).
These industries have continued to receive special interest and extensive support programs from policymakers. Accordingly, the
Government of Ethiopia has implemented various reforms and continuously provided a basket of incentives, such as tax holidays
and tariff-free policies for FDI equipment imports to attract and nurture investments in these priority sectors. As can be seen from
our survey data (Table 2), the sectoral distribution of foreign firms is concentrated on these priority areas, which suggests that the
survey provides a good characterisation of the general trends of FDI in Ethiopia.
Table 5
Impact of foreign ownership on productivity.
Dependent variable: ln
Y

L
ÀÁ
of domestic and foreign firms
Independent variables (1) (2)
Constant 3.469*** 3.506***
(0.270) (0.270)
lnKI 0.076*** 0.073***

(0.015) (0.015)
lnMI 0.672*** 0.668***
(0.025) (0.025)
lnL 0.897*** 0.883***
(0.025) (0.027)
SKILL 0.151*** 0.161***
(0.030) (0.030)
CFOR – 0.224***
(0.074)
AGE 0.094** 0.114**
(0.151) (0.045)
SCALE 0.089*** 0.091***
(0.015) (0.015)
Included observations 1033 1033
Adjusted R-squared 0.894 0.899
Notes:
1. Numbers in the parenthesis are the heteroscedasticity robust standard errors.
2. The symbols ***, ** and * indicate 1%, 5% and 10% significance levels, respectively.
3. All regressions were performed with 11 2-digit industry dummies, the inclusion of which was based on the F-statistics. The excluded dummy corresponds to the last
category of wood and furniture industries.
4. Please refer to Appendix A for the definition of lnKI, lnMI, lnL and CFOR variables.
43M. Seyoum et al. / China Economic Review 33 (2015) 35–49
Following Harding and Javorcik (2012), we instrument our foreign output share variable with two sectorial measures of sector
targeting by the EIA. The first measure is an indicator variable (referred to as Sector targeted
ST
) that equals one if the sector has
been targeted by EIA in a certain year, and zero otherwise. Since our foreign output share variable reflects the presence of foreign
firms over a longer period of time, the second measure is constructed by aggregating the dummies over the period 2007–2011 to
obtain a continuous variable, referred to as Length of sector targeting
LST

. The sum may vary from 0 (no targeting over the period
under study) to 5 (continuous targeting over the period under study). To control for non-linearities, we use the second measure in
log form (adding 1 before taking the log).
We believe that utilizing information on sector targeting by EIA is well suited as an instrument, as it is uncorrelated with domestic
firm productivity,
16
but is correlated with foreign output share variable, especially since there is a time lag between these two
measures. In Subsection 5.2, we report the first stage statistical results for instrument validity.
5. Empirical findings
In this section, we introduce the results of the empirical analysis. All regression results follow various estimations of Eq. (4) and
include industry dummies at the two-digit level to control for productivity differences across industries. All reported standard errors
are robust to heteroscedasticity. We start by presenting productivity differences between foreign and domestic owned firms, and
proceed showing the results for productivity spillover effects employing ordinary least squares (OLS) and instrumental variables
two-stage least squares (IV 2SLS) procedures.
5.1. Estimation results
Table 5 presents the estimation results for the labour productivity differences for firms in the Ethiopian manufacturing sector by
ownership. Column 1 is the regression of labour productivity on all independent variables used in the estimation, except the foreign
16
It is possible that the government of Ethiopia has been selective in allowing FDI in sectors, which are more or less productive than others. If so, our instruments may
have possible biases because the exclusion restriction will probably not be satisfied. But due to data limit, we are not able to fully address this issue in the current paper.
Table 6
FDI presence and productivity spillovers.
Dependent variable: ln
Y

L
ÀÁ
of domestic firms only
Ordinary least squares Instrumental variables two-stage least squares
Independent variable (1) (2) (3) (4) (5) (6)

C 3.687*** 3.489*** 3.535*** 4.052*** 3.549*** 3.736***
(0.298) (0.287) (0.297) (0.346) (0.297) (0.336)
lnKI 0.0710*** 0.070*** 0.072*** 0.0573*** 0.0635*** 0.0659***
(0.016) (0.016) (0.016) (0.016) (0.016) (0.016)
lnMI 0.652*** 0.670*** 0.667*** 0.625*** 0.671*** 0.664***
(0.028) (0.027) (0.027) (0.033) (0.027) (0.029)
lnL 0.886*** 0.879*** 0.871*** 0.874*** 0.853*** 0.797***
(0.028) (0.028) (0.034) (0.031) (0.032) (0.052)
SKILL 0.163*** 0.194*** 0.162*** 0.159*** 0.155*** 0.165***
(0.029) (0.031) (0.031) (0.036) (0.050) (0.043)
AGE 0.119** 0.124*** 0.123*** 0.121** 0.130*** 0.132***
(0.047) (0.047) (0.047) (0.051) (0.048) (0.049)
SCALE 0.0874*** 0.090*** 0.0913*** 0.0888*** 0.0973*** 0.102***
(0.015) (0.015) (0.015) (0.015) (0.015) (0.016)
FDI
j
Y
2.054** 1.128* 0.698 11.19*** 5.941*** 5.481***
(0.948) (0.603) (0.639) (2.338) (1.420) (1.427)
FDI
j
Y
*TG −1.798*** − 5.249***
(0.635) (1.391)
FDI
j
Y
*SKILL − 0.423*** 0.0184
(0.149) (0.486)
FDI

j
Y
*SIZE 0.169 1.747
(0.482) (1.162)
Included observations 943 943 943 943 943 943
Adjusted R-squared 0.898 0.897 0.897 0.877 0.887 0.884
Notes:
1. Numbers in the parenthesis are the heteroscedasticity robust standard errors.
2. The symbols ***, ** and * indicate 1%, 5% and 10% significance levels, respectively.
3. All regressions were performed with 11 2-digit industry dummies, the inclusion of which was based on the F-statistics. The excluded dummy corresponds to the last
category of wood and furniture industries.
4. Length of sector targeting
LST
as instrument for FDI
j
Y
in columns 4 to 6. Another instrument variable, Sector targeted
ST
(not reported) was also used as instrument for FDI
i
Y
.
The results obtained were consistent to the ones reported.
5. The estimations were also run by combining all of the variables in columns 1–3and4–6 to check for robustness. The results obtained (not reported) are consistent
with the results in each column.
6. Please refer to Appendix A for the definition of each variable.
44 M. Seyoum et al. / China Economic Review 33 (2015) 35–49
ownership variable, CFOR. All variables register statistically significant coefficients with the expected signs. Coefficients on capital in-
tensity, intermediate material inputs, labour inputs, skilled labour force, age and scale of operation are positive, and highly significant
at the 1% significance level, indicating that these variables are important determinants of labour productivity.

In column 2, CFOR is entered to control for the effect of foreign ownership on labour productivity. The coefficient of foreign
ownership CFOR is positive and stati stically significant at the 1% level, indicating a significant positive effect of FDI on produc-
tivity. The coefficient on CFOR implies th at foreign owned firms are about 100 (.224) = 22.4% more productive than domestic
firms, ceteris paribus. All other explanatory variables exhibit statistically significant coefficients with the expected signs. Thus,
our findings confirm the high number of studies pointing toward a significant labour productivity advantage in favour of foreign
firms.
Table 6 presents the regression results for productivity spillover effects on domestic firms. Columns 1 to 3 show estimates using
OLS, while columns 4 to 6 report the results applying IV 2SLS procedure. When examining the effects of local firm's absorptive capacity
on technology spillovers, we must preclude the possibility that the observed effect of a firm's absorptive capacity does not capture an-
other absorptive capacity for which we do not instantaneously control. The correlation coefficient matrix (available upon request)
shows that the correlation between the four measures of absorptive capacity is reasonably low implying that there are no substantial
multicollinearity problems; all of the pairwise correlation are less than 0.33, with only one correlation being greater than 0.33 (skill
level and firm size show a correlation of 0.43), which is still within an acceptable range.
Foreign output share reveals a positive and significant impact on labour productivity, in all specifications. Given the OLS and
IV 2SLS results, it is not surprising to note that technology spillovers in t he OLS and IV 2SLS estimates are quite different, indi-
cating the existence of potential endogeneity between FDI presence and domestic firm productivity. Capital intensity, material
inputs, labour, skilled labour force, age and scale of operation have a positive and significant effect in all specifications. Domes-
tic firms' characteristics in terms of their absorptive capacities are interacted with our FDI presence variable. In Eq. (5) of
Table 6, we interact a dummy, TG, with our FDI measure to validate the effect of technology gap on the extent of technology
spillovers. The TG dummy takes the value one if the tec hnology gap varia ble is positive and zero o therwise, as de fined in
Section 4.1.
Asshownincolumn4ofTable 6,t
hecoefficients on FDI presence are positive and significant, while the coefficients of the inter-
action terms with technology gap (TG) are negative and significant. The coefficient estimates on FDI presence imply that the presence
of foreign firms exercise positive spillover effects on domestic firms with high absorptive capacity (when the dummy TG = 0), while
the coefficients on the interaction terms (when the dummy TG = 1) suggest that domestic firms with low absorptive capacity suffer
Table 7
Productivity spillovers and trade orientation.
Dependent variable: ln
Y


L
ÀÁ
of domestic firms only
Ordinary least squares Instrumental variables two-stage least squares
Independent variable Non-exporting firms Exporting firms Non-exporting firms Exporting firms
6
(1) (2) (3) (4)
C 3.605*** 2.946 3.659*** 2.788
(0.292) (1.910) (0.296) (2.714)
lnKI 0.062*** 0.389** 0.055*** 0.398*
(0.015) (0.170) (0.015) (0.215)
lnMI 0.674*** 0.467*** 0.674*** 0.476***
(0.028) (0.093) (0.028) (0.095)
lnL 0.867*** 0.791*** 0.845*** 0.757**
(0.027) (0.111) (0.028) (0.319)
SKILL 0.172*** 0.0787 0.164*** 0.0982
(0.034) (0.055) (0.043) (0.152)
AGE 0.109
⁎⁎
0.164 0.114** 0.166
(0.048) (0.156) (0.049) (0.150)
SCALE 0.087
⁎⁎⁎
0.142** 0.094*** 0.145
⁎
(0.015) (0.070) (0.015) (0.085)
FDI
j
Y

0.793 − 0.111 5.614*** 0.558
(0.605) (1.597) (1.457) (6.486)
Included observations 889 54 889 54
Adjusted R-squared 0.896 0.643 0.897 0.642
Notes:
1. Numbers in the parenthesis are the heteroscedasticity robust standard errors.
2. The symbols ***, ** and * indicate 1%, 5% and 10% significance levels, respectively.
3. All regressions were performed with 11 2-digit industry dummies, the inclusion of which was based on the F-statistics. The excluded dummy corresponds to the last
category of wood and furniture industries.
4. Length of sector targeting
LST
as instrument for FDI
j
Y
in columns 3 and 4. Another instrument variable, Sector targeted
ST
(not reported) was also used as instrument for
FDI
j
Y
. The results obtained were consistent to the ones reported.
5. The estimations were run including all variables of absorptive capacity appearing in Table 7, but only the coefficients for trade orientation are reported for simplicity.
6. Results for exporting firms do not include industry dummies due to small sample size; hence caution should be exercised when interpreting the results.
7. Please refer to Appendix A for the definition of lnKI, lnMI, lnL and FDI
j
Y
variables.
45M. Seyoum et al. / China Economic Review 33 (2015) 35–49
negative spillover effects. Our results argue against the findings of other studies stressing that larger technology gap between foreign
and domestic firms is beneficial for domestic firms, since their catching up potential increases (Findlay, 1978; Wang & Blomstrom,

1992), and instead lends strong empirical support for the notion that too large technology gap deters the likelihood of positive
spillover (Blomstrom et al., 1999).
The interaction term with a domestic firm's skill level of employees (SKILL) does not influence domestic labour productivity
(column 5), confirming the results by Sinani and Meyer (2004) who find that firms' own human capital does not increase their ability
to benefit from positive spillovers. Putting it differently, while the SKILL level of employees contributes to productivity, the interaction
term with FDI indicates that skill level of employees and FDI do not mutually facilitate productivity among local firms. It suggests that
a certain skill intensity threshold must be met in order for a firm to modify and apply the advanced technology of foreign firms.
As shown in Table 6, when firm's SIZE is taken into account, the coefficient on FDI presence is positive and significant, while
the interaction term with SIZE is insignificant (column 6). The coefficient estimates su ggest that while small firms enjoy pos itive
spillovers, large firms do not see m to be influenced by the presence of foreign firms.
17
OurresultsareinlinewithSinani and
Meyer (2004) and Dimelis and Louri (2004) who find that productivity spillovers ac crue mostly for small firms in Estonia and
Greece, respectively.
Moreover, as shown in Table 6 above, the overall results indicate that FDI spillovers in the OLS estimates are downward biased than
those reported by IV 2SLS; for instance, the FDI
i
Y
variable in the IV 2SLS estimate is 11.19 (column 4), which is greater than the OLS
estimate of 2.054 (column 1); similarly, the IV 2SLS estimate of 5.481 (column 6) is greater than the 0.698 estimate reported by
the OLS (column 3).
Exporting firms tend to have stronger capacity than non-exporting ones, which puts them in a better position to enjoy positive
spillover effects that arise from foreign firms’ presence (Crespo & Fontoura, 2007). On the other hand, exporting firms have additional
channels through which they can learn about advanced knowledge, skill and technology from their international connections and
hence the potential for FDI-induced spillovers is limited (Sinani & Meyer, 2004).
Thus, we spilt the sample into firms that export and firms that sell only for the domestic market, and perform separate regression
estimates to investigate spillover effects in each group. The separate estimates are presented in Table 7. The signs of the coefficients for
the spillover variable (FDI
i
Y

) are similar for both exporting and non-exporting firms, although the size of coefficient is not significant
for the former, indicating that the presence of foreign firms in the same industry exercises a positive and significant influence on the
productivity of non-exporting firms.
Furthermore, as seen from Table 7, FDI spillovers in the OLS estimates are downward biased than those provided by IV 2SLS. For
non-exporting firms, FDI spillover in the IV 2SLS is 5.614 (column 3), which is greater than the OLS estimate of 0.793 (column 1).
The same is true for exporting firms.
5.2. Instruments validity results
Table 8 below presents the first stage statistical results for instruments validity corresponding to Table 6 (columns 4 to 6). The co-
efficients of our instruments (length of sector targeting [LST] and its interaction terms) are significant at the 1% significance level, and
the first stage F-statistics in columns 4 to 6 substantially exceed the conventional critical value of 10; similarly, the Kleibergen-Paap
Wald rk F statistics for the weak identification test are far greater than the critical value of 7.03. Thus, we reject the null hypothesis
that the instruments are weak, confirming the validity of our instruments.
Table 8
First stage for FDI
i
Y
and its interaction terms.
Instruments (4) (5) (6)
FDI
i
Y
FDI
i
Y
*TG FDI
i
Y
FDI
i
Y

*SKILL FDI
i
Y
FDI
i
Y
*SIZE
[LST] .0840*** .0286*** .1028*** −.0298** .1036*** .0004
(.0185) (.0171) (.0196) (.0414) (.0192) (.0139)
[LST*TG] .0218*** .1034***
(.0076) (.0076)
[LST*SKILL] − .0060** .1077***
(.0025) (.0294)
[LST*SIZE] − .0132** .0713***
(.0052) (.0062)
R
2
0.8637 0.8126 0.8607 0.5582 0.8614 0.6663
No of obs. 943 943 943 943 943 943
F test 14.76 90.43 14.54 6.73 16.08 66.60
Kleibergen-Paap Wald rk F statistic 74.13 87.67 85.44
Notes:
1. [LST] is the length of sector targeting.
2. Numbers in the parenthesis are the heteroscedasticity robust standard errors.
3. The symbols ***, ** and * indicate 1%, 5% and 10% significance levels, respectively.
4. The estimations were run including all control variables appearing in Table 6,butonlythecoefficients for the instruments are reported for simplicity.
5. Kleibergen-Paap Wald rk F statistic is based on heteroscedasticity robust standard errors. The Stock-Yogo weak identification test critical values for two endogenous
variables and two instruments is 7.03 for 10% maximal IV size under the desired maximal nominal size of a 5% Wald test.
6. Please refer to Appendix A for the definition of each variable.
46 M. Seyoum et al. / China Economic Review 33 (2015) 35–49

Similarly, for the estimation sample of exporting and non-exporting firms (Table 7), our instruments are not weak, with first stage
F-statistics substantially exceeding the conventional critical value of 10 (Table 9).
6. Concluding remarks
Using a sample of 1033 Ethiopian manufacturing firms operating in 2011, 8.6% of which are foreign owned, this study inves-
tigates the impact of Chinese ODI on domestic firm productivity. Besides OLS, this s tudy also empl oys IV 2SLS procedure to tack le
the potential endogeneity between FDI-induced spill over effects and domestic firm producti vity. This empirical study is, to our
knowledge, the first to present a deta iled analysis of Chinese ODI spillover effects on a host country in developing Sub-Saha ran
economies.
We find that increases in foreign equity participation are positively associated with increases in productivity, indicating that
foreign-owned firms enjoy higher productivity levels due to the possession of su perior productive assets, such as technological
know-how and management skills.
We also find that the productivity of domestic firms is positively correlated with the presence of foreign firms in the same industry,
providing new solid empirical evidence on spillovers from Chinese ODI in manufacturing. Our findings for positive FDI spillovers in
Ethiopia are consistent with the conventional wisdom that FDI is superior to foreign portfolio investment, that it produces positive
spillover effects not internalized by any agents in the economy.
When domestic firm characteristics are taken into account, we obtain the following results: (i) FDI has a positive spillover effects
on local firms when the technology gap between foreign and d omestic firms is smaller, and that domestic firms with lower ab-
sor ptive capacity suffer negative spillover effects from FDI; (ii) skilled labou r of domestic firms does not enhance their capaci ty
to attract FDI spillovers; and (iii) small firms and n on-exporting firms benefit more from spillovers than do other types of do-
mestic firms.
Theimportanceofdomesticfirms' absorptive capacity in influencing FDI spillovers highlighted in our study also
helps shed light on the appropriate government policies to pursue regarding FDI. To fully benefit from the positive FDI spill-
overs, the Ethiopian government might wish to pursue proactive strategies to promote and broaden linkages between
domestic firms and FDIs through intermediate inputs and technology upgrading. For instance, the EIA could design
supplier/buyer-identification programs to facilitate foreign firms locate potential domestic supply/buyer sources. Similarly,
the government could devise mechanisms and incentive packages to encourage foreign inve stors to undertake local supplier
development programs to increase the prospects of domestic firms becoming suppliers to them. Furthermore, creating a
conducive and enabling investment/business environment is crucial in promoting FDI as well as boosting the likelihood of
linkages with domestic firms.
Further research is needed to examine vertical (inter-industry) spillovers from Chinese ODI in manufacturing in Ethiopia or other

African countries. If firm survey data become available, the effect of Chinese ODI on downstream (local customers or buyers) sectors or
upstream industries (local suppliers of intermediate inputs) should be considered to further enhance our understanding on the nature
and impact of Chinese ODI on developing Sub-Saharan host economies.
Acknowledgements
We thank Seyoum Mesfin
for helpful conversations and comments. We are indebted to Long, Cheryl Xiaoning and three anony-
mous referees for useful comments and suggestions. We are thankful to Huajian International Shoe City (Ethiopian) P.L.C for financial
support in covering fieldwork and data collection expenses in Ethiopia, including travel and accommodations. The authors would also
like to thank Michal Bernert for excellent research assistance. Any remaining errors are the sole responsibility of the authors.
Table 9
First stage for FDI
i
Y
.
Instrument FDI
i
Y
FDI
i
Y
(3) (4)
[LST] .1128*** .0524***
(.0230) (.0165)
R
2
0.8697 0.3164
No of obs. 889 54
F test 24.02 10.07
Notes:
1. [LST] is the length of sector targeting.

2. Numbers in the parenthesis are the heteroscedasticity robust standard errors.
3. The symbols ***, ** and * indicate 1%, 5% and 10% significance levels, respectively.
4. The estimations were run including all control variables appearing in Table 7,butonlythecoefficients for the instruments are reported for simplicity.
5. Please refer to Appendix A for the definition of FDI
i
Y
variable.
47M. Seyoum et al. / China Economic Review 33 (2015) 35–49
Appendix A. Variables definitions.
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Variables Definitions
Dependent variable
(Labour productivity, LP)
Gross sales as reported by firms in the large and medium scale manufacturing industries survey (2012), in log form.
Capital labour ratio (KI) The ratio of fixed assets to total employment in the individual firm (in log form).
Material input labour
ratio (MI)
The ratio of material input purchases to total employment in the individual firm (in log form).

Total labour force (L) Total number of employees (in log form).
Human capital intensity
(SKILL)
The ratio of managers, scientist, engineers and technicians, and clerical and office workers to the total number of workers.
SCALE (within firms) The individual firm's output relative to the average output in the sector at 4-digit industry level to which the firm belongs.
Firm's age (AGE) Firm's age in year 2011 (in log form).
Exporting behaviour
(EXP)
A firm is considered to be an exporter if its export sales are equal to or greater than 5% of its total sales.
Size of firm (SIZE) A binary variable equal to 1 if the firm has N 50 employees and 0 otherwise.
Foreign-owned firms
(CFOR)
A dummy variable equal to 1 for a firm with foreign equity (majority or minority) and 0 otherwise. Following the literature, we
consider firms as foreign with a foreign equity of 10% or more.
Proxy for foreign presence
(FDI
j
Y
)
The ratio of the output of foreign firms to total gross output in each sub-sector at the 4-digit industry level.
Technology gab (TG) The technological gap between foreign and local firms, as defined in Section 5.1.
Industry dummies 12-digit industry dummies are used in the regression to control for industry specific effects not captured by the above explan-
atory variables.
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