Are the East African countries ready for a
common currency? Traditional indicators
and cointegration analysis
Matteo Falagiarda*†
This version: February 2010
ABSTRACT
This paper investigates the suitability of a common currency in the East African
Community (EAC), a regional block comprising Kenya, Tanzania, Uganda, Rwanda
and Burundi. First of all, a handful of traditional OCA criteria have been considered,
namely the degree of intraregional trade and the degree of openness, the degree product
diversification, and the similarity of the economic structures. Then, I perform specific
G-PPP cointegration analysis, in order to observe the behavior of the real exchange
rates. The findings of this paper suggest that a monetary union in East Africa could be a
viable option. However, some country-specific and statistical anomalies emerge, casting
doubts on the results.
JEL classification: E42, F33
Keywords: optimum currency areas, exchange rate regimes, cointegration, East African
Community
CEAFE field: International economics
*
The author (and speaker) is a graduate student at the School of Economics of the University of Reading.
Correspondence to: Matteo Falagiarda, Upper Redlands Road, Reading RG1 5JW, UK
Tel: +447552138951
Email: m.falagiarda@student.reading.ac.uk
†
This article is based on parts of my Master thesis at the University of Trento, Italy (March 2009). I thank
my supervisor Andrea Fracasso for his support.
1. INTRODUCTION
After the birth of the Euro in 1999, interest in economic and monetary integration has
increased and several regional blocks around the world have been assessing the
possibility to establish common markets and monetary unions. Also some African
regional groups have monetary integration as one of their short- and medium-term
goals. The revival of the East African Community (EAC) in 1999 brought to the fore,
besides the old idea of a political federation in East Africa, the possibility to reintroduce
a common currency in the region. Initially, the EAC was composed of three countries,
Kenya, Tanzania and Uganda. Today, after the entry of Burundi and Rwanda in 2007,
the EAC covers an area of 1.85 million square kilometers with a combined population
of about 128 million, and it appears to be one of the most ambitious integration projects
in Africa. The declared final objective of the Community consists in establishing a
monetary union and a political federation. This paper investigates the suitability of a
monetary union in the EAC by carrying out different techniques in an attempt to find
empirical evidence on the desirability of this project.
The classical theoretical framework for discussing monetary integration is the
Optimum Currency Area (OCA) theory, introduced by Mundell (1961). The OCA
theory, far from being a unified framework, tries to indicate whether and under which
conditions a monetary union may be appropriate, and which countries should participate
or not. The main OCA criteria that should be satisfied are the following: high labour
and capital mobility among the candidate countries (Mundell, 1961), high price and
wage flexibility (Mundell, 1961), a high degree of economic openness (McKinnon,
1963), a high degree of diversification in production and consumption (Kenen, 1969), a
high degree of fiscal integration (Kenen, 1969), a high similarity of inflation rates
(Fleming, 1971), a high degree of financial market integration (Ingram, 1962). More
recent studies stress the ineffectiveness of monetary policy (Phelps (1967), Friedman
(1968) and Lucas (1972)), the time-inconsistency and credibility issues (Barro &
Gordon, 1983), and the endogeneity of the OCA criteria (Frankel & Rose, 1998).1
The OCA empirical literature on the EAC is very poor. Mkenda (2001) and
Buigut and Valev (2005 and 2006) carried out studies on this issue, by using
1
For a review of the OCA theory, see Mongelli (2008).
1
cointegration and SVAR techniques, respectively. The contribution of this article to the
existing literature is threefold. Firstly, it is the first study that takes into account the new
EAC members (Burundi and Rwanda). Secondly, the three traditional OCA criteria
considered in this paper have been discussed comprehensively, and recent data have
been used. Thirdly, the Generalized Purchasing Power Parity (G-PPP) analysis, carried
out in section 3, updates, extends and improves Mkenda’s (2001) cointegration analysis.
The main findings of this paper suggest that a monetary union in East Africa
could be a viable option. However, some country-specific and statistical anomalies
emerge, casting doubts on the results.
This paper is organized as follows. The next section reports the empirical
analysis concerning three of the traditional OCA criteria: the degree of trade
interdependence among the EAC countries, and their degree of openness (sub-section
2.1), the degree of their product diversification (sub-section 2.2), and the similarity of
their economic structures (sub-section 2.3). The cointegration analysis (G-PPP) is
carried out in section 3. Section 4 reports the main conclusions.
2. SOME TRADITIONAL OCA CRITERIA
2.1. Trade interdependence and the degree of openness
According to the OCA theory, the level of intra-regional trade and the degree of
openness are crucial factors in determining the optimality of a potential currency area
(Mundell (1961), McKinnon (1963)). Indeed, countries with intense trade linkages
among themselves could in principle mainly benefit from the introduction of a common
currency. In addition, the higher the degree of openness of a country, the more inclined
countries should be to join a currency area with their closest partners, being the
exchange rate tool less useful as an adjustment tool.
Table 1 shows the extent of intra-regional trade among the EAC countries.2 In
the light of these figures, some comments are in order. Firstly, the Kenya-centric nature
2
Notice that the amount of unofficial cross-border trade may be relevant among the EAC countries, as
estimated by Ackello-Ogutu (1997) and Ackello-Ogutu and Echessah (1998). According to these studies,
cross-border trade between Kenya and Uganda in the period 1994-1995 was about 49% of official trade,
between Kenya and Tanzania it was about 12% and between Tanzania and Uganda 45%.
2
of the EAC seems to be confirmed by the high shares of Burundian, Rwandan and
Ugandan trade with Kenya. For these three countries Kenya’s markets represent an
important import source. Furthermore, it is worth noting that the imports of Kenya from
the other EAC members are negligible (just 2.13% of total imports in 2007).
Conversely, Kenyan exports depend heavily on the other EAC countries (36.61% of her
total exports). Secondly, Tanzanian trade with the other EAC members is quite low. The
reasons of that can be found in the lack of adequate infrastructures between Tanzania
and the other EAC members and in the fact that Tanzanian trading relations are carried
out mostly by sea and with SADC countries.3 Thirdly, the trade links of Burundi and
Rwanda (and to some extent those of Uganda) with the other EAC members, is quite
high, both in terms of exports and imports. It is not surprising, since these countries are
small and landlocked. The introduction of a common currency would probably bring
benefits to these three countries. Finally, it is important to stress that the intra-regional
trade in the EAC has become increasingly important and a further growth is likely to
happen in the next years, thanks to the advances towards a common market.
In conclusion, the trade interdependence criterion points out that the EAC
countries (except Tanzania) would probably benefit from the introduction of a common
currency. Tanzania seems to be relatively isolated from the other EAC members.
Table 1. Intra-regional trade: exports (imports) per country
of destination as a share of total exports (imports) (%)
1990-1995 1996-2000
2001
2002
2003
2004
2005
2006
2007
11.97
6.54
7.30
2.10
27.91
6.70
0.38
8.06
0.58
15.72
21.12
11.39
0.43
0.96
33.90
12.13
0.64
10.38
0.98
24.14
1.70
6.24
0.85
2.16
10.95
14.93
0.47
12.67
5.91
33.97
2.90 1.63 40.53
10.91 4.41 6.22
0.49 0.11 2.17
2.09 1.93 3.32
16.39 8.08 52.23
16.77 12.64 8.23
0.54 0.41 0.24
13.70 10.32 1.76
6.89 5.19 3.93
37.91 28.57 14.17
18.16
16.80
3.16
5.96
44.09
13.79
0.40
2.95
6.58
23.72
Burundi
Exports to
Kenya
Rwanda
Tanzania
Uganda
EAC
Imports from Kenya
Rwanda
Tanzania
Uganda
EAC
6.23
4.59
1.56
0.97
13.36
3.56
0.26
3.86
0.96
8.65
2.30
3.68
2.99
0.08
9.04
4.95
0.83
5.49
0.63
11.90
3
The SADC is the Southern African Development Community, another African regional integration
block.
3
Kenya
Exports to
Burundi
Rwanda
Tanzania
Uganda
EAC
Imports from Burundi
Rwanda
Tanzania
Uganda
EAC
0.86
3.13
6.44
11.87
22.31
0.17
0.29
0.55
0.43
1.45
0.41
2.65
10.44
16.64
30.14
0.01
0.02
0.55
0.19
0.77
0.48 0.77 1.69 1.47 1.49 1.03 1.08
2.87 2.86 3.18 3.75 3.50 3.53 3.69
5.57 5.05 8.28 7.96 8.03 9.30 9.72
14.48 14.78 14.82 17.48 16.28 21.16 22.12
23.41 23.48 27.97 30.66 29.30 35.02 36.61
0.09 0.20 0.00 0.00 0.00 0.06 0.03
0.00 0.00 0.00 0.00 0.02 0.04 0.01
1.32 1.20 1.32 1.42 1.20 0.86 1.10
2.04 2.08 2.32 2.49 2.11 0.25 0.99
3.45 3.48 3.64 3.91 3.34 1.21 2.13
1.11
0.15
0.01
0.06
1.33
1.30
14.09
10.99
0.34
26.72
1.85
29.41
5.81
1.31
38.37
1.11
21.72
7.31
4.66
34.80
0.57
28.33
5.70
2.97
37.57
0.97
24.79
3.85
3.24
32.86
1.48
32.31
0.62
2.45
36.87
1.69
32.83
6.48
6.97
47.97
2.13
35.78
7.02
23.23
68.15
0.96
31.29
5.96
9.33
47.54
1.86
1.79
4.79
1.34
9.78
0.09
5.22
0.00
0.08
5.39
0.92
4.49
1.89
1.59
8.89
0.07
9.27
0.03
0.37
9.73
1.45
4.95
0.40
0.86
7.66
0.01
5.60
0.00
0.66
6.28
1.49
3.92
0.47
0.83
6.71
0.00
5.80
0.00
0.16
5.96
1.63 1.64 1.65 2.23 3.55
6.99 6.12 5.58 5.98 6.17
0.48 0.53 0.48 0.58 0.88
0.96 3.78 2.92 2.55 2.30
10.05 12.07 10.63 11.33 12.90
0.02 0.00 0.01 0.00 0.00
5.67 5.80 5.50 8.28 8.18
0.04 0.00 0.00 0.00 0.00
0.39 0.31 0.29 0.28 0.28
6.12 6.12 5.81 8.56 8.46
0.18
2.88
2.00
0.54
5.61
0.21
27.88
0.04
1.34
29.47
0.31
8.53
4.97
1.23
15.04
0.01
23.64
0.05
1.10
24.79
1.05
12.94
3.64
2.49
20.11
0.00
17.66
0.02
0.42
18.10
1.42
15.28
3.20
1.30
21.19
0.01
28.15
0.12
0.68
28.95
1.79
15.36
4.07
1.14
22.37
0.00
28.59
0.04
0.86
29.50
Rwanda
Exports to
Burundi
Kenya
Tanzania
Uganda
EAC
Imports from Burundi
Kenya
Tanzania
Uganda
EAC
0.98
22.59
0.95
4.83
29.35
1.09
35.57
7.58
16.99
61.23
1.69
26.03
0.18
1.40
29.29
1.60
26.76
4.99
11.19
44.55
2.46
21.68
0.63
1.28
26.05
0.71
29.94
6.78
15.26
52.69
3.96
18.69
0.25
1.75
24.65
0.83
21.63
6.76
13.97
43.18
Tanzania
Exports to
Burundi
Kenya
Rwanda
Uganda
EAC
Imports from Burundi
Kenya
Rwanda
Uganda
EAC
Uganda
Exports to
Burundi
Kenya
Rwanda
Tanzania
EAC
Imports from Burundi
Kenya
Rwanda
Tanzania
EAC
2.05
12.82
3.40
1.38
19.64
0.00
23.20
0.03
0.78
24.02
2.53 2.12 2.75
15.76 9.07 7.60
4.18 4.38 5.36
1.88 1.42 1.97
24.36 16.98 17.67
0.01 0.05 0.05
28.29 29.03 26.04
0.04 0.04 0.05
1.59 1.15 0.84
29.93 30.27 26.97
Source: Calculated from UN Comtrade Statistics and IMF Direction of Trade Statistics
The figures reported in Table 2 indicate that the merchandise exports within the
pre-enlargement EAC (i.e. without Burundi and Rwanda), as a percentage of the total
4
exports of the three countries, are relatively high in comparison with other African
regional initiatives. This finding is in line with Buigut (2006) and Masson and Pattillo
(2005). Since the pre-enlargement EAC is the African regional block with the smaller
number of members, these figures are even more significant in the light of what claimed
by Iapadre (2004), who argues the higher the number of countries in the region, the
larger its intra-regional trade share will be. In other words, other things being equal, a
region with a high number of member countries would show a larger intra-regional
trade than a region of the same total trade size, but with a smaller number of members.
Table 2. Merchandise exports within African regional blocs (% of total bloc exports)
CEMAC
COMESA
EAC
ECCAS
ECOWAS
SADC
WAEMU
1990
1995
1999
2000
2001
2002
2003
2004
2005
2.3
6.6
13.4
1.4
7.9
17.0
13.0
2.1
7.7
17.4
1.5
9.0
31.6
10.3
1.7
7.4
14.4
1.3
10.4
11.9
13.1
1.1
6.1
20.5
1.1
7.9
9.3
13.1
1.4
7.9
21.4
1.3
8.5
8.6
12.7
1.5
7.4
19.3
1.1
10.9
9.5
12.2
1.4
7.4
18.2
1.0
8.6
9.8
13.3
1.3
6.8
16.6
0.9
9.4
9.5
12.9
0.9
5.9
15.0
0.6
9.5
7.7
13.4
Source: WB African Development Indicators
Notes: Burundi and Rwanda are not included in the EAC. CEMAC, Economic and Monetary Community of Central
Africa; COMESA, Common Market for Eastern and Southern Africa; ECCAS, Economic Community of Central
African States; ECOWAS, Economic Community of West African States; SADC, Southern African Development
Community; WAEMU, West African Economic and Monetary Union.
In Table 3 is represented the degree of openness of the EAC economies, and
other neighboring countries, as terms of comparison, calculated as the total trade over
GDP. Kenya is the most open EAC economy, followed by Burundi and Tanzania.
Rwanda and Uganda are quite closed. These results could be surprising, because small
countries are usually more open than big ones. The reason could be that the biggest
EAC economies (Kenya and Tanzania) have access to the sea and they have high
volumes of trade by sea. However, Burundi, Rwanda and Uganda have become
progressively more open in the last 15 years. All in all, the EAC economies, compared
to those of some neighboring countries, do not appear very open, except for the case of
Kenya.
5
Table 3. Degree of openness – Trade as a share of GDP (%)
Burundi
Kenya
Rwanda
Tanzania
Uganda
Dem. Rep. Congo
Ethiopia
Malawi
Mozambique
South Africa
Sudan
Zambia
90-95
96-2001
2002
2003
2004
2005
2006
37.97
63.54
32.77
56.88
29.63
42.10
17.55
65.61
54.20
41.33
25.82
75.21
25.38
50.57
31.81
43.27
34.45
46.82
33.60
63.32
47.65
50.53
25.69
66.96
28.48
53.35
32.21
40.83
38.57
47.24
41.13
92.96
74.87
62.14
32.85
69.85
36.10
52.36
35.83
45.95
39.03
59.48
43.42
81.42
74.17
53.84
33.63
69.66
43.49
59.41
38.69
51.83
41.25
69.52
49.19
68.15
72.79
53.71
39.07
66.82
56.69
63.11
41.54
54.39
40.26
70.88
54.74
58.40
75.19
55.89
45.49
71.70
58.70
62.18
43.18
55.13
44.37
70.41
57.47
46.42
88.92
63.06
42.76
67.77
Source: Calculated from WB World Development Indicators
To sum up, while Rwandan and Ugandan economies are rather closed, Burundi,
Kenya and Tanzania show higher values of openness. Thus, according to this OCA
criterion, Rwanda and Uganda seem to be the less qualified to join a monetary union.
2.2. Degree of product diversification
Kenen (1969) argued that well-diversified economies are better candidates for a
currency union, since diversification provides insulation against sector-specific shocks.
In what follows, I analyze the degree of diversification of the industrial structure, using
two different sources of data.
As in Mkenda (2001), I calculate the Herfindal index for the EAC countries,
based on the data from UNIDO’s International Yearbook of Industrial Statistics. The
data are incomplete and, except for Kenya, quite dated. Moreover, data for Burundi are
not available. Nevertheless, I construct a Herfindahl index for the EAC economies. The
index is calculated as follows:
𝑃𝑟𝑜𝑑𝐷𝑖𝑣𝑖 = 100 ∗
𝑛
2
𝑗 =1 𝑠𝑗
(1)
where sj is the share of the value added of sector j on total production in country i. The
value of the index can vary from 0 to 100. A higher value indicates a smaller degree of
product diversification. The data refer to manufacturing industries classified at three-
6
digit level of ISIC, except Tanzanian data, which are classified at three-digit level of
ISIC – Rev.2. In Table 4 are reported the Herfindahl indices for the years for which data
are available. Kenya and Tanzania have a relatively diversified industry structure,
whereas Uganda a little less diversified, and Rwanda not well diversified at all. It is not
surprising, since big countries have usually a more diversified economic structures.
Table 4. Degree of product diversification (1996-2005)
1996
Kenya
n.c.
Rwanda
n.a.
Tanzania 9.997
Uganda
n.a.
1997 1998 1999 2000
n.c.
n.c.
n.c.
n.c.
n.a.
n.a. 33.922 n.a.
9.996 9.997 9.997
n.a.
8.366 13.261 12.926 13.223
2001
n.c.
n.a.
n.a.
n.a.
2002
6.644
n.a.
n.a.
n.a.
2003
6.900
n.a.
n.a.
n.a.
2004
7.035
n.a.
n.a.
n.a.
2005
7.745
n.a.
n.a.
n.a.
Source: Calculated from UNIDO, International Yearbook of Industrial Statistics, various issues
Notes: n.a. not available, n.c. not calculated
Given that the UNIDO’s data are very poor, I construct the Herfindahl index by
using data on exports, since they reflect more or less the domestic structure of the
economy. In such a case, the index measures the extent to which exports are diversified
across products: a higher index indicates more export diversification. The data come
from UN’s Comtrade and are classified at two-digit level of SITC 3. Observing Table 5,
Kenya, Tanzania and Uganda appear much more diversified than Burundi and Rwanda.
It is crucial to underline the significant trend of the five economies towards lower values
of the index, suggesting a progressive diversification of their export structure.
Table 5. Degree of exports diversification (2000-2007)
Burundi
Kenya
Rwanda
Tanzania
Uganda
2000
65.65
18.76
n.a.
12.00
20.25
2001
47.21
17.07
46.59
13.22
14.33
2002
48.00
13.63
44.42
13.80
15.30
2003
41.08
11.50
32.39
19.09
13.57
2004
40.44
12.63
32.11
15.94
12.22
2005
40.47
10.13
33.57
14.47
12.64
2006
21.78
9.13
39.74
14.23
12.10
2007
21.28
8.14
32.51
10.22
9.18
Source: Calculated from UN Comtrade
Notes: n.a. not available
Summing up, the economies of Kenya, Tanzania and Uganda seem to be more
diversified and thus more suitable for a monetary union. Sector-specific socks in these
countries can be better absorbed even without the use of the national monetary policy
tool. Although Burundian and Rwandan economic structure is less diversified and so
7
more vulnerable to sector-specific shocks, they experienced a satisfactory trend towards
more diversification in the last years.
2.3. Similarity of the economic structure
The more similar the economic structures of the potential candidates to a monetary
union are, the more similar are the effects of the impact of sector-specific shocks on
their economies, and less useful are national monetary policies.
Table 6 shows the percentage contribution to GDP of the different economic
activities. The five EAC countries, having relatively underdeveloped economies, are
characterized by a high percentage of the agricultural sector to GDP. However, while
the industry accounts by and large for the same percentage for all the EAC members,
Kenya and Uganda have lower percentage of agriculture and higher values of the
services in comparison with the other three members, reflecting a more advanced
economic structure.
Table 6. GDP by kind of economic activity (%)
Agriculture, hunting,
forestry, fishing
Industry
Service
Burundi
Kenya
Rwanda
Tanzania
Uganda
Burundi
Kenya
Rwanda
Tanzania
Uganda
Burundi
Kenya
Rwanda
Tanzania
Uganda
1990-1995
50.82
30.09
42.72
44.80
50.09
20.70
20.32
21.20
14.72
14.03
28.48
49.58
36.08
40.48
35.88
1996-2001
46.35
31.68
43.55
44.06
38.57
17.44
17.60
19.04
14.83
19.45
36.21
50.72
37.40
41.11
41.99
2002
40.53
28.85
43.06
43.44
31.11
18.59
17.24
18.59
15.81
21.74
40.87
53.91
38.34
40.74
47.15
2003
40.08
28.65
41.31
43.74
33.30
18.93
17.32
21.29
16.04
20.81
40.99
54.02
37.41
40.22
45.88
2004
40.07
27.64
41.91
44.84
31.42
18.93
17.82
19.77
16.31
21.89
41.00
54.53
38.32
38.84
46.69
2005
35.01
26.78
42.09
44.90
32.01
20.11
18.38
19.88
16.57
21.47
44.89
54.84
38.02
38.53
46.52
2006
38.39
27.68
41.77
44.49
32.25
19.32
17.85
20.31
16.31
21.39
42.29
54.47
37.91
39.20
46.36
Source: Calculated from UNCTAD Handbook of Statistics 2008
Now it is important to investigate in a deeper way the composition of the
industrial and agricultural sectors. I begin with the analysis of the industrial sector.
Given the unavailability of complete UNIDO’s data, I use, also in this paragraph, data
8
relative to exports, taken from UN Comtrade. The percentage contribution of each
economic sector to total exports is summarized in Table 7. The exports of agricultural
products are very high for all the EAC countries. However, for Burundi, Rwanda and
Tanzania the sector of gold and ores accounts for more than 35% of total exports.
Conversely, in Kenya, although primary products remain important, manufactured
products contribute significantly. This is not surprising given Kenya’s more advanced
industrial structure.
Table 7. Percentage contribution of the economic sectors to total exports (2007)
Burundi
Kenya
33.98
28.83
9.15
3.94
3.78
Coffee, tea, cocoa, spices, and manufactures thereof
Petroleum, petroleum products and related materials
21.43
11.10
9.85
5.90
4.19
3.02
Miscellaneous manufactured articles, n.e.s.
3.64
2.35
2.05
1.56
1.39
Tobacco and tobacco manufactures
3.13
3.08
2.81
2.51
Gold, non-monetary
Road vehicles (including air-cushion vehicles)
45.30
34.20
3.64
Hides, skins and furskins, raw
1.95
Crude animal and vegetable materials, n.e.s.
Machinery specialized for particular industries
1.55
1.37
1.24
1.05
0.90
Beverages
0.73
Gold, non-monetary
Coffee, tea, cocoa, spices, and manufactures thereof
Road vehicles (including air-cushion vehicles)
Sugars, sugar preparations and honey
Petroleum, petroleum products and related materials
Prefabricated buildings; sanitary, plumbing, etc.,
n.e.s.
Hides, skins and furskins, raw
Metalliferous ores and metal scrap
Textile fibres and their wastes
Machinery specialized for particular industries
Crude animal and vegetable materials, n.e.s.
Vegetables and fruit
Articles of apparel and clothing accessories
Iron and steel
Non-metallic mineral manufactures, n.e.s.
Inorganic chemicals
Rwanda
Metalliferous ores and metal scrap
Coffee, tea, cocoa, spices, and manufactures thereof
General industrial machinery and equipment, n.e.s.
Live animals other than animals of division 03
Manufactures of metals, n.e.s.
Tanzania
Metalliferous ores and metal scrap
Coffee, tea, cocoa, spices, and manufactures thereof
Fish (not marine mammals), etc., and preparations
thereof
Tobacco and tobacco manufactures
Textile fibres and their wastes
Vegetables and fruit
Cereals and cereal preparations
Non-metallic mineral manufactures, n.e.s.
Textile yarn, fabrics, made-up articles, n.e.s., and
related products
Uganda
Coffee, tea, cocoa, spices, and manufactures thereof
Fish (not marine mammals), etc., and preparations
thereof
Telecommunications and reproducing apparatus and
equipment
Tobacco and tobacco manufactures
Gold, non-monetary (excluding gold ores and
concentrates)
Iron and steel
Crude animal and vegetable materials, n.e.s.
Animal or vegetable fats and oils, processed, etc.,
n.e.s.
Cereals and cereal preparations
Coal, coke and briquettes
25.15
8.81
6.65
5.03
4.86
4.72
3.75
3.37
3.22
3.01
Source: Calculated from UN Comtrade
Notes: Only the ten sectors with the highest values are reported.
9
25.89
9.90
8.47
7.74
4.52
4.50
4.38
4.25
3.68
2.61
In order to better gauge the similarity of the export structure of the EAC
members, I calculated an index of dissimilarity, given by:
1
𝐸𝑥𝑝𝐷𝑖𝑠𝑠𝑐1,𝑐2 = 100 ∗ 2
𝑖
𝑠𝑐1,𝑖 − 𝑠𝑐2,𝑡
(2)
where sc1,i is the share of exports in sector i on total exports of country c1 and sc2,i is the
share of exports in sector i on total exports of country c2. The index can vary from 0 to
100. If the export structures of the two countries are identical, the index is 0, while if
they are completely dissimilar, the index is 100. The values of the index of dissimilarity
are illustrated in Table 8. The export structures of Kenya, Tanzania and Uganda are not
very dissimilar, while higher degree of dissimilarity can be found between Burundi and
Kenya, Kenya and Rwanda, and Rwanda and Tanzania. Relative low values of the index
are observable between Burundi and Tanzania and between Burundi and Uganda.
Table 8. Exports dissimilarity index (2007)
Burundi
Kenya
Rwanda
Tanzania
Uganda
Burundi
X
61.03
56.39
51.52
53.44
Kenya
Rwanda
Tanzania
Uganda
X
64.85
56.17
42.96
X
70.02
59.62
X
48.69
X
Source: Calculated from UN Comtrade
Since a big share of the economic activity in the EAC countries is occupied by
the agriculture sector, it is useful to decompose it into its principal sub-sectors. Also in
this case, given the lack of data on value added, I use data on exports from FAO
database. Table 9 shows the structure of agricultural exports. For Burundi, Kenya,
Rwanda and Uganda, coffee and tea are the crops that contribute to a greater extent to
total agricultural exports. However, for Kenya, tea is the most important crop. Tanzania
agricultural exports seem to be more diversified: the crops that contribute more to total
agricultural exports are tobacco and coffee. It is possible to conclude that the
agricultural sectors of Burundi, Rwanda and Uganda are quite similar. Kenyan and
Tanzanian figures are a little different. Thus, in the case of a crop-specific shock (e.g. in
10
the price), Burundi, Rwanda and Uganda (and albeit to a certain extent Kenya) would be
probably affected in similar ways.
Table 9. Main agricultural exports – as % of total agricultural exports (2006)
Burundi
Kenya
Rwanda
Tanzania
Uganda
Coffee
88.64
Tea
54.14
Coffee
63.16
Tobacco
21.94 Coffee
46.82
Tea
6.39
Coffee
10.37
Tea
33.29
Coffee
15.36 Tea
12.61
Cotton
2.65
Beans
4.80
Beans
1.39
Cotton
9.66
Tobacco
6.68
Sugar
1.08
Pineapples
3.45
Maize
0.26
Cashew nuts
7.50
Cotton
4.93
3.05
Copra
0.23
Tea
7.09
Maize
3.97
Tobacco
0.72 Sugar
Source: Calculated from FAOSTAT
In conclusion, the analysis has shown that the economic structures of the EAC
countries is not very dissimilar, suggesting that the EAC could potentially constitute an
OCA. Little differences can be individuated in the higher industrial development of
Kenya, which produces and exports more capital intensive goods, in the presence of
important mining activities in Burundi, Rwanda and Tanzania, and in the agricultural
export structure of Kenya and Tanzania, which are characterized by a smaller
contribution of coffee and, in the case of Tanzania, a smaller contribution of tea, in
comparison with the other EAC countries.
3. A COINTEGRATION ANALYSIS (G-PPP)
3.1. Methodology
Enders and Hurn (1994) introduced a new method for assessing the suitability of a
currency area, the Generalized Purchasing Power Parity (G-PPP) approach.4 This
methodology uses cointegration analysis to test the level of similarity in the movements
of the real exchange rate against a central anchor currency. This methodology builds on
the fact that real exchange rates are often non-stationary (fact consistent with the failure
of the PPP5) because the fundamental macroeconomic variables that determine real
4
In particular, they applied this approach to Pacific Rim countries, finding that these nations do not
constitute an OCA.
5
For an exhaustive literature review of the PPP, see Rogoff (1996).
11
exchange rates are also non-stationary.6 Indeed, the basic assumption of this approach is
that real exchange rates depend on non-stationary macroeconomic fundamentals (i.e. the
“forcing variables”), such as output level, long run productivity growth, terms of trade,
technology transfer, capital movements, government consumption, etc. In an OCA, the
macroeconomic fundamentals that drive the real exchange rates should be sufficiently
interrelated. Therefore, the real exchange rates in an OCA should have common
stochastic trends and should be cointegrated; this implies that the bilateral exchange
rates of the candidate countries of a monetary union should have at least one linear
combination that is stationary. Indeed, the idea that a non-stationary series has an
equilibrium relationship with one or more other non-stationary series is captured by the
concept of cointegration.
The basic tenets of G-PPP may be summarized as follows (Enders and Hurn,
1994):
-
The real fundamental macroeconomic variables (the “forcing variables”)
determining real exchange rates tend to be non-stationary, so that, in general, the
real rates themselves will be non-stationary.
-
Within a currency area, the real fundamentals share common trends. In an
appropriately defined currency area, the forcing variables will be sufficiently
interrelated that the real exchange rates share a reduced number of common
trends. Given that a vector of bilateral real rates shares common trends, there
exists (at least one) linear combination of the real rates which is stationary and
the real rates are thus cointegrated.
It is worth noting that the G-PPP permits a test of PPP that goes beyond the
traditional two-country tests. Indeed, in a two-country setting, relative PPP suggests
that, for a successful currency area, the real exchange rate of the two countries should
be stationary to a long-run mean or have a common trend over time (Bernstein, 2000).
G-PPP is the generalization of this idea to a multi-country setting, where the individual
bilateral real exchange rates between the countries are non-stationary.
I start with formally analyzing the model. Assume that n+1 is the number of
countries constituting a potential OCA. For these n+1 countries, there will exist a long6
According to most empirical studies, PPP fails in the short run. However, in the long run, deviations
from PPP seem to die out, even though very slowly (Rogoff, 1996).
12
run equilibrium relationship between their n bilateral real exchange rates with respect to
a common base country 1, such that:
𝑟𝑒𝑟12𝑡 = 𝛽0 + 𝛽13 𝑟𝑒𝑟13𝑡 + 𝛽14 𝑟𝑒𝑟14𝑡 + ⋯ + 𝛽1𝑛 𝑟𝑒𝑟1𝑛𝑡 + 𝜀𝑡
(3)
where rer1it are the logarithmic bilateral real exchange rates between the base country 1
and country i in period t, β0 is the intercept term, β1i are the parameters of the
cointegrating vector (representing the degree of co-movement of the real exchange
rates), and εt is a stationary stochastic disturbance. G-PPP holds when there exists at
least one linear combination of such bilateral real exchange rates. The smaller the
cointegrating coefficients (β1i) are, the more similar are a country’s “forcing variables”
vis-à-vis those of the base country.7
Factually, the G-PPP approach consists in determining whether there are
cointegrated vectors between the n exchange rates; in other words, whether there is
cointegration in the equation (3). One of the possible methods to identify cointegration
was developed by Johansen (see Johansen (1988) and Johansen and Juselius (1990)).
Consider first the following Vector Autoregression model (VAR) of order p:
𝑦𝑡 = 𝐴1 𝑦𝑡−1 + ⋯ + 𝐴𝑝 𝑦𝑡−𝑝 + 𝐵𝑥𝑡 + 𝜀𝑡
(4)
where yt is the n-vector of endogenous non-stationary variables (in this case the bilateral
real exchange rates), xt is a d-vector of deterministic variables (constant, or constant and
trend) and εt is the stationary disturbance term. We can rewrite this VAR(n) as an ndimensional Vector Error-Correction model (VEC):
∆𝑦𝑡 = П𝑦𝑡−1 +
𝑛−1
𝑖=1 Г𝑖 ∆𝑦𝑡−𝑖
+ 𝐵𝑥𝑡 + 𝜀𝑡
(5)
where
П=
7
𝑝
𝑖=1 𝐴𝑖
−𝐼
(6)
The absolute traditional PPP is observed if the coefficients of the cointegrating vectors (β1i) are zero.
13
And
Г𝑖 = −
𝑝
𝑗 =𝑖+1 𝐴𝑗
(7)
Granger's representation theorem asserts that if the coefficient matrix П has
reduced rank r smaller than k, then there exist k·r matrices α and β, each with rank r,
such that П = α·β' and that β'yt is stationary. r is the number of existing cointegrating
relations (the cointegrating rank) and each column of β is a cointegrating vector. The
elements of α are known as the adjustment parameters in the VEC model. Johansen's
method consists in estimating the matrix π from an unrestricted VAR and in testing
whether we can reject the restrictions implied by imposing a reduced rank of П.8
In the literature, the G-PPP has been used by several scholars to test for OCA,
but it has not grown as popular as other empirical methods. Enders and Hurn (1997)
examined the G7 countries. Antonucci and Girardi (2005) applied the G-PPP approach
to the Euro area, whereas Liang (1999), Gao (2006), Ahn, Kim and Chang (2006) and
Kawasaki and Ogawa (2006) focused on East Asia; Lee (2003) looked to Australia,
Japan and New Zealand. Recently, Neves, Stocco and Da Silva (2008) used this
methodology for an analysis on Mercosur. With regard to Africa, G-PPP was adopted
by Mkenda (2001) and by Bigsten and Mkenda (2002) to assess the suitability of a
currency union in East Africa (three-country setting and sample period 1981-1998)9 and
by Grandes (2003), who applied the G-PPP approach to the Common Monetary Area
(CMA) in Southern Africa.
Past G-PPP empirical analyses were not homogeneous in terms of the choice of
the base country and the choice of the deterministic component to include in the
cointegration, and led to conflicting results. To overcome these problems, the G-PPP
implemented here is undertaken following two different methods: the first one consists
8
The G-PPP analysis has been subject to criticisms, essentially because it is not able to identify the
factors influencing the real exchange rates, as suggested by Enders and Hurn (1994). They argue, for
example, that real income processes might be linked by supply side considerations such as technology
transfers, factor movements and common real productivity, or by Keynesian-type demand-side factors.
Since G-PPP does not distinguish the two, it cannot properly individuate the underlying differences
across countries. SVAR Blanchard and Quah’s methodology (1989) is able to distinguish the nature of the
different shocks (see, for example, Buigut and Valev (2006)).
9
In particular, Mkenda (2001) found that real exchange rates of Kenya, Tanzania and Uganda are
cointegrated over the period 1981-1998.
14
in following Enders and Hurn’s methodology (1994), which used a base country
external to the potential OCA; the second one is an extension of Mkenda’s work (2001),
where Kenya is considered as the base country.10 In addition, the Pantula principle
(Beirne, 2008) will be applied in order to select the most appropriate deterministic
component of the model.
3.2. The first G-PPP analysis: the US and the UK as the base countries
a. Data and tests of stationarity
In this G-PPP analysis, I use quarterly data on consumer price indices and nominal
exhange rates for the five EAC countries, the US, the UK and Japan, covering the
sample period for yt from 1990(Q1) to 2007(Q1). Data are collected from the IMF’s
International Financial Statistics. First, I calculate the bilateral exchange rates between
the base country and the other countries. Two different seires of bilateral real exchange
rates are constructed: the first one using the US as the base country, the second one
using the UK as the base country. The bilateral real exchange rate of country i with
respect to the base country 1 (rer1it) is calculated as follows:
𝑟𝑒𝑟1𝑖𝑡 = 𝑠1𝑖𝑡
∗
𝑃1𝑡
(8)
𝑃𝑖𝑡
where s1it is the nominal exchange rate between the country 1 and country i at time t
(expressed as the number of country i currency units for one unit of currency of the
country 1), P1t* is the base country’s price level and Pit is the domestic price level of
country i.
The resulting bilateral real exchange rates (in logs) vis-à-vis the US dollar are
plotted in Figure 1 (UK and Japan) and Figure 2 (EAC countries). As in Enders and
Hurn (1994), all the series are normalized so that the real rates in the first quarter of
1990 are equal to zero. The bilateral real exchange rates of the EAC countries follow
similar patterns, although Tanzania’s currency in the last years of the sample
experiences a real depreciation against the USD. In addition, it is worth noting that
Kenya is the EAC country with the lowest variability of its bilateral real exchange rate
10
Bigsten and Mkenda (2002) used the UK as the base country.
15
against the USD, except in the most recent years, when its currency has experienced a
strong real appreciation.
Figure 1. Evolution of the real exchange rates of the UK and Japan - US base country (1990-2007)
0,2
0,1
0
-0,1
-0,2
-0,3
-0,4
-0,5
UK
2007Q1
2006Q1
2005Q1
2004Q1
2003Q1
2002Q1
2001Q1
2000Q1
1999Q1
1998Q1
1997Q1
1996Q1
1995Q1
1994Q1
1993Q1
1992Q1
1991Q1
1990Q1
-0,6
Japan
Figure 2. Evolution of the real exchange rates of the EAC countries - US base country (1990-2007)
1
0,8
0,6
0,4
0,2
0
-0,2
-0,4
Burundi
Kenya
Rwanda
16
Tanzania
Uganda
2007Q1
2006Q1
2005Q1
2004Q1
2003Q1
2002Q1
2001Q1
2000Q1
1999Q1
1998Q1
1997Q1
1996Q1
1995Q1
1994Q1
1993Q1
1992Q1
1991Q1
1990Q1
-0,6
Visual inspection suggests that the series are non-stationary and this is
confirmed by the unit root tests, reported in Table 10, Table 11 and Table 12. As seen
above, a unit root test is the first step in G-PPP, since all the variables must be nonstationary in order to carry out cointegration. I implement the ADF (Augmented Dickey
Fuller) test for the full sample period (Table 10) and two sub-periods covering the years
1990-1998 and 1999-2007 for the EAC countries (Table 11 and Table 12); in the
following paragraphas, I will undertake the cointegration analysis for the same sample
periods.11 The optimal number of lags is chosen as indicated by the information criteria
and the test is carried out both with and without a trend component. By observing the
results of the ADF tests, it is possible to confirm that the series are non-stationary I(1).
Indeed, the tests fail to reject the null hypothesis of unit root in all the cases. These
results confirm the failure of the PPP for these currencies versus the USD: the nonstationarity of the bilateral real exchange rate series could be explained by the nonstationary relationship of the macroeconomic variables of the US and of the other
countries considered.
Table 10. Unit Root Test - US base country (full sample)
UK
Japan
Burundi
Kenya
Rwanda
Tanzania
Uganda
Lags
1
3
1
1
2
2
4
Full Sample - US Base Country
Constant
Constant and Trend
ADF
5% CV
Lags
ADF
5% CV
-1.988
-2.906
0
-1.948
-3.477
-1.258
-2.906
3
-2.539
-3.477
-1.377
-2.906
0
-2.351
-3.477
-1.447
-2.906
1
-2.634
-3.477
-2.451
-2.906
2
-2.888
-3.477
-0.893
-2.906
2
-0.924
-3.477
-1.735
-2.906
4
-2.240
-3.477
Table 11. Unit Root Test - US base country (1990Q1-1998Q4)
Burundi
Kenya
Rwanda
Tanzania
Uganda
Lags
3
1
0
2
1
1999Q1-2007Q1 – US Base Country
Constant
Constant and Trend
ADF
5% CV
Lags
ADF
5% CV
-2.760
-2.906
3
-2.841
-3.477
-2.620
-2.906
1
-3.269
-3.477
-2.851
-2.906
0
-2.761
-3.477
-0.412
-2.906
1
-2.712
-3.477
-1.658
-2.906
1
-1.790
-3.477
11
UK and Japan series are not present in the ADF tests for the sub-periods, since they will not be
included in the relative cointegration tests.
17
Table 12. Unit Root Test - US base country (1999Q1-2007Q1)
1999Q1-2007Q1 – US Base Country
Constant
Constant and Trend
ADF
5% CV
Lags
ADF
5% CV
-2.397
-2.906
0
-2.251
-3.477
-2.484
-2.906
2
-0.691
-3.477
-1.904
-2.906
1
-0.457
-3.477
-2.436
-2.906
0
-3.052
-3.477
-2.057
-2.906
0
-1.706
-3.477
Lags
0
2
1
0
0
Burundi
Kenya
Rwanda
Tanzania
Uganda
The bilateral real exchange rates are represented in Figure 3 and Figure 4,
considering the UK as the base country. The observations made for Figure 2 remain
valid and the patterns of the bilateral real exchange rates of the EAC countries versus
the Pound Sterling are similar to those against the USD. In addition, also in this case,
Kenyan shilling has been appreciating sharply in the last years.
Figure 3. Evolution of the real exchange rates of the US and Japan - UK base country (1990-2007)
0,4
0,3
0,2
0,1
0
-0,1
-0,2
-0,3
-0,4
-0,5
Japan
18
US
2007Q1
2006Q1
2005Q1
2004Q1
2003Q1
2002Q1
2001Q1
2000Q1
1999Q1
1998Q1
1997Q1
1996Q1
1995Q1
1994Q1
1993Q1
1992Q1
1991Q1
1990Q1
-0,6
Figure 4. Evolution of the real exchange rates of the EAC countries - UK base country (1990-2007)
1
0,8
0,6
0,4
0,2
0
-0,2
Burundi
Kenya
Rwanda
Tanzania
2007Q1
2006Q1
2005Q1
2004Q1
2003Q1
2002Q1
2001Q1
2000Q1
1999Q1
1998Q1
1997Q1
1996Q1
1995Q1
1994Q1
1993Q1
1992Q1
1991Q1
1990Q1
-0,4
Uganda
In order to verify if these series are non-stationary, ADF tests have been
undertaken. The results are reported in Table 13. The tests indicate that the null
hypothesis of unit root cannot be rejected for any of the variables. In other words, the
series are non-stationary and the cointegration test can be conducted.
Table 13. Unit Root Test - UK base country (full sample)
US
Japan
Burundi
Kenya
Rwanda
Tanzania
Uganda
Lags
1
3
0
1
2
2
0
Full Sample – UK Base Country
Constant
Constant and Trend
ADF
5% CV
Lags
ADF
5% CV
-1.988
-2.906
1
-1.948
-3.477
-1.113
-2.906
3
-2.717
-3.477
-1.446
-2.906
0
-2.414
-3.477
-2.434
-2.906
2
-2.721
-3.477
-1.778
-2.906
2
-2.706
-3.477
-0.825
-2.906
3
-0.464
-3.477
-2.124
-2.906
0
-2.018
-3.477
b. Cointegration analysis
Two cointegration tests are used in the analysis: the Trace statistic (λtrace) and the MaxEigen statistic (λmax). The λtrace statistic is of a Log Likelihood Ratio type and it tests the
null hypothesis that the number of different cointegrating vectors is less or equal to r
19
(the rank of matrix П) against a general alternative. Alternatively, for certain
cointegration tests, also a cointegration test based on a “maximum” statistic (λmax
statistic) is performed, which tests the null of r cointegrating vectors against the
alternative of r + 1 vectors.
In order to determine the most appropriate deterministic component to include in
the model, the Pantula principle is applied.12 This principle suggests to choose a model
with intercept and trend in the cointegrating equations and without trend in VAR in all
the cases considered. Thus, all the cointegration tests implemented here include an
intercept and a trend component. I use 9 or 10 lags for cointegrations regarding the full
period (1990Q1 - 2007Q1) and 4 or 5 lags for cointegrations for the sub-periods
mentioned earlier.
The US, the UK and Japan represent the reference developed countries.
Following Enders and Hurn (1994), the first step is to consider whether there exists a
cointegrating vector between the bilateral real exchange rates of these three countries.
The results of the cointegration tests are reported in Table 14. The US is used as base
country.
Table 14. Cointegration tests – UK, Japan, US (base country) 1990Q1-2007Q1
Trace Statistic (9 lags)
Max-Eigen Statistic (9 lags)
H0: r≤p H1: r>p λtrace 5% CV 1% CV H0: r=p H1: r=p+1 λmax 5% CV 1% CV
r=0
r>0
22.73 25.32
30.45
r=0
r=1
16.04 18.96
23.65
r≤1
r>1
6.69 12.25
16.26
r=1
r=2
6.69 12.25
16.26
Both the λtrace statistic and the λmax statistic indicate that there are no
cointegrating vectors between the variables. Indeed, we cannot reject the null hypothesis
of no cointegration at a significant level. The bilateral real exchange rates of the US, the
UK and Japan are not cointegrated. As in Enders and Hurn (1994), G-PPP does not hold
among these large reference economies. Thus it is possible to conclude that these
countries do not constitute an OCA.
12
The Pantula principle involves estimating three alternative models (i.e. no intercept or trend, intercept
and no trend, intercept and trend) and moving from the most restrictive to the least restrictive model. The
most appropriate model is the one where the null hypothesis is not rejected for the first time (Beirne,
2008).
20
The next step consists in examining wheter there are cointegrating relationships
between the bilateral real exchange rates of these three countries and those of the EAC
countries. The cointegrating equation assumes the following representation:
𝑟𝑒𝑟1𝑖𝑡 = 𝛽0 + 𝑇 + 𝛽1𝑈𝐾 𝑟𝑒𝑟1𝑈𝐾𝑡 + 𝛽1𝐽𝐴𝑃 𝑟𝑒𝑟1𝐽𝐴𝑃𝑡 + 𝜀𝑡
(9)
where rer1it, rer1UKt and rer1JAPt refer to the logarithms of the bilateral real exchange
rates of each EAC country i, of the UK and of Japan, respectively, vis-à-vis the USD. β0
and T are the intercept and the trend component.
Table 15 reports the λtrace statistic and the λmax statistic for each EAC country
under the null hypothesis that there exist no cointegrating vectors among the bilateral
real exchange rates. It is worth noting that, if one considers the UK as the base country,
the results would be identical to those reported in Table 15 for the US. For all the
countries, except Burundian and Kenyan λmax statistics, it is possible to reject the null
hypothesis of no cointegration at the 99% level. Just for Burundi and Kenya, we can
reject the null hypothesis at most at the 95% level.
G-PPP does hold for each EAC country with the US, the UK and Japan, but it
does not hold between these latter three countries alone. As in Enders and Hurn (1994),
the interpretation of this finding is that the bilateral real exchange rates of the EAC
countries follow a time path dictated by events in these larger countries. This is not
surprising, since the EAC countries are typical underdeveloped countries characterized
by strong trading and financial links with these advanced economies.
Table 15. Values of λtrace for UK, Japan, US and each EAC country (1990Q1-2007Q1)
Burundi
Kenya
Rwanda
Tanzania
Uganda
λtrace
53.16**
57.96**
63.57**
52.90**
59.99**
Model with Intercept and Trend (lags=9)
5% CV
1% CV
λmax
5% CV
42.44
48.45
30.10*
25.54
42.44
48.45
32.63**
25.54
42.44
48.45
28.98*
25.54
42.44
48.45
32.54**
25.54
42.44
48.45
39.84**
25.54
1% CV
30.34
30.34
30.34
30.34
30.34
Notes: * (**) indicates rejection of the null hypothesis at 5% (1%) significance level.
We have seen that all the bilateral real exchange rates of the EAC countries are
cointegrated with those of the three reference economies. However, as pointed out by
21
Enders and Hurn (1994), there is no reason to expect transitivity across the various
cointegrating relationships. Therefore, it is interesting to observe whether there is
cointegration among the exchange rates of the EAC countries.
The results of the λtrace cointegration statistics for pairs of bilateral real exchange
rates of the EAC countries, using first the US and then the UK as base country, are
reported in Table 16 and Table 17, respectively. Considering the US as the base
country, I find cointegration relationships between six of the ten possible combinations
of pairs. Considering the UK as the base country, I cannot reject the null hypothesis of
no cointegration only for two combinations (Burundi-Kenya and Kenya-Rwanda). It is
very interesting to observe that the cointegrations between the old members of the EAC
(Kenya, Tanzania and Uganda) are particularly strong with high values of the λtrace
statistic in both specifications of the base country.
Table 16. Values of λtrace for pairs of bilateral real exchange rates - US base country (full sample)
Burundi
Kenya
Rwanda
Tanzania
Uganda
Burundi
X
18.89 (9)
20.97 (9)
27.61* (10)
22.92 (10)
Kenya
Rwanda
Tanzania
Uganda
X
34.39** (9)
32.43** (10)
31.17** (9)
X
20.35 (9)
29.98* (9)
X
39.85** (9)
X
Notes: Critical Values for the null of no Cointegration Vector: 95%, 25.32; 99%, 30.45. * (**) indicates rejection of
the null hypothesis at 5% (1%) significance level. Number of lags in brackets.
Table 17. Values of λtrace for pairs of bilateral real exchange rates - UK base country (full sample)
Burundi
Kenya
Rwanda
Tanzania
Uganda
Burundi
X
20.05 (9)
28.87* (9)
37.51** (10)
41.63** (10)
Kenya
Rwanda
Tanzania
Uganda
X
23.84 (9)
38.94** (10)
40.07** (9)
X
38.94** (9)
34.73** (9)
X
39.58** (9)
X
Notes: Critical Values for the null of no Cointegration Vector: 95%, 25.32; 99%, 30.45. * (**) indicates rejection of
the null hypothesis at 5% (1%) significance level. Number of lags in brackets.
The cointegration tests have been implemented also over the two sub-periods
1990-1998 and 1999-2007, only in the case where the US is the base country (Table 18
and Table 19). Only three cointegration relationships have been found in the first subperiod, whereas in the second one there exist nine significant cointegrated
22
combinations. These results indicate that the “forcing variables” that affect the bilateral
real exchange rates have become increasingly interrelated in the recent years.13
Table 18. Values of λtrace for pairs of bilateral real exchange rates - US base country (1990Q1-1998Q4)
Burundi
Kenya
Rwanda
Tanzania
Uganda
Burundi
X
26.44 (4)
16.06 (4)
27.51 (5)
22.10 (5)
Kenya
Rwanda
Tanzania
Uganda
X
30.07 (4)
21.18 (5)
23.77 (4)
X
15.14 (4)
11.65 (4)
X
23.57 (4)
X
Notes: Critical Values for the null of no Cointegration Vector: 95%, 25.32; 99%, 30.45. * (**) indicates rejection of
the null hypothesis at 5% (1%) significance level. Number of lags in brackets.
Table 19. Values of λtrace for pairs of bilateral real exchange rates - US base country (1999Q1-2007Q1)
Burundi
Kenya
Rwanda
Tanzania
Uganda
Burundi
X
26.53 (4)
26.81 (4)
28.93 (5)
22.45 (5)
Kenya
Rwanda
Tanzania
Uganda
X
26.68 (4)
31.64 (5)
31.60 (4)
X
35.77 (4)
40.17 (4)
X
48.27 (4)
X
Notes: Critical Values for the null of no Cointegration Vector: 95%, 25.32; 99%, 30.45. * (**) indicates rejection of
the null hypothesis at 5% (1%) significance level. Number of lags in brackets.
Following Enders and Hurn (1994), it would be interesting to test cointegration
between all the countries that seem to have cointegrated bilateral exchange rates.
Although some pairs have been found not cointegrated, I will implement cointegration
tests for all the EAC contries in any case. The cointegrating equations will assume the
following representation:
𝑟𝑒𝑟1𝐵𝑈𝑅𝑡 = 𝛽0 + 𝑇 + 𝛽1𝐾𝐸𝑁𝑡 𝑟𝑒𝑟1𝐾𝐸𝑁𝑡 + 𝛽1𝑅𝑊𝐴𝑡 𝑟𝑒𝑟1𝑅𝑊𝐴𝑡 + 𝛽1𝑇𝐴𝑁𝑡 𝑟𝑒𝑟1𝑇𝐴𝑁𝑡 + 𝛽1𝑈𝐺𝐴𝑡 𝑟𝑒𝑟1𝑈𝐺𝐴𝑡 + 𝜀𝑡
(10)
and the base country 1 will be first the US, then the UK.
The results are reported in Table 20. The null of no cointegration can be strongly
rejected at the 99% significance level. Furthermore, both the λtrace statistic and the λmax
statistic indicate that there are five cointegrating vectors. Although the base countries
are the US and the UK, the bilateral real exchange rates of the EAC countries seem to
13
The results of tests on such short periods of time, however, should be taken with care.
23
be cointegrated. Thus, G-PPP holds among the EAC countries, the US and the UK,
respectively.
Table 20. Cointegration tests for the EAC countries (full sample)
Trace Statistic
H0: r≤p
r=0
r≤1
r≤2
r≤3
r≤4
H1: r>p
r>0
r>1
r>2
r>3
r>4
H0: r≤p
r=0
r≤1
r≤2
r≤3
r≤4
H1: r>p
r>0
r>1
r>2
r>3
r>4
Max-Eigen Statistic
Full sample (lags=9) – US Base Country
λtrace
5% CV 1% CV H0: r=p H1: r=p+1
283.00** 87.31
96.58
r=0
r=1
177.94** 62.99
70.05
r=1
r=2
83.39**
42.44
48.45
r=2
r=3
40.11**
25.32
30.45
r=3
r=4
16.99**
12.25
16.26
r=4
r=5
Full sample (lags=9) – UK Base Country
λtrace
5% CV 1% CV H0: r=p H1: r=p+1
303.27** 87.31
96.58
r=0
r=1
179.59** 62.99
70.05
r=1
r=2
97.60**
42.44
48.45
r=2
r=3
42.66**
25.32
30.45
r=3
r=4
19.15**
12.25
16.26
r=4
r=5
λmax
5% CV
105.06** 37.52
94.55**
31.46
43.28**
25.54
23.12*
18.96
16.99**
12.25
1% CV
42.36
36.65
30.34
23.65
16.26
λmax
5% CV
123.68** 37.52
81.99**
31.46
54.94**
25.54
23.51*
18.96
19.15**
12.25
1% CV
42.36
36.65
30.34
23.65
16.26
Notes: * (**) indicates rejection of the null hypothesis at 5% (1%) significance level.
It is useful to look at the structure of the cointegrating vectors and to test
restrictions on them, as in Enders and Hurn (1994). For simplicity, I will use just the
first one cointegrating vector identified for each base country. The cointegrating vector
relative to the US as base country is reported in Table 21 and it is normalized so that
Burundi’s long-run coefficient is equal to one, as in equation (10).
Table 21. Normalized Cointegrating Equation - US base country
Long-run Coefficients (β)
Adjustment Coefficients (α)
Burundi
1
-0.840***
Kenya
2.869***
-0.331**
Rwanda
-0.475**
-0.104
Tanzania
2.140***
-0.048
Uganda
-2.453***
-0.521***
Notes: * (**, ***) indicates statistical significance at 10% (5%, 1%) level.
The cointegrating coefficients (β) may be interpreted as long-run elasticities and
represent the interrelationships among the various bilateral real exchange rates. For
example, the Burundian bilateral real exchange rate with the US changes by 2.869% in
response to a 1% change in the US/Kenya bilateral rate. All the long-run coefficients
reported in Table 21 are statically significant. As stressed by Beirne (2008), monetary
integration would appear more appropriate where the long-run coefficients have the
24
same sign and are similar and small in magnitude. Indeed, a high dissimilarity of these
coefficients could be indicative of dissimilarity between the “forcing variables” behind
the real exchage rates. The values of the cointegrating coefficients of the EAC countries
are indeed quite dissimilar: only Kenya and Tanzania show similar figures, while
Rwandan and Ugandan coefficients have even a negative sign.
The adjustment coefficients (α) may be interpreted as the “speed of adjustment”
towards long-run equilibrium, that is how quickly each of the bilateral real exchange
rates converges towards G-PPP. Similar speeds of adjustment would be indicative of
coordination on exchange rate policy (Bernie, 2008). The speed of adjustment
coefficients are very dissimilar across EAC countries: Burundi, Kenya and Uganda have
the highest coefficients, indicating a faster adjustment process towards long-run
equilibrium than Rwanda and Tanzania, which have not statistically significant values.
Considering the UK as the base country, the first cointegrating vector identified
is reported in Table 22. The coefficients of the cointegrating vector are not very
dissimilar to those reported in Table 21, except for Tanzania’s long-run coefficient
which is very low. Also in this case the speed of adjustment coefficients of Burundi,
Kenya and Uganda are quite high and similar, while those of Rwanda and Tanzania are
low and statistically insignificant.
Table 22. Normalized Cointegrating Equation - UK base country
Long-run Coefficients (β)
Adjustment Coefficients (α)
Burundi
1
-0.567***
Kenya
2.357***
-0.384***
Rwanda
-1.110***
0.145
Tanzania
0.203***
0.091
Uganda
-0.576***
-0.230**
Notes: * (**, ***) indicates statistical significance at 10% (5%, 1%) level.
These results cast some doubts on the meaningfulness of the cointegrating
relations between the exchange rates of the EAC members. Thus, it is interesting to
investigate the issue further and to test a restriction on the cointegrating vector, as in
Enders and Hurn (1994),14 i.e. the long-run coefficients of the five EAC countries sum
to zero (this is Enders and Hurn’s H1). If the sum of the five coefficients is equal to
zero, US (or UK) variables do not enter into equation (10). The calculated chi-square
14
I test only the first restriction introduced by Enders and Hurn (1994), since the second one tests the
exclusion of the variables of each EAC countries. This restriction is clearly of no interest for the purpose
of this article.
25
statistic is 10.477, considering the US as the base country, and 15.980, considering the
UK as the base country. In both cases, since the critical value at 1% is χ20.01 = 6.63, the
restriction is rejected. The hypothesis that the US (or UK) variables do not enter into
equation (10) is rejected. The real exchange rates of these two large countries seem to
be important in explaining the movements of the real exchange rates of the EAC
countries. It is physiological that small and medium developing countries have the real
exchange rates heavily influenced by those of the largest economies in the world.
Finally, as in Enders and Hurn (1994), I compare the standard deviations of the
residuals of the bilateral real exchange rates of the EAC countries when estimated by
equation (9) with the standard deviations of the residuals when the rates are estimated
by equation (10), in order to find whether the real exchange rates of the EAC countries
are more influenced by each other or by the developed economies considered. The
estimated standard deviations of the residuals are summarized in Table 23. For all the
bilateral real exchange rates of the EAC countries, the residuals have the highest
standard deviation when estimated by equation (9), except for Kenya when the UK is
the base country. This is not surprising, since Kenya is the biggest EAC economy and
its real exchange rate is likely to be less influenced by the rates of the other EAC
countries. Furthermore, notice that the difference between the values of the standard
deviations of the two equations is on average lower for the largest EAC countries.
Following Enders and Hurn’s argument (1994), it can be concluded that the movements
of the real exchange rates for the EAC countries (except Kenya, considering the UK as
base country) are more heavily influenced by each other than by the US, the UK and
Japan.
Table 23. Standard deviations of residuals
Burundi
Kenya
Rwanda
Tanzania
Uganda
US Base Country
Equation (9)
Equation (10)
0.0758
0.0364
0.0406
0.0367
0.0534
0.0360
0.0299
0.0274
0.0378
0.0192
UK Base Country
Equation (9)
Equation (10)
0.0686
0.0474
0.0287
0.0299
0.0402
0.0313
0.0547
0.0464
0.0381
0.0308
In conclusion, although the developed economies considered play an important
role in determining the movements of the bilateral real exchange rates of the EAC
countries, the latter are also heavily influenced by each other.
26
The results of first G-PPP analysis lead to a twofold conclusion. Firstly, it is
difficult to reject the idea that EAC might be an OCA, despite anomalies in
and β
coefficients. Secondly, the role played by Kenya is peculiar since, on the one hand, its
real exchange rate is strongly related to that of developed countries and, on the other
hand, it remarkably influences those of other EAC countries. Therefore, it will be
interesting to undertake cointegration considering Kenya as the base country, as in
Mkenda (2001).
3.3. The second G-PPP analysis: Kenya as the base country
a. Data and tests of stationarity
This second G-PPP analysis is an extension of Mkenda’s work (2001). Indeed, it
includes the new EAC members (Rwanda and Burundi), and extends the analysis by
encompassing more recent years. As in the previous G-PPP analysis, I use quarterly
data on consumer price indices and nominal exhange rates for the five EAC countries,
covering the sample period from 1990(Q1) to 2007(Q1). Data are obtained from the
IMF’s International Financial Statistics. I calculate the bilateral real exhange rate as in
equation (8), but now Kenya is considered the base country. Kenya is the country that
trades more intensively with the other EAC countries, it plays better the role of anchor
of the future EAC monetary union and it is the most affected by foreign determinants,
according to my previous analysis.
The resulting bilateral real exchange rates (in logs) are graphed in Figure 5. The
series are normalized so that the real rates in the second quarter of 1990 are equal to
zero. All the countries experienced a progressivley upward common trend, suggesting a
gradual real depreciation of their currencies vis à vis the Kenyan Shilling. The common
upward trend seems to begin in 1998-1999. Nevertheless, the four series exhibite quite
similar movements even since 1990.
27
Figure 5. Evolution of the real exchange rates of the EAC countries - Kenya base country (1990-2007)
1,2
1
0,8
0,6
0,4
0,2
0
-0,2
Burundi
Rwanda
Tanzania
2007Q1
2006Q1
2005Q1
2004Q1
2003Q1
2002Q1
2001Q1
2000Q1
1999Q1
1998Q1
1997Q1
1996Q1
1995Q1
1994Q1
1993Q1
1992Q1
1991Q1
1990Q1
-0,4
Uganda
The ADF tests (Table 24, Table 25 and Table 26) indicate that all the series are
non-stationary, except that of Uganda, which is stationary over a trend in the second
sub-period. However, the null hypothesis of unit root for Uganda is rejected only at the
5% significance level. As in Mkenda (2001), I include this variable in the cointegrating
equation, notwithstanding such unclear result. Also in this case, PPP does not hold and
cointegration can be performed.
Table 24. Unit Root Test - Kenya base country (full sample)
Burundi
Rwanda
Tanzania
Uganda
Lags
0
0
0
5
Constant
ADF
-0.945
-1.229
-0.508
-0.349
5% CV
-2.906
-2.906
-2.906
-2.906
Lags
0
1
0
8
Constant and Trend
ADF
5% CV
-3.318
-3.477
-3.387
-3.477
-1.279
-3.477
-3.001
-3.477
Table 25. Unit Root Test - Kenya base country (1990Q1-1998Q4)
Burundi
Rwanda
Tanzania
Uganda
Lags
2
1
0
1
Constant
ADF
-2.769
-2.655
-2.444
-2.469
5% CV
-2.906
-2.906
-2.906
-2.906
28
Lags
2
1
0
1
Constant and Trend
ADF
5% CV
-3.110
-3.477
-3.026
-3.477
-2.774
-3.477
-2.448
-3.477
Table 26. Unit Root Test - Kenya base country (1999Q1-2007Q1)
Burundi
Rwanda
Tanzania
Uganda
Lags
0
0
1
4
Constant
ADF
-1.233
-0.686
-0.740
-1.384
5% CV
-2.906
-2.906
-2.906
-2.906
Constant and Trend
Lags
ADF
5% CV
0
-3.369
-3.477
0
-2.323
-3.477
0
-2.744
-3.477
4
-3.880
-3.477
b. Cointegration analysis
First of all, I carry out cointegration tests to see if all the EAC countries may be
qualified to form an OCA over the full sample period and over the sub-periods, judging
in terms of the presence of a cointegrating relationship among the different series of the
bilateral real exchange rates. The cointegration results are reported in Table 27. As
above, the Pantula principle is applied and the model with intercept and trend seems to
be the most appropriate for these series. The optimal lag number is selected as usual, so
that error terms are approximately white noise. Both the λtrace statistic and the λmax
statistic indicate that, for the full sample, cointegration does exist between the real
exchange rates: in particular, λtrace statistic indicates three cointegrating vectors at the
1% significance level, whereas λmax statistic one cointegrating vector at the 1% and
three at the 5% significance level. In the long run, the real exchange rates of the five
EAC members move together. Interestingly, as far as the two sub-periods are
concerned, one cointegrating vector is found in the first period, whereas in the second
period there exist two cointegration vectors. The cointegration relationships seem to
have increased in the most recent period.
Thus, G-PPP holds for the five EAC countries, even without the presence of the
US or the UK as base countries, both over the full sample and over the sub-periods.
These results suggest that the real variables that affect the real exchange rates of the
EAC countries seem to be interrelated, and, apparently, this interrelation has increased
in the last years. According to these findings, the EAC has the potential to constitute an
OCA.
29
Table 27. Cointegration tests for the EAC countries - Kenya base country
Trace Statistic
H0: r≤p H1: r>p
λtrace
r=0
r>0
110.81**
r≤1
r>1
62.14**
r≤2
r>2
33.71**
r≤3
r>3
10.49
H0: r≤p H1: r>p
r=0
r>0
r≤1
r>1
r≤2
r>2
r≤3
r>3
λtrace
75.48**
29.13
15.56
4.81
H0: r≤p H1: r>p
λtrace
r=0
r>0
101.72**
r≤1
r>1
45.60*
r≤2
r>2
12.16
r≤3
r>3
2.43
Max-Eigen Statistic
Full sample (lags=9)
5% CV 1% CV H0: r=p H1: r=p+1
62.99
70.05
r=0
r=1
42.44
48.45
r=1
r=2
25.32
30.45
r=2
r=3
12.25
16.26
r=3
r=4
Sub-period 1990(Q1)-1998(Q4) (lags=4)
5% CV 1% CV H0: r=p H1: r=p+1
62.99
70.05
r=0
r=1
42.44
48.45
r=1
r=2
25.32
30.45
r=2
r=3
12.25
16.26
r=3
r=4
Sub-period 1999(Q1)-2007(Q1) (lags=4)
5% CV 1% CV H0: r=p H1: r=p+1
62.99
70.05
r=0
r=1
42.44
48.45
r=1
r=2
25.32
30.45
r=2
r=3
12.25
16.26
r=3
r=4
λmax
48.68**
28.43*
23.22*
10.49
5% CV
31.46
25.54
18.96
12.25
1% CV
36.65
30.34
23.65
16.26
λmax
46.35**
13.57
10.75
4.81
5% CV
31.46
25.54
18.96
12.25
1% CV
36.65
30.34
23.65
16.26
λmax
56.12**
33.44**
9.73
2.43
5% CV
31.46
25.54
18.96
12.25
1% CV
36.65
30.34
23.65
16.26
Notes: * (**) indicates rejection of the null hypothesis at 5% (1%) significance level.
A more detailed analysis of the cointegrating vectors, however, shows that most
of the long-run and adjustment coefficients are not statistically significant and are, in
any case, quite large and dissimilar in sign and magnitude. These results have not been
reported here for the sake of brevity. This is in contrast with the G-PPP analysis of in
the previous sub-section, considering the US and the UK as reference countries, where
all the long-run coefficients were significant. It may be possible that the cointegration
tests are better specified including the variables of these big economies, but this
incongruence reveals one of intrinsic weaknesses of the G-PPP approach.
Finally, it is worth investigating the causes of the behaviour of the bilateral real
exchange rates graphed in Figure 5, especially since 1998. Indeed, Burundi, Rwanda,
Tanzania and Uganda have experienced a common real depreciation of their currencies
against the Kenyan Shilling. The reason of this trend is basically the considerable
appreciation experienced by Kenyan Shilling against world major currencies in the
recent years (see Figure 2 and Figure 4), as a result of a significant increase in the
inflow of foreign capital (primarily for speculative purposes) together with the inflows
of foreign aid and the revenues from tourism (Paciello, 2008). This path is not
consistent with the introduction of a common currency in the short period.
30
These findings leads to the conclusion that, while the analysis does not exclude
that the EAC may form an OCA (cointegrating vectors have been found), greater
monetary policy coordination is needed to remove the appreciation trend of the Kenyan
currency, which appears deterministic, yet it could be influenced by appropriate
modifications to the EAC economic policies. Before introducing a common currency it
would be desirable, for instance, to introduce a system like the the European ERM
(Exchange Rate Mechanism), in order to reduce exchange rate variability and to
improve monetary policy coordination among the EAC countries.
4. CONCLUSION
This article investigates the suitability of a monetary union in the EAC, by observing
three traditional OCA criteria, and by performing a cointegration G-PPP analysis.
Although the analysis suggests that a monetary union could be a feasible option, some
country-specific or statistical weaknesses have been identified.
Despite the quite optimistic results of this study, other elements should be
considered before reaching a definitive conclusion. These aspects concern further OCA
criteria (e.g. the degree of the fiscal and financial integration, the degree of labour
mobility) and political factors (e.g. political will and support, political stability), which
play a crucial role in the creation of currency unions. Moreover, recent theoretical
developments, such as the credibility issue and the endogeneity of OCA, should not be
ignored. These seem to be very promising areas for future research.
31
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