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Drivers of Industrial Competitiveness in Tanzania: A Capability and Sectoral Approach

Presentation by Manuel Albaladejo, Queen Elizabeth House University of Oxford , 2004

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What: A presentation made in 2003 to an UNCTAD Expert Meeting giving an analysis of the competitiveness of different sectors in Tanzania. It gives an overview of growth in both the manufacturing and other sectors in Tanzania before looking at some of the reasons for underperformance and suggesting possible capacity upgrading strategies. Who: Useful to anyone concerned with economic development in Tanzania but also African LDCs more generally, particularly regarding policy options How: This presentation provides a good example of how to use empirical data to draw out specific policy inclusions and with additional background reading on could be the basis of an excellent country case study.

Drivers of Industrial Competitiveness
in Tanzania: A capability and sectoral


approach


Manuel Albaladejo
Queen Elizabeth House


University of Oxford


UNCTAD, Expert Meeting on Programmes and
Policies for Technology Development and Mastery of


Foreign Investment
16-18 July 2003




Overview of Tanzania’s manufacturing sector


• Context:
– Achieved macro-economic stability (control of inflation,


reduction of deficit, etc)
– Strong presence of international organisations. Yet, most


emphasise health and education programmes.


• MVA as % to GDP has declined from already low 9.2% in


1990 to 7.5% in 2000


• Main industries are processed foods (account for 53% of
industrial input and employs 30% of manufacturing
employment), beverages and tobacco.


• Industrial activity concentrated in Dar, Arusha and
Kilimanjaro areas


• Overrepresentation of family-owned microenterprises (5 or
less employees) in manufacturing




MVA growth and capacity


MVA Annual Grow th rate 1990-2000


5.6%


3.0%


8.5%


2.3%


0%
1%
2%
3%
4%
5%
6%
7%
8%
9%


Tanzania Kenya Uganda SSA


MVA per capita


14.2


36.9


14.1


91.8


11.8


28.3
18.7


80.7


0


20


40


60


80


100


Tanzania Kenya Uganda SSA


1990
2000




Manufactured export growth and capacity


Manufactured exports per capita 1995 2000


3.3


31.8


2.1
4.4


22


3


0


5


10


15


20


25


30


35


Tanzania Kenya Uganda


1995


2000


Annual grow th rate in manufactured exports 1995-2000


5.50%


-7.10%


7.90%


5.1%


-8%
-6%


-4%
-2%


0%


2%
4%
6%


8%
10%


Tanzania Kenya Uganda SSA




The desired path: technological upgrading


0%


5%


10%


15%


20%


25%


30%


35%


40%


45%


50%


0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Share of manufactured exports in total exports


S


h


a


r


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o


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m


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Mauritius 2000


Uganda 1995


Tanzania 2000


Uganda 2000


Malaw i 2000


S Africa 2000


S Africa 1995


Tanzania 1997


Malaw i 1995


Mauritius 1995


Mozambique 1995


Zimbabwe 2000


Keny a 1995


Zimbabwe 1995


Mozambique 2000


Keny a 2000


Desired
Industrial Path




The social dimension of competitiveness
Manufacturing employment 1990-1999


Annual growth rate in brakets


0


50,000


100,000


150,000


200,000


250,000


1990 1995 1999


Tanzania


Kenya


(1.76%)


(1.03%)


Annual manufacturing wages (US$)


298.3 229.2 322.5


1,604.90
1,453.30


2,227.50


0


500


1000


1500


2000


2500


1990 1995 1999


Tanzania


Kenya




The productivity gap in manufacturing


0


1,000


2,000


3,000


4,000


5,000


6,000


1990 1991 1994 1995 1996 1997 1998 1999


Tanzania Keny a


Source: UNIDO, Industrial Statistics 2002


Gap: 5.1


Gap: 3.3




Economic Arguments for Tanzania’s
underperformance in manufacturing


• Argument 1:
– Tanzania’s private sector does not have the


capabilities (i.e. skills, technology) to take
advantage of sectors of comparative
advantage (e.g. agro-processing industries)


• Evidence:
– The inability to transform agricultural inputs


in basic agro-processed products has led to
post harvest losses of around 25%-40% in
the fruit and fish industries


– Tanzania has a positive trade balance in
unprocessed food exports and a negative
trade balance in processed food exports




Capacity and upgrading in the food industry


Figure. Changes in the share of food exports in total exports and in the share of processed food
exports in total food exports for Tanzania and other African countries, 1995-2000


-10%


-5%


0%


5%


10%


15%


20%


25%


30%


35%


40%


0% 20% 40% 60% 80%


Share of food exports in total exports


S


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x


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s


Mauritius 2000 (263)


Zimbabwe 2000 (262)


Keny a 2000 (878)


Tanzania 2000 (306)


S Africa 2000 (1,724)


Uganda 2000 (207)


Bubble size indicates the v alue of food ex ports (US$ million)


Keny a 1995 (931)


Zimbabwe 1995 (294)


S Africa 1995 (1,790)


Tanzania 1997 (290)


Malaw i 1995 (79)


Mauritius 1995 (437)


Malaw i 2000 (98)
Uganda 1995 (470)




Capacity and upgrading in the fruit industry


-10%


0%


10%


20%


30%


40%


50%


60%


70%


-5% 0% 5% 10% 15% 20% 25%


Share of fruit and nut exports in total exports


S


h


a


r


e




o


f




p


r


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c


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s


s


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s Kenya 2000 (177)


Tanzania 2000 (91)


Zimbabwe 2000 (42)


Mozambique 2000 (42)


Bubble size indicates the value
of fruit and nut exports (US$ million)


Kenya 1995 (131)


Zimbabwe 1995 (41)
Tanzania 1997 (75)


Mozambique 1995 (15)


Malaw i 2000 (10)Malaw i 1995 (11)


Uganda 2000 (10)


Uganda 1995 (19)




Capacity and upgrading in the fish industry


-20%


0%


20%


40%


60%


80%


100%


120%


-10% 0% 10% 20% 30% 40% 50% 60%


Share of fish exports in total exports


S


h


a


r


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o


f




p


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s


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d




f


i


s


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i


n




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o


t


a


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f


i


s


h




e


x


p


o


r


t


s


Kenya 2000 (39)


Tanzania 2000 (74)


Mauritius 2000 (38)


Mozambique 2000 (74)


Bubble size indicates the value of fish exports (US$ million)


Kenya 1995 (35)


Mauritius 1995 (36)


Tanzania 1997 (60) Mozambique 1995 (81)




Tanzania’s trade balance for selected food
industries (US$ million 1995, 2000)


-100


-50


0


50


100


150


200


250
Unprocessed


Processed


Unprocessed 57.7 73.9 66.9 85.6 214.1 202.3


Processed 2.3 0.5 -1.1 -0.7 -64.6 -61.2


1995 2000 1995 2000 1995 2000


Fish Fruit and nuts Total food




Differentials in productivity and wages for the
Fish and fruit industries (US$


Productivity differentials,US$ (relative to average productivity in
manufacturing)


-670.9


1,430.80
1,864.40


-276.3


-962.4
-1,255.30


-1500
-1000


-500
0


500
1000


1500
2000


2500


1990 1995 1999


Fish Fruit and nuts


Wage differentials, US$ (relative to average wages in manufacturing)


-0.7


163.3


226.3


-38.7 -33.5 -44.9


-100


-50


0


50


100


150


200


250


1990 1995 1999


Fish Fruit and nuts




The leather value chain and
Tanzania’s performance (US$ million, 2000)


V


A


L


U


E




A


D


D


E


D


-


+


Manufactures
of Leather


Skins and
Hides


Leather


-1


0


1


2


3


4


5


6


7


Exports Imports Trade balance


Hides and skins


Leather


Manufactures of leather




Economic Arguments for Tanzania’s
underperformance in manufacturing


• Argument 2:
– Trade liberalisation and regional integration


has not triggered industrial growth.


• Evidence:
– Manufactured export performance in EAC and


SADC has been disappointing.
– More worrying, trade agreements have


reinforced Tanzania’s role as exporter of
primary and very low added manufactures.
This is more significant in SADC given the
differences in the sophistication and maturity
of members’ manufacturing sectors.




Manufactured export performance in EAC


0%


20%


40%


60%


80%


100%


0% 20% 40% 60% 80% 100%


Share of manufactured exports in total exports to EAC


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E


A


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Bubble size indicates total manufactured exports to EAC (US$ million )


Uganda 1995 (7)


Tanzania 1997 (9.3)


Kenya 2000 (315.2)


Tanzania 2000 (16.3)


Uganda 2000 (5.9)


Kenya 1995 (462)




Manufactured export performance in SADC


40%


50%


60%


70%


80%


90%


100%


30% 40% 50% 60% 70% 80% 90% 100%


Share of manufactured exports in total exports to SADC


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S


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D


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Bubble size indicates total manufactured exports to SADC (US$ million )


Mauritius 1995 (17)


Malawi 1995 (34)


S Africa 1995 (2,926)


S Africa 2000 (2,883)


Mauritius 2000 (24)


Malawi 2000 (34)
Zimbabwe 2000 (366)


Zimbabwe 1995 (387)


Mozambique 1995 (21)


Mozambique 2000
(30)


o


m


Tanzania 1997 (14) Tanzania 2000 (16)




Tanzania’s trade balance in EAC and SADC
markets (1995, 2000)


-160


-140


-120


-100


-80


-60


-40


-20


0
1995 2000 1995 2000


EAC SADC


Manufactured exports
Simple industries


Complex industies




Economic Arguments for Tanzania’s
underperformance in manufacturing


• Argument 3:
– Tanzania’s manufacturing has not plugged into global


production systems, hence not benefiting from
technology transfer


• Evidence:
– FDI inflows remain very low (US$ 193 million in 2000)
– Most FDI inflows in the country have not targeted the


manufacturing sector (only 2-3%)


FDI per capita (US$)


0


2


4


6


8


10


12


Tanzania Kenya Uganda


1995


2000




Economic Arguments for Tanzania’s
underperformance in manufacturing


• Argument 4:
– Tanzania lacks the dynamic technology-based


SMEs that undertake innovation and demand
highly qualified staff.


• Evidence:
– Tanzania counts with few growth-oriented


SMEs (which are often run by non-Africans)
and many survivalist microenterprises.


– R&D financed by productive enterprises is nil.


THIS DOES NOT MAKE TANZANIA
DIFFERENT FROM MOST COUNTRIES
IN SUB-SAHARAN AFRICA




IMPLICATIONS FOR POLICY:
WHERE CAN AFRICAN COUNTRIES START?


1. Defining a country’s INDUSTRIAL VISION
(short, medium and long term goals), based
on an thorough industrial assessment using
benchmarking exercises to:
– Find major bottlenecks to industrial activity
– Find competitive strengths to be exploited (e.g. low


wages, high skill level)
– Identify sectors with growth potential (‘picking


winners’)


BUT WHY IS AN INDUSTRIAL VISION SO IMPORTANT?
a) It helps developing countries define the kind of


technology and skills to be developed, and FDI to be
attracted


b) Capability building is faster and more cost-effective




IMPLICATIONS FOR POLICY:
WHERE CAN AFRICAN COUNTRIES START?


2. Design & implementation of POLICY TOOLS to
achieve the industrial vision


• Industrial policy
Licensing and other requirements; competition policy; ownership
policy; corporate tax rates


• Technology policy
Technology licensing; tax regime for R&D activities; technology
finance; technology institutes- industrial sector linkages; IPR regime


• Trade policy
Restrictions (tariff bands and average tariff rates); customs
administration (efficiency and speed); access to world-price inputs
for export activity; export taxes or incentives


• FDI policy
Restriction on investment; costs of entry and doing business (legal &
bureaucratic barriers); special incentives for investors




CAN SUCCESSFUL EAST ASIAN POLICIES BE
REPLICATED IN AFRICA?


• They can but success is unlikely to occur as
the international context has changed:


– Competition is much tougher today
– There are new rules of the game (e.g. WTO)


and SSA’s capability base is lower than EA’s in
the past


• For SSA, it is therefore better to learn from
PRINCIPLES rather than particular POLICIES


– EXPORT ORIENTATION (CONSENSUS)
– ROLE OF THE GOVERNMENT ON COMPETITIVENESS
(MORE CONTROVERSIAL)




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