III. The Business Cost Data
As noted already, the business cost survey data that we analyse comes from four sources.
The main one is the Economist Intelligence Unit, which administers a six-monthly survey
in 54 major capitals and business centres. We use their survey results from mid-2002 and
supplemented them with questions of our own. To provide comparable data on small
economies, the Commonwealth Secretariat commissioned identical surveys to be
conducted by regional organisations in various (mostly small) economies: Imani
Capricorn in Africa, The Caribbean Community in the Caribbean and The Pacific Islands
Forum in the Pacific. These surveys were completed between x and y 2002. A specimen
survey is included as Annex 1 of this report. 1
The full sample of countries was defined in table 2.1 above, along with information about
their survey organisation, their size (population and aggregate GDP), average income
(GDP per capita) and their region in 2000. The data were received by the authors as Word
or Excel files of the questionnaire with answers included. These were transcribed,
including the large number of side comments – solicited and not – into Excel
spreadsheets which were then examined carefully for inconsistencies and eccentricities.
All survey data are subject to error and ours are no exception. Many of the survey
questions seek averages or tendencies and so are subject to unconscious selectivity by
respondents, while others, despite our best efforts, proved open to different
interpretations. The surveys for large and small countries were implemented by different
organisations and so may have been subject to different reporting biases: for example, in
some cases requested ranges of data were collapsed into single averages, or vice versa. In
addition, there are also simple random errors arising from, for example, the use of the
wrong units or illegible replies, and the omission of various answers.
None of this should suggest that the survey results are useless: indeed, we believe the
data are very useful and should be made available to other researchers. However, it is
important to recognise their inherent limitations and noisiness. Two illustrations of the
latter are available. First, two African countries – Kenya and Zimbabwe – were involved
in both the EIU and the Imani samples. Comparing the two sets of answers is salutary.
For the questions requesting cardinal answers the mean absolute proportionate difference
between the two sources was 29.4% for Kenya (45 variables) and 56.8% for Zimbabwe
(44 variables). 2 Even excluding the four largest deviations, to allow for random misreporting, the figures are 21.2% and 44.3% respectively. Among the categorical questions
the corresponding statistic was 38.3% for Kenya and 10.0% for Zimbabwe (10 variables
1
In several cases we were unable to formulate questions sufficiently precisely to make them worth
including – for example, on insurance (impossible to devise an equivalent product) and the risk to
sovereign debt (small countries are not rated publicly). In both cases, we suspect that small countries are at
a disadvantge.
2
The statistic is the mean of
z 1 − z 2 / 0.5 * ( z1 + z 2 ) where z1 and z2 are the two replies to the same
question from the two sources.
1
each). 3 In the areas of dispute, we have used the EIU data, because we found them, on the
whole, more plausible.
The second area in which we have two estimates of the same phenomenon concerns air
freight costs from 5 Caribbean countries. The mean absolute difference of these data,
which are explained and reported in Annex IV.1 below, is 69.8%!
In order to increase the value of the data we have corrected the most obvious of the errors
– for example, re-scaling prices that have been reported in cents rather tha n dollars.
We were surprised by the large number of eccentricities in the simple macro-economic
data collected in the survey. These were frequently at variance with international sources
and sometimes at variance with common sense. Ms Anna Yartseva attempted to clean
and clarify these series and we believe that they are now reasonably representative. We
became conscious, however, that having 92 sources of macro data (one per country) was
likely to generate inconsistencies and so, in view of their centrality to our exercise, we
decided to collect additional data for 2000 on population and GDP from international
sources.
In general, data for GDP (current million US dollars), GDP Power Purchasing Parity
(current million US dollars) and Population were taken from the World Development
Indicators 2002 database. Data refer to the year 2000. In those countries not covered by
the WDI 2002 database (Anguilla, Cook Islands, Nauru, Niue, Taiwan and Tuvalu) or to
fill missing values, data from the Survey were primarily used and complemented by the
CIA Factbook 2002 (downloadable from the CIA website). ADB data were also used.
We complemented our Population statistics with the survey values for Anguilla, Cook
Islands, Nauru, Niue, Taiwan and Tuvalu, which probably correspond to the year 2001.
GDP (US$) for Anguilla, Cook Islands, Niue and Taiwan were taken from the Survey
(2000), while for Nauru and Tuvalu the source is ‘Business Information Guide to the
Pacific’ published by the ADB, since there no data were given in the Survey. Finally, for
Dominica, Marshall Islands and Micronesia data on GDP (PPP, US$) refer to the Survey,
while for Anguilla, Cook Islands, Kiribati, Marshall, Nauru, Niue, Palau, Seychelles,
Taiwan, Tonga and Tuvalu, where data from the Survey were unavailable, values were
taken from the CIA Factbook 2002 (different years). Per capita values for GDP (US$)
and GDP (PPP, US$) were derived from the aggregate variables described above.
As a cross check, we compared these values with other sources. We ranked countries by
GDP per capita and compared them with the World Bank’s income classification of
countries. One doubt arises: Kiribati (US$475, lower middle income) has lower GDP per
capita than, for example, Indonesia (US$728, low income). However, we have to bear in
mind that the World Bank’s classification is based on the GNI Atlas Method.
3
If the categories chosen by the two surveys for categorical variables were i1 and i2 , we calculate the
average of
(i1 − i2 ) /( n − 1) , where n is the number of categories, so that (n-1) is the maximum
difference.
2
Regarding GDP (PPP, US$), the values from the Survey differ substantially from those of
the CIA Factbook 2002, in three cases
US$428m – US$262m for Dominica
US$62m – US$115m Marshall Islands
US$214m – US$269m Micronesia,
but we tend to believe that data from the Survey is more accurate.
Furthermore, values for GDP per capita (PPP, US$) were compared with values from the
WDI 2002 and CIA Factbook 2002. Minor inconsistencies were found, and two major
differences: Niue (US$4251 – US$3600) and Cook Islands (US$6522 – US$5000).
Finally, It is worth noting that, while GDP (PPP, US$) is normally higher than GDP
(US$) for developing countries, this is not the case of some of our small countries (e.g.
Anguilla, Marshall Islands, Micronesia and Seychelles). We take this as a substantive
result, however.
In fact, these freshly collected data from 2000 are the only macro-economic data that we
use in this report, although the original series are included in the dataset.
In all cases we record these changes and substitutions in notes appended to the Excel data
spreadsheets. Some are also discussed briefly in the analytical sections of chapter IV.
While wishing to respect the original sources, we believe that they increase the value of
the dataset overall.
Even after the first round of cleansing, the data still contain a number of obvious
surprises and outliers. Where possible we have verified these from secondary sources, but
have not over-ridden the reported values. We have, however, omitted them from our
analysis in chapter IV. In addition, during the analysis a further set of outliers was
identified in the form of absolutely large residuals from our estimated relationships. Since
our aim is to test the relationship between the various business costs and size, we have in
general eliminated these from the regressions in order to preserve the normality of the
residuals and hence the legitimacy of the statistical inference. In all cases, however, we
report the direction in which the observation is outlying and checking that the nature of
the estimated relationship is not greatly changed by the elimination. If it is, we exercise
great caution in drawing conclusions.
Table 3.1 summarises some of the key data from the survey by calculating averages by
region, by size class and by income group. Such summary statistics are not powerful tools
for inference because, for example, regions and size are highly correlated as we saw in
section II.4, but the table certainly suggests a prima face case to be answered. Small
countries appear to have higher costs in many dimensions. The next chapter sets about
answering this case in a systematic fashion.
3
ANNEX TO CHAPTER 3
REGION
Pacific
Caribbean
SSA
LA
South Asia
Rest Asia
OECD
Employment
Constru Checkout Kitchen Bank
ction
operator Porter
Clerk/Tell
worker in large
(hourly $) er (annual
(hourly supermark
$) "local
$)
ets
banks"
(hourly $)
1.47
1.45
1.33
4586
2.83
1.90
1.81
7494
0.72
0.68
0.63
3844
1.55
1.09
0.95
4536
0.31
0.32
0.28
1355
3.00
2.29
1.54
5594
8.52
6.08
6.19
18549
Bank
Garage
Payroll
Qualified Branch
Clerk/Tell Mechanic Clerk
Teacher in Manager
er (annual (annual $) (annual $) State
(annual $)
$)
School
"local
"foreign
(annual $) bank"
banks"
4891
5022
5305
5254
20175
8082
7478
7145
9081
28293
4316
2977
2676
3644
16329
4535
3810
6112
5798
17665
1980
873
1274
1255
4337
6335
5353
6999
7633
18741
19610
19203
19079
21169
43711
Branch
Manager
(annual $)
"foreign
bank"
General Unemploy Literacy
Registere ment rate rate
d Nurse
(annual $)
Manufact
uring
labour
cost per
hour
23076
35614
22572
22157
6809
20982
46554
5557
10420
3820
4392
989
6412
18370
17.90
11.33
32.64
11.50
6.40
5.71
6.61
88.68
90.39
70.03
91.50
54.50
89.11
97.15
1.80
2.75
2.06
2.86
0.47
2.81
13.22
POPULATION
<0.4 million
0.4 –2.0 million
2-10 million
10-50 million
> 50 million
2.37
1.00
8.00
3.01
5.07
1.89
0.86
6.12
2.13
3.50
1.75
0.93
5.47
2.19
3.51
6320
4124
16325
7668
14694
6915
4314
15562
7909
16060
6946
3604
15570
7177
13555
6840
3506
15813
7677
14309
7850
5164
17419
7836
16763
27921
13889
40629
21627
39399
35136
19232
40391
24322
44660
8515
5428
16479
6986
13211
11.29
19.0
14.1
16.7
8.04
93.1
82.8
86.8
85.3
105.7
2.61
0.77
13.6
6.35
12.1
GDP
<0.4 billion
0.4-2.0 billion
2-10 billion
10 –100 billion
> 100 billion
2.08
1.44
1.15
2.48
6.87
1.61
1.19
0.98
1.61
5.08
1.53
1.10
0.94
1.39
5.00
5343
5464
4557
5724
15260
5684
6075
4797
6107
16245
5806
4398
4313
5529
15395
5970
4556
3647
6446
15889
6819
5952
4468
5493
18227
22659
23357
17912
19885
36290
25040
34578
21450
24439
38990
7060
5818
5734
4701
15697
14.9
15.0
28.9
10.1
7.77
93.4
83.0
70.1
84.6
93.0
2.44
2.01
0.57
3.57
11.0
0.53
1.24
2.31
7.95
10.05
0.51
0.99
1.82
5.52
7.16
0.46
0.96
1.66
3.58
7.28
3007
4361
6564
11957
21271
3412
5221
6662
12050
22251
1776
4810
6440
11319
21835
1784
4960
7017
15078
21692
2045
5468
7445
16986
24786
11535
19701
23083
34065
49536
16246
23774
28270
36683
51782
2299
5212
7739
14168
21454
29.5
13.9
10.6
4.30
6.21
63.9
89.0
91.5
89.7
97.9
0.54
1.86
2.70
5.95
15.9
INCOME
Low income
Lower middle income
Upper middle income
High income Non OECD
High income OECD
4
Electricity
Costs of
Electricity
(standard
commercia
l line)
Connection
fee
(standard
commercia
l line)
Water
Costs of
Water
(standard
commercia
l rate)
Connection
fee
(standard
commercia
l line)
0.21
0.16
0.37
0.08
0.08
0.07
0.15
73
259
499
112
691
110
154
3.56
3.41
1.92
0.62
0.16
0.45
1.33
POPULATION Electricity
<0.4 million
0.19
0.4 –2.0 million
0.37
2-10 million
0.16
10-50 million
0.13
> 50 million
0.31
109
240
40
320
789
GDP
<0.4 billion
0.4-2.0 billion
2-10 billion
10 –100 billion
> 100 billion
Electricity
0.19
0.17
0.30
0.28
0.09
132
152
143
578
207
INCOME
Electricity
Low income
0.24
Lower middle income
0.12
Upper middle income
0.31
High income Non OECD
0.09
High income OECD
0.09
510
193
185
109
111
REGION
Pacific
Caribbean
SSA
LA
South Asia
Rest Asia
OECD
Telephone
Installation
fee (stand.
comm.
line)
Line rental
fee (stand.
comm.
line)
Rate per
minute
local calls
(peak hour
61
338
143
153
553
114
564
61
66
105
73
195
85
118
9.3
20.2
6.4
12.7
3.7
7.4
20.0
3.86
0.85
1.94
1.05
1.75
83
386
612
177
417
Telephone
72
66
85
113
153
4.02
1.66
2.42
1.14
1.06
65
95
312
276
498
2.88
1.59
2.13
0.56
1.04
181
120
320
2.94
591
Water
Water
Water
5
0.09
0.06
0.12
0.03
0.04
0.04
0.05
Rate per
minute of
internation
al calls to
London
during
peak hour
($)
1.86
1.21
1.20
0.73
0.80
0.87
0.33
Rate per
minute of
internation
al calls to
Tokyo
during
peak hour
($)
1.85
1.80
1.44
0.80
0.85
0.90
0.68
Rate per
minute of
internation
al calls to
New York
during
peak hour
($)
1.71
1.06
1.39
0.60
0.89
0.78
0.31
15.6
8.2
15.4
11.6
13.0
0.08
0.12
0.05
0.06
0.06
1.74
1.17
0.35
0.80
0.95
1.92
1.76
0.64
0.97
1.16
1.54
1.30
0.38
0.85
0.85
Telephone
62
61
121
126
97
11.0
11.8
11.2
10.6
16.4
0.10
0.03
0.13
0.04
0.05
1.8
1.13
1.24
0.72
0.47
1.92
1.53
1.60
0.85
0.74
1.70
0.99
1.44
0.68
0.44
Telephone
114
72
95
55
115
5.84
8.77
16.2
8.56
21.2
0.08
0.05
0.09
0.02
0.04
1.20
1.25
1.11
0.50
0.29
1.28
1.42
1.49
0.51
0.62
1.32
1.12
1.08
0.43
0.26
Fuel
Retail
price of
diesel (per
litre)
Retail
price of
petrol (per
litre)
0.56
0.47
0.53
0.39
0.34
0.36
0.70
0.57
0.55
0.63
0.65
0.54
0.49
0.87
0.60
0.36
0.59
0.55
0.64
0.63
0.46
0.83
0.71
0.79
0.57
0.52
0.50
0.43
0.60
0.57
0.60
0.63
0.63
0.76
0.49
0.47
0.50
0.54
0.70
0.58
0.57
0.61
0.82
0.88
Fuel
Fuel
Fuel
Taxes
Corporate
tax rate
for
residents
Corporate
tax rate
for nonresident
21.71
35.35
31.58
31.09
36.93
27.83
32.44
29.30
35.35
32.28
32.28
41.25
27.83
32.44
9.06
10.50
14.61
15.63
12.50
7.50
12.92
14.22
13.50
8.25
4.50
6.09
POPULATION
<0.4 million
0.4 –2.0 million
2-10 million
10-50 million
> 50 million
25.7
33.0
28.3
31.7
44.2
29.7
33.5
30.0
32.4
45.5
8.55
12.8
13.4
15.1
7.43
3.50
11.7
10.9
8.61
8.92
18.8
21.0
22.5
26.6
89.4
GDP
<0.4 billion
0.4-2.0 billion
2-10 billion
10 –100 billion
> 100 billion
23.7
33.9
32.1
29.6
32.8
28.9
34.4
33.6
30.8
33.1
8.21
11.2
14.9
12.1
12.3
6.00
17.5
12.0
9.56
5.45
16.7
26.7
22.7
24.6
39.5
32.06
30.25
28.86
21.83
33.38
35.45
32.38
29.74
21.83
33.38
13.48
11.29
12.50
3.00
12.50
12.10
8.00
8.06
2.00
6.35
21.67
28.00
55.70
60.00
19.54
REGION
Pacific
Caribbean
SSA
LA
South Asia
Rest Asia
OECD
INCOME
Low income
Lower middle income
Upper middle income
High income Non OECD
High income OECD
6
Value
(…) Min
added tax
(VAT) or
sales tax
rate.
6.00
(…) Max Export
duty rate
(duties
from
exports as
percentag
e of total
governme
nt tax
revenues)
20.00
4.39
17.50
6.84
24.60
4.95
118.75
0.10
17.50
0.10
28.00
1.00
20.43
Bank
Receipts Lending
from
rate
import
duties and
taxes
Import
Duty:
weighted
average
(nominal)
tariff rate
Import
Duty: Unweighted
average
(nominal)
tariff rate
14.36
26.08
24.85
11.19
30.20
12.90
4.83
14.57
15.95
21.98
10.71
28.93
10.14
3.80
53.17
48.87
38.31
8.36
20.40
11.66
2.44
12.12
13.33
21.14
28.49
14.97
8.68
10.72
2.81
5.29
8.66
12.38
9.37
4.83
6.56
3.88
2.94
4.06
0.45
20.2
26.8
8.62
12.0
20.9
11.9
18.1
7.35
11.3
20.3
55.0
45.9
12.2
17.9
17.4
11.5
15.5
13.1
16.6
21.0
3.54
6.28
4.41
7.92
12.5
4.92
5.12
1.15
0.25
16.9
25.3
25.5
13.3
7.84
11.2
17.7
23.7
12.6
6.37
54.6
48.5
38.2
11.1
6.86
11.8
18.3
18.8
15.4
13.3
3.33
7.21
6.97
7.24
7.73
24.45
15.14
17.01
8.80
3.93
23.95
13.55
9.55
5.20
2.87
30.81
37.91
27.41
2.20
2.05
20.7
17.3
16.5
6.03
6.87
8.28
9.02
6.98
2.00
3.19
4.06
3.38
1.05
Deposit
Rate
REGION
Pacific
Caribbean
SSA
LA
South Asia
Rest Asia
OECD
Transport
Airfreight Airfreight Airfreight Airfreight Airfreight Airfreight Shipping
cost to
cost to
cost to
cost from cost from cost from cost to
London Tokyo ($) New York London Tokyo ($) New York Rotterdam
($)
($)
($)
($)
($)
798
507
599
1419
1135
1018
2717
598
844
360
630
1082
406
2213
462
680
520
896
1822
1250
1617
696
966
579
781
1584
890
1502
387
362
430
667
996
769
1012
709
413
683
1119
420
745
892
446
686
527
485
1074
630
973
Shipping
cost to
Yokoham
a ($)
2139
3388
1666
1470
804
597
1260
Shipping
cost to
New York
($)
3248
2977
2706
1581
2333
2742
2013
Shipping Shipping
cost from cost from
Rotterdam Yokoham
($)
a ($)
3577
2619
2440
3707
1931
1822
1497
2038
1314
1281
668
481
965
1521
Shipping
cost from
New York
($)
3448
3187
3344
1520
2598
1976
2127
POPULATION
<0.4 million
0.4 –2.0 million
2-10 million
10-50 million
> 50 million
715
517
382
508
867
647
637
388
731
1018
538
368
378
527
943
938
1084
562
800
986
1075
1450
1030
1309
1636
679
819
596
944
1175
2673
1799
875
1407
1355
2928
1799
1061
1340
1368
3431
2584
1982
2240
3191
3105
1998
841
1631
990
3335
2280
1382
1702
1258
3510
3238
1789
2598
2658
GDP
<0.4 billion
0.4-2.0 billion
2-10 billion
10 –100 billion
> 100 billion
818
523
433
539
540
683
591
575
641
719
629
316
389
562
594
1206
724
996
618
695
1138
1352
1838
965
1099
874
544
1306
761
697
2941
1873
1689
1168
998
2769
2193
1905
1183
1084
3570
2687
2795
1951
2186
3450
2345
1881
1231
925
3084
2832
2286
1460
1321
3648
3514
3036
2096
2105
INCOME
Low income
Lower middle income
Upper middle income
High income Non OECD
High in come OECD
518
708
530
566
497
647
633
654
338
758
569
521
453
543
588
980
874
681
2016
531
1647
1273
995
420
1097
1261
715
666
546
695
1695
1749
1725
801
960
1507
1762
2152
462
1197
2698
2205
2650
3217
2044
1869
2201
1720
690
1035
1617
2212
2465
412
1536
3321
2285
2689
2101
2153
7